Search

Soybean [Glycine max (L.) Merr.] seeding rate research across North America is typically conducted in small geo‐political regions where environmental effects on the seeding rate × yield relationship are minimized. Data from 211 individual field studies (∼21,000 data points, 2007–2017) were combined from across North America ranging in yield from 1,000– 7,500 kg ha−1. Cluster analysis was used to stratify each individual field study into similar environmental (soil × climate) clusters and into high (HYL), medium (MYL), and low (LYL) yield levels. Agronomically optimal seeding rates (AOSR) were calculated and Monte Carlo risk analysis was implemented. Within the two northern most clusters the AOSR was higher in the LYL followed by the MYL and then HYL. Within the farthest south cluster, a relatively small (±15,000 seeds ha−1) change in seeding rate from the MYL was required to reach the AOSR of the LYL and HYL, respectively. The increase in seeding rate to reach the LYL AOSR was relatively greater (5x) than the decrease to reach the HYL AOSR within the northern most cluster. Regardless, seeding rates below the AOSR presented substantial risk and potential yield loss, while seeding rates above provided slight risk reduction and yield increases. Specific to LYLs and MYLs, establishing and maintaining an adequate plant stand until harvest maximized yield regardless of the seeding rate, while maximizing seed number was important with lower seeding rates. These findings will help growers manage their soybean seed investment by adjusting seeding rates based upon the productivity of the environment. [ABSTRACT FROM AUTHOR]

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 yieldto- plant density relationship via the understanding of plant establishment and yield components and by exploring the influence of weather variables defining soybean YEs. [ABSTRACT FROM AUTHOR]

Canola (Brassica napus L. cv. 'Canola') production has both economic and agronomic advantages. The objectives of this review were to summarize the key management factors determining crop productivity and to propose plausible pathways to narrow the gap between actual and potential yield. A synthesis study was conducted on data available from performance trials and by reviewing >100 reports in peerreviewed journals, extension publications, and websites. The main outcomes obtained from this synthesis suggested that canola attainable yield could be 4 Mg ha-1 with a potential maximum yield of 7 Mg ha-1. However, actual average yields in North America region were ~1.7 Mg ha-1 for the period 2000 to 2014. Available inseason water, water distribution at critical stages, and nutrient supply (soil plus fertilizer) all contribute to a significant portion of canola yield. Other management factors such as seeding rate, rotation, and cultivar selection substantially affect plant performance. Tillage might have an economic and environmental effect, but overall, the outcome of the meta-analysis did not show significant effect on yield. The review suggests that water supply, balanced nutrition, early planting (for both winter and spring types) in shallow depth (10-19 mm), high seeding rate (6 kg ha-1), and diverse rotation (canola every 3 or 4 yr) are among the best management practices to increase yields. Future lines of research should focus on improving planting operations that diminish early-season heterogeneity, finetuning optimal seeding rates based on modern varieties at varying yield environments, and searching for compatible hybrids to replant without heterogeneity at harvest. [ABSTRACT FROM AUTHOR]

Targeting the right agronomic optimum plant density (AOPD) for maize (Zea mays L.) is a critical management decision, but even more when the seed cost and grain selling price are accounted for, i.e. economic OPD (EOPD). From the perspective of improving those estimates, past studies have focused on utilizing a Frequentist (classical) approach for obtaining single-point estimates for the yield-density models. Alternative analysis models such as Bayesian computational methods can provide more reliable estimation for AOPD, EOPD and yield at those optimal densities and better quantify the scope of uncertainty and variability that may be in the data. Thus, the aims of this research were to (i) quantify AOPD, EOPD and yield at those plant densities, (ii) obtain and compare clusters of yield-density for different attainable yields and latitudes, and (iii) characterize their influence on EOPD variability under different economic scenarios, i.e. seed cost to corn price ratios. Maize hybrid by seeding rate trials were conducted in 24 US states from 2010 to 2019, in at least one county per state. This study identified common yield-density response curves as well as plant density and yield optimums for 460 site-years. Locations below 40.5 N latitude showed a positive relationship between AOPD and maximum yield, in parallel to the high potential level of productivity. At these latitudes, EOPD depended mostly on the maximum attainable yield. For the northern latitudes, EOPD was not only dependent on the attainable yield but on the cost:price ratio, with high ratios favoring reductions in EOPD at similar yields. A significant contribution from the Bayesian method was realizing that the variability of the estimators for AOPD is sometimes greater than the adjustment accounting for seed cost. Our results point at the differential response across latitudes and commercial relative maturity, as well as the significant uncertainty in the prediction of AOPD, relative to the economic value of the crop and the seed cost adjustments. [ABSTRACT FROM AUTHOR]

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.