Do Satellite Data Correlate with In Situ Rainfall and Smallholder Crop Yields? Implications for Crop Insurance

Wonga Masiza, Johannes George Chirima, Hamisai Hamandawana, Ahmed Mukalazi Kalumba and Hezekiel Bheki Magagula
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Wonga Masiza: Department of Geography and Environmental Science, University of Fort Hare, Private Bag X1314, Alice 5700, South Africa
Johannes George Chirima: Agricultural Research Council-Institute for Soil, Climate and Water, Private Bag X79, Pretoria 0001, South Africa
Hamisai Hamandawana: Afromontane Research Unit, Risk and Vulnerability Science Centre, University of the Free State, Private Bag X13, Phuthaditjhaba 9866, South Africa
Ahmed Mukalazi Kalumba: Department of Geography and Environmental Science, University of Fort Hare, Private Bag X1314, Alice 5700, South Africa
Hezekiel Bheki Magagula: Department of Geography and Environmental Science, University of Fort Hare, Private Bag X1314, Alice 5700, South Africa

Sustainability, 2022, vol. 14, issue 3, 1-13

Abstract: Adverse weather is one of the most prevalent sources of risk in agriculture. Its impacts are aggravated by the lack of effective risk management mechanisms. That is why resource-poor farmers tend to respond to weather risks by adopting low-capital investment, low-return, and low-risk agricultural practices. This challenge needs to be addressed with innovative risk management strategies. One of the tools that is gaining traction, especially in the developing countries, is weather-index-based insurance (WII). However, WII uptake is still low because of several constraints, one of which is basis risk. This study attempts to address this problem by evaluating the suitability of TAMSAT, CHIRPS, MODIS, and Sentinel-2 data for WII. We evaluated the first three datasets against in situ rainfall measurements at different spatial and temporal scales over the maize-growing season in a smallholder farming area in South Africa. CHIRPS had higher correlations with in situ measured rainfall data than TAMSAT and MODIS NDVI. CHIRPS performed equally well at 10 km and 25 km spatial scales, and better at monthly than daily and 16-day time steps (maximum R = 0.78, mean R = 0.72). Due to the lack of reliable historical yield data, we conducted yield surveys over three consecutive seasons using an objective crop cut method. We then assessed how well rainfall and NDVI related with maize yield. There was a poor relationship between these variables and maize yield (R 2 ≤ 0.14). The study concludes by pointing out that crop yield does not always have a linear relationship with weather and vegetation indices, and that water is not always the main yield-limiting factor in smallholder farming systems. To minimize basis risk, the process of designing WII must include identification of main yield-limiting factors for specific localities. Alternatively, insurers could use crop water requirement methods to design WII.

Keywords: smallholder; crop insurance; weather index insurance; CHIRPS; NDVI (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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