Anexo 3 - Output do R

---

DADOS

#Curvas
curvas=read.csv("C:/Users/Samsung/Documents/Doutorado CENA_USP/Aulas e disciplinas/Metodos agronomicos/Pratica 1/curva.csv")
str(curvas)

'data.frame':   5 obs. of  5 variables:
 $ Score1 : int  1 2 3 4 5
 $ Prato1 : int  6 8 29 44 35
 $ Altura2: int  6 14 26 37 56
 $ MF1    : int  1296 2016 5728 6536 7780
 $ MF2    : int  1528 2024 4700 6188 7484

curvas

#Metodos indiretos
indireto=read.csv("C:/Users/Samsung/Documents/Doutorado CENA_USP/Aulas e disciplinas/Metodos agronomicos/Pratica 1/indireto.csv")
str(indireto)

'data.frame':   100 obs. of  3 variables:
 $ Estacoes: int  1 2 3 4 5 6 7 8 9 10 ...
 $ Nota1   : num  1.5 2.5 2 4 3.5 4 4 3.5 3 2.5 ...
 $ Altura2 : int  14 13 12 14 11 15 11 8 6 14 ...

indireto

#Metodos destrutivos
destrutivo=read.csv("C:/Users/Samsung/Documents/Doutorado CENA_USP/Aulas e disciplinas/Metodos agronomicos/Pratica 1/destrutivo.csv")
destrutivo$Metodo=as.factor(destrutivo$Metodo)
destrutivo$MF=as.numeric(destrutivo$MF)
str(destrutivo)

'data.frame':   14 obs. of  3 variables:
 $ ID    : chr  "Felipe" "Isaac" "Talita" "Vagner" ...
 $ Metodo: Factor w/ 2 levels "Coordenadas",..: 2 2 2 2 2 2 2 2 1 1 ...
 $ MF    : num  3832 5388 7492 5324 5552 ...

destrutivo

CURVAS DE CALIBRAÇÃO

Escore visual

score=lm(MF1~Score1,data=curvas)
summary(score)

Call:
lm(formula = MF1 ~ Score1, data = curvas)

Residuals:
     1      2      3      4      5 
 122.4 -906.4 1056.8  116.0 -388.8 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept)   -575.2      881.2  -0.653  0.56050   
Score1        1748.8      265.7   6.582  0.00714 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 840.2 on 3 degrees of freedom
Multiple R-squared:  0.9352,    Adjusted R-squared:  0.9136 
F-statistic: 43.32 on 1 and 3 DF,  p-value: 0.007137

library(ggplot2)

p1=ggplot(curvas,aes(y=MF1,x=Score1))+
  geom_smooth(color='black', method='lm', formula=y ~ x, se=F)+geom_point(size = 2)+
  scale_x_continuous(name="Escores visuais",breaks=seq(1,5,1))+
  scale_y_continuous(name=expression(paste("Massa de forragem (kg MS ",ha^-1,")")),breaks=seq(1000,8000,1000))+
  theme(axis.line = element_line(colour = "black", size = 0.7, linetype = "solid"),
  panel.background = element_rect(fill = "transparent"),
  axis.title.x = element_text(size = 14),
  axis.title.y = element_text(size = 14),
  axis.text.x = element_text(size = 12),
  axis.text.y = element_text(size = 12))+
  annotate(geom="text", x=2, y=8000, 
  label=expression(paste("y = 1748.8x - 575.2    ", R^2, " = 0.9352")), size= 5, color="black")+
  annotate(geom="text", x=2, y=7000, 
  label=expression(paste("EP = 840.2    P-value = 0.007")), size= 5, color="black")
p1

Disco medidor

prato=lm(MF1~Prato1,data=curvas)
summary(prato)

Call:
lm(formula = MF1 ~ Prato1, data = curvas)

