A Bayesian analysis of longitudinal farm surveys in Central Malawi reveals yield determinants and site-specific management strategies
Understanding the challenges to increasing maize productivity in sub-Saharan Africa, especially agronomic factors that reduce on-farm crop yield, has important implications for policies to reduce national and global food insecurity. Previous research on the maize yield gap has tended to emphasize th...
| Autores principales: | , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
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Public Library of Science
2019
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| Acceso en línea: | https://hdl.handle.net/10568/110310 |
| _version_ | 1855513028498292736 |
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| author | Snapp, Sieglinde S. Han Wang Fisher, M. Viens, F. |
| author_browse | Fisher, M. Han Wang Snapp, Sieglinde S. Viens, F. |
| author_facet | Snapp, Sieglinde S. Han Wang Fisher, M. Viens, F. |
| author_sort | Snapp, Sieglinde S. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Understanding the challenges to increasing maize productivity in sub-Saharan Africa, especially agronomic factors that reduce on-farm crop yield, has important implications for policies to reduce national and global food insecurity. Previous research on the maize yield gap has tended to emphasize the size of the gap (theoretical vs. achievable yields), rather than what determines maize yield in specific contexts. As a result, there is insufficient evidence on the key agronomic and environmental factors that influence maize yield in a smallholder farm environment. In this study, we implemented a Bayesian analysis with plot-level longitudinal household survey data covering 1,197 plots and 320 farms in Central Malawi. Households were interviewed and monitored three times per year, in 2015 and 2016, to document farmer management practices and seasonal rainfall, and direct measurements were taken of plant and soil characteristics to quantify impact on plot-level maize yield stability. The results revealed a high positive association between a leaf chlorophyll indicator and maize yield, with significance levels exceeding 95% Bayesian credibility at all sites and a regression coefficient posterior mean from 28% to 42% on a relative scale. A parasitic weed, Striga asiatica, was the variable most consistently negatively associated with maize yield, exceeding 95% credibility in most cases, of high intensity, with regression means ranging from 23% to 38% on a relative scale. The influence of rainfall, either directly or indirectly, varied by site and season. We conclude that the factors preventing Striga infestation and enhancing nitrogen fertility will lead to higher maize yield in Malawi. To improve plant nitrogen status, fertilizer was effective at higher productivity sites, whereas soil carbon and organic inputs were important at marginal sites. Uniquely, a Bayesian approach allowed differentiation of response by site for a relatively modest sample size study (given the complexity of farm environments and management practices). Considering the biophysical constraints, our findings highlight management strategies for crop yields, and point towards area-specific recommendations for nitrogen management and crop yield. |
| format | Journal Article |
| id | CGSpace110310 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | Public Library of Science |
| publisherStr | Public Library of Science |
| record_format | dspace |
| spelling | CGSpace1103102024-05-23T19:41:36Z A Bayesian analysis of longitudinal farm surveys in Central Malawi reveals yield determinants and site-specific management strategies Snapp, Sieglinde S. Han Wang Fisher, M. Viens, F. intensification agronomy farming systems crop yield yield gap Understanding the challenges to increasing maize productivity in sub-Saharan Africa, especially agronomic factors that reduce on-farm crop yield, has important implications for policies to reduce national and global food insecurity. Previous research on the maize yield gap has tended to emphasize the size of the gap (theoretical vs. achievable yields), rather than what determines maize yield in specific contexts. As a result, there is insufficient evidence on the key agronomic and environmental factors that influence maize yield in a smallholder farm environment. In this study, we implemented a Bayesian analysis with plot-level longitudinal household survey data covering 1,197 plots and 320 farms in Central Malawi. Households were interviewed and monitored three times per year, in 2015 and 2016, to document farmer management practices and seasonal rainfall, and direct measurements were taken of plant and soil characteristics to quantify impact on plot-level maize yield stability. The results revealed a high positive association between a leaf chlorophyll indicator and maize yield, with significance levels exceeding 95% Bayesian credibility at all sites and a regression coefficient posterior mean from 28% to 42% on a relative scale. A parasitic weed, Striga asiatica, was the variable most consistently negatively associated with maize yield, exceeding 95% credibility in most cases, of high intensity, with regression means ranging from 23% to 38% on a relative scale. The influence of rainfall, either directly or indirectly, varied by site and season. We conclude that the factors preventing Striga infestation and enhancing nitrogen fertility will lead to higher maize yield in Malawi. To improve plant nitrogen status, fertilizer was effective at higher productivity sites, whereas soil carbon and organic inputs were important at marginal sites. Uniquely, a Bayesian approach allowed differentiation of response by site for a relatively modest sample size study (given the complexity of farm environments and management practices). Considering the biophysical constraints, our findings highlight management strategies for crop yields, and point towards area-specific recommendations for nitrogen management and crop yield. 2019-08-08 2020-11-26T08:49:58Z 2020-11-26T08:49:58Z Journal Article https://hdl.handle.net/10568/110310 en Open Access Public Library of Science Wang, H., Snapp, S.S., Fisher, M. and Viens, F. 2019. A Bayesian analysis of longitudinal farm surveys in Central Malawi reveals yield determinants and site-specific management strategies. PLoS ONE 14(8):e0219296. |
| spellingShingle | intensification agronomy farming systems crop yield yield gap Snapp, Sieglinde S. Han Wang Fisher, M. Viens, F. A Bayesian analysis of longitudinal farm surveys in Central Malawi reveals yield determinants and site-specific management strategies |
| title | A Bayesian analysis of longitudinal farm surveys in Central Malawi reveals yield determinants and site-specific management strategies |
| title_full | A Bayesian analysis of longitudinal farm surveys in Central Malawi reveals yield determinants and site-specific management strategies |
| title_fullStr | A Bayesian analysis of longitudinal farm surveys in Central Malawi reveals yield determinants and site-specific management strategies |
| title_full_unstemmed | A Bayesian analysis of longitudinal farm surveys in Central Malawi reveals yield determinants and site-specific management strategies |
| title_short | A Bayesian analysis of longitudinal farm surveys in Central Malawi reveals yield determinants and site-specific management strategies |
| title_sort | bayesian analysis of longitudinal farm surveys in central malawi reveals yield determinants and site specific management strategies |
| topic | intensification agronomy farming systems crop yield yield gap |
| url | https://hdl.handle.net/10568/110310 |
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