Modeling Farmers’ Adoption Potential to New Bioenergy Crops

Close on the heals of the last post on farming, we have a new paper co-authored with Kazi Masel entitled "Modelling Farmers’ Adoption Potential to New Bioenergy Crops: An Agent-based Approach" which was presented at the 2022 Computational Social Science Society of the Americas (CSS 2022) Annual Conference. In the paper we explore the potential of farmers to adopt carinata in the state of Georgia. Carinata in an oilseed crop which could be used as a sustainable aviation fuel. Through our agent-based model our results suggest that a viable contract price made by investors could persuade farmers to adopt carinata. If this sounds of interest, below we provide the abstract to the paper along with a movie showing the model running along with some figures of the model logic and an example of one of the results. At the bottom of the post you can find the full reference to the paper and a link to a pdf of it. Similar to our other papers a detailed Overview, Design concepts and Details (ODD) protocol along with the model and the data needed to run the model has been made available at https://www.comses.net/codebase-release/5c2c06f0-3f6d-4f8d-b198-ce24b55feb2f/. This additional material allows for a more in-depth description of the model, as well as facilitates the replication of results or extension of the model.

Abstract: The use of fossil fuels is the primary source of greenhouse gas emissions but there are alternatives to these especially in the form of biofuels, fuels derived from bioenergy crops. This paper aims to determine farmers’ potential adoption rates of newly introduced bioenergy crops with a specific example of carinata in the state of Georgia. The determination is done using an agent-based modeling technique with two principal assumptions – farmers are profit maximizer and they are influenced by neighboring farmers. Two diffusion parameters (traditional and expansion) are followed along with two willingness (high and low) scenarios to switch at varying production economics to carinata and other prominent traditional field crops (cotton, peanuts, corn) in the study region. The paper finds that a contract prices around $9, $8 and $7 can be a viable option for encouraging farmers to adopt carinata in low, average, and high profit conditions, respectively. Expansion diffusion (that diffuses all over the geographical area), rather than centered to the few places like traditional diffusion at the early stage of adoption in conjunction with higher willingness conditions influences higher adoption rates in the short-term. As such, the model can be used to understand the behavioral economics of carinata in Georgia and beyond, as well as offering a potential tool to study similar bioenergy crops.

Keywords: Adoption, Agent-based modeling, Bioenergy Crops, Farming.

County-wise land availability for carinata production

Process, overview and scheduling of the model

Number of farmers who adopt carinata in the rotation years with high profit condition  (carinata yield = 60 bu/acre, carinata production cost = $260/acre)

Full Reference:

Ullah, K. and Crooks A.T., (2022), Modelling Farmers’ Adoption Potential to New Bioenergy Crops: An Agent-based Approach, The 2022 Computational Social Science Society of Americas Conference, Santa Fe, NM. (PDF)

Water reuse adoption by farmers & the impacts on local water resources using an ABM

In the past we heave explored a how farmers might sell their land but not how they might adapt new technologies or farming practices such as water reuse. But this has now changed with a new paper co-authored with Farshid Shoushtarian and  Masoud Negahban-Azar entitled "Investigating the micro-level dynamics of water reuse adoption by farmers and the impacts on local water resources using an agent-based model" which was recently published in the journal Socio-Environmental Systems Modelling. In the paper we introduce the WRAF  (water  reuse  adoption  by  farmers) model which explores how farmers might adopt water recycled water (reuse) practices. Using the model, results suggest that it might be possible through freshwater shortage or groundwater withdrawal regulations could increase recycled water use by farmers. If this sounds of interest, below we provide an abstract to the model, some figures from the agent logic (i.e., decision making), an overview of simulation results and the  full reference to the paper. Along with the paper, we have also provided more details  about the WRAF  model following the Overview, Design concepts, Details, and Decision-making (ODD) protocol along with the  NetLogo source code which can be found at https://www.comses.net/codebase-release/cc6d551e-cf0f-472e-a54b-28591cd39b4d/.

Abstract: Agricultural water reuse is gaining momentum to address freshwater scarcity worldwide. The main objective of this paper was to investigate the micro-level dynamics of water reuse adoption by farmers at the watershed scale. An agent-based model was developed to simulate agricultural water consumption and socio-hydrological dynamics. Using a case study in California, the developed model was tested, and the results showed that agricultural water reuse adoption by farmers is a gradual and time-consuming process. In addition, results also showed that agricultural water reuse could significantly decrease the water shortage (by 57.7%) and groundwater withdrawal (by 74.1%). Furthermore, our results suggest that recycled water price was the most influential factor in total recycled water consumption by farmers. Results also showed how possible freshwater shortage or groundwater withdrawal regulations could increase recycled water use by farmers. The developed model can significantly help assess how the current water reuse management practices and strategies would affect the sustainability of agricultural water resources.

Keywords: Water reuse; agent-based modelling; agricultural water management; recycled water for irrigation

(a) WRAF framework; (b) Farmers' decision-making flowchart

(a) Water reuse adoption sub-model framework; (b) Wastewater treatment plants flowchart

Representative simulation results: farmers’ water resources distribution in year one (a) andyear84(b);  available recycled water in the storage ponds of Modesto (c) and Turlock (d)wastewater treatment plants; total recycled water used by farmers in year two (e) and year 84(f)

Full Reference:

Shoushtarian, F., Negahban-Azar, M. and Crooks A.T. (2022), Investigating the Micro-level Dynamics of Water Reuse Adoption by Farmers and the Impacts on Local Water Resources using an Agent-based Model, Socio-Environmental Systems Modelling, 4: 18148. Available at https://doi.org/10.18174/sesmo.18148. (pdf)