Abstract
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.
Highlights
Maize yield has increased over the last several decades as a product of improved genetics and agronomic management practices[1,2], as the outcome of the complex genotype by environment by management (GxExM) interaction
The above-mentioned aspects of maize physiology explain a better fit of quadratic functions for response curves (Eq 1), where yield reaches an optimum at a certain agronomic optimum plant density (AOPD)[14]
A threshold for different responses to plant density was found at 40.5 N, with maize hybrid comparative relative maturity (CRM) rapidly declining for higher values based on the dataset (Fig. 2A) and as a result of the for the upper boundary (99 quantile) linear-plateau regression of the y ieldAOPD as a function of latitude (Fig. 2B2)
Summary
Maize yield has increased over the last several decades as a product of improved genetics and agronomic management practices[1,2], as the outcome of the complex genotype by environment by management (GxExM) interaction. From a breeding perspective, improved stress tolerance has been highlighted as one of the more relevant traits for the over-time yield gain process in m aize[1,2,3,4]. In this definition, ‘stress’ refers to any factor that reduces the capture or use of one or more growth resources, affecting individual plant’s growth, which is closely related to kernel set[5,6,7]. A general model for this type of response can be written out as
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