The maize seed pedicel, an important tissue connecting the seed to the cob, plays a crucial role in seed development. However, surprisingly little is known about the genetic basis of the maize seed pedicel-placenta (SP) and its impact on seed germination. Here, we established a biomechanical model of maize seed water absorption and dehydration using 50 maize cultivars, and conducted a comparative analysis of SP and its related traits, as well as their effects on seed germination and vigor. The results revealed that SP size is closely related to seed size and that there are differences in SP traits among various cultivars. Removal of the SP can improve seed germination and enhance seed vigor in maize. Additionally, the genetic basis of four SP traits-pedicel-placenta length (PL), pedicel-placenta width (PW), pedicel-placenta thickness (PT), and pedicel-placenta cross-sectional area (PC)- was investigated in a population of recombinant inbred lines (RILs) derived from Xinzi517 & times; B73 and an association panel comprising 368 inbred lines. A total of nine quantitative trait loci (QTLs) controlling these traits were detected: one for PL, three for PW, three for PT, and two for PC. Furthermore, 58 single nucleotide polymorphisms (SNPs) significantly associated with these four traits were identified in the association panel across four environments: 16 for PL, 18 for PW, 19 for PT, and 5 for PC. A total of 61 candidate genes were identified via association analysis and linkage mapping: 18 for PL, 18 for PW, 13 for PT, and 12 for PC. These findings provide insights into the mechanism of seed germination and support the improvement of molecular marker-assisted selection (MAS) for high-vigor maize breeding.

Context: The use of high-yielding, stable, and widely adaptable summer maize hybrids is essential for the sustainable cultivation of maize globally. However, contemporary breeding programs face challenges in identifying genotypes that exhibit consistent performance while maintaining desired agronomic characteristics across diverse environments. Dynamic shifts in global meteorological and edaphic conditions have impacted the evaluation of summer maize performance in China's multi-environment trials (METs). Multi-perspective analysis of genotype-by-environment interactions (GEI) is then essential for characterizing maize yield stability and trait expression across agroecological zones. Objective: This study aims to comprehensively evaluate the performance and stability of maize genotypes in the Huang-Huai-Hai region by integrating environmental techniques (ETs) with multi-trait selection methods. Methods: Twenty-eight maize hybrids and a check hybrid (ZD958) were evaluated across 29 locations in the Huang-Huai-Hai region of China during the 2019-2021 cropping seasons, using a randomized complete block design (RCBD) with three replications. Results: Based on 30 years (1993-2023) of environmental data, which included 19 meteorological and 6 soil physicochemical factors, the ETs classified the 29 locations across eight provinces into six distinct mega-environments (MEs). The additive main effects and multiplicative interaction (AMMI) model analysis revealed that genotype (G), environment (E), and their interaction (GxE) significantly influenced (p < 0.05) for all agronomic parameters from 2019 to 2021. The combined performance of grain yield and other agronomic traits-such as growth period, plant height, ear height, lodging rate, barren stalk rate, grain moisture content at harvest, ear row number, bare tip length, and 100-grain weight-across different MEs was assessed using the genotype by yield x trait (GYT) biplot approach. The integration of GYT biplots with ETs effectively identified dominant hybrids across different MEs. Among the evaluated hybrids from 2019 to 2021, HY1604 exhibited both high yield and stability in MEs 1-4, categorizing it as a high-yielding, stable hybrid. HY573 and SD610 demonstrated relatively balanced performance in yield-trait combinations in MEs 5 and 6, respectively. The control hybrid, ZD958, showed strong stability but average yield performance over the three-year MET period. Implications: The use of environmental characterization techniques to delineate MEs, combined with the GYT biplot approach to evaluate yield, adaptability, and stability, facilitated precise variety placement, and provided a robust theoretical framework for the comprehensive evaluation of multiple traits in summer maize hybrids in the Huang-Huai-Hai region of China.

