Crop Simulation | ScienceGate

SUMMARYBrazil is one of the most important soybean producers in the world. Soybean is a very important crop for the country as it is used for several purposes, from food to biodiesel production. The levels of soybean yield in the different growing regions of the country vary substantially, which results in yield gaps of considerable magnitude. The present study aimed to investigate the soybean yield gaps in Brazil, their magnitude and causes, as well as possible solutions for a more sustainable production. The concepts of yield gaps were reviewed and their values for the soybean crop determined in 15 locations across Brazil. Yield gaps were determined using potential and attainable yields, estimated by a crop simulation model for the main maturity groups of each region, as well as the average actual famers’ yield, obtained from national surveys provided by the Brazilian Government for a period of 32 years (1980–2011). The results showed that the main part of the yield gap was caused by water deficit, followed by sub-optimal crop management. The highest yield gaps caused by water deficit were observed mainly in the south of Brazil, with gaps higher than 1600 kg/ha, whereas the lowest were observed in Tapurah, Jataí, Santana do Araguaia and Uberaba, between 500 and 1050 kg/ha. The yield gaps caused by crop management were mainly concentrated in South-central Brazil. In the soybean locations in the mid-west, north and north-east regions, the yield gap caused by crop management was <500 kg/ha. When evaluating the integrated effects of water deficit and crop management on soybean yield gaps, special attention should be given to Southern Brazil, which has total yield gaps >2000 kg/ha. For reducing the present soybean yield gaps observed in Brazil, several solutions should be adopted by growers, which can be summarized as irrigation, crop rotation and precision agriculture. Improved dissemination of agricultural knowledge and the use of crop simulation models as a tool for improving crop management could further contribute to reduce the Brazilian soybean yield gap.

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Overcoming basis risk in agricultural index insurance using crop simulation modeling and satellite crop phenology

10.5194/egusphere-egu21-9534 ◽

2021 ◽

Author(s):

Mehdi H. Afshar ◽

Timothy Foster ◽

Thomas P. Higginbottom ◽

Ben Parkes ◽

Koen Hufkens ◽

...

Keyword(s):

Smallholder Farmers ◽

Simulation Models ◽

Extreme Weather ◽

Index Insurance ◽

Yield Estimation ◽

Crop Phenology ◽

Yield Data ◽

Basis Risk ◽

Crop Simulation ◽

Crop Simulation Models

Extreme weather causes substantial damage to livelihoods of smallholder farmers globally and are projected to become more frequent in the coming decades as a result of climate change. Index insurance can theoretically help farmers to adapt and mitigate the risks posed by extreme weather events, providing a financial safety net in the event of crop damage or harvest failure. However, uptake of index insurance in practice has lagged far behind expectations. A key reason is that many existing index insurance products suffer from high levels of basis risk, where insurance payouts correlate poorly with actual crop losses due to deficiencies in the underlying index relationship, contract structure or data used to trigger insurance payouts to farmers.&#160;In this study, we analyse to what extent the use of crop simulation models and crop phenology monitoring from satellite remote sensing can reduce basis risk in index insurance. Our approach uses a calibrated biophysical process-based crop model (APSIM) to generate a large synthetic crop yield training dataset in order to overcome lack of detailed in-situ observational yield datasets &#8211; a common limitation and source of uncertainty in traditional index insurance product design. We use this synthetic yield dataset to train a simple statistical model of crop yields as a function of meteorological and crop growth conditions that can be quantified using open-access earth observation imagery, radiative transfer models, and gridded weather products. Our approach thus provides a scalable tool for yield estimation in smallholder environments, which leverages multiple complementary sources of data that to date have largely been used in isolation in the design and implementation of index insuranceWe apply our yield estimation framework to a case study of rice production in Odisha state in eastern India, an area where agriculture is exposed to significant production risks from monsoonal rainfall variability. Our results demonstrate that yield estimation accuracy improves when using meteorological and crop growth data in combination as predictors, and when accounting for the timing of critical crop development stages using satellite phenological monitoring. Validating against observed yield data from crop cutting experiments, our framework is able to explain around 54% of the variance in rice yields at the village cluster (Gram Panchayat) level that is the key spatial unit for area-yield index insurance products covering millions of smallholder farmers in India. Crucially, our modelling approach significantly outperforms vegetation index-based models that were trained directly on the observed yield data, highlighting the added value obtained from use of crop simulation models in combination with other data sources commonly used in index design.

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