Back to Contents6.1.3 Spatial Variability of Value-Added Components of Corn, Soybeans and Wheat in Kentucky Soils
Investigators: Sam McNeill, Charles Poneleit, Scott Shearer and Steve Riggins
Significant spatial yield variations are known to exist for corn, soybeans and wheat within many different Kentucky soils. It has been speculated that similar variations might also be found among the highly valued chemical components of these crops (moisture, protein, oil or fiber). For example, the development of protein in wheat depends to some extent on the availability of nitrogen at various growth stages. Hence, the application of nitrogen between tillering and heading can improve grain protein without stimulating excessive vegetative growth (Cook and Veseth, 1991).
The protein and oil content of grain has been investigated by many research institutions where some have reported a negative correlation between grain yield and grain protein concentration (Pendleton and Dungan, 1960). A two-year Kansas study quantified the spatial distribution of protein for corn and hard red winter wheat within one field each by measuring grain nitrogen (Eisele et al., 1998). They found poor correlation between grain nitrogen and yield for corn but concluded that the protein content range of wheat might justify monitoring protein with a sensor on the combine.
Value-added components have been measured with Near-Infrared (NIR) analysis for different varieties of corn from different regions of Kentucky (Pearce and Poneleit, 1998). If significant variations occur within a given field, it might motivate the farmer to generate maps based on the specific component (e.g. high oil) and ultimately change the spatial distribution of fertilizer applications or other inputs in areas that show higher economic returns. Moreover, the equipment industry might be motivated to modify combines or develop methods that permit efficient separation of the crop in the field during harvest. Rugged on-the-go sensors would be needed to withstand machine vibrations and the dusty environment that is inherent in the field. Such harvest tools would allow farmers to automatically separate their crops from different areas of the field by quality parameters. This endeavor would add value to the grain when delivered to the elevator or milling facility where premiums are usually offered (Horstmeier, 1998; Cayce, 1998).
This project will identify the spatial variability of moisture, protein and fiber components of corn, soybeans and soft red winter wheat and the oil components of corn and soybeans within Kentucky soils that historically have high, moderate and low yield potential. High oil/phytase/ protein/starch corn, high oil/protein soybeans and high protein wheat will be investigated from production-scale fields in central and western Kentucky. A typical corn-wheat-soybean crop rotation sequence from the same fields will be included in this study. Grain quality parameters will be correlated to other spatial variables that are known to effect yield. A potential environ-mental benefit of this research includes improved fertilizer efficiency, reduced runoff and improved ground/surface water quality. Potential economic benefits could be realized, if grain were segregated by quality parameters thereby increasing crop value when delivered to the buyer.
Cooperators with yield monitors from each county of the Purchase, Pennyrile, Green River, Mammoth Cave and Lincoln Trail areas will be incorporated in this study. Two 1/100-acre plots for each indigenous value-added commodity will be established in each area to encompass the full range of yield potential. Boundary maps of the areas to be hand-harvested will be generated in May and June at each location. Noted areas will be harvested by hand just prior to combining. Grab samples will also be collected from the grain tank on the combine from selected areas within the field. All samples will be threshed/shelled by hand, dried with natural air and cleaned as needed prior to NIR analysis. A statistical analysis will be performed to compare the mean, standard deviation and coefficient of variation between hand harvested and machine harvested samples.
Annual milestones for this project are as follows: 1) identify cooperators/plot areas within fields/crop types and plant fields; 2) survey plot areas with backpack DGPS receiver and generate field maps; 3) purchase harvest equipment and calibration equations for NIR analysis; 4) harvest plots by hand from within plot areas and analyze grain samples with NIR; 5) harvest random areas within fields with combine with yield monitor and simultaneously collect grain samples for NIR analysis; 6) perform geo-statistical analysis and summarize results.
Deliverables from this project will include: 1) presentation of results at extension workshops, producer meetings and at the International Annual Meeting of the American Society of Agricultural Engineers; 2) publication of results in the Journal of Applied Engineering for Agriculture; and 3) extension publications.