Evaluation of Inbred Maize (Zea mays L.) for Tolerance to Low Phosphorus at the Seedling Stage.

In underdeveloped nations where low-input agriculture is practiced, low phosphorus (LP) in the soil reduces the production of maize. In the present study, a total of 550 inbred maize lines were assessed for seedling traits under LP (2.5 × 10-6 mol L-1 of KH2PO4) and NP (2.5 × 10-4 mol L-1 of KH2PO4) hydroponic conditions. The purpose of this study was to quantify the amount of variation present in the measured traits, estimate the genetic involvement of these characteristics, examine the phenotypic correlation coefficients between traits, and to integrate this information to prepare a multi-trait selection index for LP tolerance in maize. A great deal of variability in the maize genotype panel was confirmed by descriptive statistics and analysis of variance (ANOVA). Estimated broad-sense heritability (h2) ranged from 0.7 to 0.91, indicating intermediate to high heritability values for the measured traits. A substantial connection between MSL and other root traits suggested that the direct selection of MSL (maximum shoot length) could be beneficial for the enhancement of other traits. The principal component analysis (PCA) of the first two main component axes explained approximately 81.27% of the variation between lines for the eight maize seedling variables. TDM (total dry matter), SDW (shoot dry weight), RDW (root dry weight), SFW (shoot fresh weight), RFW (root fresh weight), MRL (maximum root length), and MSL measurements accounted for the majority of the first principal component (59.35%). The multi-trait indices were calculated based on PCA using all the measured traits, and 30 genotypes were selected. These selected lines might be considered as the potential source for the improvement of LP tolerance in maize.

Progressive Genomic Approaches to Explore Drought- and Salt-Induced Oxidative Stress Responses in Plants under Changing Climate.

Drought and salinity are the major environmental abiotic stresses that negatively impact crop development and yield. To improve yields under abiotic stress conditions, drought- and salinity-tolerant crops are key to support world crop production and mitigate the demand of the growing world population. Nevertheless, plant responses to abiotic stresses are highly complex and controlled by networks of genetic and ecological factors that are the main targets of crop breeding programs. Several genomics strategies are employed to improve crop productivity under abiotic stress conditions, but traditional techniques are not sufficient to prevent stress-related losses in productivity. Within the last decade, modern genomics studies have advanced our capabilities of improving crop genetics, especially those traits relevant to abiotic stress management. This review provided updated and comprehensive knowledge concerning all possible combinations of advanced genomics tools and the gene regulatory network of reactive oxygen species homeostasis for the appropriate planning of future breeding programs, which will assist sustainable crop production under salinity and drought conditions.

[Establishment of The Crop Growth and Nitrogen Nutrition State Model Using Spectral Parameters Canopy Cover].

In order to explore a non-destructive monitoring technique, the use of digital photo pixels canopy cover (CC) diagnosis and prediction on maize growth and its nitrogen nutrition status. This study through maize canopy digital photo images on relationship between color index in the photo and the leaf area index (LAI), shoot dry matter weight (DM), leaf nitrogen content percentage (N%). The test conducted in the Chinese Academy of Agricultural Science from 2012 to 2013, based on Maize canopy Visual Image Analysis System developed by Visual Basic Version 6.0, analyzed the correlation of CC, color indices, LAI, DM, N% on maize varieties (Zhongdan909, ZD 909) under three nitrogen levels treatments, furthermore the indicators significantly correlated were fitted with modeling, The results showed that CC had a highly significant correlation with LAI (r = 0.93, p < 0.01), DM (r = 0. 94, p < 0.01), N% (r = 0.82, p < 0.01). Estimating the model of LAI, DM and N% by CC were all power function, and the equation respectively were y = 3.281 2x(0.763 9), y = 283.658 1x(0.553 6) and y = 3.064 5x(0.932 9); using independent data from modeling for model validation indicated that R2, RMSE and RE based on 1 : 1 line relationship between measured values and simulated values in the model of CC estimating LAI were 0.996, 0.035 and 1.46%; R2, RMSE and RE in the model of CC estimating DM were 0.978, 5.408 g and 2.43%; R2, RMSE and RE in the model of CC estimating N% were 0.990, 0.054 and 2.62%. In summary, the model can comparatively accurately estimate the LAI, DM and N% by CC under different nitrogen levels at maize grain filling stage, indicating that it is feasible to apply digital camera on real-time undamaged rapid monitoring and prediction for maize growth conditions and its nitrogen nutrition status. This research finding is to be verified in the field experiment, and further analyze the applicability throughout the growing period in other maize varieties and different planting density.