Food security and agricultural challenges in West-African rural communities: a machine learning analysis
This article investigated household-level food security for Ghana, Liberia, and Senegal. Different agroclimatic, ecological, social, and farming conditions in West Africa were represented. Using data-driven Random Forest and Chi-Square Automatic Interaction Detection (CHAID) decision tree methodolog...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | International Journal of Food Properties |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/10942912.2022.2066124 |
| _version_ | 1811300103151943680 |
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| author | Jaehyun Ahn Gary Briers Mathew Baker Edwin Price Dagbegnon Clement Sohoulande Djebou Robert Strong Manuel Piña Shahriar Kibriya |
| author_facet | Jaehyun Ahn Gary Briers Mathew Baker Edwin Price Dagbegnon Clement Sohoulande Djebou Robert Strong Manuel Piña Shahriar Kibriya |
| author_sort | Jaehyun Ahn |
| collection | DOAJ |
| description | This article investigated household-level food security for Ghana, Liberia, and Senegal. Different agroclimatic, ecological, social, and farming conditions in West Africa were represented. Using data-driven Random Forest and Chi-Square Automatic Interaction Detection (CHAID) decision tree methodology, this study classified 644 Ghanaian, 323 Liberian, and 510 Senegalese households for comparison and interpretation on food security. The predictors growing Liberian and Senegalese decision trees imply community support, diverse selling channels outside villages, resolving the dispute over farmland, and increasing community-level investment for food availability and access demonstrate household food security. Predictor importance on food security for Ghana highlighted the role of independent producers and food suppliers toward stability. Household food security or insecurity was distinguished by location-specific and gender-led households in Liberia and Senegal. Practically, the results presented a need to step-up agricultural education and extension based on an empirical field survey and its interpretations. The results can add considerations to the role of farming households as independent and individual suppliers and consumers to long-standing dimensions of food security, i.e., food availability, access, and stability. |
| first_indexed | 2024-04-13T06:46:17Z |
| format | Article |
| id | doaj.art-0728f8208eff43019feca26205498f6a |
| institution | Directory Open Access Journal |
| issn | 1094-2912 1532-2386 |
| language | English |
| last_indexed | 2024-04-13T06:46:17Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Food Properties |
| spelling | doaj.art-0728f8208eff43019feca26205498f6a2022-12-22T02:57:33ZengTaylor & Francis GroupInternational Journal of Food Properties1094-29121532-23862022-12-0125182784410.1080/10942912.2022.2066124Food security and agricultural challenges in West-African rural communities: a machine learning analysisJaehyun Ahn0Gary Briers1Mathew Baker2Edwin Price3Dagbegnon Clement Sohoulande Djebou4Robert Strong5Manuel Piña6Shahriar Kibriya7Department of Agricultural Leadership, Education, and Communications, Texas A&M University, College Station, TX, USADepartment of Agricultural Leadership, Education, and Communications, Texas A&M University, College Station, TX, USADepartment of Agricultural Leadership, Education, and Communications, Texas A&M University, College Station, TX, USADepartment of Agricultural Economics, Texas A&M University, College Station, TX, USAUSDA-ARSCoastal Plain Soil, Water and Plant Research Center, Florence, SC, USADepartment of Agricultural Leadership, Education, and Communications, Texas A&M University, College Station, TX, USADepartment of Agricultural Leadership, Education, and Communications, Texas A&M University, College Station, TX, USADepartment of Agricultural Economics, Texas A&M University, College Station, TX, USAThis article investigated household-level food security for Ghana, Liberia, and Senegal. Different agroclimatic, ecological, social, and farming conditions in West Africa were represented. Using data-driven Random Forest and Chi-Square Automatic Interaction Detection (CHAID) decision tree methodology, this study classified 644 Ghanaian, 323 Liberian, and 510 Senegalese households for comparison and interpretation on food security. The predictors growing Liberian and Senegalese decision trees imply community support, diverse selling channels outside villages, resolving the dispute over farmland, and increasing community-level investment for food availability and access demonstrate household food security. Predictor importance on food security for Ghana highlighted the role of independent producers and food suppliers toward stability. Household food security or insecurity was distinguished by location-specific and gender-led households in Liberia and Senegal. Practically, the results presented a need to step-up agricultural education and extension based on an empirical field survey and its interpretations. The results can add considerations to the role of farming households as independent and individual suppliers and consumers to long-standing dimensions of food security, i.e., food availability, access, and stability.https://www.tandfonline.com/doi/10.1080/10942912.2022.2066124Food securityMachine learningSmall-scale farmersWest Africa |
| spellingShingle | Jaehyun Ahn Gary Briers Mathew Baker Edwin Price Dagbegnon Clement Sohoulande Djebou Robert Strong Manuel Piña Shahriar Kibriya Food security and agricultural challenges in West-African rural communities: a machine learning analysis International Journal of Food Properties Food security Machine learning Small-scale farmers West Africa |
| title | Food security and agricultural challenges in West-African rural communities: a machine learning analysis |
| title_full | Food security and agricultural challenges in West-African rural communities: a machine learning analysis |
| title_fullStr | Food security and agricultural challenges in West-African rural communities: a machine learning analysis |
| title_full_unstemmed | Food security and agricultural challenges in West-African rural communities: a machine learning analysis |
| title_short | Food security and agricultural challenges in West-African rural communities: a machine learning analysis |
| title_sort | food security and agricultural challenges in west african rural communities a machine learning analysis |
| topic | Food security Machine learning Small-scale farmers West Africa |
| url | https://www.tandfonline.com/doi/10.1080/10942912.2022.2066124 |
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