Author
Listed:
- Mohammad Rondhi
(Department of Agribusiness, Faculty of Agriculture, University of Jember, Land and Water Resources Management Research Group, Artificial Intelligence of Industrial Agriculture Research Group, Jember 68121, Indonesia)
- Yasuhiro Mori
(Department of Sustainable Agriculture, Rakuno Gakuen University, Ebetsu 069-8501, Hokkaido, Japan)
- Tri Candra Setiawati
(Department of Soil Science, University of Jember, Jember 68121, Indonesia)
- Anik Suwandari
(Department of Agribusiness, Faculty of Agriculture, University of Jember, Land and Water Resources Management Research Group, Artificial Intelligence of Industrial Agriculture Research Group, Jember 68121, Indonesia)
- Morioka Masako
(Research Department, Agricultural Economics Program, Obihiro University of Agriculture and Veterinary Medicine, Obihiro 080-8555, Hokkaido, Japan)
- Ebban Bagus Kuntadi
(Department of Agribusiness, Faculty of Agriculture, University of Jember, Land and Water Resources Management Research Group, Artificial Intelligence of Industrial Agriculture Research Group, Jember 68121, Indonesia)
- Subhan Arif Budiman
(Department of Soil Science, University of Jember, Jember 68121, Indonesia)
- Shohibul Ulum
(Department of Agribusiness, Faculty of Agriculture, University of Jember, Land and Water Resources Management Research Group, Artificial Intelligence of Industrial Agriculture Research Group, Jember 68121, Indonesia)
- Rizky Yanuarti
(Department of Agribusiness, Faculty of Agriculture, University of Jember, Land and Water Resources Management Research Group, Artificial Intelligence of Industrial Agriculture Research Group, Jember 68121, Indonesia)
- Rokhani
(Department of Agricultural Extension, Faculty of Agriculture, University of Jember, Jember 68121, Indonesia)
Abstract
The impact of climate change (CC) includes a decline in agricultural production due to crop damage caused by flooding and drought, which destroys crops before harvest, particularly in small-scale irrigation areas. This has led farmers to look for alternative irrigation methods, i.e., groundwater through dug-wells. However, the volume of water obtained through dug-wells is limited. This has led farmers to select the crops they would cultivate. This study aimed to examine the factors that influence farmers in selecting the crops to be cultivated through multinomial logistic regression (MLR). A total of 118 farmers in Jember and Lumajang were randomly selected and interviewed regarding the use of wells and the selection of crops to be cultivated. The dependent variables consist of three crop pattern categories. The results showed that water access variables—particularly well depth, pumped water volume, pipe length, and pump power—significantly influence crop pattern selection ( p < 0.01). Farmers adopting diversified crop patterns (food-other and mixed crop pattern) extracted substantially higher groundwater volumes (>76,659 m 3 ha −1 annually) and relied on deeper wells (>90 m) compared with the food-crop-dominated pattern. In contrast, water-use-efficient strategies were characterized by lower extraction volumes (<56,755.99 m 3 ha −1 annually), longer distribution pipes, and shallower wells (<90 m). Future research should examine the impacts of CC on aquifer depletion and the consequent implications for agricultural activities.
Suggested Citation
Mohammad Rondhi & Yasuhiro Mori & Tri Candra Setiawati & Anik Suwandari & Morioka Masako & Ebban Bagus Kuntadi & Subhan Arif Budiman & Shohibul Ulum & Rizky Yanuarti & Rokhani, 2026.
"Climate Change, Water Scarcity, and Farmer Adaptation in Small-Scale Dug-Well Irrigation Systems,"
Sustainability, MDPI, vol. 18(4), pages 1-20, February.
Handle:
RePEc:gam:jsusta:v:18:y:2026:i:4:p:2027-:d:1866374
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