Small-scale farming accounts for 78% of total agricultural production in Kenya and contributes to 23.5% of the country’s GDP. Their crop production activities are mostly rainfed subsistence with any surplus being sol...Small-scale farming accounts for 78% of total agricultural production in Kenya and contributes to 23.5% of the country’s GDP. Their crop production activities are mostly rainfed subsistence with any surplus being sold to bring in some income. Timely decisions on farm practices such as farm preparation and planting are critical determinants of the seasonal outcomes. In Kenya, most small-scale farmers have no reliable source of information that would help them make timely and accurate decisions. County governments have extension officers who are mandated with giving farmers advisory services to farmers but they are not able to reach most farmers due to facilitation constraints. The mode and format of sharing information is also critical since it’s important to ensure that it’s timely, well-understood and usable. This study sought to assess access to geospatial derived and other crop production information by farmers in four selected counties of Kenya. Specific objectives were to determine the profile of small-scale farmers in terms of age, education and farm size;to determine the type of information that is made available to them by County and Sub-County extension officers including the format and mode of provision;and to determine if the information provided was useful in terms of accuracy, timeliness and adequacy. The results indicated that over 80% of the farmers were over 35 years of age and over 56% were male. Majority had attained primary education (34%) or secondary education (29%) and most farmers in all the counties grew maize (71%). Notably, fellow farmers were a source of information (71%) with the frequency of sharing information being mostly seasonal (37%) and when information was available (43%). Over 66% of interviewed farmers indicating that they faced challenges while using provided information. The results from the study are insightful and helpful in determining effective ways of providing farmers with useful information to ensure maximum benefits.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
Climate change effects have had negative effects on most farmers, both small and large-scale, with weather patterns increasingly becoming unpredictable, such that farmers are unable to plan well for their farming, res...Climate change effects have had negative effects on most farmers, both small and large-scale, with weather patterns increasingly becoming unpredictable, such that farmers are unable to plan well for their farming, resulting in reduced harvests and sometimes losses for farmers. Better availability of information such as weather patterns, suitable crops, nutrient requirements based on soil types and conditions would greatly alleviate these challenges. While geospatial information is being developed and improved continuously by researchers, its accessibility and use by the counties has not been established and cannot be identified as contributing to better crop production outcomes. The aim of this study, therefore, was to assess the awareness and status of geospatial data availability and use for crop production, and the level of the relevant capacities, both human and infrastructural, in selected Counties of Kenya. A survey was conducted in the four counties of Vihiga, Kilifi, Wajir and Nyeri and key informant interviews were conducted with both management and technical County Agricultural Officers, as well as sub-county agricultural extension officers. From the results of the survey, out of the four counties, only one has adequate infrastructure in terms of hard-ware, software and connectivity to conduct useful geospatial data acquisition and processing. While most indicated awareness of the existence of geospatial data, limited resources, low skills and knowledge have restricted any meaningful sourcing of and access to data, with only 38% moderately or highly skilled in acquisition, 48% in processing and 57% in interpretation and use of geospatial data. The study concludes that moderate skills and capacities available within the counties have considerable potential to make use the available geospatial data to inform farmers accordingly and improve their farming outcomes.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
The East African Community is a regional block that brings together Kenya, Uganda, Tanzania, Rwanda, Burundi and South Sudan into various forms of economic partnership, the eventual dream being to achieve political fe...The East African Community is a regional block that brings together Kenya, Uganda, Tanzania, Rwanda, Burundi and South Sudan into various forms of economic partnership, the eventual dream being to achieve political federation. The current activities within this community, plus the block’s further development, require the generation and sharing of much geo-information to support the attendant decision-making. Such geo-information can be best served through a harmonized cartographic service with common standards. Such a harmonized service is not only lacking, but even the status of the current national services is also largely unknown. This paper reports on a study undertaken to establish this status, as represented by twelve elements of a cartographic service that the authors are able to establish. Results of the study have shown that the present national services are characterized by inadequate basic datasets that remain largely analogue. In addition, there are non-uniform spatial reference systems, inadequate cartographic human resources and lack of common mapping standards;further, funding for mapping activities remains low in national budgets. Given that over 80% of decisions are influenced by geo-spatial data, these findings point to an urgent need to improve, harmonize and digitize these services as the way forward, if the East African Community is to remain globally competitive.展开更多
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
