Development Of A Stochastic Model To Evaluate Plant Growers' Enterprise Budgets

Increased domestic concentration and international competition in the floricultural industry are forcing growers to improve resource management efficiency. Cost management and cost accounting methods are becoming key tools as growers attempt to reduce costs. These tools allow growers to allocate costs for each crop, increasing their greenhouse planning abilities. Growers have a relative high degree of risk due to potential crop and market failure. Individual growers have different tolerance for risk and risk bearing capacity. Growers need a cost accounting system that incorporates production and market risk, a system that allows them to make informed business decisions. The research reported in this paper developed a greenhouse budgeting model that incorporated risk to allow growers to compare production costs for flowers with different genetics and production technologies. This enables greenhouse growers to make production management decisions that incorporate production and market risk. The model gives growers the option of imputing their own production data to evaluate how various yield and price assumptions influence income and expense projections, and ultimately, profit. The model allows growers to compare total production cost and revenue varying grower type, production time, geographical location, operation size, and cost structure. The model evaluates budgets for growers who market to mass-market retail operations or wholesale intermediaries who sell to merchandisers or flower shops distribution channels. The model was demonstrated with sample data to illustrate how incorporating risk analysis into a grower's greenhouse budget model effects resource allocation and production decisions as compare to a budget model that does not incorporate risk. Deterministic and stochastic models were used to demonstrate differences in production decisions under various assumptions. The stochastic model introduced prices and flowering characteristics variability. The @Risk software was used to generate the random number simulation of the stochastic model, and stochastic dominance analysis was used to rank the alternatives. The result for both the deterministic and stochastic models identified the same cultivar as most profitable. However, there were differences in crop profits levels and rankings for subsequent cultivars that could influence growers' production choice decisions. The grower's risk aversion level influenced his/her choice of the most profitable cultivars in the stochastic model. The model summarizes the sources of variability that affect cost and revenue. The model enables the grower to measure effects that change in productivity might have on profit. Growers can identify items in their budget that have a greater effect on profitability, and make adjustments. The model can be used to allocate cost across activities, so the grower would be able to measure the economic impact of an item on the budget.