The development of superior summer maize hybrids with high-yield potential and essential agronomic traits, such as resistance to lodging, is crucial for ensuring the sustainability of maize cultivation. However, the task of identifying and breeding genotypes that exhibit exceptional performance and stability across multiple environment conditions, while considering a wide range of traits, is challenging. Given the backdrop of global climate change, understanding which climate variables and soil properties most significantly impact environmental similarity is essential for selecting hybrids with improved adaptability to regions with diverse climatic and soil conditions. This study aimed to integrate envirotyping techniques (ETs) with a multi-trait selection approach to carry out a comprehensive evaluation of maize genotypes for performance and stability. The grain yields of 13 maize hybrids, along with their four critical agronomic parameters, were assessed in the Huang-Huai-Hai Plain of China across 40 locations in eight provinces. By considering 20 years of climatic factors and soil covariates, these 40 locations were divided into six mega-environments (MEs) based on similar long-term weather patterns and soil characteristics. Additive main effects and multiplicative interaction (AMMI) analyses revealed that genotype (G), environment (E), and the GxE interaction had significant effects on all agronomic parameters (P < 0.001). The mean performance and stability of the genotypes in each mega-environment were assessed, allowing for the identification of superior hybrids using the multi-trait stability index (MTSI). In two of the MEs (ME2 and ME3), only two hybrids, HY321 and HY9112, were selected concurrently. Overall, this study provides valuable insights into the effects of ETs on maize hybrids and enhances our understanding of GxE interactions in multi-environment trials. This understanding is essential for improving maize cultivation practices and breeding program in diverse environments.

IntroductionWinter wheat is a crucial crop extensively cultivated in northern China, where its grain yield is influenced by genetic factors (G), environmental conditions (E), and their interactions (GEI). Accurate yield estimation depends on understanding the patterns of GEI in multi-environment trials (METs).MethodsFrom 2014 to 2018, continuous experiments were conducted in the Heilonggang region of the North China Plain (NCP), evaluating 71 winter wheat genotypes across 16 locations over five years. Leveraging 30 years of environmental data, including 19 meteorological parameters and 6 soil physicochemical properties, the study analyzed GEI and identified four distinct mega-environments (MEs) using advanced environmental classification techniques.ResultsVariance analysis of genotype-year combinations at individual locations revealed significant differences among genotypes. Furthermore, the joint analysis showed that GEI variance exceeded the variance attributed to genotypic effects alone. The Additive Main Effects and Multiplicative Interaction (AMMI) model indicates that the first three interaction principal component axes (IPCAs) account for over 70% of the GEI variance, thereby demonstrating the relevance of this model to the current study. Principal Component Analysis (PCA) across the five-year study period revealed positive correlations between grain yield and vapor pressure deficit (VPD), evapotranspiration potential (ETP), temperature range (TRANGE), available soil water (ASKSW), and sunshine duration. Conversely, negative correlations were observed with relative humidity at 2 meters (RH2M), total precipitation (PRECTOT), potential evapotranspiration (PETP), and dew point temperature at 2 meters (T2MDEW). Among the meteorological and soil variables, minimum temperature (TMIN), fruiting rate (FRUE), temperature at 2 meters (T2M), and clay content (CLAY) emerged as the most significant contributors to yield variation during the study period. Based on GGE biplot analysis, superior genotypes were identified for their respective regions: JM196, WN4176, and HN6119 in 2014; ZX4899, H9966, and LM22 in 2015; BM7, KN8162, and KM3 in 2016; HH14-4019, HM15-1, and HH1603 in 2017; and S14-6111 and JM5172 in 2018. Feixiang and Shenzhou were identified as the most discriminative and representative locations.DiscussionThese findings provide a scientific basis for optimizing winter wheat cultivation strategies in northern regions. Based on long-term data from the North China Plain, future work can further validate their applicability in other regions.

Under global climate changes, understanding climate variables that are most associated with environmental kinships can contribute to improving the success of hybrid selection, mainly in environments with high climate variations. The main goal of this study is to integrate envirotyping techniques and multi-trait selection for mean performance and the stability of maize genotypes growing in the Huanghuaihai plain in China. A panel of 26 maize hybrids growing in 10 locations in two crop seasons was evaluated for 9 traits. Considering 20 years of climate information and 19 environmental covariables, we identified four mega-environments (ME) in the Huanghuaihai plain which grouped locations that share similar long-term weather patterns. All the studied traits were significantly affected by the genotype × mega-environment × year interaction, suggesting that evaluating maize stability using single-year, multi-environment trials may provide misleading recommendations. Counterintuitively, the highest yields were not observed in the locations with higher accumulated rainfall, leading to the hypothesis that lower vapor pressure deficit, minimum temperatures, and high relative humidity are climate variables that –under no water restriction– reduce plant transpiration and consequently the yield. Utilizing the multi-trait mean performance and stability index (MTMPS) prominent hybrids with satisfactory mean performance and stability across cultivation years were identified. G23 and G25 were selected within three out of the four mega-environments, being considered the most stable and widely adapted hybrids from the panel. The G5 showed satisfactory yield and stability across contrasting years in the drier, warmer, and with higher vapor pressure deficit mega-environment, which included locations in the Hubei province. Overall, this study opens the door to a more systematic and dynamic characterization of the environment to better understand the genotype-by-environment interaction in multi-environment trials.

