One of the important tasks we use crop model for is characterization of environments (EC) for the major abiotic production constraints (“envirotyping”). This approach helps us to understand the stress dynamics, its effects on the plant production and defines how to quantify the frequencies of particular stress types. Detailed description of drought stress characterization of post-rainy sorghum production systems in India is described in Kholová et al 2013. In principle, we have to gather sensible daily weather records (Tmax, Tmin, rainfall, solar radiation) and soil profile characteristics (depth, water holding capacity, nutrient content) from the area of interest. Subsequently, the point simulations of particular genotype with the common management practices (fertilizers dosage, irrigation, sowing time) are conducted using the mechanistic crop model (e.g. APSIM, specific genotypic coefficients can be derived using the “parameterization protocol” above). For each simulated cropping season, the trajectory of the crop water status index through the crop cycle is outputted (in sorghum module of APSIM this is water supply/demand ratio which can be outputted directly; S/D). The average of water S/D every 100 degree day intervals is calculated and the cluster analysis is performed for series of these averages to identify the major drought patterns experienced by the crop. The frequency of particular stress types is further expressed in relation to total number of the evaluated seasons.
1. R code allowing clustering analysis of trajectories -
### R script for Principal Components and Plots
### Each sheet in the Excel file needs to be saved as a .csv file, extra heading rows and tables need to be removed.
#ABSvalues <- read.table("ABSvaluesWWFIN2.csv",header=T,sep=",")
ABSvalues <- read.table("westraj.csv",header=T,sep=",",dec='.')
ABSvalues <- na.omit(ABSvalues) #PCA does not like missing values
#For the PCA you need a matrix with only the trait values
#ABSvalues.mat <- ABSvalues[,10:21] #This means ALL rows (ie [,) and colums 10 - 21 (ie ,10:21)
ABSvalues.mat <- ABSvalues[,6:37] #This means ALL rows (ie [,) and colums 7 - 18 (ie ,10:21)
#dimnames(ABSvalues.mat)[] <- ABSvalues$stg.shortcut.trt #puts the names in to be used as labels
dimnames(ABSvalues.mat)[] <- ABSvalues$name.shortcut.trt #puts the names in to be used as labels
For us gems means GEMS, or G*E*M*S (genotype by environment by management by society) interactions, i.e. the fact that crop yields results from complex biophysical interactions while acceptance depends on farmer/consumer preferences. This complexity becomes an opportunity when it is cracked into components that can be analysed, understood, predicted, and then used to prioritise research investments to maximise return. This is what we do, and this is when GEMS become gems!
For us gems means GEMS, or G*E*M*S (genotype by environment by management by society) interactions, i.e. the fact that crop yields results from complex biophysical interactions while acceptance depends on farmer/consumer preferences. This complexity becomes an opportunity when it is cracked into components that can be analyzed, understood, predicted, and then used to prioritize research investments to maximise return. This is what we do, and this is when GEMS become gems!
A crop performs in different ways in different sites, years and agronomic managements. These are called genotype-by-environment-by management(G*E*M) interactions, and they are a main challenge for breeders and agronomists. There is one more layer of interaction, even more complex: the society (S). Farmers and consumers have different desires, needs, expectations, and a cultivar that fits one may not fit the other (G*E*M*S interactions). The puzzle is complex and challenging but if its components are understood, specific interventions can be undertaken.For instance, breeding for a particular genotype (G, with particular physiological characteristics), for a particular environment (E, with a particular kind of drought pattern that requires a specific adaptive trait), in a particular management practice (M, for instance a sowing density, or a N fertilizer treatment), and targeted to particular farmer/consumer (S, for instance a genotype that produces a lot of rich stover for cattle ranchers) is the need of the hour.