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Browsing IFDC Publications by Author "Anselme K. K. Kouame"
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- ItemEstimation of Water-Limited Maize Yield using the LINTUL-2 Model and Spatial Analysis of the Yield Gap in Ghana(2023) K.B.D. Simperegui; Bindraban, Prem S.; Anselme K. K. Kouame; D.H. Peluffo-Ordóñez; Williams K. AtakoraMaize 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.
- ItemIdentifying Drivers for Variability in Maize (Zea mays L.) Yield in Ghana: A Meta-Regression Approach(IFDC, 2023-04-05) Bindraban, Prem S. ; Anselme K. K. Kouame; Isaac N. Kissiedu; Williams K. Atakora; Khalil El MejahedCONTEXT: Maize is the main cereal crop in Ghana, but its production is adversely affected by various biotic and abiotic factors. OBJECTIVE: This study aimed to highlight the factors related to maize yield variability. To this end, yields from 978 data points within 3 agro-ecological zones (AEZs) were used in crop-based and statistical modelling. METHODS: The Quantitative Evaluation of the Fertility of Tropical Soils (QUEFTS) model, the Linear Mixed Effects Model (LMM), and the Random Forest (RF) model were used to evaluate multiple effect sizes. RESULTS AND CONCLUSIONS: Analyzing an entire set of yield data points with QUEFTS, and LMM explained 19%, and 26% of yield variability, respectively. Considering all data points in the RF model, nitrogen fertilizer (NF) rate, temperature, root zone depth, rainfall, and variety accounted for 27%, 15%, 13%, 10%, and 9% of yield variation, respectively. In Guinea Savanna (GS), Transition Zone (TZ), and Deciduous Forest (DF), QUEFTS explained 30%, 20%, and 4% of yield variability, respectively. LMM, however, explained 47%, 51%, and 79% of yield variability in those AEZs. LMM showed that the phosphorus fertilizer (PF) rate was important and exceeded the importance of the NF rate in GS. LMM showed also that yield variability was significantly related to maize variety at the AEZ scale. In DF, soil chemistry (marginal R2 = R2m = 0.48) and environmental variables (R²m = 0.43) contributed more to explaining yield variability, whereas in GS and TZ, fertilizer rates (R²m = 0.35 in GS and 0.26 in TZ) and variety (R²m = 0.04 in GS and 0.20 in TZ) played a much larger role. In GS, TZ, and DF, the RF model explained 74%, 79%, and 84% of the variance in yield, respectively. These findings suggest low impact of fertilization on yield on the inherently fertile soils in the DF, while fertilization drives yield increase in the less fertile TZ and GS AEZs. We may conclude that QUEFTS was unable to capture yield variability and, according to RF and LMM analysis, the NF rate was the most important factor in explaining yield variability in the data. It can also be concluded that the factors responsible for yield variability are AEZ dependent. SIGNIFICANCE: We discuss the implications of these findings to uncover factors driving maize yield variability. It also provides information to guide and prioritize actions to be taken based on the importance of these factors in contributing to yield variability
- ItemLight Use Efficiency Crop Model Effective for Identifying Driving Factors for Maize Yield Gap in Ghana(2023) Mohamed Boullouz; Isaac N. Kissiedu; Anselme K. K. Kouame; Krishna P. Devkota; Williams K. Atakora; Bindraban, Prem S.In Ghana, maize (Zea mays) is a crucial crop for achieving food security. The population of Ghana, which has grown exponentially over the past decades, consumes about 25% of its calories from maize. In order to assist in decisionmaking and guide investment in Ghana’s agricultural intensification process, this study set out to quantify and explain the yield gap for maize using a new methodological approach. The yield gap for maize was found to range from 14% to 96%. The variation in the yield gap within a single station was related to the varying levels of yield obtained with different fertilizer treatments. None of the fertilizer combinations led to total closure of the gap in the studied locations. To identify the drivers for the yield gap, a multiple linear regression (MLR) analysis appeared to explain 68% of the yield difference. The main factors influencing the yield gap in the study areas were soil organic matter, soil water-holding capacity, root zone depth, rainfall, sulfur (S) fertilizer, and nitrogen (N) fertilizer. By adding 1% more soil organic matter, the gap could be reduced by 1.3 metric tons per hectare (mt/ha). However, an increase in the pH of the soil and the application of potassium fertilizer could increase the yield gap of maize in Ghana.
- ItemYield Gap Analysis of Wheat (Triticum aestivum) Production in Morocco using a Light Use Efficiency Model (LINTUL) and Geostatistical Approaches(2023) Bouchra Darkaoui; Bindraban, Prem S.; Isaac N. Kissiedu; Martin Jemo; Anselme K. K. Kouame; Williams K. Atakora ; William AdzawlaWheat (Triticum aestivum) is a staple food crop in Morocco that plays an important role in the food security of the country. However, the crop production capacity of wheat at the national level is among the lowest at only at 1.6 metrictons per hectare(mt ha-1), compared to Egypt at 6.6 mt ha-1. Several detrimental biotic and abiotic factors curtailing wheat yield include climatic limitations, insufficient soil fertility, and inadequate management interventions. To better understand these drivers, this work simulates yield using a modeling approach based on light interception and utilization (LINTUL-1). The potential and observed yield data cover the period from 2011 to 2019 in various provinces of Morocco. The LINTUL-1 model was calibrated using crop characteristics and preliminary data generated from wheat production in Morocco. Geostatic techniques were further employed to physically map the levels of current yield production. The results showed that at the national scale the average simulated potential yield reached 5.5 mt ha-1, compared to an average observed yield of only 1.6 mt ha-1. The resulting yield gap was calculated for several different regions at an average of 3.9 mt ha-1. The yield gaps are controlled by many biotic and abiotic constraints, and the adoption of effective management techniques, such as fertilizer application, appropriate pest and disease management, and water management via irrigation, can reduce the gaps and contribute to food security in Morocco. Further studies to identify key factors that drive wheat yield variability at the regional yield level are envisaged to refine recommendations for farmers.