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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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Cogent Food & Agriculture |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/23311932.2024.2448597 |
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| Summary: | 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. |
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| ISSN: | 2331-1932 |