Despite the large differences between winter wheat (Triticum aestivum L.) current and potential yields (i.e., yield gap, YG) in Kansas, limited research is available on individual agronomic practices, or their combination, economically increasing yield. Our objective was to quantify the contribution of individual and combined management practices to reduce the wheat YG. An incomplete factorial treatment structure established in a randomized complete block design was conducted to evaluate the effects of 14 treatments on yield, YG, protein concentration, and net returns. The variety 'Everest' was evaluated at three locations in 2016 and 2017. We individually added six treatments to a farmer practice control (FP) or removed from a water-limited yield control (Y w ), which received all treatments. Treatments were: additional N, S, Cl, increased plant population, foliar fungicide, and plant growth regulator. Under no-till which had low disease pressure, the Y w increased grain yield by 0.4 Mg ha -1 as compared with FP, mostly led by additional N, S, increased population, and fungicide (0.2-0.4 Mg ha -1 ). In conventional till which had high-disease pressure, the Y w increased grain yield by 1.2 Mg ha -1 as compared with the FP, and foliar fungicide increased grain yield by 1.4 Mg ha -1 . Foliar fungicide and increased plant population economically reduced the YG for conventional till and notill, respectively. Net return analysis indicated that intensifying wheat management might be justifiable when using low-cost fungicides and if protein premiums are expected. Our results suggest that an integrated pest management should be preferred over an Y w approach with prophylactic pesticide application.

There is limited information on agronomic practices affecting wheat (Triticum aestivum L.) yield in intensively managed dryland systems despite the opportunity to narrow the existing yield gap (YG). We used a unique database of 100 intensively managed field‐years entered in the Kansas Wheat Yield Contest during the 2010 to 2017 harvest seasons to (i) quantify the YG, (ii) describe wheat management, and (iii) identify management opportunities and weather patterns associated with yield. We simulated wheat water‐limited yield (Yw) using Simple Simulation Modeling–Wheat (SSM‐Wheat) model for each field‐year to estimate YG as the difference between Yw and actual yield (Ya) and used 11 statistical approaches to test the association of management practices and weather variables with Ya. Wheat Ya averaged 5.5 Mg ha−1, and simulated Yw averaged 6.4 Mg ha−1, resulting in a YG of 0.9 Mg ha−1 (15% of Yw). High‐yielding fields had lower maximum and minimum temperatures and greater cumulative solar radiation and precipitation during grain fill. Varieties susceptible to fungal diseases responded to foliar fungicide (0.8–1.4 Mg ha−1), whereas resistant varieties did not. Seeding rate was negatively associated with Ya, as yield quantile 0.99 was 7.5 Mg ha−1 and decreased by 2.7 Mg ha−1 for every 100‐seed m−2 increase in seeding rate above 305 seeds m−2. In‐furrow P fertilizer, previous crop, tillage practice, and N timing were also associated with Ya. We conclude that fields entered in yield contests have closed the exploitable YG, and there are opportunities to improve Ya through improved management in regions with stagnant wheat yield.

Existing crop monitoring programs determine the incidence and distribution of plant diseases and pathogens and assess the damage caused within a crop production region. These programs have traditionally used observed or predicted disease and pathogen data and environmental information to prescribe management practices that minimize crop loss. Monitoring programs are especially important for crops with broad geographic distribution or for diseases that can cause rapid and great economic losses. Successful monitoring programs have been developed for several plant diseases, including downy mildew of cucurbits, Fusarium head blight of wheat, potato late blight, and rusts of cereal crops. A recent example of a successful disease-monitoring program for an economically important crop is the soybean rust (SBR) monitoring effort within North America. SBR, caused by the fungus Phakopsora pachyrhizi, was first identified in the continental United States in November 2004. SBR causes moderate to severe yield losses globally. The fungus produces foliar lesions on soybean (Glycine max) and other legume hosts. P. pachyrhizi diverts nutrients from the host to its own growth and reproduction. The lesions also reduce photosynthetic area. Uredinia rupture the host epidermis and diminish stomatal regulation of transpiration to cause tissue desiccation and premature defoliation. Severe soybean yield losses can occur if plants defoliate during the mid-reproductive growth stages. The rapid response to the threat of SBR in North America resulted in an unprecedented amount of information dissemination and the development of a real-time, publicly available monitoring and prediction system known as the Soybean Rust-Pest Information Platform for Extension and Education (SBR-PIPE). The objectives of this article are (i) to highlight the successful response effort to SBR in North America, and (ii) to introduce researchers to the quantity and type of data generated by SBR-PIPE. Data from this system may now be used to answer questions about the biology, ecology, and epidemiology of an important pathogen and disease of soybean.

Many millions of US soybean acres that would have received at least one fungicide application remained untreated for soybean rust in 2005 due to information disseminated through the US Department of Agriculture Soybean Rust Information System website. The information provided by the system increased US producers' profits by between $11 and $299 million at a low cost of between $2.6 and $5 million (8). This savings and the positive environmental implications of not spraying millions of acres with fungicides demonstrates the value of a coordinated national pest management framework and stimulated the development of the 2006 Pest Information Platform for Extension and Education. Accepted for publication 26 June 2006. Published 15 September 2006.

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