Limpeza e calculo dos dados do microcosmo no picarro - Isabelle
Vagner Ovani
18/08/2026
1 - Dados brutos
Leitura 1 - 15/04/2026
library(dplyr)
# Caminho dos arquivos .dat
file_leitura = "C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 1" #aqui
file_leitura[1] "C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 1"# Obter a lista de arquivos .dat
file_list_leitura = list.files(file_leitura, pattern = ".dat", full.names = TRUE)
# Ler os dados de todos os arquivos
data_list_leitura = lapply(file_list_leitura, read.table, header = TRUE, sep = "",fill = TRUE)
# Juntar os dados
dados_leitura = do.call(rbind, data_list_leitura)
dados_leitura# Excluir colunas desnecessarias
dados_leitura = subset(dados_leitura, select = c("DATE","TIME","JULIAN_DAYS","MPVPosition","N2O_dry","CO2_dry","CH4_dry","H2O","NH3"))
print(dados_leitura) DATE TIME JULIAN_DAYS MPVPosition N2O_dry CO2_dry CH4_dry H2O NH3
1 2026-04-14 23:56:27.846 105.0392 5 0.3307019 445.1082 2.187245 2.771877 291.1186
2 2026-04-14 23:56:28.930 105.0392 5 0.3311638 445.1326 2.187225 2.772407 291.1577
3 2026-04-14 23:56:29.726 105.0392 5 0.3311643 445.1506 2.187210 2.772798 291.1865
4 2026-04-14 23:56:30.055 105.0392 5 0.3303446 445.1580 2.187204 2.772959 291.1983
5 2026-04-14 23:56:31.223 105.0393 5 0.3274368 445.1843 2.186775 2.773531 291.2404
6 2026-04-14 23:56:32.032 105.0393 5 0.3274374 445.2026 2.186478 2.773928 291.2696
7 2026-04-14 23:56:32.704 105.0393 5 0.3240288 445.2177 2.186231 2.774257 291.2939
8 2026-04-14 23:56:33.530 105.0393 5 0.3198461 445.2406 2.185943 2.775186 291.2430
9 2026-04-14 23:56:34.648 105.0393 5 0.3141843 445.2374 2.185552 2.776444 291.1742
10 2026-04-14 23:56:35.460 105.0393 5 0.3141915 445.2351 2.185269 2.777358 291.1243
11 2026-04-14 23:56:35.803 105.0393 5 0.3178068 445.2341 2.185149 2.777744 291.1032
12 2026-04-14 23:56:36.993 105.0393 5 0.3303398 445.2308 2.185843 2.779082 291.0302
13 2026-04-14 23:56:37.782 105.0393 5 0.3303471 445.2286 2.186305 2.779971 290.9816
14 2026-04-14 23:56:38.430 105.0393 5 0.3296730 445.2267 2.186684 2.780700 290.9418
15 2026-04-14 23:56:39.233 105.0393 5 0.3288297 445.2175 2.187128 2.780738 290.9736
16 2026-04-14 23:56:40.325 105.0394 5 0.3276816 445.2146 2.187732 2.780789 291.0169
17 2026-04-14 23:56:41.131 105.0394 5 0.3276800 445.2124 2.188179 2.780827 291.0489
18 2026-04-14 23:56:41.480 105.0394 5 0.3253675 445.2115 2.188372 2.780843 291.0627
19 2026-04-14 23:56:42.671 105.0394 5 0.3174759 445.2082 2.187548 2.780899 291.1098
20 2026-04-14 23:56:43.457 105.0394 5 0.3174743 445.2061 2.187004 2.780936 291.1410
21 2026-04-14 23:56:44.127 105.0394 5 0.3213143 445.2043 2.186540 2.780967 291.1676
22 2026-04-14 23:56:44.934 105.0394 5 0.3259558 445.2137 2.186023 2.782451 291.1624
23 2026-04-14 23:56:46.033 105.0394 5 0.3322833 445.2775 2.185317 2.784471 291.1554
24 2026-04-14 23:56:46.851 105.0394 5 0.3322917 445.3249 2.184794 2.785973 291.1501
25 2026-04-14 23:56:47.191 105.0394 5 0.3303134 445.3446 2.184575 2.786599 291.1479
26 2026-04-14 23:56:48.361 105.0394 5 0.3235311 445.4125 2.185189 2.788747 291.1403
27 2026-04-14 23:56:49.128 105.0395 5 0.3235388 445.4571 2.185591 2.790157 291.1354
28 2026-04-14 23:56:49.783 105.0395 5 0.3249684 445.4952 2.185935 2.791362 291.1312
29 2026-04-14 23:56:50.575 105.0395 5 0.3266814 445.5240 2.186289 2.790688 291.1241
30 2026-04-14 23:56:51.654 105.0395 5 0.3290173 445.4566 2.186772 2.789772 291.1144
31 2026-04-14 23:56:52.464 105.0395 5 0.3290138 445.4059 2.187135 2.789083 291.1072
32 2026-04-14 23:56:52.812 105.0395 5 0.3268673 445.3841 2.187291 2.788787 291.1040
33 2026-04-14 23:56:53.996 105.0395 5 0.3195706 445.3101 2.186906 2.787783 291.0933
34 2026-04-14 23:56:54.786 105.0395 5 0.3195673 445.2607 2.186648 2.787111 291.0862
35 2026-04-14 23:56:55.448 105.0395 5 0.3206885 445.2193 2.186432 2.786549 291.0803
36 2026-04-14 23:56:56.249 105.0395 5 0.3220452 445.1758 2.186195 2.786689 291.1181
37 2026-04-14 23:56:57.341 105.0396 5 0.3238976 445.1978 2.185870 2.786879 291.1696
38 2026-04-14 23:56:58.132 105.0396 5 0.3238963 445.2137 2.185635 2.787016 291.2069
39 2026-04-14 23:56:58.480 105.0396 5 0.3273677 445.2207 2.185532 2.787076 291.2234
40 2026-04-14 23:56:59.683 105.0396 5 0.3393581 445.2448 2.185464 2.787285 291.2802
41 2026-04-14 23:57:00.494 105.0396 5 0.3393569 445.2611 2.185419 2.787426 291.3185
42 2026-04-14 23:57:01.142 105.0396 5 0.3395438 445.2742 2.185383 2.787538 291.3490
43 2026-04-14 23:57:01.934 105.0396 5 0.3397781 445.2965 2.185362 2.788485 291.3557
44 2026-04-14 23:57:03.048 105.0396 5 0.3401074 445.2491 2.185332 2.789820 291.3652
45 2026-04-14 23:57:03.842 105.0396 5 0.3401119 445.2152 2.185311 2.790773 291.3719
46 2026-04-14 23:57:04.193 105.0396 5 0.3336633 445.2002 2.185302 2.791193 291.3748
47 2026-04-14 23:57:05.359 105.0396 5 0.3121612 445.1505 2.185949 2.792592 291.3845
48 2026-04-14 23:57:06.151 105.0397 5 0.3121652 445.1168 2.186387 2.793540 291.3912
49 2026-04-14 23:57:06.802 105.0397 5 0.3167730 445.0890 2.186748 2.794322 291.3968
50 2026-04-14 23:57:07.582 105.0397 5 0.3222805 445.0489 2.187155 2.794393 291.4160
51 2026-04-14 23:57:08.687 105.0397 5 0.3300833 445.0755 2.187732 2.794491 291.4433
52 2026-04-14 23:57:09.484 105.0397 5 0.3300828 445.0948 2.188148 2.794562 291.4629
53 2026-04-14 23:57:09.815 105.0397 5 0.3296166 445.1027 2.188320 2.794591 291.4710
54 2026-04-14 23:57:10.936 105.0397 5 0.3280329 445.1298 2.188369 2.794690 291.4986
55 2026-04-14 23:57:11.710 105.0397 5 0.3280323 445.1485 2.188402 2.794759 291.5177
56 2026-04-14 23:57:12.365 105.0397 5 0.3282535 445.1643 2.188430 2.794817 291.5338
57 2026-04-14 23:57:13.132 105.0397 5 0.3285180 445.1854 2.188472 2.795206 291.4963
58 2026-04-14 23:57:14.217 105.0397 5 0.3288913 445.1702 2.188532 2.795756 291.4434
59 2026-04-14 23:57:15.032 105.0398 5 0.3288955 445.1588 2.188577 2.796169 291.4036
60 2026-04-14 23:57:15.375 105.0398 5 0.3272631 445.1540 2.188596 2.796343 291.3869
61 2026-04-14 23:57:16.552 105.0398 5 0.3216612 445.1376 2.188087 2.796940 291.3293
62 2026-04-14 23:57:17.332 105.0398 5 0.3216652 445.1267 2.187749 2.797335 291.2913
63 2026-04-14 23:57:17.996 105.0398 5 0.3240257 445.1174 2.187461 2.797672 291.2588
64 2026-04-14 23:57:18.795 105.0398 5 0.3268604 445.1138 2.187143 2.799017 291.2688
65 2026-04-14 23:57:19.858 105.0398 5 0.3306365 445.0597 2.186719 2.800809 291.2820
66 2026-04-14 23:57:20.638 105.0398 5 0.3306424 445.0200 2.186407 2.802126 291.2917
67 2026-04-14 23:57:20.976 105.0398 5 0.3317374 445.0028 2.186272 2.802696 291.2959
68 2026-04-14 23:57:22.161 105.0398 5 0.3355765 444.9426 2.186034 2.804695 291.3107
69 2026-04-14 23:57:22.960 105.0398 5 0.3355828 444.9020 2.185874 2.806043 291.3206
70 2026-04-14 23:57:23.622 105.0399 5 0.3335320 444.8683 2.185741 2.807159 291.3288
71 2026-04-14 23:57:24.394 105.0399 5 0.3311317 444.8137 2.185531 2.806550 291.3143
72 2026-04-14 23:57:25.450 105.0399 5 0.3278491 444.8360 2.185243 2.805715 291.2944
73 2026-04-14 23:57:26.260 105.0399 5 0.3278468 444.8531 2.185023 2.805074 291.2792
74 2026-04-14 23:57:26.594 105.0399 5 0.3272120 444.8601 2.184932 2.804810 291.2729
75 2026-04-14 23:57:27.762 105.0399 5 0.3249855 444.8847 2.184399 2.803886 291.2509
76 2026-04-14 23:57:28.563 105.0399 5 0.3249832 444.9016 2.184033 2.803252 291.2358
77 2026-04-14 23:57:29.233 105.0399 5 0.3255223 444.9157 2.183727 2.802722 291.2232
78 2026-04-14 23:57:30.020 105.0399 5 0.3261669 444.9514 2.183436 2.804473 291.2541
79 2026-04-14 23:57:31.095 105.0399 5 0.3270465 445.0006 2.183037 2.806861 291.2963
80 2026-04-14 23:57:31.888 105.0400 5 0.3270540 445.0369 2.182744 2.808623 291.3274
81 2026-04-14 23:57:32.247 105.0400 5 0.3254171 445.0533 2.182611 2.809420 291.3414
82 2026-04-14 23:57:33.394 105.0400 5 0.3201915 445.1058 2.181914 2.811967 291.3863
83 2026-04-14 23:57:34.187 105.0400 5 0.3201988 445.1421 2.181431 2.813731 291.4174
84 2026-04-14 23:57:34.841 105.0400 5 0.3224833 445.1720 2.181034 2.815182 291.4431
85 2026-04-14 23:57:35.611 105.0400 5 0.3251696 445.1902 2.180504 2.814772 291.4780
86 2026-04-14 23:57:36.720 105.0400 5 0.3290304 445.2778 2.179743 2.814180 291.5282
87 2026-04-14 23:57:37.499 105.0400 5 0.3290268 445.3394 2.179208 2.813764 291.5635
88 2026-04-14 23:57:37.832 105.0400 5 0.3270611 445.3658 2.178978 2.813585 291.5786
89 2026-04-14 23:57:39.003 105.0400 5 0.3201698 445.4584 2.178614 2.812960 291.6315
90 2026-04-14 23:57:39.786 105.0400 5 0.3201662 445.5203 2.178370 2.812542 291.6669
91 2026-04-14 23:57:40.441 105.0401 5 0.3219085 445.5722 2.178166 2.812191 291.6967
92 2026-04-14 23:57:41.240 105.0401 5 0.3240335 445.6363 2.177921 2.811881 291.6896
93 2026-04-14 23:57:42.322 105.0401 5 0.3269075 445.5708 2.177589 2.811465 291.6801
94 2026-04-14 23:57:43.102 105.0401 5 0.3269060 445.5235 2.177349 2.811164 291.6731
95 2026-04-14 23:57:43.437 105.0401 5 0.3277184 445.5032 2.177246 2.811036 291.6702
96 2026-04-14 23:57:44.582 105.0401 5 0.3305060 445.4338 2.177449 2.810595 291.6601
97 2026-04-14 23:57:45.377 105.0401 5 0.3305045 445.3857 2.177589 2.810289 291.6531
98 2026-04-14 23:57:46.039 105.0401 5 0.3285428 445.3456 2.177705 2.810034 291.6472
99 2026-04-14 23:57:46.816 105.0401 5 0.3262405 445.3015 2.177853 2.810108 291.6195
100 2026-04-14 23:57:47.917 105.0401 5 0.3229858 445.3017 2.178062 2.810211 291.5804
101 2026-04-14 23:57:48.744 105.0401 5 0.3229878 445.3019 2.178220 2.810288 291.5510
102 2026-04-14 23:57:49.073 105.0402 5 0.3222705 445.3020 2.178282 2.810319 291.5393
103 2026-04-14 23:57:50.244 105.0402 5 0.3197175 445.3023 2.178280 2.810428 291.4976
104 2026-04-14 23:57:51.049 105.0402 5 0.3197194 445.3024 2.178278 2.810503 291.4690
105 2026-04-14 23:57:51.697 105.0402 5 0.3222249 445.3026 2.178276 2.810564 291.4461
106 2026-04-14 23:57:52.476 105.0402 5 0.3252410 445.2969 2.178254 2.809901 291.4311
107 2026-04-14 23:57:53.594 105.0402 5 0.3295603 445.3135 2.178221 2.808952 291.4097
108 2026-04-14 23:57:54.394 105.0402 5 0.3295577 445.3255 2.178197 2.808272 291.3943
109 2026-04-14 23:57:54.734 105.0402 5 0.3276243 445.3306 2.178187 2.807983 291.3877
110 2026-04-14 23:57:55.901 105.0402 5 0.3209939 445.3479 2.177642 2.806992 291.3652
111 2026-04-14 23:57:56.707 105.0402 5 0.3209914 445.3599 2.177265 2.806307 291.3498
[ reached 'max' / getOption("max.print") -- omitted 104673 rows ]#Criar datetime
dados_leitura = dados_leitura %>% mutate(datetime = as.POSIXct(paste(DATE, TIME),
format = "%Y-%m-%d %H:%M:%OS", tz = "America/Sao_Paulo"))
print(dados_leitura) DATE TIME JULIAN_DAYS MPVPosition N2O_dry CO2_dry CH4_dry H2O NH3
1 2026-04-14 23:56:27.846 105.0392 5 0.3307019 445.1082 2.187245 2.771877 291.1186
2 2026-04-14 23:56:28.930 105.0392 5 0.3311638 445.1326 2.187225 2.772407 291.1577
3 2026-04-14 23:56:29.726 105.0392 5 0.3311643 445.1506 2.187210 2.772798 291.1865
4 2026-04-14 23:56:30.055 105.0392 5 0.3303446 445.1580 2.187204 2.772959 291.1983
5 2026-04-14 23:56:31.223 105.0393 5 0.3274368 445.1843 2.186775 2.773531 291.2404
6 2026-04-14 23:56:32.032 105.0393 5 0.3274374 445.2026 2.186478 2.773928 291.2696
7 2026-04-14 23:56:32.704 105.0393 5 0.3240288 445.2177 2.186231 2.774257 291.2939
8 2026-04-14 23:56:33.530 105.0393 5 0.3198461 445.2406 2.185943 2.775186 291.2430
9 2026-04-14 23:56:34.648 105.0393 5 0.3141843 445.2374 2.185552 2.776444 291.1742
10 2026-04-14 23:56:35.460 105.0393 5 0.3141915 445.2351 2.185269 2.777358 291.1243
11 2026-04-14 23:56:35.803 105.0393 5 0.3178068 445.2341 2.185149 2.777744 291.1032
12 2026-04-14 23:56:36.993 105.0393 5 0.3303398 445.2308 2.185843 2.779082 291.0302
13 2026-04-14 23:56:37.782 105.0393 5 0.3303471 445.2286 2.186305 2.779971 290.9816
14 2026-04-14 23:56:38.430 105.0393 5 0.3296730 445.2267 2.186684 2.780700 290.9418
15 2026-04-14 23:56:39.233 105.0393 5 0.3288297 445.2175 2.187128 2.780738 290.9736
16 2026-04-14 23:56:40.325 105.0394 5 0.3276816 445.2146 2.187732 2.780789 291.0169
17 2026-04-14 23:56:41.131 105.0394 5 0.3276800 445.2124 2.188179 2.780827 291.0489
18 2026-04-14 23:56:41.480 105.0394 5 0.3253675 445.2115 2.188372 2.780843 291.0627
19 2026-04-14 23:56:42.671 105.0394 5 0.3174759 445.2082 2.187548 2.780899 291.1098
20 2026-04-14 23:56:43.457 105.0394 5 0.3174743 445.2061 2.187004 2.780936 291.1410
21 2026-04-14 23:56:44.127 105.0394 5 0.3213143 445.2043 2.186540 2.780967 291.1676
22 2026-04-14 23:56:44.934 105.0394 5 0.3259558 445.2137 2.186023 2.782451 291.1624
23 2026-04-14 23:56:46.033 105.0394 5 0.3322833 445.2775 2.185317 2.784471 291.1554
24 2026-04-14 23:56:46.851 105.0394 5 0.3322917 445.3249 2.184794 2.785973 291.1501
25 2026-04-14 23:56:47.191 105.0394 5 0.3303134 445.3446 2.184575 2.786599 291.1479
26 2026-04-14 23:56:48.361 105.0394 5 0.3235311 445.4125 2.185189 2.788747 291.1403
27 2026-04-14 23:56:49.128 105.0395 5 0.3235388 445.4571 2.185591 2.790157 291.1354
28 2026-04-14 23:56:49.783 105.0395 5 0.3249684 445.4952 2.185935 2.791362 291.1312
29 2026-04-14 23:56:50.575 105.0395 5 0.3266814 445.5240 2.186289 2.790688 291.1241
30 2026-04-14 23:56:51.654 105.0395 5 0.3290173 445.4566 2.186772 2.789772 291.1144
31 2026-04-14 23:56:52.464 105.0395 5 0.3290138 445.4059 2.187135 2.789083 291.1072
32 2026-04-14 23:56:52.812 105.0395 5 0.3268673 445.3841 2.187291 2.788787 291.1040
33 2026-04-14 23:56:53.996 105.0395 5 0.3195706 445.3101 2.186906 2.787783 291.0933
34 2026-04-14 23:56:54.786 105.0395 5 0.3195673 445.2607 2.186648 2.787111 291.0862
35 2026-04-14 23:56:55.448 105.0395 5 0.3206885 445.2193 2.186432 2.786549 291.0803
36 2026-04-14 23:56:56.249 105.0395 5 0.3220452 445.1758 2.186195 2.786689 291.1181
37 2026-04-14 23:56:57.341 105.0396 5 0.3238976 445.1978 2.185870 2.786879 291.1696
38 2026-04-14 23:56:58.132 105.0396 5 0.3238963 445.2137 2.185635 2.787016 291.2069
39 2026-04-14 23:56:58.480 105.0396 5 0.3273677 445.2207 2.185532 2.787076 291.2234
40 2026-04-14 23:56:59.683 105.0396 5 0.3393581 445.2448 2.185464 2.787285 291.2802
41 2026-04-14 23:57:00.494 105.0396 5 0.3393569 445.2611 2.185419 2.787426 291.3185
42 2026-04-14 23:57:01.142 105.0396 5 0.3395438 445.2742 2.185383 2.787538 291.3490
43 2026-04-14 23:57:01.934 105.0396 5 0.3397781 445.2965 2.185362 2.788485 291.3557
44 2026-04-14 23:57:03.048 105.0396 5 0.3401074 445.2491 2.185332 2.789820 291.3652
45 2026-04-14 23:57:03.842 105.0396 5 0.3401119 445.2152 2.185311 2.790773 291.3719
46 2026-04-14 23:57:04.193 105.0396 5 0.3336633 445.2002 2.185302 2.791193 291.3748
47 2026-04-14 23:57:05.359 105.0396 5 0.3121612 445.1505 2.185949 2.792592 291.3845
48 2026-04-14 23:57:06.151 105.0397 5 0.3121652 445.1168 2.186387 2.793540 291.3912
49 2026-04-14 23:57:06.802 105.0397 5 0.3167730 445.0890 2.186748 2.794322 291.3968
50 2026-04-14 23:57:07.582 105.0397 5 0.3222805 445.0489 2.187155 2.794393 291.4160
51 2026-04-14 23:57:08.687 105.0397 5 0.3300833 445.0755 2.187732 2.794491 291.4433
52 2026-04-14 23:57:09.484 105.0397 5 0.3300828 445.0948 2.188148 2.794562 291.4629
53 2026-04-14 23:57:09.815 105.0397 5 0.3296166 445.1027 2.188320 2.794591 291.4710
54 2026-04-14 23:57:10.936 105.0397 5 0.3280329 445.1298 2.188369 2.794690 291.4986
55 2026-04-14 23:57:11.710 105.0397 5 0.3280323 445.1485 2.188402 2.794759 291.5177
56 2026-04-14 23:57:12.365 105.0397 5 0.3282535 445.1643 2.188430 2.794817 291.5338
57 2026-04-14 23:57:13.132 105.0397 5 0.3285180 445.1854 2.188472 2.795206 291.4963
58 2026-04-14 23:57:14.217 105.0397 5 0.3288913 445.1702 2.188532 2.795756 291.4434
59 2026-04-14 23:57:15.032 105.0398 5 0.3288955 445.1588 2.188577 2.796169 291.4036
60 2026-04-14 23:57:15.375 105.0398 5 0.3272631 445.1540 2.188596 2.796343 291.3869
61 2026-04-14 23:57:16.552 105.0398 5 0.3216612 445.1376 2.188087 2.796940 291.3293
62 2026-04-14 23:57:17.332 105.0398 5 0.3216652 445.1267 2.187749 2.797335 291.2913
63 2026-04-14 23:57:17.996 105.0398 5 0.3240257 445.1174 2.187461 2.797672 291.2588
64 2026-04-14 23:57:18.795 105.0398 5 0.3268604 445.1138 2.187143 2.799017 291.2688
65 2026-04-14 23:57:19.858 105.0398 5 0.3306365 445.0597 2.186719 2.800809 291.2820
66 2026-04-14 23:57:20.638 105.0398 5 0.3306424 445.0200 2.186407 2.802126 291.2917
67 2026-04-14 23:57:20.976 105.0398 5 0.3317374 445.0028 2.186272 2.802696 291.2959
68 2026-04-14 23:57:22.161 105.0398 5 0.3355765 444.9426 2.186034 2.804695 291.3107
69 2026-04-14 23:57:22.960 105.0398 5 0.3355828 444.9020 2.185874 2.806043 291.3206
70 2026-04-14 23:57:23.622 105.0399 5 0.3335320 444.8683 2.185741 2.807159 291.3288
71 2026-04-14 23:57:24.394 105.0399 5 0.3311317 444.8137 2.185531 2.806550 291.3143
72 2026-04-14 23:57:25.450 105.0399 5 0.3278491 444.8360 2.185243 2.805715 291.2944
73 2026-04-14 23:57:26.260 105.0399 5 0.3278468 444.8531 2.185023 2.805074 291.2792
74 2026-04-14 23:57:26.594 105.0399 5 0.3272120 444.8601 2.184932 2.804810 291.2729
75 2026-04-14 23:57:27.762 105.0399 5 0.3249855 444.8847 2.184399 2.803886 291.2509
76 2026-04-14 23:57:28.563 105.0399 5 0.3249832 444.9016 2.184033 2.803252 291.2358
77 2026-04-14 23:57:29.233 105.0399 5 0.3255223 444.9157 2.183727 2.802722 291.2232
78 2026-04-14 23:57:30.020 105.0399 5 0.3261669 444.9514 2.183436 2.804473 291.2541
79 2026-04-14 23:57:31.095 105.0399 5 0.3270465 445.0006 2.183037 2.806861 291.2963
80 2026-04-14 23:57:31.888 105.0400 5 0.3270540 445.0369 2.182744 2.808623 291.3274
81 2026-04-14 23:57:32.247 105.0400 5 0.3254171 445.0533 2.182611 2.809420 291.3414
82 2026-04-14 23:57:33.394 105.0400 5 0.3201915 445.1058 2.181914 2.811967 291.3863
83 2026-04-14 23:57:34.187 105.0400 5 0.3201988 445.1421 2.181431 2.813731 291.4174
84 2026-04-14 23:57:34.841 105.0400 5 0.3224833 445.1720 2.181034 2.815182 291.4431
85 2026-04-14 23:57:35.611 105.0400 5 0.3251696 445.1902 2.180504 2.814772 291.4780
86 2026-04-14 23:57:36.720 105.0400 5 0.3290304 445.2778 2.179743 2.814180 291.5282
87 2026-04-14 23:57:37.499 105.0400 5 0.3290268 445.3394 2.179208 2.813764 291.5635
88 2026-04-14 23:57:37.832 105.0400 5 0.3270611 445.3658 2.178978 2.813585 291.5786
89 2026-04-14 23:57:39.003 105.0400 5 0.3201698 445.4584 2.178614 2.812960 291.6315
90 2026-04-14 23:57:39.786 105.0400 5 0.3201662 445.5203 2.178370 2.812542 291.6669
91 2026-04-14 23:57:40.441 105.0401 5 0.3219085 445.5722 2.178166 2.812191 291.6967
92 2026-04-14 23:57:41.240 105.0401 5 0.3240335 445.6363 2.177921 2.811881 291.6896
93 2026-04-14 23:57:42.322 105.0401 5 0.3269075 445.5708 2.177589 2.811465 291.6801
94 2026-04-14 23:57:43.102 105.0401 5 0.3269060 445.5235 2.177349 2.811164 291.6731
95 2026-04-14 23:57:43.437 105.0401 5 0.3277184 445.5032 2.177246 2.811036 291.6702
96 2026-04-14 23:57:44.582 105.0401 5 0.3305060 445.4338 2.177449 2.810595 291.6601
97 2026-04-14 23:57:45.377 105.0401 5 0.3305045 445.3857 2.177589 2.810289 291.6531
98 2026-04-14 23:57:46.039 105.0401 5 0.3285428 445.3456 2.177705 2.810034 291.6472
99 2026-04-14 23:57:46.816 105.0401 5 0.3262405 445.3015 2.177853 2.810108 291.6195
100 2026-04-14 23:57:47.917 105.0401 5 0.3229858 445.3017 2.178062 2.810211 291.5804
datetime
1 2026-04-14 23:56:27
2 2026-04-14 23:56:28
3 2026-04-14 23:56:29
4 2026-04-14 23:56:30
5 2026-04-14 23:56:31
6 2026-04-14 23:56:32
7 2026-04-14 23:56:32
8 2026-04-14 23:56:33
9 2026-04-14 23:56:34
10 2026-04-14 23:56:35
11 2026-04-14 23:56:35
12 2026-04-14 23:56:36
13 2026-04-14 23:56:37
14 2026-04-14 23:56:38
15 2026-04-14 23:56:39
16 2026-04-14 23:56:40
17 2026-04-14 23:56:41
18 2026-04-14 23:56:41
19 2026-04-14 23:56:42
20 2026-04-14 23:56:43
21 2026-04-14 23:56:44
22 2026-04-14 23:56:44
23 2026-04-14 23:56:46
24 2026-04-14 23:56:46
25 2026-04-14 23:56:47
26 2026-04-14 23:56:48
27 2026-04-14 23:56:49
28 2026-04-14 23:56:49
29 2026-04-14 23:56:50
30 2026-04-14 23:56:51
31 2026-04-14 23:56:52
32 2026-04-14 23:56:52
33 2026-04-14 23:56:53
34 2026-04-14 23:56:54
35 2026-04-14 23:56:55
36 2026-04-14 23:56:56
37 2026-04-14 23:56:57
38 2026-04-14 23:56:58
39 2026-04-14 23:56:58
40 2026-04-14 23:56:59
41 2026-04-14 23:57:00
42 2026-04-14 23:57:01
43 2026-04-14 23:57:01
44 2026-04-14 23:57:03
45 2026-04-14 23:57:03
46 2026-04-14 23:57:04
47 2026-04-14 23:57:05
48 2026-04-14 23:57:06
49 2026-04-14 23:57:06
50 2026-04-14 23:57:07
51 2026-04-14 23:57:08
52 2026-04-14 23:57:09
53 2026-04-14 23:57:09
54 2026-04-14 23:57:10
55 2026-04-14 23:57:11
56 2026-04-14 23:57:12
57 2026-04-14 23:57:13
58 2026-04-14 23:57:14
59 2026-04-14 23:57:15
60 2026-04-14 23:57:15
61 2026-04-14 23:57:16
62 2026-04-14 23:57:17
63 2026-04-14 23:57:17
64 2026-04-14 23:57:18
65 2026-04-14 23:57:19
66 2026-04-14 23:57:20
67 2026-04-14 23:57:20
68 2026-04-14 23:57:22
69 2026-04-14 23:57:22
70 2026-04-14 23:57:23
71 2026-04-14 23:57:24
72 2026-04-14 23:57:25
73 2026-04-14 23:57:26
74 2026-04-14 23:57:26
75 2026-04-14 23:57:27
76 2026-04-14 23:57:28
77 2026-04-14 23:57:29
78 2026-04-14 23:57:30
79 2026-04-14 23:57:31
80 2026-04-14 23:57:31
81 2026-04-14 23:57:32
82 2026-04-14 23:57:33
83 2026-04-14 23:57:34
84 2026-04-14 23:57:34
85 2026-04-14 23:57:35
86 2026-04-14 23:57:36
87 2026-04-14 23:57:37
88 2026-04-14 23:57:37
89 2026-04-14 23:57:39
90 2026-04-14 23:57:39
91 2026-04-14 23:57:40
92 2026-04-14 23:57:41
93 2026-04-14 23:57:42
94 2026-04-14 23:57:43
95 2026-04-14 23:57:43
96 2026-04-14 23:57:44
97 2026-04-14 23:57:45
98 2026-04-14 23:57:46
99 2026-04-14 23:57:46
100 2026-04-14 23:57:47
[ reached 'max' / getOption("max.print") -- omitted 104684 rows ]#Filtrar por horario
filtrados_leitura1 = dados_leitura %>% filter(
(datetime >= as.POSIXct("2026-04-15 09:00:00", tz = "America/Sao_Paulo") & #aqui
datetime <= as.POSIXct("2026-04-15 12:56:00", tz = "America/Sao_Paulo"))) #aqui
print(filtrados_leitura1) #aqui DATE TIME JULIAN_DAYS MPVPosition N2O_dry CO2_dry CH4_dry H2O NH3
1 2026-04-15 09:00:00.302 105.4167 6 0.3217566 475.4381 2.280470 2.825441 195.4304
2 2026-04-15 09:00:01.078 105.4167 6 0.3217608 475.3730 2.280697 2.825593 195.3620
3 2026-04-15 09:00:01.736 105.4167 6 0.3195317 475.3179 2.280889 2.825721 195.3041
4 2026-04-15 09:00:02.496 105.4167 6 0.3169542 475.2595 2.281130 2.826495 195.2260
5 2026-04-15 09:00:03.558 105.4167 6 0.3133611 475.2594 2.281465 2.827570 195.1171
6 2026-04-15 09:00:04.345 105.4167 6 0.3133704 475.2594 2.281714 2.828369 195.0363
7 2026-04-15 09:00:04.683 105.4167 6 0.3163665 475.2594 2.281821 2.828711 195.0017
8 2026-04-15 09:00:05.855 105.4167 6 0.3267952 475.2593 2.282153 2.829900 194.8815
9 2026-04-15 09:00:06.638 105.4167 6 0.3268047 475.2593 2.282375 2.830693 194.8012
10 2026-04-15 09:00:07.306 105.4168 6 0.3245602 475.2592 2.282565 2.831372 194.7326
11 2026-04-15 09:00:08.102 105.4168 6 0.3218943 475.2683 2.282823 2.833236 194.6877
12 2026-04-15 09:00:09.191 105.4168 6 0.3182472 475.3510 2.283175 2.835785 194.6263
13 2026-04-15 09:00:10.003 105.4168 6 0.3182620 475.4126 2.283438 2.837686 194.5805
14 2026-04-15 09:00:10.339 105.4168 6 0.3219371 475.4381 2.283547 2.838473 194.5616
15 2026-04-15 09:00:11.484 105.4168 6 0.3344616 475.5250 2.283404 2.841153 194.4972
16 2026-04-15 09:00:12.275 105.4168 6 0.3344765 475.5850 2.283307 2.843003 194.4527
17 2026-04-15 09:00:12.905 105.4168 6 0.3313269 475.6329 2.283229 2.844481 194.4171
18 2026-04-15 09:00:13.663 105.4168 6 0.3275226 475.6561 2.283015 2.842264 194.3717
19 2026-04-15 09:00:14.736 105.4168 6 0.3221250 475.5939 2.282713 2.839123 194.3074
20 2026-04-15 09:00:15.542 105.4168 6 0.3221126 475.5473 2.282487 2.836768 194.2591
21 2026-04-15 09:00:15.877 105.4169 6 0.3220253 475.5279 2.282392 2.835788 194.2391
22 2026-04-15 09:00:17.006 105.4169 6 0.3217311 475.4625 2.282175 2.832484 194.1714
23 2026-04-15 09:00:17.786 105.4169 6 0.3217190 475.4174 2.282025 2.830204 194.1247
24 2026-04-15 09:00:18.463 105.4169 6 0.3217235 475.3783 2.281894 2.828224 194.0841
25 2026-04-15 09:00:19.252 105.4169 6 0.3217555 475.3670 2.281862 2.829918 194.0130
26 2026-04-15 09:00:20.348 105.4169 6 0.3218000 475.3506 2.281818 2.832276 193.9139
27 2026-04-15 09:00:21.148 105.4169 6 0.3218147 475.3386 2.281785 2.833993 193.8417
28 2026-04-15 09:00:21.484 105.4169 6 0.3210942 475.3335 2.281771 2.834718 193.8113
29 2026-04-15 09:00:22.618 105.4169 6 0.3186720 475.3166 2.281904 2.837154 193.7089
30 2026-04-15 09:00:23.407 105.4169 6 0.3186864 475.3047 2.281997 2.838852 193.6375
31 2026-04-15 09:00:24.073 105.4169 6 0.3204466 475.2948 2.282075 2.840284 193.5774
32 2026-04-15 09:00:24.865 105.4170 6 0.3225230 475.2633 2.282099 2.839704 193.4995
33 2026-04-15 09:00:25.977 105.4170 6 0.3254452 475.2857 2.282133 2.838885 193.3899
34 2026-04-15 09:00:26.799 105.4170 6 0.3254459 475.3022 2.282159 2.838281 193.3089
35 2026-04-15 09:00:27.134 105.4170 6 0.3260386 475.3089 2.282169 2.838034 193.2759
36 2026-04-15 09:00:28.275 105.4170 6 0.3280555 475.3318 2.281707 2.837196 193.1636
37 2026-04-15 09:00:29.055 105.4170 6 0.3280562 475.3475 2.281390 2.836621 193.0867
38 2026-04-15 09:00:29.732 105.4170 6 0.3258087 475.3611 2.281116 2.836124 193.0200
39 2026-04-15 09:00:30.536 105.4170 6 0.3231423 475.3862 2.280822 2.836582 192.9404
40 2026-04-15 09:00:31.627 105.4170 6 0.3195235 475.3268 2.280422 2.837205 192.8322
41 2026-04-15 09:00:32.434 105.4170 6 0.3195304 475.2828 2.280126 2.837667 192.7522
42 2026-04-15 09:00:32.789 105.4170 6 0.3208175 475.2635 2.279997 2.837869 192.7171
43 2026-04-15 09:00:33.940 105.4171 6 0.3250023 475.2008 2.280125 2.838527 192.6032
44 2026-04-15 09:00:34.723 105.4171 6 0.3250091 475.1581 2.280212 2.838974 192.5256
45 2026-04-15 09:00:35.394 105.4171 6 0.3225517 475.1215 2.280287 2.839357 192.4591
46 2026-04-15 09:00:36.188 105.4171 6 0.3196402 475.0813 2.280387 2.840173 192.4195
47 2026-04-15 09:00:37.279 105.4171 6 0.3156483 475.0436 2.280523 2.841290 192.3653
48 2026-04-15 09:00:38.086 105.4171 6 0.3156554 475.0156 2.280624 2.842118 192.3251
49 2026-04-15 09:00:38.421 105.4171 6 0.3175190 475.0040 2.280666 2.842460 192.3085
50 2026-04-15 09:00:39.588 105.4171 6 0.3240304 474.9635 2.280355 2.843656 192.2505
51 2026-04-15 09:00:40.388 105.4171 6 0.3240376 474.9358 2.280142 2.844477 192.2107
52 2026-04-15 09:00:41.049 105.4171 6 0.3262776 474.9129 2.279966 2.845154 192.1779
53 2026-04-15 09:00:41.828 105.4172 6 0.3289183 474.8826 2.279747 2.845566 192.1513
54 2026-04-15 09:00:42.900 105.4172 6 0.3325535 475.0085 2.279446 2.846129 192.1147
55 2026-04-15 09:00:43.703 105.4172 6 0.3325584 475.1028 2.279220 2.846550 192.0873
56 2026-04-15 09:00:44.042 105.4172 6 0.3318019 475.1427 2.279124 2.846729 192.0757
57 2026-04-15 09:00:45.171 105.4172 6 0.3292918 475.2752 2.279084 2.847321 192.0371
58 2026-04-15 09:00:45.947 105.4172 6 0.3292965 475.3665 2.279057 2.847729 192.0106
59 2026-04-15 09:00:46.615 105.4172 6 0.3282012 475.4449 2.279033 2.848079 191.9878
60 2026-04-15 09:00:47.401 105.4172 6 0.3269103 475.5268 2.278969 2.847290 191.8855
61 2026-04-15 09:00:48.486 105.4172 6 0.3251256 475.5403 2.278882 2.846201 191.7441
62 2026-04-15 09:00:49.283 105.4172 6 0.3251263 475.5502 2.278817 2.845400 191.6401
63 2026-04-15 09:00:49.640 105.4172 6 0.3249834 475.5546 2.278788 2.845043 191.5937
64 2026-04-15 09:00:50.778 105.4173 6 0.3245265 475.5688 2.278674 2.843901 191.4454
65 2026-04-15 09:00:51.556 105.4173 6 0.3245272 475.5785 2.278595 2.843120 191.3439
66 2026-04-15 09:00:52.239 105.4173 6 0.3251683 475.5870 2.278527 2.842435 191.2550
67 2026-04-15 09:00:53.056 105.4173 6 0.3259399 475.6120 2.278496 2.843343 191.2997
68 2026-04-15 09:00:54.150 105.4173 6 0.3269699 475.4796 2.278456 2.844560 191.3594
69 2026-04-15 09:00:54.943 105.4173 6 0.3269725 475.3836 2.278426 2.845443 191.4027
70 2026-04-15 09:00:55.293 105.4173 6 0.3279528 475.3410 2.278413 2.845834 191.4219
71 2026-04-15 09:00:56.443 105.4173 6 0.3311620 475.2018 2.278235 2.847113 191.4846
72 2026-04-15 09:00:57.227 105.4173 6 0.3311646 475.1067 2.278114 2.847987 191.5275
73 2026-04-15 09:00:57.898 105.4173 6 0.3304053 475.0256 2.278010 2.848734 191.5641
74 2026-04-15 09:00:58.684 105.4173 6 0.3295179 474.9205 2.277854 2.848465 191.4119
75 2026-04-15 09:00:59.763 105.4174 6 0.3283001 475.0149 2.277640 2.848088 191.2028
76 2026-04-15 09:01:00.558 105.4174 6 0.3283074 475.0844 2.277482 2.847811 191.0491
77 2026-04-15 09:01:00.904 105.4174 6 0.3281871 475.1147 2.277413 2.847690 190.9819
78 2026-04-15 09:01:02.032 105.4174 6 0.3277963 475.2134 2.277550 2.847297 190.7636
79 2026-04-15 09:01:02.811 105.4174 6 0.3278034 475.2814 2.277645 2.847025 190.6129
80 2026-04-15 09:01:03.486 105.4174 6 0.3278312 475.3404 2.277727 2.846790 190.4823
81 2026-04-15 09:01:04.289 105.4174 6 0.3278566 475.4105 2.277824 2.846490 190.4655
82 2026-04-15 09:01:05.351 105.4174 6 0.3278895 475.4179 2.277953 2.846094 190.4432
83 2026-04-15 09:01:06.128 105.4174 6 0.3278886 475.4232 2.278046 2.845805 190.4269
84 2026-04-15 09:01:06.479 105.4174 6 0.3277621 475.4257 2.278089 2.845675 190.4196
85 2026-04-15 09:01:07.631 105.4174 6 0.3273458 475.4336 2.277986 2.845247 190.3954
86 2026-04-15 09:01:08.421 105.4175 6 0.3273449 475.4391 2.277915 2.844953 190.3789
87 2026-04-15 09:01:09.072 105.4175 6 0.3272608 475.4435 2.277857 2.844711 190.3652
88 2026-04-15 09:01:09.871 105.4175 6 0.3271679 475.4571 2.277814 2.845352 190.2699
89 2026-04-15 09:01:10.963 105.4175 6 0.3270403 475.3797 2.277755 2.846230 190.1397
90 2026-04-15 09:01:11.767 105.4175 6 0.3270494 475.3227 2.277711 2.846878 190.0437
91 2026-04-15 09:01:12.107 105.4175 6 0.3266513 475.2987 2.277693 2.847150 190.0033
92 2026-04-15 09:01:13.256 105.4175 6 0.3253007 475.2172 2.277547 2.848075 189.8661
93 2026-04-15 09:01:14.035 105.4175 6 0.3253094 475.1621 2.277449 2.848701 189.7733
94 2026-04-15 09:01:14.713 105.4175 6 0.3262921 475.1140 2.277363 2.849248 189.6923
95 2026-04-15 09:01:15.517 105.4175 6 0.3274407 475.0428 2.277211 2.848235 189.6731
96 2026-04-15 09:01:16.578 105.4176 6 0.3289569 475.0191 2.277012 2.846896 189.6478
97 2026-04-15 09:01:17.388 105.4176 6 0.3289512 475.0011 2.276859 2.845874 189.6284
98 2026-04-15 09:01:17.732 105.4176 6 0.3271508 474.9934 2.276795 2.845441 189.6202
99 2026-04-15 09:01:18.871 105.4176 6 0.3211726 474.9680 2.276685 2.844004 189.5929
100 2026-04-15 09:01:19.655 105.4176 6 0.3211673 474.9506 2.276609 2.843014 189.5741
datetime
1 2026-04-15 09:00:00
2 2026-04-15 09:00:01
3 2026-04-15 09:00:01
4 2026-04-15 09:00:02
5 2026-04-15 09:00:03
6 2026-04-15 09:00:04
7 2026-04-15 09:00:04
8 2026-04-15 09:00:05
9 2026-04-15 09:00:06
10 2026-04-15 09:00:07
11 2026-04-15 09:00:08
12 2026-04-15 09:00:09
13 2026-04-15 09:00:10
14 2026-04-15 09:00:10
15 2026-04-15 09:00:11
16 2026-04-15 09:00:12
17 2026-04-15 09:00:12
18 2026-04-15 09:00:13
19 2026-04-15 09:00:14
20 2026-04-15 09:00:15
21 2026-04-15 09:00:15
22 2026-04-15 09:00:17
23 2026-04-15 09:00:17
24 2026-04-15 09:00:18
25 2026-04-15 09:00:19
26 2026-04-15 09:00:20
27 2026-04-15 09:00:21
28 2026-04-15 09:00:21
29 2026-04-15 09:00:22
30 2026-04-15 09:00:23
31 2026-04-15 09:00:24
32 2026-04-15 09:00:24
33 2026-04-15 09:00:25
34 2026-04-15 09:00:26
35 2026-04-15 09:00:27
36 2026-04-15 09:00:28
37 2026-04-15 09:00:29
38 2026-04-15 09:00:29
39 2026-04-15 09:00:30
40 2026-04-15 09:00:31
41 2026-04-15 09:00:32
42 2026-04-15 09:00:32
43 2026-04-15 09:00:33
44 2026-04-15 09:00:34
45 2026-04-15 09:00:35
46 2026-04-15 09:00:36
47 2026-04-15 09:00:37
48 2026-04-15 09:00:38
49 2026-04-15 09:00:38
50 2026-04-15 09:00:39
51 2026-04-15 09:00:40
52 2026-04-15 09:00:41
53 2026-04-15 09:00:41
54 2026-04-15 09:00:42
55 2026-04-15 09:00:43
56 2026-04-15 09:00:44
57 2026-04-15 09:00:45
58 2026-04-15 09:00:45
59 2026-04-15 09:00:46
60 2026-04-15 09:00:47
61 2026-04-15 09:00:48
62 2026-04-15 09:00:49
63 2026-04-15 09:00:49
64 2026-04-15 09:00:50
65 2026-04-15 09:00:51
66 2026-04-15 09:00:52
67 2026-04-15 09:00:53
68 2026-04-15 09:00:54
69 2026-04-15 09:00:54
70 2026-04-15 09:00:55
71 2026-04-15 09:00:56
72 2026-04-15 09:00:57
73 2026-04-15 09:00:57
74 2026-04-15 09:00:58
75 2026-04-15 09:00:59
76 2026-04-15 09:01:00
77 2026-04-15 09:01:00
78 2026-04-15 09:01:02
79 2026-04-15 09:01:02
80 2026-04-15 09:01:03
81 2026-04-15 09:01:04
82 2026-04-15 09:01:05
83 2026-04-15 09:01:06
84 2026-04-15 09:01:06
85 2026-04-15 09:01:07
86 2026-04-15 09:01:08
87 2026-04-15 09:01:09
88 2026-04-15 09:01:09
89 2026-04-15 09:01:10
90 2026-04-15 09:01:11
91 2026-04-15 09:01:12
92 2026-04-15 09:01:13
93 2026-04-15 09:01:14
94 2026-04-15 09:01:14
95 2026-04-15 09:01:15
96 2026-04-15 09:01:16
97 2026-04-15 09:01:17
98 2026-04-15 09:01:17
99 2026-04-15 09:01:18
100 2026-04-15 09:01:19
[ reached 'max' / getOption("max.print") -- omitted 17186 rows ]range(filtrados_leitura1$datetime) #aqui[1] "2026-04-15 09:00:00 -03" "2026-04-15 12:55:59 -03"# Carregar ID da leitura
ID.leitura1=read.csv("C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 1/ID leitura1.csv") #aqui
str(ID.leitura1) #aqui'data.frame': 30 obs. of 12 variables:
$ Bottle : int 1 2 3 4 5 6 7 8 9 10 ...
