Site-specific fertilizer application is of primary interest among those investigating site-specific crop management. Site-specific soil moisture levels may be associated with the relative success of certain weed species, and weed competition can be affected by fertilizer application. A field study was conducted to investigate how landscape position–based site-specific nitrogen fertilizer application would influence wild oat interference in spring wheat. Wild oat was allowed to grow in spring wheat at foot and knoll landscape positions in the presence or absence of 80 to 90 kg ha−1 of spring preseed broadcast nitrogen fertilizer. In all three site–years of the study, landscape position did not affect wild oat competitiveness in wheat or relative wild oat biomass, and there were no significant landscape position by nitrogen fertilizer interactions for these variables. The lack of landscape position effect may be attributed to the lack of substantive differences in soil characteristics between landscape positions at the sites used in this study and to normal to above-average seasonal precipitation levels in all three site–years. Wild oat competitiveness in wheat was significantly greater in the presence of nitrogen fertilizer for all three site–years. The results of this study suggest that, when there are not great differences in typical soil characteristics between landscape positions and precipitation levels are normal or above normal, landscape-based site-specific nitrogen fertilizer application does not affect wild oat competitiveness in spring wheat, but preseed spring broadcast nitrogen fertilizer does make wild oat more competitive in spring wheat.

The environmental effect of weed control systems has stimulated research into new practices for weed control, such as selective herbicide application methods on weed-infested crop areas. This research used the color co-occurrence method (CCM) texture analysis to determine the effects of plant maturity on the accuracy of weed species classification of digitized images. Two different experimental combinations of weed species and maturity level were examined. The weed species evaluated were ivyleaf morningglory, giant foxtail, large crabgrass, and velvetleaf, with soil image sets added to each experiment. One study examined classification accuracies for two weed species at three maturity levels, and the second study examined four weed species at two maturity levels. For each species-maturity level combination, 40 digital images were collected from a manually seeded outdoor plant bed. Digitized images were transformed from the red–green–blue (RGB) color format into hue–saturation–intensity (HSI) format to generate CCM texture feature data. Stepwise variable reduction procedures were used to select texture variables with the greatest discriminant capacity. Then discriminant analysis was used to determine the classification accuracy for the two different experiments. When using HSI texture statistics, discriminant analysis correctly classified weed species within and across maturity levels with an accuracy above 97% for both experimental groups. These image processing algorithms demonstrate potential use for weed scouting, weed infestation mapping, and weed control applications using site-specific farming technology.

This study identified the factors that influenced whether farmers in theSoutheastern United States perceived an improvement in environmental qualityfrom adopting precision farming technologies (PFTs). Farmers with largerfarms or higher yields were more likely to believe that they observedpositive externalities associated with PFTs. Farmers who found PFTsprofitable or who believed input reduction was important had higherprobabilities whereas those with higher incomes or who were more dependenton farm income were less likely to perceive such benefits. Interestingly,the importance of environmental quality and length of time using PFTs werenot found to affect the probability of perceiving an improvement inenvironmental quality.