ANÁLISIS RENDIMIENTO PAPA DIACOL CAPIRO R12 FCULTAD UNAL OCTUBRE 2020 - JUNIO 2021

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Se realiza cosecha y toma de datos de rendimiento del cultivo de papa facultad de UNAL . Por cada tratamiento se tomó un 1 m^2 , y se separaron en tres tamaños, de la siguiente forma: los tubérculos recolectados, Grande (cero más de 7,5 cm), mediana ( pareja entre 4,5 – 7,5 cm ) y pequeña (Riche menor a 4,5 cm) también se registró número de tubérculos por cada categoría,

library(XLConnect)

package 㤼㸱XLConnect㤼㸲 was built under R version 4.0.5XLConnect 1.0.3 by Mirai Solutions GmbH [aut],
  Martin Studer [cre],
  The Apache Software Foundation [ctb, cph] (Apache POI),
  Graph Builder [ctb, cph] (Curvesapi Java library)
https://mirai-solutions.ch
https://github.com/miraisolutions/xlconnect

library(agricolae)

package 㤼㸱agricolae㤼㸲 was built under R version 4.0.5

library(readxl)

package 㤼㸱readxl㤼㸲 was built under R version 4.0.5

library(readxl)
Rendimiento_papa_facultad_t_ha_1 <- read_excel("C:/Users/Eduardo/Downloads/Rendimiento papa facultad t-ha-1.xlsx")
View(Rendimiento_papa_facultad_t_ha_1)

Con los datos tomados de rendimiento, se siguió el procedimiento encontrado en Seminario Cunya,(2017) en el cual el rendimiento por hectárea lo obtuvieron mediante la ecuación de Hay y Walker (1989, 190) que considera tres componentes básicos del rendimiento.

Para este ensayo se realizó de la siguiente forma : Rendimiento (kg∙ha-1) = 27 778 plantas x N° tubérculos por planta x peso promedio del tubérculo fresco (kg). Finalmente, el rendimiento se expresó en toneladas por hectárea (t∙ha-1).

(Seminario Cunya,2017)

Datos<- read_excel("C:/Users/Eduardo/Downloads/Rendimiento papa facultad t-ha-1.xlsx")
attach(Datos)
names(Datos)

[1] "Tratamientos" "Tratamineto"  "planta"       "Riche"        "Pareja"       "Cero"        
[7] "total"       

str(Datos)

tibble [25 x 7] (S3: tbl_df/tbl/data.frame)
 $ Tratamientos: chr [1:25] "C- (0 L/ha TV) 75% FC" NA NA NA ...
 $ Tratamineto : num [1:25] 1 1 1 1 1 2 2 2 2 2 ...
 $ planta      : num [1:25] 1 2 3 4 5 1 2 3 4 5 ...
 $ Riche       : num [1:25] 4.28 1.56 3 4.33 1.33 ...
 $ Pareja      : num [1:25] 12.61 16.94 13.28 17.5 2.78 ...
 $ Cero        : num [1:25] 13.83 19.11 10.72 0 3.78 ...
 $ total       : num [1:25] 30.72 37.61 27 21.83 7.89 ...

plan<-factor(planta)
Trat<-factor(Tratamineto)

Modelo<-lm(Riche~Trat+plan)
ANOVA<-aov(Modelo)
summary(ANOVA)

            Df Sum Sq Mean Sq F value Pr(>F)  
Trat         4  12.13   3.032   3.541 0.0298 *
plan         4  13.67   3.417   3.990 0.0197 *
Residuals   16  13.70   0.856                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

tratam<- LSD.test(y = ANOVA, trt = "Trat",group = T, console = T)

Study: ANOVA ~ "Trat"

LSD t Test for Riche 

Mean Square Error:  0.856418 

Trat,  means and individual ( 95 %) CI

Alpha: 0.05 ; DF Error: 16
Critical Value of t: 2.119905 

least Significant Difference: 1.240764 

Treatments with the same letter are not significantly different.

bar.group(x = tratam$groups, 
          ylim=c(0,6),
          main=" Comparación rendimiento papa tamaño Riche ",
          xlab="Tratamiento ",
          ylab="Rendimiento (ton/ha) ",
          col="grey")

