Crop insurance, though clearly needed, has not taken root in Kenyan agriculture, and what little exists is indemnity based, meaning that a farmer is compensated only based on assessed crop damage or harvest shortfall....Crop insurance, though clearly needed, has not taken root in Kenyan agriculture, and what little exists is indemnity based, meaning that a farmer is compensated only based on assessed crop damage or harvest shortfall. This is often cumbersome and expensive for the average subsistence farmer. A better approach is to use index based insurance, whereby an agreed index is computed and the farmer is compensated or not compensated depending on its value. Remote sensing technology, which is now widely available globally, provides such an index, the Normalized Difference Vegetation Index (NDVI), which is an acknowledged indicator of crop health at different stages of crop growth. This paper reports on a study carried out in mid-2019 to investigate the possibility of applying remote sensing in this way to enable crop insurance for maize farmers in Migori County, Kenya. Sentinel 2 imagery from May 2017 (taken as the insurance year) was acquired, classified and NDVI generated over the study area. An 8 Km × 8 Km grid was overlaid and average NDVI computed per such grid cell. Similar imagery for May 2016 was acquired and similarly processed to provide reference NDVI averages. For any grid cell then, if Ap be the insurance year NDVI and Ar the reference NDVI, the insurance index was computed as (Ap - Ar), and farmer compensation would be triggered if this value was negative. Results show that out of about 85 small holder farms in the study area, 30 would have qualified for such compensations. These results are recommended for further refining and pilot testing in the study area and similar maize growing areas.展开更多
There are estimated to be approximately 600 million small scale farmers globally, and they produce most of the food consumed, especially in the developing countries. The farmers, however, are often unable to obtain op...There are estimated to be approximately 600 million small scale farmers globally, and they produce most of the food consumed, especially in the developing countries. The farmers, however, are often unable to obtain optimal crop yields due to their exclusion from the financial systems in their countries, which deem them too high risk to lend to. This results in the farmers being unable to afford optimal inputs into their farms, hence depressing their yields and the level of food security. This study aimed to statistically determine whether the small scale farmers of Migori County in Kenya are financially excluded or not, and to what extent. Data were collected from the farmers through a questionnaire survey, and subsequent statistical analysis has shown that indeed the small scale farmers of Migori are financially excluded to a large extent. Consideration of non-financial data in the farmers’ credit rating has been recommended as a way forward towards their financial inclusivity. This study provides scientific proof of smallholder farmer financial exclusion, which proof is generally difficult to find, especially in the developing countries.展开更多
According to the Food and Agriculture Organization of the United Nations (FAO), there are about 500 million smallholder farmers in the world, and in developing countries, such farmers produce about 80% of the food con...According to the Food and Agriculture Organization of the United Nations (FAO), there are about 500 million smallholder farmers in the world, and in developing countries, such farmers produce about 80% of the food consumed there;their farming activities are therefore critical to the economies of their countries and to the global food security. However, these farmers face the challenges of limited access to credit, often due to the fact that many of them farm on unregistered land that cannot be offered as collateral to lending institutions;but even when they are on registered land, the fear of losing such land that they should default on loan payments often prevents them from applying for farm credit;and even if they apply, they still get disadvantaged by low credit scores (a measure of creditworthiness). The result is that they are often unable to use optimal farm inputs such as fertilizer and good seeds among others. This depresses their yields, and in turn, has negative implications for the food security in their communities, and in the world, hence making it difficult for the UN to achieve its sustainable goal no.2 (no hunger). This study aimed to demonstrate how geospatial technology can be used to leverage farm credit scoring for the benefit of smallholder farmers. A survey was conducted within the study area to identify the smallholder farms and farmers. A sample of surveyed farmers was then subjected to credit scoring by machine learning. In the first instance, the traditional financial data approach was used and the results showed that over 40% of the farmers could not qualify for credit. When non-financial geospatial data, i.e. Normalized Difference Vegetation Index (NDVI) was introduced into the scoring model, the number of farmers not qualifying for credit reduced significantly to 24%. It is concluded that the introduction of the NDVI variable into the traditional scoring model could improve significantly the smallholder farmers’ chances of accessing credit, thus enabling such a farmer to be better evaluated for credit on the basis of the health of their crop, rather than on a traditional form of collateral.展开更多
