Final Project
Margaret Nelson
2026-04-22
Import libraries
Import data
# BAU ----
BAU_base_ex <- read_excel('ENVS-505 County-Level Data Inputs.xlsx', sheet = 'BAU baseline', na = 'NA')
BAU_cov_ex <- read_excel('ENVS-505 County-Level Data Inputs.xlsx', sheet = 'BAU cover crops', na = 'NA')
BAU_m_ex <- read_excel("ENVS-505 County-Level Data Inputs.xlsx", sheet = 'BAU manure', na = 'NA')
BAU_r_ex <- read_excel("ENVS-505 County-Level Data Inputs.xlsx", sheet = 'BAU reduced', na = 'NA')
BAU_cont_ex <- read_excel("ENVS-505 County-Level Data Inputs.xlsx", sheet = 'BAU cont', na = 'NA')
# 4R ----
R_base_ex <- read_excel("ENVS-505 County-Level Data Inputs.xlsx", sheet = '4R baseline', na = 'NA')
R_cov_ex <- read_excel("ENVS-505 County-Level Data Inputs.xlsx", sheet = '4R cover crops', na = 'NA')
R_m_ex <- read_excel("ENVS-505 County-Level Data Inputs.xlsx", sheet = '4R manure', na = 'NA')
R_r_ex <- read_excel("ENVS-505 County-Level Data Inputs.xlsx", sheet = '4R reduced', na = 'NA')
R_cont_ex <- read_excel("ENVS-505 County-Level Data Inputs.xlsx", sheet = '4R cont', na = 'NA')
# EEF ----
EEF_base_ex <- read_excel("ENVS-505 County-Level Data Inputs.xlsx", sheet = 'EEF baseline', na = 'NA')
EEF_cov_ex <- read_excel("ENVS-505 County-Level Data Inputs.xlsx", sheet = 'EEF cover crops', na = 'NA')
EEF_m_ex <- read_excel("ENVS-505 County-Level Data Inputs.xlsx", sheet = 'EEF manure', na = 'NA')
EEF_r_ex <- read_excel("ENVS-505 County-Level Data Inputs.xlsx", sheet = 'EEF reduced', na = 'NA')
EEF_cont_ex <- read_excel("ENVS-505 County-Level Data Inputs.xlsx", sheet = 'EEF cont', na = 'NA')Clean data
# BAU ----
BAU_base <- as.data.frame(t(BAU_base_ex)) |>
janitor::clean_names() |>
mutate(v2 = as.double(v2))
BAU_cov <- as.data.frame(t(BAU_cov_ex)) |>
janitor::clean_names() |>
mutate(v2 = as.double(v2), v25 = as.double(v25), v27 = as.double(v27))
BAU_m <- as.data.frame(t(BAU_m_ex)) |>
janitor::clean_names() |>
mutate(v2 = as.double(v2), v25 = as.double(v25), v27 = as.double(v27))
BAU_r <- as.data.frame(t(BAU_r_ex)) |>
janitor::clean_names() |>
mutate(v2 = as.double(v2), v25 = as.double(v25), v27 = as.double(v27))
BAU_cont <- as.data.frame(t(BAU_cont_ex)) |>
janitor::clean_names() |>
mutate(v2 = as.double(v2), v25 = as.double(v25), v27 = as.double(v27))
# 4R----
R_base <- as.data.frame(t(R_base_ex)) |>
janitor::clean_names() |>
mutate(v2 = as.double(v2), v25 = as.double(v25), v27 = as.double(v27))
R_cov <- as.data.frame(t(R_cov_ex)) |>
janitor::clean_names()|>
mutate(v2 = as.double(v2), v25 = as.double(v25), v27 = as.double(v27))
R_m <- as.data.frame(t(R_m_ex)) |>
janitor::clean_names()|>
mutate(v2 = as.double(v2), v25 = as.double(v25), v27 = as.double(v27))
R_r <- as.data.frame(t(R_r_ex)) |>
janitor::clean_names()|>
mutate(v2 = as.double(v2), v25 = as.double(v25), v27 = as.double(v27))
R_cont <- as.data.frame(t(R_cont_ex)) |>
janitor::clean_names()|>
mutate(v2 = as.double(v2), v25 = as.double(v25), v27 = as.double(v27))
# EEF ----
EEF_base<- as.data.frame(t(EEF_base_ex)) |>
janitor::clean_names()|>
mutate(v2 = as.double(v2), v25 = as.double(v25), v27 = as.double(v27))
EEF_cov <- as.data.frame(t(EEF_cov_ex)) |>
janitor::clean_names()|>
mutate(v2 = as.double(v2), v25 = as.double(v25), v27 = as.double(v27))
EEF_m <- as.data.frame(t(EEF_m_ex)) |>
janitor::clean_names()|>
mutate(v2 = as.double(v2), v25 = as.double(v25), v27 = as.double(v27))
EEF_r <- as.data.frame(t(EEF_r_ex)) |>
janitor::clean_names()|>
mutate(v2 = as.double(v2), v25 = as.double(v25), v27 = as.double(v27))
EEF_cont <- as.data.frame(t(EEF_cont_ex)) |>
janitor::clean_names()|>
mutate(v2 = as.double(v2), v25 = as.double(v25), v27 = as.double(v27))Basic stats
Counties ranked high - low
# BAU ----
BAU_cov |>
select(v25, v27) |>
arrange(desc(v25))## v25 v27
