The objective of this research was to evaluate producers’ perspectives of four key precision agriculture technologies (variable rate fertilizer application, precision soil sampling, guidance and autosteer, and yield monitoring) in terms of the benefits they provide to their farms (increased yield, reduced production costs, and increased convenience) using a best-worst scaling choice experiment. Results indicate that farmers’ perceptions of the benefits derived from various precision agriculture technologies are heterogeneous. To better understand farmers’ adoption decisions, or lack thereof, it is important to first understand their perceptions of the benefits precision agriculture technologies provide.

We explore the relationship between precision agriculture (PA) technology adoption and technical efficiency using the 2016 USDA Agricultural Resource Management Survey (ARMS). Efficiency gains from PA are likely cumulative, that is, the true impact of precision farming depends on the integration of complementary tools. To examine the efficiency benefits of different PA bundles, we perform a two‐step analysis. First, we use cluster analysis to identify distinct producer groups based on patterns in PA technology adoption. These producer groups map naturally onto the classic technology adoption curve (laggards, late majority, early majority, innovators). Second, we use stochastic frontier analysis (SFA) and stochastic meta‐frontier analysis (SMFA) to estimate differences in technical efficiency between PA adoption groups. We find that farms with advanced PA technology bundles are significantly more technically efficient than non‐adopters. Differences in technical efficiency are not found to be driven by heterogeneous production technologies, but rather inefficiencies in input usage at the farm level. Our results have strong implications for farm consolidation in US agriculture.

Traditionally grown soybean [Glycine max (L.) Merr.] Maturity Groups V and VI are subject to late-season drought in the US Mid-South, resulting in yield reductions when planted in mid-May. Earlier maturing soybean, such as the more recently adapted Maturity Group III cultivars, have generated interest among farmers as a way to avoid the e ects of late-season drought. We investigated economically optimal plant population density for soybean considering seeding rate, row spacing, seed and soybean prices, and weather for Maturity Groups V, IV, and III grown on the rainfed soils in the rolling uplands region of the US Mid-South. ree separate experiments were conducted for Maturity Groups V, IV, and III in 2005 through 2007. Soybean was planted in mid-May in narrow (38-cm) and wide (76-cm) rows at seeding rates of 60 to 593 (´ 10 3 ) seeds ha -1 . ree key implications resulted from this study. First, yield did not respond to plant population density, and an extended yield plateau was found in each maturity group experiment, implying an economic incentive to reduce seed costs to achieve the plant population density at the minimum point on the yield plateau. Second, yield and net-return responses to row spacing were inconsistent and primarily in uenced by weather, but if Maturity Group III soybean were planted, narrow row spacing may provide higher expected yields and net returns. ird, planting Maturity Group III soybean may not overcome the susceptibility of Maturity Groups V and IV soybean to the late-season drought conditions in the rolling uplands region of the Mid-South.

Genetic testing is one way that feeder cattle producers can credibly signal quality to buyers. However, quality signaling in the presence of asymmetric information typically requires paying measurement costs. Given that previous research has indicated that the value of genetic information is generally not enough to offset the current cost of testing, we evaluate random sampling as a strategy to reduce the overall cost of testing. An economic approach to sample size determination is introduced utilizing a Bayesian decision theoretic framework to balance the expected costs and benefits of sampling. Data from 101 pens (2,796 animals) of commercially‐fed cattle are used to empirically evaluate optimal sampling. Assuming profit is linear (nonlinear) in genetic information, results indicate that at the baseline parameter values an optimal sample size of nine (five) out of 100 animals generates returns from sampling of $7.87/head ($5.96/head). Sensitivity analyses indicate that the degree of asymmetric information (absolute difference between seller and buyer prior expectations of quality) is the major driver of the overall results. The results provide strong evidence that random sampling generates benefits that far exceed the costs.

Late‐season applications of foliar N have the potential to increase protein content in hard red winter (HRW) wheat (Triticum aestivum L.), but the optimal N management strategy and economics of this decision have yet to be determined for the U.S. southern Great Plains. This study was conducted to determine the expected net return from HRW wheat managed for enhanced protein and marketed to receive a protein premium. Field experiments were conducted during the 2011 to 2013 harvest years at two Oklahoma locations to estimate the grain yield and protein content response of HRW wheat to N source (urea ammonium nitrate [UAN] or a low salt, controlled release [LSCR] specialty N), N timing (flag leaf or post‐flowering), and N rate (0, 7, 13, or 27 kg N ha−1). Late‐season N treatments significantly influenced protein, but did not significantly increase or decrease grain yield. However, the returns to protein management were US$10 to $194 ha−1 less than treatments that did not receive late‐season foliar N. Sensitivity analysis indicated that protein management could be profitable if wheat prices increase, but changes in N prices had only small impacts on net returns. Breakeven protein premiums ranged from $0.014 to $0.020 kg−1 suggesting the need to produce wheat with 12 to 13% protein content. Results from this study indicate that the ability to consistently achieve HRW wheat with this level of protein is limited. Therefore, to economically manage HRW wheat for protein content in the southern Great Plains will likely require within‐year/location‐specific N management.Core Ideas Late‐season foliar N applications may increase wheat grain protein percentage. Late‐season foliar N applications are costly. Southern Plains producers do not routinely receive a premium for high protein wheat. Additional returns were insufficient to offset the additional cost.