Satellite-based vegetation health indices as a criteria for insuring against drought-related yield losses

Abstract

This paper considers the use of indices built on the basis of remote-sensing data for crop 5 insurance purposes. In our analysis, we compare the capacity of two satellite-based vegetation 6 health (VH) indices, the vegetation condition index (VCI) and the temperature condition index 7 (TCI), measured for important periods of the crop vegetation to predict farmers' wheat yields in 8 two main grain producing regions of Kazakhstan. The selected indices are used for the design of 9 index-based insurance contracts. The dependence of wheat yields on vegetation conditions as measured by VCI and TCI, is modeled by applying the copula approach. Our empirical results for 47 grain-producing farms in the Northern Kazakhstan show that insurance contracts built on VH indices can provide substantial risk reductions for a group of farms, though on average for the whole sample, risk reductions were found to be moderate. The study results suggest that the effectiveness of insurance contracts can be improved using satellite data of higher resolutions and measuring indices at more disaggregated levels.

Probabilistic modelling of the dependence between rainfed crops and drought hazard

“…Agriculture is one of the activities most directly influenced by climate variability (Lesk et al, 2016;IPCC, 2012) and by extreme weather events in particular (IPCC, 2012). The latter are a major source of risk in agricultural systems, often entailing substantial crop yield losses (Bokusheva et al, 2016;Kogan et al, 2015;Saadi et al, 2015). Despite the constraints associated with the application of certain governmental policies in agricultural systems, the success of mitigating the consequences of climate extremes is largely dependent on the development of appropriate risk management strategies (Paredes et al, 2014;Quiroga et al, 2011).…”

“…On the other hand, crop models describing the biological processes are one of the existing tools used to assess crop productivity, e.g. CERES (Crop Environment REsource Synthesis) models (Capa-Morocho et al, 2016;Hlavinka et al, 2010) and AquaCrop (Paredes et al, 2016;Vergni et al, 2015). These crop models are important tools in agrometeorological studies as they are able to compute irrigation requirements and yield simulations, and they have been particularly useful for assessing the impacts of climate change on agricultural productions (Leng and Hall, 2019;Hlavinka et al, 2010).…”

Meteorological Drought Analysis in the Lower Mekong Basin Using Satellite-Based Long-Term CHIRPS Product

“…It is released by the NOAA Center online [51]. The VHI dataset has been widely applied for early drought warning, monitoring of crop yield and production and assessment of irrigated areas and excessive wetness [52][53][54][55][56]. VHI is a weighted average of two sub-indices: the Vegetation Condition Index (VCI) calculated from Normalized Difference Vegetation Index (NDVI) and the Temperature Condition Index (TCI) computed from brightness temperature (TB) data.…”

Non-Parametric Analysis of Yield Risk in Lithuanian Crop Farming

“…Agricultural business depends on multiple external factors affecting both crop yields and prices. As agricultural sector is related to such important issues as food security and viability of rural areas, public support aims to manage or reduce the agricultural risks and thus ensure implementation of the goals of sustainable rural development (Breustedt et al 2008;Bokusheva 2011;Shi, Jiang 2016;Bokusheva et al 2016). By managing agricultural risks, farmers are able to streamline their activities and ensure stability of their income.…”