## Haakon 13676 12632
## Bennett 12440 11347
## Jones 11239 10194
## Lyman 10748 9704
## Dewey 10162 9117
## Todd 9774 9029
## Tripp 9757 8547
## Fall River 8428 7384
## Corson 8422 6695
## Beadle 8263 7178
## Lawrence 7963 6919
## Faulk 7915 6678
## Potter 7872 6210
## Butte 7594 6550
## Douglas 7556 6470
## Sanborn 7297 6211
## Kingsbury 7257 6296
## Hand 7149 6064
## Hyde 7115 5453
## Hutchinson 7099 6013
## Hughes 7069 5407
## Campbell 7004 5767
## Charles Mix 6972 5886
## Sully 6948 5285
## Walworth 6936 5699
## Turner 6908 5791
## Miner 6838 5753
## Edmunds 6827 5590
## Aurora 6819 5734
## McPherson 6804 5567
## Brule 6729 5643
## Hanson 6699 5614
## Jerauld 6699 5037
## Bon Homme 6556 5471
## Buffalo 6490 5280
## Davison 6480 5395
## Lincoln 6457 5340
## Clay 6436 5320
## Spink 6367 5328
## Hamlin 6224 5262
## McCook 6174 5089
## Grant 6111 5150
## Yankton 6102 5017
## Roberts 6087 5297
## Marshall 6070 5108
## Union 6067 4987
## Minnehaha 6061 4981
## Lake 6017 4901
## Codington 5963 5002
## Deuel 5922 4960
## Brown 5905 4866
## Moody 5894 4814
## Day 5832 4870
## Clark 5821 4860
## Brookings 5694 4733
## Custer NA NA
## Gregory NA NA
## Harding NA NA
## Jackson NA NA
## Meade NA NA
## Mellette NA NA
## Ogala Lakota NA NA
## Pennington NA NA
## Perkins NA NA
## Stanley NA NA
## Ziebach NA NABAU_m |>
select(v25, v27) |>
arrange(desc(v25))## v25 v27
## Haakon 16789 5430
## Bennett 15249 4982
## Jones 13763 4549
## Lyman 13154 4372
## Dewey 12427 4160
## Todd 11945 4020
## Tripp 11924 4014
## Fall River 10275 3534
## Corson 10268 3532
## Beadle 10067 3474
## Lawrence 9698 3366
## Faulk 9638 3349
## Potter 9585 3334
## Butte 9240 3233
## Douglas 9192 3219
## Sanborn 8871 3126
## Kingsbury 8822 3111
## Hand 8688 3072
## Hyde 8646 3060
## Hutchinson 8625 3045
## Hughes 8589 3044
## Campbell 8507 3020
## Charles Mix 8467 3008
## Sully 8438 3000
## Walworth 8423 2995
## Turner 8389 2985
## Miner 8302 2960
## Edmunds 8287 2956
## Aurora 8278 2953
## McPherson 8259 2948
## Brule 8166 2921
## Hanson 8129 2910
## Jerauld 8129 2910
## Bon Homme 7952 2858
## Buffalo 7870 2835
## Davison 7857 2831
## Lincoln 7828 2822
## Clay 7803 2815
## Spink 7717 2790
## Hamlin 7539 2738
## McCook 7478 2720
## Grant 7399 2698
## Yankton 7388 2694
## Roberts 7370 2689
## Marshall 7348 2683
## Union 7344 2682
## Minnehaha 7337 2679
## Lake 7283 2664
## Codington 7216 2644
## Deuel 7164 2629
## Brown 7144 2623
## Moody 7130 2619
## Day 7053 2597
## Clark 7039 2593
## Brookings 6682 2547
## Custer NA NA
## Gregory NA NA
## Harding NA NA
## Jackson NA NA
## Meade NA NA
## Mellette NA NA
## Ogala Lakota NA NA
## Pennington NA NA
## Perkins NA NA
## Stanley NA NA
## Ziebach NA NABAU_r |>
select(v25, v27) |>
arrange(desc(v25))## v25 v27
## Haakon 12122 11677
## Bennett 11031 10664
## Jones 9977 9533
## Lyman 9546 9101
## Dewey 9030 8586
## Todd 8689 8381
## Tripp 8674 8459
## Fall River 7506 7061
## Corson 7500 6890
## Beadle 7358 6974
## Lawrence 7096 6652
## Faulk 7054 6537
## Potter 7017 6613
## Butte 6772 6327
## Douglas 6738 6353
## Sanborn 6510 6126
## Kingsbury 6476 6036
## Hand 6381 5996
## Hyde 6351 5947
## Hutchinson 6336 5951
## Hughes 6311 5906
## Campbell 6253 5736
## Charles Mix 6224 5840
## Sully 6203 5799
## Walworth 6193 5676
## Turner 6169 5744
## Miner 6107 5722
## Edmunds 6097 5580
## Aurora 6090 5705
## McPherson 6077 5560
## Brule 6011 5626
## Hanson 5985 5600
## Jerauld 5985 5581
## Bon Homme 5859 5474
## Buffalo 5801 5586
## Davison 5792 5407
## Lincoln 5771 5346
## Clay 5754 5329
## Spink 5693 5252
## Hamlin 5566 5127
## McCook 5523 5138
## Grant 5468 5028
## Yankton 5460 5075
## Roberts 5447 4992
## Marshall 5431 4992
## Union 5429 5150
## Minnehaha 5423 5145
## Lake 5385 4960
## Codington 5338 4898
## Deuel 5301 4862
## Brown 5286 4846
## Moody 5277 4998
## Day 5222 4783
## Clark 5212 4773
## Brookings 5101 4661
## Custer NA NA
## Gregory NA NA
## Harding NA NA
## Jackson NA NA
## Meade NA NA
## Mellette NA NA
## Ogala Lakota NA NA
## Pennington NA NA
## Perkins NA NA
## Stanley NA NA
## Ziebach NA NABAU_cont |>
select(v25, v27) |>
