Contour perennial buffers within cropland reduce pollutants from watersheds, but may interfere and affect crop yields at the crop‐buffer interface. The objective of this study was to evaluate the temporal and spatial effects of agroforestry (AGF) and contour grass (CGS) buffers on no‐till corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] yields in the claypan region of Missouri. The CGS buffers (4.5‐m width) contained redtop (Agrostis gigantean Roth), brome grass (Bromus spp.), and birdsfoot trefoil (Lotus corniculatus L.), established at 35 m spacings. The AGF buffers contained, a single row of pin oak (Quercus palustris Muenchh.), swamp white oak (Q. bicolor Willd.), and bur oak (Q. macrocarpa Michx.) trees planted at 3‐m spacings in the middle of grass strips. Mean yields of corn in 2004, 2006, and 2008 and soybean in 2005, 2007, and 2009 at distances 0 to 5 m, 5 to 10 m, 10 to 15 m, and 15 to 20 m from AGF and CGS buffers were determined using geo‐referenced yield maps and ArcGIS software. Corn yield reductions at 0 to 5 m from buffers, ranged from 22 to 49% in AGF and 15 to 32% in CGS watersheds, compared to the yield at 15 to 20 m during 2004 and 2006. This reduction may have been enhanced from soil moisture stress, late planting, and different hybrids between study years. Soybean yields were not affected by buffers. Reduction of corn yields could be potentially minimized with early planting, drought‐tolerant varieties and reduction of buffers root competition with pruning or barriers.
A study was set forth to determine if lower cost GPS receivers are suitable for precision farming practices such as yield mapping. A combine equipped with a yield monitoring system was configured so that yield data could be collected simultaneously using a "lower cost" GPS receiver and a DGPS receiver. Yield maps were then produced from the same field using standard yield mapping techniques. Comparisons were then made between the maps. One comparison made was analyzing the positioning differences between the two receivers. Results showed average relative positioning differences of 7.9 feet (2.4 meters). The other comparison made was how similar the yield maps were at showing variability and yield. Visually one could not tell any difference between the maps. Using the yield maps to create management zones, similarity between the maps ranged from 66.4% to 79.7% of being the same.
The commercialization of synthetic auxin-resistant crops and the commensurate increase in post-emergent auxin-mimic herbicide applications has resulted in millions of hectares of injury to sensitive soybeans in the United States since 2016. Visual yield loss estimations following auxin injury can be difficult. The goal of this research was to determine if spectral variations following auxin injury to soybean allow for more precise yield loss estimations. Identical field experiments were performed in 2018, 2019, and 2020 in Columbia, Missouri to compare the ability of established vegetative indices to differentiate between exposure levels of 2,4-D and dicamba in soybean and predict yield loss. Soybeans were planted at three timings for growth stage separation and were exposed to sublethal rates of 2,4-D and dicamba at the R2, R1, and V3 growth stages. A UAV-mounted multispectral sensor was flown over the trial 14 days after the herbicide treatments. The results of this research found that vegetative indices incorporating the red-edge wavelength were more consistent in estimating yield loss than indices comprised of only visible or NIR wavelengths. Yield loss estimations became difficult when soybean injury occurred during later reproductive stages when soybean biomass was increased. This research also determined that when injury occurs to soybean in vegetative growth stages late in the growing season there is a greater likelihood for yield loss to occur due to decreased time for recovery. The results of this research could provide direction for more objective and accurate evaluations of yield loss following synthetic auxin injury than what is currently available.
The USA and Germany have compared the issues that surround the adoption of digital technology on the farm that will foster more environmentally sustainable food production/processing systems. Both countries lack robust broadband internet pathways to foster the adoption of these technologies. The problem is currently relevant to making this data technology available on every farm and field. The implementation of this infrastructure is even more important as society demands more and more information on the product and production process of agriculture and industry.
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