This paper introduces a novel hybrid approach, combining machine learning algorithms with feature selection, for efficient modelling and forecasting of complex phenomenon governed by multifactorial and nonlinear behaviours, such as crop yield. We have attempted to harness the benefits of the soft computing algorithm multivariate adaptive regression spline (MARS) for feature selection coupled with support vector regression (SVR) and artificial neural network (ANN) for efficiently mapping the relationship between the predictors and predictand variables using the MARS-ANN and MARS-SVR hybrid frameworks. The performances of the algorithms are com-pared on different fit statistics such as RMSE, MAD, MAPE, etc., using numeric agronomic traits of 518 lentil genotypes to predict grain yield. The proposed MARS-based hybrid models outperformed individual models such as MARS, SVR and ANN. This is largely due to the enhanced feature ex-traction capability of the MARS model coupled with the nonlinear adaptive learning ability of ANN and SVR. The superiority of the proposed hybrid models MARS-ANN and MARS-SVM in terms of model building and generalisation ability was demonstrated.

Cultivars with efficient root systems play a major role in enhancing resource use efficiency, particularly water absorption, and thus in drought tolerance. In this study, a diverse wheat association panel of 136 wheat accessions including mini core subset was genotyped using Axiom 35k Breeders’ Array to identify genomic regions associated with seedling stage root architecture and shoot traits using multi-locus genome-wide association studies (ML-GWAS). The association panel revealed a wide variation of 1.5- to 50-fold and were grouped into six clusters based on 15 traits. Six different ML-GWAS models revealed 456 significant quantitative trait nucleotides (QTNs) for various traits with phenotypic variance in the range of 0.12–38.60%. Of these, 87 QTNs were repeatedly detected by two or more models and were considered reliable genomic regions for the respective traits. Among these QTNs, eleven were associated with average diameter and nine each for second order lateral root number (SOLRN), root volume (RV) and root length density (RLD). A total of eleven genomic regions were pleiotropic and each controlled two or three traits. Some important candidate genes such as Formin homology 1, Ubiquitin-like domain superfamily and ATP-dependent 6-phosphofructokinase were identified from the associated genomic regions. The genomic regions/genes identified in this study could potentially be targeted for improving root traits and drought tolerance in wheat.