arrange(desc(v25))## v25 v27
## Haakon 12122 9768
## Bennett 11031 9336
## Jones 9977 7624
## Lyman 9546 7193
## Dewey 9030 6677
## Todd 8689 6857
## Tripp 8674 7499
## Fall River 7506 5153
## Corson 7500 4555
## Beadle 7358 4769
## Lawrence 7096 4743
## Faulk 7054 4709
## Potter 7017 5319
## Butte 6772 4419
## Douglas 6738 4149
## Sanborn 6510 3922
## Kingsbury 6476 2962
## Hand 6381 3792
## Hyde 6351 4653
## Hutchinson 6336 3747
## Hughes 6311 4613
## Campbell 6253 3908
## Charles Mix 6224 3635
## Sully 6203 4506
## Walworth 6193 3848
## Turner 6169 2834
## Miner 6107 3518
## Edmunds 6097 3752
## Aurora 6090 3501
## McPherson 6077 3732
## Brule 6011 3422
## Hanson 5985 3396
## Jerauld 5985 4287
## Bon Homme 5859 3270
## Buffalo 5801 4626
## Davison 5792 3203
## Lincoln 5771 2436
## Clay 5754 2419
## Spink 5693 2850
## Hamlin 5566 2053
## McCook 5523 2934
## Grant 5468 1954
## Yankton 5460 2871
## Roberts 5447 1648
## Marshall 5431 1918
## Union 5429 2755
## Minnehaha 5423 2750
## Lake 5385 2050
## Codington 5338 1824
## Deuel 5301 1788
## Brown 5286 2444
## Moody 5277 2603
## Day 5222 1709
## Clark 5212 1699
## Brookings 5101 1597
## Custer NA NA
## Gregory NA NA
## Harding NA NA
## Jackson NA NA
## Meade NA NA
## Mellette NA NA
## Ogala Lakota NA NA
## Pennington NA NA
## Perkins NA NA
## Stanley NA NA
## Ziebach NA NA# 4R ----
R_cov |>
select(v25, v27) |>
arrange(desc(v25))## v25 v27
## Haakon 8497 7453
## Bennett 7979 6886
## Jones 7472 6427
## Lyman 7265 6221
## Dewey 7019 5974
## Todd 6855 6110
## Tripp 6848 5638
## Fall River 6290 5245
## Corson 6287 4560
## Beadle 6222 5136
## Lawrence 6094 5050
## Faulk 6073 4837
## Potter 6056 4393
## Butte 5939 4894
## Douglas 5922 4837
## Sanborn 5814 4728
## Kingsbury 5797 4836
## Hand 5751 4666
## Hyde 5737 4075
## Hutchinson 5730 4645
## Hughes 5718 4055
## Campbell 5690 4454
## Charles Mix 5677 4591
## Sully 5667 4004
## Walworth 5662 4425
## Turner 5650 4533
## Miner 5621 4535
## Edmunds 5616 4379
## Aurora 5613 4527
## McPherson 5606 4370
## Brule 5575 4489
## Hanson 5562 4477
## Jerauld 5562 3900
## Bon Homme 5502 4416
## Buffalo 5474 4264
## Davison 5470 4385
## Lincoln 5460 4343
## Clay 5452 4335
## Spink 5422 4384
## Hamlin 5362 4401
## McCook 5341 4256
## Grant 5315 4353
## Yankton 5311 4226
## Roberts 5305 4515
## Marshall 5297 4336
## Union 5296 4216
## Minnehaha 5294 4214
## Lake 5275 4159
## Codington 5253 4291
## Deuel 5235 4274
## Brown 5228 4189
## Moody 5224 4143
## Day 5197 4236
## Clark 5193 4232
## Brookings 5139 4178
## Custer NA NA
## Gregory NA NA
## Harding NA NA
## Jackson NA NA
## Meade NA NA
## Mellette NA NA
## Ogala Lakota NA NA
## Pennington NA NA
## Perkins NA NA
## Stanley NA NA
## Ziebach NA NAR_m |>
select(v25, v27) |>
arrange(desc(v25)) |>
rename('Feedstock CI w/o SOC' = v25,
'Feedstock CI w/ SOC' = v27)## Feedstock CI w/o SOC Feedstock CI w/ SOC
## Haakon 11609 251
## Bennett 10788 521
## Jones 9996 782
## Lyman 9672 889
## Dewey 9284 1017
## Todd 9027 1102
## Tripp 9016 1106
## Fall River 8137 1396
## Corson 8133 1397
## Beadle 8026 1432
## Lawrence 7829 1497
## Faulk 7797 1508
## Potter 7769 1517
## Butte 7585 1578
## Douglas 7559 1586
## Sanborn 7388 1643
## Kingsbury 7361 1651
## Hand 7290 1675
## Hyde 7268 1682
## Hutchinson 7257 1686
## Hughes 7237 1692
## Campbell 7194 1706
## Charles Mix 7173 1714
## Sully 7157 1719
## Walworth 7149 1721
## Turner 7130 1727
## Miner 7084 1743
## Edmunds 7077 1745
## Aurora 7072 1747
## McPherson 7061 1750
## Brule 7012 1767
## Hanson 6992 1773
## Jerauld 6992 1773
## Bon Homme 6898 1804
## Buffalo 6854 1819
## Davison 6847 1821
## Lincoln 6832 1826
## Clay 6818 1830
## Spink 6772 1845
## Hamlin 6677 1877
## McCook 6645 1887
## Grant 6603 1901
## Yankton 6597 1903
## Roberts 6587 1906
## Marshall 6576 1910
## Union 6574 1911
## Minnehaha 6570 1912
## Lake 6541 1922
## Codington 6505 1933
## Deuel 6478 1943
## Brown 6467 1946
## Moody 6459 1949
## Day 6418 1962
## Clark 6411 1965
## Brookings 6327 1992
## Custer NA NA
## Gregory NA NA
## Harding NA NA
## Jackson NA NA
## Meade NA NA
## Mellette NA NA
## Ogala Lakota NA NA
## Pennington NA NA
## Perkins NA NA
## Stanley NA NA
## Ziebach NA NAR_r |>
select(v25, v27) |>
