Evaluation of agriculture land transformations with socio-economic influences on wheat demand and supply for food sustainability
Accurate insights into the spatial distribution of cultivated areas, land use for effective agricultural management, and improvement of food security planning, especially in developing countries. Therefore, this study examined the impact of land changes and population growth on agricultural land and...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Cogent Food & Agriculture |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/23311932.2024.2448597 |
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| author | Danish Raza Hong Shu Muhsan Ehsan Hong Fan Kamal Abdelrahman Hasnat Aslam Abdul Quddoos Rana Waqar Aslam Majid Nazeer Mohammed S. Fnais Azeem Sardar |
| author_facet | Danish Raza Hong Shu Muhsan Ehsan Hong Fan Kamal Abdelrahman Hasnat Aslam Abdul Quddoos Rana Waqar Aslam Majid Nazeer Mohammed S. Fnais Azeem Sardar |
| author_sort | Danish Raza |
| collection | DOAJ |
| description | Accurate insights into the spatial distribution of cultivated areas, land use for effective agricultural management, and improvement of food security planning, especially in developing countries. Therefore, this study examined the impact of land changes and population growth on agricultural land and wheat crop productivity. First, by incorporating more than three decades of satellite data (1990–2022) and different Landsat missions with machine learning algorithms, high-confidence classes were defined for different land features, including cropland. Second, the wheat grown area was identified using the cropland extraction based wheat acreage assessment method (CLE-WAAM). Third, population dynamics were examined by applying an exponential growth model to forecast population growth and predict food demand. These findings necessitate the integrated methodological development for wheat demand and supply mechanisms using the two-step floating catchment area (2SFCA) approach for a more thorough analysis of socioeconomic developments. The results revealed that the cropland area was transformed into non-cropland, with a percentage of 8.01. A 79% rise in the population occured between 1990 and 2022, with a projected increase of 112% by 2030. Specifically, the wheat cultivation area decreased by 28%, despite stagnant parameters observed since 2000. The proposed method contributes efficiently to the United Nations’ sustainable development goal (02: Zero Hunger) using satellite, geospatial, and statistical data integration. |
| format | Article |
| id | doaj-art-eb40181d63ef48b48ace1bf5e53e36ac |
| institution | Kabale University |
| issn | 2331-1932 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Food & Agriculture |
| spelling | doaj-art-eb40181d63ef48b48ace1bf5e53e36ac2025-01-07T13:10:44ZengTaylor & Francis GroupCogent Food & Agriculture2331-19322025-12-0111110.1080/23311932.2024.2448597Evaluation of agriculture land transformations with socio-economic influences on wheat demand and supply for food sustainabilityDanish Raza0Hong Shu1Muhsan Ehsan2Hong Fan3Kamal Abdelrahman4Hasnat Aslam5Abdul Quddoos6Rana Waqar Aslam7Majid Nazeer8Mohammed S. Fnais9Azeem Sardar10State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaDepartment of Earth and Environmental Sciences, Bahria University, Islamabad, PakistanState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaDepartment of Geology and Geophysics, College of Science, King Saud University, Riyadh, Saudi ArabiaCollege of Agriculture and Natural Resources, University of Nebraska-Lincoln, Lincoln, Nebraska, USAState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaDepartment of Land Surveying and Geo-Informatics (LSGI), The Hong Kong Polytechnic University, Hong Kong, SAR ChinaDepartment of Geology and Geophysics, College of Science, King Saud University, Riyadh, Saudi ArabiaDepartment of Agricultural Economics, University of Agriculture, Faisalabad, PakistanAccurate insights into the spatial distribution of cultivated areas, land use for effective agricultural management, and improvement of food security planning, especially in developing countries. Therefore, this study examined the impact of land changes and population growth on agricultural land and wheat crop productivity. First, by incorporating more than three decades of satellite data (1990–2022) and different Landsat missions with machine learning algorithms, high-confidence classes were defined for different land features, including cropland. Second, the wheat grown area was identified using the cropland extraction based wheat acreage assessment method (CLE-WAAM). Third, population dynamics were examined by applying an exponential growth model to forecast population growth and predict food demand. These findings necessitate the integrated methodological development for wheat demand and supply mechanisms using the two-step floating catchment area (2SFCA) approach for a more thorough analysis of socioeconomic developments. The results revealed that the cropland area was transformed into non-cropland, with a percentage of 8.01. A 79% rise in the population occured between 1990 and 2022, with a projected increase of 112% by 2030. Specifically, the wheat cultivation area decreased by 28%, despite stagnant parameters observed since 2000. The proposed method contributes efficiently to the United Nations’ sustainable development goal (02: Zero Hunger) using satellite, geospatial, and statistical data integration.https://www.tandfonline.com/doi/10.1080/23311932.2024.2448597Agriculturemachine learning2SFCAwheat demandsustainable development goalAgriculture |
| spellingShingle | Danish Raza Hong Shu Muhsan Ehsan Hong Fan Kamal Abdelrahman Hasnat Aslam Abdul Quddoos Rana Waqar Aslam Majid Nazeer Mohammed S. Fnais Azeem Sardar Evaluation of agriculture land transformations with socio-economic influences on wheat demand and supply for food sustainability Cogent Food & Agriculture Agriculture machine learning 2SFCA wheat demand sustainable development goal Agriculture |
| title | Evaluation of agriculture land transformations with socio-economic influences on wheat demand and supply for food sustainability |
| title_full | Evaluation of agriculture land transformations with socio-economic influences on wheat demand and supply for food sustainability |
| title_fullStr | Evaluation of agriculture land transformations with socio-economic influences on wheat demand and supply for food sustainability |
| title_full_unstemmed | Evaluation of agriculture land transformations with socio-economic influences on wheat demand and supply for food sustainability |
| title_short | Evaluation of agriculture land transformations with socio-economic influences on wheat demand and supply for food sustainability |
| title_sort | evaluation of agriculture land transformations with socio economic influences on wheat demand and supply for food sustainability |
| topic | Agriculture machine learning 2SFCA wheat demand sustainable development goal Agriculture |
| url | https://www.tandfonline.com/doi/10.1080/23311932.2024.2448597 |
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