Sequenced crop evapotranspiration and water requirement in developing a multi-trigger rainfall index insurance and risk-contingent credit

2021 ◽  
Author(s):  
Michael K. Ndegwa ◽  
Apurba Shee ◽  
Calum Turvey ◽  
Liangzhi You
Keyword(s):  
Smallholder Farmers ◽  
Daily Rainfall ◽  
Crop Growth ◽  
Water Requirement ◽  
Index Insurance ◽  
Growth Stages ◽  
Water Requirements ◽  
Basis Risk ◽  
Risk Modelling ◽  
Seasonal Basis

AbstractWeather index insurance (WII) has been a promising innovation that protects smallholder farmers against drought risks and provides resilience against adverse rainfall conditions. However, the uptake of WII has been hampered by high spatial and intra-seasonal basis risk. To minimize intra-seasonal basis risk, the standard approaches to designing WII based on seasonal cumulative rainfall have shown to be ineffective in some cases as they do not incorporate different water requirements across each phenological stage of crop growth. One of the challenges in incorporating crop phenology in insurance design is to determine the water requirement in crop growth stages. Borrowing from agronomy, crop science, and agro-meteorology we adopt evapotranspiration methods in determining water requirements for a crop to survive in each stage, that can be used as a trigger level for a WII product. Using daily rainfall and evapotranspiration data, we illustrate the use of Monte Carlo risk modelling to price an operational WII and WII-linked credit product. The risk modelling approach we develop includes incorporation of correlation between rainfall and evapotranspiration indexes that can minimise significant intertemporal basis risk in WII.

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

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

<p>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. </p><p>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 – 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 insurance</p><p>We 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.</p>

scholarly journals TIME SERIES ANALYSIS OF REMOTE SENSING OBSERVATIONS FOR CITRUS CROP GROWTH STAGE AND EVAPOTRANSPIRATION ESTIMATION

2016 ◽  
Vol XLI-B8 ◽  
pp. 1037-1042 ◽  
Author(s):  
S. A. Sawant ◽  
M. Chakraborty ◽  
S. Suradhaniwar ◽  
J. Adinarayana ◽  
S. S. Durbha
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Growth Stage ◽  
Crop Growth ◽  
Water Requirement ◽  
Growth Stages ◽  
Crop Water ◽  
Horticultural Crops ◽  
Crop Growth Stages ◽  
Crop Growth Stage

Satellite based earth observation (EO) platforms have proved capability to spatio-temporally monitor changes on the earth's surface. Long term satellite missions have provided huge repository of optical remote sensing datasets, and United States Geological Survey (USGS) Landsat program is one of the oldest sources of optical EO datasets. This historical and near real time EO archive is a rich source of information to understand the seasonal changes in the horticultural crops. Citrus (Mandarin / Nagpur Orange) is one of the major horticultural crops cultivated in central India. Erratic behaviour of rainfall and dependency on groundwater for irrigation has wide impact on the citrus crop yield. Also, wide variations are reported in temperature and relative humidity causing early fruit onset and increase in crop water requirement. Therefore, there is need to study the crop growth stages and crop evapotranspiration at spatio-temporal scale for managing the scarce resources. In this study, an attempt has been made to understand the citrus crop growth stages using Normalized Difference Time Series (NDVI) time series data obtained from Landsat archives (<a href="http://earthexplorer.usgs.gov/"target="_blank">http://earthexplorer.usgs.gov/</a>). Total 388 Landsat 4, 5, 7 and 8 scenes (from year 1990 to Aug. 2015) for Worldwide Reference System (WRS) 2, path 145 and row 45 were selected to understand seasonal variations in citrus crop growth. Considering Landsat 30 meter spatial resolution to obtain homogeneous pixels with crop cover orchards larger than 2 hectare area was selected. To consider change in wavelength bandwidth (radiometric resolution) with Landsat sensors (i.e. 4, 5, 7 and 8) NDVI has been selected to obtain continuous sensor independent time series. The obtained crop growth stage information has been used to estimate citrus basal crop coefficient information (Kcb). Satellite based Kcb estimates were used with proximal agrometeorological sensing system observed relevant weather parameters for crop ET estimation. The results show that time series EO based crop growth stage estimates provide better information about geographically separated citrus orchards. Attempts are being made to estimate regional variations in citrus crop water requirement for effective irrigation planning. In future high resolution Sentinel 2 observations from European Space Agency (ESA) will be used to fill the time gaps and to get better understanding about citrus crop canopy parameters.