$ Tratamento : chr "Controle - Apenas água" "Fezes ovinas tratadas com oleo de girassol ozonizado " "Fezes ovinas tratadas com oleo de girassol" "Água ozonizada" ...
$ Abreviacao : chr "CON" "FOGO3" "FOG" "AO3" ...
$ Bloco : int 1 1 1 1 1 2 2 2 2 2 ...
$ Rep : num 1 1 1 1 1 2 2 2 2 2 ...
$ Leitura : int 1 1 1 1 1 1 1 1 1 1 ...
$ Date : chr "15/04/2026" "15/04/2026" "15/04/2026" "15/04/2026" ...
$ Solo.Seco : num 247 246 246 246 246 ...
$ Beginning.reading : chr "09:09" "09:16" "09:23" "09:30" ...
$ End.reading : chr "09:12" "09:19" "09:26" "09:33" ...
$ External.temperature: num 22.9 22.5 22.1 22.8 22.4 22.8 21.5 21.3 21.7 20.6 ...
$ Internal.temperature: num 25.5 24.6 25.1 25.3 23.8 24.8 25.1 25.1 25 25.5 ...print(ID.leitura1) #aqui Bottle Tratamento Abreviacao Bloco Rep Leitura Date Solo.Seco
1 1 Controle - Apenas água CON 1 1 1 15/04/2026 246.61
2 2 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 1 1 1 15/04/2026 246.39
3 3 Fezes ovinas tratadas com oleo de girassol FOG 1 1 1 15/04/2026 246.21
4 4 Água ozonizada AO3 1 1 1 15/04/2026 246.31
5 5 Adubo nitrogenado (ureia) NMIN 1 1 1 15/04/2026 246.29
6 6 Controle - Apenas água CON 2 2 1 15/04/2026 246.26
7 7 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 2 2 1 15/04/2026 246.14
8 8 Fezes ovinas tratadas com oleo de girassol FOG 2 2 1 15/04/2026 246.21
9 9 Água ozonizada AO3 2 2 1 15/04/2026 246.14
10 10 Adubo nitrogenado NMIN 2 2 1 15/04/2026 246.35
11 11 Controle - Apenas água CON 3 3 1 15/04/2026 246.26
12 12 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 3 3 1 15/04/2026 246.30
13 13 Fezes ovinas tratadas com oleo de girassol FOG 3 3 1 15/04/2026 246.38
14 14 Água ozonizada AO3 3 3 1 15/04/2026 246.37
15 15 Adubo nitrogenado NMIN 3 3 1 15/04/2026 246.15
16 16 Controle - Apenas água CON 4 4 1 15/04/2026 246.50
17 17 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 4 4 1 15/04/2026 246.36
18 18 Fezes ovinas tratadas com oleo de girassol FOG 4 4 1 15/04/2026 246.30
19 19 Água ozonizada AO3 4 4 1 15/04/2026 246.25
20 20 Adubo nitrogenado NMIN 4 4 1 15/04/2026 246.34
21 21 Controle - Apenas água CON 5 5 1 15/04/2026 246.23
22 22 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 5 5 1 15/04/2026 246.18
23 23 Fezes ovinas tratadas com oleo de girassol FOG 5 5 1 15/04/2026 246.21
24 24 Água ozonizada AO3 5 5 1 15/04/2026 246.31
25 25 Adubo nitrogenado NMIN 5 5 1 15/04/2026 246.35
26 26 Controle - Apenas água CON 6 6 1 15/04/2026 246.25
27 27 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 6 6 1 15/04/2026 246.25
28 28 Fezes ovinas tratadas com oleo de girassol FOG 6 6 1 15/04/2026 246.36
29 29 Água ozonizada AO3 6 6 1 15/04/2026 246.23
30 30 Adubo nitrogenado NMIN 6 6 1 15/04/2026 246.35
Beginning.reading End.reading External.temperature Internal.temperature
1 09:09 09:12 22.9 25.5
2 09:16 09:19 22.5 24.6
3 09:23 09:26 22.1 25.1
4 09:30 09:33 22.8 25.3
5 09:51 09:54 22.4 23.8
6 09:44 09:47 22.8 24.8
7 09:58 10:01 21.5 25.1
8 10:05 10:08 21.3 25.1
9 10:12 10:15 21.7 25.0
10 10:19 10:22 20.6 25.5
11 10:26 10:29 22.3 25.3
12 10:33 10:36 22.1 25.1
13 10:40 10:43 21.4 25.0
14 10:47 10:50 21.3 25.3
15 10:54 10:57 21.5 25.6
16 11:01 11:04 21.3 25.1
17 11:08 11:11 21.6 25.1
18 11:15 11:18 21.5 25.0
19 11:22 11:25 21.6 25.3
20 11:50 11:53 21.3 24.8
21 11:36 11:39 20.7 25.3
22 11:43 11:46 21.9 25.1
23 11:57 12:00 22.3 25.1
24 12:04 12:07 21.7 25.3
25 12:11 12:14 22.3 25.3
26 12:18 12:21 21.7 25.2
27 12:25 12:28 20.9 25.2
28 12:32 12:35 21.1 25.0
29 12:53 12:56 20.9 24.4
30 12:46 12:49 21.5 24.8Leitura 2 - 16/04/2026
# Caminho dos arquivos .dat
file_leitura = "C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 2" #aqui
# Obter a lista de arquivos .dat
file_list_leitura = list.files(file_leitura, pattern = ".dat", full.names = TRUE)
# Ler os dados de todos os arquivos
data_list_leitura = lapply(file_list_leitura, read.table, header = TRUE, sep = "",fill = TRUE)
# Juntar os dados
dados_leitura = do.call(rbind, data_list_leitura)
# Excluir colunas desnecessarias
dados_leitura = subset(dados_leitura, select = c("DATE","TIME","JULIAN_DAYS","MPVPosition","N2O_dry","CO2_dry","CH4_dry","H2O","NH3"))
#Criar datetime
dados_leitura = dados_leitura %>% mutate(datetime = as.POSIXct(paste(DATE, TIME),
format = "%Y-%m-%d %H:%M:%OS", tz = "America/Sao_Paulo"))
#Filtrar por horario
filtrados_leitura2 = dados_leitura %>% filter( #aqui
(datetime >= as.POSIXct("2026-04-16 08:00:00", tz = "America/Sao_Paulo") & #aqui
datetime <= as.POSIXct("2026-04-16 14:00:00", tz = "America/Sao_Paulo"))) #aqui
head(filtrados_leitura2) #aquirange(filtrados_leitura2$datetime) #aqui[1] "2026-04-16 08:00:00 -03" "2026-04-16 13:59:59 -03"# Carregar ID da leitura
ID.leitura2=read.csv("C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 2/ID leitura2.csv") #aqui
str(ID.leitura2) #aqui'data.frame': 30 obs. of 12 variables:
$ Bottle : int 1 2 3 4 5 6 7 8 9 10 ...
$ Tratamento : chr "Controle - Apenas água" "Fezes ovinas tratadas com oleo de girassol ozonizado " "Fezes ovinas tratadas com oleo de girassol" "Água ozonizada" ...
$ Abreviacao : chr "CON" "FOGO3" "FOG" "AO3" ...
$ Bloco : int 1 1 1 1 1 2 2 2 2 2 ...
$ Rep : num 1 1 1 1 1 2 2 2 2 2 ...
$ Leitura : int 2 2 2 2 2 2 2 2 2 2 ...
$ Date : chr "16/04/2026" "16/04/2026" "16/04/2026" "16/04/2026" ...
$ Solo.Seco : num 247 246 246 246 246 ...
$ Beginning.reading : chr "08:49" "08:56" "09:03" "09:10" ...
$ End.reading : chr "08:52" "08:59" "09:06" "09:13" ...
$ External.temperature: num 24.1 24.1 24.4 24.3 24.3 24.3 23.8 22.1 21.8 22.6 ...
$ Internal.temperature: num 24.9 25.3 25.2 25.3 25.5 25.3 25.5 25.2 25.3 25.3 ...head(ID.leitura2) #aquiLeitura 3 - 17/04/2026
# Caminho dos arquivos .dat
file_leitura = "C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 3" #aqui
# Obter a lista de arquivos .dat
file_list_leitura = list.files(file_leitura, pattern = ".dat", full.names = TRUE)
# Ler os dados de todos os arquivos
data_list_leitura = lapply(file_list_leitura, read.table, header = TRUE, sep = "",fill = TRUE)
# Juntar os dados
dados_leitura = do.call(rbind, data_list_leitura)
# Excluir colunas desnecessarias
dados_leitura = subset(dados_leitura, select = c("DATE","TIME","JULIAN_DAYS","MPVPosition","N2O_dry","CO2_dry","CH4_dry","H2O","NH3"))
#Criar datetime
dados_leitura = dados_leitura %>% mutate(datetime = as.POSIXct(paste(DATE, TIME),
format = "%Y-%m-%d %H:%M:%OS", tz = "America/Sao_Paulo"))
#Filtrar por horario
filtrados_leitura3 = dados_leitura %>% filter( #aqui
(datetime >= as.POSIXct("2026-04-17 09:00:00", tz = "America/Sao_Paulo") & #aqui
datetime <= as.POSIXct("2026-04-17 13:00:00", tz = "America/Sao_Paulo"))) #aqui
head(filtrados_leitura3) #aquirange(filtrados_leitura3$datetime) #aqui[1] "2026-04-17 09:00:01 -03" "2026-04-17 12:59:59 -03"# Carregar ID da leitura
ID.leitura3=read.csv("C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 3/ID leitura3.csv") #aqui
str(ID.leitura3) #aqui'data.frame': 30 obs. of 12 variables:
$ Bottle : int 1 2 3 4 5 6 7 8 9 10 ...
$ Tratamento : chr "Controle - Apenas água" "Fezes ovinas tratadas com oleo de girassol ozonizado " "Fezes ovinas tratadas com oleo de girassol" "Água ozonizada" ...
$ Abreviacao : chr "CON" "FOGO3" "FOG" "AO3" ...
$ Bloco : int 1 1 1 1 1 2 2 2 2 2 ...
$ Rep : num 1 1 1 1 1 2 2 2 2 2 ...
$ Leitura : int 3 3 3 3 3 3 3 3 3 3 ...
$ Date : chr "17/04/2026" "17/04/2026" "17/04/2026" "17/04/2026" ...
$ Solo.Seco : num 247 246 246 246 246 ...
$ Beginning.reading : chr "09:09" "09:16" "09:23" "09:30" ...
$ End.reading : chr "09:12" "09:19" "09:26" "09:33" ...
$ External.temperature: num 22.8 21.8 21.8 21.9 22 20.8 21.1 21.9 20.8 21.3 ...
$ Internal.temperature: num 25.1 24.9 25 25.3 25.3 25.2 25.6 25.5 25.3 25.3 ...head(ID.leitura3) #aquiLeitura 4 - 19/04/2026
# Caminho dos arquivos .dat
file_leitura = "C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 4" #aqui
# Obter a lista de arquivos .dat
file_list_leitura = list.files(file_leitura, pattern = ".dat", full.names = TRUE)
# Ler os dados de todos os arquivos
data_list_leitura = lapply(file_list_leitura, read.table, header = TRUE, sep = "",fill = TRUE)
# Juntar os dados
dados_leitura = do.call(rbind, data_list_leitura)
# Excluir colunas desnecessarias
dados_leitura = subset(dados_leitura, select = c("DATE","TIME","JULIAN_DAYS","MPVPosition","N2O_dry","CO2_dry","CH4_dry","H2O","NH3"))
#Criar datetime
dados_leitura = dados_leitura %>% mutate(datetime = as.POSIXct(paste(DATE, TIME),
format = "%Y-%m-%d %H:%M:%OS", tz = "America/Sao_Paulo"))
#Filtrar por horario
filtrados_leitura4 = dados_leitura %>% filter( #aqui
(datetime >= as.POSIXct("2026-04-19 09:00:00", tz = "America/Sao_Paulo") & #aqui
datetime <= as.POSIXct("2026-04-19 14:00:00", tz = "America/Sao_Paulo"))) #aqui
head(filtrados_leitura4) #aquirange(filtrados_leitura4$datetime) #aqui[1] "2026-04-19 09:00:00 -03" "2026-04-19 13:59:59 -03"# Carregar ID da leitura
ID.leitura4=read.csv("C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 4/ID leitura4.csv") #aqui
str(ID.leitura4) #aqui'data.frame': 30 obs. of 12 variables:
$ Bottle : int 1 2 3 4 5 6 7 8 9 10 ...
$ Tratamento : chr "Controle - Apenas água" "Fezes ovinas tratadas com oleo de girassol ozonizado " "Fezes ovinas tratadas com oleo de girassol" "Água ozonizada" ...
$ Abreviacao : chr "CON" "FOGO3" "FOG" "AO3" ...
$ Bloco : int 1 1 1 1 1 2 2 2 2 2 ...
$ Rep : int 1 1 1 1 1 2 2 2 2 2 ...
$ Leitura : int 4 4 4 4 4 4 4 4 4 4 ...
$ Date : chr "19/04/2026" "19/04/2026" "19/04/2026" "19/04/2026" ...
$ Solo.Seco : num 247 246 246 246 246 ...
$ Beginning.reading : chr "09:31" "09:38" "09:45" "09:52" ...
$ End.reading : chr "09:34" "09:41" "09:48" "09:55" ...
$ External.temperature: num 23.1 21.5 22.3 21.9 20.9 21.9 20.9 21.9 22.5 20.5 ...
$ Internal.temperature: num 25.2 25 25.5 25.3 25.1 25.3 25.2 25.2 25.3 25.3 ...head(ID.leitura4) #aquiLeitura 5 - 22/04/2026
# Caminho dos arquivos .dat
file_leitura = "C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 5" #aqui
# Obter a lista de arquivos .dat
file_list_leitura = list.files(file_leitura, pattern = ".dat", full.names = TRUE)
# Ler os dados de todos os arquivos
data_list_leitura = lapply(file_list_leitura, read.table, header = TRUE, sep = "",fill = TRUE)
# Juntar os dados
dados_leitura = do.call(rbind, data_list_leitura)
# Excluir colunas desnecessarias
dados_leitura = subset(dados_leitura, select = c("DATE","TIME","JULIAN_DAYS","MPVPosition","N2O_dry","CO2_dry","CH4_dry","H2O","NH3"))
#Criar datetime
dados_leitura = dados_leitura %>% mutate(datetime = as.POSIXct(paste(DATE, TIME),
format = "%Y-%m-%d %H:%M:%OS", tz = "America/Sao_Paulo"))
#Filtrar por horario
filtrados_leitura5 = dados_leitura %>% filter( #aqui
(datetime >= as.POSIXct("2026-04-22 08:00:00", tz = "America/Sao_Paulo") & #aqui
datetime <= as.POSIXct("2026-04-22 12:00:00", tz = "America/Sao_Paulo"))) #aqui
head(filtrados_leitura5) #aquirange(filtrados_leitura5$datetime) #aqui[1] "2026-04-22 08:00:00 -03" "2026-04-22 11:59:59 -03"# Carregar ID da leitura
ID.leitura5=read.csv("C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 5/ID leitura5.csv") #aqui
str(ID.leitura5) #aqui'data.frame': 30 obs. of 12 variables:
$ Bottle : int 1 2 3 4 5 6 7 8 9 10 ...
$ Tratamento : chr "Controle - Apenas água" "Fezes ovinas tratadas com oleo de girassol ozonizado " "Fezes ovinas tratadas com oleo de girassol" "Água ozonizada" ...
$ Abreviacao : chr "CON" "FOGO3" "FOG" "AO3" ...
$ Bloco : int 1 1 1 1 1 2 2 2 2 2 ...
$ Rep : int 1 1 1 1 1 2 2 2 2 2 ...
$ Leitura : int 5 5 5 5 5 5 5 5 5 5 ...
$ Date : chr "22/04/2026" "22/04/2026" "22/04/2026" "22/04/2026" ...
$ Solo.Seco : num 247 246 246 246 246 ...
$ Beginning.reading : chr "08:22" "08:29" "08:36" "08:43" ...
$ End.reading : chr "08:25" "08:32" "08:39" "08:46" ...
$ External.temperature: num 22.8 20.6 21.3 21 20.8 21.5 20.1 20.8 20.6 20.8 ...
$ Internal.temperature: num 25 25.2 25.3 25.2 25.2 25.4 25.1 25.3 25.1 25.5 ...head(ID.leitura5) #aquiLeitura 6 - 27/04/2026
# Caminho dos arquivos .dat
file_leitura = "C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 6" #aqui
# Obter a lista de arquivos .dat
file_list_leitura = list.files(file_leitura, pattern = ".dat", full.names = TRUE)
# Ler os dados de todos os arquivos
data_list_leitura = lapply(file_list_leitura, read.table, header = TRUE, sep = "",fill = TRUE)
# Juntar os dados
dados_leitura = do.call(rbind, data_list_leitura)
# Excluir colunas desnecessarias
dados_leitura = subset(dados_leitura, select = c("DATE","TIME","JULIAN_DAYS","MPVPosition","N2O_dry","CO2_dry","CH4_dry","H2O","NH3"))
#Criar datetime
dados_leitura = dados_leitura %>% mutate(datetime = as.POSIXct(paste(DATE, TIME),
format = "%Y-%m-%d %H:%M:%OS", tz = "America/Sao_Paulo"))
#Filtrar por horario
filtrados_leitura6 = dados_leitura %>% filter( #aqui
(datetime >= as.POSIXct("2026-04-27 09:00:00", tz = "America/Sao_Paulo") & #aqui
datetime <= as.POSIXct("2026-04-27 13:00:00", tz = "America/Sao_Paulo"))) #aqui
head(filtrados_leitura6) #aquirange(filtrados_leitura6$datetime) #aqui[1] "2026-04-27 09:00:00 -03" "2026-04-27 12:59:59 -03"# Carregar ID da leitura
ID.leitura6=read.csv("C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 6/ID leitura6.csv") #aqui
str(ID.leitura6) #aqui'data.frame': 30 obs. of 12 variables:
$ Bottle : int 1 2 3 4 5 6 7 8 9 10 ...
$ Tratamento : chr "Controle - Apenas água" "Fezes ovinas tratadas com oleo de girassol ozonizado " "Fezes ovinas tratadas com oleo de girassol" "Água ozonizada" ...
$ Abreviacao : chr "CON" "FOGO3" "FOG" "AO3" ...
$ Bloco : int 1 1 1 1 1 2 2 2 2 2 ...
$ Rep : num 1 1 1 1 1 2 2 2 2 2 ...
$ Leitura : int 6 6 6 6 6 6 6 6 6 6 ...
$ Date : chr "27/04/2026" "27/04/2026" "27/04/2026" "27/04/2026" ...
$ Solo.Seco : num 247 246 246 246 246 ...
$ Beginning.reading : chr "09:06" "09:13" "09:20" "09:27" ...
$ End.reading : chr "09:09" "09:16" "09:23" "09:30" ...
$ External.temperature: num 23.1 22.4 23.1 22.3 20.7 20.9 20.5 20.9 20.1 21.9 ...
$ Internal.temperature: num 25.3 25.3 25.5 25.5 25.3 25.5 25.4 25.2 25.2 25.1 ...head(ID.leitura6) #aquiLeitura 7 - 05/05/2026
# Caminho dos arquivos .dat
file_leitura = "C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 7" #aqui
# Obter a lista de arquivos .dat
file_list_leitura = list.files(file_leitura, pattern = ".dat", full.names = TRUE)
# Ler os dados de todos os arquivos
data_list_leitura = lapply(file_list_leitura, read.table, header = TRUE, sep = "",fill = TRUE)
# Juntar os dados
dados_leitura = do.call(rbind, data_list_leitura)
# Excluir colunas desnecessarias
dados_leitura = subset(dados_leitura, select = c("DATE","TIME","JULIAN_DAYS","MPVPosition","N2O_dry","CO2_dry","CH4_dry","H2O","NH3"))
#Criar datetime
dados_leitura = dados_leitura %>% mutate(datetime = as.POSIXct(paste(DATE, TIME),
format = "%Y-%m-%d %H:%M:%OS", tz = "America/Sao_Paulo"))
#Filtrar por horario
filtrados_leitura7 = dados_leitura %>% filter( #aqui
(datetime >= as.POSIXct("2026-05-05 10:00:00", tz = "America/Sao_Paulo") & #aqui
datetime <= as.POSIXct("2026-05-05 14:00:00", tz = "America/Sao_Paulo"))) #aqui
head(filtrados_leitura7) #aquirange(filtrados_leitura7$datetime) #aqui[1] "2026-05-05 10:00:00 -03" "2026-05-05 13:59:59 -03"# Carregar ID da leitura
ID.leitura7=read.csv("C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 7/ID leitura7.csv") #aqui
str(ID.leitura7) #aqui'data.frame': 30 obs. of 12 variables:
$ Bottle : int 1 2 3 4 5 6 7 8 9 10 ...
$ Tratamento : chr "Controle - Apenas água" "Fezes ovinas tratadas com oleo de girassol ozonizado " "Fezes ovinas tratadas com oleo de girassol" "Água ozonizada" ...
$ Abreviacao : chr "CON" "FOGO3" "FOG" "AO3" ...
$ Bloco : int 1 1 1 1 1 2 2 2 2 2 ...
$ Rep : num 1 1 1 1 1 2 2 2 2 2 ...
$ Leitura : int 7 7 7 7 7 7 7 7 7 7 ...
$ Date : chr "05/05/2026" "05/05/2026" "05/05/2026" "05/05/2026" ...
$ Solo.Seco : num 247 246 246 246 246 ...
$ Beginning.reading : chr "10:12" "10:19" "10:25" "10:33" ...
$ End.reading : chr "10:15" "10:22" "10:28" "10:36" ...
$ External.temperature: num 23.6 22.4 22.4 22.6 20.9 22.1 22.3 20.7 21.8 21.6 ...
$ Internal.temperature: num 25.1 25.1 25.2 25.3 25.3 25.5 25.4 25.3 25.5 25.3 ...head(ID.leitura7) #aquiLeitura 8 - 18/05/2026
# Caminho dos arquivos .dat
file_leitura = "C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 8" #aqui
# Obter a lista de arquivos .dat
file_list_leitura = list.files(file_leitura, pattern = ".dat", full.names = TRUE)
# Ler os dados de todos os arquivos
data_list_leitura = lapply(file_list_leitura, read.table, header = TRUE, sep = "",fill = TRUE)
# Juntar os dados
dados_leitura = do.call(rbind, data_list_leitura)
# Excluir colunas desnecessarias
dados_leitura = subset(dados_leitura, select = c("DATE","TIME","JULIAN_DAYS","MPVPosition","N2O_dry","CO2_dry","CH4_dry","H2O","NH3"))
#Criar datetime
dados_leitura = dados_leitura %>% mutate(datetime = as.POSIXct(paste(DATE, TIME),
format = "%Y-%m-%d %H:%M:%OS", tz = "America/Sao_Paulo"))
#Filtrar por horario
filtrados_leitura8 = dados_leitura %>% filter( #aqui
(datetime >= as.POSIXct("2026-05-18 08:00:00", tz = "America/Sao_Paulo") & #aqui
datetime <= as.POSIXct("2026-05-18 13:00:00", tz = "America/Sao_Paulo"))) #aqui
head(filtrados_leitura8) #aquirange(filtrados_leitura8$datetime) #aqui[1] "2026-05-18 08:00:00 -03" "2026-05-18 12:59:59 -03"# Carregar ID da leitura
ID.leitura8=read.csv("C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 8/ID leitura8.csv") #aqui
str(ID.leitura8) #aqui'data.frame': 30 obs. of 12 variables:
$ Bottle : int 1 2 3 4 5 6 7 8 9 10 ...
$ Tratamento : chr "Controle - Apenas água" "Fezes ovinas tratadas com oleo de girassol ozonizado " "Fezes ovinas tratadas com oleo de girassol" "Água ozonizada" ...
$ Abreviacao : chr "CON" "FOGO3" "FOG" "AO3" ...
$ Bloco : int 1 1 1 1 1 2 2 2 2 2 ...
$ Rep : num 1 1 1 1 1 2 2 2 2 2 ...
$ Leitura : int 8 8 8 8 8 8 8 8 8 8 ...
$ Date : chr "18/05/2026" "18/05/2026" "18/05/2026" "18/05/2026" ...
$ Solo.Seco : num 247 246 246 246 246 ...
$ Beginning.reading : chr "09:00" "09:07" "09:14" "09:21" ...
$ End.reading : chr "09:03" "09:10" "09:17" "09:24" ...
$ External.temperature: num 23.5 22.3 21.6 22.5 23.6 22.4 22.8 22.1 22.5 21.7 ...
$ Internal.temperature: num 24.9 24.8 25.1 25 25.1 25.3 25.3 25.1 25.1 25.1 ...head(ID.leitura8) #aqui2 - Identificar leituras dos frascos e background
Leitura 1 - 15/04/2026
library(data.table)
########################### PREPARAR DADOS ###########################
dados1 = as.data.table(filtrados_leitura1)
id = as.data.table(ID.leitura1)
# Criar início e fim das leituras
id[, start := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), Beginning.reading),
format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")] Bottle Tratamento Abreviacao Bloco Rep Leitura Date
<int> <char> <char> <int> <num> <int> <char>
1: 1 Controle - Apenas água CON 1 1 1 15/04/2026
2: 2 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 1 1 1 15/04/2026
3: 3 Fezes ovinas tratadas com oleo de girassol FOG 1 1 1 15/04/2026
4: 4 Água ozonizada AO3 1 1 1 15/04/2026
5: 5 Adubo nitrogenado (ureia) NMIN 1 1 1 15/04/2026
6: 6 Controle - Apenas água CON 2 2 1 15/04/2026
7: 7 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 2 2 1 15/04/2026
8: 8 Fezes ovinas tratadas com oleo de girassol FOG 2 2 1 15/04/2026
9: 9 Água ozonizada AO3 2 2 1 15/04/2026
10: 10 Adubo nitrogenado NMIN 2 2 1 15/04/2026
11: 11 Controle - Apenas água CON 3 3 1 15/04/2026
12: 12 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 3 3 1 15/04/2026
13: 13 Fezes ovinas tratadas com oleo de girassol FOG 3 3 1 15/04/2026
14: 14 Água ozonizada AO3 3 3 1 15/04/2026
15: 15 Adubo nitrogenado NMIN 3 3 1 15/04/2026
16: 16 Controle - Apenas água CON 4 4 1 15/04/2026
17: 17 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 4 4 1 15/04/2026
18: 18 Fezes ovinas tratadas com oleo de girassol FOG 4 4 1 15/04/2026
19: 19 Água ozonizada AO3 4 4 1 15/04/2026
20: 20 Adubo nitrogenado NMIN 4 4 1 15/04/2026
21: 21 Controle - Apenas água CON 5 5 1 15/04/2026
22: 22 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 5 5 1 15/04/2026
23: 23 Fezes ovinas tratadas com oleo de girassol FOG 5 5 1 15/04/2026
24: 24 Água ozonizada AO3 5 5 1 15/04/2026
25: 25 Adubo nitrogenado NMIN 5 5 1 15/04/2026
26: 26 Controle - Apenas água CON 6 6 1 15/04/2026
27: 27 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 6 6 1 15/04/2026
28: 28 Fezes ovinas tratadas com oleo de girassol FOG 6 6 1 15/04/2026
29: 29 Água ozonizada AO3 6 6 1 15/04/2026
30: 30 Adubo nitrogenado NMIN 6 6 1 15/04/2026
Bottle Tratamento Abreviacao Bloco Rep Leitura Date
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature start
<num> <char> <char> <num> <num> <POSc>
1: 246.61 09:09 09:12 22.9 25.5 2026-04-15 09:09:00
2: 246.39 09:16 09:19 22.5 24.6 2026-04-15 09:16:00
3: 246.21 09:23 09:26 22.1 25.1 2026-04-15 09:23:00
4: 246.31 09:30 09:33 22.8 25.3 2026-04-15 09:30:00
5: 246.29 09:51 09:54 22.4 23.8 2026-04-15 09:51:00
6: 246.26 09:44 09:47 22.8 24.8 2026-04-15 09:44:00
7: 246.14 09:58 10:01 21.5 25.1 2026-04-15 09:58:00
8: 246.21 10:05 10:08 21.3 25.1 2026-04-15 10:05:00
9: 246.14 10:12 10:15 21.7 25.0 2026-04-15 10:12:00
10: 246.35 10:19 10:22 20.6 25.5 2026-04-15 10:19:00
11: 246.26 10:26 10:29 22.3 25.3 2026-04-15 10:26:00
12: 246.30 10:33 10:36 22.1 25.1 2026-04-15 10:33:00
13: 246.38 10:40 10:43 21.4 25.0 2026-04-15 10:40:00
14: 246.37 10:47 10:50 21.3 25.3 2026-04-15 10:47:00
15: 246.15 10:54 10:57 21.5 25.6 2026-04-15 10:54:00
16: 246.50 11:01 11:04 21.3 25.1 2026-04-15 11:01:00
17: 246.36 11:08 11:11 21.6 25.1 2026-04-15 11:08:00
18: 246.30 11:15 11:18 21.5 25.0 2026-04-15 11:15:00
19: 246.25 11:22 11:25 21.6 25.3 2026-04-15 11:22:00
20: 246.34 11:50 11:53 21.3 24.8 2026-04-15 11:50:00
21: 246.23 11:36 11:39 20.7 25.3 2026-04-15 11:36:00
22: 246.18 11:43 11:46 21.9 25.1 2026-04-15 11:43:00
23: 246.21 11:57 12:00 22.3 25.1 2026-04-15 11:57:00
24: 246.31 12:04 12:07 21.7 25.3 2026-04-15 12:04:00
25: 246.35 12:11 12:14 22.3 25.3 2026-04-15 12:11:00
26: 246.25 12:18 12:21 21.7 25.2 2026-04-15 12:18:00
27: 246.25 12:25 12:28 20.9 25.2 2026-04-15 12:25:00
28: 246.36 12:32 12:35 21.1 25.0 2026-04-15 12:32:00
29: 246.23 12:53 12:56 20.9 24.4 2026-04-15 12:53:00
30: 246.35 12:46 12:49 21.5 24.8 2026-04-15 12:46:00
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature startid[, end := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), End.reading),
format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")] Bottle Tratamento Abreviacao Bloco Rep Leitura Date
<int> <char> <char> <int> <num> <int> <char>
1: 1 Controle - Apenas água CON 1 1 1 15/04/2026
2: 2 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 1 1 1 15/04/2026
3: 3 Fezes ovinas tratadas com oleo de girassol FOG 1 1 1 15/04/2026
4: 4 Água ozonizada AO3 1 1 1 15/04/2026
5: 5 Adubo nitrogenado (ureia) NMIN 1 1 1 15/04/2026
6: 6 Controle - Apenas água CON 2 2 1 15/04/2026
7: 7 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 2 2 1 15/04/2026
8: 8 Fezes ovinas tratadas com oleo de girassol FOG 2 2 1 15/04/2026
9: 9 Água ozonizada AO3 2 2 1 15/04/2026
10: 10 Adubo nitrogenado NMIN 2 2 1 15/04/2026
11: 11 Controle - Apenas água CON 3 3 1 15/04/2026
12: 12 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 3 3 1 15/04/2026
13: 13 Fezes ovinas tratadas com oleo de girassol FOG 3 3 1 15/04/2026
14: 14 Água ozonizada AO3 3 3 1 15/04/2026
15: 15 Adubo nitrogenado NMIN 3 3 1 15/04/2026
16: 16 Controle - Apenas água CON 4 4 1 15/04/2026
17: 17 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 4 4 1 15/04/2026
18: 18 Fezes ovinas tratadas com oleo de girassol FOG 4 4 1 15/04/2026
19: 19 Água ozonizada AO3 4 4 1 15/04/2026
20: 20 Adubo nitrogenado NMIN 4 4 1 15/04/2026
21: 21 Controle - Apenas água CON 5 5 1 15/04/2026
22: 22 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 5 5 1 15/04/2026
23: 23 Fezes ovinas tratadas com oleo de girassol FOG 5 5 1 15/04/2026
24: 24 Água ozonizada AO3 5 5 1 15/04/2026
25: 25 Adubo nitrogenado NMIN 5 5 1 15/04/2026
26: 26 Controle - Apenas água CON 6 6 1 15/04/2026
27: 27 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 6 6 1 15/04/2026
28: 28 Fezes ovinas tratadas com oleo de girassol FOG 6 6 1 15/04/2026
29: 29 Água ozonizada AO3 6 6 1 15/04/2026
30: 30 Adubo nitrogenado NMIN 6 6 1 15/04/2026
Bottle Tratamento Abreviacao Bloco Rep Leitura Date
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature start
<num> <char> <char> <num> <num> <POSc>
1: 246.61 09:09 09:12 22.9 25.5 2026-04-15 09:09:00
2: 246.39 09:16 09:19 22.5 24.6 2026-04-15 09:16:00
3: 246.21 09:23 09:26 22.1 25.1 2026-04-15 09:23:00
4: 246.31 09:30 09:33 22.8 25.3 2026-04-15 09:30:00
5: 246.29 09:51 09:54 22.4 23.8 2026-04-15 09:51:00
6: 246.26 09:44 09:47 22.8 24.8 2026-04-15 09:44:00
7: 246.14 09:58 10:01 21.5 25.1 2026-04-15 09:58:00
8: 246.21 10:05 10:08 21.3 25.1 2026-04-15 10:05:00
9: 246.14 10:12 10:15 21.7 25.0 2026-04-15 10:12:00
10: 246.35 10:19 10:22 20.6 25.5 2026-04-15 10:19:00
11: 246.26 10:26 10:29 22.3 25.3 2026-04-15 10:26:00
12: 246.30 10:33 10:36 22.1 25.1 2026-04-15 10:33:00
13: 246.38 10:40 10:43 21.4 25.0 2026-04-15 10:40:00
14: 246.37 10:47 10:50 21.3 25.3 2026-04-15 10:47:00
15: 246.15 10:54 10:57 21.5 25.6 2026-04-15 10:54:00
16: 246.50 11:01 11:04 21.3 25.1 2026-04-15 11:01:00
17: 246.36 11:08 11:11 21.6 25.1 2026-04-15 11:08:00
18: 246.30 11:15 11:18 21.5 25.0 2026-04-15 11:15:00
19: 246.25 11:22 11:25 21.6 25.3 2026-04-15 11:22:00
20: 246.34 11:50 11:53 21.3 24.8 2026-04-15 11:50:00
21: 246.23 11:36 11:39 20.7 25.3 2026-04-15 11:36:00
22: 246.18 11:43 11:46 21.9 25.1 2026-04-15 11:43:00
23: 246.21 11:57 12:00 22.3 25.1 2026-04-15 11:57:00
24: 246.31 12:04 12:07 21.7 25.3 2026-04-15 12:04:00
25: 246.35 12:11 12:14 22.3 25.3 2026-04-15 12:11:00
26: 246.25 12:18 12:21 21.7 25.2 2026-04-15 12:18:00
27: 246.25 12:25 12:28 20.9 25.2 2026-04-15 12:25:00
28: 246.36 12:32 12:35 21.1 25.0 2026-04-15 12:32:00
29: 246.23 12:53 12:56 20.9 24.4 2026-04-15 12:53:00
30: 246.35 12:46 12:49 21.5 24.8 2026-04-15 12:46:00
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature start
end
<POSc>
1: 2026-04-15 09:12:00
2: 2026-04-15 09:19:00
3: 2026-04-15 09:26:00
4: 2026-04-15 09:33:00
5: 2026-04-15 09:54:00
6: 2026-04-15 09:47:00
7: 2026-04-15 10:01:00
8: 2026-04-15 10:08:00
9: 2026-04-15 10:15:00
10: 2026-04-15 10:22:00
11: 2026-04-15 10:29:00
12: 2026-04-15 10:36:00
13: 2026-04-15 10:43:00
14: 2026-04-15 10:50:00
15: 2026-04-15 10:57:00
16: 2026-04-15 11:04:00
17: 2026-04-15 11:11:00
18: 2026-04-15 11:18:00
19: 2026-04-15 11:25:00
20: 2026-04-15 11:53:00
21: 2026-04-15 11:39:00
22: 2026-04-15 11:46:00
23: 2026-04-15 12:00:00
24: 2026-04-15 12:07:00
25: 2026-04-15 12:14:00
26: 2026-04-15 12:21:00
27: 2026-04-15 12:28:00
28: 2026-04-15 12:35:00
29: 2026-04-15 12:56:00
30: 2026-04-15 12:49:00
end########################### MPV 1 - LEITURA DOS FRASCOS ###########################
leitura1 = copy(dados1)
leitura1[, c(
"Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") := .(
NA_integer_, NA_character_, NA_integer_, NA_integer_,NA_integer_, NA_real_, NA_real_)] DATE TIME JULIAN_DAYS MPVPosition N2O_dry CO2_dry CH4_dry H2O NH3
<char> <char> <num> <num> <num> <num> <num> <num> <num>
1: 2026-04-15 09:00:00.302 105.4167 6 0.3217566 475.4381 2.280470 2.825441 195.4304
2: 2026-04-15 09:00:01.078 105.4167 6 0.3217608 475.3730 2.280697 2.825593 195.3620
3: 2026-04-15 09:00:01.736 105.4167 6 0.3195317 475.3179 2.280889 2.825721 195.3041
4: 2026-04-15 09:00:02.496 105.4167 6 0.3169542 475.2595 2.281130 2.826495 195.2260
5: 2026-04-15 09:00:03.558 105.4167 6 0.3133611 475.2594 2.281465 2.827570 195.1171
---
17282: 2026-04-15 12:55:56.891 105.5805 1 0.3281264 568.5016 2.942714 4.671953 202.7369
17283: 2026-04-15 12:55:57.677 105.5805 1 0.3305572 568.6779 2.942535 4.684007 203.1691
17284: 2026-04-15 12:55:58.753 105.5805 1 0.3338910 569.1185 2.942289 4.700526 203.7612
17285: 2026-04-15 12:55:59.581 105.5806 1 0.3339351 569.4571 2.942101 4.713218 204.2159
17286: 2026-04-15 12:55:59.910 105.5806 1 0.3339171 569.5919 2.942026 4.718269 204.3968
datetime Bottle Abreviacao Bloco Rep Leitura Solo.Seco Internal.temperature
<POSc> <int> <char> <int> <int> <int> <num> <num>
1: 2026-04-15 09:00:00 NA <NA> NA NA NA NA NA
2: 2026-04-15 09:00:01 NA <NA> NA NA NA NA NA
3: 2026-04-15 09:00:01 NA <NA> NA NA NA NA NA
4: 2026-04-15 09:00:02 NA <NA> NA NA NA NA NA
5: 2026-04-15 09:00:03 NA <NA> NA NA NA NA NA
---
17282: 2026-04-15 12:55:56 NA <NA> NA NA NA NA NA
17283: 2026-04-15 12:55:57 NA <NA> NA NA NA NA NA
17284: 2026-04-15 12:55:58 NA <NA> NA NA NA NA NA
17285: 2026-04-15 12:55:59 NA <NA> NA NA NA NA NA
17286: 2026-04-15 12:55:59 NA <NA> NA NA NA NA NAleitura1[MPVPosition == 1,c(
"Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") :=
id[.SD,.(Bottle, Abreviacao, Bloco, Rep,Leitura, Solo.Seco, Internal.temperature),
on = .(start <= datetime, end > datetime),mult = "first"]]Index: <MPVPosition>
DATE TIME JULIAN_DAYS MPVPosition N2O_dry CO2_dry CH4_dry H2O NH3
<char> <char> <num> <num> <num> <num> <num> <num> <num>
1: 2026-04-15 09:00:00.302 105.4167 6 0.3217566 475.4381 2.280470 2.825441 195.4304
2: 2026-04-15 09:00:01.078 105.4167 6 0.3217608 475.3730 2.280697 2.825593 195.3620
3: 2026-04-15 09:00:01.736 105.4167 6 0.3195317 475.3179 2.280889 2.825721 195.3041
4: 2026-04-15 09:00:02.496 105.4167 6 0.3169542 475.2595 2.281130 2.826495 195.2260
5: 2026-04-15 09:00:03.558 105.4167 6 0.3133611 475.2594 2.281465 2.827570 195.1171
---
17282: 2026-04-15 12:55:56.891 105.5805 1 0.3281264 568.5016 2.942714 4.671953 202.7369
17283: 2026-04-15 12:55:57.677 105.5805 1 0.3305572 568.6779 2.942535 4.684007 203.1691
17284: 2026-04-15 12:55:58.753 105.5805 1 0.3338910 569.1185 2.942289 4.700526 203.7612
17285: 2026-04-15 12:55:59.581 105.5806 1 0.3339351 569.4571 2.942101 4.713218 204.2159
17286: 2026-04-15 12:55:59.910 105.5806 1 0.3339171 569.5919 2.942026 4.718269 204.3968
datetime Bottle Abreviacao Bloco Rep Leitura Solo.Seco Internal.temperature
<POSc> <int> <char> <int> <int> <int> <num> <num>
1: 2026-04-15 09:00:00 NA <NA> NA NA NA NA NA
2: 2026-04-15 09:00:01 NA <NA> NA NA NA NA NA
3: 2026-04-15 09:00:01 NA <NA> NA NA NA NA NA
4: 2026-04-15 09:00:02 NA <NA> NA NA NA NA NA
5: 2026-04-15 09:00:03 NA <NA> NA NA NA NA NA
---
17282: 2026-04-15 12:55:56 29 AO3 6 6 1 246.23 24.4
17283: 2026-04-15 12:55:57 29 AO3 6 6 1 246.23 24.4
17284: 2026-04-15 12:55:58 29 AO3 6 6 1 246.23 24.4
