An analytical study on farmland allocation in the conversion from conventional to organic farming
Creators: name: Jahantab, Mahboubeh
Publisher: name: RMIT University
Year: 2024
Subjects: classificationCode: 350301
subject: Business analytics
classificationCode: 490108
subject: Operations research

description: Organic farming refers to an agricultural system that relies on farm resources and excludes or strictly limits the use of external inputs such as synthetic fertilizers and pesticides. It enhances food quality and public health and contributes to a more sustainable environment. Although there is a growth in certified organic farmland (from 11 to 72.3 million hectares between 1999 and 2019), this constitutes only 1.5 percent of the world's agricultural farmlands in 2019. The main impediment to the conversion to organic farming is the financial difficulties that a farmer experiences in terms of a decrease in production and an increase in farming costs owning to such transitional practices. Furthermore, uncertainty in the yield, weather, and price may intensify the adverse effects of transitional practices.

This study aims to find the optimal cropping pattern and conversion plan to optimise a farmer’s payoff in the conversion to organic farming, with respect to the uncertainty in the crop’s revenue that is drawn by uncertainty in its yield and price. Reviewing the relevant literature, crop production planning has been considered in a wide range of decision-making models in the upstream level of the agricultural supply chain including cropping policy, harvesting operations, capital investments, and pest control strategies. However, there are few studies on farmland allocation while planning conversion from conventional to organic farming, and to the best of my knowledge, this work is the first that focuses on farmland allocation decisions under uncertainty during the conversion period. Also, this is the first study that numerically compares the different approaches in the conversion to organic farming. This thesis proposes three models to seek the optimal farmland allocation in conversion from conventional to organic farming.

To establish the first model, utilizing studies and surveys on organic farming methods, conversion to organic farming, and crop production planning, the technical parameters (including transitional and rotational parameters affecting the farmer’s payoff) are explored and estimated. A multi-period optimisation model in the presence of crop rotation and revenue uncertainty is then developed to sequentially decide on the starting year of conversion and cropping pattern. The multistage stochastic optimization model is approximated with a two-stage stochastic optimization model, and to embed the dynamic nature of sequential decision making a rolling horizon framework is considered for the two-stage stochastic model. The model is calibrated to represent a grower of corn and soybean in Iowa and using a seemingly unrelated regression model, crops’ revenues are simulated and utilized in the numerical experiments. The performance of the farmland allocation model is examined against other policies in the agriculture industry, including monoculture (growing only one crop) and systematic crop rotation in terms of profit shortfall (when obtained profit is less than the acceptable profit), average profit, and the minimum yearly profit. The results are reported and studied to extract insights on the cropping pattern in the conversion to organic farming. This chapter presents the excellence of the developed model in terms of the proposed rotation pattern. 

In the second model with the goal of enhancing farmer’s profit shortfall in the conversion period, by adopting gradual conversion (where the farmland is converted to organic farming on a field-by-field basis), this study tries to reduce the adverse effects of transitional farming in comparison with a whole-farmland conversion (the first model). The stochastic multi-period optimisation model is developed into a chance-constrained multi-stage optimisation model to decide on the agricultural method in addition to the cropping pattern. Conducting numerical experiments, the performance of the developed model is examined against different cropping pattern policies in both gradual and whole-farmland conversion approaches and findings are translated into insights. This chapter highlights the improvements in the profit shortfall made by the developed gradual conversion model in the farmland allocation decisions.

Finally, considering the ability of reinforcement learning to overcome the curse of dimensionality in comparison with multi-stage stochastic programming solving methods, required components for reinforcement learning including the set of states, the set of actions, and the reward function are presented to find the optimal farmland allocation policy. The performance of the reinforcement learning method under different designs of reward function is evaluated to provide insights into the conversion planning and application of reinforcement algorithms in farmland allocation decision-making.