On‐farm research is becoming widespread due to a growing interest among farmers, agronomists and the research community. While the concept of on‐farm research is not new, the scale and influence of on‐farm studies conducted by farmers in partnership with industry and university researchers are expanding. New technologies, tools, and analytical approaches are rapidly evolving to advance the reach and impact of on‐farm research. Papers in this special issue include the use of on‐farm testing to increase economic and environmental sustainability in Africa and South America, the development of a new analytical collaborative frameworks for better summarization, visualization and communication of research results, and a new analytical approaches based on Bayesian hierarchical and multivariate analyses that can be used to assess complex relationships between management, genetics, soils, and the environment. Simulation approaches were also tested to identify the most efficient experimental designs and estimation methods of statistical parameters. In all situations, on‐farm research leads to improved productivity, better economics, higher adoption of conservation practice and greater farmer satisfaction.Core Ideas Precision agriculture technology adoption benefits on‐farm research expansion. New analytical and visual frameworks aid in farmer decision making. On‐farm research reduces barriers to adoption of new management techniques. On‐farm research increases farmer interest and motivation.

Current systems for developing N recommendations for corn (Zea mays L.) lack methods to quantify the effects of factors influencing yield responses to N and quantify the uncertainty in N recommendations. We utilized hierarchical modeling and Bayesian analysis to quantify the risk from reducing N to corn using on‐farm observations. Across Iowa, farmers conducted 34 trials in 2006 and 22 trials in 2007. Each trial had a farmer’s normal N rate alternating with a reduced rate (by about 30% less) in three or more replications. Yield losses (YLs) from reduced N were calculated at 35‐m intervals. Posterior distributions were used to identify across‐field and within‐field factors affecting YL and to quantify the risk of economic YL (>0.31 Mg ha⁻¹) from reducing N in unobserved fields. In 2006 (dry May and June), the economic YL for corn after soybean (C‐S) was predicted to be 20% larger than that for corn after corn. Also in 2006, C‐S fields with above‐normal June rainfall had economic YLs 35% larger than those with below‐normal June rainfall, and sidedress applications were about 20% riskier than spring applications. In 2007 for C‐S, N reductions with above‐normal spring rainfall were riskier than with below‐normal spring rainfall. Areas with higher soil organic matter (SOM) had economic YLs about 20% smaller than those with lower SOM. Many on‐farm trials can be conducted across the state and the use of the proposed statistical methodology can improve decisions on where to reduce N applications across and within fields.

From 2005 through 2009, 282 on-farm evaluation trials were conducted in soybean fields across Iowa to identify when a foliar application of pyraclostrobin produced profitable yield responses. Because of a delay in plant maturity, 218 trials exhibited a fungicide-induced “greening effect” documented using the late-season color infrared (CIR) digital aerial imagery of the soybean canopy. These 218 trials were approximately 35% more likely to produce profitable yield responses (65% vs 30%) than those without “the greening effect.” In addition, greater yield responses were observed in trials that received more than 12 inches of cumulative March through May rainfall. Potentially, site-specific observations of spring rainfall could be used to identify fields that are or are not likely to produce above break-even yield responses, and therefore, help farmers avoid unnecessary foliar fungicide applications on soybean. Accepted for publication 30 July 2013. Published 28 September 2013.