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Spatial Pattern of Agricultural Productivity Trends in Malawi
Journal article   Peer reviewed

Spatial Pattern of Agricultural Productivity Trends in Malawi

Leah M. Mungai, Joseph P. Messina and Sieglinde Snapp
Sustainability, Vol.12(4), p.1313
02/11/2020
DOI: https://doi.org/10.3390/su12041313
url
https://doi.org/10.3390/su12041313View
Published (Version of record) Open

Abstract

Environmental Sciences Environmental Sciences & Ecology Environmental Studies Green & Sustainable Science & Technology Life Sciences & Biomedicine Science & Technology Science & Technology - Other Topics
This study aims to assess spatial patterns of Malawian agricultural productivity trends to elucidate the influence of weather and edaphic properties on Moderate Resolution Imaging Spectroradiometer (MODIS)-Normalized Difference Vegetation Index (NDVI) seasonal time series data over a decade (2006-2017). Spatially-located positive trends in the time series that can't otherwise be accounted for are considered as evidence of farmer management and agricultural intensification. A second set of data provides further insights, using spatial distribution of farmer reported maize yield, inorganic and organic inputs use, and farmer reported soil quality information from the Malawi Integrated Household Survey (IHS3) and (IHS4), implemented between 2010-2011 and 2016-2017, respectively. Overall, remote-sensing identified areas of intensifying agriculture as not fully explained by biophysical drivers. Further, productivity trends for maize crop across Malawi show a decreasing trend over a decade (2006-2017). This is consistent with survey data, as national farmer reported yields showed low yields across Malawi, where 61% (2010-11) and 69% (2016-17) reported yields as being less than 1000 Kilograms/Hectare. Yields were markedly low in the southern region of Malawi, similar to remote sensing observations. Our generalized models provide contextual information for stakeholders on sustainability of productivity and can assist in targeting resources in needed areas. More in-depth research would improve detection of drivers of agricultural variability.

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