scholarly journals TIME SERIES ANALYSIS OF REMOTE SENSING OBSERVATIONS FOR CITRUS CROP GROWTH STAGE AND EVAPOTRANSPIRATION ESTIMATION

2016 ◽  
Vol XLI-B8 ◽  
pp. 1037-1042 ◽  
Author(s):  
S. A. Sawant ◽  
M. Chakraborty ◽  
S. Suradhaniwar ◽  
J. Adinarayana ◽  
S. S. Durbha
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Growth Stage ◽  
Crop Growth ◽  
Water Requirement ◽  
Growth Stages ◽  
Crop Water ◽  
Horticultural Crops ◽  
Crop Growth Stages ◽  
Crop Growth Stage

Satellite based earth observation (EO) platforms have proved capability to spatio-temporally monitor changes on the earth's surface. Long term satellite missions have provided huge repository of optical remote sensing datasets, and United States Geological Survey (USGS) Landsat program is one of the oldest sources of optical EO datasets. This historical and near real time EO archive is a rich source of information to understand the seasonal changes in the horticultural crops. Citrus (Mandarin / Nagpur Orange) is one of the major horticultural crops cultivated in central India. Erratic behaviour of rainfall and dependency on groundwater for irrigation has wide impact on the citrus crop yield. Also, wide variations are reported in temperature and relative humidity causing early fruit onset and increase in crop water requirement. Therefore, there is need to study the crop growth stages and crop evapotranspiration at spatio-temporal scale for managing the scarce resources. In this study, an attempt has been made to understand the citrus crop growth stages using Normalized Difference Time Series (NDVI) time series data obtained from Landsat archives (<a href="http://earthexplorer.usgs.gov/"target="_blank">http://earthexplorer.usgs.gov/</a>). Total 388 Landsat 4, 5, 7 and 8 scenes (from year 1990 to Aug. 2015) for Worldwide Reference System (WRS) 2, path 145 and row 45 were selected to understand seasonal variations in citrus crop growth. Considering Landsat 30 meter spatial resolution to obtain homogeneous pixels with crop cover orchards larger than 2 hectare area was selected. To consider change in wavelength bandwidth (radiometric resolution) with Landsat sensors (i.e. 4, 5, 7 and 8) NDVI has been selected to obtain continuous sensor independent time series. The obtained crop growth stage information has been used to estimate citrus basal crop coefficient information (Kcb). Satellite based Kcb estimates were used with proximal agrometeorological sensing system observed relevant weather parameters for crop ET estimation. The results show that time series EO based crop growth stage estimates provide better information about geographically separated citrus orchards. Attempts are being made to estimate regional variations in citrus crop water requirement for effective irrigation planning. In future high resolution Sentinel 2 observations from European Space Agency (ESA) will be used to fill the time gaps and to get better understanding about citrus crop canopy parameters.

scholarly journals Assessment of crop water requirement of field pea (Pisum sativum L.) in foothills valley areas of Manipur, North East India

2021 ◽  
Vol 23 (3) ◽  
pp. 306-309
Author(s):  
LAISHRAM KANTA SINGH ◽  
INGUDAM BHUPENCHANDRA ◽  
S. ROMA DEVI
Keyword(s):  
Pisum Sativum ◽  
Water Demand ◽  
Crop Growth ◽  
Water Requirement ◽  
Crop Water Requirement ◽  
Field Pea ◽  
Growth Stages ◽  
Crop Water ◽  
Pisum Sativum L ◽  
Crop Growth Stages

