Determining and managing maize yield gaps in Rwanda

Charles Bucagu (), Alain Ndoli, Athanase R. Cyamweshi, Leon N. Nabahungu, Athanase Mukuralinda and Philip Smethurst
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Charles Bucagu: University of Rwanda(UR), Busogo Campus – Musanze
Alain Ndoli: International Union for Conservation of Nature (IUCN)
Athanase R. Cyamweshi: Rwanda Agriculture and Animal Resources Development Board (RAB)
Leon N. Nabahungu: International Institute of Tropical Agriculture (IITA) –RDC
Athanase Mukuralinda: International Centre for Research in Agroforestry
Philip Smethurst: Commonwealth Scientific and Industrial Research Organisation (CSIRO) – Australia

Food Security: The Science, Sociology and Economics of Food Production and Access to Food, 2020, vol. 12, issue 6, No 6, 1269-1282

Abstract: Abstract Smallholder maize growers are experiencing significant yield gaps due to sub-optimal agricultural practices. Adequate agricultural inputs, particularly nutrient amendments and best management practices, are essential to reverse this trend. There is a need to understand the cause of variations in maize yield, provide reliable early estimates of yields, and make necessary recommendations for fertilizer applications. Maize yield prediction and estimates of yield gaps using objective and spatial analytical tools could provide accurate and objective information that underpin decision support. A study was conducted in Rwanda at Nyakiliba sector and Gashora sector located in Birunga and Central Bugesera agro-ecological zones, with the objectives of (1) determining factors influencing maize yield, (2) predicting maize yield (using the Normalized Difference Vegetation Index (NDVI) approach), and (3) assessing the maize yield gaps and the impact on food security. Maize grain yield was significantly higher at Nyakiliba (1.74 t ha−1) than at Gashora (0.6 t ha−1). NDVI values correlated positively with maize grain yield at both sites (R2 = 0.50 to 0.65) and soil fertility indicators (R2 = 0.55 to 0.70). Maize yield was highest at 40 kg P ha−1 and response to N fertilizer was adequately simulated at Nyakiliba (R2 = 0.85, maximum yield 3.3 t ha−1). Yield gap was 4.6 t ha−1 in Nyakiliba and 5.1 t ha−1 in Gashora. Soil variables were more important determinants of social class than family size. Knowledge that low nutrient inputs are a major cause of yield gaps in Rwanda should prioritize increasing the rate of fertilizer use in these agricultural systems.

Keywords: Maize grain yield; Social classification; Prediction; Crop intensification (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s12571-020-01059-2

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