Assessment of maize yield gap and major determinant factors between smallholder farmers in the Dedza district of Malawi

Tamene, Lulseged; Mponela, Powell; Ndengu, Gift; Kihara, Job

doi:10.1007/s10705-015-9692-7

Cited by 43 publications

(33 citation statements)

References 38 publications

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“…The GLMM and CART analysis identified a number of agroecology‐specific factors influencing maize yield gaps. The findings are consistent with previous recommendations for site‐specific extension services on soil and crop management strategies to reduce yield gaps (Banerjee et al, ; Krupnik et al, ; Tamene, Mponela, Ndengu, & Kihara, ; Yengoh, ).…”

Section: Discussionsupporting

confidence: 91%

Soil and management‐related factors contributing to maize yield gaps in western Kenya

Munialo

Dahlin

Onyango

et al. 2019

Food and Energy Security

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The solution to reducing existing yield gaps on smallholder farms lies in understanding factors limiting yield in areas with agricultural intensification potential. This study applied an integrated analysis approach comprising Classification and Regression Tree (CART), generalized linear mixed model (GLMM), and factor analysis (FA), to explain soil and management‐related factors influencing maize yield gaps, in order to enhance yields. The study was conducted in Mukuyu and Shikomoli in western Kenya, sites with, respectively, high and low agroecological potential regarding soil fertility. Maize yield gaps were quantified by comparing yields on the 90th percentile of farms to yields determined in 189 fields on 70 randomly sampled smallholdings. Soil and management‐related factors were determined at early and late maize development stages. Maize yield on the 90th percentile of farms in Mukuyu and Shikomoli was 5.1 and 4.8 t/ha, respectively, and the average yield gap was 1.8 and 2.6 t/ha, representing 35% and 54% unachieved yield for Mukuyu and Shikomoli, respectively. In FA, soil was revealed to be the main factor influencing maize yield gaps at both sites, rather than management‐related variables. The CART method identified maize density, chlorophyll values, maize height, and depth to compact layer as consistent factors affecting yield at both sites, while GLMM identified soil texture (silt content) as important. According to CART, weed cover at early stages and maize density at late stages were the most limiting factor in maize production in Mukuyu and Shikomoli, respectively. Generalized linear mixed model analysis identified agroecology‐specific factors influencing maize yield gaps as soil‐available phosphorus and zinc, plus weed pressure at early maize stages in Mukuyu, and plus soil cation exchange capacity and exchangeable magnesium in Shikomoli. Through an integrated approach, it was possible to identify both consistent and agroecology‐specific factors limiting crop yields. This can increase the applicability of the findings to smallholder farms.

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Section: Discussionsupporting

confidence: 91%

Soil and management‐related factors contributing to maize yield gaps in western Kenya

Munialo

Dahlin

Onyango

et al. 2019

Food and Energy Security

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“…In a recent review of literature, Beza et al (2017) found that most agronomic yield gap studies consider management and edaphic factors as the main explaining factors, whereas farm(er) characteristics and socioeconomic conditions are rarely considered. An exception is Tamene et al (2015), which showed that soil nutrient content, socio-economic and agronomic practices together explain maize yield gaps in Malawi, highlighting the importance of an integrated approach. However, they used the highest farmers' yield as an estimate of potential yield, which is likely to underestimate the yield gap when all farmers face similar constraints and their practices are agronomically sub-optimal (Lobell et al 2009).…”

Section: Introductionmentioning

confidence: 99%

Unravelling the variability and causes of smallholder maize yield gaps in Ethiopia

Assefa¹,

Chamberlin

Reidsma³

et al. 2019

Food Sec.

