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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  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 multi-environment trials (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 genotype (G) main effects and genotype (G) -by-environment (E) interaction (GGE) technician, genotype à yield à 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.

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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.

Crop performance is seriously affected by high salt concentrations in soils. To develop improved seed pre-sowing treatment technologies, it is crucial to improve the salt tolerance of seed germination. Here, we isolated and identified the strain Bacillus sp. MGW9 and developed the seed biostimulant MGW9. The effects of seed biopriming with the seed biostimulant MGW9 in maize (Zea mays L.) under saline conditions were studied. The results show that the strain Bacillus sp. MGW9 has characteristics such as salt tolerance, nitrogen fixation, phosphorus dissolution, and indole-3-acetic acid production. Seed biopriming with the seed biostimulant MGW9 enhanced the performance of maize during seed germination under salinity stress, improving the germination energy, germination percentage, shoot/seedling length, primary root length, shoot/seedling fresh weight, shoot/seedling dry weight, root fresh weight and root dry weight. Seed biostimulant MGW9 biopriming also alleviated the salinity damage to maize by improving the relative water content, chlorophyll content, proline content, soluble sugar content, root activity, and activities of superoxide dismutase, catalase, peroxidase and ascorbate peroxidase, while decreasing the malondialdehyde content. In particular, the field seedling emergence of maize seeds in saline-alkali soil can be improved by biopriming with the seed biostimulant MGW9. Therefore, maize seed biopriming with the seed biostimulant MGW9 could be an effective approach to overcoming the inhibitory effects of salinity stress and promoting seed germination and seedling growth.

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.

In this study, a comparative analysis of seed quality indicators of 1196 hybrid maize seed samples from the main maize-producing areas in China from 2013 to 2018 was carried out. The results showed that the maize seed quality in China had changed obviously in the past six years, and was mainly as follows: The percentage of samples with coated seed in 2015–2018 was higher than 62.8% in 2013 and all exceeded 97%; the sample rate of packaging according to seed number was from 24.5% in 2013 to 58.6% in 2018, and the percentage of samples which met the prescribed quality standards was from 89.2% in 2013 to 98.4% in 2018. Principal component analysis indicated that standard germination energy (SGE), standard germination percentage (SGP), cold test germination percentage (CTGP), accelerated aging test germination percentage (AATGP), and mean field seedling emergence (FSE) were the primary predictors of seed germination and seedling emergence. Meanwhile, combining other statistical methods, regression models of SGE, SGP, CTGP, and AATGP were established to predict the field seedling emergence. Furthermore, seed bulk density and total starch content were correlated with seed vigor, which needs to be further studied. This study offered a theoretical basis and data support to better understand the changes of maize quality in China over the past six years, and provided an important reference to further improve the maize seed quality in the future.

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.