Lake Nakuru is one of Kenya’s Rift Valley Lakes and lies within the Lake Nakuru National Park. As a key habitat for flamingos and other water birds, the lake is a major tourist attraction. Lake Nakuru National Park c...Lake Nakuru is one of Kenya’s Rift Valley Lakes and lies within the Lake Nakuru National Park. As a key habitat for flamingos and other water birds, the lake is a major tourist attraction. Lake Nakuru National Park covers an area of approximately 188 km<sup>2</sup> and is fully enclosed with a perimeter fence. The park is home to about 56 different species of mammals, 550 plant species, and 450 species of terrestrial birds as well as flamingos and other water birds. In the last decade, the lake has experienced continuous flooding, increasing the lake area from 35 km<sup>2</sup> in 2009 to 54 km<sup>2</sup> in 2018. This impacted negatively on the available space for wildlife. The main objective of this study was to investigate the effects of this flooding on the wildlife and their habitats in Lake Nakuru National Park. The methodology used Land use Land cover (LULC) interpretation of Landsat Satellite imagery from two epochs, 2009 and 2018, and integration of the results with relevant wildlife data provided by Kenya Wildlife Service. The results, which include LULC change maps and wildlife distribution maps, have shown that the flooding impacted negatively on the available space for wildlife. In addition, the floods also compromised key park infrastructures such as roads and the main gate making it very difficult to maintain the normal park operations, and hence adversely affecting the local and national economies. The information provided by this study is useful for planning mitigation measures in respect of the current and potential future flooding.展开更多
The purpose of this study was to use Geographic Information Systems (GIS) to assess the magnitude of the environmental problems caused by the Standard Gauge Railway (SGR) project on Nairobi National Park (NNP) and hen...The purpose of this study was to use Geographic Information Systems (GIS) to assess the magnitude of the environmental problems caused by the Standard Gauge Railway (SGR) project on Nairobi National Park (NNP) and hence model GIS aided solutions to the problems. People may know the impacts the SGR has or can have on the park. However, there is no research that has been done to unearth the magnitude of these impacts, hence a knowledge gap that needs to be filled. Furthermore, a deeper understanding of these impacts will open up a door for the formulation of the most appropriate solutions for the identified problems. Relevant spatial and non-spatial data, based on the objectives, were collected for processing and analysis using geospatial technologies to assess the environmental footprints before and after the planned SGR on the Nairobi National Park. The layers were overlaid to identify the most impacted areas and spatial statistical methods used to predict the expected continued impact over 5 years and 10 years. The results successfully demonstrated how the Standard Gauge Railway (SGR) has and will cause negative environmental impacts on Nairobi National Park by use of the various GIS analysis tools. The SGR-I has indeed encroached on Nairobi National Park occupying an area of 87.29 Hectares and the proposed SGR-IIA will cut across the park caving out an area of 42 Hectares. Moreover, approximately 500.61 Hectares of vegetation cover will be lost to construction and operation of the SGR. Ultimately, the noise and air pollution produced due to the SGR construction and operation will fragment the wild animals, affect the herbivores vegetation, and personnel as well. SGR encroachment into the park particularly affects the wildlife migration routes negatively. Some of the recommendations of the study are wet-spraying of cement and wet drilling to reduce dust emissions during the construction of SGR-IIA;often investigations of the construction sites and recommendation of a suitability analysis of the best SGR route to be carried out using GIS.展开更多
文摘Small-scale farming accounts for 78% of total agricultural production in Kenya and contributes to 23.5% of the country’s GDP. Their crop production activities are mostly rainfed subsistence with any surplus being sold to bring in some income. Timely decisions on farm practices such as farm preparation and planting are critical determinants of the seasonal outcomes. In Kenya, most small-scale farmers have no reliable source of information that would help them make timely and accurate decisions. County governments have extension officers who are mandated with giving farmers advisory services to farmers but they are not able to reach most farmers due to facilitation constraints. The mode and format of sharing information is also critical since it’s important to ensure that it’s timely, well-understood and usable. This study sought to assess access to geospatial derived and other crop production information by farmers in four selected counties of Kenya. Specific objectives were to determine the profile of small-scale farmers in terms of age, education and farm size;to determine the type of information that is made available to them by County and Sub-County extension officers including the format and mode of provision;and to determine if the information provided was useful in terms of accuracy, timeliness and adequacy. The results indicated that over 80% of the farmers were over 35 years of age and over 56% were male. Majority had attained primary education (34%) or secondary education (29%) and most farmers in all the counties grew maize (71%). Notably, fellow farmers were a source of information (71%) with the frequency of sharing information being mostly seasonal (37%) and when information was available (43%). Over 66% of interviewed farmers indicating that they faced challenges while using provided information. The results from the study are insightful and helpful in determining effective ways of providing farmers with useful information to ensure maximum benefits.
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.
