Field-level rice yield estimations under different farm practices using the crop simulation model for better yield

Abstract

Crop yield estimation is essential for decision-making systems and insurance policy makers. Numerous methodologies for yield estimation have been developed, encompassing crop models, remote sensing techniques, and empirical equations. Each approach holds unique limitations and advantages. The primary aim of this study was to assess the accuracy of the DSSAT (Decision Support System for Agro Technology Transfer) model in predicting rice yields and LAI (Leaf Area Index) across various management methods. Additionally, the study sought to identify the optimal management practice for attaining higher yields. Crop models facilitate the expeditious evaluation of management strategies aimed at improving crop yield and analyzing the balance between production, resource efficiency, and environmental impacts. The study region selected for analysis is Karimnagar district of Telangana state. DSSAT has been chosen as the preferred tool due to its high efficiency in evaluating crop yield. The model's simulated yield was compared to the observed yield obtained from crop-cutting experiments. The results indicate a correlation of 0.81 and 0.85 between observed and simulated yields, as well as between model LAI and yield. An observation was made regarding a discrepancy between predicted and actual yields, which can be attributed to biotic stress. However, it should be noted that the current model does not account for this factor. The observed average yield was 5200 kg ha-1, whereas the projected yield was 5400 kg ha-1. The findings indicate that the model's performance is influenced by both the timing of sowing and the amount of nitrogen applied. The findings indicate that the DSSAT model has demonstrated a high level of accuracy in predicting both yields and leaf area index (LAI) across various management strategies. This study showcases the potential use of crop simulation models as a technology-driven tool to identify the most effective management strategies for rice production.