Facing the trend of increasing population, how to increase maize grain yield is a very important issue to ensure food security. In this study, 28 nationally approved maize hybrids were evaluated across 24 different climatic conditions for two consecutive years (2018-2019). The purpose of this study was to select high-yield with stable genotypes and identify important agronomic traits for maize breeding program improvement. The results of this study showed that the genotype x environment interaction effects of the 12 evaluated agronomic traits was highly significant (P < 0.001). We introduced a novel multi-trait genotype-ideotype distance index (MGIDI) to select genotypes based on multiple agronomic traits. The selection process exhibited by this method is unique and easy to understand, so the MGIDI index will have more and more important applications in future (please indicate what does it mean MET before abbreviating it) METs research. The genotypes selected by the MGIDI index were G22, G10, G12 and G1 as the high yielding and stable genotypes. The parents of these selected genotypes have the ability to play a greater role as the basic germplasm in the breeding process. A new form of (please indicate what does it mean GGE before abbreviating it) GGE technician, geno-type*yield*trait (GYT) biplot, based on multiple traits for genotypes selection was also applied in this study. The GYT biplot ranked genotypes by combining grain yield with other evaluated agronomic traits, and displayed the distribution of their traits, namely strengths and weaknesses.

Increasing the maize production capacity to ensure food security is still the primary goal of global maize planting. The purpose of this study was to evaluate genotypes with high yield and stability in summer maize hybrids grown in the Huanghuaihai region of China using additive main effects and multiplicative interaction (AMMI) analysis and best linear unbiased prediction (BLUP) technique. A total of 18 summer maize hybrids with one check hybrid were used for this study using a randomized complete block design (RCBD) with three replicates at 74 locations during two consecutive years (2018–2019). A three-way analysis of variance (ANOVA) and an AMMI analysis showed that genotype (G), environment (E), year (Y) and their interactions were highly significant (p < 0.001) except G × E × Y for all evaluated traits viz., grain yield (GY), ear length (EL), hundred seed weight (HSW) and E × Y for hundred seed weight. The first seven interaction principal components (IPCs) were highly significant and explained 81.74% of the genotype by environment interaction (GEI). By comparing different models, the best linear unbiased prediction (BLUP) was considered the best model for data analysis in this study. The combination of AMMI model and BLUP technology to use the WAASB (weighted average of absolute scores from the singular value decomposition of the matrix of BLUP for GEI effects generated by linear mixed model) index was considered promising for similar research in the future. Genotypes H321 and Y23 had high yield and good stability, and could be used as new potential genetic resources for improving and stabilizing grain yield in maize breeding practices in the Huanghuaihai region of China. Genotypes H9, H168, Q218, Y303 and L5 had narrow adaptability and only apply to specific areas. The check genotype Z958 had good adaptability in most environments due to its good stability, but it also needs the potential to increase grain yield. Significant positive correlations were also found between the tested agronomic traits.

The correct interpretation of the adaptability and stability of maize ( Zea mays L.) hybrids in different ecological environments is very important for plant breeders. Additive main effect and multiplicative interaction model and genotype main effects and genotype × environment interaction biplot are the two most popular methods in the analysis of genotype × environment interaction in multienvironment trials. We conducted an experiment designed to examine and evaluate the adaptability and stability of four agronomic traits of 19 tested maize genotypes in two consecutive growth cycles across seven provinces including 37 locations using a randomized completely block design with three replicates. The combined ANOVA for all traits showed that the effect of genotype, environment, and genotype × environment interaction were significant at 0.1% probability levels. In order to evaluate multiple agronomic traits more accurately, multitrait stability index (MTSI) as an emerging selection method based on mean performance and stability was adopted. Agronomic trait grain yield (GY) was positively correlated with grain weight per ear and growth period. In addition, it was also found that GY and bar tip length showed obvious negative correlation. The MTSI is very helpful for breeders who hope to select mean performance and stability based on a variety of agronomic traits because it provides a convenient selection process while taking into account the relevant results of the traits.