17285: 2026-04-15 12:55:59 29 AO3 6 6 1 246.23 24.4
17286: 2026-04-15 12:55:59 29 AO3 6 6 1 246.23 24.4# Manter apenas as leituras válidas dos frascos
leitura1 = leitura1[MPVPosition == 1 & !is.na(Bottle)]
View(leitura1)Leitura 2 - 16/04/2026
########################### PREPARAR DADOS ###########################
dados2 = as.data.table(filtrados_leitura2)
id = as.data.table(ID.leitura2)
# Criar início e fim das leituras
id[, start := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), Beginning.reading),
format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")] Bottle Tratamento Abreviacao Bloco Rep Leitura Date
<int> <char> <char> <int> <num> <int> <char>
1: 1 Controle - Apenas água CON 1 1 2 16/04/2026
2: 2 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 1 1 2 16/04/2026
3: 3 Fezes ovinas tratadas com oleo de girassol FOG 1 1 2 16/04/2026
4: 4 Água ozonizada AO3 1 1 2 16/04/2026
5: 5 Adubo nitrogenado (ureia) NMIN 1 1 2 16/04/2026
6: 6 Controle - Apenas água CON 2 2 2 16/04/2026
7: 7 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 2 2 2 16/04/2026
8: 8 Fezes ovinas tratadas com oleo de girassol FOG 2 2 2 16/04/2026
9: 9 Água ozonizada AO3 2 2 2 16/04/2026
10: 10 Adubo nitrogenado NMIN 2 2 2 16/04/2026
11: 11 Controle - Apenas água CON 3 3 2 16/04/2026
12: 12 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 3 3 2 16/04/2026
13: 13 Fezes ovinas tratadas com oleo de girassol FOG 3 3 2 16/04/2026
14: 14 Água ozonizada AO3 3 3 2 16/04/2026
15: 15 Adubo nitrogenado NMIN 3 3 2 16/04/2026
16: 16 Controle - Apenas água CON 4 4 2 16/04/2026
17: 17 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 4 4 2 16/04/2026
18: 18 Fezes ovinas tratadas com oleo de girassol FOG 4 4 2 16/04/2026
19: 19 Água ozonizada AO3 4 4 2 16/04/2026
20: 20 Adubo nitrogenado NMIN 4 4 2 16/04/2026
21: 21 Controle - Apenas água CON 5 5 2 16/04/2026
22: 22 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 5 5 2 16/04/2026
23: 23 Fezes ovinas tratadas com oleo de girassol FOG 5 5 2 16/04/2026
24: 24 Água ozonizada AO3 5 5 2 16/04/2026
25: 25 Adubo nitrogenado NMIN 5 5 2 16/04/2026
26: 26 Controle - Apenas água CON 6 6 2 16/04/2026
27: 27 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 6 6 2 16/04/2026
28: 28 Fezes ovinas tratadas com oleo de girassol FOG 6 6 2 16/04/2026
29: 29 Água ozonizada AO3 6 6 2 16/04/2026
30: 30 Adubo nitrogenado NMIN 6 6 2 16/04/2026
Bottle Tratamento Abreviacao Bloco Rep Leitura Date
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature start
<num> <char> <char> <num> <num> <POSc>
1: 246.61 08:49 08:52 24.1 24.9 2026-04-16 08:49:00
2: 246.39 08:56 08:59 24.1 25.3 2026-04-16 08:56:00
3: 246.21 09:03 09:06 24.4 25.2 2026-04-16 09:03:00
4: 246.31 09:10 09:13 24.3 25.3 2026-04-16 09:10:00
5: 246.29 09:17 09:20 24.3 25.5 2026-04-16 09:17:00
6: 246.26 09:24 09:27 24.3 25.3 2026-04-16 09:24:00
7: 246.14 09:31 09:34 23.8 25.5 2026-04-16 09:31:00
8: 246.21 09:38 09:41 22.1 25.2 2026-04-16 09:38:00
9: 246.14 09:45 09:48 21.8 25.3 2026-04-16 09:45:00
10: 246.35 09:52 09:55 22.6 25.3 2026-04-16 09:52:00
11: 246.26 09:59 10:02 21.9 25.3 2026-04-16 09:59:00
12: 246.30 10:06 10:09 21.0 25.3 2026-04-16 10:06:00
13: 246.38 10:13 10:16 22.5 25.3 2026-04-16 10:13:00
14: 246.37 10:20 10:23 21.0 25.2 2026-04-16 10:20:00
15: 246.15 10:27 10:30 21.4 25.3 2026-04-16 10:27:00
16: 246.50 10:34 10:37 21.3 25.3 2026-04-16 10:34:00
17: 246.36 10:41 10:44 21.8 25.3 2026-04-16 10:41:00
18: 246.30 10:48 10:51 22.5 25.3 2026-04-16 10:48:00
19: 246.25 10:55 10:58 22.1 25.2 2026-04-16 10:55:00
20: 246.34 11:02 11:05 21.9 25.8 2026-04-16 11:02:00
21: 246.23 11:23 11:26 22.5 25.7 2026-04-16 11:23:00
22: 246.18 12:32 12:35 23.4 24.9 2026-04-16 12:32:00
23: 246.21 12:39 12:42 22.8 24.8 2026-04-16 12:39:00
24: 246.31 12:46 12:49 22.6 25.1 2026-04-16 12:46:00
25: 246.35 12:53 12:56 20.3 24.9 2026-04-16 12:53:00
26: 246.25 13:00 13:03 22.7 24.8 2026-04-16 13:00:00
27: 246.25 13:07 13:10 20.6 24.6 2026-04-16 13:07:00
28: 246.36 13:14 13:17 22.1 25.3 2026-04-16 13:14:00
29: 246.23 13:21 13:24 22.8 25.1 2026-04-16 13:21:00
30: 246.35 13:28 13:31 21.3 25.2 2026-04-16 13:28:00
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature startid[, end := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), End.reading),
format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")] Bottle Tratamento Abreviacao Bloco Rep Leitura Date
<int> <char> <char> <int> <num> <int> <char>
1: 1 Controle - Apenas água CON 1 1 2 16/04/2026
2: 2 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 1 1 2 16/04/2026
3: 3 Fezes ovinas tratadas com oleo de girassol FOG 1 1 2 16/04/2026
4: 4 Água ozonizada AO3 1 1 2 16/04/2026
5: 5 Adubo nitrogenado (ureia) NMIN 1 1 2 16/04/2026
6: 6 Controle - Apenas água CON 2 2 2 16/04/2026
7: 7 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 2 2 2 16/04/2026
8: 8 Fezes ovinas tratadas com oleo de girassol FOG 2 2 2 16/04/2026
9: 9 Água ozonizada AO3 2 2 2 16/04/2026
10: 10 Adubo nitrogenado NMIN 2 2 2 16/04/2026
11: 11 Controle - Apenas água CON 3 3 2 16/04/2026
12: 12 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 3 3 2 16/04/2026
13: 13 Fezes ovinas tratadas com oleo de girassol FOG 3 3 2 16/04/2026
14: 14 Água ozonizada AO3 3 3 2 16/04/2026
15: 15 Adubo nitrogenado NMIN 3 3 2 16/04/2026
16: 16 Controle - Apenas água CON 4 4 2 16/04/2026
17: 17 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 4 4 2 16/04/2026
18: 18 Fezes ovinas tratadas com oleo de girassol FOG 4 4 2 16/04/2026
19: 19 Água ozonizada AO3 4 4 2 16/04/2026
20: 20 Adubo nitrogenado NMIN 4 4 2 16/04/2026
21: 21 Controle - Apenas água CON 5 5 2 16/04/2026
22: 22 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 5 5 2 16/04/2026
23: 23 Fezes ovinas tratadas com oleo de girassol FOG 5 5 2 16/04/2026
24: 24 Água ozonizada AO3 5 5 2 16/04/2026
25: 25 Adubo nitrogenado NMIN 5 5 2 16/04/2026
26: 26 Controle - Apenas água CON 6 6 2 16/04/2026
27: 27 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 6 6 2 16/04/2026
28: 28 Fezes ovinas tratadas com oleo de girassol FOG 6 6 2 16/04/2026
29: 29 Água ozonizada AO3 6 6 2 16/04/2026
30: 30 Adubo nitrogenado NMIN 6 6 2 16/04/2026
Bottle Tratamento Abreviacao Bloco Rep Leitura Date
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature start
<num> <char> <char> <num> <num> <POSc>
1: 246.61 08:49 08:52 24.1 24.9 2026-04-16 08:49:00
2: 246.39 08:56 08:59 24.1 25.3 2026-04-16 08:56:00
3: 246.21 09:03 09:06 24.4 25.2 2026-04-16 09:03:00
4: 246.31 09:10 09:13 24.3 25.3 2026-04-16 09:10:00
5: 246.29 09:17 09:20 24.3 25.5 2026-04-16 09:17:00
6: 246.26 09:24 09:27 24.3 25.3 2026-04-16 09:24:00
7: 246.14 09:31 09:34 23.8 25.5 2026-04-16 09:31:00
8: 246.21 09:38 09:41 22.1 25.2 2026-04-16 09:38:00
9: 246.14 09:45 09:48 21.8 25.3 2026-04-16 09:45:00
10: 246.35 09:52 09:55 22.6 25.3 2026-04-16 09:52:00
11: 246.26 09:59 10:02 21.9 25.3 2026-04-16 09:59:00
12: 246.30 10:06 10:09 21.0 25.3 2026-04-16 10:06:00
13: 246.38 10:13 10:16 22.5 25.3 2026-04-16 10:13:00
14: 246.37 10:20 10:23 21.0 25.2 2026-04-16 10:20:00
15: 246.15 10:27 10:30 21.4 25.3 2026-04-16 10:27:00
16: 246.50 10:34 10:37 21.3 25.3 2026-04-16 10:34:00
17: 246.36 10:41 10:44 21.8 25.3 2026-04-16 10:41:00
18: 246.30 10:48 10:51 22.5 25.3 2026-04-16 10:48:00
19: 246.25 10:55 10:58 22.1 25.2 2026-04-16 10:55:00
20: 246.34 11:02 11:05 21.9 25.8 2026-04-16 11:02:00
21: 246.23 11:23 11:26 22.5 25.7 2026-04-16 11:23:00
22: 246.18 12:32 12:35 23.4 24.9 2026-04-16 12:32:00
23: 246.21 12:39 12:42 22.8 24.8 2026-04-16 12:39:00
24: 246.31 12:46 12:49 22.6 25.1 2026-04-16 12:46:00
25: 246.35 12:53 12:56 20.3 24.9 2026-04-16 12:53:00
26: 246.25 13:00 13:03 22.7 24.8 2026-04-16 13:00:00
27: 246.25 13:07 13:10 20.6 24.6 2026-04-16 13:07:00
28: 246.36 13:14 13:17 22.1 25.3 2026-04-16 13:14:00
29: 246.23 13:21 13:24 22.8 25.1 2026-04-16 13:21:00
30: 246.35 13:28 13:31 21.3 25.2 2026-04-16 13:28:00
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature start
end
<POSc>
1: 2026-04-16 08:52:00
2: 2026-04-16 08:59:00
3: 2026-04-16 09:06:00
4: 2026-04-16 09:13:00
5: 2026-04-16 09:20:00
6: 2026-04-16 09:27:00
7: 2026-04-16 09:34:00
8: 2026-04-16 09:41:00
9: 2026-04-16 09:48:00
10: 2026-04-16 09:55:00
11: 2026-04-16 10:02:00
12: 2026-04-16 10:09:00
13: 2026-04-16 10:16:00
14: 2026-04-16 10:23:00
15: 2026-04-16 10:30:00
16: 2026-04-16 10:37:00
17: 2026-04-16 10:44:00
18: 2026-04-16 10:51:00
19: 2026-04-16 10:58:00
20: 2026-04-16 11:05:00
21: 2026-04-16 11:26:00
22: 2026-04-16 12:35:00
23: 2026-04-16 12:42:00
24: 2026-04-16 12:49:00
25: 2026-04-16 12:56:00
26: 2026-04-16 13:03:00
27: 2026-04-16 13:10:00
28: 2026-04-16 13:17:00
29: 2026-04-16 13:24:00
30: 2026-04-16 13:31:00
end########################### MPV 1 - LEITURA DOS FRASCOS ###########################
leitura2 = copy(dados2)
leitura2[, c(
"Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") := .(
NA_integer_, NA_character_, NA_integer_, NA_integer_,NA_integer_, NA_real_, NA_real_)] DATE TIME JULIAN_DAYS MPVPosition N2O_dry CO2_dry CH4_dry H2O NH3
<char> <char> <num> <num> <num> <num> <num> <num> <num>
1: 2026-04-16 08:00:00.319 106.375 2 0.3453462 519.8073 2.843718 1.980916 174.8344
2: 2026-04-16 08:00:00.990 106.375 2 0.3408664 519.7384 2.843515 1.981191 174.8154
3: 2026-04-16 08:00:01.782 106.375 2 0.3355795 519.6579 2.843278 1.981595 174.7870
4: 2026-04-16 08:00:02.869 106.375 2 0.3283175 519.6893 2.842953 1.982147 174.7481
5: 2026-04-16 08:00:03.670 106.375 2 0.3283217 519.7123 2.842713 1.982553 174.7195
---
23326: 2026-04-16 13:59:56.207 106.625 2 0.3313497 473.5128 2.751786 1.478828 134.6767
23327: 2026-04-16 13:59:56.976 106.625 2 0.3313584 473.4986 2.751614 1.480540 134.7796
23328: 2026-04-16 13:59:57.631 106.625 2 0.3310794 473.4865 2.751467 1.481998 134.8673
23329: 2026-04-16 13:59:58.388 106.625 2 0.3307511 473.4638 2.751260 1.482631 134.9263
23330: 2026-04-16 13:59:59.447 106.625 2 0.3302906 473.3283 2.750971 1.483521 135.0090
datetime Bottle Abreviacao Bloco Rep Leitura Solo.Seco Internal.temperature
<POSc> <int> <char> <int> <int> <int> <num> <num>
1: 2026-04-16 08:00:00 NA <NA> NA NA NA NA NA
2: 2026-04-16 08:00:00 NA <NA> NA NA NA NA NA
3: 2026-04-16 08:00:01 NA <NA> NA NA NA NA NA
4: 2026-04-16 08:00:02 NA <NA> NA NA NA NA NA
5: 2026-04-16 08:00:03 NA <NA> NA NA NA NA NA
---
23326: 2026-04-16 13:59:56 NA <NA> NA NA NA NA NA
23327: 2026-04-16 13:59:56 NA <NA> NA NA NA NA NA
23328: 2026-04-16 13:59:57 NA <NA> NA NA NA NA NA
23329: 2026-04-16 13:59:58 NA <NA> NA NA NA NA NA
23330: 2026-04-16 13:59:59 NA <NA> NA NA NA NA NAleitura2[MPVPosition == 1,c(
"Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") :=
id[.SD,.(Bottle, Abreviacao, Bloco, Rep,Leitura, Solo.Seco, Internal.temperature),
on = .(start <= datetime, end > datetime),mult = "first"]]Index: <MPVPosition>
DATE TIME JULIAN_DAYS MPVPosition N2O_dry CO2_dry CH4_dry H2O NH3
<char> <char> <num> <num> <num> <num> <num> <num> <num>
1: 2026-04-16 08:00:00.319 106.375 2 0.3453462 519.8073 2.843718 1.980916 174.8344
2: 2026-04-16 08:00:00.990 106.375 2 0.3408664 519.7384 2.843515 1.981191 174.8154
3: 2026-04-16 08:00:01.782 106.375 2 0.3355795 519.6579 2.843278 1.981595 174.7870
4: 2026-04-16 08:00:02.869 106.375 2 0.3283175 519.6893 2.842953 1.982147 174.7481
5: 2026-04-16 08:00:03.670 106.375 2 0.3283217 519.7123 2.842713 1.982553 174.7195
---
23326: 2026-04-16 13:59:56.207 106.625 2 0.3313497 473.5128 2.751786 1.478828 134.6767
23327: 2026-04-16 13:59:56.976 106.625 2 0.3313584 473.4986 2.751614 1.480540 134.7796
23328: 2026-04-16 13:59:57.631 106.625 2 0.3310794 473.4865 2.751467 1.481998 134.8673
23329: 2026-04-16 13:59:58.388 106.625 2 0.3307511 473.4638 2.751260 1.482631 134.9263
23330: 2026-04-16 13:59:59.447 106.625 2 0.3302906 473.3283 2.750971 1.483521 135.0090
datetime Bottle Abreviacao Bloco Rep Leitura Solo.Seco Internal.temperature
<POSc> <int> <char> <int> <int> <int> <num> <num>
1: 2026-04-16 08:00:00 NA <NA> NA NA NA NA NA
2: 2026-04-16 08:00:00 NA <NA> NA NA NA NA NA
3: 2026-04-16 08:00:01 NA <NA> NA NA NA NA NA
4: 2026-04-16 08:00:02 NA <NA> NA NA NA NA NA
5: 2026-04-16 08:00:03 NA <NA> NA NA NA NA NA
---
23326: 2026-04-16 13:59:56 NA <NA> NA NA NA NA NA
23327: 2026-04-16 13:59:56 NA <NA> NA NA NA NA NA
23328: 2026-04-16 13:59:57 NA <NA> NA NA NA NA NA
23329: 2026-04-16 13:59:58 NA <NA> NA NA NA NA NA
23330: 2026-04-16 13:59:59 NA <NA> NA NA NA NA NA# Manter apenas as leituras válidas dos frascos
leitura2 = leitura2[MPVPosition == 1 & !is.na(Bottle)]
View(leitura2) Leitura 3 - 17/04/2026
########################### PREPARAR DADOS ###########################
dados3 = as.data.table(filtrados_leitura3)
id = as.data.table(ID.leitura3)
# Criar início e fim das leituras
id[, start := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), Beginning.reading),
format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")] Bottle Tratamento Abreviacao Bloco Rep Leitura Date
<int> <char> <char> <int> <num> <int> <char>
1: 1 Controle - Apenas água CON 1 1 3 17/04/2026
2: 2 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 1 1 3 17/04/2026
3: 3 Fezes ovinas tratadas com oleo de girassol FOG 1 1 3 17/04/2026
4: 4 Água ozonizada AO3 1 1 3 17/04/2026
5: 5 Adubo nitrogenado (ureia) NMIN 1 1 3 17/04/2026
6: 6 Controle - Apenas água CON 2 2 3 17/04/2026
7: 7 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 2 2 3 17/04/2026
8: 8 Fezes ovinas tratadas com oleo de girassol FOG 2 2 3 17/04/2026
9: 9 Água ozonizada AO3 2 2 3 17/04/2026
10: 10 Adubo nitrogenado NMIN 2 2 3 17/04/2026
11: 11 Controle - Apenas água CON 3 3 3 17/04/2026
12: 12 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 3 3 3 17/04/2026
13: 13 Fezes ovinas tratadas com oleo de girassol FOG 3 3 3 17/04/2026
14: 14 Água ozonizada AO3 3 3 3 17/04/2026
15: 15 Adubo nitrogenado NMIN 3 3 3 17/04/2026
16: 16 Controle - Apenas água CON 4 4 3 17/04/2026
17: 17 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 4 4 3 17/04/2026
18: 18 Fezes ovinas tratadas com oleo de girassol FOG 4 4 3 17/04/2026
19: 19 Água ozonizada AO3 4 4 3 17/04/2026
20: 20 Adubo nitrogenado NMIN 4 4 3 17/04/2026
21: 21 Controle - Apenas água CON 5 5 3 17/04/2026
22: 22 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 5 5 3 17/04/2026
23: 23 Fezes ovinas tratadas com oleo de girassol FOG 5 5 3 17/04/2026
24: 24 Água ozonizada AO3 5 5 3 17/04/2026
25: 25 Adubo nitrogenado NMIN 5 5 3 17/04/2026
26: 26 Controle - Apenas água CON 6 6 3 17/04/2026
27: 27 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 6 6 3 17/04/2026
28: 28 Fezes ovinas tratadas com oleo de girassol FOG 6 6 3 17/04/2026
29: 29 Água ozonizada AO3 6 6 3 17/04/2026
30: 30 Adubo nitrogenado NMIN 6 6 3 17/04/2026
Bottle Tratamento Abreviacao Bloco Rep Leitura Date
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature start
<num> <char> <char> <num> <num> <POSc>
1: 246.61 09:09 09:12 22.8 25.1 2026-04-17 09:09:00
2: 246.39 09:16 09:19 21.8 24.9 2026-04-17 09:16:00
3: 246.21 09:23 09:26 21.8 25.0 2026-04-17 09:23:00
4: 246.31 09:30 09:33 21.9 25.3 2026-04-17 09:30:00
5: 246.29 09:37 09:40 22.0 25.3 2026-04-17 09:37:00
6: 246.26 09:44 09:47 20.8 25.2 2026-04-17 09:44:00
7: 246.14 09:51 09:54 21.1 25.6 2026-04-17 09:51:00
8: 246.21 09:58 10:01 21.9 25.5 2026-04-17 09:58:00
9: 246.14 10:05 10:08 20.8 25.3 2026-04-17 10:05:00
10: 246.35 10:12 10:15 21.3 25.3 2026-04-17 10:12:00
11: 246.26 10:19 10:22 21.2 25.3 2026-04-17 10:19:00
12: 246.30 10:26 10:29 20.6 25.6 2026-04-17 10:26:00
13: 246.38 10:33 10:36 21.0 25.3 2026-04-17 10:33:00
14: 246.37 10:40 10:43 20.7 25.3 2026-04-17 10:40:00
15: 246.15 10:47 10:50 20.9 25.1 2026-04-17 10:47:00
16: 246.50 10:54 10:57 20.8 24.9 2026-04-17 10:54:00
17: 246.36 11:01 11:04 18.5 25.4 2026-04-17 11:01:00
18: 246.30 11:08 11:11 19.0 25.3 2026-04-17 11:08:00
19: 246.25 11:15 11:18 20.7 25.1 2026-04-17 11:15:00
20: 246.34 11:22 11:25 19.8 25.1 2026-04-17 11:22:00
21: 246.23 11:29 11:32 20.3 25.1 2026-04-17 11:29:00
22: 246.18 11:36 11:39 20.6 25.4 2026-04-17 11:36:00
23: 246.21 11:43 11:46 20.0 25.1 2026-04-17 11:43:00
24: 246.31 11:50 11:53 20.5 25.0 2026-04-17 11:50:00
25: 246.35 11:57 12:00 19.8 25.1 2026-04-17 11:57:00
26: 246.25 12:04 12:07 19.6 25.3 2026-04-17 12:04:00
27: 246.25 12:11 12:14 20.6 25.2 2026-04-17 12:11:00
28: 246.36 12:18 12:21 19.9 25.3 2026-04-17 12:18:00
29: 246.23 12:25 12:28 19.2 24.9 2026-04-17 12:25:00
30: 246.35 12:32 12:35 18.9 24.9 2026-04-17 12:32:00
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature startid[, end := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), End.reading),
format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")] Bottle Tratamento Abreviacao Bloco Rep Leitura Date
<int> <char> <char> <int> <num> <int> <char>
1: 1 Controle - Apenas água CON 1 1 3 17/04/2026
2: 2 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 1 1 3 17/04/2026
3: 3 Fezes ovinas tratadas com oleo de girassol FOG 1 1 3 17/04/2026
4: 4 Água ozonizada AO3 1 1 3 17/04/2026
5: 5 Adubo nitrogenado (ureia) NMIN 1 1 3 17/04/2026
6: 6 Controle - Apenas água CON 2 2 3 17/04/2026
7: 7 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 2 2 3 17/04/2026
8: 8 Fezes ovinas tratadas com oleo de girassol FOG 2 2 3 17/04/2026
9: 9 Água ozonizada AO3 2 2 3 17/04/2026
10: 10 Adubo nitrogenado NMIN 2 2 3 17/04/2026
11: 11 Controle - Apenas água CON 3 3 3 17/04/2026
12: 12 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 3 3 3 17/04/2026
13: 13 Fezes ovinas tratadas com oleo de girassol FOG 3 3 3 17/04/2026
14: 14 Água ozonizada AO3 3 3 3 17/04/2026
15: 15 Adubo nitrogenado NMIN 3 3 3 17/04/2026
16: 16 Controle - Apenas água CON 4 4 3 17/04/2026
17: 17 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 4 4 3 17/04/2026
18: 18 Fezes ovinas tratadas com oleo de girassol FOG 4 4 3 17/04/2026
19: 19 Água ozonizada AO3 4 4 3 17/04/2026
20: 20 Adubo nitrogenado NMIN 4 4 3 17/04/2026
21: 21 Controle - Apenas água CON 5 5 3 17/04/2026
22: 22 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 5 5 3 17/04/2026
23: 23 Fezes ovinas tratadas com oleo de girassol FOG 5 5 3 17/04/2026
24: 24 Água ozonizada AO3 5 5 3 17/04/2026
25: 25 Adubo nitrogenado NMIN 5 5 3 17/04/2026
26: 26 Controle - Apenas água CON 6 6 3 17/04/2026
27: 27 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 6 6 3 17/04/2026
28: 28 Fezes ovinas tratadas com oleo de girassol FOG 6 6 3 17/04/2026
29: 29 Água ozonizada AO3 6 6 3 17/04/2026
30: 30 Adubo nitrogenado NMIN 6 6 3 17/04/2026
Bottle Tratamento Abreviacao Bloco Rep Leitura Date
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature start
<num> <char> <char> <num> <num> <POSc>
1: 246.61 09:09 09:12 22.8 25.1 2026-04-17 09:09:00
2: 246.39 09:16 09:19 21.8 24.9 2026-04-17 09:16:00
3: 246.21 09:23 09:26 21.8 25.0 2026-04-17 09:23:00
4: 246.31 09:30 09:33 21.9 25.3 2026-04-17 09:30:00
5: 246.29 09:37 09:40 22.0 25.3 2026-04-17 09:37:00
6: 246.26 09:44 09:47 20.8 25.2 2026-04-17 09:44:00
7: 246.14 09:51 09:54 21.1 25.6 2026-04-17 09:51:00
8: 246.21 09:58 10:01 21.9 25.5 2026-04-17 09:58:00
9: 246.14 10:05 10:08 20.8 25.3 2026-04-17 10:05:00
10: 246.35 10:12 10:15 21.3 25.3 2026-04-17 10:12:00
11: 246.26 10:19 10:22 21.2 25.3 2026-04-17 10:19:00
12: 246.30 10:26 10:29 20.6 25.6 2026-04-17 10:26:00
13: 246.38 10:33 10:36 21.0 25.3 2026-04-17 10:33:00
14: 246.37 10:40 10:43 20.7 25.3 2026-04-17 10:40:00
15: 246.15 10:47 10:50 20.9 25.1 2026-04-17 10:47:00
16: 246.50 10:54 10:57 20.8 24.9 2026-04-17 10:54:00
17: 246.36 11:01 11:04 18.5 25.4 2026-04-17 11:01:00
18: 246.30 11:08 11:11 19.0 25.3 2026-04-17 11:08:00
19: 246.25 11:15 11:18 20.7 25.1 2026-04-17 11:15:00
20: 246.34 11:22 11:25 19.8 25.1 2026-04-17 11:22:00
21: 246.23 11:29 11:32 20.3 25.1 2026-04-17 11:29:00
22: 246.18 11:36 11:39 20.6 25.4 2026-04-17 11:36:00
23: 246.21 11:43 11:46 20.0 25.1 2026-04-17 11:43:00
24: 246.31 11:50 11:53 20.5 25.0 2026-04-17 11:50:00
25: 246.35 11:57 12:00 19.8 25.1 2026-04-17 11:57:00
26: 246.25 12:04 12:07 19.6 25.3 2026-04-17 12:04:00
27: 246.25 12:11 12:14 20.6 25.2 2026-04-17 12:11:00
28: 246.36 12:18 12:21 19.9 25.3 2026-04-17 12:18:00
29: 246.23 12:25 12:28 19.2 24.9 2026-04-17 12:25:00
30: 246.35 12:32 12:35 18.9 24.9 2026-04-17 12:32:00
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature start
end
<POSc>
1: 2026-04-17 09:12:00
2: 2026-04-17 09:19:00
3: 2026-04-17 09:26:00
4: 2026-04-17 09:33:00
5: 2026-04-17 09:40:00
6: 2026-04-17 09:47:00
7: 2026-04-17 09:54:00
8: 2026-04-17 10:01:00
9: 2026-04-17 10:08:00
10: 2026-04-17 10:15:00
11: 2026-04-17 10:22:00
12: 2026-04-17 10:29:00
13: 2026-04-17 10:36:00
14: 2026-04-17 10:43:00
15: 2026-04-17 10:50:00
16: 2026-04-17 10:57:00
17: 2026-04-17 11:04:00
18: 2026-04-17 11:11:00
19: 2026-04-17 11:18:00
20: 2026-04-17 11:25:00
21: 2026-04-17 11:32:00
22: 2026-04-17 11:39:00
23: 2026-04-17 11:46:00
24: 2026-04-17 11:53:00
25: 2026-04-17 12:00:00
26: 2026-04-17 12:07:00
27: 2026-04-17 12:14:00
28: 2026-04-17 12:21:00
29: 2026-04-17 12:28:00
30: 2026-04-17 12:35:00
end########################### MPV 1 - LEITURA DOS FRASCOS ###########################
leitura3 = copy(dados3)
leitura3[, c(
"Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") := .(
NA_integer_, NA_character_, NA_integer_, NA_integer_,NA_integer_, NA_real_, NA_real_)] DATE TIME JULIAN_DAYS MPVPosition N2O_dry CO2_dry CH4_dry H2O NH3
<char> <char> <num> <num> <num> <num> <num> <num> <num>
1: 2026-04-17 09:00:01.065 107.4167 2 0.3422074 567.7332 3.080015 1.930018 187.99025
2: 2026-04-17 09:00:01.825 107.4167 2 0.3422014 567.5623 3.080052 1.929129 187.96281
3: 2026-04-17 09:00:02.450 107.4167 2 0.3410180 567.4215 3.080083 1.928398 187.94019
4: 2026-04-17 09:00:03.190 107.4167 2 0.3396211 567.2517 3.080105 1.927198 187.81842
5: 2026-04-17 09:00:04.282 107.4167 2 0.3375602 567.0791 3.080139 1.925427 187.63873
---
18167: 2026-04-17 12:59:56.684 107.5833 2 0.3364050 459.2750 2.172541 1.357984 84.98730
18168: 2026-04-17 12:59:57.448 107.5833 2 0.3364013 459.2459 2.172491 1.357547 84.98544
18169: 2026-04-17 12:59:58.125 107.5833 2 0.3344320 459.2201 2.172447 1.357160 84.98376
18170: 2026-04-17 12:59:58.907 107.5833 2 0.3321558 459.1934 2.172407 1.357096 85.01648
18171: 2026-04-17 12:59:59.974 107.5833 2 0.3290474 459.2013 2.172352 1.357008 85.06117
datetime Bottle Abreviacao Bloco Rep Leitura Solo.Seco Internal.temperature
<POSc> <int> <char> <int> <int> <int> <num> <num>
1: 2026-04-17 09:00:01 NA <NA> NA NA NA NA NA
2: 2026-04-17 09:00:01 NA <NA> NA NA NA NA NA
3: 2026-04-17 09:00:02 NA <NA> NA NA NA NA NA
4: 2026-04-17 09:00:03 NA <NA> NA NA NA NA NA
5: 2026-04-17 09:00:04 NA <NA> NA NA NA NA NA
---
18167: 2026-04-17 12:59:56 NA <NA> NA NA NA NA NA
18168: 2026-04-17 12:59:57 NA <NA> NA NA NA NA NA
18169: 2026-04-17 12:59:58 NA <NA> NA NA NA NA NA
18170: 2026-04-17 12:59:58 NA <NA> NA NA NA NA NA
18171: 2026-04-17 12:59:59 NA <NA> NA NA NA NA NAleitura3[MPVPosition == 1,c(
"Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") :=
id[.SD,.(Bottle, Abreviacao, Bloco, Rep,Leitura, Solo.Seco, Internal.temperature),
on = .(start <= datetime, end > datetime),mult = "first"]]Index: <MPVPosition>
DATE TIME JULIAN_DAYS MPVPosition N2O_dry CO2_dry CH4_dry H2O NH3
<char> <char> <num> <num> <num> <num> <num> <num> <num>
1: 2026-04-17 09:00:01.065 107.4167 2 0.3422074 567.7332 3.080015 1.930018 187.99025
2: 2026-04-17 09:00:01.825 107.4167 2 0.3422014 567.5623 3.080052 1.929129 187.96281
3: 2026-04-17 09:00:02.450 107.4167 2 0.3410180 567.4215 3.080083 1.928398 187.94019
4: 2026-04-17 09:00:03.190 107.4167 2 0.3396211 567.2517 3.080105 1.927198 187.81842
5: 2026-04-17 09:00:04.282 107.4167 2 0.3375602 567.0791 3.080139 1.925427 187.63873
---
18167: 2026-04-17 12:59:56.684 107.5833 2 0.3364050 459.2750 2.172541 1.357984 84.98730
18168: 2026-04-17 12:59:57.448 107.5833 2 0.3364013 459.2459 2.172491 1.357547 84.98544
18169: 2026-04-17 12:59:58.125 107.5833 2 0.3344320 459.2201 2.172447 1.357160 84.98376
18170: 2026-04-17 12:59:58.907 107.5833 2 0.3321558 459.1934 2.172407 1.357096 85.01648
18171: 2026-04-17 12:59:59.974 107.5833 2 0.3290474 459.2013 2.172352 1.357008 85.06117
datetime Bottle Abreviacao Bloco Rep Leitura Solo.Seco Internal.temperature
<POSc> <int> <char> <int> <int> <int> <num> <num>
1: 2026-04-17 09:00:01 NA <NA> NA NA NA NA NA
2: 2026-04-17 09:00:01 NA <NA> NA NA NA NA NA
3: 2026-04-17 09:00:02 NA <NA> NA NA NA NA NA
4: 2026-04-17 09:00:03 NA <NA> NA NA NA NA NA
5: 2026-04-17 09:00:04 NA <NA> NA NA NA NA NA
---
18167: 2026-04-17 12:59:56 NA <NA> NA NA NA NA NA
18168: 2026-04-17 12:59:57 NA <NA> NA NA NA NA NA
18169: 2026-04-17 12:59:58 NA <NA> NA NA NA NA NA
18170: 2026-04-17 12:59:58 NA <NA> NA NA NA NA NA
18171: 2026-04-17 12:59:59 NA <NA> NA NA NA NA NA# Manter apenas as leituras válidas dos frascos
leitura3 = leitura3[MPVPosition == 1 & !is.na(Bottle)]
View(leitura3)Leitura 4 - 19/04/2026
########################### PREPARAR DADOS ###########################
dados4 = as.data.table(filtrados_leitura4)
id = as.data.table(ID.leitura4)
# Criar início e fim das leituras
id[, start := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), Beginning.reading),
format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")] Bottle Tratamento Abreviacao Bloco Rep Leitura Date
<int> <char> <char> <int> <int> <int> <char>
1: 1 Controle - Apenas água CON 1 1 4 19/04/2026
2: 2 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 1 1 4 19/04/2026
3: 3 Fezes ovinas tratadas com oleo de girassol FOG 1 1 4 19/04/2026
4: 4 Água ozonizada AO3 1 1 4 19/04/2026
5: 5 Adubo nitrogenado (ureia) NMIN 1 1 4 19/04/2026
6: 6 Controle - Apenas água CON 2 2 4 19/04/2026
7: 7 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 2 2 4 19/04/2026
8: 8 Fezes ovinas tratadas com oleo de girassol FOG 2 2 4 19/04/2026
9: 9 Água ozonizada AO3 2 2 4 19/04/2026
10: 10 Adubo nitrogenado NMIN 2 2 4 19/04/2026
11: 11 Controle - Apenas água CON 3 3 4 19/04/2026
12: 12 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 3 3 4 19/04/2026
13: 13 Fezes ovinas tratadas com oleo de girassol FOG 3 3 4 19/04/2026
14: 14 Água ozonizada AO3 3 3 4 19/04/2026
15: 15 Adubo nitrogenado NMIN 3 3 4 19/04/2026
16: 16 Controle - Apenas água CON 4 4 4 19/04/2026
17: 17 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 4 4 4 19/04/2026
18: 18 Fezes ovinas tratadas com oleo de girassol FOG 4 4 4 19/04/2026
19: 19 Água ozonizada AO3 4 4 4 19/04/2026
20: 20 Adubo nitrogenado NMIN 4 4 4 19/04/2026
21: 21 Controle - Apenas água CON 5 5 4 19/04/2026
22: 22 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 5 5 4 19/04/2026
23: 23 Fezes ovinas tratadas com oleo de girassol FOG 5 5 4 19/04/2026
24: 24 Água ozonizada AO3 5 5 4 19/04/2026
25: 25 Adubo nitrogenado NMIN 5 5 4 19/04/2026
26: 26 Controle - Apenas água CON 6 6 4 19/04/2026
27: 27 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 6 6 4 19/04/2026
28: 28 Fezes ovinas tratadas com oleo de girassol FOG 6 6 4 19/04/2026
29: 29 Água ozonizada AO3 6 6 4 19/04/2026
30: 30 Adubo nitrogenado NMIN 6 6 4 19/04/2026
Bottle Tratamento Abreviacao Bloco Rep Leitura Date
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature start
<num> <char> <char> <num> <num> <POSc>
1: 246.61 09:31 09:34 23.1 25.2 2026-04-19 09:31:00
2: 246.39 09:38 09:41 21.5 25.0 2026-04-19 09:38:00
3: 246.21 09:45 09:48 22.3 25.5 2026-04-19 09:45:00
4: 246.31 09:52 09:55 21.9 25.3 2026-04-19 09:52:00
5: 246.29 09:59 10:02 20.9 25.1 2026-04-19 09:59:00
6: 246.26 10:06 10:09 21.9 25.3 2026-04-19 10:06:00
7: 246.14 10:13 10:16 20.9 25.2 2026-04-19 10:13:00
8: 246.21 10:20 10:23 21.9 25.2 2026-04-19 10:20:00
9: 246.14 10:27 10:30 22.5 25.3 2026-04-19 10:27:00
10: 246.35 10:34 10:37 20.5 25.3 2026-04-19 10:34:00
11: 246.26 10:41 10:44 21.8 24.9 2026-04-19 10:41:00
12: 246.30 10:48 10:51 20.4 25.1 2026-04-19 10:48:00
13: 246.38 10:55 10:58 19.6 25.3 2026-04-19 10:55:00
14: 246.37 11:02 11:05 20.8 25.1 2026-04-19 11:02:00
15: 246.15 11:09 11:12 20.7 25.3 2026-04-19 11:09:00
16: 246.50 11:16 11:19 20.3 25.2 2026-04-19 11:16:00
17: 246.36 11:23 11:26 20.6 25.2 2026-04-19 11:23:00
18: 246.30 12:05 12:08 22.5 24.1 2026-04-19 12:05:00
19: 246.25 12:12 12:15 20.6 25.3 2026-04-19 12:12:00
20: 246.34 12:19 12:22 19.1 25.0 2026-04-19 12:19:00
21: 246.23 12:26 12:29 19.4 25.1 2026-04-19 12:26:00
22: 246.18 12:33 12:36 20.0 25.1 2026-04-19 12:33:00
23: 246.21 12:40 12:43 19.9 24.9 2026-04-19 12:40:00
24: 246.31 13:01 13:04 19.9 24.6 2026-04-19 13:01:00
25: 246.35 12:54 12:57 22.6 25.1 2026-04-19 12:54:00
26: 246.25 13:15 13:18 21.7 25.1 2026-04-19 13:15:00
27: 246.25 13:22 13:25 20.5 25.3 2026-04-19 13:22:00
28: 246.36 13:29 13:32 20.1 24.9 2026-04-19 13:29:00
29: 246.23 13:36 13:39 20.0 24.8 2026-04-19 13:36:00
30: 246.35 13:43 13:46 19.6 25.2 2026-04-19 13:43:00
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature startid[, end := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), End.reading),
format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")] Bottle Tratamento Abreviacao Bloco Rep Leitura Date
<int> <char> <char> <int> <int> <int> <char>
1: 1 Controle - Apenas água CON 1 1 4 19/04/2026
2: 2 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 1 1 4 19/04/2026
3: 3 Fezes ovinas tratadas com oleo de girassol FOG 1 1 4 19/04/2026
4: 4 Água ozonizada AO3 1 1 4 19/04/2026
5: 5 Adubo nitrogenado (ureia) NMIN 1 1 4 19/04/2026
6: 6 Controle - Apenas água CON 2 2 4 19/04/2026
7: 7 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 2 2 4 19/04/2026
8: 8 Fezes ovinas tratadas com oleo de girassol FOG 2 2 4 19/04/2026
9: 9 Água ozonizada AO3 2 2 4 19/04/2026
10: 10 Adubo nitrogenado NMIN 2 2 4 19/04/2026
11: 11 Controle - Apenas água CON 3 3 4 19/04/2026
12: 12 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 3 3 4 19/04/2026
13: 13 Fezes ovinas tratadas com oleo de girassol FOG 3 3 4 19/04/2026
14: 14 Água ozonizada AO3 3 3 4 19/04/2026
15: 15 Adubo nitrogenado NMIN 3 3 4 19/04/2026
16: 16 Controle - Apenas água CON 4 4 4 19/04/2026
17: 17 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 4 4 4 19/04/2026
18: 18 Fezes ovinas tratadas com oleo de girassol FOG 4 4 4 19/04/2026
19: 19 Água ozonizada AO3 4 4 4 19/04/2026
20: 20 Adubo nitrogenado NMIN 4 4 4 19/04/2026
21: 21 Controle - Apenas água CON 5 5 4 19/04/2026
22: 22 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 5 5 4 19/04/2026
23: 23 Fezes ovinas tratadas com oleo de girassol FOG 5 5 4 19/04/2026
24: 24 Água ozonizada AO3 5 5 4 19/04/2026
