Legumes constitute a major component of sustainable cropping systems due to their biological nitrogen fixing potential. A field study was conducted in 2020 and 2021 at Ashanti-Mampong in the forest transition zone of ...Legumes constitute a major component of sustainable cropping systems due to their biological nitrogen fixing potential. A field study was conducted in 2020 and 2021 at Ashanti-Mampong in the forest transition zone of Ghana to quantify nitrogen credits to carrot from early (70 - 75 days) and medium maturing (80 - 85 days) cowpea varieties (Asetenapa and Soronko) respectively, and Obatanpa maize variety as a reference crop. The experimental design was a split plot with five Nitrogen levels (0, 30, 45, 60 and 90 N kg/ha) applied to carrot as sub-plots following the legumes and the maize variety as main plots. NPK (15:15:15) was applied at the rate of 250 kg/ha to provide the nitrogen. The sub-plot treatments (0, 30, 45, 60 and 90 N kg/ha) were planted following the two cowpea varieties and the maize variety as a reference crop. Soronko had the highest number of nodules (176) while Asetenapa had the lowest nodules (55). Nitrogen credit to carrot from the early-maturing cowpea (Asetenapa) was 32 N kg/ha in the first year of incorporation and 18 N kg/ha in the second year after incorporation. N-credit from the medium-maturing cowpea (Soronko) was 18 N kg/ha and 29 N kg/ha in the first and second year after incorporation respectively. Obatanpa maize variety with 0 kg N/ha fertilizer level produced the lowest carrot yield, indicating that the soil amendment increased yields. The species and maturity of legumes are important determinants of their N credit contribution to crops in rotation.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Legumes constitute a major component of sustainable cropping systems due to their biological nitrogen fixing potential. A field study was conducted in 2020 and 2021 at Ashanti-Mampong in the forest transition zone of Ghana to quantify nitrogen credits to carrot from early (70 - 75 days) and medium maturing (80 - 85 days) cowpea varieties (Asetenapa and Soronko) respectively, and Obatanpa maize variety as a reference crop. The experimental design was a split plot with five Nitrogen levels (0, 30, 45, 60 and 90 N kg/ha) applied to carrot as sub-plots following the legumes and the maize variety as main plots. NPK (15:15:15) was applied at the rate of 250 kg/ha to provide the nitrogen. The sub-plot treatments (0, 30, 45, 60 and 90 N kg/ha) were planted following the two cowpea varieties and the maize variety as a reference crop. Soronko had the highest number of nodules (176) while Asetenapa had the lowest nodules (55). Nitrogen credit to carrot from the early-maturing cowpea (Asetenapa) was 32 N kg/ha in the first year of incorporation and 18 N kg/ha in the second year after incorporation. N-credit from the medium-maturing cowpea (Soronko) was 18 N kg/ha and 29 N kg/ha in the first and second year after incorporation respectively. Obatanpa maize variety with 0 kg N/ha fertilizer level produced the lowest carrot yield, indicating that the soil amendment increased yields. The species and maturity of legumes are important determinants of their N credit contribution to crops in rotation.
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.
