The ability of CROPGRO-Tomato model to simulate the growth characteristics of Thomas F1 tomato cultivar grown under open field conditions

Abstract

There are few studies about the ability of CROPGRO-Tomato model to simulate tomato growth under field conditions as a function of both local weather and soil conditions. The aim of this work was to calibrate the CROPGRO-Tomato model, included in the Decision Support System for Agrotechnology Transfer (DSSAT) software, for the Thomas F1 indeterminate tomato cultivar grown under open field conditions at two locations in the Czech Republic with different soil and climate conditions. Additionally, this paper focuses on modelling the impact of compound weather events (CEs) on the growth characteristics of the hybrid field tomato variety. The genotype file, including the main parameters of crop phenology and plant growth, was adapted to the Thomas F1 indeterminate tomato cultivar. The CROPGRO-Tomato model was calibrated by inputting the soil characteristics, weather data and crop management data and then by adjusting the genetic coefficients to simulate the observed Leaf Area Index (LAI) and Above Ground Biomass (AGB) from transplanting to harvest under the farmers' field conditions. The comparison of the LAI simulated by the model and measured under field conditions showed adequate representation with the root mean square error of 0.86 and 1.11 m2/m2. Although there was a good fit for LAI and AGB between the simulated and measured data during the first part of the growing season, increasing differences were found in the growing season with cool-wet and/or hot-dry thresholds of CEs.

Climate Change Impacts Assessment Using Crop Simulation Model Intercomparison Approach in Northern Indo-Gangetic Basin of Bangladesh

“…The calibration outputs and the validation of different crop simulation models need to operate in different ways. Two statistical criteria were used to validate the model: the normalized root mean square error (NRMSE) and the root mean square error (RMSE) [ 37 ]. where s are the simulated values, o are the observed values, and n are the number of data points.…”