Automation of the Dssat Crop Growth Simulation Model

Abstract

<p><strong>Abstract.</strong> Crop Simulation Models (CSM) simulate the growth, development, and yield of crops using various inputs such as soil water, carbon and nitrogen processes, and management practices. DSSAT (Decision Support System for Agrotechnology Transfer) is a software program that comprises dynamic crop growth simulation models for over 42 crops. It incorporates modules for crop, soil, and weather to simulate long-term outcomes of crop management strategies. DSSAT-CSM requires various data for model operation. This includes data on the site where the model is to be operated, on the daily weather during the growth cycle, on the characteristics of the soil at the beginning of the growing cycle or crop sequence, and on the management of the crop. Acquisition of the data and providing the data to the DSSAT model is tedious and time-consuming as each individual value has to be manually entered. Additionally, crop simulation models can only be run for specific points and not for entire locations. Sometimes site-specific data especially weather data cannot be obtained. The output thus produced is difficult to analyze spatially at a large scale. The main purpose of this paper is to take the required dataset directly from spatial data. This is done by dividing locations into grids and taking the data from each grid. Python scripts are then used to convert this data into crop model format which is then run through DSSAT on an individual basis. The output thus obtained is be entered back into their respective grids as spatial data.</p>

Synergy between digital soil mapping and crop modeling: influence of soil data on sugarcane attainable yield

“…Process-based crop models simulate plant growth, development, and yield, according to the genotype, weather, soil, and crop management (Jones et al, 2003(Jones et al, , 2017. These models have been widely applied to understand the impacts of climate variability and change in agricultural production (Karthikeyan et al, 2020;Liang et al, 2020;Sachin et al, 2019), to rationalize resources in precision agriculture (Thorp et al, 2008) and to support improvements in sustainable agricultural practices (Miao et al, 2006;Sentelhas et al, 2015). They are usually employed in site-specific evaluations (i.e., simulations considering only a pair of geographic coordinates) at a field-scale.…”