25: 25 Adubo nitrogenado NMIN 5 5 4 19/04/2026
26: 26 Controle - Apenas água CON 6 6 4 19/04/2026
27: 27 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 6 6 4 19/04/2026
28: 28 Fezes ovinas tratadas com oleo de girassol FOG 6 6 4 19/04/2026
29: 29 Água ozonizada AO3 6 6 4 19/04/2026
30: 30 Adubo nitrogenado NMIN 6 6 4 19/04/2026
Bottle Tratamento Abreviacao Bloco Rep Leitura Date
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature start
<num> <char> <char> <num> <num> <POSc>
1: 246.61 09:31 09:34 23.1 25.2 2026-04-19 09:31:00
2: 246.39 09:38 09:41 21.5 25.0 2026-04-19 09:38:00
3: 246.21 09:45 09:48 22.3 25.5 2026-04-19 09:45:00
4: 246.31 09:52 09:55 21.9 25.3 2026-04-19 09:52:00
5: 246.29 09:59 10:02 20.9 25.1 2026-04-19 09:59:00
6: 246.26 10:06 10:09 21.9 25.3 2026-04-19 10:06:00
7: 246.14 10:13 10:16 20.9 25.2 2026-04-19 10:13:00
8: 246.21 10:20 10:23 21.9 25.2 2026-04-19 10:20:00
9: 246.14 10:27 10:30 22.5 25.3 2026-04-19 10:27:00
10: 246.35 10:34 10:37 20.5 25.3 2026-04-19 10:34:00
11: 246.26 10:41 10:44 21.8 24.9 2026-04-19 10:41:00
12: 246.30 10:48 10:51 20.4 25.1 2026-04-19 10:48:00
13: 246.38 10:55 10:58 19.6 25.3 2026-04-19 10:55:00
14: 246.37 11:02 11:05 20.8 25.1 2026-04-19 11:02:00
15: 246.15 11:09 11:12 20.7 25.3 2026-04-19 11:09:00
16: 246.50 11:16 11:19 20.3 25.2 2026-04-19 11:16:00
17: 246.36 11:23 11:26 20.6 25.2 2026-04-19 11:23:00
18: 246.30 12:05 12:08 22.5 24.1 2026-04-19 12:05:00
19: 246.25 12:12 12:15 20.6 25.3 2026-04-19 12:12:00
20: 246.34 12:19 12:22 19.1 25.0 2026-04-19 12:19:00
21: 246.23 12:26 12:29 19.4 25.1 2026-04-19 12:26:00
22: 246.18 12:33 12:36 20.0 25.1 2026-04-19 12:33:00
23: 246.21 12:40 12:43 19.9 24.9 2026-04-19 12:40:00
24: 246.31 13:01 13:04 19.9 24.6 2026-04-19 13:01:00
25: 246.35 12:54 12:57 22.6 25.1 2026-04-19 12:54:00
26: 246.25 13:15 13:18 21.7 25.1 2026-04-19 13:15:00
27: 246.25 13:22 13:25 20.5 25.3 2026-04-19 13:22:00
28: 246.36 13:29 13:32 20.1 24.9 2026-04-19 13:29:00
29: 246.23 13:36 13:39 20.0 24.8 2026-04-19 13:36:00
30: 246.35 13:43 13:46 19.6 25.2 2026-04-19 13:43:00
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature start
end
<POSc>
1: 2026-04-19 09:34:00
2: 2026-04-19 09:41:00
3: 2026-04-19 09:48:00
4: 2026-04-19 09:55:00
5: 2026-04-19 10:02:00
6: 2026-04-19 10:09:00
7: 2026-04-19 10:16:00
8: 2026-04-19 10:23:00
9: 2026-04-19 10:30:00
10: 2026-04-19 10:37:00
11: 2026-04-19 10:44:00
12: 2026-04-19 10:51:00
13: 2026-04-19 10:58:00
14: 2026-04-19 11:05:00
15: 2026-04-19 11:12:00
16: 2026-04-19 11:19:00
17: 2026-04-19 11:26:00
18: 2026-04-19 12:08:00
19: 2026-04-19 12:15:00
20: 2026-04-19 12:22:00
21: 2026-04-19 12:29:00
22: 2026-04-19 12:36:00
23: 2026-04-19 12:43:00
24: 2026-04-19 13:04:00
25: 2026-04-19 12:57:00
26: 2026-04-19 13:18:00
27: 2026-04-19 13:25:00
28: 2026-04-19 13:32:00
29: 2026-04-19 13:39:00
30: 2026-04-19 13:46:00
end########################### MPV 1 - LEITURA DOS FRASCOS ###########################
leitura4 = copy(dados4)
leitura4[, c(
"Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") := .(
NA_integer_, NA_character_, NA_integer_, NA_integer_,NA_integer_, NA_real_, NA_real_)] DATE TIME JULIAN_DAYS MPVPosition N2O_dry CO2_dry CH4_dry H2O NH3
<char> <char> <num> <num> <num> <num> <num> <num> <num>
1: 2026-04-19 09:00:00.414 109.4167 2 0.3251889 515.2003 3.100977 1.942901 209.1246
2: 2026-04-19 09:00:01.065 109.4167 2 0.3298713 515.1399 3.100505 1.942570 209.1125
3: 2026-04-19 09:00:01.825 109.4167 2 0.3353444 515.0749 3.099976 1.942788 209.2270
4: 2026-04-19 09:00:02.881 109.4167 2 0.3429575 515.1224 3.099241 1.943086 209.3864
5: 2026-04-19 09:00:03.687 109.4167 2 0.3429540 515.1585 3.098681 1.943314 209.5077
---
22258: 2026-04-19 13:59:56.516 109.6250 1 0.3363921 455.7402 2.814126 1.588747 235.1718
22259: 2026-04-19 13:59:57.160 109.6250 1 0.3338485 455.7401 2.814224 1.588953 235.2667
22260: 2026-04-19 13:59:57.924 109.6250 1 0.3308305 455.7482 2.814379 1.590230 235.4322
22261: 2026-04-19 13:59:59.000 109.6250 1 0.3265808 455.8966 2.814597 1.592024 235.6652
22262: 2026-04-19 13:59:59.799 109.6250 1 0.3265830 456.0068 2.814758 1.593357 235.8382
datetime Bottle Abreviacao Bloco Rep Leitura Solo.Seco Internal.temperature
<POSc> <int> <char> <int> <int> <int> <num> <num>
1: 2026-04-19 09:00:00 NA <NA> NA NA NA NA NA
2: 2026-04-19 09:00:01 NA <NA> NA NA NA NA NA
3: 2026-04-19 09:00:01 NA <NA> NA NA NA NA NA
4: 2026-04-19 09:00:02 NA <NA> NA NA NA NA NA
5: 2026-04-19 09:00:03 NA <NA> NA NA NA NA NA
---
22258: 2026-04-19 13:59:56 NA <NA> NA NA NA NA NA
22259: 2026-04-19 13:59:57 NA <NA> NA NA NA NA NA
22260: 2026-04-19 13:59:57 NA <NA> NA NA NA NA NA
22261: 2026-04-19 13:59:59 NA <NA> NA NA NA NA NA
22262: 2026-04-19 13:59:59 NA <NA> NA NA NA NA NAleitura4[MPVPosition == 1,c(
"Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") :=
id[.SD,.(Bottle, Abreviacao, Bloco, Rep,Leitura, Solo.Seco, Internal.temperature),
on = .(start <= datetime, end > datetime),mult = "first"]]Index: <MPVPosition>
DATE TIME JULIAN_DAYS MPVPosition N2O_dry CO2_dry CH4_dry H2O NH3
<char> <char> <num> <num> <num> <num> <num> <num> <num>
1: 2026-04-19 09:00:00.414 109.4167 2 0.3251889 515.2003 3.100977 1.942901 209.1246
2: 2026-04-19 09:00:01.065 109.4167 2 0.3298713 515.1399 3.100505 1.942570 209.1125
3: 2026-04-19 09:00:01.825 109.4167 2 0.3353444 515.0749 3.099976 1.942788 209.2270
4: 2026-04-19 09:00:02.881 109.4167 2 0.3429575 515.1224 3.099241 1.943086 209.3864
5: 2026-04-19 09:00:03.687 109.4167 2 0.3429540 515.1585 3.098681 1.943314 209.5077
---
22258: 2026-04-19 13:59:56.516 109.6250 1 0.3363921 455.7402 2.814126 1.588747 235.1718
22259: 2026-04-19 13:59:57.160 109.6250 1 0.3338485 455.7401 2.814224 1.588953 235.2667
22260: 2026-04-19 13:59:57.924 109.6250 1 0.3308305 455.7482 2.814379 1.590230 235.4322
22261: 2026-04-19 13:59:59.000 109.6250 1 0.3265808 455.8966 2.814597 1.592024 235.6652
22262: 2026-04-19 13:59:59.799 109.6250 1 0.3265830 456.0068 2.814758 1.593357 235.8382
datetime Bottle Abreviacao Bloco Rep Leitura Solo.Seco Internal.temperature
<POSc> <int> <char> <int> <int> <int> <num> <num>
1: 2026-04-19 09:00:00 NA <NA> NA NA NA NA NA
2: 2026-04-19 09:00:01 NA <NA> NA NA NA NA NA
3: 2026-04-19 09:00:01 NA <NA> NA NA NA NA NA
4: 2026-04-19 09:00:02 NA <NA> NA NA NA NA NA
5: 2026-04-19 09:00:03 NA <NA> NA NA NA NA NA
---
22258: 2026-04-19 13:59:56 NA <NA> NA NA NA NA NA
22259: 2026-04-19 13:59:57 NA <NA> NA NA NA NA NA
22260: 2026-04-19 13:59:57 NA <NA> NA NA NA NA NA
22261: 2026-04-19 13:59:59 NA <NA> NA NA NA NA NA
22262: 2026-04-19 13:59:59 NA <NA> NA NA NA NA NA# Manter apenas as leituras válidas dos frascos
leitura4 = leitura4[MPVPosition == 1 & !is.na(Bottle)]
View(leitura4)Leitura 5 - 22/04/2026
########################### PREPARAR DADOS ###########################
dados5 = as.data.table(filtrados_leitura5)
id = as.data.table(ID.leitura5)
# Criar início e fim das leituras
id[, start := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), Beginning.reading),
format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")] Bottle Tratamento Abreviacao Bloco Rep Leitura Date
<int> <char> <char> <int> <int> <int> <char>
1: 1 Controle - Apenas água CON 1 1 5 22/04/2026
2: 2 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 1 1 5 22/04/2026
3: 3 Fezes ovinas tratadas com oleo de girassol FOG 1 1 5 22/04/2026
4: 4 Água ozonizada AO3 1 1 5 22/04/2026
5: 5 Adubo nitrogenado (ureia) NMIN 1 1 5 22/04/2026
6: 6 Controle - Apenas água CON 2 2 5 22/04/2026
7: 7 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 2 2 5 22/04/2026
8: 8 Fezes ovinas tratadas com oleo de girassol FOG 2 2 5 22/04/2026
9: 9 Água ozonizada AO3 2 2 5 22/04/2026
10: 10 Adubo nitrogenado NMIN 2 2 5 22/04/2026
11: 11 Controle - Apenas água CON 3 3 5 22/04/2026
12: 12 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 3 3 5 22/04/2026
13: 13 Fezes ovinas tratadas com oleo de girassol FOG 3 3 5 22/04/2026
14: 14 Água ozonizada AO3 3 3 5 22/04/2026
15: 15 Adubo nitrogenado NMIN 3 3 5 22/04/2026
16: 16 Controle - Apenas água CON 4 4 5 22/04/2026
17: 17 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 4 4 5 22/04/2026
18: 18 Fezes ovinas tratadas com oleo de girassol FOG 4 4 5 22/04/2026
19: 19 Água ozonizada AO3 4 4 5 22/04/2026
20: 20 Adubo nitrogenado NMIN 4 4 5 22/04/2026
21: 21 Controle - Apenas água CON 5 5 5 22/04/2026
22: 22 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 5 5 5 22/04/2026
23: 23 Fezes ovinas tratadas com oleo de girassol FOG 5 5 5 22/04/2026
24: 24 Água ozonizada AO3 5 5 5 22/04/2026
25: 25 Adubo nitrogenado NMIN 5 5 5 22/04/2026
26: 26 Controle - Apenas água CON 6 6 5 22/04/2026
27: 27 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 6 6 5 22/04/2026
28: 28 Fezes ovinas tratadas com oleo de girassol FOG 6 6 5 22/04/2026
29: 29 Água ozonizada AO3 6 6 5 22/04/2026
30: 30 Adubo nitrogenado NMIN 6 6 5 22/04/2026
Bottle Tratamento Abreviacao Bloco Rep Leitura Date
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature start
<num> <char> <char> <num> <num> <POSc>
1: 246.61 08:22 08:25 22.8 25.0 2026-04-22 08:22:00
2: 246.39 08:29 08:32 20.6 25.2 2026-04-22 08:29:00
3: 246.21 08:36 08:39 21.3 25.3 2026-04-22 08:36:00
4: 246.31 08:43 08:46 21.0 25.2 2026-04-22 08:43:00
5: 246.29 08:50 08:53 20.8 25.2 2026-04-22 08:50:00
6: 246.26 08:57 09:00 21.5 25.4 2026-04-22 08:57:00
7: 246.14 09:04 09:07 20.1 25.1 2026-04-22 09:04:00
8: 246.21 09:11 09:14 20.8 25.3 2026-04-22 09:11:00
9: 246.14 09:18 09:21 20.6 25.1 2026-04-22 09:18:00
10: 246.35 09:25 09:28 20.8 25.5 2026-04-22 09:25:00
11: 246.26 09:32 09:35 21.4 25.4 2026-04-22 09:32:00
12: 246.30 09:39 09:42 20.5 25.5 2026-04-22 09:39:00
13: 246.38 09:46 09:49 21.2 25.5 2026-04-22 09:46:00
14: 246.37 09:53 09:56 21.6 25.5 2026-04-22 09:53:00
15: 246.15 10:00 10:03 21.1 25.2 2026-04-22 10:00:00
16: 246.50 10:07 10:10 20.6 25.2 2026-04-22 10:07:00
17: 246.36 10:14 10:17 20.8 25.2 2026-04-22 10:14:00
18: 246.30 10:21 10:24 20.3 25.5 2026-04-22 10:21:00
19: 246.25 10:28 10:31 21.5 25.3 2026-04-22 10:28:00
20: 246.34 10:35 10:38 21.1 25.1 2026-04-22 10:35:00
21: 246.23 10:42 10:45 20.6 25.1 2026-04-22 10:42:00
22: 246.18 10:49 10:52 21.3 25.3 2026-04-22 10:49:00
23: 246.21 10:56 10:59 21.2 25.5 2026-04-22 10:56:00
24: 246.31 11:03 11:06 20.8 25.2 2026-04-22 11:03:00
25: 246.35 11:10 11:13 21.5 25.3 2026-04-22 11:10:00
26: 246.25 11:17 11:20 20.9 25.4 2026-04-22 11:17:00
27: 246.25 11:24 11:27 21.6 25.1 2026-04-22 11:24:00
28: 246.36 11:45 11:48 19.8 24.6 2026-04-22 11:45:00
29: 246.23 11:38 11:41 21.9 25.6 2026-04-22 11:38:00
30: 246.35 11:52 11:55 19.5 25.2 2026-04-22 11:52:00
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature startid[, end := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), End.reading),
format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")] Bottle Tratamento Abreviacao Bloco Rep Leitura Date
<int> <char> <char> <int> <int> <int> <char>
1: 1 Controle - Apenas água CON 1 1 5 22/04/2026
2: 2 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 1 1 5 22/04/2026
3: 3 Fezes ovinas tratadas com oleo de girassol FOG 1 1 5 22/04/2026
4: 4 Água ozonizada AO3 1 1 5 22/04/2026
5: 5 Adubo nitrogenado (ureia) NMIN 1 1 5 22/04/2026
6: 6 Controle - Apenas água CON 2 2 5 22/04/2026
7: 7 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 2 2 5 22/04/2026
8: 8 Fezes ovinas tratadas com oleo de girassol FOG 2 2 5 22/04/2026
9: 9 Água ozonizada AO3 2 2 5 22/04/2026
10: 10 Adubo nitrogenado NMIN 2 2 5 22/04/2026
11: 11 Controle - Apenas água CON 3 3 5 22/04/2026
12: 12 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 3 3 5 22/04/2026
13: 13 Fezes ovinas tratadas com oleo de girassol FOG 3 3 5 22/04/2026
14: 14 Água ozonizada AO3 3 3 5 22/04/2026
15: 15 Adubo nitrogenado NMIN 3 3 5 22/04/2026
16: 16 Controle - Apenas água CON 4 4 5 22/04/2026
17: 17 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 4 4 5 22/04/2026
18: 18 Fezes ovinas tratadas com oleo de girassol FOG 4 4 5 22/04/2026
19: 19 Água ozonizada AO3 4 4 5 22/04/2026
20: 20 Adubo nitrogenado NMIN 4 4 5 22/04/2026
21: 21 Controle - Apenas água CON 5 5 5 22/04/2026
22: 22 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 5 5 5 22/04/2026
23: 23 Fezes ovinas tratadas com oleo de girassol FOG 5 5 5 22/04/2026
24: 24 Água ozonizada AO3 5 5 5 22/04/2026
25: 25 Adubo nitrogenado NMIN 5 5 5 22/04/2026
26: 26 Controle - Apenas água CON 6 6 5 22/04/2026
27: 27 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 6 6 5 22/04/2026
28: 28 Fezes ovinas tratadas com oleo de girassol FOG 6 6 5 22/04/2026
29: 29 Água ozonizada AO3 6 6 5 22/04/2026
30: 30 Adubo nitrogenado NMIN 6 6 5 22/04/2026
Bottle Tratamento Abreviacao Bloco Rep Leitura Date
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature start
<num> <char> <char> <num> <num> <POSc>
1: 246.61 08:22 08:25 22.8 25.0 2026-04-22 08:22:00
2: 246.39 08:29 08:32 20.6 25.2 2026-04-22 08:29:00
3: 246.21 08:36 08:39 21.3 25.3 2026-04-22 08:36:00
4: 246.31 08:43 08:46 21.0 25.2 2026-04-22 08:43:00
5: 246.29 08:50 08:53 20.8 25.2 2026-04-22 08:50:00
6: 246.26 08:57 09:00 21.5 25.4 2026-04-22 08:57:00
7: 246.14 09:04 09:07 20.1 25.1 2026-04-22 09:04:00
8: 246.21 09:11 09:14 20.8 25.3 2026-04-22 09:11:00
9: 246.14 09:18 09:21 20.6 25.1 2026-04-22 09:18:00
10: 246.35 09:25 09:28 20.8 25.5 2026-04-22 09:25:00
11: 246.26 09:32 09:35 21.4 25.4 2026-04-22 09:32:00
12: 246.30 09:39 09:42 20.5 25.5 2026-04-22 09:39:00
13: 246.38 09:46 09:49 21.2 25.5 2026-04-22 09:46:00
14: 246.37 09:53 09:56 21.6 25.5 2026-04-22 09:53:00
15: 246.15 10:00 10:03 21.1 25.2 2026-04-22 10:00:00
16: 246.50 10:07 10:10 20.6 25.2 2026-04-22 10:07:00
17: 246.36 10:14 10:17 20.8 25.2 2026-04-22 10:14:00
18: 246.30 10:21 10:24 20.3 25.5 2026-04-22 10:21:00
19: 246.25 10:28 10:31 21.5 25.3 2026-04-22 10:28:00
20: 246.34 10:35 10:38 21.1 25.1 2026-04-22 10:35:00
21: 246.23 10:42 10:45 20.6 25.1 2026-04-22 10:42:00
22: 246.18 10:49 10:52 21.3 25.3 2026-04-22 10:49:00
23: 246.21 10:56 10:59 21.2 25.5 2026-04-22 10:56:00
24: 246.31 11:03 11:06 20.8 25.2 2026-04-22 11:03:00
25: 246.35 11:10 11:13 21.5 25.3 2026-04-22 11:10:00
26: 246.25 11:17 11:20 20.9 25.4 2026-04-22 11:17:00
27: 246.25 11:24 11:27 21.6 25.1 2026-04-22 11:24:00
28: 246.36 11:45 11:48 19.8 24.6 2026-04-22 11:45:00
29: 246.23 11:38 11:41 21.9 25.6 2026-04-22 11:38:00
30: 246.35 11:52 11:55 19.5 25.2 2026-04-22 11:52:00
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature start
end
<POSc>
1: 2026-04-22 08:25:00
2: 2026-04-22 08:32:00
3: 2026-04-22 08:39:00
4: 2026-04-22 08:46:00
5: 2026-04-22 08:53:00
6: 2026-04-22 09:00:00
7: 2026-04-22 09:07:00
8: 2026-04-22 09:14:00
9: 2026-04-22 09:21:00
10: 2026-04-22 09:28:00
11: 2026-04-22 09:35:00
12: 2026-04-22 09:42:00
13: 2026-04-22 09:49:00
14: 2026-04-22 09:56:00
15: 2026-04-22 10:03:00
16: 2026-04-22 10:10:00
17: 2026-04-22 10:17:00
18: 2026-04-22 10:24:00
19: 2026-04-22 10:31:00
20: 2026-04-22 10:38:00
21: 2026-04-22 10:45:00
22: 2026-04-22 10:52:00
23: 2026-04-22 10:59:00
24: 2026-04-22 11:06:00
25: 2026-04-22 11:13:00
26: 2026-04-22 11:20:00
27: 2026-04-22 11:27:00
28: 2026-04-22 11:48:00
29: 2026-04-22 11:41:00
30: 2026-04-22 11:55:00
end########################### MPV 1 - LEITURA DOS FRASCOS ###########################
leitura5 = copy(dados5)
leitura5[, c(
"Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") := .(
NA_integer_, NA_character_, NA_integer_, NA_integer_,NA_integer_, NA_real_, NA_real_)] DATE TIME JULIAN_DAYS MPVPosition N2O_dry CO2_dry CH4_dry H2O NH3
<char> <char> <num> <num> <num> <num> <num> <num> <num>
1: 2026-04-22 08:00:00.259 112.3750 2 0.3418550 489.4310 2.514914 1.739537 170.4868
2: 2026-04-22 08:00:01.322 112.3750 2 0.3383274 489.8048 2.515608 1.740927 170.4470
3: 2026-04-22 08:00:02.138 112.3750 2 0.3383381 490.0915 2.516139 1.741994 170.4166
4: 2026-04-22 08:00:02.476 112.3750 2 0.3366002 490.2103 2.516360 1.742436 170.4039
5: 2026-04-22 08:00:03.631 112.3750 2 0.3306664 490.6158 2.516621 1.743944 170.3608
---
17963: 2026-04-22 11:59:56.857 112.5416 1 0.3283243 485.9612 2.407098 2.780741 133.8801
17964: 2026-04-22 11:59:57.194 112.5416 1 0.3286898 485.9406 2.407094 2.780189 133.8402
17965: 2026-04-22 11:59:58.361 112.5416 1 0.3299555 485.8689 2.406784 2.778276 133.7019
17966: 2026-04-22 11:59:59.138 112.5417 1 0.3299510 485.8212 2.406578 2.777002 133.6098
17967: 2026-04-22 11:59:59.785 112.5417 1 0.3270313 485.7815 2.406406 2.775942 133.5331
datetime Bottle Abreviacao Bloco Rep Leitura Solo.Seco Internal.temperature
<POSc> <int> <char> <int> <int> <int> <num> <num>
1: 2026-04-22 08:00:00 NA <NA> NA NA NA NA NA
2: 2026-04-22 08:00:01 NA <NA> NA NA NA NA NA
3: 2026-04-22 08:00:02 NA <NA> NA NA NA NA NA
4: 2026-04-22 08:00:02 NA <NA> NA NA NA NA NA
5: 2026-04-22 08:00:03 NA <NA> NA NA NA NA NA
---
17963: 2026-04-22 11:59:56 NA <NA> NA NA NA NA NA
17964: 2026-04-22 11:59:57 NA <NA> NA NA NA NA NA
17965: 2026-04-22 11:59:58 NA <NA> NA NA NA NA NA
17966: 2026-04-22 11:59:59 NA <NA> NA NA NA NA NA
17967: 2026-04-22 11:59:59 NA <NA> NA NA NA NA NAleitura5[MPVPosition == 1,c(
"Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") :=
id[.SD,.(Bottle, Abreviacao, Bloco, Rep,Leitura, Solo.Seco, Internal.temperature),
on = .(start <= datetime, end > datetime),mult = "first"]]Index: <MPVPosition>
DATE TIME JULIAN_DAYS MPVPosition N2O_dry CO2_dry CH4_dry H2O NH3
<char> <char> <num> <num> <num> <num> <num> <num> <num>
1: 2026-04-22 08:00:00.259 112.3750 2 0.3418550 489.4310 2.514914 1.739537 170.4868
2: 2026-04-22 08:00:01.322 112.3750 2 0.3383274 489.8048 2.515608 1.740927 170.4470
3: 2026-04-22 08:00:02.138 112.3750 2 0.3383381 490.0915 2.516139 1.741994 170.4166
4: 2026-04-22 08:00:02.476 112.3750 2 0.3366002 490.2103 2.516360 1.742436 170.4039
5: 2026-04-22 08:00:03.631 112.3750 2 0.3306664 490.6158 2.516621 1.743944 170.3608
---
17963: 2026-04-22 11:59:56.857 112.5416 1 0.3283243 485.9612 2.407098 2.780741 133.8801
17964: 2026-04-22 11:59:57.194 112.5416 1 0.3286898 485.9406 2.407094 2.780189 133.8402
17965: 2026-04-22 11:59:58.361 112.5416 1 0.3299555 485.8689 2.406784 2.778276 133.7019
17966: 2026-04-22 11:59:59.138 112.5417 1 0.3299510 485.8212 2.406578 2.777002 133.6098
17967: 2026-04-22 11:59:59.785 112.5417 1 0.3270313 485.7815 2.406406 2.775942 133.5331
datetime Bottle Abreviacao Bloco Rep Leitura Solo.Seco Internal.temperature
<POSc> <int> <char> <int> <int> <int> <num> <num>
1: 2026-04-22 08:00:00 NA <NA> NA NA NA NA NA
2: 2026-04-22 08:00:01 NA <NA> NA NA NA NA NA
3: 2026-04-22 08:00:02 NA <NA> NA NA NA NA NA
4: 2026-04-22 08:00:02 NA <NA> NA NA NA NA NA
5: 2026-04-22 08:00:03 NA <NA> NA NA NA NA NA
---
17963: 2026-04-22 11:59:56 NA <NA> NA NA NA NA NA
17964: 2026-04-22 11:59:57 NA <NA> NA NA NA NA NA
17965: 2026-04-22 11:59:58 NA <NA> NA NA NA NA NA
17966: 2026-04-22 11:59:59 NA <NA> NA NA NA NA NA
17967: 2026-04-22 11:59:59 NA <NA> NA NA NA NA NA# Manter apenas as leituras válidas dos frascos
leitura5 = leitura5[MPVPosition == 1 & !is.na(Bottle)]
View(leitura5)Leitura 6 - 27/04/2026
########################### PREPARAR DADOS ###########################
dados6 = as.data.table(filtrados_leitura6)
id = as.data.table(ID.leitura6)
# Criar início e fim das leituras
id[, start := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), Beginning.reading),
format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")] Bottle Tratamento Abreviacao Bloco Rep Leitura Date
<int> <char> <char> <int> <num> <int> <char>
1: 1 Controle - Apenas água CON 1 1 6 27/04/2026
2: 2 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 1 1 6 27/04/2026
3: 3 Fezes ovinas tratadas com oleo de girassol FOG 1 1 6 27/04/2026
4: 4 Água ozonizada AO3 1 1 6 27/04/2026
5: 5 Adubo nitrogenado (ureia) NMIN 1 1 6 27/04/2026
6: 6 Controle - Apenas água CON 2 2 6 27/04/2026
7: 7 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 2 2 6 27/04/2026
8: 8 Fezes ovinas tratadas com oleo de girassol FOG 2 2 6 27/04/2026
9: 9 Água ozonizada AO3 2 2 6 27/04/2026
10: 10 Adubo nitrogenado NMIN 2 2 6 27/04/2026
11: 11 Controle - Apenas água CON 3 3 6 27/04/2026
12: 12 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 3 3 6 27/04/2026
13: 13 Fezes ovinas tratadas com oleo de girassol FOG 3 3 6 27/04/2026
14: 14 Água ozonizada AO3 3 3 6 27/04/2026
15: 15 Adubo nitrogenado NMIN 3 3 6 27/04/2026
16: 16 Controle - Apenas água CON 4 4 6 27/04/2026
17: 17 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 4 4 6 27/04/2026
18: 18 Fezes ovinas tratadas com oleo de girassol FOG 4 4 6 27/04/2026
19: 19 Água ozonizada AO3 4 4 6 27/04/2026
20: 20 Adubo nitrogenado NMIN 4 4 6 27/04/2026
21: 21 Controle - Apenas água CON 5 5 6 27/04/2026
22: 22 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 5 5 6 27/04/2026
23: 23 Fezes ovinas tratadas com oleo de girassol FOG 5 5 6 27/04/2026
24: 24 Água ozonizada AO3 5 5 6 27/04/2026
25: 25 Adubo nitrogenado NMIN 5 5 6 27/04/2026
26: 26 Controle - Apenas água CON 6 6 6 27/04/2026
27: 27 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 6 6 6 27/04/2026
28: 28 Fezes ovinas tratadas com oleo de girassol FOG 6 6 6 27/04/2026
29: 29 Água ozonizada AO3 6 6 6 27/04/2026
30: 30 Adubo nitrogenado NMIN 6 6 6 27/04/2026
Bottle Tratamento Abreviacao Bloco Rep Leitura Date
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature start
<num> <char> <char> <num> <num> <POSc>
1: 246.61 09:06 09:09 23.1 25.3 2026-04-27 09:06:00
2: 246.39 09:13 09:16 22.4 25.3 2026-04-27 09:13:00
3: 246.21 09:20 09:23 23.1 25.5 2026-04-27 09:20:00
4: 246.31 09:27 09:30 22.3 25.5 2026-04-27 09:27:00
5: 246.29 09:34 09:37 20.7 25.3 2026-04-27 09:34:00
6: 246.26 09:41 09:44 20.9 25.5 2026-04-27 09:41:00
7: 246.14 09:48 09:51 20.5 25.4 2026-04-27 09:48:00
8: 246.21 09:55 09:58 20.9 25.2 2026-04-27 09:55:00
9: 246.14 10:02 10:05 20.1 25.2 2026-04-27 10:02:00
10: 246.35 10:09 10:12 21.9 25.1 2026-04-27 10:09:00
11: 246.26 10:16 10:19 20.0 25.1 2026-04-27 10:16:00
12: 246.30 10:23 10:26 20.7 25.6 2026-04-27 10:23:00
13: 246.38 10:30 10:33 21.2 25.5 2026-04-27 10:30:00
14: 246.37 10:37 10:40 20.9 25.4 2026-04-27 10:37:00
15: 246.15 10:44 10:47 19.5 24.9 2026-04-27 10:44:00
16: 246.50 10:51 10:54 19.2 24.9 2026-04-27 10:51:00
17: 246.36 10:58 11:01 19.7 24.9 2026-04-27 10:58:00
18: 246.30 11:05 11:08 20.0 25.3 2026-04-27 11:05:00
19: 246.25 11:12 11:15 20.1 25.1 2026-04-27 11:12:00
20: 246.34 11:19 11:22 20.1 25.1 2026-04-27 11:19:00
21: 246.23 11:26 11:29 20.0 25.0 2026-04-27 11:26:00
22: 246.18 11:33 11:36 20.4 25.3 2026-04-27 11:33:00
23: 246.21 11:40 11:43 19.9 25.5 2026-04-27 11:40:00
24: 246.31 11:47 11:50 19.9 25.1 2026-04-27 11:47:00
25: 246.35 11:54 11:57 20.0 25.0 2026-04-27 11:54:00
26: 246.25 12:01 12:04 19.8 24.9 2026-04-27 12:01:00
27: 246.25 12:08 12:11 20.1 24.9 2026-04-27 12:08:00
28: 246.36 12:15 12:18 20.4 24.9 2026-04-27 12:15:00
29: 246.23 12:22 12:25 20.0 25.1 2026-04-27 12:22:00
30: 246.35 12:29 12:32 20.0 25.4 2026-04-27 12:29:00
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature startid[, end := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), End.reading),
format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")] Bottle Tratamento Abreviacao Bloco Rep Leitura Date
<int> <char> <char> <int> <num> <int> <char>
1: 1 Controle - Apenas água CON 1 1 6 27/04/2026
2: 2 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 1 1 6 27/04/2026
3: 3 Fezes ovinas tratadas com oleo de girassol FOG 1 1 6 27/04/2026
4: 4 Água ozonizada AO3 1 1 6 27/04/2026
5: 5 Adubo nitrogenado (ureia) NMIN 1 1 6 27/04/2026
6: 6 Controle - Apenas água CON 2 2 6 27/04/2026
7: 7 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 2 2 6 27/04/2026
8: 8 Fezes ovinas tratadas com oleo de girassol FOG 2 2 6 27/04/2026
9: 9 Água ozonizada AO3 2 2 6 27/04/2026
10: 10 Adubo nitrogenado NMIN 2 2 6 27/04/2026
11: 11 Controle - Apenas água CON 3 3 6 27/04/2026
12: 12 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 3 3 6 27/04/2026
13: 13 Fezes ovinas tratadas com oleo de girassol FOG 3 3 6 27/04/2026
14: 14 Água ozonizada AO3 3 3 6 27/04/2026
15: 15 Adubo nitrogenado NMIN 3 3 6 27/04/2026
16: 16 Controle - Apenas água CON 4 4 6 27/04/2026
17: 17 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 4 4 6 27/04/2026
18: 18 Fezes ovinas tratadas com oleo de girassol FOG 4 4 6 27/04/2026
19: 19 Água ozonizada AO3 4 4 6 27/04/2026
20: 20 Adubo nitrogenado NMIN 4 4 6 27/04/2026
21: 21 Controle - Apenas água CON 5 5 6 27/04/2026
22: 22 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 5 5 6 27/04/2026
23: 23 Fezes ovinas tratadas com oleo de girassol FOG 5 5 6 27/04/2026
24: 24 Água ozonizada AO3 5 5 6 27/04/2026
25: 25 Adubo nitrogenado NMIN 5 5 6 27/04/2026
26: 26 Controle - Apenas água CON 6 6 6 27/04/2026
27: 27 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 6 6 6 27/04/2026
28: 28 Fezes ovinas tratadas com oleo de girassol FOG 6 6 6 27/04/2026
29: 29 Água ozonizada AO3 6 6 6 27/04/2026
30: 30 Adubo nitrogenado NMIN 6 6 6 27/04/2026
Bottle Tratamento Abreviacao Bloco Rep Leitura Date
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature start
<num> <char> <char> <num> <num> <POSc>
1: 246.61 09:06 09:09 23.1 25.3 2026-04-27 09:06:00
2: 246.39 09:13 09:16 22.4 25.3 2026-04-27 09:13:00
3: 246.21 09:20 09:23 23.1 25.5 2026-04-27 09:20:00
4: 246.31 09:27 09:30 22.3 25.5 2026-04-27 09:27:00
5: 246.29 09:34 09:37 20.7 25.3 2026-04-27 09:34:00
6: 246.26 09:41 09:44 20.9 25.5 2026-04-27 09:41:00
7: 246.14 09:48 09:51 20.5 25.4 2026-04-27 09:48:00
8: 246.21 09:55 09:58 20.9 25.2 2026-04-27 09:55:00
9: 246.14 10:02 10:05 20.1 25.2 2026-04-27 10:02:00
10: 246.35 10:09 10:12 21.9 25.1 2026-04-27 10:09:00
11: 246.26 10:16 10:19 20.0 25.1 2026-04-27 10:16:00
12: 246.30 10:23 10:26 20.7 25.6 2026-04-27 10:23:00
13: 246.38 10:30 10:33 21.2 25.5 2026-04-27 10:30:00
14: 246.37 10:37 10:40 20.9 25.4 2026-04-27 10:37:00
15: 246.15 10:44 10:47 19.5 24.9 2026-04-27 10:44:00
16: 246.50 10:51 10:54 19.2 24.9 2026-04-27 10:51:00
17: 246.36 10:58 11:01 19.7 24.9 2026-04-27 10:58:00
18: 246.30 11:05 11:08 20.0 25.3 2026-04-27 11:05:00
19: 246.25 11:12 11:15 20.1 25.1 2026-04-27 11:12:00
20: 246.34 11:19 11:22 20.1 25.1 2026-04-27 11:19:00
21: 246.23 11:26 11:29 20.0 25.0 2026-04-27 11:26:00
22: 246.18 11:33 11:36 20.4 25.3 2026-04-27 11:33:00
23: 246.21 11:40 11:43 19.9 25.5 2026-04-27 11:40:00
24: 246.31 11:47 11:50 19.9 25.1 2026-04-27 11:47:00
25: 246.35 11:54 11:57 20.0 25.0 2026-04-27 11:54:00
26: 246.25 12:01 12:04 19.8 24.9 2026-04-27 12:01:00
27: 246.25 12:08 12:11 20.1 24.9 2026-04-27 12:08:00
28: 246.36 12:15 12:18 20.4 24.9 2026-04-27 12:15:00
29: 246.23 12:22 12:25 20.0 25.1 2026-04-27 12:22:00
30: 246.35 12:29 12:32 20.0 25.4 2026-04-27 12:29:00
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature start
end
<POSc>
1: 2026-04-27 09:09:00
2: 2026-04-27 09:16:00
3: 2026-04-27 09:23:00
4: 2026-04-27 09:30:00
5: 2026-04-27 09:37:00
6: 2026-04-27 09:44:00
7: 2026-04-27 09:51:00
8: 2026-04-27 09:58:00
9: 2026-04-27 10:05:00
10: 2026-04-27 10:12:00
11: 2026-04-27 10:19:00
12: 2026-04-27 10:26:00
13: 2026-04-27 10:33:00
14: 2026-04-27 10:40:00
15: 2026-04-27 10:47:00
16: 2026-04-27 10:54:00
17: 2026-04-27 11:01:00
18: 2026-04-27 11:08:00
19: 2026-04-27 11:15:00
20: 2026-04-27 11:22:00
21: 2026-04-27 11:29:00
22: 2026-04-27 11:36:00
23: 2026-04-27 11:43:00
24: 2026-04-27 11:50:00
25: 2026-04-27 11:57:00
26: 2026-04-27 12:04:00
27: 2026-04-27 12:11:00
28: 2026-04-27 12:18:00
29: 2026-04-27 12:25:00
30: 2026-04-27 12:32:00
end########################### MPV 1 - LEITURA DOS FRASCOS ###########################
leitura6 = copy(dados6)
leitura6[, c(
"Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") := .(
NA_integer_, NA_character_, NA_integer_, NA_integer_,NA_integer_, NA_real_, NA_real_)] DATE TIME JULIAN_DAYS MPVPosition N2O_dry CO2_dry CH4_dry H2O NH3
<char> <char> <num> <num> <num> <num> <num> <num> <num>
1: 2026-04-27 09:00:00.727 117.4167 1.00000 0.3496643 563.8459 2.881716 2.092712 912.0522
2: 2026-04-27 09:00:01.492 117.4167 1.00000 0.3497405 564.1107 2.881741 2.092165 910.4254
3: 2026-04-27 09:00:02.153 117.4167 1.00000 0.3467633 564.3399 2.881763 2.091692 909.0180
4: 2026-04-27 09:00:02.938 117.4167 1.00000 0.3432238 564.6146 2.881798 2.091357 907.3144
5: 2026-04-27 09:00:04.029 117.4167 1.00000 0.3383082 564.6062 2.881847 2.090904 904.9498
---
18038: 2026-04-27 12:59:57.095 117.5833 1.88862 0.3333136 444.0541 2.100215 1.763829 197.4754
18039: 2026-04-27 12:59:57.437 117.5833 2.00000 0.3299605 444.0880 2.100372 1.763344 197.4533
18040: 2026-04-27 12:59:58.573 117.5833 2.00000 0.3188556 444.2002 2.099961 1.761736 197.3798
18041: 2026-04-27 12:59:59.361 117.5833 2.00000 0.3188512 444.2780 2.099676 1.760621 197.3289
18042: 2026-04-27 12:59:59.993 117.5833 2.00000 0.3221046 444.3404 2.099447 1.759727 197.2881
datetime Bottle Abreviacao Bloco Rep Leitura Solo.Seco Internal.temperature
<POSc> <int> <char> <int> <int> <int> <num> <num>
1: 2026-04-27 09:00:00 NA <NA> NA NA NA NA NA
2: 2026-04-27 09:00:01 NA <NA> NA NA NA NA NA
3: 2026-04-27 09:00:02 NA <NA> NA NA NA NA NA
4: 2026-04-27 09:00:02 NA <NA> NA NA NA NA NA
5: 2026-04-27 09:00:04 NA <NA> NA NA NA NA NA
---
18038: 2026-04-27 12:59:57 NA <NA> NA NA NA NA NA
18039: 2026-04-27 12:59:57 NA <NA> NA NA NA NA NA
18040: 2026-04-27 12:59:58 NA <NA> NA NA NA NA NA
18041: 2026-04-27 12:59:59 NA <NA> NA NA NA NA NA
18042: 2026-04-27 12:59:59 NA <NA> NA NA NA NA NAleitura6[MPVPosition == 1,c(
"Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") :=
id[.SD,.(Bottle, Abreviacao, Bloco, Rep,Leitura, Solo.Seco, Internal.temperature),
on = .(start <= datetime, end > datetime),mult = "first"]]Index: <MPVPosition>
DATE TIME JULIAN_DAYS MPVPosition N2O_dry CO2_dry CH4_dry H2O NH3
<char> <char> <num> <num> <num> <num> <num> <num> <num>
1: 2026-04-27 09:00:00.727 117.4167 1.00000 0.3496643 563.8459 2.881716 2.092712 912.0522
2: 2026-04-27 09:00:01.492 117.4167 1.00000 0.3497405 564.1107 2.881741 2.092165 910.4254
3: 2026-04-27 09:00:02.153 117.4167 1.00000 0.3467633 564.3399 2.881763 2.091692 909.0180
4: 2026-04-27 09:00:02.938 117.4167 1.00000 0.3432238 564.6146 2.881798 2.091357 907.3144
5: 2026-04-27 09:00:04.029 117.4167 1.00000 0.3383082 564.6062 2.881847 2.090904 904.9498
---
18038: 2026-04-27 12:59:57.095 117.5833 1.88862 0.3333136 444.0541 2.100215 1.763829 197.4754
18039: 2026-04-27 12:59:57.437 117.5833 2.00000 0.3299605 444.0880 2.100372 1.763344 197.4533