Modelo<-lm(Pareja~Trat+plan)
ANOVA<-aov(Modelo)
summary(ANOVA)

            Df Sum Sq Mean Sq F value Pr(>F)  
Trat         4   46.6   11.65   0.463 0.7619  
plan         4  241.2   60.31   2.396 0.0935 .
Residuals   16  402.7   25.17                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

tratam<- LSD.test(y = ANOVA, trt = "Trat",group = T, console = T)

Study: ANOVA ~ "Trat"

LSD t Test for Pareja 

Mean Square Error:  25.16957 

Trat,  means and individual ( 95 %) CI

Alpha: 0.05 ; DF Error: 16
Critical Value of t: 2.119905 

least Significant Difference: 6.726426 

Treatments with the same letter are not significantly different.

bar.group(x = tratam$groups, 
          ylim=c(0,20),
          main=" Comparación rendimiento papa tamaño Pareja ",
          xlab="Tratamiento ",
          ylab="Rendimiento (ton/ha) ",
          col="grey")

Modelo<-lm(Cero~Trat+plan)
ANOVA<-aov(Modelo)
summary(ANOVA)

            Df Sum Sq Mean Sq F value   Pr(>F)    
Trat         4   85.9   21.48   1.769    0.184    
plan         4  605.8  151.45  12.475 8.52e-05 ***
Residuals   16  194.2   12.14                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

tratam<- LSD.test(y = ANOVA, trt = "Trat",group = T, console = T)

Study: ANOVA ~ "Trat"

LSD t Test for Cero 

Mean Square Error:  12.14032 

Trat,  means and individual ( 95 %) CI

Alpha: 0.05 ; DF Error: 16
Critical Value of t: 2.119905 

least Significant Difference: 4.671555 

Treatments with the same letter are not significantly different.

bar.group(x = tratam$groups, 
          ylim=c(0,13),
          main=" Comparación rendimiento papa tamaño Cero ",
          xlab="Tratamiento ",
          ylab="Rendimiento (ton/ha) ",
          col="grey")

Modelo<-lm(total~Trat+plan)
ANOVA<-aov(Modelo)
summary(ANOVA)

            Df Sum Sq Mean Sq F value   Pr(>F)    
Trat         4  102.4   25.60   1.203    0.348    
plan         4 1191.2  297.81  13.995 4.31e-05 ***
Residuals   16  340.5   21.28                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

tratam<- LSD.test(y = ANOVA, trt = "Trat",group = T, console = T)

Study: ANOVA ~ "Trat"

LSD t Test for total 

Mean Square Error:  21.28046 

Trat,  means and individual ( 95 %) CI

Alpha: 0.05 ; DF Error: 16
Critical Value of t: 2.119905 

least Significant Difference: 6.184962 

Treatments with the same letter are not significantly different.

bar.group(x = tratam$groups, 
          ylim=c(0,33),
          main=" Comparación rendimiento papa total ",
          xlab="Tratamiento ",
          ylab="Rendimiento (ton/ha) ",
          col="grey")

library(multcompView)

package 㤼㸱multcompView㤼㸲 was built under R version 4.0.5

library(daewr)

package 㤼㸱daewr㤼㸲 was built under R version 4.0.5Registered S3 method overwritten by 'DoE.base':
  method           from       
  factorize.factor conf.design

Modelo<-lm(Riche~Trat+plan)
ANOVA<-aov(Modelo)
summary(ANOVA)

            Df Sum Sq Mean Sq F value Pr(>F)  
Trat         4  12.13   3.032   3.541 0.0298 *
plan         4  13.67   3.417   3.990 0.0197 *
Residuals   16  13.70   0.856                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

TUKEY <- TukeyHSD(x=ANOVA, 'Trat', conf.level=0.95)
summary(TUKEY)

     Length Class  Mode   
Trat 40     -none- numeric

tratam<- LSD.test(y = ANOVA, trt = "Trat",group = T, console = T)

Study: ANOVA ~ "Trat"