文摘Climate change effects have had negative effects on most farmers, both small and large-scale, with weather patterns increasingly becoming unpredictable, such that farmers are unable to plan well for their farming, resulting in reduced harvests and sometimes losses for farmers. Better availability of information such as weather patterns, suitable crops, nutrient requirements based on soil types and conditions would greatly alleviate these challenges. While geospatial information is being developed and improved continuously by researchers, its accessibility and use by the counties has not been established and cannot be identified as contributing to better crop production outcomes. The aim of this study, therefore, was to assess the awareness and status of geospatial data availability and use for crop production, and the level of the relevant capacities, both human and infrastructural, in selected Counties of Kenya. A survey was conducted in the four counties of Vihiga, Kilifi, Wajir and Nyeri and key informant interviews were conducted with both management and technical County Agricultural Officers, as well as sub-county agricultural extension officers. From the results of the survey, out of the four counties, only one has adequate infrastructure in terms of hard-ware, software and connectivity to conduct useful geospatial data acquisition and processing. While most indicated awareness of the existence of geospatial data, limited resources, low skills and knowledge have restricted any meaningful sourcing of and access to data, with only 38% moderately or highly skilled in acquisition, 48% in processing and 57% in interpretation and use of geospatial data. The study concludes that moderate skills and capacities available within the counties have considerable potential to make use the available geospatial data to inform farmers accordingly and improve their farming outcomes.
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The East African Community is a regional block that brings together Kenya, Uganda, Tanzania, Rwanda, Burundi and South Sudan into various forms of economic partnership, the eventual dream being to achieve political federation. The current activities within this community, plus the block’s further development, require the generation and sharing of much geo-information to support the attendant decision-making. Such geo-information can be best served through a harmonized cartographic service with common standards. Such a harmonized service is not only lacking, but even the status of the current national services is also largely unknown. This paper reports on a study undertaken to establish this status, as represented by twelve elements of a cartographic service that the authors are able to establish. Results of the study have shown that the present national services are characterized by inadequate basic datasets that remain largely analogue. In addition, there are non-uniform spatial reference systems, inadequate cartographic human resources and lack of common mapping standards;further, funding for mapping activities remains low in national budgets. Given that over 80% of decisions are influenced by geo-spatial data, these findings point to an urgent need to improve, harmonize and digitize these services as the way forward, if the East African Community is to remain globally competitive.
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Lake Nakuru is one of Kenya’s Rift Valley Lakes and lies within the Lake Nakuru National Park. As a key habitat for flamingos and other water birds, the lake is a major tourist attraction. Lake Nakuru National Park covers an area of approximately 188 km<sup>2</sup> and is fully enclosed with a perimeter fence. The park is home to about 56 different species of mammals, 550 plant species, and 450 species of terrestrial birds as well as flamingos and other water birds. In the last decade, the lake has experienced continuous flooding, increasing the lake area from 35 km<sup>2</sup> in 2009 to 54 km<sup>2</sup> in 2018. This impacted negatively on the available space for wildlife. The main objective of this study was to investigate the effects of this flooding on the wildlife and their habitats in Lake Nakuru National Park. The methodology used Land use Land cover (LULC) interpretation of Landsat Satellite imagery from two epochs, 2009 and 2018, and integration of the results with relevant wildlife data provided by Kenya Wildlife Service. The results, which include LULC change maps and wildlife distribution maps, have shown that the flooding impacted negatively on the available space for wildlife. In addition, the floods also compromised key park infrastructures such as roads and the main gate making it very difficult to maintain the normal park operations, and hence adversely affecting the local and national economies. The information provided by this study is useful for planning mitigation measures in respect of the current and potential future flooding.
文摘The purpose of this study was to use Geographic Information Systems (GIS) to assess the magnitude of the environmental problems caused by the Standard Gauge Railway (SGR) project on Nairobi National Park (NNP) and hence model GIS aided solutions to the problems. People may know the impacts the SGR has or can have on the park. However, there is no research that has been done to unearth the magnitude of these impacts, hence a knowledge gap that needs to be filled. Furthermore, a deeper understanding of these impacts will open up a door for the formulation of the most appropriate solutions for the identified problems. Relevant spatial and non-spatial data, based on the objectives, were collected for processing and analysis using geospatial technologies to assess the environmental footprints before and after the planned SGR on the Nairobi National Park. The layers were overlaid to identify the most impacted areas and spatial statistical methods used to predict the expected continued impact over 5 years and 10 years. The results successfully demonstrated how the Standard Gauge Railway (SGR) has and will cause negative environmental impacts on Nairobi National Park by use of the various GIS analysis tools. The SGR-I has indeed encroached on Nairobi National Park occupying an area of 87.29 Hectares and the proposed SGR-IIA will cut across the park caving out an area of 42 Hectares. Moreover, approximately 500.61 Hectares of vegetation cover will be lost to construction and operation of the SGR. Ultimately, the noise and air pollution produced due to the SGR construction and operation will fragment the wild animals, affect the herbivores vegetation, and personnel as well. SGR encroachment into the park particularly affects the wildlife migration routes negatively. Some of the recommendations of the study are wet-spraying of cement and wet drilling to reduce dust emissions during the construction of SGR-IIA;often investigations of the construction sites and recommendation of a suitability analysis of the best SGR route to be carried out using GIS.
