Bayesian approach for maize yield response to plant density from both agronomic and economic viewpoints in North America

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.

A probabilistic framework for forecasting maize yield response to agricultural inputs with sub-seasonal climate predictions

“…EOPD was set as 10 log ( cp αβ ) β−1 , where the slope equals the cost:price ratio cp-a random variable following a normal distribution N (0.22, 0.01). A theoretical maximum EOPD of 14 plants m −2 was set [32].…”

“…Usually, a proxy to define optimum plant density is the combination of genotype and yield environment [30,31]. But, different yields may present similar OPD [32] and thus the cost of seeds needs to be considered to estimate an economic optimum plant density (EOPD). There is a need for a shift towards a new framework that predicts crop management responses accounting for the uncertainty associated to weather, maintaining the interpretability of the model at its core.…”

Differential Maize Yield Hybrid Responses to Stand Density Are Correlated to Their Response to Radiation Reductions Around Flowering

“…Crop managers are seeking information about hybrid × stand density interactions, and many seed companies are currently providing hybrid-specific recommendations for stand density management. The generation of this information comes with a large effort, in which commercial and precommercial hybrids are tested at a range of stand densities and environments to provide accurate recommendations ( Lacasa et al, 2020 ).…”

The financial analysis of nitrogen fertilizers and planting systems and its implications on maize agribusiness: Evidence from Peshawar, Pakistan

“…However, as plant population density increases, the conversion of intercepted solar radiation into maize yield decreases due to mutual shading. Furthermore, plant population density affects vegetative and reproductive growth Lacasa et al, 2020;Shi et al, 2016).…”