TY - JOUR
T1 - Evaluation of agriculture land transformations with socio-economic influences on wheat demand and supply for food sustainability
AU - Raza, Danish
AU - Shu, Hong
AU - Ehsan, Muhsan
AU - Fan, Hong
AU - Abdelrahman, Kamal
AU - Aslam, Hasnat
AU - Quddoos, Abdul
AU - Aslam, Rana Waqar
AU - Nazeer, Majid
AU - Fnais, Mohammed S.
AU - Sardar, Azeem
N1 - Publisher Copyright:
© 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - 2SFCA
KW - Agriculture
KW - GIS, Remote Sensing, & Cartography
KW - Machine Learning
KW - machine learning
KW - sustainable development goal
KW - wheat demand
UR - https://www.scopus.com/pages/publications/85214942753
U2 - 10.1080/23311932.2024.2448597
DO - 10.1080/23311932.2024.2448597
M3 - Journal article
AN - SCOPUS:85214942753
SN - 2331-1932
VL - 11
JO - Cogent Food and Agriculture
JF - Cogent Food and Agriculture
IS - 1
M1 - 2448597
ER -