Quantifying the relationship between adaptive traits and agro-climatic conditions
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Teklezgi Gebre. (1/12/2018). Quantifying the relationship between adaptive traits and agro-climatic conditions.
Abstract
Durum wheat is an economically important and regularly eaten food for billions of people
in the world. Consequently, wheat breeders over the past century have increased
the productivity and adaptability via strong selection applied to genes controlling agronomical
important traits. In the International Center for Agriculture Research in Dry
Areas (ICARDA), genbanks are using Focused Identification of the Germplasm Strategy
(FIGS) to find out and quantify relationships between agro-climatic conditions and the
presence of specific traits. Hence, the study is aimed to investigate the predictive value of
various types of long-term agro-climatic variables on the future values of different traits
as well as the association between these traits and those of the different agro-climatic
characteristics.
Ordinary multiple linear regression with stepwise variable selection method, and multiple
linear regression models with predictors selected by penalized methods with mean square
error cross-validation as a model selection criterion, are used to analyze 238 durum wheat
landraces which were chosen from the International Center for Agriculture Research in
the Dry Areas (ICARDA) genebanks. Each of the models are fitted on Days to Heading,
Days to Maturity, Plant Height, Grain Weight and Thousand Kernel Weight response
variables with 57 predictor variables, independently. The penalized based models used
data splitting into training on which the model is fitted and test data set on which the
fitted model is validated. Ordinary least square and weighted least square estimation
methods are also used for parameter estimation and prediction of post model selection.
Findings implied that there is high multicollinearity among the predictor variables. It
is found that there are some predictors which affect positively and some others affect
negatively for Days to Heading, Days to Maturity, Plant Height and Grain Weight using
both ordinary and shrinkage based models. Longitude affects significantly the Thousand
Kernel Weight using the ordinary MLR model, However, there is no significant predictor
which affects the Thousand Kernel Weight from the shrinkage based MLR models. But
longitude affects it significantly using the ordinary MLR model. It is revealed that model
with predictors selected by Elastic net method seem to have good prediction on the Plant
Height for both OLS and WLS estimation methods, while the prediction from the Lasso
based model is not that much reasonable. Furthermore, for the Days to Heading and
Grain Weight showed that there seems better prediction as their predicted values increase
continuously as a function of the actual values though there is considerable variability.
However, the Lasso based model used for Thousand Kernel Weight is not predicting well.
In conclusion, inferences and predictions by the ordinary MLR models are not trusted
due to the presence of multicollinearity in the model fitting, and violation of some model
assumptions after model fitting. However, predictions using the models with predictors
selected by the shrinkage methods may be better as the effects of the variability on these
methods are minimal. Moreover, the WLS methods might give more sensible predictions
than the OLS estimation methods. Better predictions were found on the Plant Height,
Days to Heading and grain Weight.