Feasible crop insurance indexes for drought risk management in Northern Ethiopia

Eze, Emmanuel; Girma, Atkilt; Zenebe, Amanuel; Gebreyohannes, Zenebe

doi:10.1016/j.ijdrr.2020.101544

Cited by 42 publications

(33 citation statements)

References 15 publications

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“…The outputs of the CROWRAYEM model were applied as yield inputs in determination of insurance payouts, based on the area yield index insurance payout structure (equation 5) used by (Choudhury et al, 2016;Eze et al, 2020) The overall model evaluation statistics, especially for the CROWRAYEM, indicates prospects of improvements on the model's ability to estimate crop yields, owing to its high accuracy (Table 3).…”

Section: Application Of the Use Of Crowrayem Estimated Yields For Insmentioning

confidence: 99%

“…This idea is considered novel in the Ethiopian context, as no other known work has attempted yield estimation using our approach. The models were validated and found highly promising, especially with use for remote calculation of the area's yield insurance payouts based on an earlier work of Eze et al (2020). A study as this will address the expressed shortcoming of index insurance as lacking empirical validation and monitoring of experiences, in data scarce regions (Clarke, 2016)n.…”

Section: Introductionmentioning

confidence: 98%

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Exploring the possibilities of remote yield estimation using crop water requirements for area yield index insurance in a data-scarce dryland

Eze

Girma

Zenebe

et al. 2020

Journal of Arid Environments

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The need for accurate and meaningful agricultural data as the means of making vibrant policies and informed decisions, is an increasing concern for policymakers in developing countries such as Ethiopia, where such information is usually scarce. In Ethiopia, the impacts of climate change on crops yields is rarely available at the lowest administrative levels such as wards/villages, for the benefits of the grassroots' populace. Thus, this research sought to evaluate the use of crop water requirements in the estimation of crops' yield. FAO's CROPWAT 8.0 application was used to predetermine the possibility, preceding the use of CROWRAYEM. Both CROPWAT and CROWRAYEM had high coefficients of determination, when tested with a survey data of barley and sorghum farmers' yield for the 2015 to 2018 cropping season in semi-arid southern Tigray, northern Ethiopia. Furthermore, the infusion of the crop yield into a recently published area yield index insurance payout structure, increases the functionality of the proposed yield estimated model (CROWRAYEM).

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Section: Application Of the Use Of Crowrayem Estimated Yields For Insmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Exploring the possibilities of remote yield estimation using crop water requirements for area yield index insurance in a data-scarce dryland

Eze

Girma

Zenebe

et al. 2020

Journal of Arid Environments

Self Cite

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“…They stated that the impact of non-weather factors on crop yield might have affected the regression results. In Ethiopia, Eze et al Ref [31] evaluated the relationship between crop yield and indices derived from rainfall and the Normalized Difference Vegetation Index (NDVI). They found minimum and maximum correlations of −0.007 and 0.64, respectively, with yield showing better correlation with annual NDVI than the other indices.…”

Section: Introductionmentioning

confidence: 99%

Do Satellite Data Correlate with In Situ Rainfall and Smallholder Crop Yields? Implications for Crop Insurance

et al. 2022

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Adverse weather is one of the most prevalent sources of risk in agriculture. Its impacts are aggravated by the lack of effective risk management mechanisms. That is why resource-poor farmers tend to respond to weather risks by adopting low-capital investment, low-return, and low-risk agricultural practices. This challenge needs to be addressed with innovative risk management strategies. One of the tools that is gaining traction, especially in the developing countries, is weather-index-based insurance (WII). However, WII uptake is still low because of several constraints, one of which is basis risk. This study attempts to address this problem by evaluating the suitability of TAMSAT, CHIRPS, MODIS, and Sentinel-2 data for WII. We evaluated the first three datasets against in situ rainfall measurements at different spatial and temporal scales over the maize-growing season in a smallholder farming area in South Africa. CHIRPS had higher correlations with in situ measured rainfall data than TAMSAT and MODIS NDVI. CHIRPS performed equally well at 10 km and 25 km spatial scales, and better at monthly than daily and 16-day time steps (maximum R = 0.78, mean R = 0.72). Due to the lack of reliable historical yield data, we conducted yield surveys over three consecutive seasons using an objective crop cut method. We then assessed how well rainfall and NDVI related with maize yield. There was a poor relationship between these variables and maize yield (R2 ≤ 0.14). The study concludes by pointing out that crop yield does not always have a linear relationship with weather and vegetation indices, and that water is not always the main yield-limiting factor in smallholder farming systems. To minimize basis risk, the process of designing WII must include identification of main yield-limiting factors for specific localities. Alternatively, insurers could use crop water requirement methods to design WII.

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“…Global climate change has resulted in increasing occurrence of extreme weather and climatic events such as oods and droughts. According to Eze et al (2020), Negash et al (2019Negash et al ( , 2020, outputs of rain-fed agriculture are at risk of in uence by drought events. Droughts occur when a shortage of precipitation is received in a given season, or when there is a departure from a known average.…”

Section: Introductionmentioning

confidence: 99%

Yield and yield-reduction in recent drought years in southern Tigray, northern Ethiopia: Implications on food security

Eze

Okolo

Negash

2020

Preprint

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The consequences of prolonged precipitation-deficient periods are primarily substantial water deficit, with the spatial characteristics of a place being (semi)arid, and various socioeconomic factors worsening its impacts and deepening poverty among agrarian communities.This study utilizes a combination of climate, remote sensing and field survey data from farmers to obtain a first-hand information on the impacts of recent (2015 and 2017) droughts on crop yield in Raya Azebo and Endamehoni woredas in Ethiopia. Annual rainfall, kiremt rainfall, annual NDVI and Dev-NDVI, kiremt season Dev-NDVI, monthly Dev-NDVI (for June to October), and monthly SPI-1, SPI-3 and SPI-12 (for June to October) were considered as likely factors that could relate with yield and yield loss in the area. Correlation and multiple linear stepwise regression statistical techniques were used to determine drought-yield relationships, and identify more accurate predictors of yield and yield losses in each of the drought years.Results obtained show droughts as having spatiotemporal variations and impacts, with its primary and common reflection being reduced vegetation amount, translating to crop failures, food shortages and reduced income of smallholders.Spate irrigation should be further popularized in the low-lying areas of Raya Azebo to augment for future deficiencies in the kiremt rainfall.

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Feasible crop insurance indexes for drought risk management in Northern Ethiopia

Cited by 42 publications

References 15 publications

Exploring the possibilities of remote yield estimation using crop water requirements for area yield index insurance in a data-scarce dryland

Exploring the possibilities of remote yield estimation using crop water requirements for area yield index insurance in a data-scarce dryland

Do Satellite Data Correlate with In Situ Rainfall and Smallholder Crop Yields? Implications for Crop Insurance

Yield and yield-reduction in recent drought years in southern Tigray, northern Ethiopia: Implications on food security

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