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 lowrisk
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 weatherindex-
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 (R2 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.