arrange(desc(v25))## v25 v27
## Haakon 6942 6498
## Bennett 6570 6203
## Jones 6211 5766
## Lyman 6063 5619
## Dewey 5887 5443
## Todd 5771 5463
## Tripp 5766 5550
## Fall River 5367 4922
## Corson 5365 4755
## Beadle 5317 4932
## Lawrence 5227 4782
## Faulk 5213 4695
## Potter 5200 4796
## Butte 5117 4672
## Douglas 5105 4720
## Sanborn 5027 4642
## Kingsbury 5015 4576
## Hand 4983 4598
## Hyde 4973 4569
## Hutchinson 4968 4583
## Hughes 4959 4555
## Campbell 4939 4422
## Charles Mix 4930 4545
## Sully 4922 4518
## Walworth 4919 4402
## Turner 4911 4486
## Miner 4890 4505
## Edmunds 4886 4369
## Aurora 4884 4499
## McPherson 4879 4362
## Brule 4857 4472
## Hanson 4848 4463
## Jerauld 4848 4444
## Bon Homme 4805 4420
## Buffalo 4785 4570
## Davison 4782 4397
## Lincoln 4775 4350
## Clay 4769 4344
## Spink 4748 4308
## Hamlin 4705 4266
## McCook 4690 4305
## Grant 4671 4232
## Yankton 4669 4284
## Roberts 4664 4210
## Marshall 4659 4220
## Union 4658 4380
## Minnehaha 4656 4378
## Lake 4643 4218
## Codington 4627 4188
## Deuel 4614 4175
## Brown 4609 4169
## Moody 4606 4328
## Day 4587 4148
## Clark 4584 4145
## Brookings 4546 4107
## Custer NA NA
## Gregory NA NA
## Harding NA NA
## Jackson NA NA
## Meade NA NA
## Mellette NA NA
## Ogala Lakota NA NA
## Pennington NA NA
## Perkins NA NA
## Stanley NA NA
## Ziebach NA NAR_cont |>
select(v25, v27) |>
arrange(desc(v25))## v25 v27
## Haakon 6942 4589
## Bennett 6570 4875
## Jones 6211 3857
## Lyman 6063 3710
## Dewey 5887 3534
## Todd 5771 3939
## Tripp 5766 4591
## Fall River 5367 3014
## Corson 5365 2419
## Beadle 5317 2728
## Lawrence 5227 2874
## Faulk 5213 2868
## Potter 5200 3502
## Butte 5117 2763
## Douglas 5105 2516
## Sanborn 5027 2438
## Kingsbury 5015 1502
## Hand 4983 2394
## Hyde 4973 3275
## Hutchinson 4968 2379
## Hughes 4959 3261
## Charles Mix 4930 2341
## Sully 4922 3225
## Walworth 4919 2574
## Turner 4911 1576
## Miner 4890 2301
## Edmunds 4886 2541
## Aurora 4884 2295
## McPherson 4879 2534
## Brule 4857 2268
## Hanson 4848 2259
## Jerauld 4848 3150
## Bon Homme 4805 2216
## Buffalo 4785 3610
## Davison 4782 2193
## Lincoln 4775 1440
## Clay 4769 1434
## Spink 4748 1905
## Hamlin 4705 1192
## McCook 4690 2101
## Grant 4671 1158
## Yankton 4669 2080
## Roberts 4664 865
## Marshall 4659 1146
## Union 4658 1984
## Minnehaha 4656 1982
## Lake 4643 1308
## Codington 4627 1114
## Deuel 4614 1101
## Brown 4609 1767
## Moody 4606 1932
## Day 4587 1074
## Clark 4584 1071
## Brookings 4546 1033
## Campbell 4393 2594
## Custer NA NA
## Gregory NA NA
## Harding NA NA
## Jackson NA NA
## Meade NA NA
## Mellette NA NA
## Ogala Lakota NA NA
## Pennington NA NA
## Perkins NA NA
## Stanley NA NA
## Ziebach NA NA# EEF ----
EEF_cov |>
select(v25, v27) |>
arrange(desc(v25))## v25 v27
