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dc.contributor.authorBucagu, C.
dc.contributor.authorNdoli, A.
dc.contributor.authorCyamweshi, A.R.
dc.contributor.authorNabahungu, L.N.
dc.contributor.authorMukuralinda, A.
dc.contributor.authorSmethurst, P.
dc.date.accessioned2022-09-19T08:42:03Z
dc.date.available2022-09-19T08:42:03Z
dc.date.issued2020
dc.identifier.citationBucagu, C., Ndoli, A., Cyamweshi, A.R., Nabahungu, L.N., Mukuralinda, A. & Smethurst, P. (2020). Determining and managing maize yield gaps in Rwanda. Food Security, 12, 1269-1282.
dc.identifier.issn1876-4517
dc.identifier.urihttps://hdl.handle.net/20.500.12478/7764
dc.description.abstractSmallholder 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.
dc.description.sponsorshipSwedish International Development Cooperation Agency
dc.format.extent1269-1282
dc.language.isoen
dc.subjectMaize
dc.subjectGrain
dc.subjectYields
dc.subjectForecasting
dc.subjectCrops
dc.subjectIntensification
dc.titleDetermining and managing maize yield gaps in Rwanda
dc.typeJournal Article
cg.contributor.crpRoots, Tubers and Bananas
cg.contributor.affiliationUniversity of Rwanda
cg.contributor.affiliationInternational Union for Conservation of Nature, Rwanda
cg.contributor.affiliationRwanda Agriculture and Animal Resources Development Board
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.contributor.affiliationInternational Centre for Research in Agroforestry
cg.contributor.affiliationCommonwealth Scientific and Industrial Research Organisation
cg.coverage.regionAfrica
cg.coverage.regionCentral Africa
cg.coverage.countryRwanda
cg.coverage.hubCentral Africa Hub
cg.researchthemeNatural Resource Management
cg.identifier.bibtexciteidBUCAGU:2020
cg.isijournalISI Journal
cg.authorship.typesCGIAR and developing country institute
cg.iitasubjectAgronomy
cg.iitasubjectFood Security
cg.iitasubjectMaize
cg.iitasubjectPlant Breeding
cg.iitasubjectPlant Production
cg.iitasubjectSocioeconomy
cg.iitasubjectValue Chains
cg.journalFood Security
cg.notesPublished online: 24 Jul 2020
cg.accessibilitystatusOpen Access
cg.reviewstatusPeer Review
cg.usagerightslicenseCreative Commons Attribution 4.0 (CC BY 0.0)
cg.targetaudienceScientists
cg.identifier.doihttps://dx.doi.org/10.1007/s12571-020-01059-2
cg.iitaauthor.identifierNsharwasi Nabahungu: 0000-0002-2104-3777
cg.futureupdate.requiredNo
cg.identifier.volume12


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