On‐farm research has emerged in recent years as a unique approach to involve farmers and other agricultural stakeholders as active participants in knowledge development and as an effective method of technology and innovation transfer across farms. This study assessed the perspective and knowledge of US farmers regarding on‐farm research via the implementation of a 24‐question survey distributed across most of the US Midwest and South‐Central regions. We found that farmers generally are willing to engage with universities to conduct on‐farm research and were 40% more likely to adopt practices supported by on‐farm research findings than research not conducted on‐farm. Notably, a shift toward conservation practices was made, with cover crops and no‐till at the forefront. Insights of this nature have implications for fostering collaborations, addressing constraints, and maximizing the impact of on‐farm research, offering guidance for sustainable agriculture progress in the United States and beyond. Results from this research survey could be used to initiate much‐needed policies to promote on‐farm research. Further, information on the benefits and drawbacks of on‐farm research could be used in the development of studies that benefit both farmers and researchers.

Despite improvements to grain yield in maize ( Zea mays L.) during the Green Revolution, maize remained predominantly tall unlike other major cereals such as wheat ( Triticum aestivum L.) and rice ( Oryza sativa L.). Although short‐statured maize (SSM) has been a topic of interest for many decades, historical efforts to introduce it commercially have remained unsuccessful. Commercial interest in SSM has recently returned mainly due to their lodging resistance, potential for high‐density planting, and better in‐season accessibility for pest and disease management. In this article, we conduct a systematic review to examine the limitations of past SSM hybrids in regard to genetic backgrounds and traits associated with agronomic performance, and renewed interest in SSM. Our objectives are to (i) identify the limiting factors of early SSM to understand why maize lagged behind in adopting dwarf traits compared to other cereal crops, and (ii) analyze the drivers behind renewed interest in SSM to assess its agronomic significance and potential role in future crop improvement strategies. This study analyses 45 studies and 17 patents and patent applications published between 1965 and 2024. Linear regression models were used to analyze changes in yield, height, harvest index, and lodging over time in both short and tall hybrids. Based on our review results, traits used to reduce height in maize introduced undesirable defects in plant reproductive development and architecture in early SSM hybrids. Modern improvements in SSM hybrid performance are due to less severe dwarfing traits or through concentrating trait effects away from reproductive structures.

Recent climate variability and emerging diseases have led farmers toward using multiple tactics to protect corn ( Zea mays L.) yield, including fungicide applications. Despite observed yield increases from various fungicide application methods in corn, minimal research has addressed the yield component mechanisms driving these gains. Therefore, this research examined how fungicide application methods can impact corn grain fill duration, kernel weight, and grain yield. In 2022 and 2023, a research trial was established in West Lafayette, IN to examine kernel weight accumulation differences between applied fungicide treatments. In addition, two research trials were sampled in Indiana to assess corn harvest kernel number, kernel weight, and yield differences between designated treatments. Examined treatments include the following: (1) nontreated control treatment, (2) subsurface banded fungicide (flutriafol) applied at planting (starter), and (3) foliar fungicide (prothioconazole, trifloxystrobin, and fluopyram) applied at the R1 growth stage. Trial results observed corn grain yield increases at 3 of 6 site‐years and 4 of 6 site‐years from starter fungicide and R1 foliar fungicide, respectively, when compared to the nontreated due to reduced leaf disease severity. Furthermore, starter fungicide and R1 foliar fungicide increased grain fill duration on average by 3.5 and 4.5 days, respectively, and increased maximum dry kernel weight on average by 5.7% and 9.4%, respectively, across 2022 and 2023. Overall, research data show the ability of various fungicide programs to reduce leaf disease severity, lengthen grain fill duration, and increase final kernel weight, each of which helps explain the yield component mechanisms when foliar diseases are controlled.

The high demands of corn (Zea mays L.) grain production coupled with water quality goals and phosphorus (P) conservation pose a great challenge to farmers and society and necessitate improved P utilization efficiency (PUtE: grain yield per mass total P (TP) content). The objective of this study was to evaluate PUtE among six Pioneer corn hybrids released over a span of 75 years. Corn was grown in a sand culture hydroponics system that eliminated confounding plant–soil interactions and root architecture and allowed for precise control of nutrient availability. Four P concentration levels (4, 7, 10, and 12 mg P L⁻¹) were applied to hybrids released in 1936, 1942, 1946, 1952, 2008, and 2011. Nutrients other than P were applied at sufficient levels. Shoots and roots were harvested at maturity (R6) and biomass and P concentration determined. Results showed that total biomass did not differ among hybrids, but partitioning of biomass varied with hybrid. Grain yield varied between hybrids, but there was no trend with the year of release. Grain P content was negatively correlated with stem P content (R² = 0.89). PUtE differed between the most recently released hybrids (2008 and 2011) whereas older hybrids had intermediate and similar PUtE. Grain yield was not solely determined by TP in the plant, but was strongly influenced by biomass and P partitioning, which was manifested as relative differences in PUtE between hybrids. While the PUtE did not necessarily change as a function of the breeding period, there were differences between hybrids. The findings highlight the critical role of the source–sink relationship in determining PUtE and grain yield.

Predicting crop nitrogen (N) fertilizer needs is a major challenge in contemporary agriculture. Despite the success of current N recommendation tools, environmental concerns over N pollution from agriculture, and the adoption of improved corn ( Zea mays L.) technologies with enhanced N efficiencies highlight the need for more accurate N fertilizer recommendation systems. Here, we aimed to develop a methodology to predict corn N requirements based on delta yield (dY = maximum yield−unfertilized yield). To develop this delta yield‐based nitrogen (dY‐based N) tool, we selected 486 quadratic‐plateau corn yield response to N curves (from 732 N rate trials across northern US) to calculate dY and N fertilizer required to reach the yield plateau (N _x ). The economic optimum nitrogen rate (EONR) was calculated using different fertilizer:crop price ratios (PR). The response curve outputs were then partitioned into calibration and validation sets. The calibration set was used to select linear models to predict N _x based on dY, resulting in nine state, agroecosystem region, and irrigation‐specific sub‐models. These sub‐models predicted N _x of the validation set with a mean absolute error (MAE) of 33.0 kg N ha ⁻¹ . Predicted values from the site‐year quadratic‐plateau response fits were used to improve further predictions’ outcomes. Predictions of EONR based on dY had a lower MAE than the predictions of N _x , ranging between 19.9 and 25.4 kg N ha ⁻¹ depending on the PR, highlighting the system's predictive power. The exclusion of non‐responsive and linear‐response trials in our proposed dY‐based approach enables future model refinement to improve EONR prediction accuracy across a broader range of yield responses to fertilizer‐N rates. The proposed dY‐based N system, which integrates both economic and agronomic inputs (including management, environmental effects on soil N supply, and maximum yields), could help to reduce N losses and provide functional benefits for N optimization.