Core Ideas Soybean seed yield response to plant density is dependent on yield environment.Low yield environments required higher plant densities than high yield environments.Plant density mainly affected per‐plant seed number.No differences in plant survival were observed among yield environments. Inconsistent soybean [Glycine max (L.) Merr.] seed yield response to plant density has been previously reported. Moreover, recent economic and productive circumstances have caused interest in within‐field variation of the agronomic optimal plant density (AOPD) for soybean. Thus, the objectives of this study were to: (i) determine the AOPD by yield environments (YE) and (ii) study variations in yield components (seed number and weight) related to the changes in seed yield response to plant density for soybean in North America. During 2013 and 2014, a total of 78 yield‐to‐plant density responses were evaluated in different regions of the United States and Canada. A soybean database evaluating multiple seeding rates ranging from 170,000 to 670,000 seeds ha−1 was collected, including final number of plants, seed yield, and its components (seed number and weight). The data was classified in YEs: low (LYE, <4 Mg ha−1), medium (MYE, 4–4.3 Mg ha−1), and high (HYE, >4.3 Mg ha−1). The main outcomes were: (i) AOPD increased by 24% from HYE to LYE, (ii) per‐plant yield increased due to a decrease in plant density: HYE > MYE > LYE, and (iii) per‐plant yield was mainly driven by seed number across plant densities within a YE, but both yield components influenced per‐plant yield across YEs. This study presents the first attempt to investigate the seed yield‐to‐plant density relationship via the understanding of plant establishment and yield components and by exploring the influence of weather variables defining soybean YEs.

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Wheat (Triticum aestivum L.) grain yield response to plant density is inconsistent, and the mechanisms driving this response are unclear. A better understanding of the factors governing this relationship could improve plant density recommendations according to specific environmental and genetics characteristics. Therefore, the aims of this paper were to: i) execute a synthesis-analysis of existing literature related to yield-plant density relationship to provide an indication of the need for different agronomic optimum plant density (AOPD) in different yield environments (YEs), and ii) explore a data set of field research studies conducted in Kansas (USA) on yield response to plant density to determine the AOPD at different YEs, evaluate the effect of tillering potential (TP) on the AOPD, and explain changes in AOPD via variations in wheat yield components. Major findings of this study are: i) the synthesis-analysis portrayed new insights of differences in AOPD at varying YEs, reducing the AOPD as the attainable yield increases (with AOPD moving from 397 pl m -2 for the low YE to 191 pl m -2 for the high YE); ii) the field dataset confirmed the trend observed in the synthesis-analysis but expanded on the physiological mechanisms underpinning the yield response to plant density for wheat, mainly highlighting the following points: a) high TP reduces the AOPD mainly in high and low YEs, b) at canopy-scale, both final number of heads and kernels per square meter were the main factors improving yield response to plant density under high TP, c) under varying YEs, at per-plant-scale, a compensation between heads per plant and kernels per head was the main factor contributing to yield with different TP.

Core Ideas Soybean yield response to seeding rate was dependent on yield environment. Optimum seeding rate increased as yield environments were reduced. Seeding rate could be reduced by 18% for high‐yielding relative to low‐yielding environments, without penalizing yields. Planting date interacts with seed yield response to seeding rate, optimum seeding rates increase with late planting. For high‐yielding environment, late planting time decreased yields regardless of the seeding rate. Optimizing seed inputs while increasing farming profit is the main purpose of variable rate seeding (VRS) technology adoption. Previous studies in corn (Zea mays L.) suggested that optimal seeding rates increase as yield productivity level increased. For soybean [Glycine max (L.) Merr.], optimal yield‐to‐seeding rate by yield level has not been fully investigated, representing a scientific knowledge gap. Therefore, a dataset was collected from 109 replicated field trials from Southern Brazil (2180 experimental units) presenting the following objectives: (i) identify the optimum seeding rate at varying yield levels (herein termed as yield environments), and (ii) explore the contribution of management factors (i.e., seeding rate, planting date, row spacing, maturity groups, growing season, yield environment, and ecological region) on soybean seed yield. Hierarchical modeling and Bayesian statistical inference were used to predict optimum seeding rate at varying yield environments, while conditional inference tree analysis was explored to identify and rank factors contributing to yield variation. The main results were: (i) soybean seeding rate increased from high‐ to low‐yielding environments; (ii) seeding rate could be reduced by 18% in high‐yielding (>5 Mg ha−1) relative to the low‐yielding (<4 Mg ha−1) environments, without penalizing yields. For improving site‐specific soybean seeding rate prescriptions, future studies should focus on the physiological mechanisms underpinning yield formation and on understanding the main factors (soil × plant × weather) contributing to the differential optimum seeding rate response.

