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.

Waterlogging has negative effects on crop yield. Physiological and transcriptome data of two peanut cultivars [Zhongkaihua 1 (ZKH 1) and Huayu 39 (HY 39)] were studied under normal water supply and waterlogging stress for 5 or 10 days at the flowering stage. The results showed that the main stem height, the number of lateral branches, lateral branch length, and the stem diameter increased under waterlogging stress, followed by an increase in dry matter accumulation, which was correlated with the increase in the soil and plant analysis development (SPAD) and net photosynthetic rate (Pn) and the upregulation of genes related to porphyrin and chlorophyll metabolism and photosynthesis. However, the imbalance of the source–sink relationship under waterlogging was the main cause of yield loss, and waterlogging caused an increase in the sucrose and soluble sugar contents and a decrease in the starch content; it also decreased the activities of sucrose synthetase (SS) and sucrose phosphate synthetase (SPS), which may be due to the changes in the expression of genes related to starch and sucrose metabolism. However, the imbalance of the source–sink relationship led to the accumulation of photosynthate in the stems and leaves, which resulted in the decrease of the ratio of pod dry weight to total dry weight (PDW/TDW) and yield. Compared with ZKH 1, the PDW of HY 39 decreased more probably because more photosynthate accumulated in the stem and leaves of HY 39 and could not be effectively transported to the pod.

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.