Agricultural Systems Identifying Drivers for Variability in Maize (Zea mays L.) Yield in Ghana: A Meta-Regression Approach
| Author | Bindraban, Prem S. | |
| Author | Anselme K. K. Kouame | |
| Author | Isaac N. Kissiedu | |
| Author | Williams K. Atakora | |
| Author | Khalil El Mejahed | |
| Jurisdiction: | Ghana | |
| Date of acession | 2023-05-08T12:02:47Z | |
| Date of availability | 2023-05-08T12:02:47Z | |
| Date of issue | 2023-04-05 | |
| Abstract | CONTEXT: 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 | |
| URL | https://hub.ifdc.org/handle/20.500.14297/2139 | |
| Language | en_US | |
| Publisher | IFDC | |
| Subject | Maize | |
| Subject | Fertilizers | |
| Title | Agricultural Systems Identifying Drivers for Variability in Maize (Zea mays L.) Yield in Ghana: A Meta-Regression Approach | |
| Type | Article |