18040: 2026-04-27 12:59:58.573 117.5833 2.00000 0.3188556 444.2002 2.099961 1.761736 197.3798
18041: 2026-04-27 12:59:59.361 117.5833 2.00000 0.3188512 444.2780 2.099676 1.760621 197.3289
18042: 2026-04-27 12:59:59.993 117.5833 2.00000 0.3221046 444.3404 2.099447 1.759727 197.2881
datetime Bottle Abreviacao Bloco Rep Leitura Solo.Seco Internal.temperature
<POSc> <int> <char> <int> <int> <int> <num> <num>
1: 2026-04-27 09:00:00 NA <NA> NA NA NA NA NA
2: 2026-04-27 09:00:01 NA <NA> NA NA NA NA NA
3: 2026-04-27 09:00:02 NA <NA> NA NA NA NA NA
4: 2026-04-27 09:00:02 NA <NA> NA NA NA NA NA
5: 2026-04-27 09:00:04 NA <NA> NA NA NA NA NA
---
18038: 2026-04-27 12:59:57 NA <NA> NA NA NA NA NA
18039: 2026-04-27 12:59:57 NA <NA> NA NA NA NA NA
18040: 2026-04-27 12:59:58 NA <NA> NA NA NA NA NA
18041: 2026-04-27 12:59:59 NA <NA> NA NA NA NA NA
18042: 2026-04-27 12:59:59 NA <NA> NA NA NA NA NA# Manter apenas as leituras válidas dos frascos
leitura6 = leitura6[MPVPosition == 1 & !is.na(Bottle)]
View(leitura6)Leitura 7 - 05/05/2026
########################### PREPARAR DADOS ###########################
dados7 = as.data.table(filtrados_leitura7)
id = as.data.table(ID.leitura7)
# Criar início e fim das leituras
id[, start := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), Beginning.reading),
format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")] Bottle Tratamento Abreviacao Bloco Rep Leitura Date
<int> <char> <char> <int> <num> <int> <char>
1: 1 Controle - Apenas água CON 1 1 7 05/05/2026
2: 2 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 1 1 7 05/05/2026
3: 3 Fezes ovinas tratadas com oleo de girassol FOG 1 1 7 05/05/2026
4: 4 Água ozonizada AO3 1 1 7 05/05/2026
5: 5 Adubo nitrogenado (ureia) NMIN 1 1 7 05/05/2026
6: 6 Controle - Apenas água CON 2 2 7 05/05/2026
7: 7 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 2 2 7 05/05/2026
8: 8 Fezes ovinas tratadas com oleo de girassol FOG 2 2 7 05/05/2026
9: 9 Água ozonizada AO3 2 2 7 05/05/2026
10: 10 Adubo nitrogenado NMIN 2 2 7 05/05/2026
11: 11 Controle - Apenas água CON 3 3 7 05/05/2026
12: 12 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 3 3 7 05/05/2026
13: 13 Fezes ovinas tratadas com oleo de girassol FOG 3 3 7 05/05/2026
14: 14 Água ozonizada AO3 3 3 7 05/05/2026
15: 15 Adubo nitrogenado NMIN 3 3 7 05/05/2026
16: 16 Controle - Apenas água CON 4 4 7 05/05/2026
17: 17 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 4 4 7 05/05/2026
18: 18 Fezes ovinas tratadas com oleo de girassol FOG 4 4 7 05/05/2026
19: 19 Água ozonizada AO3 4 4 7 05/05/2026
20: 20 Adubo nitrogenado NMIN 4 4 7 05/05/2026
21: 21 Controle - Apenas água CON 5 5 7 05/05/2026
22: 22 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 5 5 7 05/05/2026
23: 23 Fezes ovinas tratadas com oleo de girassol FOG 5 5 7 05/05/2026
24: 24 Água ozonizada AO3 5 5 7 05/05/2026
25: 25 Adubo nitrogenado NMIN 5 5 7 05/05/2026
26: 26 Controle - Apenas água CON 6 6 7 05/05/2026
27: 27 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 6 6 7 05/05/2026
28: 28 Fezes ovinas tratadas com oleo de girassol FOG 6 6 7 05/05/2026
29: 29 Água ozonizada AO3 6 6 7 05/05/2026
30: 30 Adubo nitrogenado NMIN 6 6 7 05/05/2026
Bottle Tratamento Abreviacao Bloco Rep Leitura Date
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature start
<num> <char> <char> <num> <num> <POSc>
1: 246.61 10:12 10:15 23.6 25.1 2026-05-05 10:12:00
2: 246.39 10:19 10:22 22.4 25.1 2026-05-05 10:19:00
3: 246.21 10:25 10:28 22.4 25.2 2026-05-05 10:25:00
4: 246.31 10:33 10:36 22.6 25.3 2026-05-05 10:33:00
5: 246.29 10:40 10:43 20.9 25.3 2026-05-05 10:40:00
6: 246.26 10:47 10:50 22.1 25.5 2026-05-05 10:47:00
7: 246.14 10:54 10:57 22.3 25.4 2026-05-05 10:54:00
8: 246.21 11:01 11:04 20.7 25.3 2026-05-05 11:01:00
9: 246.14 11:08 11:11 21.8 25.5 2026-05-05 11:08:00
10: 246.35 11:15 11:18 21.6 25.3 2026-05-05 11:15:00
11: 246.26 11:22 11:25 21.9 25.6 2026-05-05 11:22:00
12: 246.30 11:43 11:46 23.1 25.0 2026-05-05 11:43:00
13: 246.38 11:36 11:39 22.9 25.5 2026-05-05 11:36:00
14: 246.37 11:50 11:53 21.1 25.3 2026-05-05 11:50:00
15: 246.15 11:57 12:00 21.2 25.3 2026-05-05 11:57:00
16: 246.50 12:04 12:07 22.2 25.3 2026-05-05 12:04:00
17: 246.36 12:11 12:14 21.9 25.2 2026-05-05 12:11:00
18: 246.30 12:18 12:21 22.5 25.1 2026-05-05 12:18:00
19: 246.25 12:25 12:28 21.1 25.3 2026-05-05 12:25:00
20: 246.34 12:32 12:35 20.6 25.2 2026-05-05 12:32:00
21: 246.23 12:39 12:42 21.0 25.1 2026-05-05 12:39:00
22: 246.18 12:46 12:49 20.7 25.3 2026-05-05 12:46:00
23: 246.21 12:53 12:56 20.7 25.4 2026-05-05 12:53:00
24: 246.31 13:00 13:03 20.8 25.1 2026-05-05 13:00:00
25: 246.35 13:07 13:10 21.0 25.3 2026-05-05 13:07:00
26: 246.25 13:14 13:17 20.3 24.9 2026-05-05 13:14:00
27: 246.25 13:21 13:24 20.5 24.8 2026-05-05 13:21:00
28: 246.36 13:27 13:30 19.5 25.0 2026-05-05 13:27:00
29: 246.23 13:34 13:37 19.9 25.1 2026-05-05 13:34:00
30: 246.35 13:41 13:44 20.0 24.8 2026-05-05 13:41:00
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature startid[, end := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), End.reading),
format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")] Bottle Tratamento Abreviacao Bloco Rep Leitura Date
<int> <char> <char> <int> <num> <int> <char>
1: 1 Controle - Apenas água CON 1 1 7 05/05/2026
2: 2 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 1 1 7 05/05/2026
3: 3 Fezes ovinas tratadas com oleo de girassol FOG 1 1 7 05/05/2026
4: 4 Água ozonizada AO3 1 1 7 05/05/2026
5: 5 Adubo nitrogenado (ureia) NMIN 1 1 7 05/05/2026
6: 6 Controle - Apenas água CON 2 2 7 05/05/2026
7: 7 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 2 2 7 05/05/2026
8: 8 Fezes ovinas tratadas com oleo de girassol FOG 2 2 7 05/05/2026
9: 9 Água ozonizada AO3 2 2 7 05/05/2026
10: 10 Adubo nitrogenado NMIN 2 2 7 05/05/2026
11: 11 Controle - Apenas água CON 3 3 7 05/05/2026
12: 12 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 3 3 7 05/05/2026
13: 13 Fezes ovinas tratadas com oleo de girassol FOG 3 3 7 05/05/2026
14: 14 Água ozonizada AO3 3 3 7 05/05/2026
15: 15 Adubo nitrogenado NMIN 3 3 7 05/05/2026
16: 16 Controle - Apenas água CON 4 4 7 05/05/2026
17: 17 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 4 4 7 05/05/2026
18: 18 Fezes ovinas tratadas com oleo de girassol FOG 4 4 7 05/05/2026
19: 19 Água ozonizada AO3 4 4 7 05/05/2026
20: 20 Adubo nitrogenado NMIN 4 4 7 05/05/2026
21: 21 Controle - Apenas água CON 5 5 7 05/05/2026
22: 22 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 5 5 7 05/05/2026
23: 23 Fezes ovinas tratadas com oleo de girassol FOG 5 5 7 05/05/2026
24: 24 Água ozonizada AO3 5 5 7 05/05/2026
25: 25 Adubo nitrogenado NMIN 5 5 7 05/05/2026
26: 26 Controle - Apenas água CON 6 6 7 05/05/2026
27: 27 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 6 6 7 05/05/2026
28: 28 Fezes ovinas tratadas com oleo de girassol FOG 6 6 7 05/05/2026
29: 29 Água ozonizada AO3 6 6 7 05/05/2026
30: 30 Adubo nitrogenado NMIN 6 6 7 05/05/2026
Bottle Tratamento Abreviacao Bloco Rep Leitura Date
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature start
<num> <char> <char> <num> <num> <POSc>
1: 246.61 10:12 10:15 23.6 25.1 2026-05-05 10:12:00
2: 246.39 10:19 10:22 22.4 25.1 2026-05-05 10:19:00
3: 246.21 10:25 10:28 22.4 25.2 2026-05-05 10:25:00
4: 246.31 10:33 10:36 22.6 25.3 2026-05-05 10:33:00
5: 246.29 10:40 10:43 20.9 25.3 2026-05-05 10:40:00
6: 246.26 10:47 10:50 22.1 25.5 2026-05-05 10:47:00
7: 246.14 10:54 10:57 22.3 25.4 2026-05-05 10:54:00
8: 246.21 11:01 11:04 20.7 25.3 2026-05-05 11:01:00
9: 246.14 11:08 11:11 21.8 25.5 2026-05-05 11:08:00
10: 246.35 11:15 11:18 21.6 25.3 2026-05-05 11:15:00
11: 246.26 11:22 11:25 21.9 25.6 2026-05-05 11:22:00
12: 246.30 11:43 11:46 23.1 25.0 2026-05-05 11:43:00
13: 246.38 11:36 11:39 22.9 25.5 2026-05-05 11:36:00
14: 246.37 11:50 11:53 21.1 25.3 2026-05-05 11:50:00
15: 246.15 11:57 12:00 21.2 25.3 2026-05-05 11:57:00
16: 246.50 12:04 12:07 22.2 25.3 2026-05-05 12:04:00
17: 246.36 12:11 12:14 21.9 25.2 2026-05-05 12:11:00
18: 246.30 12:18 12:21 22.5 25.1 2026-05-05 12:18:00
19: 246.25 12:25 12:28 21.1 25.3 2026-05-05 12:25:00
20: 246.34 12:32 12:35 20.6 25.2 2026-05-05 12:32:00
21: 246.23 12:39 12:42 21.0 25.1 2026-05-05 12:39:00
22: 246.18 12:46 12:49 20.7 25.3 2026-05-05 12:46:00
23: 246.21 12:53 12:56 20.7 25.4 2026-05-05 12:53:00
24: 246.31 13:00 13:03 20.8 25.1 2026-05-05 13:00:00
25: 246.35 13:07 13:10 21.0 25.3 2026-05-05 13:07:00
26: 246.25 13:14 13:17 20.3 24.9 2026-05-05 13:14:00
27: 246.25 13:21 13:24 20.5 24.8 2026-05-05 13:21:00
28: 246.36 13:27 13:30 19.5 25.0 2026-05-05 13:27:00
29: 246.23 13:34 13:37 19.9 25.1 2026-05-05 13:34:00
30: 246.35 13:41 13:44 20.0 24.8 2026-05-05 13:41:00
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature start
end
<POSc>
1: 2026-05-05 10:15:00
2: 2026-05-05 10:22:00
3: 2026-05-05 10:28:00
4: 2026-05-05 10:36:00
5: 2026-05-05 10:43:00
6: 2026-05-05 10:50:00
7: 2026-05-05 10:57:00
8: 2026-05-05 11:04:00
9: 2026-05-05 11:11:00
10: 2026-05-05 11:18:00
11: 2026-05-05 11:25:00
12: 2026-05-05 11:46:00
13: 2026-05-05 11:39:00
14: 2026-05-05 11:53:00
15: 2026-05-05 12:00:00
16: 2026-05-05 12:07:00
17: 2026-05-05 12:14:00
18: 2026-05-05 12:21:00
19: 2026-05-05 12:28:00
20: 2026-05-05 12:35:00
21: 2026-05-05 12:42:00
22: 2026-05-05 12:49:00
23: 2026-05-05 12:56:00
24: 2026-05-05 13:03:00
25: 2026-05-05 13:10:00
26: 2026-05-05 13:17:00
27: 2026-05-05 13:24:00
28: 2026-05-05 13:30:00
29: 2026-05-05 13:37:00
30: 2026-05-05 13:44:00
end########################### MPV 1 - LEITURA DOS FRASCOS ###########################
leitura7 = copy(dados7)
leitura7[, c(
"Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") := .(
NA_integer_, NA_character_, NA_integer_, NA_integer_,NA_integer_, NA_real_, NA_real_)] DATE TIME JULIAN_DAYS MPVPosition N2O_dry CO2_dry CH4_dry H2O NH3
<char> <char> <num> <num> <num> <num> <num> <num> <num>
1: 2026-05-05 10:00:00.236 125.4583 1 0.3331141 500.3166 2.552657 2.091699 162.98052
2: 2026-05-05 10:00:00.579 125.4583 1 0.3327318 499.9666 2.552479 2.091127 162.95755
3: 2026-05-05 10:00:01.704 125.4584 1 0.3314818 498.8224 2.552563 2.089258 162.88243
4: 2026-05-05 10:00:02.496 125.4584 1 0.3314693 498.0179 2.552623 2.087945 162.82962
5: 2026-05-05 10:00:03.148 125.4584 1 0.3316185 497.3538 2.552672 2.086860 162.78602
---
17672: 2026-05-05 13:59:56.174 125.6250 2 0.3236655 438.3252 2.012625 1.533754 79.76837
17673: 2026-05-05 13:59:57.309 125.6250 2 0.3219233 438.3822 2.013018 1.533108 79.82989
17674: 2026-05-05 13:59:58.084 125.6250 2 0.3219186 438.4211 2.013286 1.532668 79.87185
17675: 2026-05-05 13:59:58.742 125.6250 2 0.3228059 438.4542 2.013514 1.532293 79.90753
17676: 2026-05-05 13:59:59.496 125.6250 2 0.3238297 438.4951 2.013785 1.532259 79.84567
datetime Bottle Abreviacao Bloco Rep Leitura Solo.Seco Internal.temperature
<POSc> <int> <char> <int> <int> <int> <num> <num>
1: 2026-05-05 10:00:00 NA <NA> NA NA NA NA NA
2: 2026-05-05 10:00:00 NA <NA> NA NA NA NA NA
3: 2026-05-05 10:00:01 NA <NA> NA NA NA NA NA
4: 2026-05-05 10:00:02 NA <NA> NA NA NA NA NA
5: 2026-05-05 10:00:03 NA <NA> NA NA NA NA NA
---
17672: 2026-05-05 13:59:56 NA <NA> NA NA NA NA NA
17673: 2026-05-05 13:59:57 NA <NA> NA NA NA NA NA
17674: 2026-05-05 13:59:58 NA <NA> NA NA NA NA NA
17675: 2026-05-05 13:59:58 NA <NA> NA NA NA NA NA
17676: 2026-05-05 13:59:59 NA <NA> NA NA NA NA NAleitura7[MPVPosition == 1,c(
"Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") :=
id[.SD,.(Bottle, Abreviacao, Bloco, Rep,Leitura, Solo.Seco, Internal.temperature),
on = .(start <= datetime, end > datetime),mult = "first"]]Index: <MPVPosition>
DATE TIME JULIAN_DAYS MPVPosition N2O_dry CO2_dry CH4_dry H2O NH3
<char> <char> <num> <num> <num> <num> <num> <num> <num>
1: 2026-05-05 10:00:00.236 125.4583 1 0.3331141 500.3166 2.552657 2.091699 162.98052
2: 2026-05-05 10:00:00.579 125.4583 1 0.3327318 499.9666 2.552479 2.091127 162.95755
3: 2026-05-05 10:00:01.704 125.4584 1 0.3314818 498.8224 2.552563 2.089258 162.88243
4: 2026-05-05 10:00:02.496 125.4584 1 0.3314693 498.0179 2.552623 2.087945 162.82962
5: 2026-05-05 10:00:03.148 125.4584 1 0.3316185 497.3538 2.552672 2.086860 162.78602
---
17672: 2026-05-05 13:59:56.174 125.6250 2 0.3236655 438.3252 2.012625 1.533754 79.76837
17673: 2026-05-05 13:59:57.309 125.6250 2 0.3219233 438.3822 2.013018 1.533108 79.82989
17674: 2026-05-05 13:59:58.084 125.6250 2 0.3219186 438.4211 2.013286 1.532668 79.87185
17675: 2026-05-05 13:59:58.742 125.6250 2 0.3228059 438.4542 2.013514 1.532293 79.90753
17676: 2026-05-05 13:59:59.496 125.6250 2 0.3238297 438.4951 2.013785 1.532259 79.84567
datetime Bottle Abreviacao Bloco Rep Leitura Solo.Seco Internal.temperature
<POSc> <int> <char> <int> <int> <int> <num> <num>
1: 2026-05-05 10:00:00 NA <NA> NA NA NA NA NA
2: 2026-05-05 10:00:00 NA <NA> NA NA NA NA NA
3: 2026-05-05 10:00:01 NA <NA> NA NA NA NA NA
4: 2026-05-05 10:00:02 NA <NA> NA NA NA NA NA
5: 2026-05-05 10:00:03 NA <NA> NA NA NA NA NA
---
17672: 2026-05-05 13:59:56 NA <NA> NA NA NA NA NA
17673: 2026-05-05 13:59:57 NA <NA> NA NA NA NA NA
17674: 2026-05-05 13:59:58 NA <NA> NA NA NA NA NA
17675: 2026-05-05 13:59:58 NA <NA> NA NA NA NA NA
17676: 2026-05-05 13:59:59 NA <NA> NA NA NA NA NA# Manter apenas as leituras válidas dos frascos
leitura7 = leitura7[MPVPosition == 1 & !is.na(Bottle)]
View(leitura7)Leitura 8 - 18/05/2026
########################### PREPARAR DADOS ###########################
dados8 = as.data.table(filtrados_leitura8)
id = as.data.table(ID.leitura8)
# Criar início e fim das leituras
id[, start := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), Beginning.reading),
format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")] Bottle Tratamento Abreviacao Bloco Rep Leitura Date
<int> <char> <char> <int> <num> <int> <char>
1: 1 Controle - Apenas água CON 1 1 8 18/05/2026
2: 2 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 1 1 8 18/05/2026
3: 3 Fezes ovinas tratadas com oleo de girassol FOG 1 1 8 18/05/2026
4: 4 Água ozonizada AO3 1 1 8 18/05/2026
5: 5 Adubo nitrogenado (ureia) NMIN 1 1 8 18/05/2026
6: 6 Controle - Apenas água CON 2 2 8 18/05/2026
7: 7 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 2 2 8 18/05/2026
8: 8 Fezes ovinas tratadas com oleo de girassol FOG 2 2 8 18/05/2026
9: 9 Água ozonizada AO3 2 2 8 18/05/2026
10: 10 Adubo nitrogenado NMIN 2 2 8 18/05/2026
11: 11 Controle - Apenas água CON 3 3 8 18/05/2026
12: 12 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 3 3 8 18/05/2026
13: 13 Fezes ovinas tratadas com oleo de girassol FOG 3 3 8 18/05/2026
14: 14 Água ozonizada AO3 3 3 8 18/05/2026
15: 15 Adubo nitrogenado NMIN 3 3 8 18/05/2026
16: 16 Controle - Apenas água CON 4 4 8 18/05/2026
17: 17 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 4 4 8 18/05/2026
18: 18 Fezes ovinas tratadas com oleo de girassol FOG 4 4 8 18/05/2026
19: 19 Água ozonizada AO3 4 4 8 18/05/2026
20: 20 Adubo nitrogenado NMIN 4 4 8 18/05/2026
21: 21 Controle - Apenas água CON 5 5 8 18/05/2026
22: 22 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 5 5 8 18/05/2026
23: 23 Fezes ovinas tratadas com oleo de girassol FOG 5 5 8 18/05/2026
24: 24 Água ozonizada AO3 5 5 8 18/05/2026
25: 25 Adubo nitrogenado NMIN 5 5 8 18/05/2026
26: 26 Controle - Apenas água CON 6 6 8 18/05/2026
27: 27 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 6 6 8 18/05/2026
28: 28 Fezes ovinas tratadas com oleo de girassol FOG 6 6 8 18/05/2026
29: 29 Água ozonizada AO3 6 6 8 18/05/2026
30: 30 Adubo nitrogenado NMIN 6 6 8 18/05/2026
Bottle Tratamento Abreviacao Bloco Rep Leitura Date
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature start
<num> <char> <char> <num> <num> <POSc>
1: 246.61 09:00 09:03 23.5 24.9 2026-05-18 09:00:00
2: 246.39 09:07 09:10 22.3 24.8 2026-05-18 09:07:00
3: 246.21 09:14 09:17 21.6 25.1 2026-05-18 09:14:00
4: 246.31 09:21 09:24 22.5 25.0 2026-05-18 09:21:00
5: 246.29 09:28 09:31 23.6 25.1 2026-05-18 09:28:00
6: 246.26 09:35 09:38 22.4 25.3 2026-05-18 09:35:00
7: 246.14 09:42 09:45 22.8 25.3 2026-05-18 09:42:00
8: 246.21 09:49 09:52 22.1 25.1 2026-05-18 09:49:00
9: 246.14 09:56 09:59 22.5 25.1 2026-05-18 09:56:00
10: 246.35 10:03 10:06 21.7 25.1 2026-05-18 10:03:00
11: 246.26 10:10 10:13 22.3 25.3 2026-05-18 10:10:00
12: 246.30 10:17 10:20 22.4 25.5 2026-05-18 10:17:00
13: 246.38 10:24 10:27 21.8 25.5 2026-05-18 10:24:00
14: 246.37 10:31 10:34 21.6 25.3 2026-05-18 10:31:00
15: 246.15 10:38 10:41 21.1 25.5 2026-05-18 10:38:00
16: 246.50 10:45 10:48 21.1 25.1 2026-05-18 10:45:00
17: 246.36 10:52 10:55 21.8 25.0 2026-05-18 10:52:00
18: 246.30 10:59 11:02 21.8 25.1 2026-05-18 10:59:00
19: 246.25 11:06 11:09 21.4 25.2 2026-05-18 11:06:00
20: 246.34 11:13 11:16 21.5 25.4 2026-05-18 11:13:00
21: 246.23 11:20 11:23 21.6 25.5 2026-05-18 11:20:00
22: 246.18 11:27 11:30 21.3 25.2 2026-05-18 11:27:00
23: 246.21 11:34 11:37 21.9 25.1 2026-05-18 11:34:00
24: 246.31 11:41 11:44 21.5 25.4 2026-05-18 11:41:00
25: 246.35 11:48 11:51 22.1 25.1 2026-05-18 11:48:00
26: 246.25 11:55 11:58 22.1 25.0 2026-05-18 11:55:00
27: 246.25 12:02 12:05 21.0 24.9 2026-05-18 12:02:00
28: 246.36 12:09 12:12 21.5 25.0 2026-05-18 12:09:00
29: 246.23 12:16 12:19 21.6 25.5 2026-05-18 12:16:00
30: 246.35 12:23 12:26 21.1 25.0 2026-05-18 12:23:00
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature startid[, end := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), End.reading),
format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")] Bottle Tratamento Abreviacao Bloco Rep Leitura Date
<int> <char> <char> <int> <num> <int> <char>
1: 1 Controle - Apenas água CON 1 1 8 18/05/2026
2: 2 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 1 1 8 18/05/2026
3: 3 Fezes ovinas tratadas com oleo de girassol FOG 1 1 8 18/05/2026
4: 4 Água ozonizada AO3 1 1 8 18/05/2026
5: 5 Adubo nitrogenado (ureia) NMIN 1 1 8 18/05/2026
6: 6 Controle - Apenas água CON 2 2 8 18/05/2026
7: 7 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 2 2 8 18/05/2026
8: 8 Fezes ovinas tratadas com oleo de girassol FOG 2 2 8 18/05/2026
9: 9 Água ozonizada AO3 2 2 8 18/05/2026
10: 10 Adubo nitrogenado NMIN 2 2 8 18/05/2026
11: 11 Controle - Apenas água CON 3 3 8 18/05/2026
12: 12 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 3 3 8 18/05/2026
13: 13 Fezes ovinas tratadas com oleo de girassol FOG 3 3 8 18/05/2026
14: 14 Água ozonizada AO3 3 3 8 18/05/2026
15: 15 Adubo nitrogenado NMIN 3 3 8 18/05/2026
16: 16 Controle - Apenas água CON 4 4 8 18/05/2026
17: 17 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 4 4 8 18/05/2026
18: 18 Fezes ovinas tratadas com oleo de girassol FOG 4 4 8 18/05/2026
19: 19 Água ozonizada AO3 4 4 8 18/05/2026
20: 20 Adubo nitrogenado NMIN 4 4 8 18/05/2026
21: 21 Controle - Apenas água CON 5 5 8 18/05/2026
22: 22 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 5 5 8 18/05/2026
23: 23 Fezes ovinas tratadas com oleo de girassol FOG 5 5 8 18/05/2026
24: 24 Água ozonizada AO3 5 5 8 18/05/2026
25: 25 Adubo nitrogenado NMIN 5 5 8 18/05/2026
26: 26 Controle - Apenas água CON 6 6 8 18/05/2026
27: 27 Fezes ovinas tratadas com oleo de girassol ozonizado FOGO3 6 6 8 18/05/2026
28: 28 Fezes ovinas tratadas com oleo de girassol FOG 6 6 8 18/05/2026
29: 29 Água ozonizada AO3 6 6 8 18/05/2026
30: 30 Adubo nitrogenado NMIN 6 6 8 18/05/2026
Bottle Tratamento Abreviacao Bloco Rep Leitura Date
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature start
<num> <char> <char> <num> <num> <POSc>
1: 246.61 09:00 09:03 23.5 24.9 2026-05-18 09:00:00
2: 246.39 09:07 09:10 22.3 24.8 2026-05-18 09:07:00
3: 246.21 09:14 09:17 21.6 25.1 2026-05-18 09:14:00
4: 246.31 09:21 09:24 22.5 25.0 2026-05-18 09:21:00
5: 246.29 09:28 09:31 23.6 25.1 2026-05-18 09:28:00
6: 246.26 09:35 09:38 22.4 25.3 2026-05-18 09:35:00
7: 246.14 09:42 09:45 22.8 25.3 2026-05-18 09:42:00
8: 246.21 09:49 09:52 22.1 25.1 2026-05-18 09:49:00
9: 246.14 09:56 09:59 22.5 25.1 2026-05-18 09:56:00
10: 246.35 10:03 10:06 21.7 25.1 2026-05-18 10:03:00
11: 246.26 10:10 10:13 22.3 25.3 2026-05-18 10:10:00
12: 246.30 10:17 10:20 22.4 25.5 2026-05-18 10:17:00
13: 246.38 10:24 10:27 21.8 25.5 2026-05-18 10:24:00
14: 246.37 10:31 10:34 21.6 25.3 2026-05-18 10:31:00
15: 246.15 10:38 10:41 21.1 25.5 2026-05-18 10:38:00
16: 246.50 10:45 10:48 21.1 25.1 2026-05-18 10:45:00
17: 246.36 10:52 10:55 21.8 25.0 2026-05-18 10:52:00
18: 246.30 10:59 11:02 21.8 25.1 2026-05-18 10:59:00
19: 246.25 11:06 11:09 21.4 25.2 2026-05-18 11:06:00
20: 246.34 11:13 11:16 21.5 25.4 2026-05-18 11:13:00
21: 246.23 11:20 11:23 21.6 25.5 2026-05-18 11:20:00
22: 246.18 11:27 11:30 21.3 25.2 2026-05-18 11:27:00
23: 246.21 11:34 11:37 21.9 25.1 2026-05-18 11:34:00
24: 246.31 11:41 11:44 21.5 25.4 2026-05-18 11:41:00
25: 246.35 11:48 11:51 22.1 25.1 2026-05-18 11:48:00
26: 246.25 11:55 11:58 22.1 25.0 2026-05-18 11:55:00
27: 246.25 12:02 12:05 21.0 24.9 2026-05-18 12:02:00
28: 246.36 12:09 12:12 21.5 25.0 2026-05-18 12:09:00
29: 246.23 12:16 12:19 21.6 25.5 2026-05-18 12:16:00
30: 246.35 12:23 12:26 21.1 25.0 2026-05-18 12:23:00
Solo.Seco Beginning.reading End.reading External.temperature Internal.temperature start
end
<POSc>
1: 2026-05-18 09:03:00
2: 2026-05-18 09:10:00
3: 2026-05-18 09:17:00
4: 2026-05-18 09:24:00
5: 2026-05-18 09:31:00
6: 2026-05-18 09:38:00
7: 2026-05-18 09:45:00
8: 2026-05-18 09:52:00
9: 2026-05-18 09:59:00
10: 2026-05-18 10:06:00
11: 2026-05-18 10:13:00
12: 2026-05-18 10:20:00
13: 2026-05-18 10:27:00
14: 2026-05-18 10:34:00
15: 2026-05-18 10:41:00
16: 2026-05-18 10:48:00
17: 2026-05-18 10:55:00
18: 2026-05-18 11:02:00
19: 2026-05-18 11:09:00
20: 2026-05-18 11:16:00
21: 2026-05-18 11:23:00
22: 2026-05-18 11:30:00
23: 2026-05-18 11:37:00
24: 2026-05-18 11:44:00
25: 2026-05-18 11:51:00
26: 2026-05-18 11:58:00
27: 2026-05-18 12:05:00
28: 2026-05-18 12:12:00
29: 2026-05-18 12:19:00
30: 2026-05-18 12:26:00
end########################### MPV 1 - LEITURA DOS FRASCOS ###########################
leitura8 = copy(dados8)
leitura8[, c(
"Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") := .(
NA_integer_, NA_character_, NA_integer_, NA_integer_,NA_integer_, NA_real_, NA_real_)] DATE TIME JULIAN_DAYS MPVPosition N2O_dry CO2_dry CH4_dry H2O NH3
<char> <char> <num> <num> <num> <num> <num> <num> <num>
1: 2026-05-18 08:00:00.234 138.3750 1 0.3363889 498.0335 2.522241 1.941849 154.5771
2: 2026-05-18 08:00:01.026 138.3750 1 0.3363787 498.0620 2.521272 1.940609 154.6118
3: 2026-05-18 08:00:01.676 138.3750 1 0.3360332 498.0854 2.520478 1.939592 154.6402
4: 2026-05-18 08:00:02.437 138.3750 1 0.3356440 498.1288 2.519605 1.940208 154.5930
5: 2026-05-18 08:00:03.490 138.3750 1 0.3351059 498.0852 2.518400 1.941061 154.5279
---
22691: 2026-05-18 12:59:56.377 138.5833 1 0.3371623 501.9600 2.175345 2.466752 154.2754
22692: 2026-05-18 12:59:57.453 138.5833 1 0.3383252 502.0502 2.175419 2.466417 154.3238
22693: 2026-05-18 12:59:58.226 138.5833 1 0.3383222 502.1149 2.175473 2.466176 154.3586
22694: 2026-05-18 12:59:58.573 138.5833 1 0.3369384 502.1441 2.175497 2.466067 154.3742
22695: 2026-05-18 12:59:59.707 138.5833 1 0.3324290 502.2390 2.175433 2.465713 154.4252
datetime Bottle Abreviacao Bloco Rep Leitura Solo.Seco Internal.temperature
<POSc> <int> <char> <int> <int> <int> <num> <num>
1: 2026-05-18 08:00:00 NA <NA> NA NA NA NA NA
2: 2026-05-18 08:00:01 NA <NA> NA NA NA NA NA
3: 2026-05-18 08:00:01 NA <NA> NA NA NA NA NA
4: 2026-05-18 08:00:02 NA <NA> NA NA NA NA NA
5: 2026-05-18 08:00:03 NA <NA> NA NA NA NA NA
---
22691: 2026-05-18 12:59:56 NA <NA> NA NA NA NA NA
22692: 2026-05-18 12:59:57 NA <NA> NA NA NA NA NA
22693: 2026-05-18 12:59:58 NA <NA> NA NA NA NA NA
22694: 2026-05-18 12:59:58 NA <NA> NA NA NA NA NA
22695: 2026-05-18 12:59:59 NA <NA> NA NA NA NA NAleitura8[MPVPosition == 1,c(
"Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") :=
id[.SD,.(Bottle, Abreviacao, Bloco, Rep,Leitura, Solo.Seco, Internal.temperature),
on = .(start <= datetime, end > datetime),mult = "first"]]Index: <MPVPosition>
DATE TIME JULIAN_DAYS MPVPosition N2O_dry CO2_dry CH4_dry H2O NH3
<char> <char> <num> <num> <num> <num> <num> <num> <num>
1: 2026-05-18 08:00:00.234 138.3750 1 0.3363889 498.0335 2.522241 1.941849 154.5771
2: 2026-05-18 08:00:01.026 138.3750 1 0.3363787 498.0620 2.521272 1.940609 154.6118
3: 2026-05-18 08:00:01.676 138.3750 1 0.3360332 498.0854 2.520478 1.939592 154.6402
4: 2026-05-18 08:00:02.437 138.3750 1 0.3356440 498.1288 2.519605 1.940208 154.5930
5: 2026-05-18 08:00:03.490 138.3750 1 0.3351059 498.0852 2.518400 1.941061 154.5279
---
22691: 2026-05-18 12:59:56.377 138.5833 1 0.3371623 501.9600 2.175345 2.466752 154.2754
22692: 2026-05-18 12:59:57.453 138.5833 1 0.3383252 502.0502 2.175419 2.466417 154.3238
22693: 2026-05-18 12:59:58.226 138.5833 1 0.3383222 502.1149 2.175473 2.466176 154.3586
22694: 2026-05-18 12:59:58.573 138.5833 1 0.3369384 502.1441 2.175497 2.466067 154.3742
22695: 2026-05-18 12:59:59.707 138.5833 1 0.3324290 502.2390 2.175433 2.465713 154.4252
datetime Bottle Abreviacao Bloco Rep Leitura Solo.Seco Internal.temperature
<POSc> <int> <char> <int> <int> <int> <num> <num>
1: 2026-05-18 08:00:00 NA <NA> NA NA NA NA NA
2: 2026-05-18 08:00:01 NA <NA> NA NA NA NA NA
3: 2026-05-18 08:00:01 NA <NA> NA NA NA NA NA
4: 2026-05-18 08:00:02 NA <NA> NA NA NA NA NA
5: 2026-05-18 08:00:03 NA <NA> NA NA NA NA NA
---
22691: 2026-05-18 12:59:56 NA <NA> NA NA NA NA NA
22692: 2026-05-18 12:59:57 NA <NA> NA NA NA NA NA
22693: 2026-05-18 12:59:58 NA <NA> NA NA NA NA NA
22694: 2026-05-18 12:59:58 NA <NA> NA NA NA NA NA
22695: 2026-05-18 12:59:59 NA <NA> NA NA NA NA NA# Manter apenas as leituras válidas dos frascos
leitura8 = leitura8[MPVPosition == 1 & !is.na(Bottle)]
View(leitura8)Backgorunds
########################### FUNÇÃO PARA EXTRAIR O BACKGROUND ###########################
criar_background = function(dados, leitura) {
# Identificar o início real da leitura de cada frasco
mapa = leitura[, .(
inicio_leitura = min(datetime),
Abreviacao = data.table::first(Abreviacao),
Bloco = data.table::first(Bloco),
Rep = data.table::first(Rep),
Leitura = data.table::first(Leitura),
Solo.Seco = data.table::first(Solo.Seco),
Internal.temperature = data.table::first(Internal.temperature)), by = Bottle]
# Definir os 4 minutos anteriores à leitura
mapa[, background_start := inicio_leitura - 240]
# Manter apenas dados do MPVPosition 2
bg_raw = dados[MPVPosition == 2]
# Extrair separadamente o background de cada frasco
background = data.table::rbindlist(
lapply(seq_len(nrow(mapa)), function(i) {
m = mapa[i]
bg = data.table::copy(
bg_raw[datetime >= m$background_start & datetime < m$inicio_leitura])
if (nrow(bg) == 0) return(NULL)
bg[, `:=`(
Bottle = m$Bottle,
Abreviacao = m$Abreviacao,
Bloco = m$Bloco,
Rep = m$Rep,
Leitura = m$Leitura,
Solo.Seco = m$Solo.Seco,
Internal.temperature = m$Internal.temperature,
inicio_leitura = m$inicio_leitura,
# Tempo desde o início dos 4 min de background
t_background = as.numeric(
difftime(datetime,m$background_start,units = "secs")))]
bg}),use.names = TRUE,fill = TRUE)
return(background)}
########################### CRIAR OS 8 BACKGROUNDS ###########################
background1 = criar_background(dados1, leitura1)
background2 = criar_background(dados2, leitura2)
background3 = criar_background(dados3, leitura3)
background4 = criar_background(dados4, leitura4)
background5 = criar_background(dados5, leitura5)
background6 = criar_background(dados6, leitura6)
background7 = criar_background(dados7, leitura7)
background8 = criar_background(dados8, leitura8)
Juntar as 8 leituras e backgrounds
########################### Leituras ###########################
Leituras_completas = dplyr::bind_rows(
leitura1,leitura2,leitura3,leitura4,leitura5,leitura6,leitura7,leitura8)
View(Leituras_completas)
dim(Leituras_completas)[1] 46905 17table(Leituras_completas$Leitura)
1 2 3 4 5 6 7 8
5048 6296 5105 5845 5370 6545 6299 6397 str(Leituras_completas)Classes ‘data.table’ and 'data.frame': 46905 obs. of 17 variables:
$ DATE : chr "2026-04-15" "2026-04-15" "2026-04-15" "2026-04-15" ...
$ TIME : chr "09:09:52.979" "09:09:53.765" "09:09:54.828" "09:09:55.624" ...
$ JULIAN_DAYS : num 105 105 105 105 105 ...
$ MPVPosition : num 1 1 1 1 1 1 1 1 1 1 ...
$ N2O_dry : num 0.339 0.351 0.368 0.368 0.399 ...
$ CO2_dry : num 783 783 817 843 854 ...
$ CH4_dry : num 2.85 2.86 2.87 2.88 2.89 ...
$ H2O : num 2.09 2.15 2.22 2.28 2.31 ...
$ NH3 : num 173 175 179 182 183 ...
$ datetime : POSIXct, format: "2026-04-15 09:09:52" "2026-04-15 09:09:53" "2026-04-15 09:09:54" "2026-04-15 09:09:55" ...
$ Bottle : int 1 1 1 1 1 1 1 1 1 1 ...
$ Abreviacao : chr "CON" "CON" "CON" "CON" ...
$ Bloco : int 1 1 1 1 1 1 1 1 1 1 ...
$ Rep : int 1 1 1 1 1 1 1 1 1 1 ...
$ Leitura : int 1 1 1 1 1 1 1 1 1 1 ...
$ Solo.Seco : num 247 247 247 247 247 ...
$ Internal.temperature: num 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5 ...
- attr(*, ".internal.selfref")=<externalptr> ########################### backgrounds ###########################
background_completo = dplyr::bind_rows(
background1,background2,background3,background4,background5,background6,background7,background8)
View(background_completo)
dim(background_completo)[1] 71118 19table(background_completo$Leitura)
1 2 3 4 5 6 7 8
8707 8909 9001 9011 8829 8940 8739 8982 str(background_completo)Classes ‘data.table’ and 'data.frame': 71118 obs. of 19 variables:
$ DATE : chr "2026-04-15" "2026-04-15" "2026-04-15" "2026-04-15" ...
$ TIME : chr "09:05:53.203" "09:05:53.529" "09:05:54.671" "09:05:55.453" ...
$ JULIAN_DAYS : num 105 105 105 105 105 ...
$ MPVPosition : num 2 2 2 2 2 2 2 2 2 2 ...
$ N2O_dry : num 0.332 0.333 0.338 0.338 0.335 ...
$ CO2_dry : num 771 771 770 770 769 ...
$ CH4_dry : num 2.85 2.85 2.85 2.85 2.85 ...
$ H2O : num 2.01 2.01 2.01 2.01 2.01 ...
$ NH3 : num 173 173 173 172 172 ...
$ datetime : POSIXct, format: "2026-04-15 09:05:53" "2026-04-15 09:05:53" "2026-04-15 09:05:54" "2026-04-15 09:05:55" ...
$ Bottle : int 1 1 1 1 1 1 1 1 1 1 ...
$ Abreviacao : chr "CON" "CON" "CON" "CON" ...
$ Bloco : int 1 1 1 1 1 1 1 1 1 1 ...
$ Rep : int 1 1 1 1 1 1 1 1 1 1 ...
$ Leitura : int 1 1 1 1 1 1 1 1 1 1 ...
$ Solo.Seco : num 247 247 247 247 247 ...
$ Internal.temperature: num 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5 ...
$ inicio_leitura : POSIXct, format: "2026-04-15 09:09:52" "2026-04-15 09:09:52" "2026-04-15 09:09:52" "2026-04-15 09:09:52" ...
$ t_background : num 0.224 0.55 1.692 2.474 3.126 ...
