Modelling the response of maize to nitrogen rates and planting windows in the semi-arid savannas of Nigeria - Tofa, A.I.; Kamara, A.Y.; Aliyu, K.T.; Garba, I.I.; Omoigui, L.O.; Bebeley, J.F.; Solomon, R.; Peter-Jerome, H.; Kofarmata, A.H. (Elsevier, 2025)
Context Maize production in the semi-arid savannas of Nigeria is limited by poor soil fertility and erratic rainfall, both of which contribute to low and unstable yields. Optimizing cultivars choice, planting windows, and nitrogen application is critical for improving maize yield. However, ideal combination of these varies with climatic and soil conditions. Decision support tools can support optimizing agronomic practices to enhance productivity. Objective This study was conducted to determine the optimal combinations of planting window and nitrogen rates for two maize cultivars to optimized maize yield in Kano State in Nigeria using the Decision Support System for Agro-technology Transfer (DSSAT) and Geographic Information Systems (GIS). Methodology DSSAT-CERES-Maize model was used to calibrate the genetic coefficients of two maize cultivars: SAMMAZ-15 and SAMMAZ-27, using a dataset generated from 14 consecutive field experiments which ran from 2014 to 2019 season across three locations in Kano, Nigeria. Model validation was performed using independent datasets from 2015 and 2016 seasons for SAMMAZ-15, and the 2016 and 2017 seasons for SAMMAZ-27. The model was then used to simulate long-term maize grain yield under varying nitrogen rates and sowing windows in 66 sites across Sahel, Sudan and Guinea savanna agroecological zones (AEZs) in Kano State, Nigeria. GIS was then used to interpolate the yield across the study area. Results Maize grain yield declined with late planting windows with reductions of 17–34 % in the Guinea savanna, 25–44 % in the Sudan savanna, and 32–58 % in the Sahel savanna. Nitrogen application showed a quadratic yield response in the Guinea and Sudan savannas at 90 kg N ha⁻¹ (R² > 0.85; p < 0.05) but had no significant effect beyond the application of 30 kg N ha−1 in the Sahel savanna with a yield of ∼1000 kg ha−1 for both cultivars. The optimal genotype × management (G × M) combination was sowing between June 1–15 with 90 kg N ha⁻¹ , which resulted in yields above 4000 kg ha⁻¹ for SAMMAZ-15 and 3700 kg ha⁻¹ for SAMMAZ-27 in the Guinea Savanna. In Sudan Savanna, sowing between 16 and 30 June at 90 kg N ha⁻¹ yielded 2500 kg ha⁻¹ for SAMMAZ-15 and 2100 kg ha⁻¹ for SAMMAZ-27. When simulated, the maps indicate a high spatial variability, with yields in the Sahel ranging from less than 1000–2000 kg ha⁻¹ , and those in the Sudan Savanna ranging from 1000 to 4000 kg ha⁻¹ for both cultivars. In the Guinea Savanna AEZ, yields ranged from 4000 to 6000 kg ha⁻¹ for SAMMAZ-15 and from 3000 to 5000 kg ha⁻¹ for SAMMAZ-27. Conclusion Between the two cultivars, SAMMAZ-15 performed better and responded well to higher nitrogen rates in both Guinea and Sudan Savanna Zones at N application rate of 90 kg ha⁻¹ , while in the Sahel Savanna, increasing nitrogen beyond 30 kg ha⁻¹ had little effect. However, SAMMAZ-27 offers more stable performance under variable planting window and nitrogen levels. Therefore, matching of crop cultivars to appropriate planting window, N rate and agroecological zone can help growers maximize crop productivity and stability in the savanna.