## Haakon 12710 11666
## Bennett 11566 10473
## Jones 10455 9410
## Lyman 10001 8957
## Dewey 9458 8414
## Todd 9099 8354
## Tripp 9084 7874
## Fall River 7855 6811
## Corson 7849 6122
## Beadle 7702 6617
## Lawrence 7472 6380
## Faulk 7380 6143
## Potter 7340 5678
## Butte 7083 6039
## Douglas 7047 5962
## Sanborn 6808 5723
## Kingsbury 6711 5810
## Hand 6671 5586
## Hyde 6640 4977
## Hutchinson 6625 5539
## Hughes 6598 4935
## Campbell 6537 5301
## Charles Mix 6507 5422
## Sully 6485 4822
## Walworth 6474 5237
## Turner 6448 5332
## Miner 6384 5298
## Edmunds 6373 5137
## Aurora 6369 5281
## McPherson 6352 5115
## Brule 6282 5197
## Hanson 6255 5170
## Jerauld 6255 4593
## Bon Homme 6123 5037
## Buffalo 6062 4852
## Davison 6053 4967
## Lincoln 6031 4914
## Clay 6012 4895
## Spink 5948 4909
## Hamlin 5815 4854
## McCook 5770 4684
## Grant 5711 4750
## Yankton 5703 4617
## Roberts 5689 4899
## Marshall 5673 4711
## Union 5670 4590
## Minnehaha 5665 4585
## Lake 5624 4508
## Codington 5574 4613
## Deuel 5536 4574
## Brown 5520 4482
## Moody 5510 4430
## Day 5453 4491
## Clark 5443 4481
## Brookings 5325 4364
## Custer NA NA
## Gregory NA NA
## Harding NA NA
## Jackson NA NA
## Meade NA NA
## Mellette NA NA
## Ogala Lakota NA NA
## Pennington NA NA
## Perkins NA NA
## Stanley NA NA
## Ziebach NA NAEEF_m |>
select(v25, v27) |>
arrange(desc(v25))## v25 v27
## Haakon 15822 4463
## Bennett 14375 4108
## Jones 12979 3765
## Lyman 12407 3625
## Dewey 11723 3457
## Todd 11271 3346
## Tripp 11251 3341
## Fall River 9702 2961
## Corson 9695 2959
## Beadle 9507 2913
## Lawrence 9159 2828
## Faulk 9103 2817
## Potter 9053 2802
## Butte 8729 2722
## Douglas 8684 2711
## Sanborn 8382 2637
## Kingsbury 8336 2626
## Hand 8210 2595
## Hyde 8171 2585
## Hutchinson 8151 2580
## Hughes 8117 2572
## Campbell 8041 2553
## Charles Mix 8003 2544
## Sully 7975 2537
## Walworth 7961 2534
## Turner 7929 2526
## Miner 7847 2506
## Edmunds 7834 2502
## Aurora 7825 2500
## McPherson 7807 2496
## Brule 7720 2474
## Hanson 7685 2466
## Jerauld 7685 2466
## Bon Homme 7518 2425
## Buffalo 7442 2406
## Davison 7430 2403
## Lincoln 7402 2396
## Clay 7379 2391
## Spink 7298 2371
## Hamlin 7130 2330
## McCook 7073 2316
## Grant 6999 2298
## Yankton 6989 2295
## Roberts 6972 2291
## Marshall 6951 2286
## Union 6948 2285
## Minnehaha 6941 2283
## Lake 6890 2271
## Codington 6827 2255
## Deuel 6778 2243
## Brown 6759 2239
## Moody 6746 2235
## Day 6674 2218
## Clark 6661 2215
## Brookings 6513 2178
## Custer NA NA
## Gregory NA NA
## Harding NA NA
## Jackson NA NA
## Meade NA NA
## Mellette NA NA
## Ogala Lakota NA NA
## Pennington NA NA
## Perkins NA NA
## Stanley NA NA
## Ziebach NA NAEEF_r |>
select(v25, v27) |>
arrange(desc(v25))## v25 v27
## Haakon 11155 10711
## Bennett 10157 9790
## Jones 9194 8749
## Lyman 8799 8354
## Dewey 8327 7882
## Todd 8015 7707
## Tripp 8001 7786
## Fall River 6932 6488
## Corson 6927 6317
## Beadle 6797 6413
## Lawrence 6558 6113
## Faulk 6519 6002
## Potter 6485 6081
## Butte 6261 5816
## Douglas 6230 5845
## Sanborn 6022 5637
## Kingsbury 5990 5550
## Hand 5903 5518
## Hyde 5876 5471
## Hutchinson 5862 5477
## Hughes 5839 5435
## Campbell 5786 5269
## Charles Mix 5760 5357
## Sully 5741 5337
## Walworth 5713 5214
## Turner 5709 5284
## Miner 5653 5268
## Edmunds 5643 5126
## Aurora 5637 5252
## McPherson 5625 5108
## Brule 5565 5180
## Hanson 5541 5156
## Jerauld 5541 5137
## Bon Homme 5426 5041
## Buffalo 5373 5158
## Davison 5365 4980
## Lincoln 5345 4920
## Clay 5329 4904
## Spink 5273 4833
## Hamlin 5158 4719
## McCook 5118 4734
## Grant 5068 4628
## Yankton 5060 4676
## Roberts 5048 4594
## Marshall 5034 4595
## Union 5031 4754
## Minnehaha 5027 4749
## Lake 4992 4567
## Codington 4949 4509
## Deuel 4915 4476
## Brown 4902 4461
## Moody 4893 4615
## Day 4843 4404
## Clark 4834 4395
## Brookings 4732 4293
## Custer NA NA
## Gregory NA NA
## Harding NA NA
## Jackson NA NA
## Meade NA NA
## Mellette NA NA
## Ogala Lakota NA NA
## Pennington NA NA
## Perkins NA NA
## Stanley NA NA
## Ziebach NA NAEEF_cont |>
select(v25, v27) |>
arrange(desc(v25))## v25 v27
