| The complexity of machinery investment decisionmaking, the substantial amounts of capital resources involved and the importance of the effects of machinery on dairy farm costs, provide the motivation for this study. Attention focuses on the decision environment in which New York State dairy farmers attempt to determine the optimum level of forage machinery investment. The principal objective is to construct and evaluate a model of this environment which can be used for research purposes and which may at a later stage provide the basis for a computerized decision aid in this area. A second objective is to investigate the consequences of using alternative methods for selecting optimum machinery complements.; The simulation model developed in this study may be used to evaluate alternative forage machinery complements on farms where forage production accounts for a major proportion of machinery operations. Machinery performance is evaluated in terms of the costs and benefits arising out of its use in forage production. The model considers investment costs, repair costs, insurance and housing costs, fuel and lubrication costs and labor costs. The benefits of forage machinery, associated with the quantity and quality of forage production, are physically measured in terms of forage nutrients produced. An estimate of the value of these nutrients in producing milk is obtained by considering the cost of nutrients not now required to be purchased. It is assumed that the nutrient requirements of the dairy herd are a function of annual milk production, that annual milk production per cow is fixed and that the farmers feeding policy is such as feed for this level of production. With a fixed nutrient requirement, variation in forage nutrient production will be reflected in changes in the purchase of feed ingredients, primarily concentrates, and these purchases may be valued at market prices.; Weather conditions are regarded as being of critical importance and their effects on machinery performance are explicitly considered. In particular the model considers the effect of weather on the scheduling of machinery operations. A simulation period of one year is used in conjunction with a daily timestep. This allows detailed consideration of the interaction between weather, crop growth, machinery scheduling and crop harvesting. The model is designed to accept daily weather observations and these were obtained from historical records.; The model is used to evaluate 12 machinery complements in four farm situations, and these were chosen to represent the range of conditions typically found in New York State dairy farms. In general, the results show that machinery complements perform as expected and the model may be accepted as an adequate representation of the system being modelled.; The study examines the effects of using alternative selection criteria in choosing the optimum machinery complement. Two versions of the profit maximization criteria are examined; a full-information version based on all available cost data, and a limited-information version which uses only data from a single "representative" year. Five criteria derived from the expected utility maximization hypothesis are examined; the mean-standard-deviation criterion, the mean-standard-deviation-skewness criterion and three criteria based on stochastic dominance analysis.; The main results of this analysis were: (a) that there is little difference between the alternative versions of the profit maximization criteria; (b) that the mean-standard-deviation-skewness criterion offers no advantage over the mean-standard-deviation criterion; and (c) that the unique optimum chosen under the profit maximization criteria is always contained in the dominant set of optima chosen by the utility maximization criteria. |