Residuals:
       1        2        3        4        5 
 -431.50   -31.47   320.88 -1270.88  1412.97 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept)    767.6      973.8   0.788   0.4881  
Prato1         160.0       34.0   4.706   0.0182 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1140 on 3 degrees of freedom
Multiple R-squared:  0.8807,    Adjusted R-squared:  0.8409 
F-statistic: 22.14 on 1 and 3 DF,  p-value: 0.01816

p2=ggplot(curvas,aes(y=MF1,x=Prato1))+
  geom_smooth(color='black', method='lm', formula=y ~ x, se=F)+geom_point(size = 2)+
  scale_x_continuous(name="Leituras do prato",breaks=seq(0,45,5))+
  scale_y_continuous(name=expression(paste("Massa de forragem (kg MS ",ha^-1,")")),breaks=seq(1000,8000,1000))+
  theme(axis.line = element_line(colour = "black", size = 0.7, linetype = "solid"),
  panel.background = element_rect(fill = "transparent"),
  axis.title.x = element_text(size = 14),
  axis.title.y = element_text(size = 14),
  axis.text.x = element_text(size = 12),
  axis.text.y = element_text(size = 12))+
  annotate(geom="text", x=15, y=8000, 
  label=expression(paste("y = 160.0x + 767.6    ", R^2, " = 0.8807")), size= 5, color="black")+
  annotate(geom="text", x=15, y=7000, 
  label=expression(paste("EP = 1140    P-value = 0.0182")), size= 5, color="black")
p2

Altura

altura=lm(MF2~Altura2,data=curvas)
summary(altura)

Call:
lm(formula = MF2 ~ Altura2, data = curvas)

Residuals:
      1       2       3       4       5 
 -65.97 -594.13  545.64  625.42 -510.95 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept)   825.85     551.90   1.496  0.23145   
Altura2       128.02      16.77   7.632  0.00467 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 660.1 on 3 degrees of freedom
Multiple R-squared:  0.951, Adjusted R-squared:  0.9347 
F-statistic: 58.25 on 1 and 3 DF,  p-value: 0.00467

p3=ggplot(curvas,aes(y=MF2,x=Altura2))+
  geom_smooth(color='black', method='lm', formula=y ~ x, se=F)+geom_point(size = 2)+
  scale_x_continuous(name="Medidas de altura",breaks=seq(0,60,5))+
  scale_y_continuous(name=expression(paste("Massa de forragem (kg MS ",ha^-1,")")),breaks=seq(1000,8000,1000))+
  theme(axis.line = element_line(colour = "black", size = 0.7, linetype = "solid"),
  panel.background = element_rect(fill = "transparent"),
  axis.title.x = element_text(size = 14),
  axis.title.y = element_text(size = 14),
  axis.text.x = element_text(size = 12),
  axis.text.y = element_text(size = 12))+
  annotate(geom="text", x=20, y=8000, 
  label=expression(paste("y = 128.02x + 825.85    ", R^2, " = 0.951")), size= 5, color="black")+
  annotate(geom="text", x=20, y=7000, 
  label=expression(paste("EP = 660.1    P-value = 0.0047")), size= 5, color="black")
p3

MF POR METODOS INDIRETOS

Escore visual

indireto$MF1=(1748.8*indireto$Nota1)-575.2
indireto

#Escore médio 
psych::describeBy(indireto[,2])

Warning: no grouping variable requested

#MF média
psych::describeBy(indireto[,4])

Warning: no grouping variable requested

Altura

indireto$MF2=(128.02*indireto$Altura2)+825.85
indireto

#Altura média 
psych::describeBy(indireto[,3])

Warning: no grouping variable requested

#MF média
psych::describeBy(indireto[,5])

Warning: no grouping variable requested

Disco medidor

Somatoria.100.leituras=25139-22722
Somatoria.100.leituras

[1] 2417

Valor.medio=Somatoria.100.leituras/100
Valor.medio

[1] 24.17

MF.disco=(160*Valor.medio)+767.6
MF.disco

[1] 4634.8

MF POR METODOS DESTRUTIVOS

a1=lm(MF~Metodo, data=destrutivo)
anova(a1)

Analysis of Variance Table

Response: MF
          Df   Sum Sq  Mean Sq F value   Pr(>F)   
Metodo     1 21761282 21761282  18.448 0.001041 **
Residuals 12 14155285  1179607                    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

medias=emmeans(a1,~Metodo)
pairs(medias)

 contrast               estimate  SE df t.ratio p.value
 Coordenadas - Dirigido    -2519 587 12 -4.295  0.0010 

multcomp::cld(medias)

 Metodo      emmean  SE df lower.CL upper.CL .group
 Coordenadas   3007 443 12     2041     3973  1    
 Dirigido      5526 384 12     4689     6363   2   

Confidence level used: 0.95 
significance level used: alpha = 0.05 

#COORDENADAS
psych::describeBy(destrutivo[c(9:14),3])

Warning: no grouping variable requested

#DIRIGIDO
psych::describeBy(destrutivo[c(1:8),3])

Warning: no grouping variable requested