Stability and adaptability of promising maize hybrids in terms of three agronomic traits (grain yield, ear weight and 100-kernel weight) in multi-environments trials were evaluated. The analysis of AMMI model indicated that the all three agronomic traits showed highly significant differences (p < 0.01) on genotype, environment and genotype by environment interaction. Results showed that genotypes Hengyu321 (G9), Yufeng303 (G10) and Huanong138 (G3) were of higher stability on grain yield, ear weight and 100-kernel weight, respectively. Genotypes Hengyu1587 (G8) and Hengyu321 (G9) showed good performance in terms of grain yield, whereas Longping208 (G2) and Weike966 (G12) showed broad adaptability for ear weight. It was also found that the genotypes with better adaptability in terms of 100-kernel weight were Zhengdan958 (G5) and Weike966 (G12). The genotype and environment interaction model based on AMMI analysis indicated that Hengyu1587 and Hengyu321 were the ideal genotypes, due to extensive adaptability and high grain yield under both testing sites. Bangladesh J. Bot. 50(2): 343-350, 2021 (June)

Abiotic stresses, including cold and drought, negatively affect maize ( Zea mays L.) seed field emergence and later yield and quality. In order to reveal the molecular mechanism of maize seed resistance to abiotic stress at seed germination, the global transcriptome of high- vigour variety Zhongdi175 exposed to cold- and drought- stress was analyzed by RNA-seq. In the comparison between the control and different stressed sample, 12,299 differentially expressed genes (DEGs) were detected, of which 9605 and 7837 DEGs were identified under cold- and drought- stress, respectively. Functional annotation analysis suggested that stress response mediated by the pathways involving ribosome, phenylpropanoid biosynthesis and biosynthesis of secondary metabolites, among others. Of the obtained DEGs (12,299), 5,143 genes are common to cold- and drought- stress, at least 2248 TFs in 56 TF families were identified that are involved in cold and/or drought treatments during seed germination, including bHLH, NAC, MYB and WRKY families, which suggested that common mechanisms may be originated during maize seed germination in response to different abiotic stresses. This study will provide a better understanding of the molecular mechanism of response to abiotic stress during maize seed germination, and could be useful for cultivar improvement and breeding of high vigour maize cultivars.

To evaluate the adaptability and stability of silage maize cultivars and identify the representativeness and discrimination of each testing site, a two-year field research in a randomized complete block design (RCBD) with three replicates at 10 testing sites was conducted. An additive main effect and multiplicative interaction (AMMI) model and a genotype plus genotype environment interactions (GEI) biplot (referred to as GGE hereafter) were used to analyze the data. The two-year test revealed that four cultivars (Zhongdi 175 (ZD175), Qiushuo 008 (Q008), Hengyu 1587 (H1587), and Yayuqingzhu 8 (Y8) exhibited high yield and good stability, whereas two cultivars (Zhongbeiqingzhu 410 (Z410) and Fangyu 36 (F36) had low yield and poor stability. The comprehensive application of the AMMI model and the GGE biplot could accurately and intuitively evaluate the high yield, stability, and adaptability of each cultivar.