- attr(*, ".internal.selfref")=<externalptr> 3 - Controle de qualidade e definição da janela de leitura
Leituras
dados_proc = Leituras_completas %>%
dplyr::mutate(BottleID = paste(Leitura,Bottle,Abreviacao,Bloco,Rep,sep = "_")) %>%
dplyr::group_by(BottleID) %>%
dplyr::arrange(datetime, .by_group = TRUE) %>%
dplyr::mutate(t_sec = as.numeric(difftime(datetime, min(datetime), units = "secs"))) %>%
dplyr::ungroup()
head(dados_proc)
########################### PREPARAR DADOS ###########################
dados_diagnostico = dados_proc %>% dplyr::filter(t_sec <= 120) %>%
dplyr::select(BottleID, t_sec,CO2_dry, N2O_dry, CH4_dry, H2O, NH3) %>%
tidyr::pivot_longer(cols = c(CO2_dry, N2O_dry, CH4_dry, H2O, NH3),
names_to = "Gas",values_to = "Concentracao") %>%
dplyr::mutate(Gas = dplyr::recode(Gas,
"CH4_dry"="CH4 (ppm)","CO2_dry"="CO2 (ppm)","N2O_dry"="N2O (ppm)","H2O"="H2O (%)","NH3"= "NH3 (ppb)"))
########################### FIGURA S2 - PERFIL DAS LEITURAS ###########################
fig_S2 = ggplot(dados_diagnostico, aes(x = t_sec, y = Concentracao, group = BottleID)) +
geom_line(alpha = 0.35) +
geom_vline(xintercept = c(15, 45), linetype = "dashed") +
facet_wrap(~ Gas, scales = "free_y", ncol = 3) +
labs(x = "Reading time (s)",y = "Concentration") + theme_bw() +
scale_x_continuous(breaks = seq(0, 120, by = 15))+
theme(strip.text = element_text(hjust = 0.5, face = "bold"),panel.spacing = unit(1, "lines"))
fig_S2
ggsave("Figure S2.png",fig_S2, width = 10, height = 6, dpi = 300, bg = "white")
########################### AVALIAR JANELAS CANDIDATAS ###########################
gases_qc = c("CO2_dry", "N2O_dry", "CH4_dry")
janelas = data.frame(Janela = c("15-35", "15-45", "15-60","20-40", "20-50", "20-60"),
inicio = c(15, 15, 15, 20, 20, 20), fim = c(35, 45, 60, 40, 50, 60))
avaliacao_janelas = dplyr::bind_rows(lapply(seq_len(nrow(janelas)), function(i) {
ini = janelas$inicio[i]
fim = janelas$fim[i]
dplyr::bind_rows(lapply(gases_qc, function(g) {dados_proc %>%
dplyr::filter(t_sec >= ini, t_sec <= fim) %>%
dplyr::group_by(BottleID) %>%
dplyr::summarise(media = mean(.data[[g]], na.rm = TRUE),sd = sd(.data[[g]], na.rm = TRUE),
slope = cov(t_sec,.data[[g]],use = "complete.obs") / var(t_sec, na.rm = TRUE),.groups = "drop") %>%
dplyr::mutate(Gas = g,Janela = janelas$Janela[i],
# Coeficiente de variação dentro da janela
CV = (sd / abs(media)) * 100,
# Mudança estimada da concentração ao longo da janela
Drift = abs(slope) * (fim - ini) / abs(media) * 100)}))}))
########################### ESTABILIDADE DAS JANELAS ###########################
tab_S2 = avaliacao_janelas %>% dplyr::group_by(Gas, Janela) %>%
dplyr::summarise(CV_mediano = median(CV, na.rm = TRUE), CV_P90 = quantile(CV, 0.90, na.rm = TRUE),
Drift_mediano = median(Drift, na.rm = TRUE),
Drift_P90 = quantile(Drift, 0.90, na.rm = TRUE),.groups = "drop") %>%
dplyr::mutate(Janela = factor(Janela, levels = janelas$Janela)) %>%
dplyr::arrange(Gas, Janela)
print(tab_S2)# A tibble: 18 × 6
Gas Janela CV_mediano CV_P90 Drift_mediano Drift_P90
<chr> <fct> <dbl> <dbl> <dbl> <dbl>
1 CH4_dry 15-35 0.0267 0.0439 0.0482 0.109
2 CH4_dry 15-45 0.0301 0.0496 0.0449 0.118
3 CH4_dry 15-60 0.0351 0.0544 0.0509 0.120
4 CH4_dry 20-40 0.0258 0.0437 0.0456 0.112
5 CH4_dry 20-50 0.0293 0.0521 0.0449 0.120
6 CH4_dry 20-60 0.0339 0.0538 0.0457 0.133
7 CO2_dry 15-35 0.131 0.328 0.440 1.09
8 CO2_dry 15-45 0.228 0.558 0.761 1.88
9 CO2_dry 15-60 0.391 0.928 1.30 3.19
10 CO2_dry 20-40 0.151 0.366 0.506 1.24
11 CO2_dry 20-50 0.256 0.612 0.863 2.08
12 CO2_dry 20-60 0.358 0.875 1.22 3.04
13 N2O_dry 15-35 1.27 1.84 1.07 3.24
14 N2O_dry 15-45 1.34 1.80 1.12 2.67
15 N2O_dry 15-60 1.37 1.83 1.20 2.81
16 N2O_dry 20-40 1.26 1.88 1.24 3.24
17 N2O_dry 20-50 1.31 1.85 1.10 2.82
18 N2O_dry 20-60 1.35 1.83 1.33 2.86 ########################### FIGURA S3 - COMPARAÇÃO DAS JANELAS ###########################
dados_fig_S3 = tab_S2 %>%
dplyr::select(Gas, Janela, CV_P90, Drift_P90) %>%
tidyr::pivot_longer(cols = c(CV_P90, Drift_P90),names_to = "Metrica",values_to = "Valor") %>%
dplyr::mutate(
Gas = dplyr::recode(Gas,"CH4_dry"="CH4 (ppm)","CO2_dry"="CO2 (ppm)","N2O_dry"="N2O (ppm)"),
Metrica = dplyr::recode(Metrica,"CV_P90" = "CV P90 (%)","Drift_P90" = "Drift P90 (%)"))
fig_S3 = ggplot(dados_fig_S3,aes(x = Janela, y = Valor, group = Gas)) +
geom_line() + geom_point(size = 2) +
facet_grid(Metrica ~ Gas, scales = "free_y") +
labs(x = "Reading window (s)",y = "Value (%)") + theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),strip.text = element_text(face = "bold", hjust = 0.5))
fig_S3
ggsave("Figure S3.png",fig_S3, width = 10, height = 6, dpi = 400, bg = "white")
########################### JANELA FINAL: 15-45 s ###########################
dados_estaveis = dados_proc %>% dplyr::filter(t_sec >= 15, t_sec <= 45)
########################### CONCENTRAÇÕES MÉDIAS ###########################
medias_leituras = dados_estaveis %>% dplyr::group_by(
Leitura, Bottle, Abreviacao, Bloco, Rep) %>%
dplyr::summarise(
Temperatura = mean(Internal.temperature, na.rm = TRUE),
Peso.solo = mean(Solo.Seco, na.rm = TRUE),
N2O = mean(N2O_dry, na.rm = TRUE),
CO2 = mean(CO2_dry, na.rm = TRUE),
CH4 = mean(CH4_dry, na.rm = TRUE),
# Mantido apenas como controle de qualidade
H2O_QC = mean(H2O, na.rm = TRUE),
n_obs = dplyr::n(),.groups = "drop")
View(medias_leituras)Background
########################### PREPARAR DADOS ###########################
background_proc = background_completo %>% dplyr::mutate(
BottleID = paste(Leitura, Bottle, Abreviacao, Bloco, Rep,sep = "_"))
head(background_proc)
########################### PREPARAR DADOS PARA DIAGNÓSTICO ###########################
background_diagnostico = background_proc %>% dplyr::filter(
t_background >= 0,t_background <= 240) %>%
dplyr::select(BottleID,t_background,CO2_dry,N2O_dry,CH4_dry,H2O) %>%
tidyr::pivot_longer(cols = c(CO2_dry, N2O_dry, CH4_dry, H2O),names_to = "Gas",values_to = "Concentracao") %>%
dplyr::mutate(
Gas = dplyr::recode(Gas,"CH4_dry"="CH4 (ppm)","CO2_dry"="CO2 (ppm)","N2O_dry"="N2O (ppm)","H2O"="H2O (%)"))
########################### FIGURA S4 - PERFIL DO BACKGROUND ###########################
fig_S4 = ggplot(background_diagnostico,aes(x = t_background,y = Concentracao,group = BottleID)) +
geom_line(alpha = 0.30) +
geom_vline(xintercept = c(60, 90),linetype = "dashed") +
facet_wrap(~ Gas,scales = "free_y",ncol = 2) +
scale_x_continuous(breaks = seq(0, 240, by = 30)) +
labs(x = "Background measurement time (s)",y = "Concentration") +
theme_bw()+theme(strip.text = element_text(hjust = 0.5,face = "bold"),panel.spacing = grid::unit(1, "lines"))
fig_S4
ggsave("Figure S4.png",fig_S4,width = 9,height = 7,dpi = 300,bg = "white")
########################### AVALIAR JANELAS DO BACKGROUND ###########################
gases_bg = c("CO2_dry","N2O_dry","CH4_dry")
janelas_bg = data.frame(Janela = c("20-50","30-60","30-90","60-90","60-120","180-240"),
inicio = c(20,30,30,60,60,180),fim = c(50,60,90,90,120,240))
avaliacao_bg = dplyr::bind_rows(
lapply(seq_len(nrow(janelas_bg)), function(i) {
ini = janelas_bg$inicio[i]
fim = janelas_bg$fim[i]
dplyr::bind_rows(lapply(gases_bg, function(g) {
background_proc %>%
dplyr::filter(t_background >= ini,t_background <= fim) %>%
dplyr::group_by(BottleID) %>%
dplyr::summarise(media = mean(.data[[g]],na.rm = TRUE),
sd = sd(.data[[g]],na.rm = TRUE),
slope = cov(t_background,.data[[g]],
use = "complete.obs") /var(t_background,na.rm = TRUE),.groups = "drop") %>%
dplyr::mutate(Gas = g,
Janela = janelas_bg$Janela[i],
# Coeficiente de variação dentro da janela
CV = (sd / abs(media)) * 100,
# Mudança relativa estimada ao longo da janela
Drift = abs(slope) * (fim - ini) / abs(media) * 100)}))}))
########################### RESUMO DAS JANELAS ###########################
resumo_bg = avaliacao_bg %>%
dplyr::group_by(Gas,Janela) %>%
dplyr::summarise(CV_mediano = median(CV,na.rm = TRUE),
CV_P90 = quantile(CV,0.90,na.rm = TRUE),
Drift_mediano = median(Drift,na.rm = TRUE),
Drift_P90 = quantile(Drift,0.90,na.rm = TRUE),.groups = "drop") %>%
dplyr::mutate(Janela = factor(Janela,levels = janelas_bg$Janela)) %>%
dplyr::arrange(Gas,Janela)
print(resumo_bg)# A tibble: 18 × 6
Gas Janela CV_mediano CV_P90 Drift_mediano Drift_P90
<chr> <fct> <dbl> <dbl> <dbl> <dbl>
1 CH4_dry 20-50 0.0686 0.193 0.149 0.492
2 CH4_dry 30-60 0.0594 0.211 0.144 0.582
3 CH4_dry 30-90 0.0954 0.340 0.194 1.06
4 CH4_dry 60-90 0.0560 0.193 0.120 0.598
5 CH4_dry 60-120 0.0836 0.310 0.167 0.834
6 CH4_dry 180-240 0.110 0.402 0.229 1.20
7 CO2_dry 20-50 0.628 2.79 1.02 5.76
8 CO2_dry 30-60 0.550 3.18 1.00 6.29
9 CO2_dry 30-90 0.883 3.88 1.14 6.81
10 CO2_dry 60-90 0.565 2.75 0.998 5.13
11 CO2_dry 60-120 0.768 3.30 0.982 5.06
12 CO2_dry 180-240 1.01 5.06 1.10 8.57
13 N2O_dry 20-50 1.61 2.24 1.77 4.43
14 N2O_dry 30-60 1.46 1.92 1.33 3.01
15 N2O_dry 30-90 1.52 1.88 1.12 2.84
16 N2O_dry 60-90 1.43 1.91 1.19 3.11
17 N2O_dry 60-120 1.48 1.83 0.915 2.45
18 N2O_dry 180-240 1.51 1.85 0.903 2.54 ########################### FIGURA S5 - COMPARAÇÃO DAS JANELAS ###########################
dados_fig_S5 = resumo_bg %>% dplyr::select(
Gas,Janela,CV_P90,Drift_P90) %>%
tidyr::pivot_longer(cols = c(CV_P90,Drift_P90),
names_to = "Metrica",values_to = "Valor") %>%
dplyr::mutate(Gas = dplyr::recode(
Gas,"CH4_dry" = "CH4 (ppm)","CO2_dry" = "CO2 (ppm)","N2O_dry" = "N2O (ppm)"),
Metrica = dplyr::recode(Metrica,"CV_P90" = "CV P90 (%)","Drift_P90" = "Drift P90 (%)"))
fig_S5 = ggplot(dados_fig_S5,aes(x = Janela,y = Valor,group = Gas)) +
geom_line() +
geom_point(size = 2) +
facet_grid(Metrica ~ Gas,scales = "free_y") +
labs(x = "Background window (s)",y = "Value (%)") +
theme_bw() + theme(axis.text.x = element_text(angle = 45,hjust = 1),
strip.text = element_text(face = "bold",hjust = 0.5))
fig_S5
ggsave("Figure S5.png",fig_S5,width = 10,height = 6,dpi = 400,bg = "white")
########################### BACKGROUND FINAL: 60-90 s ###########################
background_estavel = background_proc %>%
dplyr::filter(t_background >= 60,t_background <= 90)
########################### CONCENTRAÇÕES MÉDIAS DO BACKGROUND ###########################
medias_background = background_estavel %>%
dplyr::group_by(Leitura,Bottle) %>%
dplyr::summarise(
CO2_bg = mean(CO2_dry,na.rm = TRUE),
N2O_bg = mean(N2O_dry,na.rm = TRUE),
CH4_bg = mean(CH4_dry,na.rm = TRUE),
# Mantido apenas como controle de qualidade
H2O_bg = mean(H2O,na.rm = TRUE),
n_obs_bg = dplyr::n(),.groups = "drop")
nrow(medias_background)[1] 239View(medias_background)4 - Calcular emissão e normalizar por g de solo seco
########################### JUNTAR LEITURA E BACKGROUND ###########################
dados_fluxo = medias_leituras %>%
dplyr::left_join(medias_background,by = c("Leitura", "Bottle"))
# Conferir se todos os frascos receberam background
nrow(dados_fluxo)[1] 240colSums(is.na(dados_fluxo[, c("CO2_bg","N2O_bg","CH4_bg")]))CO2_bg N2O_bg CH4_bg
1 1 1 View(dados_fluxo)
########################### CONSTANTES ###########################
R = 0.082057 # L atm mol-1 K-1
P = 0.927 # atm - estimada para altitude de 633 m
Vh = 0.700 # L - volume do headspace
tempo = 4 # min - período de fechamento do frasco
MM.CH4 = 16.04 # g mol-1
MM.N2O = 44.013 # g mol-1
MM.CO2 = 44.0095 # g mol-1
########################### CORRIGIR PELO BACKGROUND ###########################
dados_fluxo = dados_fluxo %>% dplyr::mutate(
# Temperatura do headspace em Kelvin
T_K = Temperatura + 273.15,
# Diferença entre concentração do frasco e ar ambiente
Delta_CH4 = CH4 - CH4_bg,
Delta_N2O = N2O - N2O_bg,
Delta_CO2 = CO2 - CO2_bg)
########################### MASSA ACUMULADA EM 4 MIN ###########################
dados_fluxo = dados_fluxo %>% dplyr::mutate(
# Massa acumulada no headspace durante os 4 min (µg)
CH4_ug_4min = Delta_CH4 * Vh * MM.CH4 * P / (R * T_K),
N2O_ug_4min = Delta_N2O * Vh * MM.N2O * P / (R * T_K),
CO2_ug_4min = Delta_CO2 * Vh * MM.CO2 * P / (R * T_K))
########################### NORMALIZAR POR SOLO SECO E TEMPO ###########################
dados_fluxo = dados_fluxo %>% dplyr::mutate(
# µg de gás g-1 de solo seco h-1
Emissao_CH4_ug_g_h = CH4_ug_4min * (60 / tempo) / Peso.solo,
Emissao_N2O_ug_g_h = N2O_ug_4min * (60 / tempo) / Peso.solo,
Emissao_CO2_ug_g_h = CO2_ug_4min * (60 / tempo) / Peso.solo)
View(dados_fluxo)5 - Calcular emissões acumuladas
########################### IDENTIFICAR OS DIAS ###########################
dias_leitura = c(1, 2, 3, 5, 8, 13, 21, 34)
dados_acumulo = dados_fluxo %>%
dplyr::mutate(Dia = dias_leitura[Leitura]) %>%
dplyr::arrange(Bottle,Dia)
View(dados_acumulo)
########################### CALCULAR EMISSÕES ACUMULADAS ###########################
emissoes_acumuladas = dados_acumulo %>%
dplyr::group_by(Bottle,Abreviacao,Bloco,Rep) %>%
dplyr::arrange(Dia, .by_group = TRUE) %>%
dplyr::summarise(
CH4_acumulado_ug_g =sum((
Emissao_CH4_ug_g_h+dplyr::lead(Emissao_CH4_ug_g_h))/2*(dplyr::lead(Dia)-Dia)*24,na.rm = TRUE),
N2O_acumulado_ug_g =sum((
Emissao_N2O_ug_g_h+dplyr::lead(Emissao_N2O_ug_g_h))/2*(dplyr::lead(Dia)-Dia)*24,na.rm = TRUE),
CO2_acumulado_ug_g =sum((
Emissao_CO2_ug_g_h+dplyr::lead(Emissao_CO2_ug_g_h))/2*(dplyr::lead(Dia)-Dia)*24,na.rm = TRUE),.groups = "drop")
View(emissoes_acumuladas)5 - Análise estatística
6.1 - Dinâmica temporal das emissões
6.1.1 - MODELOS CANDIDATOS
dados_acumulo$Abreviacao=as.factor(dados_acumulo$Abreviacao)
dados_acumulo$Dia=as.factor(dados_acumulo$Dia)
dados_acumulo$Bloco=as.factor(dados_acumulo$Bloco)
str(dados_acumulo)tibble [240 × 28] (S3: tbl_df/tbl/data.frame)
$ Leitura : int [1:240] 1 2 3 4 5 6 7 8 1 2 ...
$ Bottle : int [1:240] 1 1 1 1 1 1 1 1 2 2 ...
$ Abreviacao : Factor w/ 5 levels "AO3","CON","FOG",..: 2 2 2 2 2 2 2 2 4 4 ...
$ Bloco : Factor w/ 6 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Rep : int [1:240] 1 1 1 1 1 1 1 1 1 1 ...
$ Temperatura : num [1:240] 25.5 24.9 25.1 25.2 25 25.3 25.1 24.9 24.6 25.3 ...
$ Peso.solo : num [1:240] 247 247 247 247 247 ...
$ N2O : num [1:240] 0.514 0.336 0.348 0.35 0.398 ...
$ CO2 : num [1:240] 977 825 899 715 1525 ...
$ CH4 : num [1:240] 2.9 2.7 3.1 3.25 2.94 ...
$ H2O_QC : num [1:240] 3.26 3.28 3.14 3.21 3.13 ...
$ n_obs : int [1:240] 37 38 38 38 38 38 37 38 37 38 ...
$ CO2_bg : num [1:240] 779 604 645 544 521 ...
$ N2O_bg : num [1:240] 0.329 0.332 0.332 0.334 0.332 ...
$ CH4_bg : num [1:240] 2.84 2.66 3.12 3.25 2.57 ...
$ H2O_bg : num [1:240] 2.04 2.04 1.86 2.17 1.79 ...
$ n_obs_bg : int [1:240] 37 38 38 37 38 38 37 38 37 37 ...
$ T_K : num [1:240] 299 298 298 298 298 ...
$ Delta_CH4 : num [1:240] 0.06337 0.03605 -0.01267 -0.00668 0.36543 ...
$ Delta_N2O : num [1:240] 0.1854 0.0049 0.016 0.0161 0.0661 ...
$ Delta_CO2 : num [1:240] 198 221 254 171 1004 ...
$ CH4_ug_4min : num [1:240] 0.02692 0.01534 -0.00539 -0.00284 0.15547 ...
$ N2O_ug_4min : num [1:240] 0.21606 0.00572 0.01864 0.01878 0.07717 ...
$ CO2_ug_4min : num [1:240] 230 258 297 199 1172 ...
$ Emissao_CH4_ug_g_h: num [1:240] 0.001637 0.000933 -0.000328 -0.000173 0.009456 ...
$ Emissao_N2O_ug_g_h: num [1:240] 0.013142 0.000348 0.001134 0.001142 0.004694 ...
$ Emissao_CO2_ug_g_h: num [1:240] 14 15.7 18 12.1 71.3 ...
$ Dia : Factor w/ 8 levels "1","2","3","5",..: 1 2 3 4 5 6 7 8 1 2 ...########################### CO2 ###########################
library(lme4)
library(lmerTest)
# Sem bloco e sem efeito aleatório
mod1 = lm(Emissao_CO2_ug_g_h ~ Abreviacao * Dia,data = dados_acumulo)
# Com bloco
mod2 = lm(Emissao_CO2_ug_g_h ~ Abreviacao * Dia + Bloco,data = dados_acumulo)
# Com Bottle como efeito aleatório
mod3 = lmer(Emissao_CO2_ug_g_h ~ Abreviacao * Dia + (1 | Bottle),data = dados_acumulo,REML = FALSE)
# Com bloco + Bottle
mod4 = lmer(Emissao_CO2_ug_g_h~Abreviacao* Dia + Bloco + (1|Bottle), data = dados_acumulo,REML = FALSE)
########################### COMPARAR MODELOS ###########################
AIC(mod1, mod2, mod3, mod4)BIC(mod1, mod2, mod3, mod4)VarCorr(mod3) Groups Name Std.Dev.
Bottle (Intercept) 1.6032
Residual 49.8551 VarCorr(mod4) Groups Name Std.Dev.
Bottle (Intercept) 0.000
Residual 48.978 isSingular(mod3)[1] FALSEisSingular(mod4)[1] TRUE# Resultado: mod1 é mais simples, com menor AIC e BIC, mod2 e mod3 piorou AIC/BIC e mod4 é singular.
########################### MODELO FINAL - CO2 ###########################
mod_CO2 = mod1
########################### CH4 ###########################
# Sem bloco e sem efeito aleatório
mod1 = lm(Emissao_CH4_ug_g_h ~ Abreviacao * Dia,data = dados_acumulo)
# Com bloco
mod2 = lm(Emissao_CH4_ug_g_h ~ Abreviacao * Dia + Bloco,data = dados_acumulo)
# Com Bottle como efeito aleatório
mod3 = lmer(Emissao_CH4_ug_g_h ~ Abreviacao * Dia + (1 | Bottle),data = dados_acumulo,REML = FALSE)
# Com bloco + Bottle
mod4 = lmer(Emissao_CH4_ug_g_h~Abreviacao * Dia + Bloco + (1|Bottle), data = dados_acumulo, REML = FALSE)
########################### COMPARAR MODELOS ###########################
AIC(mod1, mod2, mod3, mod4)BIC(mod1, mod2, mod3, mod4)VarCorr(mod3) Groups Name Std.Dev.
Bottle (Intercept) 0.00043979
Residual 0.00157671VarCorr(mod4) Groups Name Std.Dev.
Bottle (Intercept) 0.00027098
Residual 0.00157683isSingular(mod3)[1] FALSEisSingular(mod4)[1] FALSE# Resultado: Mesma coisa para metano
########################### MODELO FINAL - CH4 ###########################
mod_CH4 = mod1
########################### N2O ###########################
# Sem bloco e sem efeito aleatório
mod1 = lm(Emissao_N2O_ug_g_h ~ Abreviacao * Dia,data = dados_acumulo)
# Com bloco
mod2 = lm(Emissao_N2O_ug_g_h ~ Abreviacao * Dia + Bloco, data = dados_acumulo)
# Com Bottle como efeito aleatório
mod3 = lmer(Emissao_N2O_ug_g_h ~ Abreviacao * Dia + (1 | Bottle),data = dados_acumulo,REML = FALSE)
# Com bloco + Bottle
mod4 = lmer(Emissao_N2O_ug_g_h~Abreviacao * Dia + Bloco + (1|Bottle), data = dados_acumulo,REML = FALSE)
########################### COMPARAR MODELOS ###########################
AIC(mod1, mod2, mod3, mod4)BIC(mod1, mod2, mod3, mod4)VarCorr(mod3) Groups Name Std.Dev.
Bottle (Intercept) 0.000000
Residual 0.023622VarCorr(mod4) Groups Name Std.Dev.
Bottle (Intercept) 0.00000
Residual 0.02333 isSingular(mod3)[1] TRUEisSingular(mod4)[1] TRUE# Resultado: Mesma coisa para N2O
########################### MODELO FINAL - N2O ###########################
mod_N2O = mod16.1.2 - Pressupostos
shapiro.test(resid(mod_N2O_sqrt))
Shapiro-Wilk normality test
data: resid(mod_N2O_sqrt)
W = 0.8537, p-value = 1.581e-136.1.3 - ANOVA, TUKEY E MÉDIAS
library(dplyr)
library(emmeans)
library(multcomp)
mapa_vars = data.frame(original = c("Emissao_CO2_ug_g_h","Emissao_N2O_ug_g_h","Emissao_CH4_ug_g_h"),
modelo = c("CO2_log","N2O_log","CH4_log"))
resultados = list()
for(i in 1:nrow(mapa_vars)){
var_orig = mapa_vars$original[i]
var_mod = mapa_vars$modelo[i]
cat("\n\n################ Variável:", var_orig, "################\n")
# Modelo
formula_mod = as.formula(paste0(var_mod, " ~ Abreviacao * Dia"))
mod = lm(formula_mod, data = dados_acumulo)
# ANOVA
cat("\nANOVA:\n")
print(anova(mod))
# Médias estimadas
em = emmeans(mod, ~ Abreviacao | Dia)
# Tukey
cat("\nTeste de Tukey:\n")
print(pairs(em, adjust = "tukey"))
# Letras
grupos = multcomp::cld(em,alpha = 0.05,Letters = letters,sort = TRUE,decreasing = FALSE)
letras = data.frame(Dia = grupos$Dia,Abreviacao = grupos$Abreviacao,letra = trimws(grupos$.group))
# Média e SE dos dados originais
medias = dados_acumulo %>%
group_by(Dia, Abreviacao) %>%
summarise(media = mean(.data[[var_orig]], na.rm = TRUE),
se = sd(.data[[var_orig]], na.rm = TRUE)/sqrt(sum(!is.na(.data[[var_orig]]))),.groups = "drop")
# Tabela
tabela = medias %>%
left_join(letras, by = c("Dia", "Abreviacao")) %>%
dplyr::mutate(variavel = var_orig) %>%
dplyr::select(variavel,Dia,Tratamento = Abreviacao,media,se,letra)
resultados[[var_orig]] = tabela}
################ Variável: Emissao_CO2_ug_g_h ################
ANOVA:
Analysis of Variance Table
Response: CO2_log
Df Sum Sq Mean Sq F value Pr(>F)
Abreviacao 4 66.242 16.5605 16.8172 7.339e-12 ***
Dia 7 41.273 5.8961 5.9875 2.497e-06 ***
Abreviacao:Dia 28 44.622 1.5937 1.6184 0.03196 *
Residuals 195 192.023 0.9847
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Teste de Tukey:
Dia = 1:
contrast estimate SE df t.ratio p.value
AO3 - CON 0.10777 0.601 195 0.179 0.9998
AO3 - FOG -1.34922 0.573 195 -2.355 0.1324
AO3 - FOGO3 -1.83462 0.573 195 -3.202 0.0136
AO3 - NMIN -1.08931 0.601 195 -1.813 0.3691
CON - FOG -1.45699 0.601 195 -2.425 0.1131
CON - FOGO3 -1.94239 0.601 195 -3.233 0.0124
CON - NMIN -1.19709 0.628 195 -1.907 0.3169
FOG - FOGO3 -0.48540 0.573 195 -0.847 0.9154
FOG - NMIN 0.25990 0.601 195 0.433 0.9927
FOGO3 - NMIN 0.74530 0.601 195 1.240 0.7278
Dia = 2:
contrast estimate SE df t.ratio p.value
AO3 - CON 1.75391 0.628 195 2.795 0.0448
AO3 - FOG -0.64332 0.601 195 -1.071 0.8214
AO3 - FOGO3 -0.75401 0.601 195 -1.255 0.7191
AO3 - NMIN 1.03098 0.628 195 1.643 0.4721
CON - FOG -2.39722 0.601 195 -3.989 0.0009
CON - FOGO3 -2.50792 0.601 195 -4.174 0.0004
CON - NMIN -0.72293 0.628 195 -1.152 0.7786
FOG - FOGO3 -0.11069 0.573 195 -0.193 0.9997
FOG - NMIN 1.67430 0.601 195 2.786 0.0458
FOGO3 - NMIN 1.78499 0.601 195 2.971 0.0274
Dia = 3:
contrast estimate SE df t.ratio p.value
AO3 - CON 0.22282 0.573 195 0.389 0.9951
AO3 - FOG -0.73062 0.573 195 -1.275 0.7067
AO3 - FOGO3 -0.77101 0.573 195 -1.346 0.6630
AO3 - NMIN 0.70287 0.573 195 1.227 0.7358
CON - FOG -0.95344 0.573 195 -1.664 0.4586
CON - FOGO3 -0.99384 0.573 195 -1.735 0.4152
CON - NMIN 0.48004 0.573 195 0.838 0.9185
FOG - FOGO3 -0.04040 0.573 195 -0.071 1.0000
FOG - NMIN 1.43349 0.573 195 2.502 0.0944
FOGO3 - NMIN 1.47388 0.573 195 2.573 0.0795
Dia = 5:
contrast estimate SE df t.ratio p.value
AO3 - CON -0.22394 0.573 195 -0.391 0.9950
AO3 - FOG -2.07300 0.573 195 -3.618 0.0034
AO3 - FOGO3 -1.52107 0.573 195 -2.655 0.0647
AO3 - NMIN -0.37645 0.573 195 -0.657 0.9651
CON - FOG -1.84905 0.573 195 -3.227 0.0126
CON - FOGO3 -1.29712 0.573 195 -2.264 0.1612
CON - NMIN -0.15250 0.573 195 -0.266 0.9989
FOG - FOGO3 0.55193 0.573 195 0.963 0.8712
FOG - NMIN 1.69655 0.573 195 2.961 0.0281
FOGO3 - NMIN 1.14462 0.573 195 1.998 0.2709
Dia = 8:
contrast estimate SE df t.ratio p.value
AO3 - CON -0.35972 0.573 195 -0.628 0.9704
AO3 - FOG -1.30739 0.573 195 -2.282 0.1552
AO3 - FOGO3 -1.01146 0.573 195 -1.765 0.3968
AO3 - NMIN 0.77192 0.573 195 1.347 0.6620
CON - FOG -0.94767 0.573 195 -1.654 0.4649
CON - FOGO3 -0.65174 0.573 195 -1.138 0.7864
CON - NMIN 1.13163 0.573 195 1.975 0.2820
FOG - FOGO3 0.29593 0.573 195 0.517 0.9856
FOG - NMIN 2.07930 0.573 195 3.629 0.0033
FOGO3 - NMIN 1.78337 0.573 195 3.113 0.0180
Dia = 13:
contrast estimate SE df t.ratio p.value
AO3 - CON -0.59104 0.573 195 -1.032 0.8404
AO3 - FOG -1.48831 0.573 195 -2.598 0.0747
AO3 - FOGO3 -1.88480 0.573 195 -3.290 0.0103
AO3 - NMIN 0.00799 0.573 195 0.014 1.0000
CON - FOG -0.89727 0.573 195 -1.566 0.5209
CON - FOGO3 -1.29376 0.573 195 -2.258 0.1632
CON - NMIN 0.59903 0.573 195 1.046 0.8338
FOG - FOGO3 -0.39649 0.573 195 -0.692 0.9580
FOG - NMIN 1.49630 0.573 195 2.612 0.0721
FOGO3 - NMIN 1.89279 0.573 195 3.304 0.0099
Dia = 21:
contrast estimate SE df t.ratio p.value
AO3 - CON -0.58997 0.573 195 -1.030 0.8413
AO3 - FOG -0.63761 0.573 195 -1.113 0.7996
AO3 - FOGO3 -0.29794 0.573 195 -0.520 0.9853
AO3 - NMIN -0.22032 0.573 195 -0.385 0.9953
CON - FOG -0.04765 0.573 195 -0.083 1.0000
CON - FOGO3 0.29203 0.573 195 0.510 0.9863
CON - NMIN 0.36965 0.573 195 0.645 0.9674
FOG - FOGO3 0.33968 0.573 195 0.593 0.9760
FOG - NMIN 0.41730 0.573 195 0.728 0.9497
FOGO3 - NMIN 0.07762 0.573 195 0.135 0.9999
Dia = 34:
contrast estimate SE df t.ratio p.value
AO3 - CON -0.40111 0.573 195 -0.700 0.9563
AO3 - FOG -0.33973 0.573 195 -0.593 0.9760
AO3 - FOGO3 0.24100 0.573 195 0.421 0.9934
AO3 - NMIN 0.19394 0.573 195 0.339 0.9972
CON - FOG 0.06138 0.573 195 0.107 1.0000
CON - FOGO3 0.64211 0.573 195 1.121 0.7954
CON - NMIN 0.59505 0.573 195 1.039 0.8371
FOG - FOGO3 0.58073 0.573 195 1.014 0.8489
FOG - NMIN 0.53367 0.573 195 0.931 0.8844
FOGO3 - NMIN -0.04706 0.573 195 -0.082 1.0000
P value adjustment: tukey method for comparing a family of 5 estimates
################ Variável: Emissao_N2O_ug_g_h ################
ANOVA:
Analysis of Variance Table
Response: N2O_log
Df Sum Sq Mean Sq F value Pr(>F)
Abreviacao 4 233.90 58.475 28.9778 < 2.2e-16 ***
Dia 7 103.50 14.786 7.3272 1.018e-07 ***
Abreviacao:Dia 28 109.84 3.923 1.9440 0.005229 **
Residuals 177 357.17 2.018
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Teste de Tukey:
Dia = 1:
contrast estimate SE df t.ratio p.value
AO3 - CON 0.3662 0.898 177 0.408 0.9942
AO3 - FOG -3.6643 0.860 177 -4.260 0.0003
AO3 - FOGO3 -3.9642 0.860 177 -4.609 0.0001
AO3 - NMIN 0.3986 0.860 177 0.463 0.9905
CON - FOG -4.0305 0.860 177 -4.686 0.0001
CON - FOGO3 -4.3304 0.860 177 -5.034 <.0001
CON - NMIN 0.0323 0.860 177 0.038 1.0000
FOG - FOGO3 -0.2999 0.820 177 -0.366 0.9962
FOG - NMIN 4.0629 0.820 177 4.954 <.0001
FOGO3 - NMIN 4.3628 0.820 177 5.320 <.0001
Dia = 2:
contrast estimate SE df t.ratio p.value
AO3 - CON 0.5192 0.917 177 0.566 0.9797
AO3 - FOG -3.4800 0.820 177 -4.243 0.0003
AO3 - FOGO3 -3.5311 0.820 177 -4.305 0.0003
AO3 - NMIN -0.7659 0.820 177 -0.934 0.8834
CON - FOG -3.9992 0.917 177 -4.361 0.0002
CON - FOGO3 -4.0503 0.917 177 -4.417 0.0002
CON - NMIN -1.2851 0.917 177 -1.401 0.6275
FOG - FOGO3 -0.0510 0.820 177 -0.062 1.0000
FOG - NMIN 2.7142 0.820 177 3.309 0.0099
FOGO3 - NMIN 2.7652 0.820 177 3.372 0.0081
Dia = 3:
contrast estimate SE df t.ratio p.value
AO3 - CON 0.9303 0.860 177 1.082 0.8158
AO3 - FOG -1.9187 0.820 177 -2.339 0.1374
AO3 - FOGO3 -2.0753 0.820 177 -2.530 0.0886
AO3 - NMIN 0.5678 0.820 177 0.692 0.9579
CON - FOG -2.8490 0.860 177 -3.312 0.0098
CON - FOGO3 -3.0056 0.860 177 -3.494 0.0054
CON - NMIN -0.3625 0.860 177 -0.421 0.9934
FOG - FOGO3 -0.1566 0.820 177 -0.191 0.9997
FOG - NMIN 2.4865 0.820 177 3.032 0.0231
FOGO3 - NMIN 2.6431 0.820 177 3.223 0.0130
Dia = 5:
contrast estimate SE df t.ratio p.value
AO3 - CON -0.0615 0.860 177 -0.072 1.0000
AO3 - FOG -2.9233 0.860 177 -3.398 0.0074
AO3 - FOGO3 -2.3778 0.860 177 -2.764 0.0489
AO3 - NMIN -1.2598 0.860 177 -1.465 0.5868
CON - FOG -2.8618 0.820 177 -3.489 0.0055
CON - FOGO3 -2.3163 0.820 177 -2.824 0.0416
CON - NMIN -1.1983 0.820 177 -1.461 0.5891
FOG - FOGO3 0.5455 0.820 177 0.665 0.9636
FOG - NMIN 1.6635 0.820 177 2.028 0.2568
FOGO3 - NMIN 1.1180 0.820 177 1.363 0.6520
Dia = 8:
contrast estimate SE df t.ratio p.value
AO3 - CON -0.9330 0.898 177 -1.038 0.8372
AO3 - FOG -2.2370 0.860 177 -2.601 0.0746
AO3 - FOGO3 -1.9814 0.860 177 -2.303 0.1486
AO3 - NMIN 0.0589 0.860 177 0.069 1.0000
CON - FOG -1.3041 0.860 177 -1.516 0.5535
CON - FOGO3 -1.0484 0.860 177 -1.219 0.7405
CON - NMIN 0.9919 0.860 177 1.153 0.7778
FOG - FOGO3 0.2557 0.820 177 0.312 0.9979
FOG - NMIN 2.2960 0.820 177 2.799 0.0445
FOGO3 - NMIN 2.0403 0.820 177 2.488 0.0980
Dia = 13:
contrast estimate SE df t.ratio p.value
AO3 - CON 0.7667 0.917 177 0.836 0.9190
AO3 - FOG -1.2410 0.917 177 -1.353 0.6582
AO3 - FOGO3 -1.8289 0.917 177 -1.995 0.2729
AO3 - NMIN 0.1997 0.917 177 0.218 0.9995
CON - FOG -2.0078 0.820 177 -2.448 0.1076
CON - FOGO3 -2.5956 0.820 177 -3.165 0.0155
CON - NMIN -0.5671 0.820 177 -0.691 0.9581
FOG - FOGO3 -0.5879 0.820 177 -0.717 0.9524
FOG - NMIN 1.4407 0.820 177 1.757 0.4023
FOGO3 - NMIN 2.0286 0.820 177 2.473 0.1014
Dia = 21:
contrast estimate SE df t.ratio p.value
AO3 - CON 1.3721 1.004 177 1.366 0.6502
AO3 - FOG 0.1655 0.917 177 0.180 0.9998
AO3 - FOGO3 0.0761 0.953 177 0.080 1.0000
AO3 - NMIN 0.2218 0.953 177 0.233 0.9993
CON - FOG -1.2066 0.917 177 -1.316 0.6817
CON - FOGO3 -1.2960 0.953 177 -1.360 0.6540
CON - NMIN -1.1503 0.953 177 -1.207 0.7473
FOG - FOGO3 -0.0894 0.860 177 -0.104 1.0000
FOG - NMIN 0.0563 0.860 177 0.065 1.0000
FOGO3 - NMIN 0.1457 0.898 177 0.162 0.9998
Dia = 34:
contrast estimate SE df t.ratio p.value
AO3 - CON 1.0212 0.898 177 1.137 0.7868
AO3 - FOG 0.3697 0.898 177 0.412 0.9939
AO3 - FOGO3 1.0500 0.953 177 1.102 0.8054
AO3 - NMIN 0.2401 0.953 177 0.252 0.9991
CON - FOG -0.6515 0.898 177 -0.725 0.9504
CON - FOGO3 0.0288 0.953 177 0.030 1.0000
CON - NMIN -0.7811 0.953 177 -0.820 0.9243
FOG - FOGO3 0.6803 0.953 177 0.714 0.9531
FOG - NMIN -0.1296 0.953 177 -0.136 0.9999
FOGO3 - NMIN -0.8099 1.004 177 -0.806 0.9284
P value adjustment: tukey method for comparing a family of 5 estimates
################ Variável: Emissao_CH4_ug_g_h ################
ANOVA:
Analysis of Variance Table
Response: CH4_log
Df Sum Sq Mean Sq F value Pr(>F)
Abreviacao 4 5.471 1.36765 1.1008 0.35817
Dia 7 19.267 2.75244 2.2154 0.03562 *
Abreviacao:Dia 28 17.719 0.63282 0.5093 0.98098
Residuals 160 198.790 1.24244
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Teste de Tukey:
Dia = 1:
contrast estimate SE df t.ratio p.value
AO3 - CON -0.55098 0.788 160 -0.699 0.9564
AO3 - FOG 0.31602 0.788 160 0.401 0.9945
AO3 - FOGO3 0.01807 0.748 160 0.024 1.0000
AO3 - NMIN 0.40016 0.788 160 0.508 0.9865
CON - FOG 0.86701 0.788 160 1.100 0.8063
CON - FOGO3 0.56906 0.748 160 0.761 0.9413
CON - NMIN 0.95114 0.788 160 1.207 0.7475
FOG - FOGO3 -0.29795 0.748 160 -0.398 0.9946
FOG - NMIN 0.08414 0.788 160 0.107 1.0000
FOGO3 - NMIN 0.38208 0.748 160 0.511 0.9862
Dia = 2:
contrast estimate SE df t.ratio p.value
AO3 - CON -0.36635 0.720 160 -0.509 0.9864
AO3 - FOG 0.29709 0.748 160 0.397 0.9947
AO3 - FOGO3 0.43487 0.851 160 0.511 0.9862
AO3 - NMIN -0.48493 0.851 160 -0.570 0.9793
CON - FOG 0.66344 0.675 160 0.983 0.8626
CON - FOGO3 0.80122 0.788 160 1.017 0.8474
CON - NMIN -0.11857 0.788 160 -0.150 0.9999
FOG - FOGO3 0.13778 0.814 160 0.169 0.9998
FOG - NMIN -0.78202 0.814 160 -0.961 0.8723
FOGO3 - NMIN -0.91980 0.910 160 -1.011 0.8502
Dia = 3:
contrast estimate SE df t.ratio p.value
AO3 - CON 0.89527 0.851 160 1.052 0.8307
AO3 - FOG 1.42058 0.748 160 1.900 0.3216
AO3 - FOGO3 0.15645 0.748 160 0.209 0.9996
AO3 - NMIN 0.22097 0.748 160 0.296 0.9983
CON - FOG 0.52532 0.814 160 0.645 0.9673
CON - FOGO3 -0.73882 0.814 160 -0.908 0.8936
CON - NMIN -0.67429 0.814 160 -0.828 0.9215
FOG - FOGO3 -1.26414 0.705 160 -1.793 0.3811
FOG - NMIN -1.19961 0.705 160 -1.702 0.4359
FOGO3 - NMIN 0.06453 0.705 160 0.092 1.0000
Dia = 5:
contrast estimate SE df t.ratio p.value
AO3 - CON 0.30578 0.748 160 0.409 0.9941
AO3 - FOG 0.51855 0.788 160 0.658 0.9649
AO3 - FOGO3 0.82480 0.788 160 1.046 0.8332
AO3 - NMIN 1.03794 0.851 160 1.219 0.7402
CON - FOG 0.21277 0.748 160 0.285 0.9986
CON - FOGO3 0.51902 0.748 160 0.694 0.9575
CON - NMIN 0.73216 0.814 160 0.899 0.8967
FOG - FOGO3 0.30625 0.788 160 0.389 0.9951
FOG - NMIN 0.51939 0.851 160 0.610 0.9733
FOGO3 - NMIN 0.21314 0.851 160 0.250 0.9991
Dia = 8:
contrast estimate SE df t.ratio p.value
AO3 - CON 0.00773 0.675 160 0.011 1.0000
AO3 - FOG 0.17113 0.675 160 0.254 0.9991
AO3 - FOGO3 -0.35976 0.675 160 -0.533 0.9838
AO3 - NMIN 0.74259 0.705 160 1.053 0.8299
CON - FOG 0.16340 0.644 160 0.254 0.9991
CON - FOGO3 -0.36749 0.644 160 -0.571 0.9791
CON - NMIN 0.73486 0.675 160 1.089 0.8121
FOG - FOGO3 -0.53089 0.644 160 -0.825 0.9226
FOG - NMIN 0.57146 0.675 160 0.847 0.9155
FOGO3 - NMIN 1.10235 0.675 160 1.633 0.4785
Dia = 13:
contrast estimate SE df t.ratio p.value
AO3 - CON -0.62317 0.644 160 -0.968 0.8690
AO3 - FOG -0.39443 0.644 160 -0.613 0.9729
AO3 - FOGO3 -0.91340 0.644 160 -1.419 0.6161
AO3 - NMIN -0.22055 0.644 160 -0.343 0.9970
CON - FOG 0.22874 0.644 160 0.355 0.9966
CON - FOGO3 -0.29023 0.644 160 -0.451 0.9914
CON - NMIN 0.40262 0.644 160 0.626 0.9708
FOG - FOGO3 -0.51897 0.644 160 -0.806 0.9283
FOG - NMIN 0.17388 0.644 160 0.270 0.9988
FOGO3 - NMIN 0.69285 0.644 160 1.077 0.8183
Dia = 21:
contrast estimate SE df t.ratio p.value
AO3 - CON -0.57005 0.644 160 -0.886 0.9018
AO3 - FOG -0.18962 0.644 160 -0.295 0.9983
AO3 - FOGO3 0.01785 0.675 160 0.026 1.0000
AO3 - NMIN -0.12504 0.644 160 -0.194 0.9997
CON - FOG 0.38044 0.644 160 0.591 0.9762
CON - FOGO3 0.58790 0.675 160 0.871 0.9071
CON - NMIN 0.44501 0.644 160 0.692 0.9581
FOG - FOGO3 0.20746 0.675 160 0.307 0.9980
FOG - NMIN 0.06458 0.644 160 0.100 1.0000
FOGO3 - NMIN -0.14289 0.675 160 -0.212 0.9995
Dia = 34:
contrast estimate SE df t.ratio p.value
AO3 - CON -0.35422 0.644 160 -0.550 0.9818
AO3 - FOG 0.16856 0.675 160 0.250 0.9991
AO3 - FOGO3 0.32753 0.644 160 0.509 0.9864
AO3 - NMIN 0.23794 0.644 160 0.370 0.9960
CON - FOG 0.52278 0.675 160 0.775 0.9376
CON - FOGO3 0.68175 0.644 160 1.059 0.8269
CON - NMIN 0.59216 0.644 160 0.920 0.8888
FOG - FOGO3 0.15897 0.675 160 0.236 0.9993
FOG - NMIN 0.06938 0.675 160 0.103 1.0000
FOGO3 - NMIN -0.08959 0.644 160 -0.139 0.9999
P value adjustment: tukey method for comparing a family of 5 estimates # Tabela final
resultado_final = do.call(rbind, resultados)
rownames(resultado_final) = NULL
View(resultado_final)6.1.4 - GRÁFICOS TEMPORAIS
########################### GRÁFICO CO2 ###########################
library(ggplot2)
library(emmeans)
# Dias em que houve diferença entre tratamentos pelo Tukey
dias_sig_CO2 = pairs(emmeans(mod_CO2_log, ~ Abreviacao | Dia),adjust = "tukey") %>%
as.data.frame()%>%
group_by(Dia) %>%
summarise(significativo = any(p.value < 0.05),.groups = "drop")
# Preparar dados
dados_CO2 = resultado_final %>%
filter(variavel == "Emissao_CO2_ug_g_h") %>%
left_join(dias_sig_CO2, by = "Dia") %>%
mutate(Dia = as.numeric(as.character(Dia)),
Tratamento = factor(Tratamento,levels = c("CON", "AO3", "FOG", "FOGO3", "NMIN")),
# Deslocamento horizontal de cada tratamento
deslocamento = case_when(
Tratamento == "CON" ~ -0.50,
Tratamento == "AO3" ~ -0.25,
Tratamento == "FOG" ~ 0.00,
Tratamento == "FOGO3" ~ 0.25,
Tratamento == "NMIN" ~ 0.50),
x_plot = Dia + deslocamento,
# Letras somente quando o Tukey detectou diferença no dia
letra_plot = ifelse(significativo, letra, ""),
# Maior afastamento vertical das letras
y_letra = media + se + 5)
anova(mod_CO2_log)Analysis of Variance Table
Response: CO2_log
Df Sum Sq Mean Sq F value Pr(>F)
Abreviacao 4 66.242 16.5605 16.8172 7.339e-12 ***
Dia 7 41.273 5.8961 5.9875 2.497e-06 ***
Abreviacao:Dia 28 44.622 1.5937 1.6184 0.03196 *
Residuals 195 192.023 0.9847
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1# PLOT
CO2=ggplot(dados_CO2,aes(x = x_plot,y = media,color = Tratamento,group = Tratamento)) +
geom_line(linewidth = 0.8) +
geom_errorbar(aes(ymin = media - se,ymax = media + se),width = 0.12,linewidth = 0.6) +
geom_point(size = 2.7) +
geom_text(aes(y = y_letra,label = letra_plot),size = 4,fontface = "bold",show.legend = FALSE) +
annotate("label",x = 26.5,y = 250,label = "Treatment: P < 0.01\nDay: P < 0.01\nTreatment × Day: P = 0.03",
hjust = 0,vjust = 0.8,size = 3.5,label.size = 0.5,label.padding=unit(0.25,"lines"),fill= "white",color="black") +
scale_color_manual(values = c("CON"="red4","AO3"="#2474A6","FOG"="#D89B00","FOGO3"="#8C6515","NMIN"="black"),
breaks = c("CON", "AO3", "FOG", "FOGO3", "NMIN")) +
scale_x_continuous(breaks = c(1, 2, 3, 5, 8, 13, 21, 34),labels = c(1, 2, 3, 5, 8, 13, 21, 34)) +
scale_y_continuous(limits = c(0, 260),breaks = seq(0, 250, by = 50),expand = expansion(mult = c(0, 0.03)))+
labs(x = "Days after treatment application",y = expression(CO[2]~emission~(mu*g~g^{-1}~h^{-1})),color = "Treatment:")+
theme_classic(base_size = 12) +
theme(axis.title = element_text(face = "bold"),
legend.position = c(0.34, 0.90),
legend.direction = "horizontal",
legend.background = element_rect(fill = "white", color = "black", linewidth = 0.5),
legend.box.background = element_rect(fill = "white", color = "black", linewidth = 0.5),
legend.text = element_text(size = 7),
legend.title = element_text(size = 8, face = "bold"))
CO2
########################### GRÁFICO CH4 ###########################
# Dias em que houve diferença entre tratamentos pelo Tukey
dias_sig_CH4 = pairs(emmeans(mod_CH4_log, ~ Abreviacao | Dia),adjust = "tukey") %>%
as.data.frame()%>%
group_by(Dia) %>%
summarise(significativo = any(p.value < 0.05),.groups = "drop")
# Preparar dados
dados_CH4 = resultado_final %>%
filter(variavel == "Emissao_CH4_ug_g_h") %>%
left_join(dias_sig_CH4, by = "Dia") %>%
mutate(Dia = as.numeric(as.character(Dia)),
Tratamento = factor(Tratamento,levels = c("CON", "AO3", "FOG", "FOGO3", "NMIN")),
# Deslocamento horizontal de cada tratamento
deslocamento = case_when(
Tratamento == "CON" ~ -0.50,
Tratamento == "AO3" ~ -0.25,
Tratamento == "FOG" ~ 0.00,
Tratamento == "FOGO3" ~ 0.25,
Tratamento == "NMIN" ~ 0.50),
x_plot = Dia + deslocamento,
