Data were collected from 25 dry farmers living in East-Juab County farming a minimum of 100 acres of land. The data includes the use of tractors, plows, weeders, drills, and combines. A comparison was made between the costs of operation for nine farms between the range of 100 and 500 acres, with an average of 302 acres, producing an average of 83 acres of grain; eleven farms in the range of 501 to 1,000 acres, with an average of 729 acres;--producing an average of 243 acres of grain; and five farms in the range of 1,001 to the largest of 2,600 acres, having an average of 1,871 acres, producing an average of 769 acres of grain. Machinery costs were prorated for other crops grown. The calculations include costs of depreciation, interest, taxes, and repairs. Machinery costs per acre of grain produced for the smallest acreage group were $10.99. Costs for the medium acreage group were $5.66, and the largest acreage group were $3.21. The total costs with estimated fuel and labor amounted to $16.27 for the smallest acreage group, $10.25 for the medium acreage group, and $7.13 for the largest acreage group. A comparison was then made between the costs of four operations with custom hiring, cooperative-owned equipment, rental equipment, and the costs of the survey data for one acre of land. The costs are as follows: the smallest acreage group, $11.07; custom hiring, $9.50; rental equipment $7.57; medium acreage group, $6.89; cooperative-owned equipment $5.37; and the largest acreage group, $4.95.

In farming operation, machinery represents a major cost; therefore, good fleet management can have a great impact on the producer’s profit, especially considering the increasing costs of fuel and production inputs in recent years. One of the tasks to be accomplished in order to improve fleet management is planning the path that the machine should take to cover the field while working. Information such as distance traveled, time and fuel consumption as well as agricultural inputs wasted due to off-target-application areas are crucial in the path planning process. Parameters such as field boundary size and geometry, machine total width as well as control width resolution present a great impact on the information necessary for path planning. Researchers around the world have proposed methods that approach specific aspects related to path planning, the majority addressing machine field efficiency per-se, which a function of total time spent in the field as well as effective working time. However, wasted inputs due to off-target-application areas in the maneuvering regions, especially in oddly shaped agricultural fields might be as important as field efficiency when it comes down to the total operation cost. Thus, the main purpose of this research was to develop a path planning method that accounts for not only machinery field efficiency, but also the supply inputs. This research was accomplished in a threefold approach where in the first step an algorithm for computing off-target application area was developed, implemented and validated resulting in a computational tool that can be used to evaluate potential savings when using automatic section control on agricultural fields of complex field boundary. This tool allowed accomplishment of the second step, which was an investigation and better understanding of field size and shape as well as machine width of the effects on off-target application areas resulting in an empirical method for such estimations based on object shape descriptors. Finally, a path planning algorithm was developed and evaluated taking into consideration the aspects of machine field efficiency as well as off-target application areas.

Master of Agribusiness / Department of Agricultural Economics / Terry Griffin / Weather uncertainty plays a large role in farm management decisions. Changes in weather trends or increased variability during the growing season may alter the optimal farm management choices regarding machinery purchases, crop allocation to available acreage, varietal trait selection, and crop management practices. These farm management decisions impact the expected length of time available from planting to harvest. The dates that farmers most actively plant and harvest crops changes from year to year based on annual weather patterns that affect the number of days suitable to conduct fieldwork. This research analyzed corn planting and harvest progress, as well as the number of days suitable for fieldwork in Iowa, Kansas, and Missouri. Variability of days suitable for fieldwork across crop reporting districts within each state was reported. The total number of days suitable for fieldwork during the ‘most active’ planting and harvest weeks in each state were then analyzed to determine if increasing or decreasing trends exist and estimated as ordinary least squares (OLS) regression. The outcomes presented in this research indicated a statistically significant decreasing trend in days suitable for spring planting in Iowa, and positive trend in Missouri during fall harvest. However, no statistically significant trends were observed in Kansas for either time period. Farm management implications were examined in relation to the results of the days suitable for fieldwork analysis, specifically regarding machinery sizing decisions. Profit maximizing producers must manage machinery such that they are not over-equipped, but have adequate equipment capacity to plant and harvest all acreage within the available days suitable for fieldwork. Results of these analyses are directly of interest to farmers desiring to optimally equip their farms, agricultural lenders providing farmers with financing of equipment, and equipment manufacturers.

This dissertation investigated issues surrounding grain harvest and transportation logistics. A discrete event simulation model of grain transportation from the field to an on-farm storage facility was developed to evaluate how truck and driver resource constraints impact material flow efficiency, resource utilization, and system throughput. Harvest rate and in-field transportation were represented as a stochastic entity generation process, and service times associated with various material handling steps were represented by a combination of deterministic times and statistical distributions. The model was applied to data collected for three distinct harvest scenarios (18 total days). The observed number of deliveries was within ± 2 standard deviations of the simulation mean for 15 of the 18 input conditions examined, and on a daily basis, the median error between the simulated and observed deliveries was -4.1%. The model was expanded to simulate the whole harvest season and include temporary wet storage capacity and grain drying. Moisture content changes due to field dry down was modeled using weather data and grain equilibrium moisture content relationships and resulted in an RMSE of 0.73 pts. Dryer capacity and performance were accounted for by adjusting the specified dryer performance to the observed level of moisture removal and drying temperature. Dryer capacity was generally underpredicted, and large variations were found in the observed data. The expanded model matched the observed cumulative mass of grain delivered well and estimated the harvest would take one partial day longer than was observed. Usefulness of the model to evaluate both costs and system performance was demonstrated by conducting a sensitivity analysis and examining system changes for a hypothetical operation. A dry year and a slow drying crop had the largest impact on the system’s operating and drying costs (12.7% decrease and 10.8% increase, respectively). The impact of reducing the drying temperature to maintain quality in drying white corn had no impact on the combined drying and operating cost, but harvest took six days longer. The reduced drying capacity at lower temperatures resulted in more field drying which counteracted the reduced drying efficiency and increased field time. The sensitivity analysis demonstrated varied benefits of increased drying and transportation capacity based on how often these systems created a bottleneck in the operation. For some combinations of longer transportation times and higher harvest rates, increasing hauling and drying capacity could shorten the harvest window by a week or more at an increase in costs of less than $12 ha-1. An additional field study was conducted to examine corn harvest losses in Kentucky. Total losses for cooperator combines were found to be between 0.8%-2.4% of total yield (86 to 222 kg ha-1). On average, the combine head accounted for 66% of the measured losses, and the total losses were highly variable, with coefficients of variation ranging from 21.7% to 77.2%. Yield and harvest losses were monitored in a single field as the grain dried from 33.9% to 14.6%. There was no significant difference in the potential yield at any moisture level, and the observed yield and losses displayed little variation for moisture levels from 33.9% to 19.8%, with total losses less than 1% (82 to 130 kg dry matter ha-1). Large amounts of lodging occurred while the grain dried from 19.8% to 14.6%, which resulted in an 18.9% reduction in yield, and harvest losses in excess of 9%. Allowing the grain to field dry generally improved test weight and reduced mechanical damage, however, there was a trend of increased mold and other damage in prolonged field drying.