Maize is one of the most important cereal crops supporting millions of people in China. The main purpose of this research was to assess the genotype by environment interaction (GEI) and yield performance of 20 maize genotypes in 16 different environments of Huang-Huai-Hai area, China. In this research, the additive main effects and multiplicative interactions (AMMI) model was applied to analyze the GEI effect and to evaluate the suitability and yield stability of 20 different maize genotypes. The AMMI model analysis indicated that genotype (G), environment (E) and GEI had significant effects on grain yield and the contribution to the total sum of squares difference was 3.10%, 35.05% and 42.25%, respectively, suggesting that GEI was the primary factor affecting grain yield. The AMMI model analysis partitioned sum of squares of GEI into fourteen interaction principal components axes (IPCA), of which all the IPCA were significant (P < 0.01) and the first five IPCA (IPCA1, IPCA2, IPCA3, IPCA4 and IPCA5) explained 77.7% of variation. The AMMI model analysis using IPCA1 scores and G main effect indicated that two genotypes Hengyul47 and Hengyu321 had relatively stable performance across the environments. Among the locations, Quwo was the most productive site in distinguishing genotypes and the most representative environment. In conclusion, this study suggested that genotype and environment interactions were the major source of variation in maize yield, and use of AMMI model seemed useful for screening and identifying the response of summer maize genotypes in different environments. (C) 2019 Friends Science Publishers

The genotype (G) by environment (E) interaction (GEI) determines the stability of maize grain yield in multi-environment trials (METs). This study evaluated the high-yielding and promising maize genotypes over years and locations by the additive main effects and multiplicative interaction (AMMI) model. The grain yield of 13 spring maize genotypes was evaluated for two consecutive years (2012-2013) when planted in six and eight ecological environments, respectively, using a randomized complete block design (RCBD) with three replications. The AMMI model explained 77.49 and 75.57% of total observed genotypic variation, respectively. A comprehensive analysis of variances showed a highly significant impact of environment, genotype and genotype x environment (GE) interaction on grain yield (P < 0.01). The AMMI model analysis of variance showed that the environment contributed the most to variations in grain yield (55.58 and 72.50% of the total variation, respectively), followed by GE interaction (24.61 and 10.71% of the total variation, respectively) and genotype (3.01 and 3.01% of the total variation, respectively). Among the interaction effects, first interaction principal component axis (IPCA1), IPCA2 and IPCA3 explained the vast majority of genetic and environmental interaction information. Two years of experimental data showed that the genotype with high yield and stability was G4 (Zhongdi175) while G3 (C807) and G8 (LY10) of poor yield and unstable. The check genotype G6 (Nongda108) had good stability and general high-yielding. The best and worst discriminative environments for each of the locations in 2012-2013 were XT (Xingtai) and LH (Longhua), WA (Wuan) and PQ (Pingquan), respectively. (C) 2019 Friends Science Publishers

A multi-environment trial of maize cultivars in the north of China was conducted at 17 sites to assess the agronomic traits and test location representativeness. An additive main effects and multiplicative interaction (AMMI) model and a genotype main effect (G) and genotype and environment interaction (GE), (GGE) biplot were used to analyze the data. Results showed that the grain yields of summer maize cultivars were remarkably influenced by environment (E), genotype (G), and genotype by environment interaction (GEI). The effect of GEI was 1.53 times higher than that of genotypes. The cultivars Hengyu1182, Longhua369 and J1302 exhibited good yield and stability. Among sites Baoding, Xinle, Yutian and Zhengding showed good discriminating ability while Langfang, Gaoyang and Tanchang sites displayed good representativeness. In correlation analysis, agronomic traits, such as ear length, kernel row number, and 1000-grain weight were positively correlated with maize yield. Conversely, barren ear tip was negatively correlated with yield. This study confirmed that the AMMI model and GGE biplot are effective methods for exploring the stability and adaptability of genotypes and representative patterns of biplots in crop breeding and subsequent cultivar recommendations. (C) 2019 Friends Science Publishers

This study evaluated the yield performance of 27 maize (Zea mays L.) genotypes from 2016 to 2017 in 80 trial environments in Huang-huai-hai summer maize growing area of China during summer by using a randomized complete block design (RCBD) with three replications. Grain-yield data obtained from the regional trials of the Kechuang Union were analyzed with a genotype-and-genotype-by-environment (GGE) biplot. Analysis of variance (ANOVA) of grain-yield data showed that the effects of environment, genotype, and genotype-by-environment interaction were significant, accounting for 45.27%, 9.18%, and 27.39% of the sum of treatment combinations in 2016, as well as 53.97%, 9.43%, and 19.81% of that in 2017, respectively. Results showed that the best performance and candidate genotypes for the 2016-2017 multi-environment trials were D56, J118, and H321, as well as S617, D205 and D56, respectively. Among the 80 test sites, the environments TA and XJ in 2016 and the environment JX in 2017 were included in the ideal environment due to their excellent discrimination and representativeness.