LSD t Test for Riche 

Mean Square Error:  0.856418 

Trat,  means and individual ( 95 %) CI

Alpha: 0.05 ; DF Error: 16
Critical Value of t: 2.119905 

least Significant Difference: 1.240764 

Treatments with the same letter are not significantly different.

bar.group(x = tratam$groups, 
          ylim=c(0,6),
          main=" Comparación rendimiento papa Riche por medio del método LSD",
          xlab="Tratamiento ",
          ylab="Rendimiento (ton/ha) ",
          col="grey")

Modelo<-lm(Pareja~Trat+plan)
ANOVA<-aov(Modelo)
summary(ANOVA)

            Df Sum Sq Mean Sq F value Pr(>F)  
Trat         4   46.6   11.65   0.463 0.7619  
plan         4  241.2   60.31   2.396 0.0935 .
Residuals   16  402.7   25.17                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

TUKEY <- TukeyHSD(x=ANOVA, 'Trat', conf.level=0.95)
summary(TUKEY)

     Length Class  Mode   
Trat 40     -none- numeric

tratam<- LSD.test(y = ANOVA, trt = "Trat",group = T, console = T)

Study: ANOVA ~ "Trat"

LSD t Test for Pareja 

Mean Square Error:  25.16957 

Trat,  means and individual ( 95 %) CI

Alpha: 0.05 ; DF Error: 16
Critical Value of t: 2.119905 

least Significant Difference: 6.726426 

Treatments with the same letter are not significantly different.

bar.group(x = tratam$groups, 
          ylim=c(0,20),
          main=" Comparación rendimiento papa Pareja por medio del método LSD",
          xlab="Tratamiento ",
          ylab="Rendimiento (ton/ha) ",
          col="grey")

Modelo<-lm(Cero~Trat+plan)
ANOVA<-aov(Modelo)
summary(ANOVA)

            Df Sum Sq Mean Sq F value   Pr(>F)    
Trat         4   85.9   21.48   1.769    0.184    
plan         4  605.8  151.45  12.475 8.52e-05 ***
Residuals   16  194.2   12.14                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

TUKEY <- TukeyHSD(x=ANOVA, 'Trat', conf.level=0.95)
summary(TUKEY)

     Length Class  Mode   
Trat 40     -none- numeric

tratam<- LSD.test(y = ANOVA, trt = "Trat",group = T, console = T)

Study: ANOVA ~ "Trat"

LSD t Test for Cero 

Mean Square Error:  12.14032 

Trat,  means and individual ( 95 %) CI

Alpha: 0.05 ; DF Error: 16
Critical Value of t: 2.119905 

least Significant Difference: 4.671555 

Treatments with the same letter are not significantly different.

bar.group(x = tratam$groups, 
          ylim=c(0,13),
          main=" Comparación rendimiento papa Cero por medio del método LSD",
          xlab="Tratamiento ",
          ylab="Rendimiento (ton/ha) ",
          col="grey")

Modelo<-lm(total~Trat+plan)
ANOVA<-aov(Modelo,data=Datos)
summary(ANOVA)

            Df Sum Sq Mean Sq F value   Pr(>F)    
Trat         4  102.4   25.60   1.203    0.348    
plan         4 1191.2  297.81  13.995 4.31e-05 ***
Residuals   16  340.5   21.28                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

TUKEY <- TukeyHSD(x=ANOVA, 'Trat', conf.level=0.95)
summary(TUKEY)

     Length Class  Mode   
Trat 40     -none- numeric

tratam<- LSD.test(y = ANOVA, trt = "Trat",group = T, console = T)

Study: ANOVA ~ "Trat"

LSD t Test for total 

Mean Square Error:  21.28046 

Trat,  means and individual ( 95 %) CI

Alpha: 0.05 ; DF Error: 16
Critical Value of t: 2.119905 

least Significant Difference: 6.184962 

Treatments with the same letter are not significantly different.

bar.group(x = tratam$groups, 
          ylim=c(0,34),
          main=" Comparación rendimiento papa total por medio del método LSD",
          xlab="Tratamiento ",
          ylab="Rendimiento (ton/ha) ",
          col="grey")

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