Integrated vegetable production planning under uncertainty

Agricultural vegetable production plays an important role in the Australian economy. Australian farmers, as the main stakeholders in this sector, need to make a wide range of decisions relating to vegetable production planning and relevant logistics arrangements, which makes it a complex decision-making process. Therefore, assisting farmers with practical decision support systems is crucial in addressing the complexity of decision-making in vegetable production planning. Mathematical programming models as an important part of decision support systems are utilised to contribute to the vegetable production planning process. Crop rotation and cropping plan decisions are the key components of vegetable production planning and affect other important farm activities such as planting and harvesting. A wide range of mathematical programming models has been developed to address different aspects of vegetable production planning with crop rotation considerations. However, they have not captured some key features of real-world agricultural production planning. For example, many growers deploy crop rotation rules for three and more consecutive growing periods and do not follow a predefined growing season. Such a situation has resulted in many developed models becoming irrelevant and impractical to vegetable growers. Therefore, an integrated vegetable production planning model is developed that incorporates crop rotation, cropping plan, and harvest schedule decisions. This model proposes a new formulation for implementing rotation rules and provides an integrated vegetable production plans that are not bounded to predefined growing seasons and gives flexible harvest schedules. The contribution of this study to the literature is an integrated decision-making framework for crop rotation and cropping plan without being limited to predefined growing seasons and being able to consider more than two consecutive growing periods for rotation rules. Another aspect of complexity in vegetable production planning is the presence of uncertain factors, including crops prices and yields. These uncertain factors cause affects the grower's profit and makes it impossible to choose a single profitable cropping plan for all the possible outcomes of uncertain factors. This issue is addressed by extending the integrated model via relevant uncertainty and risk programming approaches. A two-stage stochastic programming model is adopted to deliver a stochastic version of the integrated model. Afterwards, conditional value at risk (CVaR) as a risk measure is incorporated into the model to form a risk-averse stochastic integrated vegetable production planning model. It provides a framework for vegetable growers to determine farm decisions based on their risk-taking attitudes. They can compare different production plans by making trade-offs between expected profit values and the associated risk values. Through conducting the sensitivity analysis and model validation process, secondary data from Australian vegetable growers survey is applied. Moreover, a brief inquiry from Tasmanian farmers is made to complete the required data for the model validation purpose. Examination of results indicates that the model can successfully provide integrated plans for a different type of crops and vegetables with different growing cycles and different crop rotation rules. The model can be easily extended to cases where growers deploy rotation rules with more than three crops in a row. Moreover, the risk analysis result shows the effectiveness of the developed risk-averse model. It gives a decision-making environment for growers to choose a cropping plan based on their risk-taking attitude and makes sure that a minimum average profit is secured at the end of the planning horizon. Different measures are compared through model validation process that resulted in practical managerial insights about different aspects of the vegetable production planning problem. This study particularly contributes to the vegetable growing industry as it considers more realistic features and provides a risk analysis approach to address uncertainties and the way growers react to risks.