文摘Crop insurance, though clearly needed, has not taken root in Kenyan agriculture, and what little exists is indemnity based, meaning that a farmer is compensated only based on assessed crop damage or harvest shortfall. This is often cumbersome and expensive for the average subsistence farmer. A better approach is to use index based insurance, whereby an agreed index is computed and the farmer is compensated or not compensated depending on its value. Remote sensing technology, which is now widely available globally, provides such an index, the Normalized Difference Vegetation Index (NDVI), which is an acknowledged indicator of crop health at different stages of crop growth. This paper reports on a study carried out in mid-2019 to investigate the possibility of applying remote sensing in this way to enable crop insurance for maize farmers in Migori County, Kenya. Sentinel 2 imagery from May 2017 (taken as the insurance year) was acquired, classified and NDVI generated over the study area. An 8 Km × 8 Km grid was overlaid and average NDVI computed per such grid cell. Similar imagery for May 2016 was acquired and similarly processed to provide reference NDVI averages. For any grid cell then, if Ap be the insurance year NDVI and Ar the reference NDVI, the insurance index was computed as (Ap - Ar), and farmer compensation would be triggered if this value was negative. Results show that out of about 85 small holder farms in the study area, 30 would have qualified for such compensations. These results are recommended for further refining and pilot testing in the study area and similar maize growing areas.
文摘There are estimated to be approximately 600 million small scale farmers globally, and they produce most of the food consumed, especially in the developing countries. The farmers, however, are often unable to obtain optimal crop yields due to their exclusion from the financial systems in their countries, which deem them too high risk to lend to. This results in the farmers being unable to afford optimal inputs into their farms, hence depressing their yields and the level of food security. This study aimed to statistically determine whether the small scale farmers of Migori County in Kenya are financially excluded or not, and to what extent. Data were collected from the farmers through a questionnaire survey, and subsequent statistical analysis has shown that indeed the small scale farmers of Migori are financially excluded to a large extent. Consideration of non-financial data in the farmers’ credit rating has been recommended as a way forward towards their financial inclusivity. This study provides scientific proof of smallholder farmer financial exclusion, which proof is generally difficult to find, especially in the developing countries.
文摘According to the Food and Agriculture Organization of the United Nations (FAO), there are about 500 million smallholder farmers in the world, and in developing countries, such farmers produce about 80% of the food consumed there;their farming activities are therefore critical to the economies of their countries and to the global food security. However, these farmers face the challenges of limited access to credit, often due to the fact that many of them farm on unregistered land that cannot be offered as collateral to lending institutions;but even when they are on registered land, the fear of losing such land that they should default on loan payments often prevents them from applying for farm credit;and even if they apply, they still get disadvantaged by low credit scores (a measure of creditworthiness). The result is that they are often unable to use optimal farm inputs such as fertilizer and good seeds among others. This depresses their yields, and in turn, has negative implications for the food security in their communities, and in the world, hence making it difficult for the UN to achieve its sustainable goal no.2 (no hunger). This study aimed to demonstrate how geospatial technology can be used to leverage farm credit scoring for the benefit of smallholder farmers. A survey was conducted within the study area to identify the smallholder farms and farmers. A sample of surveyed farmers was then subjected to credit scoring by machine learning. In the first instance, the traditional financial data approach was used and the results showed that over 40% of the farmers could not qualify for credit. When non-financial geospatial data, i.e. Normalized Difference Vegetation Index (NDVI) was introduced into the scoring model, the number of farmers not qualifying for credit reduced significantly to 24%. It is concluded that the introduction of the NDVI variable into the traditional scoring model could improve significantly the smallholder farmers’ chances of accessing credit, thus enabling such a farmer to be better evaluated for credit on the basis of the health of their crop, rather than on a traditional form of collateral.
