Ascertaining land allocation decisions of farmers about the adoption of carinata as a potential crop for sustainable aviation fuel production in the Southern United States

Abstract The adoption of a bioenergy crop is affected by various factors, including but not limited to the characteristics of farmers, farm economics, market forces, and physical environment. This study develops a spatially explicit agent‐based model for ascertaining the adoption rate of carinata (B...

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Main Authors: Kazi Masel Ullah, Puneet Dwivedi
Format: Article
Language:English
Published: Wiley 2022-07-01
Series:GCB Bioenergy
Subjects:
Online Access:https://doi.org/10.1111/gcbb.12945
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author Kazi Masel Ullah
Puneet Dwivedi
author_facet Kazi Masel Ullah
Puneet Dwivedi
author_sort Kazi Masel Ullah
collection DOAJ
description Abstract The adoption of a bioenergy crop is affected by various factors, including but not limited to the characteristics of farmers, farm economics, market forces, and physical environment. This study develops a spatially explicit agent‐based model for ascertaining the adoption rate of carinata (Brassica carinata) among the farmers in the Little River Experimental Watershed located in the southern state of Georgia in the United States. Each farmer's adoption behavior is modeled using the profitability difference between traditional crop rotations (with and without carinata at different contract prices), the adoption rate of neighboring farmers, and their land allocation decisions from managing a risky portfolio of enterprises. Carinata production in the winter season once every 3 years has no conflict with the most profitable and popular traditional row crop rotations, such as cotton‐cotton‐cotton and cotton‐cotton‐peanut, to a larger extent. The results show that 28% and 85% of farmers in the watershed will adopt carinata after 33 years at a contract price of $13/bushel (bu) under two different assumptions of low (2.5%) and high (5%) initial neighborhood adoption rates. The proportions of land allocated to carinata to the total farmland under field crops are 38% and 85% after 33 years under the same low and high neighborhood adoption rates, respectively. Our results suggest that fixing the appropriate contract price of carinata will bring additional profits to farmers without any significant foreseeable agronomic risks, thereby increasing the adoption rate of carinata at a regional level.
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spelling doaj.art-c6093f0c95184104af23887a7ca72f3a2022-12-22T00:35:48ZengWileyGCB Bioenergy1757-16931757-17072022-07-0114782483910.1111/gcbb.12945Ascertaining land allocation decisions of farmers about the adoption of carinata as a potential crop for sustainable aviation fuel production in the Southern United StatesKazi Masel Ullah0Puneet Dwivedi1Warnell School of Forestry and Natural Resources University of Georgia Athens Georgia USAWarnell School of Forestry and Natural Resources University of Georgia Athens Georgia USAAbstract The adoption of a bioenergy crop is affected by various factors, including but not limited to the characteristics of farmers, farm economics, market forces, and physical environment. This study develops a spatially explicit agent‐based model for ascertaining the adoption rate of carinata (Brassica carinata) among the farmers in the Little River Experimental Watershed located in the southern state of Georgia in the United States. Each farmer's adoption behavior is modeled using the profitability difference between traditional crop rotations (with and without carinata at different contract prices), the adoption rate of neighboring farmers, and their land allocation decisions from managing a risky portfolio of enterprises. Carinata production in the winter season once every 3 years has no conflict with the most profitable and popular traditional row crop rotations, such as cotton‐cotton‐cotton and cotton‐cotton‐peanut, to a larger extent. The results show that 28% and 85% of farmers in the watershed will adopt carinata after 33 years at a contract price of $13/bushel (bu) under two different assumptions of low (2.5%) and high (5%) initial neighborhood adoption rates. The proportions of land allocated to carinata to the total farmland under field crops are 38% and 85% after 33 years under the same low and high neighborhood adoption rates, respectively. Our results suggest that fixing the appropriate contract price of carinata will bring additional profits to farmers without any significant foreseeable agronomic risks, thereby increasing the adoption rate of carinata at a regional level.https://doi.org/10.1111/gcbb.12945agent‐based modellingaviation sectordiffusion theoryeconomic modelingenergy cropfarm economics
spellingShingle Kazi Masel Ullah
Puneet Dwivedi
Ascertaining land allocation decisions of farmers about the adoption of carinata as a potential crop for sustainable aviation fuel production in the Southern United States
GCB Bioenergy
agent‐based modelling
aviation sector
diffusion theory
economic modeling
energy crop
farm economics
title Ascertaining land allocation decisions of farmers about the adoption of carinata as a potential crop for sustainable aviation fuel production in the Southern United States
title_full Ascertaining land allocation decisions of farmers about the adoption of carinata as a potential crop for sustainable aviation fuel production in the Southern United States
title_fullStr Ascertaining land allocation decisions of farmers about the adoption of carinata as a potential crop for sustainable aviation fuel production in the Southern United States
title_full_unstemmed Ascertaining land allocation decisions of farmers about the adoption of carinata as a potential crop for sustainable aviation fuel production in the Southern United States
title_short Ascertaining land allocation decisions of farmers about the adoption of carinata as a potential crop for sustainable aviation fuel production in the Southern United States
title_sort ascertaining land allocation decisions of farmers about the adoption of carinata as a potential crop for sustainable aviation fuel production in the southern united states
topic agent‐based modelling
aviation sector
diffusion theory
economic modeling
energy crop
farm economics
url https://doi.org/10.1111/gcbb.12945
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