Estimating farm machinery complements based on cropmix and farm size

Abstract

Machinery complement information is used in farm simulation models such as the FLIPSIM model when studying of the impacts of agricultural policies on representative farms. Since acquiring machinery complement data for FLIPSIM simulations is a lengthy process, it would be beneficial if researchers could estimate the machinery complement based on information provided by the producer panels. The purpose of this analysis was to develop and test an econometric model for estimating the machinery complement for representative crop farms. Machinery complement data was collected from 31 representative farms. These data were used to estimate Ordinary Least Squares regression models for 16 machinery complement categories for the current market value, purchase price, replacement cost, average economic life, and average age. Statistical tests were used to compare the actual and predicted machinery complements for eight test farms. The predicted and actual machinery complements for the eight test farms were simulated with their respective panel farm data for alternative farm policies using FLIPSIM. The results of the simulations were compared by testing the differences between the means of key output variables. Stochastic dominance rankings of the policies for the two machinery complements were also used. The results of the study were: Statistical tests indicated that the regression models for the three irrigation type categories, "trucks," "feed related," and the "harvesting - cotton" category would predict well and the "pickups and lawn tractors," 'harvesting - other," "fuevnurse tanks and trailers," and 'other machinery" categories would not predict as well as other categories. The statistical tests used to compare the actual and predicted machinery complements indicated that the regression models predicted well for the eight test farms. The test on the means of the key output variables for the predicted vs. actual machinery complement policy simulation indicated that using an estimated a machinery complement can result in statistically different outcomes. The stochastic dominance ranking of the policy alternatives indicated that six out of the eight farms ranked the policies in the same order when simulating with either the actual or predicted machinery complement. Because using a predicted machinery complement can result in statistically different outcomes when performing a farm level simulation, one may need to simulate using the actual machinery complement. However, if the object of the farm simulation is to rank policy alternatives, a predicted machinery complement can be used.