Development of an index-based insurance product: validation of a forage production index derived from medium spatial resolution fCover time series

Abstract

An index-based insurance is being developed to estimate and monitor forage production in France in near real-time based on a forage production index (FPI) derived from the fraction of green vegetation cover (fCover) integral, obtained from medium spatial resolution time series. This article presents the first step of the scientific validation implemented. The grassland parcels, the field protocol established to collect biomass production data, and the method used to get the fCover are described. Local ground measurements of biomass production are compared with FPI values obtained from high-resolution space-based images. Discrepancies between the two variables are quantified by the coefficient of determination, the mean square error and the normalised root mean square error. First, fCover derived from the four sensors are coherent demonstrating the ability of the algorithm used to provide a consistent way of calculating fCover. Second, for the whole data set, the scatter plot between FPI and biomass shows an acceptable correlation (R 2 = 0.75) improved when only taking into account data recorded up until the production maximum (R 2 = 0.81). Third, the analysis carried out on the scale of the parcels, grass species, period of mowing or climatic conditions reveals variability on the regression coefficients indicating that other explanatory variables should be integrated to better compute the FPI.

Early assessment of seasonal forage availability for mitigating the impact of drought on East African pastoralists

“…Roumiguié et al 2015), time-lapse photography of forage condition (Inoue et al 2015;Migliavacca et al 2011), or crowdsourcing approaches for the collection of forage condition information by pastoralists using mobile phones. 3) Drought recall exercises that exploit pastoralists' experiences to rank forage conditions across multiple seasons.…”

Self-insurance and multi-peril grassland crop insurance: the case of French suckler cow farms

“…The FPI performs quite well but correlations between this index and real grassland production are imperfect. The method to derive forage production from satellite images explains between 0.71 and 0.90 of observed variability (R²) (Roumiguié et al, 2015b). Errors relative to the spatial resolution must be added: R² between high-resolution estimates (10 mx10 m) and moderate resolution (6 km×6 km) range between 0.78 and 0.93 (Roumiguié et al, 2015a).…”

Validation of a Forage Production Index (FPI) Derived from MODIS fCover Time-Series Using High-Resolution Satellite Imagery: Methodology, Results and Opportunities

“…Previous work with HR data yielded satisfactory results concerning the link between biomass and the integral of biophysical parameters like fAPAR or fCover [37,63]. With this study, the comparison of the FPIMR with the FPIHR provided additional important information about the strengths and weaknesses of such data.…”

“…In a previous study [37], a direct comparison between in situ grassland biomass measurements and FPI derived from fCover measured with HR time series was conducted. Ground data for validation were collected with a motorized mower and weighed before and after drying to calculate dry matter production of each sample area.…”

“…Ground data for validation were collected with a motorized mower and weighed before and after drying to calculate dry matter production of each sample area. Results indicated that fCover was an appropriate proxy for grassland biomass production [37]. The fraction of photosynthetically active radiation absorbed by the canopy (fAPAR) was also analyzed and replaced fCover in the FPI equation [19].…”