Optimal land distribution for ambiguous profit vegetable crops using multi-objective fuzzy linear programming

Decisions in agriculture had been driven by methodical planning to increase yields to cater to the needs of overwhelming populations while also allowing farmers to prosper. Allocating land to various crops by making use of limited resources is becoming a crucial challenge for achieving higher profit...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Indonesian Journal of Electrical Engineering and Computer Science Ročník 38; číslo 2; s. 1162
Hlavní autoři: Dixit, Pranav, Tyagi, Sohan Lal
Médium: Journal Article
Jazyk:angličtina
Vydáno: 01.05.2025
ISSN:2502-4752, 2502-4760
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Decisions in agriculture had been driven by methodical planning to increase yields to cater to the needs of overwhelming populations while also allowing farmers to prosper. Allocating land to various crops by making use of limited resources is becoming a crucial challenge for achieving higher profits. To make cropping pattern decisions, farmers traditionally rely on experience, instinct, and comparisons with their neighbors. Since profit varies depending on many factors, intuition and experience usually cannot guarantee optimal (maximum) profits. A number of research studies on linear programming (LP) have shown optimum cropping patterns when crop prices (profits) are fixed. Vegetable crops, also known as cash crops, are subject to a high degree of price volatility owing to the fact that their production is costly and they carry a significant risk of not being profitable, despite the fact that they provide higher earnings than food crops. The net returns of crops in agriculture are greatly impacted by price uncertainty. With the use of the optimization tool TORA, a step-by-step process is shown in this paper to solve the model and manage the volatility in vegetable crop profitability using fuzzy multi-objective linear programming (FMOLP).
ISSN:2502-4752
2502-4760
DOI:10.11591/ijeecs.v38.i2.pp1162-1169