Using Crop Modelling to Improve Chickpea Adaptation in Variable Environments

Abstract

Chickpea (Cicer arietinum L.) is the second most important food legume crop grown in the arid and semi-arid regions of the world. The crop is valued as an important source of protein, starch and fibre, as well as for its ability to improve health of the farming systems through various rotational benefits. However, the realisation of the full potential benefit of the crop to farming systems and its contributions towards ensuring global food security have been undermined by its less than 1 t/ha average yield. For this crop to remain competitive in farming systems and to meet its projected annual demand of over 20 million tons by 2050, a substantial increase in yield is required. The ongoing efforts to improve chickpea yield through breeding and agronomy therefore need to be complemented by new approaches. One such approach could be to use chickpea crop models in novel ways to optimise relative contributions of genotype (G), environment (E) and management (M) to yield. In the approach reviewed here, a crop model is applied to characterise the target population of environments for major abiotic stresses including drought and thermal environments, define homogenous agro-ecological regions and investigate GxExM interactions in silico to guide field testing and increase resource use efficiency. Such an approach is designed to (1) allow more effective assessment of the types of major abiotic constraints affecting yield and their frequencies and to (2) determine the value of adaptive traits in mitigating adverse effects of these environmental constraints on yield. The approach could also assist (3) in defining appropriate selection environments for wide and specific adaptation, (4) geographically visualise the adaptation domains of specific varieties and (5) create relevant managed environments to speed up breeding. We believe that crop models can thus play a beneficial role in chickpea improvement.