Exploring DSSAT Model Genetic Coefficient Estimation Methodologies for Chickpea in Bundelkhand Region of Uttar Pradesh, India

Abstract

In modern crop production, essential factors that contribute to narrowing yield gaps and minimizing production costs include making informed decisions about the selection of plant varieties, determining optimal sowing dates, determining appropriate plant populations, selecting suitable fertilizer rates, and implementing effective pest control methods. Two field experiments were conducted during the Rabi seasons of 2021 and 2022 at ICAR-Indian Institute of Pulses Research (IIPR), Kanpur using split-plot experimental design, where the main plots were three different sowing dates (20-25th October, November 10-15th, and 25th November-5th December), and the sub-plots were four chickpea cultivars (JG 16, RVG 202, IPC-07-66, and IPC-05-62), each with three replications. The genetic coefficients of the cultivars were estimated using both the iterative process (IP) and Generalized Likelihood Uncertainty Estimation (GLUE) methods in DSSAT v 4.7 to simulate the yields. Upon model validation, it was found that the average relative error (ARE) in predicting grain yield across the different sowing windows was between -25.7% to 29.1% when using the iterative process, while ARE was between -23.4% to 19% when using GLUE. The findings report more accurate simulations of chickpea growth and phenological development stages were recorded in normal sowings. And the model calibration suggest that GLUE provided superior estimates of genetic coefficients compared to the IP method. Therefore, it can be inferred that Glue is a more user-friendly and precise method.