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Modeling producer responses with dynamic programming: a case for adaptive crop management
Author(s) -
Boussios David,
Preckel Paul V.,
Yigezu Yigezu A.,
Dixit Prakash N.,
Akroush Samia,
M'hamed Hatem Cheikh,
Annabi Mohamed,
AwHassan Aden,
Shakatreh Yahya,
Abdel Hadi Omar,
AlAbdallat Ayed,
Abu El Enein Jamal,
Ayad Jamal
Publication year - 2019
Publication title -
agricultural economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.29
H-Index - 82
eISSN - 1574-0862
pISSN - 0169-5150
DOI - 10.1111/agec.12469
Subject(s) - cropping , agriculture , stochastic programming , dynamic programming , econometrics , growing season , crop , economics , agricultural economics , computer science , mathematics , statistics , ecology , mathematical optimization , biology
Past research found agricultural producers’ conditional responses during the growing season are important adaptations to weather and other stochastic events. Failing to recognize these responses overstates the risks confronting producers and understates their ability to respond to adverse circumstances. Dynamic programming (DP) provides a means for determining optimal long‐term crop management plans. However, most applications in the literature base their analysis on annual time steps with fixed strategies within the year, effectively ignoring conditional responses during the year. We suggest an alternative approach that captures the strategic responses within a cropping season to random weather variables as they unfold, reflecting farmers’ ability to adapt to weather realizations. We illustrate our approach by applying it to a typical cereal farm in Karak, Jordan. The results show that including conditional within‐year responses to weather reduces the frequency of fallowing by 23% and increases expected income by 9%.