Quantifying the Relationship between Adaptive Traits and Agro-climatic Conditions
Abstract
Background & Aim: Durum wheat is an economically important and regularly eaten food for billions of people in the world. In the International Center for Agriculture Research in the 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.
Method: Ordinary multiple linear regression with stepwise variable selection method on the complete data set, 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. Each of the models are fitted on Days to Heading and Days to Maturity response variables with 57 predictor variables, independently. Ordinary least square and weighted least square estimation methods were used.
Result: 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 both Days to Heading and Days to Maturity using both ordinary and shrinkage based models. It is revealed that the prediction from the lasso based model is not that much reasonable. Furthermore, for the Days to Heading showed that there seems better prediction as their predicted value increase continuously as a function of the actual values though there is considerable variability.
Conclusion: In conclusion, inferences and predictions by the ordinary MLR models are not trusted due to the presence of multicollinearity, and violation of some model assumptions. 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 Days to Heading.
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| Files | ||
| Issue | Vol 5 No 2 (2019) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/jbe.v5i2.2343 | |
| Keywords | ||
| Cross-validation Mean Square Error MLR Penalized Methods Lasso Elastic net Bias-Variance Trade-off Weighted Least Square | ||
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