Best Linear Unbiased Predictions of Environmental Effects on Grain Yield in Maize Variety Trials of Different Maturity Groups

Abstract

Development of new cultivars and agronomic improvements are key factors of increasing in future grain yield in maize grown in environments affected by climate change. Assessment of value for cultivation and use (VCU) reflects the results of latest breeding efforts showing yield trends, whereby external environmental covariates were rarely used. This study aimed to analyze several environmental effects including stress degree days (SDD) on grain yields in Croatian VCU trials in three maturity groups using linear mixed model for the estimation of fixed and random effects. Best linear unbiased predictions (BLUPs) of location-year interaction showed no pattern among maturity groups. SDD showed mostly non-significant coefficients of regression on location BLUPs for yield. Analyzing location BLUPs, it was shown that the effect became consistently stronger with later maturity, either positive or negative. The effects of management might play more critical role in maize phenology and yield formation compared with climate change, at least in suboptimum growing conditions often found in Southeast Europe. To facilitate more robust predictions of the crop improvement, the traditional forked approach dealing with G×E by breeders and E×M by agronomists should be integrated to GxExM framework, to assess the full gradient of combinations forming the adaptation landscape.

Combining Ability Analysis for Maturity and Yield Attributes in Sweet Corn Across Environments

“…Such results indicate that the location affects the non-additive gene actions more than additive genetic effects in tasseling. However, Aly and Mousa (2011) and Zoric et al (2022) have reported significant GCA and SCA variances for most characters including tasseling in maize, emphasizing the function of both additive and non-additive components. GCA variances for tasseling were generally less in magnitude than SCA variances at both locations, revealing that a large part of total variability for tasseling inheritance was under non-additive gene legacy.…”

“…Sweet corn (Zea mays L. var. 'saccharata') is a type of corn with a creamy texture and mutant endosperm, which contains about three to eight times more sugar than other types of corn (Chozin et al, 2017;Sheng et al, 2018;Zoric et al, 2022). Global production is relatively few grown on a small area of 1.06 million ha due to restricted research work, unexpected climatic changes, and tapered genetic makeup (Lertrat and Pulam, 2007;Fahad et al, 2022;Shojaei et al, 2022).…”

“…Early maturity, with the good combining ability of the parents and their cross combinations, is a prerequisite to feed the ever-increasing demands of the current food security scenario. Early maturing high-yielding hybrids are more desirable for their commercial release into a particular target environment to reduce the climatic effect on sweet corn in Pakistan (Pacurar et al, 2018;Zoric et al, 2022). In addition, the phenotypic constitution of a crop is the function of genotype, environment, and the interactions due to genotype by environment (Smith et al, 2005;Adnan et al, 2020;Shojaei et al, 2022).…”

Predictions and Estimations in Agricultural Production under a Changing Climate

“…In the fourth article, the authors presented a method for effectively and accurately predicting the effect of environmental conditions on the grain yield of maize varieties belonging to different maturity groups [10]. The presented experiment concerned the analysis of several important environmental parameters in terms of the yield efficiency of three groups of corn varieties in VCU (value for cultivation and use) experiments in Croatia.…”

Genetic Parameters, Prediction of Gains and Intraspecific Hybrid Selection of Paspalum notatum Flügge for Forage Using REML/BLUP

“…Effectively, the BLUP presents a favorable characteristic for the shrinkage of the estimators towards the mean and reduces its variance and increases its predictive precision [35]. The REML/BLUP procedure has been widely used in annual allogamy breeding crops such as Zea mays (L.) [36][37][38] and autogamy such as Glycine max (L.) [39,40]. The procedure has also been applied to selection for perennial crops e.g., Prunus persica (L.) [41], but the approach is less common.…”