Evaluating maize and soybean grain dry-down in the field with predictive algorithms and genotype-by-environment analysis

Abstract

A delayed harvest of maize and soybean crops is associated with yield or revenue losses, whereas a premature harvest requires additional costs for artificial grain drying. Accurately predicting the ideal harvest date can increase profitability of US Midwest farms, but today’s predictive capacity is low. To fill this gap, we collected and analyzed time-series grain moisture datasets from field experiments in Iowa, Minnesota and North Dakota, US with various maize (n = 102) and soybean (n = 36) genotype-by-environment treatments. Our goal was to examine factors driving the post-maturity grain drying process, and develop scalable algorithms for decision-making. The algorithms evaluated are driven by changes in the grain equilibrium moisture content (function of air relative humidity and temperature) and require three input parameters: moisture content at physiological maturity, a drying coefficient and a power constant. Across independent genotypes and environments, the calibrated algorithms accurately predicted grain dry-down of maize ( r 2 = 0.79; root mean square error, RMSE = 1.8% grain moisture) and soybean field crops ( r 2 = 0.72; RMSE = 6.7% grain moisture). Evaluation of variance components and treatment effects revealed that genotypes, weather-years, and planting dates had little influence on the post-maturity drying coefficient, but significantly influenced grain moisture content at physiological maturity. Therefore, accurate implementation of the algorithms across environments would require estimating the initial grain moisture content, via modeling approaches or in-field measurements. Our work contributes new insights to understand the post-maturity grain dry-down and provides a robust and scalable predictive algorithm to forecast grain dry-down and ideal harvest dates across environments in the US Corn Belt.

Transcriptomic Analysis of the Maize Mutant TC19 Identifies Genes Regulating the Development of Grain Size Through the IAA and BR Pathways

“…However, the grain size and weight of TC19 under each environmental condition were always higher than in Chang 7 − 2, indicating that grain development mainly is genetically controlled. This is consistent with previous studies [17].…”

The Influence of Seed Production Environment on Seed Development and Quality of Soybean (Glycine max (L.) Merrill)

“…This is in accordance with the findings of Olivares et al [47], who showed that extension of reproductive growing period in wild oat (Avena barbata) occurred due to rainy spells during their reproductive stage. During the LR stage, moisture content of the seeds gradually decreased, and the rate of moisture reduction and the equilibrium moisture content of the harvested seeds depended completely on the surrounding environmental conditions (temperature and relative humidity) [23]. Relatively slower moisture reduction rate (1.8% day −1 ) at R6-R7 was observed in wet compared with the dry environments (3.3% day −1 ).…”

“…The above examples clearly imply that the production environment directly affects seed maturation process and the time taken to harvest the crop which determine the final seed quality. Furthermore, grain moisture loss during seed maturation depends on both the air temperature and relative humidity, and the seed moisture at harvest affected seed quality directly [23]. According to Diniz et al [24] it is not always possible to harvest the seeds at the right time due to various factors, and it leads to loss in both the physiological and sanitary qualities of seeds.…”

A regional analysis model of maize kernel moisture

“…Thus, obtaining an accurate calculation of M e becomes a key problem for accurately simulating kernel moisture concentration. In addition, although the equation (M − M e )/(M 0 − M e ) = e −kt relates M (kernel moisture concentration) to M e , this equation and the parameter k must be thoroughly studied to apply this equation in the field (Martinez-Feria et al, 2019;Piggott, 2010;Weller & Bunn, 1993). Additionally, the time t is in hours or minutes (Flood et al, 1972;Shivhare et al, 1992;Weller & Bunn, 1993), which is inconsistent with the diurnal variation of kernel moisture concentration.…”

“…Analysis of variance was conducted in IBM SPSS Statistics 22.0 to test whether the parameters a and b estimated by the model were dependent on genotype (i.e., hybrid). This was done by fitting the kernel moisture concentration model to each experimental unit (site‐year) separately and then conducting a standard one‐way type‐3 ANOVA on each parameter ( a or b ) (Martinez‐Feria et al., 2019). In addition, box‐and‐whisker plots of the parameters were drawn using GraphPad Prism 5.0 (GraphPad Software).…”