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Nematode management for cotton production has eluded farmers and researchers. Control strategies typically rely upon highly toxic nematicide application. Site-specific management provides opportunity to improve profitability while maintaining environmental stewardship. This paper determined the potential for site-specific nematicide application by using spatial econometric analyses of on-farm experimental data to estimate cotton yield response functions with respect to environmental factors and treatment applications. Results suggest that crop yield response for a given nematode infestation level or nematicide application rate differs by soil texture, providing evidence to support the potential of site-specific nematicide application and management zone delineation. The profitability analysis related is useful to provide practical recommendations for effectively controlling nematodes via site-specific management.

Root-knot nematode infestations tend to be spatially clustered within agricultural fields and result in crop yield penalties. Site-specific nematode management provides the opportunity for producers to maximize profit while maintaining acceptable yield and reducing overuse of product. This paper determines the potential of site-specific nematicide application by using spatial econometric analyses of on-farm experiments panel data to estimate cotton yield response functions with respect to environmental factors and treatment applications. The results suggest that yield response for nematicide application differs by soil texture. The nematode populations at bloom season and nematicide treatment are significant factors in explaining yield variability. Spatial spillovers from neighboring plots also significantly impact yield estimates. The results can be used to provide practical recommendations for effectively controlling nematodes via site-specific management.

Many farmland owners opt to work directly with farmer-tenants while others choose to hire a professional farm manager to assist in the management of their land. As absentee landownership increases and more landowners become less associated with farming, professional farm managers have increased opportunity to communicate their role to potential clientele. Some landowners have an opportunity to make the most of their situation by enlisting the services of a professional farm manager, but only if they know what can be expected from a professional farm manager. We discuss some of the benefits and disadvantages to landowners and tenants of professional farm management and how professional farm managers can convey these ideas to potential clients and assist landowners to choose the best farm manager for their farm.

The role of Extension in assisting farmers conducting on-farm trials with yield monitors was assessed by direct casual observation and a formal interview during a three-year case study. Results indicate that Extension has a role in assisting farmers when selecting treatments to test, designing experiments, and interpreting statistical results.

Site-specific data is spatially variable, precluding traditional econometric analysis. Some field-scale experimental designs present logistical, operational and mathematical problems in estimating treatment differences, specifically when adjacent observations are of different treatments such as with strip-trial designs. A modified spatial interaction structure is presented to analyze strip-trial designs with spatial econometrics.

The objective of this study is to examine how factors such as government payments, soil productivity ratings, commodity selling price, corn and soybean production, and spatial attributes affect cash rental rates. Baseline estimates of the effects of government payments on cash rents are determined using a fixed effect, distributed lag model. The results of this model are compared to a distributed lag model that incorporates spatial effects. A second model estimates the impact of government subsides on farm cost structure. This is accomplished estimating a fixed effect, translog cost function that also incorporates spatial effects. The data used in the analysis is the Illinois Farm Business Farm Management (FBFM) Economic Management Analysis (EMA), containing more than five thousand Illinois FBFM clients annually from 1996 to 2001.

In the Midwest, the adoption of precision farming technologies began in the early 1990s. Research has produced profiles of early adopters, evaluated adoption trends and has identified factors that influence the adoption and profitability of precision farming. Importantly, this information is available to producers, who are interested in precision farming issues. In addition, the Midwest regional agricultural industry, strong promoters of precision farming technologies, has gained the confidence of farmers who now rely on them heavily for information on farming technologies. Precision farming in Arkansas, however, is still in its infancy. Adoption levels lag far behind those in the Midwest. Two reasons for this lag have been offered. First, some suggest that much of what is believed about the technologies in the state is based on hearsay or the results of small single farm case study analyses. Because these beliefs have not been rigorously substantiated with extensive empirical evidence it has not been possible to truly assess the status of adoption, to predict potential adoption trends, or to adequately advise farmers in a decision to include precision farming in their farm management plan. Second, others suggest that agricultural industry has not taken an active role in the promotion and sale of precision farming equipment and services. Without local availability, all the research in the world will not lead to adoption of technology in the state. The objective of this paper is to provide critical information to Arkansas agricultural producers, industry and extension with answers regarding 1) the current status of precision farming 2) the amount, source and effectiveness of precision farming promotion and 3) the potential future of precision farming in Arkansas. In the Spring of 1999, three groups, early adopters of precision farming technologies (EA), Cooperative Extension Service personnel (CES) and agricultural industry personnel (AI), were surveyed to ascertain the realities and perceptions of precision farming in Arkansas. The surveys included questions related to characteristics of early adopters, factors encouraging and hindering adoption, and the roles of CES and AI in the promotion of precision farming within Arkansas. The survey response rate was over 60 percent. To build profiles of Arkansas EA to compare responses regarding sources of precision farming information across all three groups three statistical tools were used to test hypotheses regarding factors which influence adoption. The surveys revealed that Arkansas EA are young, educated, computer using, experienced farmers controlling relatively large farms predominantly devoted to rice and soybean. These farmers currently employ yield and soil mapping, as well as VRT and GIS technologies in their operations. While many reasons (such as decreased costs, improved yields, and improved management capabilities) have been cited as factors that can encourage adoption, there are still any number of reasons why many Arkansas farmers have not yet adopted these technologies, including, technical difficulties, expense and unproven profitability. In addition, AI representatives see themselves as promoters of precision farming technologies in Arkansas while EA have cited instances of a lack of available equipment and also stated that they turn to CES rather than AI for farming information because they believe this is an unbiased source of information. The authors conclude that both reasons offered for the lag in adoption are likely and hope that these insights provide both the CES and AI representatives with information to help them focus their research and outreach activities so that more Arkansas producers can make informed decisions about precision farming.