A field experiment was conducted at Shenzhou of Hebei province to study on grain-filling characteristics of maize hybrids differing in maturities, aimed to provide scientific information for the regulation of grain weight and selection of maturity. Four maize hybrids differing in maturities were used as experimental materials, under the same planting density. The main results were as follows. All the hybrids reached the maximum value of 100 grain fresh weight at 40d after pollination, and then declined slowly. The maximum value of 100 grain fresh weight of middle-maturity hybrid Zhengdan958 was the highest, and early maturity hybrid Demeiyal was the lowest. After 40 days of pollination, all the hybrids reached the maximum value of 100 seed fresh weight, and then declined slowly, and the maximum 100 fresh weight of middle-late hybrid Zhengdan958 was the highest, and early-maturity hybrid Demeiyal was the lowest. Four hybrids of 100 grain dry weight and fresh weight showed the same trend, it was a rapid growth period at 40d after pollination, the 100 grain dry weight of Hengyul182 was maximum, Demeiyal was minimum. The grain-filling rate of four hybrids showed a single peak curve, reached the maximum peak at 20d after pollination, the peak performance of four maize hybrids showed that Hengyul182> Zhengdan958> Nongda108 > Demeiyal. The percentage of grain water of Hengyul182 was the lowest, the value was 25.70% when 60d after pollination, Nongda108 was significantly higher than the other three hybrids (P<0.05), the value was 34.01%. The grain-filling processes of maize hybrids differing in maturities were analyzed by Logistic model, and the total filling period could be divided into early stage, middle stage and late stage. Middle-early hybrid Hengyul182 showed that grain weight of every stage (W-1-W-3), and mean grain-filling rate of every stage (P-1-P-3) were higher than other three types. The results showed that the increase of grain weight was related to grain filling rate at filling stage, and the middle-early maturity hybrids suitable for planting in Heilonggang area. (C) 2018 Friends Science Publishers

Three maize varieties were planted as the main corn varieties in the Huang-huai-hai Plain as materials with five planting densities. Differences in grain filling and mechanised harvest grain characteristics to planting density amongst summer maize cultivars were examined. As plant density increased, the 100-grain dry weights of the three varieties gradually decreased. In the filling period, the 100-grain fresh weights increased initially and then decreased, and the 100-grain fresh weights decreased as plant density increased. At different densities, the grain-filling rates of the three varieties showed single-peak curves, and the highest peak of the grain-filling rate was achieved 30 days after pollination. In the whole grain-filling period, the different densities of the three maize varieties exhibited a decline in the percentage of grain water (PGW). The grain-filling processes of maize varieties with different plant densities were analysed with a logistic model, and the total filling period was divided into gradual increase stage, rapid increase stage and slow growth stage. The yield factors of the three varieties were also analysed. With the increased density, the number of lines per ear (NE), the number of grains per line (NL) and 1000-grain weight (GW) decreased. The grain yield initially increased and then decreased. The maximum yields of Zhengdan958 and Liyul6 were achieved at a density of 75,000 plants ha(-1), and the yield of Hengdan6272 was obtained at 90,000 plants ha(-1). These results indicated that the negative effects of dense planting on grain filling and mechanised harvest grain functions in Hengdan6272 were lower than those in Zhengdan958 and Liyul6 and suggested that the yielding potential of the former variety was higher than that of the latter two. (C) 2018 Friends Science Publishers.

Drought is a serious threat to maize production in Hebei province. Planting drought resistant maize varieties is an effective measure to solve drought in arid and less rain areas. Drought resistance in maize is controlled by many genes, and multiple indexes should be used for comprehensive evaluation (Campos H et al.2004). In the arid rain shed, using 34 maize varieties to promote crop production compared to the drought resistance test. The experiment was conducted with two treatments of drought stress (irrigation only at seedling stage) and normal irrigation, and 12 agronomic traits related to drought resistance of maize were determined. The results showed that drought had significant effects on maize yield and main agronomic characters. Under drought stress, plant height, ear length, bare tip, ear row number, row grains, 1000-kernel weight, ASI index can be used as identification index of drought resistance of maize in different period. The results indicated that the variety with strong drought resistance is Zhongdi175, the worst drought resistance is Woyu964.