Estimation of Water-Limited Maize Yield using the LINTUL-2 Model and Spatial Analysis of the Yield Gap in Ghana
| Author | K.B.D. Simperegui | |
| Author | Bindraban, Prem S. | |
| Author | Anselme K. K. Kouame | |
| Author | D.H. Peluffo-Ordóñez | |
| Author | Williams K. Atakora | |
| Jurisdiction: | Ghana | |
| Date of acession | 2024-06-21T05:55:02Z | |
| Date of availability | 2024-06-21T05:55:02Z | |
| Date of issue | 2023 | |
| Abstract | Maize holds a significant position within Ghana’s cereal production, contributing to 45% of the total cereal production. Despite this, the average maize yield of 2.4 metric tons per hectare (mt ha-1) between 2017 and 2019 falls well below its potential range of 5-6 mt ha-1. To comprehensively grasp the dynamics of the maize yield gap in Ghana, we employed the light use efficiency(LINTUL-2) crop model alongside statistical and geospatial analyses. This allowed us to assess the variability of maize water-limited potential yield and yield gap across 10 designated study sites, extending our evaluation to a national scale. Utilizing random forest regression, followed by ridge regression, we endeavored to uncover the principal drivers behind maize yield gap in Ghana. Our findings reveal a water-limited yield gap ranging from 18% to 74% across the 10 study sites and diverse fertilizer treatments. The combined approach of random forest and ridge regression, explaining 87% of the yield gap variability (RMSE = 472.6kg ha-1), highlights noteworthy trends. Notably, at a 5% confidence level, soil organic matter, soil carbon content, base saturation, and soil nitrogen content emerge as the most influential factors, explaining 13.81%, 13.80%, 11.56% and 10.25% of the maize yield gap variability under water limited conditions, respectively. The Ridge Regression underscores the significance of soil organic matter, base saturation, soil nitrogen content, nitrogen application, phosphorus application, potassium application, and sulfur application for reducing the maize yield gap. Our research also emphasis the potential of sulfur application as a secondary nutrient to effectively decrease the maize yield gap, particularly when integrated with macronutrients (NPK) and the kriging interpolation reveals high potential for maize production in the northern part of the country. | |
| Citation | Simperegui, K.B.D., P.S. Bindraban, K.K.A. Kouame, D.H. Peluffo-Ordóñez, and W.K. Atakora. 2023. Estimation of Water-Limited Maize Yield using the LINTUL-2Model and SpatialAnalysis of the Yield Gap in Ghana. IFDC FERARI Research Report No. 13. | |
| URL | https://hub.ifdc.org/handle/20.500.14297/3064 | |
| Language | en | |
| Subject | Maize | |
| Subject | Spatial analysis | |
| Title | Estimation of Water-Limited Maize Yield using the LINTUL-2 Model and Spatial Analysis of the Yield Gap in Ghana |