Incidents and performance of healthy tubers and rotten potato field infested with naturally Ralstonia solanacearum Race 3/Bv 2A, after application of inorganic amendments and a rotation crop in Huanuco Peru, 2006.
Application of inorganic amendment and crop rotation to control bacterial wilt of the potato (MBP).
data(Hco2006)
An object of class list with two elements: wilt and yield
@details
amendment amendment
crop crop
block block
plant number platn
wilt_percent wilt percentage at 60 days
health kg/8m2, 20 plants
rot kg/8m2, 20 plants
Experimental field, 2006. Data Kindly provided by Pedro Aley.
International Potato Center. CIP - Lima Peru.
#> List of 2 #> $ wilt :'data.frame': 1347 obs. of 5 variables: #> ..$ amendment : Factor w/ 3 levels "0C","2C1Z","4C": 1 1 1 1 1 1 1 1 1 1 ... #> ..$ crop : Factor w/ 3 levels "Cabbage","Corn",..: 3 3 3 3 3 3 3 3 3 3 ... #> ..$ block : Factor w/ 3 levels "I","II","III": 1 1 1 1 1 1 1 1 1 1 ... #> ..$ plant : num [1:1347] 1 2 3 4 5 6 7 8 9 10 ... #> ..$ wilt_percent: num [1:1347] 0 0 0 0 50 0 100 0 0 0 ... #> $ yield:'data.frame': 27 obs. of 5 variables: #> ..$ amendment: Factor w/ 3 levels "0C","2C1Z","4C": 1 1 1 1 1 1 1 1 1 3 ... #> ..$ crop : Factor w/ 3 levels "Cabbage","Corn",..: 3 3 3 1 1 1 2 2 2 3 ... #> ..$ block : Factor w/ 3 levels "I","II","III": 1 2 3 1 2 3 1 2 3 1 ... #> ..$ health : num [1:27] 3.9 11.4 13.1 8.2 13.1 14.5 4.5 13.4 14.4 3.8 ... #> ..$ rot : num [1:27] 1.5 2.6 2.8 1.5 2.15 0.5 1.3 3.6 4.7 0.8 ...wilt<-Hco2006$wilt yield<-Hco2006$yield means <- tapply.stat(wilt[,5],wilt[,1:3],function(x) mean(x,na.rm=TRUE)) names(means)[4]<-"wilt_percent" model <- aov(wilt_percent ~ block + crop, means) anova(model)#> Analysis of Variance Table #> #> Response: wilt_percent #> Df Sum Sq Mean Sq F value Pr(>F) #> block 2 569.9 284.93 1.2056 0.3186 #> crop 2 496.1 248.04 1.0495 0.3670 #> Residuals 22 5199.5 236.34cv.model(model)#> [1] 69.41147yield<-yield[order(paste(yield[,1],yield[,2],yield[,3])),] correlation(means[,4],yield[,4],method="spearman")#> #> Spearman's rank correlation rho #> #> data: means[, 4] and yield[, 4] #> p-value = 0.006544646 #> alternative hypothesis: true rho is not equal to 0 #> sample estimates: #> rho #> -0.5102417