# Letras somente quando o Tukey detectou diferença no dia
letra_plot = ifelse(significativo, letra, ""),
# Maior afastamento vertical das letras
y_letra = media + se + 0.0005)
anova(mod_CH4_log)Analysis of Variance Table
Response: CH4_log
Df Sum Sq Mean Sq F value Pr(>F)
Abreviacao 4 5.471 1.36765 1.1008 0.35817
Dia 7 19.267 2.75244 2.2154 0.03562 *
Abreviacao:Dia 28 17.719 0.63282 0.5093 0.98098
Residuals 160 198.790 1.24244
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1# PLOT
CH4=ggplot(dados_CH4,aes(x = x_plot,y = media,color = Tratamento,group = Tratamento)) +
geom_line(linewidth = 0.8) +
geom_errorbar(aes(ymin = media - se,ymax = media + se),width = 0.12,linewidth = 0.6) +
geom_point(size = 2.7) +
geom_text(aes(y = y_letra,label = letra_plot),size = 4,fontface = "bold",show.legend = FALSE) +
annotate("label",x = 26,y = 0.009,label = "Treatment: P = 0.358\nDay: P < 0.036\nTreatment × Day: P = 0.980",
hjust = 0,vjust = 1,size = 3.5,label.size = 0.5,label.padding=unit(0.25,"lines"),fill= "white",color="black") +
scale_color_manual(values = c("CON"="red4","AO3"="#2474A6","FOG"="#D89B00","FOGO3"="#8C6515","NMIN"="black"),
breaks = c("CON", "AO3", "FOG", "FOGO3", "NMIN")) +
scale_x_continuous(breaks = c(1, 2, 3, 5, 8, 13, 21, 34),labels = c(1, 2, 3, 5, 8, 13, 21, 34)) +
scale_y_continuous(limits = c(-0.001, 0.009),breaks = seq(-0.001, 0.009, by = 0.001),expand = expansion(mult = c(0, 0.03)))+
labs(x = "Days after treatment application",y = expression(CH [4]~emission~(mu*g~g^{-1}~h^{-1})),color = "Treatment:")+
theme_classic(base_size = 12) +
theme(axis.title = element_text(face = "bold", size = 12),
legend.position = c(0.34, 0.90),
legend.direction = "horizontal",
legend.background = element_rect(fill = "white", color = "black", linewidth = 0.5),
legend.box.background = element_rect(fill = "white", color = "black", linewidth = 0.5),
legend.text = element_text(size = 7),
legend.title = element_text(size = 8, face = "bold"))
CH4
########################### GRÁFICO N2O ###########################
# Dias em que houve diferença entre tratamentos pelo Tukey
dias_sig_N2O = pairs(emmeans(mod_N2O_log, ~ Abreviacao | Dia),adjust = "tukey") %>%
as.data.frame()%>%
group_by(Dia) %>%
summarise(significativo = any(p.value < 0.05),.groups = "drop")
# Preparar dados
dados_N2O = resultado_final %>%
filter(variavel == "Emissao_N2O_ug_g_h") %>%
left_join(dias_sig_N2O, by = "Dia") %>%
mutate(Dia = as.numeric(as.character(Dia)),
Tratamento = factor(Tratamento,levels = c("CON", "AO3", "FOG", "FOGO3", "NMIN")),
# Deslocamento horizontal de cada tratamento
deslocamento = case_when(
Tratamento == "CON" ~ -0.50,
Tratamento == "AO3" ~ -0.25,
Tratamento == "FOG" ~ 0.00,
Tratamento == "FOGO3" ~ 0.25,
Tratamento == "NMIN" ~ 0.50),
x_plot = Dia + deslocamento,
# Letras somente quando o Tukey detectou diferença no dia
letra_plot = ifelse(significativo, letra, ""),
# Maior afastamento vertical das letras
y_letra = media + se + 0.007)
anova(mod_N2O_log)Analysis of Variance Table
Response: N2O_log
Df Sum Sq Mean Sq F value Pr(>F)
Abreviacao 4 233.90 58.475 28.9778 < 2.2e-16 ***
Dia 7 103.50 14.786 7.3272 1.018e-07 ***
Abreviacao:Dia 28 109.84 3.923 1.9440 0.005229 **
Residuals 177 357.17 2.018
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1# PLOT
N2O=ggplot(dados_N2O,aes(x = x_plot,y = media,color = Tratamento,group = Tratamento)) +
geom_line(linewidth = 0.8) +
geom_errorbar(aes(ymin = media - se,ymax = media + se),width = 0.12,linewidth = 0.6) +
geom_point(size = 2.7) +
geom_text(aes(y = y_letra,label = letra_plot),size = 4,fontface = "bold",show.legend = FALSE) +
annotate("label",x = 26.5,y = 0.18,label = "Treatment: P < 0.01\nDay: P < 0.01\nTreatment × Day: P < 0.01",
hjust = 0,vjust = 1,size = 3.5,label.size = 0.5,label.padding=unit(0.25,"lines"),fill= "white",color="black") +
scale_color_manual(values = c("CON"="red4","AO3"="#2474A6","FOG"="#D89B00","FOGO3"="#8C6515","NMIN"="black"),
breaks = c("CON", "AO3", "FOG", "FOGO3", "NMIN")) +
scale_x_continuous(breaks = c(1, 2, 3, 5, 8, 13, 21, 34),labels = c(1, 2, 3, 5, 8, 13, 21, 34)) +
scale_y_continuous(limits = c(-0.006, 0.18),breaks=seq(-0.006, 0.18, by = 0.03),expand = expansion(mult = c(0, 0.03)))+
labs(x = "Days after treatment application",y = expression(N[2]*O~emission~(mu*g~g^{-1}~h^{-1})),color = "Treatment:")+
theme_classic(base_size = 12) +
theme(axis.title = element_text(face = "bold"),
legend.position = c(0.34, 0.90),
legend.direction = "horizontal",
legend.background = element_rect(fill = "white", color = "black", linewidth = 0.5),
legend.box.background = element_rect(fill = "white", color = "black", linewidth = 0.5),
legend.text = element_text(size = 7),
legend.title = element_text(size = 8, face = "bold"))
N2O
########################### JUNTAR GRÁFICOS ###########################
library(patchwork)
Figura_gases = (CH4 / CO2 / N2O) + plot_annotation(tag_levels = "A")
Figura_gases
ggsave("Figura 1 - gases no tempo.png",Figura_gases, width = 8, height = 9, dpi = 300, bg = "white")
6.2 - Emissões acumuladas
6.2.1 - ANOVA, TUKEY E MÉDIAS
# Identificar os valores outliers
boxplot(emissoes_acumuladas$CO2_acumulado_ug_g)
out_CO2 = boxplot.stats(emissoes_acumuladas$CO2_acumulado_ug_g)$out
# Mostrar as linhas correspondentes
emissoes_acumuladas %>%
filter(CO2_acumulado_ug_g %in% out_CO2) %>%
dplyr::select(Bottle, Abreviacao, Bloco, Rep, CO2_acumulado_ug_g)emissoes_acumuladas_filtrada = emissoes_acumuladas %>%
filter(!Bottle %in% c(1, 2,7, 23))
# Ordem dos tratamentos
emissoes_acumuladas_filtrada$Abreviacao = factor(emissoes_acumuladas_filtrada$Abreviacao,
levels = c("CON", "AO3", "FOG", "FOGO3", "NMIN"))
# Variáveis
variaveis = c("CH4_acumulado_ug_g","CO2_acumulado_ug_g","N2O_acumulado_ug_g")
resultados = list()
for(var in variaveis){
cat("\n\n################ Variável:", var, "################\n")
# Modelo
formula_mod = reformulate("Abreviacao", response = var)
mod = aov(formula_mod, data = emissoes_acumuladas_filtrada)
# ANOVA
cat("\nANOVA:\n")
print(summary(mod))
# Médias
em = emmeans(mod, ~ Abreviacao)
# Tukey
cat("\nTeste de Tukey:\n")
tukey = pairs(em, adjust = "tukey")
print(tukey)
# Letras
grupos = multcomp::cld(em,alpha = 0.05,Letters = letters,sort = FALSE)
# Se nenhum contraste for significativo, retirar letras
if(!any(as.data.frame(tukey)$p.value < 0.05)){
grupos$.group = ""}
# Tabela
tabela = data.frame(variavel = var,Tratamento = grupos$Abreviacao,
media = grupos$emmean,se = grupos$SE,letra = trimws(grupos$.group),row.names = NULL)
cat("\nMédias, SE e letras:\n")
print(tabela)
resultados[[var]] = tabela}
################ Variável: CH4_acumulado_ug_g ################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
Abreviacao 4 0.372 0.09308 0.474 0.755
Residuals 21 4.127 0.19652
Teste de Tukey:
contrast estimate SE df t.ratio p.value
CON - AO3 -0.2049 0.268 21 -0.763 0.9383
CON - FOG -0.1725 0.280 21 -0.615 0.9710
CON - FOGO3 0.1453 0.297 21 0.489 0.9876
CON - NMIN -0.0927 0.268 21 -0.345 0.9967
AO3 - FOG 0.0324 0.268 21 0.121 0.9999
AO3 - FOGO3 0.3502 0.286 21 1.224 0.7381
AO3 - NMIN 0.1122 0.256 21 0.439 0.9917
FOG - FOGO3 0.3178 0.297 21 1.069 0.8203
FOG - NMIN 0.0798 0.268 21 0.297 0.9982
FOGO3 - NMIN -0.2379 0.286 21 -0.831 0.9178
P value adjustment: tukey method for comparing a family of 5 estimates
Médias, SE e letras:
variavel Tratamento media se letra
1 CH4_acumulado_ug_g CON 0.4008404 0.1982533
2 CH4_acumulado_ug_g AO3 0.6057440 0.1809797
3 CH4_acumulado_ug_g FOG 0.5733354 0.1982533
4 CH4_acumulado_ug_g FOGO3 0.2555668 0.2216539
5 CH4_acumulado_ug_g NMIN 0.4934985 0.1809797
################ Variável: CO2_acumulado_ug_g ################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
Abreviacao 4 1.023e+09 255636705 7.484 0.000654 ***
Residuals 21 7.174e+08 34159591
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Teste de Tukey:
contrast estimate SE df t.ratio p.value
CON - AO3 -1510 3539 21 -0.427 0.9926
CON - FOG -16770 3696 21 -4.537 0.0015
CON - FOGO3 -7848 3921 21 -2.002 0.2993
CON - NMIN -932 3539 21 -0.263 0.9989
AO3 - FOG -15260 3539 21 -4.312 0.0026
AO3 - FOGO3 -6338 3773 21 -1.680 0.4666
AO3 - NMIN 579 3374 21 0.171 0.9998
FOG - FOGO3 8922 3921 21 2.276 0.1920
FOG - NMIN 15839 3539 21 4.475 0.0018
FOGO3 - NMIN 6916 3773 21 1.833 0.3819
P value adjustment: tukey method for comparing a family of 5 estimates
Médias, SE e letras:
variavel Tratamento media se letra
1 CO2_acumulado_ug_g CON 8272.865 2613.794 a
2 CO2_acumulado_ug_g AO3 9782.940 2386.056 a
3 CO2_acumulado_ug_g FOG 25043.262 2613.794 b
4 CO2_acumulado_ug_g FOGO3 16120.890 2922.310 ab
5 CO2_acumulado_ug_g NMIN 9204.418 2386.056 a
################ Variável: N2O_acumulado_ug_g ################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
Abreviacao 4 151.2 37.81 3.955 0.0152 *
Residuals 21 200.7 9.56
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Teste de Tukey:
contrast estimate SE df t.ratio p.value
CON - AO3 -1.998 1.87 21 -1.067 0.8209
CON - FOG -5.289 1.96 21 -2.705 0.0870
CON - FOGO3 -6.323 2.07 21 -3.049 0.0432
CON - NMIN -0.614 1.87 21 -0.328 0.9973
AO3 - FOG -3.291 1.87 21 -1.758 0.4227
AO3 - FOGO3 -4.325 2.00 21 -2.167 0.2305
AO3 - NMIN 1.384 1.79 21 0.776 0.9349
FOG - FOGO3 -1.034 2.07 21 -0.499 0.9866
FOG - NMIN 4.675 1.87 21 2.497 0.1293
FOGO3 - NMIN 5.709 2.00 21 2.861 0.0637
P value adjustment: tukey method for comparing a family of 5 estimates
Médias, SE e letras:
variavel Tratamento media se letra
1 N2O_acumulado_ug_g CON 0.4423697 1.382676 a
2 N2O_acumulado_ug_g AO3 2.4403995 1.262204 ab
3 N2O_acumulado_ug_g FOG 5.7310269 1.382676 ab
4 N2O_acumulado_ug_g FOGO3 6.7651676 1.545878 b
5 N2O_acumulado_ug_g NMIN 1.0559925 1.262204 ab# Tabela final
resultado_acumulado = do.call(rbind, resultados)
rownames(resultado_acumulado) = NULL
View(resultado_acumulado)6.2.2 - GRÁFICOS DAS EMISSÕES ACUMULADAS
########################### GRÁFICO ###########################
rotulos_facet = as_labeller(c("CH4_acumulado_ug_g" = "'(A)'~CH[4]","CO2_acumulado_ug_g" = "'(B)'~CO[2]",
"N2O_acumulado_ug_g" = "'(C)'~N[2]*O"),default = label_parsed)
p_anova_plot = data.frame(variavel = c("CH4_acumulado_ug_g","CO2_acumulado_ug_g","N2O_acumulado_ug_g"),
Tratamento = c("NMIN", "NMIN", "NMIN"),label = c("italic(P) == 0.755","italic(P) < 0.01","italic(P) == 0.015"))
p_acumulado = ggplot(resultado_acumulado,aes(x = Tratamento, y = media, fill = Tratamento)) +
geom_col(width = 0.70,color = "black",linewidth = 0.5) +
geom_errorbar(aes(ymin = media - se,ymax = media + se),width = 0.15,linewidth = 0.6) +
geom_text(aes(y = media + se,label = letra),vjust = -0.6,size = 4,fontface = "bold") +
geom_text(data = p_anova_plot,aes(x = Tratamento,y = Inf,label = label),
inherit.aes = FALSE,parse = TRUE,hjust = 1,vjust = 2,size = 4) +
scale_fill_manual(values = c(
"CON" = "red4","AO3" = "#2474A6","FOG" = "#D89B00","FOGO3" = "#8C6515","NMIN" = "black")) +
facet_wrap(~ variavel,scales = "free_y",labeller = rotulos_facet) +
scale_y_continuous(expand = expansion(mult = c(0.05, 0.18))) +
labs(x = NULL,y = expression("Cumulative emission "~(mu*g~g^{-1}))) +
theme_classic(base_size = 12) +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,hjust = 1,color = "black"),
strip.background = element_blank(),
strip.text = element_text(face = "bold",size = 12))
p_acumulado
ggsave("Figura 2 - gases acumulados.png",p_acumulado, width = 8, height = 5, dpi = 300, bg = "white")
6.2.3 - CONTRASTE FEZES vs NMIN
variaveis = c("CH4_acumulado_ug_g","CO2_acumulado_ug_g","N2O_acumulado_ug_g")
resultados_contraste = list()
for(var in variaveis){
cat("\n\n################ Variável:", var, "################\n")
# Modelo
formula_mod = as.formula(paste(var, "~ Abreviacao"))
mod = aov(formula_mod,data = emissoes_acumuladas_filtrada)
# Médias
em = emmeans::emmeans(mod,~ Abreviacao)
# Contraste: média FOG + FOGO3 versus NMIN
cont = emmeans::contrast(em,method = list(Fezes_vs_NMIN = c(0, 0, 0.5, 0.5, -1)))
cat("\nFOG + FOGO3 vs NMIN:\n")
print(summary(cont, infer = TRUE))
resultados_contraste[[var]] = as.data.frame(cont)}
################ Variável: CH4_acumulado_ug_g ################
FOG + FOGO3 vs NMIN:
contrast estimate SE df lower.CL upper.CL t.ratio p.value
Fezes_vs_NMIN -0.079 0.234 21 -0.566 0.408 -0.337 0.7391
Confidence level used: 0.95
################ Variável: CO2_acumulado_ug_g ################
FOG + FOGO3 vs NMIN:
contrast estimate SE df lower.CL upper.CL t.ratio p.value
Fezes_vs_NMIN 11378 3088 21 4956 17800 3.684 0.0014
Confidence level used: 0.95
################ Variável: N2O_acumulado_ug_g ################
FOG + FOGO3 vs NMIN:
contrast estimate SE df lower.CL upper.CL t.ratio p.value
Fezes_vs_NMIN 5.19 1.63 21 1.79 8.59 3.178 0.0045
Confidence level used: 0.95 resultado_contraste = do.call(rbind,resultados_contraste)
rownames(resultado_contraste) = NULL
View(resultado_contraste)---
title: "Limpeza e calculo dos dados do microcosmo no picarro - Isabelle"
author: "Vagner Ovani"
date: "18/08/2026"
output:
  html_notebook:
    toc: true
    toc_depth: 2
    theme: united
---

***
***
# **1 - Dados brutos**
***
***
## _**Leitura 1 - 15/04/2026**_
***
***
```{r}
library(dplyr)
# Caminho dos arquivos .dat
file_leitura = "C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 1"                                                                        #aqui
file_leitura
# Obter a lista de arquivos .dat
file_list_leitura = list.files(file_leitura, pattern = ".dat", full.names = TRUE)
# Ler os dados de todos os arquivos
data_list_leitura = lapply(file_list_leitura, read.table, header = TRUE, sep = "",fill = TRUE)
# Juntar os dados
dados_leitura = do.call(rbind, data_list_leitura)
dados_leitura
# Excluir colunas desnecessarias
dados_leitura = subset(dados_leitura, select = c("DATE","TIME","JULIAN_DAYS","MPVPosition","N2O_dry","CO2_dry","CH4_dry","H2O","NH3"))
print(dados_leitura)
#Criar datetime
dados_leitura = dados_leitura %>% mutate(datetime = as.POSIXct(paste(DATE, TIME),
  format = "%Y-%m-%d %H:%M:%OS", tz = "America/Sao_Paulo"))
print(dados_leitura)
#Filtrar por horario
filtrados_leitura1 = dados_leitura %>% filter(
    (datetime >= as.POSIXct("2026-04-15 09:00:00", tz = "America/Sao_Paulo") &                                      #aqui
     datetime <= as.POSIXct("2026-04-15 12:56:00", tz = "America/Sao_Paulo")))                                      #aqui
print(filtrados_leitura1)                                                                                           #aqui
range(filtrados_leitura1$datetime)                                                                                  #aqui


# Carregar ID da leitura
ID.leitura1=read.csv("C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 1/ID leitura1.csv")                                                      #aqui
str(ID.leitura1)                                                                                                    #aqui
print(ID.leitura1)                                                                                                  #aqui
```
***
***
## _**Leitura 2 - 16/04/2026**_
***
***
```{r}
# Caminho dos arquivos .dat
file_leitura = "C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 2"                                                                        #aqui
# Obter a lista de arquivos .dat
file_list_leitura = list.files(file_leitura, pattern = ".dat", full.names = TRUE)
# Ler os dados de todos os arquivos
data_list_leitura = lapply(file_list_leitura, read.table, header = TRUE, sep = "",fill = TRUE)
# Juntar os dados
dados_leitura = do.call(rbind, data_list_leitura)
# Excluir colunas desnecessarias
dados_leitura = subset(dados_leitura, select = c("DATE","TIME","JULIAN_DAYS","MPVPosition","N2O_dry","CO2_dry","CH4_dry","H2O","NH3"))
#Criar datetime
dados_leitura = dados_leitura %>% mutate(datetime = as.POSIXct(paste(DATE, TIME),
  format = "%Y-%m-%d %H:%M:%OS", tz = "America/Sao_Paulo"))
#Filtrar por horario
filtrados_leitura2 = dados_leitura %>% filter(                                                                      #aqui
    (datetime >= as.POSIXct("2026-04-16 08:00:00", tz = "America/Sao_Paulo") &                                      #aqui
     datetime <= as.POSIXct("2026-04-16 14:00:00", tz = "America/Sao_Paulo")))                                      #aqui
head(filtrados_leitura2)                                                                                           #aqui
range(filtrados_leitura2$datetime)                                                                                  #aqui

# Carregar ID da leitura
ID.leitura2=read.csv("C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 2/ID leitura2.csv")                                                      #aqui
str(ID.leitura2)                                                                                                   #aqui
head(ID.leitura2)                                                                                                  #aqui
```
***
***
## _**Leitura 3 - 17/04/2026**_
***
***
```{r}
# Caminho dos arquivos .dat
file_leitura = "C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 3"                                                                        #aqui
# Obter a lista de arquivos .dat
file_list_leitura = list.files(file_leitura, pattern = ".dat", full.names = TRUE)
# Ler os dados de todos os arquivos
data_list_leitura = lapply(file_list_leitura, read.table, header = TRUE, sep = "",fill = TRUE)
# Juntar os dados
dados_leitura = do.call(rbind, data_list_leitura)
# Excluir colunas desnecessarias
dados_leitura = subset(dados_leitura, select = c("DATE","TIME","JULIAN_DAYS","MPVPosition","N2O_dry","CO2_dry","CH4_dry","H2O","NH3"))
#Criar datetime
dados_leitura = dados_leitura %>% mutate(datetime = as.POSIXct(paste(DATE, TIME),
  format = "%Y-%m-%d %H:%M:%OS", tz = "America/Sao_Paulo"))
#Filtrar por horario
filtrados_leitura3 = dados_leitura %>% filter(                                                                      #aqui
    (datetime >= as.POSIXct("2026-04-17 09:00:00", tz = "America/Sao_Paulo") &                                      #aqui
     datetime <= as.POSIXct("2026-04-17 13:00:00", tz = "America/Sao_Paulo")))                                      #aqui
head(filtrados_leitura3)                                                                                            #aqui
range(filtrados_leitura3$datetime)                                                                                  #aqui

# Carregar ID da leitura
ID.leitura3=read.csv("C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 3/ID leitura3.csv")                                                      #aqui
str(ID.leitura3)                                                                                                   #aqui
head(ID.leitura3)                                                                                                  #aqui
```
***
***
## _**Leitura 4 - 19/04/2026**_
***
***
```{r}
# Caminho dos arquivos .dat
file_leitura = "C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 4"                                                                        #aqui
# Obter a lista de arquivos .dat
file_list_leitura = list.files(file_leitura, pattern = ".dat", full.names = TRUE)
# Ler os dados de todos os arquivos
data_list_leitura = lapply(file_list_leitura, read.table, header = TRUE, sep = "",fill = TRUE)
# Juntar os dados
dados_leitura = do.call(rbind, data_list_leitura)
# Excluir colunas desnecessarias
dados_leitura = subset(dados_leitura, select = c("DATE","TIME","JULIAN_DAYS","MPVPosition","N2O_dry","CO2_dry","CH4_dry","H2O","NH3"))
#Criar datetime
dados_leitura = dados_leitura %>% mutate(datetime = as.POSIXct(paste(DATE, TIME),
  format = "%Y-%m-%d %H:%M:%OS", tz = "America/Sao_Paulo"))
#Filtrar por horario
filtrados_leitura4 = dados_leitura %>% filter(                                                                      #aqui
    (datetime >= as.POSIXct("2026-04-19 09:00:00", tz = "America/Sao_Paulo") &                                      #aqui
     datetime <= as.POSIXct("2026-04-19 14:00:00", tz = "America/Sao_Paulo")))                                      #aqui
head(filtrados_leitura4)                                                                                            #aqui
range(filtrados_leitura4$datetime)                                                                                  #aqui

# Carregar ID da leitura
ID.leitura4=read.csv("C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 4/ID leitura4.csv")                                                      #aqui
str(ID.leitura4)                                                                                                   #aqui
head(ID.leitura4)                                                                                                  #aqui
```
***
***
## _**Leitura 5 - 22/04/2026**_
***
***
```{r}
# Caminho dos arquivos .dat
file_leitura = "C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 5"                                                                        #aqui
# Obter a lista de arquivos .dat
file_list_leitura = list.files(file_leitura, pattern = ".dat", full.names = TRUE)
# Ler os dados de todos os arquivos
data_list_leitura = lapply(file_list_leitura, read.table, header = TRUE, sep = "",fill = TRUE)
# Juntar os dados
dados_leitura = do.call(rbind, data_list_leitura)
# Excluir colunas desnecessarias
dados_leitura = subset(dados_leitura, select = c("DATE","TIME","JULIAN_DAYS","MPVPosition","N2O_dry","CO2_dry","CH4_dry","H2O","NH3"))
#Criar datetime
dados_leitura = dados_leitura %>% mutate(datetime = as.POSIXct(paste(DATE, TIME),
  format = "%Y-%m-%d %H:%M:%OS", tz = "America/Sao_Paulo"))
#Filtrar por horario
filtrados_leitura5 = dados_leitura %>% filter(                                                                      #aqui
    (datetime >= as.POSIXct("2026-04-22 08:00:00", tz = "America/Sao_Paulo") &                                      #aqui
     datetime <= as.POSIXct("2026-04-22 12:00:00", tz = "America/Sao_Paulo")))                                      #aqui
head(filtrados_leitura5)                                                                                            #aqui
range(filtrados_leitura5$datetime)                                                                                  #aqui

# Carregar ID da leitura
ID.leitura5=read.csv("C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 5/ID leitura5.csv")                                                      #aqui
str(ID.leitura5)                                                                                                   #aqui
head(ID.leitura5)                                                                                                  #aqui
```
***
***
## _**Leitura 6 - 27/04/2026**_
***
***
```{r}
# Caminho dos arquivos .dat
file_leitura = "C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 6"                                                                        #aqui
# Obter a lista de arquivos .dat
file_list_leitura = list.files(file_leitura, pattern = ".dat", full.names = TRUE)
# Ler os dados de todos os arquivos
data_list_leitura = lapply(file_list_leitura, read.table, header = TRUE, sep = "",fill = TRUE)
# Juntar os dados
dados_leitura = do.call(rbind, data_list_leitura)
# Excluir colunas desnecessarias
dados_leitura = subset(dados_leitura, select = c("DATE","TIME","JULIAN_DAYS","MPVPosition","N2O_dry","CO2_dry","CH4_dry","H2O","NH3"))
#Criar datetime
dados_leitura = dados_leitura %>% mutate(datetime = as.POSIXct(paste(DATE, TIME),
  format = "%Y-%m-%d %H:%M:%OS", tz = "America/Sao_Paulo"))
#Filtrar por horario
filtrados_leitura6 = dados_leitura %>% filter(                                                                      #aqui
    (datetime >= as.POSIXct("2026-04-27 09:00:00", tz = "America/Sao_Paulo") &                                      #aqui
     datetime <= as.POSIXct("2026-04-27 13:00:00", tz = "America/Sao_Paulo")))                                      #aqui
head(filtrados_leitura6)                                                                                            #aqui
range(filtrados_leitura6$datetime)                                                                                  #aqui

# Carregar ID da leitura
ID.leitura6=read.csv("C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 6/ID leitura6.csv")                                                      #aqui
str(ID.leitura6)                                                                                                   #aqui
head(ID.leitura6)                                                                                                  #aqui
```
***
***
## _**Leitura 7 - 05/05/2026**_
***
***
```{r}
# Caminho dos arquivos .dat
file_leitura = "C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 7"                                                                        #aqui
# Obter a lista de arquivos .dat
file_list_leitura = list.files(file_leitura, pattern = ".dat", full.names = TRUE)
# Ler os dados de todos os arquivos
data_list_leitura = lapply(file_list_leitura, read.table, header = TRUE, sep = "",fill = TRUE)
# Juntar os dados
dados_leitura = do.call(rbind, data_list_leitura)
# Excluir colunas desnecessarias
dados_leitura = subset(dados_leitura, select = c("DATE","TIME","JULIAN_DAYS","MPVPosition","N2O_dry","CO2_dry","CH4_dry","H2O","NH3"))
#Criar datetime
dados_leitura = dados_leitura %>% mutate(datetime = as.POSIXct(paste(DATE, TIME),
  format = "%Y-%m-%d %H:%M:%OS", tz = "America/Sao_Paulo"))
#Filtrar por horario
filtrados_leitura7 = dados_leitura %>% filter(                                                                      #aqui
    (datetime >= as.POSIXct("2026-05-05 10:00:00", tz = "America/Sao_Paulo") &                                      #aqui
     datetime <= as.POSIXct("2026-05-05 14:00:00", tz = "America/Sao_Paulo")))                                      #aqui
head(filtrados_leitura7)                                                                                            #aqui
range(filtrados_leitura7$datetime)                                                                                  #aqui

# Carregar ID da leitura
ID.leitura7=read.csv("C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 7/ID leitura7.csv")                                                      #aqui
str(ID.leitura7)                                                                                                   #aqui
head(ID.leitura7)                                                                                                  #aqui
```
***
***
## _**Leitura 8 - 18/05/2026**_
***
***
```{r}
# Caminho dos arquivos .dat
file_leitura = "C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 8"                                                                        #aqui
# Obter a lista de arquivos .dat
file_list_leitura = list.files(file_leitura, pattern = ".dat", full.names = TRUE)
# Ler os dados de todos os arquivos
data_list_leitura = lapply(file_list_leitura, read.table, header = TRUE, sep = "",fill = TRUE)
# Juntar os dados
dados_leitura = do.call(rbind, data_list_leitura)
# Excluir colunas desnecessarias
dados_leitura = subset(dados_leitura, select = c("DATE","TIME","JULIAN_DAYS","MPVPosition","N2O_dry","CO2_dry","CH4_dry","H2O","NH3"))
#Criar datetime
dados_leitura = dados_leitura %>% mutate(datetime = as.POSIXct(paste(DATE, TIME),
  format = "%Y-%m-%d %H:%M:%OS", tz = "America/Sao_Paulo"))
#Filtrar por horario
filtrados_leitura8 = dados_leitura %>% filter(                                                                      #aqui
    (datetime >= as.POSIXct("2026-05-18 08:00:00", tz = "America/Sao_Paulo") &                                      #aqui
     datetime <= as.POSIXct("2026-05-18 13:00:00", tz = "America/Sao_Paulo")))                                      #aqui
head(filtrados_leitura8)                                                                                            #aqui
range(filtrados_leitura8$datetime)                                                                                  #aqui

# Carregar ID da leitura
ID.leitura8=read.csv("C:/Users/Samsung/OneDrive/Documentos/2 - Material de trabalho/5 - Atividades Didáticas/5.5 - Orientação/Mestrado/Isabelle/Data/Leitura 8/ID leitura8.csv")                                                      #aqui
str(ID.leitura8)                                                                                                   #aqui
head(ID.leitura8)                                                                                                  #aqui
```

***
***
# **2 - Identificar leituras dos frascos e background**
***
***
## _**Leitura 1 - 15/04/2026**_
***
***
```{r}
library(data.table)
########################### PREPARAR DADOS ###########################
dados1 = as.data.table(filtrados_leitura1)
id = as.data.table(ID.leitura1)

# Criar início e fim das leituras
id[, start := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), Beginning.reading),
  format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")]

id[, end := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), End.reading),
  format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")]

########################### MPV 1 - LEITURA DOS FRASCOS ###########################
leitura1 = copy(dados1)
leitura1[, c(
  "Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") := .(
  NA_integer_, NA_character_, NA_integer_, NA_integer_,NA_integer_, NA_real_, NA_real_)]

leitura1[MPVPosition == 1,c(
    "Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") :=
    id[.SD,.(Bottle, Abreviacao, Bloco, Rep,Leitura, Solo.Seco, Internal.temperature),
       on = .(start <= datetime, end > datetime),mult = "first"]]

# Manter apenas as leituras válidas dos frascos
leitura1 = leitura1[MPVPosition == 1 & !is.na(Bottle)]
View(leitura1)
```
***
***
## _**Leitura 2 - 16/04/2026**_
***
***
```{r}
########################### PREPARAR DADOS ###########################
dados2 = as.data.table(filtrados_leitura2)
id = as.data.table(ID.leitura2)

# Criar início e fim das leituras
id[, start := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), Beginning.reading),
  format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")]

id[, end := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), End.reading),
  format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")]

########################### MPV 1 - LEITURA DOS FRASCOS ###########################
leitura2 = copy(dados2)
leitura2[, c(
  "Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") := .(
  NA_integer_, NA_character_, NA_integer_, NA_integer_,NA_integer_, NA_real_, NA_real_)]

leitura2[MPVPosition == 1,c(
    "Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") :=
    id[.SD,.(Bottle, Abreviacao, Bloco, Rep,Leitura, Solo.Seco, Internal.temperature),
       on = .(start <= datetime, end > datetime),mult = "first"]]

# Manter apenas as leituras válidas dos frascos
leitura2 = leitura2[MPVPosition == 1 & !is.na(Bottle)]
View(leitura2)                                                                                               
```
***
***
## _**Leitura 3 - 17/04/2026**_
***
***
```{r}
########################### PREPARAR DADOS ###########################
dados3 = as.data.table(filtrados_leitura3)
id = as.data.table(ID.leitura3)

# Criar início e fim das leituras
id[, start := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), Beginning.reading),
  format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")]

id[, end := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), End.reading),
  format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")]

########################### MPV 1 - LEITURA DOS FRASCOS ###########################
leitura3 = copy(dados3)
leitura3[, c(
  "Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") := .(
  NA_integer_, NA_character_, NA_integer_, NA_integer_,NA_integer_, NA_real_, NA_real_)]

leitura3[MPVPosition == 1,c(
    "Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") :=
    id[.SD,.(Bottle, Abreviacao, Bloco, Rep,Leitura, Solo.Seco, Internal.temperature),
       on = .(start <= datetime, end > datetime),mult = "first"]]

# Manter apenas as leituras válidas dos frascos
leitura3 = leitura3[MPVPosition == 1 & !is.na(Bottle)]
View(leitura3)
```
***
***
## _**Leitura 4 - 19/04/2026**_
***
***
```{r}
########################### PREPARAR DADOS ###########################
dados4 = as.data.table(filtrados_leitura4)
id = as.data.table(ID.leitura4)

# Criar início e fim das leituras
id[, start := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), Beginning.reading),
  format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")]

id[, end := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), End.reading),
  format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")]

########################### MPV 1 - LEITURA DOS FRASCOS ###########################
leitura4 = copy(dados4)
leitura4[, c(
  "Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") := .(
  NA_integer_, NA_character_, NA_integer_, NA_integer_,NA_integer_, NA_real_, NA_real_)]

leitura4[MPVPosition == 1,c(
    "Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") :=
    id[.SD,.(Bottle, Abreviacao, Bloco, Rep,Leitura, Solo.Seco, Internal.temperature),
       on = .(start <= datetime, end > datetime),mult = "first"]]

# Manter apenas as leituras válidas dos frascos
leitura4 = leitura4[MPVPosition == 1 & !is.na(Bottle)]
View(leitura4)
```
***
***
## _**Leitura 5 - 22/04/2026**_
***
***
```{r}
########################### PREPARAR DADOS ###########################
dados5 = as.data.table(filtrados_leitura5)
id = as.data.table(ID.leitura5)

# Criar início e fim das leituras
id[, start := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), Beginning.reading),
  format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")]

id[, end := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), End.reading),
  format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")]

########################### MPV 1 - LEITURA DOS FRASCOS ###########################
leitura5 = copy(dados5)
leitura5[, c(
  "Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") := .(
  NA_integer_, NA_character_, NA_integer_, NA_integer_,NA_integer_, NA_real_, NA_real_)]

leitura5[MPVPosition == 1,c(
    "Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") :=
    id[.SD,.(Bottle, Abreviacao, Bloco, Rep,Leitura, Solo.Seco, Internal.temperature),
       on = .(start <= datetime, end > datetime),mult = "first"]]

# Manter apenas as leituras válidas dos frascos
leitura5 = leitura5[MPVPosition == 1 & !is.na(Bottle)]
View(leitura5)
```
***
***
## _**Leitura 6 - 27/04/2026**_
***
***
```{r}
########################### PREPARAR DADOS ###########################
dados6 = as.data.table(filtrados_leitura6)
id = as.data.table(ID.leitura6)

# Criar início e fim das leituras
id[, start := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), Beginning.reading),
  format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")]

id[, end := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), End.reading),
  format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")]

########################### MPV 1 - LEITURA DOS FRASCOS ###########################
leitura6 = copy(dados6)
leitura6[, c(
  "Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") := .(
  NA_integer_, NA_character_, NA_integer_, NA_integer_,NA_integer_, NA_real_, NA_real_)]

leitura6[MPVPosition == 1,c(
    "Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") :=
    id[.SD,.(Bottle, Abreviacao, Bloco, Rep,Leitura, Solo.Seco, Internal.temperature),
       on = .(start <= datetime, end > datetime),mult = "first"]]

# Manter apenas as leituras válidas dos frascos
leitura6 = leitura6[MPVPosition == 1 & !is.na(Bottle)]
View(leitura6)
```
***
***
## _**Leitura 7 - 05/05/2026**_
***
***
```{r}
########################### PREPARAR DADOS ###########################
dados7 = as.data.table(filtrados_leitura7)
id = as.data.table(ID.leitura7)

# Criar início e fim das leituras
id[, start := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), Beginning.reading),