The purpose of this study was to assess the evapotranspiration in field pea (Pisum sativum L.) in foothills valley areas of Manipur using the Hargreaves-Samani equation to predict the plant water demand. The crop coefficient (Kc) values ranged between 0.45 and 1.28 during the crop growth stages of field pea for the five crop seasons (2013-18). The average five-year effective rainfall was estimated to be 59.0 mm, with standard deviation (SD±) ranging between 4.4 to 35.1 mm. The average crop water requirement for field pea was estimated to be 221.0 mm and the average water demand for different crop growth stages of field pea was estimated to be 20.0 mm (initial stage), 52.0 mm (development stage), 100.0 mm (mid-season) and 49.0 mm (late season). Thus, the information generated may help in effective management of crop water requirements for sustainable crop production including field pea in the region.

scholarly journals Improving the Performance of Index Insurance Using Crop Models and Phenological Monitoring

2021 ◽  
Vol 13 (5) ◽  
pp. 924
Author(s):  
Mehdi H. Afshar ◽  
Timothy Foster ◽  
Thomas P. Higginbottom ◽  
Ben Parkes ◽  
Koen Hufkens ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Smallholder Farmers ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Index Insurance ◽  
Smallholder Farming ◽  
Yield Data ◽  
Basis Risk ◽  
Crop Models ◽  
Rice Yields

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.

scholarly journals Adoption and impact of index-insurance and credit for smallholder farmers in developing countries

2016 ◽  
Vol 76 (1) ◽  
pp. 94-118 ◽  
Author(s):  
Ana Marr ◽  
Anne Winkel ◽  
Marcel van Asseldonk ◽  
Robert Lensink ◽  
Erwin Bulte
Keyword(s):  
Developing Countries ◽  
Smallholder Farmers ◽  
Well Being ◽  
Index Insurance ◽  
Microfinance Institutions ◽  
Basis Risk ◽  
Content Type ◽  
Farm Productivity ◽  
Financial Products ◽  
The Impact

Purpose – The purpose of this paper is to review the most recent scientific literature on the determinants explaining the demand for index-insurance, the impact of index-insurance and the existing links between insurance and credit. In this meta-analysis, the authors identify key discoveries on the potential of index-insurance in enhancing credit supply for smallholders and thus farm productivity. Design/methodology/approach – Following a systematic literature search in Scopus and Web of Science, relevant empirical articles were identified by using the following criteria search algorithm: “insurance” and (“weather” or “micro” or “area?based” or “rain*” or “livestock” or “index”), and ((“empiric*” or “experiment” or “trial” or “RCT” or “impact”) or (“credit” or “loan*” or “debt” or “finance”)). The authors identified 1,133 related papers, 110 of which were selected as closely matching the study criteria. After removing duplicates and analysing each document, 45 papers were included in the current analysis. The framework for addressing insurance and credit issues, in the paper, entails three subsequent themes, namely, adoption of insurance, impact of insurance and links between insurance and credit. Findings – It is not confirmed yet that demand for insurance is indeed hump-shaped in risk aversion and the functional form of this relationship should be tested in more detail. This also holds for the magnitude of the effect of trust and education on actual demand. Furthermore, it is unclear to what extent other risk mitigation strategies form complements or substitutes to index-insurance. Lastly, the interaction between basis risk and price is important to the design of index-insurance products. If basis risk and price elasticity are indeed highly correlated, products that diminish basis risk are crucial in increasing demand. On the impact of bundled products, e.g. combination of insurance and credit, limited empirical research has been conducted. For example, it is unknown to what extent credit suppliers would react to the insured status of farmers or what the preferences of farmers are when it comes to a mix of financial products. In addition, several researchers have suggested that microfinance institutions or banks could insure themselves against covariate risk, yet no empirical evidence about this insurance mechanism has been conducted so far. Research limitations/implications – The authors based the research on scientific literature uploaded in Scopus and Web of Science. Other potentially insightful grey literature was not included due to lack of accessibility. Given the research findings, there is plenty of opportunity for further research particularly with regard to the effects of bundled products, e.g. insurance plus credit, on demand for index-insurance, supply of credit, loan conditions and impact on farm productivity and farmers’ well-being. Practical implications – Microfinance institutions, insurance companies, NGOs, research institutions and universities, particularly in developing countries, will be interested to learn about the systematic review of scientific research done in the area of insurance and credit for agriculture and the possibilities for application in their own practice of supplying these financial products. Social implications – A rigorous understanding of the potential of index-insurance and credit is essential for identifying the right mix of financial products that help smallholder farmers to increase farm productivity and their own well-being. Originality/value – The paper is valuable due to its rigorous evaluation of existing theoretical and empirical research around issues explaining the degree of adoption and impact of index-insurance and that of bundled financial products (i.e. index-insurance plus credit). The paper has the potential to become essential reading for academics, practitioners and policy-makers interested in researching and putting in practice the best options leading to greater farm productivity and well-being in developing countries.

