Response of common bean ( <i>Phaseolus vulgaris</i> L.) to triple superphosphate fertilization in iron-rich Luvisols

Abstract Background : Common bean ( Phaseolus vulgaris L.) is an important legume crop cultivated globally for its nutritious seeds, which are abundant in protein, fiber, and essential micronutrients. Its growth, yield, and biological nitrogen fixation ability depend heavily on phosphorus (P) availability. Determining optimal P fertilizer rates is highly soil-specific, and data is lacking for iron-rich Luvisols. These soils have a high P-fixing capacity, rendering applied P less available to plants, which poses a significant challenge to crop productivity. Methods : A field study was conducted during the 2021 and 2022 cropping seasons under rainfed conditions in the Guinea Savanna agro-ecological zone of Ghana. Three common bean genotypes; Ennepa (V1), Nsroma (V2), and Semanhyia (V3) were evaluated under three rates of triple superphosphate fertilizer: 0, 30, and 60 kg P₂O₅ per hectare. Analysis of variance and Pearson correlation analyses were performed on growth and yield data using. Grain yield efficiency index (GYEI), P utilization efficiency (PUE), and P stress factor were also computed. Results : The application of P fertilizer significantly influenced all measured parameters. The higher P rate (60 kg/ha) reduced the number of days to flowering and pod maturity while simultaneously enhancing grain yield and its components. The V2 variety recorded the highest grain yield and the lowest PUE, indicating high-stress sensitivity and a requirement for high-input systems. Conversely, V3 registered the highest PUE and the most significant stress tolerance, making it ideal for low-input agriculture. Conclusions : This study provides a valuable adaptation strategy for the “4R” nutrient stewardship framework, specifically defining the “Right rate” of P for common bean cultivation in P-fixing soils. The findings offer critical decision-support for farmers to invest efficiently in soil nutrition, enabling them to select genotypes based on their input levels to achieve optimal yields and enhance climate resilience.