Implementing nitrogen and its varieties to predict spring wheat productivity using DSSAT crop simulation modeling
Аннотация
Background. The research focuses on using the DSSAT model (v4.8.2) to simulate spring wheat (Triticum turgidum L.) performance in Russian climatic conditions, addressing the need to optimize farming strategies amongst agroecological challenges. Crop models are effective tools to predict crop productivity in different management options and climatic conditions.
Purpose. The study aimed to forecast spring wheat yield by evaluating the impact of four nitrogen (N) levels (0, 15, 30, 45 kg/ha) on leaf area index (LAI), stem weight, leaf weight, and tops weight for two spring wheat varieties (Radmira and Kanyuk) to predict the enhancement of agricultural productivity.
Materials and methods. Field experiments at the Research Institute of Agrochemistry in Barybino during summer 2024 utilized the DSSAT model (v4.8.2) to simulate spring wheat (Triticum turgidum L.) performance. Statistical analyses (t-tests, RMSE, Std. Dev.) compared observed and simulated data, confirming the model’s accuracy and significance.
Results. The DSSAT model accurately simulated outputs within observed ranges: LAI ranged from 0.42 (0 kg N/ha, V1) to 1.25 (30 kg N/ha, V1), and tops weight varied from 1704 kg/ha (45 kg N/ha) to 4248 kg/ha (30 kg N/ha). Simulated values showed less variability (LAI: 0.78–1.10; tops weight: 1749–3412 kg/ha). Model performance was strong for tops weight (r² = 0.88–0.95) and stem weight (r² = 0.90–0.95), but weaker for LAI (r² = 0.796–0.847) and leaf weight (r² = 0.74–0.90). Nitrogen rates significantly affected all parameters (p < 0.05), with Kanyuk (V2) outperforming Radmira (V1) at higher N levels.
The findings highlighted the importance of advanced DSSAT crop modeling for predicting and optimizing nitrogen use and varietal selection to improve agricultural productivity under varying agroecological conditions.
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