scholarly journals Improving performance of index insurance using crop models and phenological monitoring

2020 ◽  
Author(s):  
Mehdi H. Afshar ◽  
Timothy Foster ◽  
Ben Parkes ◽  
Koen Hufkens ◽  
Francisco Ceballos ◽  
...  
Keyword(s):  
Management Practices ◽  
Growing Season ◽  
Multivariate Adaptive Regression Splines ◽  
Extreme Weather Events ◽  
Index Insurance ◽  
Growth Stages ◽  
Crop Phenology ◽  
Weather Index ◽  
Basis Risk ◽  
Insurance Contracts

&lt;p&gt;Extreme weather events pose significant risks to the livelihoods of smallholder farmers across Asia and Africa. Weather index-based insurance provides a potential solution to mitigate risks caused by crop failures, providing farmers with a payout in the event of a poor harvest. It also reduces costs relative to traditional indemnity insurance by eliminating the need for resource-intensive, in-situ assessment of losses. However, one challenge associated with weather index-based insurance is basis risk &amp;#8211; where the payouts triggered by the index do not match actual crop losses. High levels of basis risk are observed across many existing weather index-based insurance products, and represent a key constraint to successful upscaling. &amp;#160;&lt;/p&gt;&lt;p&gt;A common feature of existing weather index-based insurance contracts is that payouts are triggered based on weather indices defined over fixed calendar periods, specified to capture the typical duration of the crop growing season or key phenological stages in a given agricultural system. In reality, however, the timing of a crop&amp;#8217;s sensitivity to weather often varies significantly between individual plots or farmers due to differences in management practices (e.g., sowing date, variety choice) and meteorological conditions (e.g., temperature and precipitation) that affect rates of crop development. Failure to consider this heterogeneity is potentially a significant driver of basis risk, and suggests that opportunities may exist to improve the quality of index insurance by designing phenology-specific insurance contracts.&amp;#160;&lt;/p&gt;&lt;p&gt;In this study, we evaluate the impacts of improved monitoring of crop phenology on the performance of index-based crop yield models through a range of synthetic model-based simulated experiments for wheat and rice production in Haryana and Odisha states in India. We use a calibrated process-based crop simulation model (APSIM) to evaluate yields for a range of potential weather realizations and agricultural management practices typically observed in our case study regions. Subsequently, we develop non-linear statistical (i.e. index-based) models using non-parametric regression techniques (Multivariate adaptive regression splines; MARS) to reproduce APSIM-simulated yields as a function of rainfall and temperature conditions during key sensitive crop growth stages.&amp;#160;&lt;/p&gt;&lt;p&gt;Our results show that by considering field-level heterogeneity in crop phenology and development, it is possible to reliably estimate (&gt;0.8 r-squared) wheat and rice yields. In contrast, model performance deteriorates significantly when variability in growth stage between individual simulated fields is not considered or when weather predictors are aggregated over the entire growing season as opposed to specific growth stages. These findings show that considering crop phenology can dramatically improve the performance of statistical yield models and, in turn, the accuracy of an index-based insurance product. Nevertheless, reductions in basis risk must also be balanced against the increasing complexity and implementation costs of these potential products in smallholder environments.&lt;/p&gt;

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