Drought and heat stresses are the major abiotic stress factors detrimental to maize (Zea mays L.) production. Much attention has been directed toward plant responses to heat or drought stress. However, maize reproductive stage responses to combined heat and drought remain less explored. Therefore, this study aimed to quantify the impact of optimum daytime (30°C, control) and warmer daytime temperatures (35°C, heat stress) on pollen germination, morpho‐physiology, and yield potential using two maize genotypes (“Mo17” and “B73”) under contrasting soil moisture content, that is, 100% and 40% irrigation during flowering. Pollen germination of both genotypes decreased under combined stresses (42%), followed by heat stress (30%) and drought stress (19%). Stomatal conductance and transpiration were comparable between control and heat stress but significantly decreased under combined stresses (83% and 72%) and drought stress (52% and 47%) compared with the control. Genotype “Mo17” reduced its green leaf area to minimize the water loss, which appears to be one of the adaptive strategies of “Mo17” under stress conditions. The leaf reflectance of both genotypes varied across treatments. Vegetation indices associated with pigments (chlorophyll index of green, chlorophyll index of red edge, and carotenoid index) and plant health (normalized difference red‐edge index) were found to be highly sensitive to drought and combined stressors than heat stress. Combined drought and heat stresses caused a significant reduction in yield and yield components in both Mo17 (49%) and B73 (86%) genotypes. The harvest index of genotype “B73” was extremely low, indicating poor partitioning efficiency. At least when it comes to “B73,” the cause of yield reduction appears to be the result of reduced sink number rather than the pollen and source size. To the best of our awareness, this is the first study that showed how the leaf‐level spectra, yield, and quality parameters respond to the short duration of independent and combined stresses during flowering in inbred maize. Further studies are required to validate the responses of potential traits involving diverse maize genotypes under field conditions. This study suggests the need to develop maize with improved tolerance to combined stresses to sustain production under increasing temperatures and low rainfall conditions.

Drought is a severe threat to agriculture production that affects all growth stages of plants, including corn (Zea mays L.). Any factor affecting early seedling growth and development will significantly impact yield. Despite the recurrence of low rainfall during the growing seasons, corn responses to different early-season soil moisture content levels have not been investigated. In this study, we investigated how corn morpho-physiological and biomass traits responded to varied soil moisture content during the early vegetative stage. Two corn hybrids were grown in a pot-culture facility under five different soil moisture treatments (0.15, 0.12, 0.09, 0.06, and 0.03 m³ m⁻³ volumetric water content, VWC) to assess the growth and developmental responses to varied soil moisture content during early-season growth (V2 to V7) stage. Sub-optimal soil moisture content limited plant growth and development by reducing physiological and phenotypic expression. Stomatal conductance and transpiration were decreased by an average of 65% and 59% across stress treatments relative to optimum conditions. On average, soil moisture deficit reduced the total leaf area by 71% and 72% compared to the control in ‘A6659VT2RIB’ and ‘P1316YHR’, respectively. Shoot and root dry weights were reduced by 74% and 43% under 0.03 m³ m⁻³ VWC. An increase in the root-to-shoot ratio was noticed under low VWC conditions compared to the control. Based on the stress tolerance index, the physiology and leaf growth parameters were more sensitive to soil moisture deficit. Our results highlight the impact of sub-optimal soil moisture on physiology and morphological traits during early-season growth. ‘P1316YHR’ demonstrated better physiological performance under stress conditions, while ‘A6659VT2RIB’ produced relatively better root growth. The findings suggest that biomass partitioning between shoot and root components is dynamic and depends on stress intensity. The current findings can help to prioritize traits associated with the early-season drought tolerance in corn. The functional relationships developed between soil moisture content and growth and developmental responses can be integrated into corn crop modeling to allow better irrigation management decisions.

Soybean Glycine max (L.) Merr. production trends have increased throughout the past century due to its versatile use in food, feed, and fuel industries. The selection of soybean cultivars with higher yields coupled with consumer-preferred seed quality has become a priority to sustain economic advantage. In this study, eighteen popular soybean cultivars from different maturity groups (MG III to V) were phenotyped for yield and quality traits under optimum water and nutrient conditions. Significant phenotypic variability was observed for days to flowering, yield, and seed quality traits. The late flowering soybean cultivars (MG V) recorded 14% lower seed weight than the early flowering (MG IV). Under optimum growing conditions, protein content increased with the MGs, but oil content decreased. Further, significant negative correlations between protein and yield, oil, and sucrose were observed. In contrast, the oil content was positively correlated with yield. Cultivars 539-T3 and GT-477CR2 were classified as high-yielding short-duration soybean cultivars. Based on the cultivar performance index, MS 4616 RXT and 7547XT were found to have a combination of desirable industry traits such as high protein, and oil content with high yield compared to other cultivars. The current research provides prospective benchmark seed weight and quality parameters under sunlit and temperature conditions with optimum water and nutrient conditions for many soybean cultivars grown in the US Mid-South. Furthermore, it can assist growers and breeders in selecting soybean cultivars with high protein and oil specific to their regional demand.

Rising temperatures and intense heatwaves combined with lower precipitations are the new norms of current global scenarios. These altered climatic conditions negatively impact soybean yield potential and quality. Ten soybean cultivars were subjected to four different growing conditions: control, drought, heat, and combined heat and drought to understand the physiological, yield, and molecular changes. Stomatal conductance was reduced by 62% and 10% under drought and heat, respectively. This reduction was further exacerbated to 93% when exposed to both stresses simultaneously. The highest canopy temperature was recorded at +8 °C with combinatorial treatments, whereas heat and drought exhibited +5.4 °C and +2 °C, respectively. Furthermore, combined stress displayed a more pronounced negative impact on greenness-associated vegetative index; the gene expression analysis further corroborated these findings. Particularly, each °C increase in temperature during flowering-seed filling reduced seed weight by ∼7% and ∼4% with and without drought, respectively. The seed protein increased under drought, whereas the oil showed a converse trend under drought and combined stresses. Most physiology and yield traits showed no significant correlations between control or individual and combined stress. This suggests that selection for combinatorial stress may not be appropriate based on nonstress or individual stress performance. Thus, incorporating stress-resilient traits into elite soybean cultivars could significantly boost soybean production under hot and dry climatic conditions.

Genome-Wide Association Studies (GWAS) are pivotal for identifying quantitative trait nucleotides (QTNs) in crops like barley. However, the performance of different methods varies depending on population structure and computational demands. This study compared eight GWAS methods (GLM, MLM, CMLM, SUPER, MLMM, FarmCPU, BLINK, and ECMLM) using a simulated dataset from the World Barley Core Collection (WBCC) with 6,332 SNPs, 318 samples, a heritability of 0.7, and 15 QTNs across 30 replicates. Statistical power, false discovery rate (FDR), Type I error, and computational efficiency were evaluated at a 10 Mb window size through Power vs FDR/Type I error curves, Area Under the Curve (AUC) boxplots, QQ plots, Manhattan plots, and timing bar graphs. MLMM consistently outperformed other methods in balancing power and error control, followed closely by BLINK and FarmCPU, which also demonstrated high mapping resolution in Manhattan plots. GLM exhibited the highest false-positive rate, as seen in its QQ plot, while ECMLM underperformed despite theoretical advantages. GLM and MLMM were the fastest, whereas CMLM was the slowest, highlighting significant computational trade-offs. These findings suggest that MLMM is ideal for high-quality QTN discovery in barley, while BLINK offers a balanced approach for routine analyses. The study provides a framework for selecting GWAS methods in barley, emphasizing the importance of balancing power, error control, and computational efficiency in structured populations like the WBCC.