Drought negatively affects the growth and yield of terrestrial crops. Seed priming, pre-exposing seed to a compound, could induce improved tolerance and adaptation to stress in germinated plants. To understand the effects and regulatory mechanism of seed priming with brassinosteroid (BR) on peanut plants, we treated seeds with five BR concentrations and examined dozens of physiological and biochemical features, and transcriptomic changes in leaves under well-watered and drought conditions. We found optimal 0.15 ppm BR priming could reduce inhibitions from drought and increase the yield of peanut, and priming effects are dependent on stage of plant development and duration of drought. BR priming induced fewer differentially expressed genes (DEGs) than no BR priming under well-watered condition. Drought with BR priming reduced the number of DEGs than drought only. These DEGs were enriched in varied gene ontologies and metabolism pathways. Downregulation of DEGs involved in both light perceiving and photosynthesis in leaves is consistent with low parameters of photosynthesis. Optimal BR priming partially rescued the levels of growth promoting auxin and gibberellin which were largely reduced by drought, and increased levels of defense associated abscisic acid and salicylic acid after long-term drought. BR priming induced many DEGs which function as kinase or transcription factor for signal cascade under drought. We proposed BR priming-induced regulatory responses will be memorized and recalled for fast adaptation in later drought stress. These results provide physiological and regulatory bases of effects of seed priming with BR, which can help to guide the framing improvement under drought stress.

Leaf area index (LAI) is used to predict crop yield, and unmanned aerial vehicles (UAVs) provide new ways to monitor LAI. In this study, we used a fixed-wing UAV with multispectral cameras for remote sensing monitoring. We conducted field experiments with two peanut varieties at different planting densities to estimate LAI from multispectral images and establish a high-precision LAI prediction model. We used eight vegetation indices (VIs) and developed simple regression and artificial neural network (BPN) models for LAI and spectral VIs. The empirical model was calibrated to estimate peanut LAI, and the best model was selected from the coefficient of determination and root mean square error. The red (660 nm) and near-infrared (790 nm) bands effectively predicted peanut LAI, and LAI increased with planting density. The predictive accuracy of the multiple regression model was higher than that of the single linear regression models, and the correlations between Modified Red-Edge Simple Ratio Index (MSR), Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI), and LAI were higher than the other indices. The combined VI BPN model was more accurate than the single VI BPN model, and the BPN model accuracy was higher. Planting density affects peanut LAI, and reflectance-based vegetation indices can help predict LAI.

Intercropping improves land utilization with more crops grown together; however, shorter crops in intercropping experience stress, being shaded by the taller crops. Systematic changes in phenotype, physiology, yield, and gene regulation under shade stress in peanut are largely unknown, although shade responses have been well analyzed in model plants. We exposed peanut plants to simulated 40% and 80% shade for 15 and 30 days at the seedling stage, flowering stage, and both stages. Shade caused the increased elongation growth of the main stem, internode, and leaf, and elongation was positively associated with auxin levels. Shade stress reduced peanut yield. Further comparative RNA-seq analyses revealed expressional changes in many metabolism pathways and common core sets of expressional regulations in all shade treatments. Expressional downregulation of most genes for light-harvesting and photosynthesis agreed with the observed decreased parameters of photosynthesis processes. Other major regulations included expressional downregulation of most core genes in the sucrose and starch metabolism, and growth-promoting genes in plant hormone signal pathways. Together, the results advance our understanding of physiological and molecular regulation in shade avoidance in peanut, which could guide the breeding designing in the intercropping system.

Peanut is an important oilseed and food crop worldwide; however, the development of new cultivars is limited by its remarkably low genetic variability. Therefore, in order to enhance peanut genetic variability, here, we treated two widely cultivated peanut genotypes, Huayu 22 and Yueyou 45, with different concentrations of the mutagen ethyl methyl sulfonate (EMS) for different durations. Based on median lethal dose (LD50) value, optimal EMS treatment concentrations for each duration were identified for each genotype. Mutants induced by EMS differed in various phenotypic traits, including plant height, number of branches, leaf characteristics, and yield and quality in plants of the M2 generation. Moreover, we identified potentially useful mutants associated with dwarfism, leaf color and shape, high oil and/or protein content, seed size and testa color, among individuals of the M2 generation. Mutations were stably inherited in M3-generation individuals. In addition to their contribution to the study and elucidation of the mechanisms underlying the regulation of the expression of some important agronomic traits, the mutants obtained in this study provide valuable germplasm resources for use in peanut improvement programs.

Understanding the responses of vegetation to climate extremes is important for revealing vegetation growth and guiding environmental management. Guangxi was selected as a case region in this study. This study investigated the spatial-temporal variations of the Normalized Difference Vegetation Index (NDVI), and quantitatively explored effects of climate extremes on vegetation on multiple time scales during 1982–2015 by applying the Pearson correlation and time-lag analyses. The annual NDVI significantly increased in most areas with a regional average rate of 0.00144 year⁻¹, and the highest greening rate appeared in spring. On an annual scale, the strengthened vegetation activity was positively correlated with the increased temperature indices, whereas on a seasonal or monthly scale, this was the case only in spring and summer. The influence of precipitation extremes mainly occurred on a monthly scale. The vegetation was negatively correlated with both the decreased precipitation in February and the increased precipitation in summer months. Generally, the vegetation significantly responded to temperature extremes with a time lag of at least one month, whereas it responded to precipitation extremes with a time lag of two months. This study highlights the importance of accounting for vegetation-climate interactions.

Climate extremes, particularly drought, often affect the ecosystem. Guangdong Province is one of the most vulnerable areas in China. Using the normalized difference vegetation index (NDVI) to capture vegetation dynamics, this study investigated vegetation responses to drought, temperature, and precipitation extremes on a monthly scale in the vegetation area of Guangdong without vegetation type changes from 1982 to 2015. As extreme temperatures rose, a drought trend occurred in most months, with a higher rate in February and April. The vegetation evenly showed a significant greening trend in all months except June and October. The vegetation activity was significantly positively correlated with the increased extreme temperatures in most months. However, it exerted a negative correlation with drought in February, April, May, June, and September, as well as precipitation extremes in February, April, and June. The response of vegetation to drought was the most sensitive in June. The vegetation tended to be more sensitive to short-term droughts (1–2 months) and had no time lag in response to drought. The results are helpful to provide references for ecological management and ecosystem protection.