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Ethiopia has achieved the second highest maize yield in sub-Saharan Africa. Yet, farmers' maize yields are still much lower than onfarm and on-station trial yields, and only ca. 20% of the estimated water-limited potential yield. This article provides a comprehensive national level analysis of the drivers of maize yields in Ethiopia, by decomposing yield gaps into efficiency, resource and technology components, and accounting for a broad set of detailed input and crop management choices. Stochastic frontier analysis was combined with concepts of production ecology to estimate and explain technically efficient yields, the efficiency yield gap and the resource yield gap. The technology yield gap was estimated based on water-limited potential yields from the Global Yield Gap Atlas. The relative magnitudes of the efficiency, resource and technology yield gaps differed across farming systems; they ranged from 15% (1.6 t/ha) to 21% (1.9 t/ha), 12% (1.3 t/ha) to 25% (2.3 t/ha) and 54% (4.8 t/ha) to 73% (7.8 t/ha), respectively. Factors that reduce the efficiency yield gap include: income from non-farm sources, value of productive assets, education and plot distance from home. The resource yield gap can be explained by sub-optimal input use, from a yield perspective. The technology yield gap comprised the largest share of the total yield gap, partly due to limited use of fertilizer and improved seeds. We conclude that targeted but integrated policy design and implementation is required to narrow the overall maize yield gap and improve food security.

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“…The varying geographical gradient, and climatic conditions play a role in influencing productivity. As shown in Table 1 , Golomoti is a low agricultural potential site located at low elevation, with high evapotranspiration and variable rainfall, Kandeu and Nsipe are medium agricultural potential, located on medium elevation, with medium rainfall and Linthipe is a high agricultural potential, high elevation site, and well-distributed rainfall (Tamene et al, 2015; Smith et al, 2016). Malawi has a unimodal rainy season occurring from November to April, and a dry season from May to October (Jury and Mwafulirwa, 2002).…”

Section: Methodsmentioning

confidence: 99%

Smallholder Farms and the Potential for Sustainable Intensification

et al. 2016

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The sustainable intensification of African agriculture is gaining momentum with the compelling need to increase food and agricultural production. In Southern Africa, smallholder farming systems are predominately maize-based and subject to erratic climatic conditions. Farmer crop and soil management decisions are influenced by a plethora of complex factors such as market access resource availability, social relations, environment, and various messages on sustainable farming practices. Such factors pose barriers to increasing sustainable intensification in Africa. This paper characterizes smallholder farming practices in Central Malawi, at Africa Research in Sustainable Intensification for the Next Generation (Africa RISING) project sites. We present findings from a survey of 324 farmers, located within four Africa RISING sites selected in a stratified random manner to represent (1) low agricultural potential (high evapotranspiration, variable rainfall), (2) medium agricultural potential (two sites), and (3) high agricultural potential (well-distributed rainfall). Soil fertility was low overall, and certain farming practices appeared to limit the sustainability of agricultural production. Nearly half of farmers did not value legume residues as a high nutrient value resource for soil amelioration, as legume residues were removed (17.9%) or burned (21.4%). Conversely, maize residues were rarely removed (4.5%) or burned (10.4%). We found that farmers do not allocate soil amendment resources to legume fields (zero instances of mineral fertilizer or manure application to legumes compared to 88 and 22% of maize systems, respectively). Policy makers in Malawi have led initiatives to intensify agricultural systems through subsidizing farmer access to mineral fertilizer as well as maize hybrid seed, and only rarely to improved legume seed. In this survey, farmers allocate mineral fertilizer to maize systems and not legume systems. There is urgent need to invest in education on sustainable reinvestment in natural resources through complementary practices, such as maximization of biological nitrogen fixation through improved legume agronomy and better organic resource and crop residue management. Recent efforts by Malawi agricultural services to promote doubled-up legumes as a sustainable intensification technology are encouraging, but benefits will not accrue unless equal attention is given to an extension campaign on management of organic resources such as crop residues.

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Assessment of maize yield gap and major determinant factors between smallholder farmers in the Dedza district of Malawi

Cited by 43 publications

References 38 publications

Soil and management‐related factors contributing to maize yield gaps in western Kenya

Soil and management‐related factors contributing to maize yield gaps in western Kenya

Unravelling the variability and causes of smallholder maize yield gaps in Ethiopia

Smallholder Farms and the Potential for Sustainable Intensification

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