  format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")]

id[, end := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), End.reading),
  format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")]

########################### MPV 1 - LEITURA DOS FRASCOS ###########################
leitura7 = copy(dados7)
leitura7[, c(
  "Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") := .(
  NA_integer_, NA_character_, NA_integer_, NA_integer_,NA_integer_, NA_real_, NA_real_)]

leitura7[MPVPosition == 1,c(
    "Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") :=
    id[.SD,.(Bottle, Abreviacao, Bloco, Rep,Leitura, Solo.Seco, Internal.temperature),
       on = .(start <= datetime, end > datetime),mult = "first"]]

# Manter apenas as leituras válidas dos frascos
leitura7 = leitura7[MPVPosition == 1 & !is.na(Bottle)]
View(leitura7)
```
***
***
## _**Leitura 8 - 18/05/2026**_
***
***
```{r}
########################### PREPARAR DADOS ###########################
dados8 = as.data.table(filtrados_leitura8)
id = as.data.table(ID.leitura8)

# Criar início e fim das leituras
id[, start := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), Beginning.reading),
  format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")]

id[, end := as.POSIXct(paste(as.Date(Date, format = "%d/%m/%Y"), End.reading),
  format = "%Y-%m-%d %H:%M",tz = "America/Sao_Paulo")]

########################### MPV 1 - LEITURA DOS FRASCOS ###########################
leitura8 = copy(dados8)
leitura8[, c(
  "Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") := .(
  NA_integer_, NA_character_, NA_integer_, NA_integer_,NA_integer_, NA_real_, NA_real_)]

leitura8[MPVPosition == 1,c(
    "Bottle", "Abreviacao", "Bloco", "Rep","Leitura", "Solo.Seco", "Internal.temperature") :=
    id[.SD,.(Bottle, Abreviacao, Bloco, Rep,Leitura, Solo.Seco, Internal.temperature),
       on = .(start <= datetime, end > datetime),mult = "first"]]

# Manter apenas as leituras válidas dos frascos
leitura8 = leitura8[MPVPosition == 1 & !is.na(Bottle)]
View(leitura8)
```
***
***
## _**Backgorunds**_
***
***
```{r}
########################### FUNÇÃO PARA EXTRAIR O BACKGROUND ###########################
criar_background = function(dados, leitura) {
  # Identificar o início real da leitura de cada frasco
  mapa = leitura[, .(
    inicio_leitura = min(datetime),
    Abreviacao = data.table::first(Abreviacao),
    Bloco = data.table::first(Bloco),
    Rep = data.table::first(Rep),
    Leitura = data.table::first(Leitura),
    Solo.Seco = data.table::first(Solo.Seco),
    Internal.temperature = data.table::first(Internal.temperature)), by = Bottle]

  # Definir os 4 minutos anteriores à leitura
  mapa[, background_start := inicio_leitura - 240]
  # Manter apenas dados do MPVPosition 2
  bg_raw = dados[MPVPosition == 2]
  # Extrair separadamente o background de cada frasco
  background = data.table::rbindlist(
    lapply(seq_len(nrow(mapa)), function(i) {
      m = mapa[i]
      bg = data.table::copy(
        bg_raw[datetime >= m$background_start & datetime <  m$inicio_leitura])
      if (nrow(bg) == 0) return(NULL)
      bg[, `:=`(
        Bottle = m$Bottle,
        Abreviacao = m$Abreviacao,
        Bloco = m$Bloco,
        Rep = m$Rep,
        Leitura = m$Leitura,
        Solo.Seco = m$Solo.Seco,
        Internal.temperature = m$Internal.temperature,
        inicio_leitura = m$inicio_leitura,
        # Tempo desde o início dos 4 min de background
        t_background = as.numeric(
          difftime(datetime,m$background_start,units = "secs")))]
      bg}),use.names = TRUE,fill = TRUE)
  return(background)}

########################### CRIAR OS 8 BACKGROUNDS ###########################

background1 = criar_background(dados1, leitura1)
background2 = criar_background(dados2, leitura2)
background3 = criar_background(dados3, leitura3)
background4 = criar_background(dados4, leitura4)
background5 = criar_background(dados5, leitura5)
background6 = criar_background(dados6, leitura6)
background7 = criar_background(dados7, leitura7)
background8 = criar_background(dados8, leitura8)

```
***
***
## _**Juntar as 8 leituras e backgrounds**_
***
***
```{r}
########################### Leituras ###########################
Leituras_completas = dplyr::bind_rows(
  leitura1,leitura2,leitura3,leitura4,leitura5,leitura6,leitura7,leitura8)
View(Leituras_completas)

dim(Leituras_completas)
table(Leituras_completas$Leitura)
str(Leituras_completas)

########################### backgrounds ###########################
background_completo = dplyr::bind_rows(
  background1,background2,background3,background4,background5,background6,background7,background8)
View(background_completo)

dim(background_completo)
table(background_completo$Leitura)
str(background_completo)
```
***
***
# **3 - Controle de qualidade e definição da janela de leitura**
***
***
## _**Leituras**_
***
***
```{r}
dados_proc = Leituras_completas %>% 
  dplyr::mutate(BottleID = paste(Leitura,Bottle,Abreviacao,Bloco,Rep,sep = "_")) %>%
  dplyr::group_by(BottleID) %>%
  dplyr::arrange(datetime, .by_group = TRUE) %>%
  dplyr::mutate(t_sec = as.numeric(difftime(datetime, min(datetime), units = "secs"))) %>%
  dplyr::ungroup()
head(dados_proc)

########################### PREPARAR DADOS ###########################
dados_diagnostico = dados_proc %>% dplyr::filter(t_sec <= 120) %>%
  dplyr::select(BottleID, t_sec,CO2_dry, N2O_dry, CH4_dry, H2O, NH3) %>%
  tidyr::pivot_longer(cols = c(CO2_dry, N2O_dry, CH4_dry, H2O, NH3),
    names_to = "Gas",values_to = "Concentracao") %>%
  dplyr::mutate(Gas = dplyr::recode(Gas,
      "CH4_dry"="CH4 (ppm)","CO2_dry"="CO2 (ppm)","N2O_dry"="N2O (ppm)","H2O"="H2O (%)","NH3"= "NH3 (ppb)"))

########################### FIGURA S2 - PERFIL DAS LEITURAS ###########################
fig_S2 = ggplot(dados_diagnostico, aes(x = t_sec, y = Concentracao, group = BottleID)) +
  geom_line(alpha = 0.35) +
  geom_vline(xintercept = c(15, 45), linetype = "dashed") +
  facet_wrap(~ Gas, scales = "free_y", ncol = 3) +
  labs(x = "Reading time (s)",y = "Concentration") + theme_bw() +
  scale_x_continuous(breaks = seq(0, 120, by = 15))+
  theme(strip.text = element_text(hjust = 0.5, face = "bold"),panel.spacing = unit(1, "lines"))
fig_S2

ggsave("Figure S2.png",fig_S2, width = 10, height = 6, dpi = 300, bg = "white")

########################### AVALIAR JANELAS CANDIDATAS ###########################
gases_qc = c("CO2_dry", "N2O_dry", "CH4_dry")
janelas = data.frame(Janela = c("15-35", "15-45", "15-60","20-40", "20-50", "20-60"),
  inicio = c(15, 15, 15, 20, 20, 20), fim = c(35, 45, 60, 40, 50, 60))

avaliacao_janelas = dplyr::bind_rows(lapply(seq_len(nrow(janelas)), function(i) {
    ini = janelas$inicio[i]
    fim = janelas$fim[i]
    dplyr::bind_rows(lapply(gases_qc, function(g) {dados_proc %>%
          dplyr::filter(t_sec >= ini, t_sec <= fim) %>%
          dplyr::group_by(BottleID) %>%
          dplyr::summarise(media = mean(.data[[g]], na.rm = TRUE),sd = sd(.data[[g]], na.rm = TRUE),
            slope = cov(t_sec,.data[[g]],use = "complete.obs") / var(t_sec, na.rm = TRUE),.groups = "drop") %>%
          dplyr::mutate(Gas = g,Janela = janelas$Janela[i],
            # Coeficiente de variação dentro da janela
            CV = (sd / abs(media)) * 100,
            # Mudança estimada da concentração ao longo da janela
            Drift = abs(slope) * (fim - ini) / abs(media) * 100)}))}))

########################### ESTABILIDADE DAS JANELAS ###########################
tab_S2 = avaliacao_janelas %>% dplyr::group_by(Gas, Janela) %>%
  dplyr::summarise(CV_mediano = median(CV, na.rm = TRUE), CV_P90 = quantile(CV, 0.90, na.rm = TRUE),
    Drift_mediano = median(Drift, na.rm = TRUE),
    Drift_P90 = quantile(Drift, 0.90, na.rm = TRUE),.groups = "drop") %>%
  dplyr::mutate(Janela = factor(Janela, levels = janelas$Janela)) %>%
  dplyr::arrange(Gas, Janela)
print(tab_S2)

########################### FIGURA S3 - COMPARAÇÃO DAS JANELAS ###########################
dados_fig_S3 = tab_S2 %>%
  dplyr::select(Gas, Janela, CV_P90, Drift_P90) %>%
  tidyr::pivot_longer(cols = c(CV_P90, Drift_P90),names_to = "Metrica",values_to = "Valor") %>%
  dplyr::mutate(
    Gas = dplyr::recode(Gas,"CH4_dry"="CH4 (ppm)","CO2_dry"="CO2 (ppm)","N2O_dry"="N2O (ppm)"),
    Metrica = dplyr::recode(Metrica,"CV_P90" = "CV P90 (%)","Drift_P90" = "Drift P90 (%)"))

fig_S3 = ggplot(dados_fig_S3,aes(x = Janela, y = Valor, group = Gas)) +
  geom_line() + geom_point(size = 2) +
  facet_grid(Metrica ~ Gas, scales = "free_y") + 
  labs(x = "Reading window (s)",y = "Value (%)") + theme_bw() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),strip.text = element_text(face = "bold", hjust = 0.5))
fig_S3

ggsave("Figure S3.png",fig_S3, width = 10, height = 6, dpi = 400, bg = "white")

########################### JANELA FINAL: 15-45 s ###########################
dados_estaveis = dados_proc %>% dplyr::filter(t_sec >= 15, t_sec <= 45)

########################### CONCENTRAÇÕES MÉDIAS ###########################
medias_leituras = dados_estaveis %>% dplyr::group_by(
    Leitura, Bottle, Abreviacao, Bloco, Rep) %>%
  dplyr::summarise(
    Temperatura = mean(Internal.temperature, na.rm = TRUE),
    Peso.solo = mean(Solo.Seco, na.rm = TRUE),
    N2O = mean(N2O_dry, na.rm = TRUE),
    CO2 = mean(CO2_dry, na.rm = TRUE),
    CH4 = mean(CH4_dry, na.rm = TRUE),
    # Mantido apenas como controle de qualidade
    H2O_QC = mean(H2O, na.rm = TRUE),
    n_obs = dplyr::n(),.groups = "drop")
View(medias_leituras)
```
***
***
## _**Background**_
***
***
```{r}
########################### PREPARAR DADOS ###########################
background_proc = background_completo %>% dplyr::mutate(
    BottleID = paste(Leitura, Bottle, Abreviacao, Bloco, Rep,sep = "_"))
head(background_proc)


########################### PREPARAR DADOS PARA DIAGNÓSTICO ###########################
background_diagnostico = background_proc %>% dplyr::filter(
    t_background >= 0,t_background <= 240) %>%
  dplyr::select(BottleID,t_background,CO2_dry,N2O_dry,CH4_dry,H2O) %>%
  tidyr::pivot_longer(cols = c(CO2_dry, N2O_dry, CH4_dry, H2O),names_to = "Gas",values_to = "Concentracao") %>%
  dplyr::mutate(
    Gas = dplyr::recode(Gas,"CH4_dry"="CH4 (ppm)","CO2_dry"="CO2 (ppm)","N2O_dry"="N2O (ppm)","H2O"="H2O (%)"))

########################### FIGURA S4 - PERFIL DO BACKGROUND ###########################
fig_S4 = ggplot(background_diagnostico,aes(x = t_background,y = Concentracao,group = BottleID)) +
  geom_line(alpha = 0.30) +
  geom_vline(xintercept = c(60, 90),linetype = "dashed") +
  facet_wrap(~ Gas,scales = "free_y",ncol = 2) +
  scale_x_continuous(breaks = seq(0, 240, by = 30)) +
  labs(x = "Background measurement time (s)",y = "Concentration") +
  theme_bw()+theme(strip.text = element_text(hjust = 0.5,face = "bold"),panel.spacing = grid::unit(1, "lines"))
fig_S4

ggsave("Figure S4.png",fig_S4,width = 9,height = 7,dpi = 300,bg = "white")

########################### AVALIAR JANELAS DO BACKGROUND ###########################
gases_bg = c("CO2_dry","N2O_dry","CH4_dry")

janelas_bg = data.frame(Janela = c("20-50","30-60","30-90","60-90","60-120","180-240"),
  inicio = c(20,30,30,60,60,180),fim = c(50,60,90,90,120,240))

avaliacao_bg = dplyr::bind_rows(
  lapply(seq_len(nrow(janelas_bg)), function(i) {
    ini = janelas_bg$inicio[i]
    fim = janelas_bg$fim[i]
    dplyr::bind_rows(lapply(gases_bg, function(g) {
        background_proc %>%
          dplyr::filter(t_background >= ini,t_background <= fim) %>%
          dplyr::group_by(BottleID) %>%
          dplyr::summarise(media = mean(.data[[g]],na.rm = TRUE),
            sd = sd(.data[[g]],na.rm = TRUE),
            slope = cov(t_background,.data[[g]],
                        use = "complete.obs") /var(t_background,na.rm = TRUE),.groups = "drop") %>%
          dplyr::mutate(Gas = g,
            Janela = janelas_bg$Janela[i],
            # Coeficiente de variação dentro da janela
            CV = (sd / abs(media)) * 100,
            # Mudança relativa estimada ao longo da janela
            Drift = abs(slope) * (fim - ini) / abs(media) * 100)}))}))

########################### RESUMO DAS JANELAS ###########################
resumo_bg = avaliacao_bg %>%
  dplyr::group_by(Gas,Janela) %>%
  dplyr::summarise(CV_mediano = median(CV,na.rm = TRUE),
    CV_P90 = quantile(CV,0.90,na.rm = TRUE),
    Drift_mediano = median(Drift,na.rm = TRUE),
    Drift_P90 = quantile(Drift,0.90,na.rm = TRUE),.groups = "drop") %>%
  dplyr::mutate(Janela = factor(Janela,levels = janelas_bg$Janela)) %>%
  dplyr::arrange(Gas,Janela)
print(resumo_bg)


########################### FIGURA S5 - COMPARAÇÃO DAS JANELAS ###########################
dados_fig_S5 = resumo_bg %>% dplyr::select(
  Gas,Janela,CV_P90,Drift_P90) %>%
  tidyr::pivot_longer(cols = c(CV_P90,Drift_P90),
    names_to = "Metrica",values_to = "Valor") %>%
  dplyr::mutate(Gas = dplyr::recode(
      Gas,"CH4_dry" = "CH4 (ppm)","CO2_dry" = "CO2 (ppm)","N2O_dry" = "N2O (ppm)"),
    Metrica = dplyr::recode(Metrica,"CV_P90" = "CV P90 (%)","Drift_P90" = "Drift P90 (%)"))

fig_S5 = ggplot(dados_fig_S5,aes(x = Janela,y = Valor,group = Gas)) +
  geom_line() +
  geom_point(size = 2) +
  facet_grid(Metrica ~ Gas,scales = "free_y") +
  labs(x = "Background window (s)",y = "Value (%)") +
  theme_bw() + theme(axis.text.x = element_text(angle = 45,hjust = 1),
    strip.text = element_text(face = "bold",hjust = 0.5))
fig_S5

ggsave("Figure S5.png",fig_S5,width = 10,height = 6,dpi = 400,bg = "white")

########################### BACKGROUND FINAL: 60-90 s ###########################
background_estavel = background_proc %>%
  dplyr::filter(t_background >= 60,t_background <= 90)

########################### CONCENTRAÇÕES MÉDIAS DO BACKGROUND ###########################
medias_background = background_estavel %>%
  dplyr::group_by(Leitura,Bottle) %>%
  dplyr::summarise(
    CO2_bg = mean(CO2_dry,na.rm = TRUE),
    N2O_bg = mean(N2O_dry,na.rm = TRUE),
    CH4_bg = mean(CH4_dry,na.rm = TRUE),
    # Mantido apenas como controle de qualidade
    H2O_bg = mean(H2O,na.rm = TRUE),
    n_obs_bg = dplyr::n(),.groups = "drop")
nrow(medias_background)
View(medias_background)
```

***
***
# **4 - Calcular emissão e normalizar por g de solo seco**
***
***
```{r}
########################### JUNTAR LEITURA E BACKGROUND ###########################
dados_fluxo = medias_leituras %>%
  dplyr::left_join(medias_background,by = c("Leitura", "Bottle"))

# Conferir se todos os frascos receberam background
nrow(dados_fluxo)
colSums(is.na(dados_fluxo[, c("CO2_bg","N2O_bg","CH4_bg")]))
View(dados_fluxo)


########################### CONSTANTES ###########################
R = 0.082057        # L atm mol-1 K-1
P = 0.927           # atm - estimada para altitude de 633 m
Vh = 0.700          # L - volume do headspace
tempo = 4           # min - período de fechamento do frasco
MM.CH4 = 16.04      # g mol-1
MM.N2O = 44.013     # g mol-1
MM.CO2 = 44.0095    # g mol-1

########################### CORRIGIR PELO BACKGROUND ###########################
dados_fluxo = dados_fluxo %>% dplyr::mutate(
    # Temperatura do headspace em Kelvin
    T_K = Temperatura + 273.15,
    # Diferença entre concentração do frasco e ar ambiente
    Delta_CH4 = CH4 - CH4_bg,
    Delta_N2O = N2O - N2O_bg,
    Delta_CO2 = CO2 - CO2_bg)

########################### MASSA ACUMULADA EM 4 MIN ###########################
dados_fluxo = dados_fluxo %>% dplyr::mutate(
    # Massa acumulada no headspace durante os 4 min (µg)
    CH4_ug_4min = Delta_CH4 * Vh * MM.CH4 * P / (R * T_K),
    N2O_ug_4min = Delta_N2O * Vh * MM.N2O * P / (R * T_K),
    CO2_ug_4min = Delta_CO2 * Vh * MM.CO2 * P / (R * T_K))

########################### NORMALIZAR POR SOLO SECO E TEMPO ###########################
dados_fluxo = dados_fluxo %>% dplyr::mutate(
    # µg de gás g-1 de solo seco h-1
    Emissao_CH4_ug_g_h = CH4_ug_4min * (60 / tempo) / Peso.solo,
    Emissao_N2O_ug_g_h = N2O_ug_4min * (60 / tempo) / Peso.solo,
    Emissao_CO2_ug_g_h = CO2_ug_4min * (60 / tempo) / Peso.solo)
View(dados_fluxo)
```
***
***
# **5 - Calcular emissões acumuladas**
***
***
```{r}
########################### IDENTIFICAR OS DIAS ###########################
dias_leitura = c(1, 2, 3, 5, 8, 13, 21, 34)
dados_acumulo = dados_fluxo %>%
  dplyr::mutate(Dia = dias_leitura[Leitura]) %>%
  dplyr::arrange(Bottle,Dia)
View(dados_acumulo)


########################### CALCULAR EMISSÕES ACUMULADAS ###########################
emissoes_acumuladas = dados_acumulo %>%
  dplyr::group_by(Bottle,Abreviacao,Bloco,Rep) %>%
  dplyr::arrange(Dia, .by_group = TRUE) %>%
  dplyr::summarise(
    CH4_acumulado_ug_g =sum((
      Emissao_CH4_ug_g_h+dplyr::lead(Emissao_CH4_ug_g_h))/2*(dplyr::lead(Dia)-Dia)*24,na.rm = TRUE),
    N2O_acumulado_ug_g =sum((
      Emissao_N2O_ug_g_h+dplyr::lead(Emissao_N2O_ug_g_h))/2*(dplyr::lead(Dia)-Dia)*24,na.rm = TRUE),
    CO2_acumulado_ug_g =sum((
      Emissao_CO2_ug_g_h+dplyr::lead(Emissao_CO2_ug_g_h))/2*(dplyr::lead(Dia)-Dia)*24,na.rm = TRUE),.groups = "drop")
View(emissoes_acumuladas)
```

***
***
# **5 - Análise estatística** 
***
***
## _**6.1 - Dinâmica temporal das emissões**_
***
***
### _**6.1.1 - MODELOS CANDIDATOS**_
***
***
```{r}
dados_acumulo$Abreviacao=as.factor(dados_acumulo$Abreviacao)
dados_acumulo$Dia=as.factor(dados_acumulo$Dia)
dados_acumulo$Bloco=as.factor(dados_acumulo$Bloco)
str(dados_acumulo)

########################### CO2 ###########################
library(lme4)
library(lmerTest)
# Sem bloco e sem efeito aleatório
mod1 = lm(Emissao_CO2_ug_g_h ~ Abreviacao * Dia,data = dados_acumulo)
# Com bloco
mod2 = lm(Emissao_CO2_ug_g_h ~ Abreviacao * Dia + Bloco,data = dados_acumulo)
# Com Bottle como efeito aleatório
mod3 = lmer(Emissao_CO2_ug_g_h ~ Abreviacao * Dia + (1 | Bottle),data = dados_acumulo,REML = FALSE)
# Com bloco + Bottle
mod4 = lmer(Emissao_CO2_ug_g_h~Abreviacao* Dia + Bloco + (1|Bottle), data = dados_acumulo,REML = FALSE)

########################### COMPARAR MODELOS ###########################
AIC(mod1, mod2, mod3, mod4)
BIC(mod1, mod2, mod3, mod4)
VarCorr(mod3)
VarCorr(mod4)
isSingular(mod3)
isSingular(mod4)
# Resultado: mod1 é mais simples, com menor AIC e BIC, mod2 e mod3 piorou AIC/BIC e mod4 é singular.

########################### MODELO FINAL - CO2 ###########################
mod_CO2 = mod1


########################### CH4 ###########################
# Sem bloco e sem efeito aleatório
mod1 = lm(Emissao_CH4_ug_g_h ~ Abreviacao * Dia,data = dados_acumulo)
# Com bloco
mod2 = lm(Emissao_CH4_ug_g_h ~ Abreviacao * Dia + Bloco,data = dados_acumulo)
# Com Bottle como efeito aleatório
mod3 = lmer(Emissao_CH4_ug_g_h ~ Abreviacao * Dia + (1 | Bottle),data = dados_acumulo,REML = FALSE)
# Com bloco + Bottle
mod4 = lmer(Emissao_CH4_ug_g_h~Abreviacao * Dia + Bloco + (1|Bottle), data = dados_acumulo, REML = FALSE)

########################### COMPARAR MODELOS ###########################
AIC(mod1, mod2, mod3, mod4)
BIC(mod1, mod2, mod3, mod4)
VarCorr(mod3)
VarCorr(mod4)
isSingular(mod3)
isSingular(mod4)
# Resultado: Mesma coisa para metano

########################### MODELO FINAL - CH4 ###########################
mod_CH4 = mod1


########################### N2O ###########################
# Sem bloco e sem efeito aleatório
mod1 = lm(Emissao_N2O_ug_g_h ~ Abreviacao * Dia,data = dados_acumulo)
# Com bloco
mod2 = lm(Emissao_N2O_ug_g_h ~ Abreviacao * Dia + Bloco, data = dados_acumulo)
# Com Bottle como efeito aleatório
mod3 = lmer(Emissao_N2O_ug_g_h ~ Abreviacao * Dia + (1 | Bottle),data = dados_acumulo,REML = FALSE)
# Com bloco + Bottle
mod4 = lmer(Emissao_N2O_ug_g_h~Abreviacao * Dia + Bloco + (1|Bottle), data = dados_acumulo,REML = FALSE)

########################### COMPARAR MODELOS ###########################
AIC(mod1, mod2, mod3, mod4)
BIC(mod1, mod2, mod3, mod4)
VarCorr(mod3)
VarCorr(mod4)
isSingular(mod3)
isSingular(mod4)
# Resultado: Mesma coisa para N2O

########################### MODELO FINAL - N2O ###########################
mod_N2O = mod1
```
***
***
### _**6.1.2 - Pressupostos**_
***
***
```{r}
########################### CO2 ###########################
# Gráfico de resíduos
plot(fitted(mod_CO2), resid(mod_CO2),xlab = "Fitted values",ylab = "Residuals")
# Normalidade dos resíduos
qqnorm(resid(mod_CO2))
shapiro.test(resid(mod_CO2))
#RAIZ QUADRADA 
dados_acumulo$CO2_sqrt = sqrt(dados_acumulo$Emissao_CO2_ug_g_h)
mod_CO2_sqrt = lm(CO2_sqrt ~ Abreviacao * Dia,data = dados_acumulo)
qqnorm(resid(mod_CO2_sqrt))
shapiro.test(resid(mod_CO2_sqrt))
#LOG
dados_acumulo$CO2_log = log(dados_acumulo$Emissao_CO2_ug_g_h)
mod_CO2_log = lm(CO2_log ~ Abreviacao * Dia,data = dados_acumulo)
qqnorm(resid(mod_CO2_log))
shapiro.test(resid(mod_CO2_log))
#Resultado: LOG funcionou

########################### CH4 ###########################
# Gráfico de resíduos
plot(fitted(mod_CH4), resid(mod_CH4),xlab = "Fitted values",ylab = "Residuals")
# Normalidade dos resíduos
qqnorm(resid(mod_CH4))
shapiro.test(resid(mod_CH4))
#RAIZ QUADRADA 
dados_acumulo$CH4_sqrt = sqrt(dados_acumulo$Emissao_CH4_ug_g_h)
mod_CH4_sqrt = lm(CH4_sqrt ~ Abreviacao * Dia,data = dados_acumulo)
qqnorm(resid(mod_CH4_sqrt))
shapiro.test(resid(mod_CH4_sqrt))
#LOG
dados_acumulo$CH4_log = log(dados_acumulo$Emissao_CH4_ug_g_h)
mod_CH4_log = lm(CH4_log ~ Abreviacao * Dia,data = dados_acumulo)
qqnorm(resid(mod_CH4_log))
shapiro.test(resid(mod_CH4_log))
#Resultado: LOG funcionou

########################### N2O ###########################
# Gráfico de resíduos
plot(fitted(mod_N2O), resid(mod_N2O),xlab = "Fitted values",ylab = "Residuals")
# Normalidade dos resíduos
qqnorm(resid(mod_N2O))
shapiro.test(resid(mod_N2O))
#RAIZ QUADRADA 
dados_acumulo$N2O_sqrt = sqrt(dados_acumulo$Emissao_N2O_ug_g_h)
mod_N2O_sqrt = lm(N2O_sqrt ~ Abreviacao * Dia,data = dados_acumulo)
qqnorm(resid(mod_N2O_sqrt))
shapiro.test(resid(mod_N2O_sqrt))
#LOG
dados_acumulo$N2O_log = log(dados_acumulo$Emissao_N2O_ug_g_h)
mod_N2O_log = lm(N2O_log ~ Abreviacao * Dia,data = dados_acumulo)
qqnorm(resid(mod_N2O_log))
shapiro.test(resid(mod_N2O_log))
#Resultado: LOG funcionou

View(dados_acumulo)
```
***
***
### _**6.1.3 - ANOVA, TUKEY E MÉDIAS**_
***
***
```{r}
library(dplyr)
library(emmeans)
library(multcomp)

mapa_vars = data.frame(original = c("Emissao_CO2_ug_g_h","Emissao_N2O_ug_g_h","Emissao_CH4_ug_g_h"),
  modelo = c("CO2_log","N2O_log","CH4_log"))

resultados = list()
for(i in 1:nrow(mapa_vars)){
  var_orig = mapa_vars$original[i]
  var_mod = mapa_vars$modelo[i]
  cat("\n\n################ Variável:", var_orig, "################\n")
  # Modelo
  formula_mod = as.formula(paste0(var_mod, " ~ Abreviacao * Dia"))
  mod = lm(formula_mod, data = dados_acumulo)
  # ANOVA
  cat("\nANOVA:\n")
  print(anova(mod))
  # Médias estimadas
  em = emmeans(mod, ~ Abreviacao | Dia)
  # Tukey
  cat("\nTeste de Tukey:\n")
  print(pairs(em, adjust = "tukey"))
  # Letras
  grupos = multcomp::cld(em,alpha = 0.05,Letters = letters,sort = TRUE,decreasing = FALSE)
  letras = data.frame(Dia = grupos$Dia,Abreviacao = grupos$Abreviacao,letra = trimws(grupos$.group))
  # Média e SE dos dados originais
  medias = dados_acumulo %>%
    group_by(Dia, Abreviacao) %>%
    summarise(media = mean(.data[[var_orig]], na.rm = TRUE),
      se = sd(.data[[var_orig]], na.rm = TRUE)/sqrt(sum(!is.na(.data[[var_orig]]))),.groups = "drop")
  # Tabela
  tabela = medias %>%
    left_join(letras, by = c("Dia", "Abreviacao")) %>%
    dplyr::mutate(variavel = var_orig) %>%
    dplyr::select(variavel,Dia,Tratamento = Abreviacao,media,se,letra)
  resultados[[var_orig]] = tabela}

# Tabela final
resultado_final = do.call(rbind, resultados)
rownames(resultado_final) = NULL
View(resultado_final)
```
***
***
### _**6.1.4 - GRÁFICOS TEMPORAIS**_
***
***
```{r}
########################### GRÁFICO CO2 ###########################
library(ggplot2)
library(emmeans)

# Dias em que houve diferença entre tratamentos pelo Tukey
dias_sig_CO2 = pairs(emmeans(mod_CO2_log, ~ Abreviacao | Dia),adjust = "tukey") %>%
  as.data.frame()%>%
  group_by(Dia) %>%
  summarise(significativo = any(p.value < 0.05),.groups = "drop")
# Preparar dados
dados_CO2 = resultado_final %>%
  filter(variavel == "Emissao_CO2_ug_g_h") %>%
  left_join(dias_sig_CO2, by = "Dia") %>%
  mutate(Dia = as.numeric(as.character(Dia)),
    Tratamento = factor(Tratamento,levels = c("CON", "AO3", "FOG", "FOGO3", "NMIN")),
    # Deslocamento horizontal de cada tratamento
    deslocamento = case_when(
      Tratamento == "CON"   ~ -0.50,
      Tratamento == "AO3"   ~ -0.25,
      Tratamento == "FOG"   ~  0.00,
      Tratamento == "FOGO3" ~  0.25,
      Tratamento == "NMIN"  ~  0.50),
    x_plot = Dia + deslocamento,
    # Letras somente quando o Tukey detectou diferença no dia
    letra_plot = ifelse(significativo, letra, ""),
    # Maior afastamento vertical das letras
    y_letra = media + se + 5)

anova(mod_CO2_log)

# PLOT
CO2=ggplot(dados_CO2,aes(x = x_plot,y = media,color = Tratamento,group = Tratamento)) +
  geom_line(linewidth = 0.8) +
  geom_errorbar(aes(ymin = media - se,ymax = media + se),width = 0.12,linewidth = 0.6) +
  geom_point(size = 2.7) +
  geom_text(aes(y = y_letra,label = letra_plot),size = 4,fontface = "bold",show.legend = FALSE) +
  annotate("label",x = 26.5,y = 250,label = "Treatment: P < 0.01\nDay: P < 0.01\nTreatment × Day: P = 0.03",
    hjust = 0,vjust = 0.8,size = 3.5,label.size = 0.5,label.padding=unit(0.25,"lines"),fill= "white",color="black") +
  scale_color_manual(values = c("CON"="red4","AO3"="#2474A6","FOG"="#D89B00","FOGO3"="#8C6515","NMIN"="black"),
    breaks = c("CON", "AO3", "FOG", "FOGO3", "NMIN")) +
  scale_x_continuous(breaks = c(1, 2, 3, 5, 8, 13, 21, 34),labels = c(1, 2, 3, 5, 8, 13, 21, 34)) +
  scale_y_continuous(limits = c(0, 260),breaks = seq(0, 250, by = 50),expand = expansion(mult = c(0, 0.03)))+
  labs(x = "Days after treatment application",y = expression(CO[2]~emission~(mu*g~g^{-1}~h^{-1})),color = "Treatment:")+
  theme_classic(base_size = 12) +
  theme(axis.title = element_text(face = "bold"),
    legend.position = c(0.34, 0.90),
        legend.direction = "horizontal",
    legend.background = element_rect(fill = "white", color = "black", linewidth = 0.5),
    legend.box.background = element_rect(fill = "white", color = "black", linewidth = 0.5),
    legend.text = element_text(size = 7),
  legend.title = element_text(size = 8, face = "bold"))
CO2

########################### GRÁFICO CH4 ###########################

# Dias em que houve diferença entre tratamentos pelo Tukey
dias_sig_CH4 = pairs(emmeans(mod_CH4_log, ~ Abreviacao | Dia),adjust = "tukey") %>%
  as.data.frame()%>%
  group_by(Dia) %>%
  summarise(significativo = any(p.value < 0.05),.groups = "drop")
# Preparar dados
dados_CH4 = resultado_final %>%
  filter(variavel == "Emissao_CH4_ug_g_h") %>%
  left_join(dias_sig_CH4, by = "Dia") %>%
  mutate(Dia = as.numeric(as.character(Dia)),
    Tratamento = factor(Tratamento,levels = c("CON", "AO3", "FOG", "FOGO3", "NMIN")),
    # Deslocamento horizontal de cada tratamento
    deslocamento = case_when(
      Tratamento == "CON"   ~ -0.50,
      Tratamento == "AO3"   ~ -0.25,
      Tratamento == "FOG"   ~  0.00,
      Tratamento == "FOGO3" ~  0.25,
      Tratamento == "NMIN"  ~  0.50),
    x_plot = Dia + deslocamento,
    # Letras somente quando o Tukey detectou diferença no dia
    letra_plot = ifelse(significativo, letra, ""),
    # Maior afastamento vertical das letras
    y_letra = media + se + 0.0005)

anova(mod_CH4_log)

# PLOT
CH4=ggplot(dados_CH4,aes(x = x_plot,y = media,color = Tratamento,group = Tratamento)) +
  geom_line(linewidth = 0.8) +
  geom_errorbar(aes(ymin = media - se,ymax = media + se),width = 0.12,linewidth = 0.6) +
  geom_point(size = 2.7) +
  geom_text(aes(y = y_letra,label = letra_plot),size = 4,fontface = "bold",show.legend = FALSE) +
  annotate("label",x = 26,y = 0.009,label = "Treatment: P = 0.358\nDay: P < 0.036\nTreatment × Day: P = 0.980",
    hjust = 0,vjust = 1,size = 3.5,label.size = 0.5,label.padding=unit(0.25,"lines"),fill= "white",color="black") +
  scale_color_manual(values = c("CON"="red4","AO3"="#2474A6","FOG"="#D89B00","FOGO3"="#8C6515","NMIN"="black"),
    breaks = c("CON", "AO3", "FOG", "FOGO3", "NMIN")) +
  scale_x_continuous(breaks = c(1, 2, 3, 5, 8, 13, 21, 34),labels = c(1, 2, 3, 5, 8, 13, 21, 34)) +
  scale_y_continuous(limits = c(-0.001, 0.009),breaks = seq(-0.001, 0.009, by = 0.001),expand = expansion(mult = c(0, 0.03)))+
  labs(x = "Days after treatment application",y = expression(CH [4]~emission~(mu*g~g^{-1}~h^{-1})),color = "Treatment:")+
  theme_classic(base_size = 12) +
  theme(axis.title = element_text(face = "bold", size = 12),
    legend.position = c(0.34, 0.90),
        legend.direction = "horizontal",
    legend.background = element_rect(fill = "white", color = "black", linewidth = 0.5),
    legend.box.background = element_rect(fill = "white", color = "black", linewidth = 0.5),
    legend.text = element_text(size = 7),
  legend.title = element_text(size = 8, face = "bold"))
CH4

########################### GRÁFICO N2O ###########################

# Dias em que houve diferença entre tratamentos pelo Tukey
dias_sig_N2O = pairs(emmeans(mod_N2O_log, ~ Abreviacao | Dia),adjust = "tukey") %>%
  as.data.frame()%>%
  group_by(Dia) %>%
  summarise(significativo = any(p.value < 0.05),.groups = "drop")
# Preparar dados
dados_N2O = resultado_final %>%
  filter(variavel == "Emissao_N2O_ug_g_h") %>%
  left_join(dias_sig_N2O, by = "Dia") %>%
  mutate(Dia = as.numeric(as.character(Dia)),
    Tratamento = factor(Tratamento,levels = c("CON", "AO3", "FOG", "FOGO3", "NMIN")),
    # Deslocamento horizontal de cada tratamento
    deslocamento = case_when(
      Tratamento == "CON"   ~ -0.50,
      Tratamento == "AO3"   ~ -0.25,
      Tratamento == "FOG"   ~  0.00,
      Tratamento == "FOGO3" ~  0.25,
      Tratamento == "NMIN"  ~  0.50),
    x_plot = Dia + deslocamento,
    # Letras somente quando o Tukey detectou diferença no dia
    letra_plot = ifelse(significativo, letra, ""),
    # Maior afastamento vertical das letras
    y_letra = media + se + 0.007)

anova(mod_N2O_log)

# PLOT
N2O=ggplot(dados_N2O,aes(x = x_plot,y = media,color = Tratamento,group = Tratamento)) +
  geom_line(linewidth = 0.8) +
  geom_errorbar(aes(ymin = media - se,ymax = media + se),width = 0.12,linewidth = 0.6) +
  geom_point(size = 2.7) +
  geom_text(aes(y = y_letra,label = letra_plot),size = 4,fontface = "bold",show.legend = FALSE) +
  annotate("label",x = 26.5,y = 0.18,label = "Treatment: P < 0.01\nDay: P < 0.01\nTreatment × Day: P < 0.01",
    hjust = 0,vjust = 1,size = 3.5,label.size = 0.5,label.padding=unit(0.25,"lines"),fill= "white",color="black") +
  scale_color_manual(values = c("CON"="red4","AO3"="#2474A6","FOG"="#D89B00","FOGO3"="#8C6515","NMIN"="black"),
    breaks = c("CON", "AO3", "FOG", "FOGO3", "NMIN")) +
  scale_x_continuous(breaks = c(1, 2, 3, 5, 8, 13, 21, 34),labels = c(1, 2, 3, 5, 8, 13, 21, 34)) +
  scale_y_continuous(limits = c(-0.006, 0.18),breaks=seq(-0.006, 0.18, by = 0.03),expand = expansion(mult = c(0, 0.03)))+
  labs(x = "Days after treatment application",y = expression(N[2]*O~emission~(mu*g~g^{-1}~h^{-1})),color = "Treatment:")+
  theme_classic(base_size = 12) +
  theme(axis.title = element_text(face = "bold"),
        legend.position = c(0.34, 0.90),
        legend.direction = "horizontal",
    legend.background = element_rect(fill = "white", color = "black", linewidth = 0.5),
    legend.box.background = element_rect(fill = "white", color = "black", linewidth = 0.5),
    legend.text = element_text(size = 7),
  legend.title = element_text(size = 8, face = "bold"))
N2O

########################### JUNTAR GRÁFICOS ###########################
library(patchwork)
Figura_gases = (CH4 / CO2 / N2O) + plot_annotation(tag_levels = "A")
Figura_gases
ggsave("Figura 1 - gases no tempo.png",Figura_gases, width = 8, height = 9, dpi = 300, bg = "white")
```

***
***
## _**6.2 - Emissões acumuladas**_
***
***
### _**6.2.1 - ANOVA, TUKEY E MÉDIAS**_
***
***
```{r}
# Identificar os valores outliers
boxplot(emissoes_acumuladas$CO2_acumulado_ug_g)
out_CO2 = boxplot.stats(emissoes_acumuladas$CO2_acumulado_ug_g)$out
# Mostrar as linhas correspondentes
emissoes_acumuladas %>%
  filter(CO2_acumulado_ug_g %in% out_CO2) %>%
  dplyr::select(Bottle, Abreviacao, Bloco, Rep, CO2_acumulado_ug_g)
emissoes_acumuladas_filtrada = emissoes_acumuladas %>%
  filter(!Bottle %in% c(1, 2,7, 23))

# Ordem dos tratamentos
emissoes_acumuladas_filtrada$Abreviacao = factor(emissoes_acumuladas_filtrada$Abreviacao,
  levels = c("CON", "AO3", "FOG", "FOGO3", "NMIN"))
# Variáveis
variaveis = c("CH4_acumulado_ug_g","CO2_acumulado_ug_g","N2O_acumulado_ug_g")

resultados = list()
for(var in variaveis){
  cat("\n\n################ Variável:", var, "################\n")
  # Modelo
  formula_mod = reformulate("Abreviacao", response = var)
  mod = aov(formula_mod, data = emissoes_acumuladas_filtrada)
  # ANOVA
  cat("\nANOVA:\n")
  print(summary(mod))
  # Médias
  em = emmeans(mod, ~ Abreviacao)
  # Tukey
  cat("\nTeste de Tukey:\n")
  tukey = pairs(em, adjust = "tukey")
  print(tukey)
  # Letras
  grupos = multcomp::cld(em,alpha = 0.05,Letters = letters,sort = FALSE)
  # Se nenhum contraste for significativo, retirar letras
  if(!any(as.data.frame(tukey)$p.value < 0.05)){
    grupos$.group = ""}

  # Tabela
  tabela = data.frame(variavel = var,Tratamento = grupos$Abreviacao,
    media = grupos$emmean,se = grupos$SE,letra = trimws(grupos$.group),row.names = NULL)
  cat("\nMédias, SE e letras:\n")
  print(tabela)
  resultados[[var]] = tabela}

# Tabela final
resultado_acumulado = do.call(rbind, resultados)
rownames(resultado_acumulado) = NULL
View(resultado_acumulado)
```
***
***
### _**6.2.2 - GRÁFICOS DAS EMISSÕES ACUMULADAS**_
***
***
```{r}
########################### GRÁFICO ###########################
rotulos_facet = as_labeller(c("CH4_acumulado_ug_g" = "'(A)'~CH[4]","CO2_acumulado_ug_g" = "'(B)'~CO[2]",
    "N2O_acumulado_ug_g" = "'(C)'~N[2]*O"),default = label_parsed)

p_anova_plot = data.frame(variavel = c("CH4_acumulado_ug_g","CO2_acumulado_ug_g","N2O_acumulado_ug_g"),
  Tratamento = c("NMIN", "NMIN", "NMIN"),label = c("italic(P) == 0.755","italic(P) < 0.01","italic(P) == 0.015"))

p_acumulado = ggplot(resultado_acumulado,aes(x = Tratamento, y = media, fill = Tratamento)) +
  geom_col(width = 0.70,color = "black",linewidth = 0.5) +
  geom_errorbar(aes(ymin = media - se,ymax = media + se),width = 0.15,linewidth = 0.6) +
  geom_text(aes(y = media + se,label = letra),vjust = -0.6,size = 4,fontface = "bold") +
  geom_text(data = p_anova_plot,aes(x = Tratamento,y = Inf,label = label),
    inherit.aes = FALSE,parse = TRUE,hjust = 1,vjust = 2,size = 4) +
  scale_fill_manual(values = c(
    "CON" = "red4","AO3" = "#2474A6","FOG" = "#D89B00","FOGO3" = "#8C6515","NMIN" = "black")) +
  facet_wrap(~ variavel,scales = "free_y",labeller = rotulos_facet) +
  scale_y_continuous(expand = expansion(mult = c(0.05, 0.18))) +
  labs(x = NULL,y = expression("Cumulative emission "~(mu*g~g^{-1}))) +
  theme_classic(base_size = 12) +
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 45,hjust = 1,color = "black"),
        strip.background = element_blank(),
        strip.text = element_text(face = "bold",size = 12))
p_acumulado
ggsave("Figura 2 - gases acumulados.png",p_acumulado, width = 8, height = 5, dpi = 300, bg = "white")
```

***
***
### _**6.2.3 - CONTRASTE FEZES vs NMIN**_
***
***
```{r}
variaveis = c("CH4_acumulado_ug_g","CO2_acumulado_ug_g","N2O_acumulado_ug_g")

resultados_contraste = list()
for(var in variaveis){
  cat("\n\n################ Variável:", var, "################\n")
  # Modelo
  formula_mod = as.formula(paste(var, "~ Abreviacao"))
  mod = aov(formula_mod,data = emissoes_acumuladas_filtrada)
  # Médias
  em = emmeans::emmeans(mod,~ Abreviacao)
  # Contraste: média FOG + FOGO3 versus NMIN
  cont = emmeans::contrast(em,method = list(Fezes_vs_NMIN = c(0, 0, 0.5, 0.5, -1)))
  cat("\nFOG + FOGO3 vs NMIN:\n")
  print(summary(cont, infer = TRUE))

  resultados_contraste[[var]] = as.data.frame(cont)}

resultado_contraste = do.call(rbind,resultados_contraste)
rownames(resultado_contraste) = NULL
View(resultado_contraste)
```

















