Agro-ecological variability effects on an index-based insurance design for extreme events

Index-based insurance (IBI) is a risk transfer tool that is widely used in low-incomes countries, especially catered to smallholder farmers. A good correlation between the selected index (in this case Normalized Difference Vegetation Index -NDVI-) and the crop losses of the insured area is a key requirement for well-performing system. Usually, IBI application area is defined by political boundaries, despite that variation in soil, topography, and climate is more important. In this work, agro-ecological variability effects will be studied in the IBI context. The case study was the rice crop failure in Babahoyo canton (Ecuador), using NDVI as the crop damage indicator. In a previous study, zones of similar soil, topographic and climatic characteristics (AHZs) were defined using principal component analysis in Babahoyo canton. Two largest AHZs were found in this canton. They were f7 and f15, which account for 91% of the total rice crop area in Babahoyo. We sampled reflectance values of red, blue, near infra-red (NIR) and medium infra-red (MIR) bands over bare soil (1st of January) by observation points, within f7 and f15. The reflectance band values were used to study whether the difference between the two AHZs could be appreciated through remote sensing. Then, we did the same but for NDVI average of the rice crop cycle, i.e., 15th of January to 15th of May (NDVI_ave). It was done for confirming that the AHZs (f7 and f15) have an influence over rice crop development. The used product was MODIS imagery (2001?2017). We stratified the sampling through the AHZs, applying 30% density for ensuring the accuracy of the selected pixels. Statistically significant differences between the f7 and f15 zones were found in all the analysed reflectance bands and in NDVI_ave. This found supported the differences that the agro-ecological map pointed out in its database. At the same time, the NDVI_ave distribution for each AHZ presented a statistically significant difference of the accumulated probability below the threshold (0.4), which is confirmed as a reliable threshold for rice crop failure. This stratified sampling could improve the representativeness and accuracy of crop state assessments and reduce the basis risk.