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Least absolute shrinkage and selection operator regression used to select important features when predicting wheat yield from various genotype groups

Published online by Cambridge University Press:  06 November 2024

Muhuddin Rajin Anwar Open the ORCID record for Muhuddin Rajin Anwar [Opens in a new window] ,
Livinus Emebiri ,
Ryan H. L. Ip ,
David J. Luckett ,
Yashvir S. Chauhan  and
Ketema T. Zeleke
Muhuddin Rajin Anwar*
Affiliation:
NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW, Australia Gulbali Institute (Agriculture, Water and Environment), Charles Sturt University, Wagga Wagga, NSW, Australia
Livinus Emebiri
Affiliation:
NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW, Australia
Ryan H. L. Ip
Affiliation:
Department of Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand School of Computing and Mathematics, Charles Sturt University, Wagga Wagga, NSW, Australia
David J. Luckett
Affiliation:
Gulbali Institute (Agriculture, Water and Environment), Charles Sturt University, Wagga Wagga, NSW, Australia
Yashvir S. Chauhan
Affiliation:
Department of Agriculture and Fisheries (DAF), Kingaroy, QLD, Australia
Ketema T. Zeleke
Affiliation:
Gulbali Institute (Agriculture, Water and Environment), Charles Sturt University, Wagga Wagga, NSW, Australia School of Agricultural, Environmental and Veterinary Sciences, Charles Sturt University, Wagga Wagga, NSW, Australia
*
Corresponding author: Muhuddin Rajin Anwar; Email: muhuddin.anwar@dpi.nsw.gov.au
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Abstract

Bread wheat and durum wheat genotypes were grown in field experiments at two locations in New South Wales, Australia across several years and using two sowing times (‘early’ v. ‘late’). Genotypes were grouped based on genetic similarity. Grain yield, grain size, soil characteristics and daily weather data were collected. The weather data were used to calculate water and heat stress indices for four key growth periods around flowering. Least absolute shrinkage and selection operator (LASSO) was used to predict grain yield and to identify the most influential features (a combination of index and growth period). A novel approach involving the crop water supply–demand ratio effectively summarized water relations during growth. LASSO predicted grain yield quite well (adjusted R2 from 0.57 to 0.98), especially in a set of durum genotypes. However, the addition of other important variables such as lodging score, disease incidence, weed incidence and insect damage could have improved modelling results. Growth period 2 (30 days pre-flowering up to flowering) was the most sensitive for yield loss from heat stress and water stress for most features. Although one group of bread wheat genotypes was more sensitive to water stress (drought) in period 3 (20 days pre-flowering to 10 days post-flowering). Evapotranspiration was a significant positive feature but only in the vegetative phase (pre-flowering, period 1). This study confirms the usefulness of LASSO modelling as a technique to make predictions that could be used to identify genotypes that are suitable candidates for further investigation by breeders for their stress-tolerance ability.

Keywords

crop modellingheat stressshrinkage estimatorTriticum aestivumwater stress
Type
Crops and Soils Research Paper
Information
The Journal of Agricultural Science , Volume 162 , Issue 3 , June 2024 , pp. 245 - 259
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Copyright © The Author(s), 2024. Published by Cambridge University Press

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