Timely information on the spatial extent of staple crops cultivated in an African country is critical to monitor food supply and plan policy responses, yet it’s often challenged by the inherent complexity in the cropping systems. Majority of cereal crops are intercropped with other crop types in sub-Saharan Africa. This contributes to the sustainable management of production risks and soil health, yet it creates high levels of intra-field spectral variability and difficult to classify using remote sensing imagery data. Hence, the identification of crop mix in the intercropped fields has been very difficult and only few previous studies addressed this.
Despite the challenge, a team of geospatial scientists at ICRISAT, led by Murali Krishna Gumma, successfully developed and implemented a MODIS-based workflow to map major staple crops in Malawi and detect major spatiotemporal changes occurred between 2010/2011 and 2016/2017. Based on the intensive collection of groundtruthing data across the country over time, the team analyzed that pigeonpea production area expanded by almost 30% during the period and contributed to the country’s export to Asian countries. The study was published in Remote Sensing (Open Access).
Malawi, in south-eastern Africa, is one of the poorest countries in the world. Food security in the country hinges on rainfed systems in which maize and sorghum are staple cereals and groundnut and pigeonpea are now major grain legume crops. While the country has experienced a considerable reduction in forest lands, population growth and demand for food production have seen an increase in the area dedicated to agricultural crops. From 2010, pigeonpea developed into a major export crop, and is commonly intercropped with cereals or grown in double-up legume systems. Information on the spatial extent of these crops is useful for estimating food supply, understanding export potential, and planning policy changes as examples of various applications. Remote sensing analysis offers a number of efficient approaches to deliver spatial, reproducible data on land use and land cover (LULC) and changes therein. Moderate Resolution Imaging Spectroradiometer (MODIS) products (fortnightly and monthly) and derived phenological parameters assist in mapping cropland areas during the agricultural season, with explicit focus on redistributed farmland. Owing to its low revisit time and the availability of long-term period data, MODIS offers several advantages, e.g., the possibility of obtaining cloud-free Normalized Difference Vegetation Index (NDVI) profile and an analysis using one methodology applied to one sensor at regular acquisition dates, avoiding incomparable results. To assess the expansion of areas used in the production of pigeonpea and groundnut resulting from the release of new varieties, the spatial distribution of cropland areas was mapped using MODIS NDVI 16-day time-series products (MOD13Q1) at a spatial resolution of 250 m for the years 2010–2011 and 2016–2017. The resultant cropland extent map was validated using intensive ground survey data. Pigeonpea is mostly grown in the southern dry districts of Mulanje, Phalombe, Chiradzulu, Blantyre and Mwanza and parts of Balaka and Chikwawa as a groundnut-pigeonpea intercrop, and sorghum-pigeonpea intercrop in Mzimba district. By 2016, groundnut extent had increased in Mwanza, Mulanje, and Phalombe and fallen in Mzimba. The result indicates that the area planted with pigeonpea had increased by 29% (75,000 ha) from 2010–2011 to 2016–2017. Pigeonpea expansion in recent years has resulted from major export opportunities to Asian countries like India, and its consumption by Asian expatriates all over the world. This study provides useful information for policy changes and the prioritization of resources allocated to sustainable food production and to support smallholder farmers.
Gumma, M. K., T. W. Tsusaka, I. Mohammed, G. Chavula, N. V. P. R. Ganga Rao, P. Okori, C. O. Ojiewo, R. Varshney, M. Siambi and A. Whitbread (2019). “Monitoring Changes in the Cultivation of Pigeonpea and Groundnut in Malawi Using Time Series Satellite Imagery for Sustainable Food Systems.” Remote Sensing 11(12): 1475.https://doi.org/10.3390/rs11121475