## Haakon 11155 8802
## Bennett 10157 8462
## Jones 9194 6840
## Lyman 8799 6446
## Dewey 8327 5974
## Todd 8015 6183
## Tripp 8001 6826
## Fall River 6932 4579
## Corson 6927 3982
## Beadle 6797 4208
## Lawrence 6558 4204
## Faulk 6519 4174
## Potter 6485 4787
## Butte 6261 3908
## Douglas 6230 3641
## Sanborn 6022 3433
## Kingsbury 5990 2476
## Hand 5903 3314
## Hyde 5876 4178
## Hutchinson 5862 3273
## Hughes 5839 4141
## Campbell 5786 3441
## Charles Mix 5760 3171
## Sully 5741 4043
## Walworth 5731 3386
## Turner 5709 2374
## Miner 5653 3064
## Edmunds 5643 3298
## Aurora 5637 3048
## McPherson 5625 3280
## Brule 5565 2976
## Hanson 5541 2952
## Jerauld 5541 3843
## Bon Homme 5426 2837
## Buffalo 5373 4198
## Davison 5365 2776
## Lincoln 5345 2010
## Clay 5329 1994
## Spink 5273 2431
## Hamlin 5158 1645
## McCook 5118 2529
## Grant 5068 1554
## Yankton 5060 2471
## Roberts 5048 1250
## Marshall 5034 1521
## Union 5032 2358
## Minnehaha 5027 2353
## Lake 4992 1657
## Codington 4949 1435
## Deuel 4915 1402
## Brown 4902 2059
## Moody 4893 2219
## Day 4843 1329
## Clark 4834 1321
## Brookings 4732 1219
## Custer NA NA
## Gregory NA NA
## Harding NA NA
## Jackson NA NA
## Meade NA NA
## Mellette NA NA
## Ogala Lakota NA NA
## Pennington NA NA
## Perkins NA NA
## Stanley NA NA
## Ziebach NA NAAverage Feedstock CI (with and without SOC)
Calculate mean
# BAU ----
avg_B_base <- mean(BAU_base$v25, na.rm = TRUE)
avg_B_base_SOC <- mean(BAU_base$v27, na.rm = TRUE)
avg_B_cov <- mean(BAU_cov$v25, na.rm = TRUE)
avg_B_cov_SOC <- mean(BAU_cov$v27, na.rm = TRUE)
avg_B_m <- mean(BAU_m$v25, na.rm = TRUE)
avg_B_m_SOC <- mean(BAU_m$v27, na.rm = TRUE)
avg_B_r <- mean(BAU_r$v25, na.rm = TRUE)
avg_B_r_SOC <- mean(BAU_r$v27, na.rm = TRUE)
avg_B_cont <- mean(BAU_cont$v25, na.rm = TRUE)
avg_B_cont_SOC <- mean(BAU_cont$v27, na.rm = TRUE)
# 4R ----
avg_R_base <- mean(R_base$v25, na.rm = TRUE)
avg_R_base_SOC <- mean(R_base$v27, na.rm = TRUE)
avg_R_cov <- mean(R_cov$v25, na.rm = TRUE)
avg_R_cov_SOC <- mean(R_cov$v27, , na.rm = TRUE)
avg_R_m <- mean(R_m$v25, na.rm = TRUE)
avg_R_m_SOC <- mean(R_m$v27, na.rm = TRUE)
avg_R_r <- mean(R_r$v25, na.rm = TRUE)
avg_R_r_SOC <- mean(R_r$v27, na.rm = TRUE)
avg_R_cont <- mean(R_cont$v25, na.rm = TRUE)
avg_R_cont_SOC <- mean(R_cont$v27, na.rm = TRUE)
# EEF ----
avg_E_base <- mean(EEF_base$v25, na.rm = TRUE)
avg_E_base_SOC <- mean(EEF_base$v27, na.rm = TRUE)
avg_E_cov <- mean(EEF_cov$v25, na.rm = TRUE)
avg_E_cov_SOC <- mean(EEF_cov$v27, na.rm = TRUE)
avg_E_m <- mean(EEF_m$v25, na.rm = TRUE)
avg_E_m_SOC <- mean(EEF_m$v27, na.rm = TRUE)
avg_E_r <- mean(EEF_r$v25, na.rm = TRUE)
avg_E_r_SOC <- mean(EEF_r$v27, na.rm = TRUE)
avg_E_cont <- mean(EEF_cont$v25, na.rm = TRUE)
avg_E_cont_SOC <- mean(EEF_cont$v27, na.rm = TRUE)Dataframes
# Base data frames ----
rownames <- c('Baseline', 'Cover crops', 'Manure', 'Reeduced till', 'Cont no till')
B_avg <- data.frame(avg_B_base, avg_B_cov, avg_B_m, avg_B_r, avg_B_cont) |>
pivot_longer(cols = starts_with('avg'),
values_to = 'BAU Average CI') |>
mutate(Scenario = case_when(name == 'avg_B_base' ~ 'Baseline',
name == 'avg_B_cov' ~ 'Cover crops',
name == 'avg_B_m' ~ 'Manure',
name == 'avg_B_r' ~ 'Reduced till',
name == 'avg_B_cont' ~ 'Cont no till')) |>
select(Scenario, 'BAU Average CI')
B_SOC_avg <- data.frame(avg_B_base_SOC, avg_B_cov_SOC, avg_B_m_SOC, avg_B_r_SOC, avg_B_cont_SOC) |>
pivot_longer(cols = starts_with('avg'),
values_to = 'BAU Average CI - SOC') |>
mutate(Scenario = case_when(name == 'avg_B_base_SOC' ~ 'Baseline',
name == 'avg_B_cov_SOC' ~ 'Cover crops',
name == 'avg_B_m_SOC' ~ 'Manure',
name == 'avg_B_r_SOC' ~ 'Reduced till',
name == 'avg_B_cont_SOC' ~ 'Cont no till')) |>
select(Scenario, 'BAU Average CI - SOC')
R_avg <- data.frame(avg_R_base, avg_R_cov, avg_R_m, avg_R_r, avg_R_cont) |>
pivot_longer(cols = starts_with('avg'),
values_to = '4R Average CI') |>
mutate(Scenario = case_when(name == 'avg_R_base' ~ 'Baseline',
name == 'avg_R_cov' ~ 'Cover crops',
name == 'avg_R_m' ~ 'Manure',
name == 'avg_R_r' ~ 'Reduced till',
name == 'avg_R_cont' ~ 'Cont no till')) |>
select(Scenario, '4R Average CI')
