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With research staff from more than 60 countries, and offices across the globe, IFPRI provides research-based policy solutions to sustainably reduce poverty and end hunger and malnutrition in developing countries.

Ahmed Akhter

researcher spotlight

Akhter Ahmed

Akhter Ahmed is a Senior Research Fellow in the IFPRI’s Poverty, Gender, and Inclusion Unit and Country Representative for IFPRI Bangladesh. He has worked on strategies for agricultural and rural development, social protection, and women’s empowerment to reduce poverty, food insecurity, and undernutrition in developing countries including Bangladesh, China, Egypt, India, Malawi, the Philippines, and Turkey.

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Since 1975, IFPRI’s research has been informing policies and development programs to improve food security, nutrition, and livelihoods around the world.

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IFPRI currently has more than 600 employees working in over 80 countries with a wide range of local, national, and international partners.

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The Mahalanobis National Crop Forecast Centre was established to provide in-season crop forecasts and assessment of drought situation using state of the art techniques and methodologies developed by Indian Space Research Organisation (ISRO).

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Publications

Resource thumbnail

Abstract

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

2021Afshar, Mehdi H.; Foster, Tim; Higginbottom, Thomas P.; Parkes, Ben; Hufkens, Koen; Mansabdar, Sanjay; Ceballos, Francisco; Kramer, Berber
Details

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

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.

Year published

2021

Authors

Afshar, Mehdi H.; Foster, Tim; Higginbottom, Thomas P.; Parkes, Ben; Hufkens, Koen; Mansabdar, Sanjay; Ceballos, Francisco; Kramer, Berber

Citation

Afshar, Mehdi H.; Foster, Tim; Higginbottom, Thomas P.; Parkes, Ben; Hufkens, Koen; Mansabdar, Sanjay; Ceballos, Francisco; and Kramer, Berber. 2021. Overcoming basis risk in agricultural index insurance using crop simulation modeling and satellite crop phenology. EGU21-9534. EGU General Assembly. https://doi.org/10.5194/egusphere-egu21-9534

Country/Region

India

Keywords

Southern Asia; Asia; Insurance; Models; Data; Remote Sensing; Leaf Area Index; Farmers; Crop Yield; Yield Forecasting; Smallholders; Extreme Weather Events; Livelihoods; Yields; Crop Modelling

Language

English

Access/Licence

Open AccessCC-BY-4.0

Links

https://doi.org/10.5194/egusphere-egu21-9534 Source record

Project

Policies, Institutions, and Markets

Record type

Abstract

Resource thumbnail

Journal Article

Improving the performance of index insurance using crop models and phenological monitoring

2021Afshar, Mehdi H.; Foster, Tim; Higginbottom, Thomas P.; Parkes, Ben; Hufkens, Koen; Mansabdar, Sanjay; Ceballos, Francisco; Kramer, Berber
Details

Improving the performance of index insurance using crop models and phenological monitoring

Extreme weather events cause considerable damage to the livelihoods of smallholder farmers globally. Whilst index insurance can help farmers cope with the financial consequences of extreme weather, a major challenge for index insurance is basis risk, where insurance payouts correlate poorly with actual crop losses. We analyse to what extent the use of crop simulation models and crop phenology monitoring can reduce basis risk in index insurance. Using a biophysical process-based crop model (Agricultural Production System sIMulator (APSIM)) applied for rice producers in Odisha, India, we simulate a synthetic yield dataset to train non-parametric statistical models to predict rice yields as a function of meteorological and phenological conditions. We find that the performance of statistical yield models depends on whether meteorological or phenological conditions are used as predictors and whether one aggregates these predictors by season or crop growth stage. Validating the preferred statistical model with observed yield data, we find that the model explains around 54% of the variance in rice yields at the village cluster (Gram Panchayat) level, outperforming vegetation index-based models that were trained directly on the observed yield data. Our methods and findings can guide efforts to design smart phenology-based index insurance and target yield monitoring resources in smallholder farming environments.

Year published

2021

Authors

Afshar, Mehdi H.; Foster, Tim; Higginbottom, Thomas P.; Parkes, Ben; Hufkens, Koen; Mansabdar, Sanjay; Ceballos, Francisco; Kramer, Berber

Citation

Afshar, Mehdi H.; Foster, Tim; Higginbottom, Thomas P.; Parkes, Ben; Hufkens, Koen; Mansabdar, Sanjay; Ceballos, Francisco; and Kramer, Berber. 2021. Improving the performance of index insurance using crop models and phenological monitoring. Remote Sensing 13(5): 924. https://doi.org/10.3390/rs13050924

Country/Region

India

Keywords

Southern Asia; Asia; Insurance; Models; Data; Remote Sensing; Leaf Area Index; Farmers; Crop Yield; Yield Forecasting; Smallholders; Extreme Weather Events; Livelihoods; Yields; Crop Modelling

Language

English

Access/Licence

Open AccessCC-BY-4.0

Links

https://doi.org/10.3390/rs13050924 Source record

Project

Policies, Institutions, and Markets

Record type

Journal Article