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.

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.

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.

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.

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.

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.

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, 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 Liyu16 were achieved at a density of 75,000 plants ha −1 , and the yield of Hengdan6272 was obtained at 90,000 plants ha. 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 Liyu16 and suggested that the yielding potential of the former variety was higher than that of the latter two.

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.