R_SOC_avg <- data.frame(avg_R_base_SOC, avg_R_cov_SOC, avg_R_m_SOC, avg_R_r_SOC, avg_R_cont_SOC) |>
pivot_longer(cols = starts_with('avg'),
values_to = '4R Average CI - SOC') |>
mutate(Scenario = case_when(name == 'avg_R_base_SOC' ~ 'Baseline',
name == 'avg_R_cov_SOC' ~ 'Cover crops',
name == 'avg_R_m_SOC' ~ 'Manure',
name == 'avg_R_r_SOC' ~ 'Reduced till',
name == 'avg_R_cont_SOC' ~ 'Cont no till')) |>
select(Scenario, '4R Average CI - SOC')
E_avg <- data.frame(avg_E_base, avg_E_cov, avg_E_m, avg_E_r, avg_E_cont) |>
pivot_longer(cols = starts_with('avg'),
values_to = 'EEF Average CI') |>
mutate(Scenario = case_when(name == 'avg_E_base' ~ 'Baseline',
name == 'avg_E_cov' ~ 'Cover crops',
name == 'avg_E_m' ~ 'Manure',
name == 'avg_E_r' ~ 'Reduced till',
name == 'avg_E_cont' ~ 'Cont no till')) |>
select(Scenario, 'EEF Average CI')
E_SOC_avg <- data.frame(avg_E_base_SOC, avg_E_cov_SOC, avg_E_m_SOC, avg_E_r_SOC, avg_E_cont_SOC) |>
pivot_longer(cols = starts_with('avg'),
values_to = 'EEF Average CI - SOC') |>
mutate(Scenario = case_when(name == 'avg_E_base_SOC' ~ 'Baseline',
name == 'avg_E_cov_SOC' ~ 'Cover crops',
name == 'avg_E_m_SOC' ~ 'Manure',
name == 'avg_E_r_SOC' ~ 'Reduced till',
name == 'avg_E_cont_SOC' ~ 'Cont no till')) |>
select(Scenario, 'EEF Average CI - SOC')
# Averages ----
BAU_averages <- left_join(B_avg, B_SOC_avg )## Joining with `by = join_by(Scenario)`R_averages <- left_join(R_avg, R_SOC_avg)## Joining with `by = join_by(Scenario)`E_averages <- left_join(E_avg, E_SOC_avg)## Joining with `by = join_by(Scenario)`BR_avg <- left_join(BAU_averages, R_averages)## Joining with `by = join_by(Scenario)`Averages <- left_join(BR_avg, E_averages)## Joining with `by = join_by(Scenario)`Averages## # A tibble: 5 × 7
## Scenario `BAU Average CI` `BAU Average CI - SOC` `4R Average CI`
## <chr> <dbl> <dbl> <dbl>
## 1 Baseline 6517. 6517. 5029.
## 2 Cover crops 7304. 6173. 5817.
## 3 Manure 8876. 3128. 7392.
## 4 Reduced till 6517. 6105. 5029.
## 5 Cont no till 6517. 3895. 5019.
## # ℹ 3 more variables: `4R Average CI - SOC` <dbl>, `EEF Average CI` <dbl>,
## # `EEF Average CI - SOC` <dbl>##Pattern by farm size
# BAU ----
BAU_base |>
arrange(desc(v2), desc(v27)) |>
ggplot(aes(v2, v27)) +
geom_point() +
labs(
title = 'Farm size and Feedstock CI at Baseline BAU', subtitle = '** Feedstock CI w/ SOC',
x = 'Farm size (acres)', y = 'Carbon intensity (g CO2/bushel')## Warning: Removed 11 rows containing missing values or values outside the scale range
## (`geom_point()`).
BAU_cov |>
arrange(desc(v2), desc(v27)) |>
ggplot(aes(v2, v27)) +
geom_point() +
labs(
title = 'Farm size and Feedstock CI at BAU w/ cover crops', subtitle = '** Feedstock CI w/ SOC',
x = 'Farm size (acres)', y = 'Carbon intensity (g CO2/bushel')## Warning: Removed 11 rows containing missing values or values outside the scale range
## (`geom_point()`).
BAU_m |>
arrange(desc(v2), desc(v27)) |>
ggplot(aes(v2, v27)) +
geom_point() +
labs(
title = 'Farm size and Feedstock CI at BAU w/ manure', subtitle = '** Feedstock CI w/ SOC',
x = 'Farm size (acres)', y = 'Carbon intensity (g CO2/bushel')## Warning: Removed 11 rows containing missing values or values outside the scale range
## (`geom_point()`).
BAU_r |>
arrange(desc(v2), desc(v27)) |>
ggplot(aes(v2, v27)) +
geom_point() +
labs(
title = 'Farm size and Feedstock CI under reduced till BAU', subtitle = '** Feedstock CI w/ SOC',
x = 'Farm size (acres)', y = 'Carbon intensity (g CO2/bushel')## Warning: Removed 11 rows containing missing values or values outside the scale range
## (`geom_point()`).
BAU_cont |>
arrange(desc(v2), desc(v27)) |>
ggplot(aes(v2, v27)) +
geom_point() +
labs(
title = 'Farm size and Feedstock CI under cont no till BAU', subtitle = '** Feedstock CI w/ SOC',
x = 'Farm size (acres)', y = 'Carbon intensity (g CO2/bushel') ## Warning: Removed 11 rows containing missing values or values outside the scale range