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For maize (Zea mays L.), early planting date could be of advantage to high yields but a review of planting date effect on high-yielding data is not yet available. Following this rationale, a synthesis-analysis was conducted from the farmer annual maize contest-winner data (n = 16171 data points; 2011–2016 period); cordially provided by the National Corn Growers Association and a scientific literature dataset collected from research publications since the last three decades. The main objectives of this study were to: (i) identify spatial yield variability within the high-yielding maize dataset; (ii) understand the impacts of planting date on yield variability; (iii) explore the effect of management practices on maize yield-planting date relationship, and (iv) utilize the yield-planting date dataset collected via farmer contest-winner as a benchmarking data to be compared to the compendium of scientific literature available for yield-planting date relationship for the primary US maize producing regions. Major findings of this study are: (i) significant correlation between planting date and latitude, (ii) maize yield was maximized when planting window was 89–106 day of the year (DOY) for the 30–35°N, 107–118 DOY for the 35–40°N, <119 DOY for 40–45°N, and <129 DOY for 45–50°N, and (iii) both, yield contest and literature datasets portrayed that planting date becomes a more relevant factor when planting late, presenting a relatively smaller planting window in high-compared to low-latitudes.

Core Ideas Corn yield response to plant density and N rate were dependent on yield environment.Agronomic optimal plant density and N rate were positively correlated to yield level.Yield to density within a yield environment was independent on year, country, and hybrid.Similarity in yield frequency data distributions lead to similar yield–factor responses. Understanding the relationship of corn (Zea mays L.) yield responses to plant density and nitrogen (N) fertilization is critical to production decisions. The main objectives of this study were to (i) evaluate yield responses to plant density and fertilizer N rate at varying yields adjusting models considering a spatial component, (ii) perform a validation for the fitted models with an independent dataset, and (iii) identify key statistical parameters for the yield data distribution governing response models. Analyses were conducted with information from seven fields with 21 studies (one study per yield environment, with three environments per field) conducted from 2009 to 2017 in southern Brazil with geospatial data collected to evaluate yield response to plant density and fertilizer N rates (28911 data points) and one additional database with 12 field studies conducted from 2012 to 2015 in the US Midwest (1773 data points). Databases were divided into training and validation datasets. Field experiments evaluating both plant density and N rate were selected as training dataset. Key research findings were (i) yield–factor response models were dependent on yield environment and within a yield environment those models remained constant regardless the year, country, and hybrid for all evaluated fields, (ii) statistical models considering spatial correlation of the random errors outperformed those considering errors independent and identically distributed and, (iii) yield distribution with comparable 50% interquartile range and mode portrayed similar yield–factor relationship. In summary, fitting spatial yield–density models considering yield data distribution is critical to upscale site‐specific models to larger spatial domains.

Core Ideas Dividing a tillering N application into tillering and heading reduced wheat yield. Additional late‐season N application increased wheat protein concentration and dough quality. Late‐season N applications are economically unfit unless there is a reward for protein. Wheat yield and quality response to N management was similar across cultivars. There are opportunities to improve N management for wheat yield and quality in Southern Brazil. Nitrogen supply, environment, and cultivar determine yield and dough properties of hard red spring wheat (Triticum aestivum L.); however, the effects of broadcasting N fertilizer at heading, a growing practice in regions such as southern Brazil, have not been explored. The objectives of this study were to: (i) compare the current producer practice vs. alternative fertilizer N management strategies and (ii) quantify their interaction with cultivar and their effects on yield and its components and relevant dough properties. Field experiments were conducted using a complete factorial arrangement in a split‐plot design of three cultivars (main plots) and five N strategies (subplots) across three environments in southern Brazil. Overall, the current producer practice (all 70 kg N ha−1 applied at tillering) was appropriate to the targeted yield (3.5 Mg ha−1); splitting this fertilizer N rate into tillering and heading applications (either 35 kg N ha−1 on tillering + 35 kg N ha−1 on heading or 45 kg N ha−1 on tillering + 25 kg N ha−1 on heading) benefited protein concentration but reduced yield. Best N management resulted in the addition of one late‐season N application (70 kg N ha−1 on tillering + 23 kg N ha−1 on heading) positively impacting yield, protein concentration, dough extensibility, and alveogram index. In‐season N management is more relevant for grain quality than yield, more importantly if deductions from low protein are projected, or if premiums from increasing protein concentration exist, justifying a late‐season fertilizer N application.