Concurrent to yield, maize (Zea Mays L.) plant density has significantly increased over the years. Unlike yield, however, the rate of change in plant density and its contribution to maize yield gain are rarely reported. The main objectives of this study were to examine the trend in the agronomic optimum plant density (AOPD) and quantify the contribution of plant density to yield gain. Maize hybrid by seeding rate trials were conducted from 1987–2016 across North America (187,662 data points). Mixed model, response surface, and simple linear regression analyses were applied on the meta-data. New outcomes from this analysis are: (i) an increase in the AOPD at rate of 700 plant ha⁻¹ yr⁻¹, (ii) increase in the AOPD of 1386, 580 and 404 plants ha⁻¹ yr⁻¹ for very high yielding (VHY, > 13 Mg ha⁻¹), high yielding (HY, 10–13 Mg ha⁻¹) and medium yielding (MY, 7–10 Mg ha⁻¹), respectively, with a lack of change for the low yielding (LY, < 7 Mg ha⁻¹) environment; (iii) plant density contribution to maize yield gain ranged from 8.5% to 17%, and (iv) yield improvement was partially explained by changes in the AOPD but we also identified positive impacts on yield components as other sources for yield gain.

Identifying an optimal plant density is a critical management decision for corn (Zea maysL.) production. The main objectives of this study were to: (i) investigate the grain yield responses to plant density (yield–density relationship), (ii) identify best fitted yield–density response curves, and (iii) explore genotype (G) × environment (E) interaction effect on yield–density response models. Analysis was conducted on meta‐data (124,374 observations) gathered from 22 US states and 2 Canadian provinces, diverse sites (E), for years from 2000–2014 on multiple hybrids (G). Yield data were further grouped into four yield environments (low [LY], 13 Mg ha⁻¹yielding groups). Primary outcomes from this analysis were: (1) strong G × E interaction; (2) a quadratic model best fitted yield–density relationship; (3) four contrasting yield–density responses identified as dominant in each yield productivity environment, i.e., a declining, a constant, an increasing, and ever‐increasing type; (4) the yield productivity environment varied for the different corn comparative relative maturity (CRM) groups, i.e., the LY environment for long‐maturing hybrids matched with a MY or HY environment for short maturing hybrids; and (5) maximum yielding plant density (MYPD) was lower but maximum yield was greater for long‐ versus short‐maturing hybrids. In summary, optimal plant density should be decided based on detailed G × E analysis of production conditions that include factors such as CRM, yield productivity environment (weather–soil × management practices), and site information.

Soybean [Glycine max (L.) Merr.] seed composition and yield are a function of genetics (G), environment (E), and management (M) practices, but contribution of each factor to seed composition and yield are not well understood. The goal of this synthesis-analysis was to identify the main effects of G, E, and M factors on seed composition (protein and oil concentration) and yield. The entire dataset (13,574 data points) consisted of 21 studies conducted across the United States (US) between 2002 and 2017 with varying treatments and all reporting seed yield and composition. Environment (E), defined as site-year, was the dominant factor accounting for more than 70% of the variation for both seed composition and yield. Of the crop management factors: (i) delayed planting date decreased oil concentration by 0.007 to 0.06% per delayed week (R²∼0.70) and a 0.01 to 0.04 Mg ha^-1 decline in seed yield per week, mainly in northern latitudes (40–45 N); (ii) crop rotation (corn-soybean) resulted in an overall positive impact for both seed composition and yield (1.60 Mg ha^-1 positive yield difference relative to continuous soybean); and (iii) other management practices such as no-till, seed treatment, foliar nutrient application, and fungicide showed mixed results. Fertilizer N application in lower quantities (10–50 kg N ha^-1) increased both oil and protein concentration, but seed yield was improved with rates above 100 kg N ha^-1. At southern latitudes (30–35 N), trends of reduction in oil and increases in protein concentrations with later maturity groups (MG, from 3 to 7) was found. Continuing coordinated research is critical to advance our understanding of G × E × M interactions.

Corn (Zea mays L.) is one of the most sensitive crops to planting pattern and early-season uniformity. The most common method to determine number of plants is by visual inspection on the ground but this field activity becomes time-consuming, labor-intensive, biased, and may lead to less profitable decisions by farmers. The objective of this study was to develop a reliable, timely, and unbiased method for counting corn plants based on ultra-high-resolution imagery acquired from unmanned aerial systems (UAS) to automatically scout fields and applied to real field conditions. A ground sampling distance of 2.4 mm was targeted to extract information at a plant-level basis. First, an excess greenness (ExG) index was used to individualized green pixels from the background, then rows and inter-row contours were identified and extracted. A scalable training procedure was implemented using geometric descriptors as inputs of the classifier. Second, a decision tree was implemented and tested using two training modes in each site to expose the workflow to different ground conditions at the time of the aerial data acquisition. Differences in performance were due to training modes and spatial resolutions in the two sites. For an object classification task, an overall accuracy of 0.96, based on the proportion of corrected assessment of corn and non-corn objects, was obtained for local (per-site) classification, and an accuracy of 0.93 was obtained for the combined training modes. For successful model implementation, plants should have between two to three leaves when images are collected (avoiding overlapping between plants). Best workflow performance was reached at 2.4 mm resolution corresponding to 10 m of altitude (lower altitude); higher altitudes were gradually penalized. The latter was coincident with the larger number of detected green objects in the images and the effectiveness of geometry as descriptor for corn plant detection.