## (`geom_point()`).
#4R ----
R_base |>
arrange(desc(v2), desc(v27)) |>
ggplot(aes(v2, v27)) +
geom_point() +
labs(
title = 'Farm size and Feedstock CI at Baseline 4R', subtitle = '** Feedstock CI w/ SOC',
x = 'Farm size (acres)', y = 'Carbon intensity (g CO2/bushel')## Warning: Removed 11 rows containing missing values or values outside the scale range
## (`geom_point()`).
R_cov |>
arrange(desc(v2), desc(v27)) |>
ggplot(aes(v2, v27)) +
geom_point() +
labs(
title = 'Farm size and Feedstock CI at 4R w/ cover crops', subtitle = '** Feedstock CI w/ SOC',
x = 'Farm size (acres)', y = 'Carbon intensity (g CO2/bushel')## Warning: Removed 11 rows containing missing values or values outside the scale range
## (`geom_point()`).
R_m |>
arrange(desc(v2), desc(v27)) |>
ggplot(aes(v2, v27)) +
geom_point() +
labs(
title = 'Farm size and Feedstock CI at 4R w/ manure', subtitle = '** Feedstock CI w/ SOC',
x = 'Farm size (acres)', y = 'Carbon intensity (g CO2/bushel')## Warning: Removed 11 rows containing missing values or values outside the scale range
## (`geom_point()`).
R_r |>
arrange(desc(v2), desc(v27)) |>
ggplot(aes(v2, v27)) +
geom_point() +
labs(
title = 'Farm size and Feedstock CI under reduced till 4R', subtitle = '** Feedstock CI w/ SOC',
x = 'Farm size (acres)', y = 'Carbon intensity (g CO2/bushel')## Warning: Removed 11 rows containing missing values or values outside the scale range
## (`geom_point()`).
R_cont |>
arrange(desc(v2), desc(v27)) |>
ggplot(aes(v2, v27)) +
geom_point() +
labs(
title = 'Farm size and Feedstock CI under cont no till 4R', subtitle = '** Feedstock CI w/ SOC',
x = 'Farm size (acres)', y = 'Carbon intensity (g CO2/bushel')## Warning: Removed 11 rows containing missing values or values outside the scale range
## (`geom_point()`).
# EEF ----
EEF_base |>
arrange(desc(v2), desc(v27)) |>
ggplot(aes(v2, v27)) +
geom_point() +
labs(
title = 'Farm size and Feedstock CI at Baseline EEF', subtitle = '** Feedstock CI w/ SOC',
x = 'Farm size (acres)', y = 'Carbon intensity (g CO2/bushel')## Warning: Removed 11 rows containing missing values or values outside the scale range
## (`geom_point()`).
EEF_cov |>
arrange(desc(v2), desc(v27)) |>
ggplot(aes(v2, v27)) +
geom_point() +
labs(
title = 'Farm size and Feedstock CI at EEF w/ cover crops', subtitle = '** Feedstock CI w/ SOC',
x = 'Farm size (acres)', y = 'Carbon intensity (g CO2/bushel')## Warning: Removed 11 rows containing missing values or values outside the scale range
## (`geom_point()`).
EEF_m |>
arrange(desc(v2), desc(v27)) |>
ggplot(aes(v2, v27)) +
geom_point() +
labs(
title = 'Farm size and Feedstock CI at EEF w/ manure', subtitle = '** Feedstock CI w/ SOC',
x = 'Farm size (acres)', y = 'Carbon intensity (g CO2/bushel')## Warning: Removed 11 rows containing missing values or values outside the scale range
## (`geom_point()`).
EEF_r |>
arrange(desc(v2), desc(v27)) |>
ggplot(aes(v2, v27)) +
geom_point() +
labs(
title = 'Farm size and Feedstock CI under reduced till EEF', subtitle = '** Feedstock CI w/ SOC',
x = 'Farm size (acres)', y = 'Carbon intensity (g CO2/bushel') ## Warning: Removed 11 rows containing missing values or values outside the scale range
## (`geom_point()`).
EEF_cont |>
arrange(desc(v2), desc(v27)) |>
ggplot(aes(v2, v27)) +
geom_point() +
labs(
title = 'Farm size and Feedstock CI under cont no till EEF', subtitle = '** Feedstock CI w/ SOC',
x = 'Farm size (acres)', y = 'Carbon intensity (g CO2/bushel') ## Warning: Removed 11 rows containing missing values or values outside the scale range
## (`geom_point()`).
#### Explore EEF cont no till relationship compared with 4R manure
model <- lm(v2 ~ v27, data = EEF_cont)
summary(model)##
## Call:
## lm(formula = v2 ~ v27, data = EEF_cont)
##
## Residuals:
## Min 1Q Median 3Q Max
## -455.51 -127.21 -32.69 101.20 519.73
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 435.70245 63.22401 6.891 6.74e-09 ***
## v27 0.02423 0.01656 1.463 0.149
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 212.3 on 53 degrees of freedom
## (11 observations deleted due to missingness)
## Multiple R-squared: 0.03883, Adjusted R-squared: 0.02069
## F-statistic: 2.141 on 1 and 53 DF, p-value: 0.1493model_r <- lm(v2 ~ v27, data = R_m)
summary(model_r)##
## Call:
## lm(formula = v2 ~ v27, data = R_m)
##
## Residuals:
## Min 1Q Median 3Q Max
## -448.06 -139.76 -34.09 124.84 541.05
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 641.51699 133.31406 4.812 1.28e-05 ***
## v27 -0.07515 0.07930 -0.948 0.348
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 214.7 on 53 degrees of freedom
## (11 observations deleted due to missingness)
## Multiple R-squared: 0.01666, Adjusted R-squared: -0.001891
## F-statistic: 0.8981 on 1 and 53 DF, p-value: 0.3476