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Both carbon limitation and developmentally driven kernel failure occur in the apical region of maize (Zea mays L.) ears. Failed kernel development in the basal and middle regions of the ear often is neglected because their spaces usually are occupied by adjacent ovaries at harvest. We tested the spatial distribution of kernel losses and potential underlying reasons, from perspectives of silk elongation and carbohydrate dynamics, when maize experienced water deficit during silk elongation. Kernel loss was distributed along the length of the ear regardless of water availability, with the highest kernel set in the middle region and a gradual reduction toward the apical and basal ends. Water deficit limited silk elongation in a manner inverse to the temporal pattern of silk initiation, more strongly in the apical and basal regions of the ear than in the middle region. The limited recovery of silk elongation, especially at the apical and basal regions following rescue irrigation was probably due to water potentials below the threshold for elongation and lower growth rates of the associated ovaries. While sugar concentrations increased or did not respond to water deficit in ovaries and silks, the calculated sugar flux into the developing ovaries was impaired and diverged among ovaries at different positions under water deficit. Water deficit resulted in 58% kernel loss, 68% of which was attributable to arrested silks within husks caused by lower water potentials and 32% to ovaries with emerged silks possibly due to impaired carbohydrate metabolism. [ABSTRACT FROM AUTHOR]

SUMMARY: Global warming, climate change, and industrial pollution are altering our environment subjecting plants, microbiomes, and ecosystems to an increasing number and complexity of abiotic stress conditions, concurrently or sequentially. These conditions, termed, "multifactorial stress combination" (MFSC), can cause a significant decline in plant growth and survival. However, the impacts of MFSC on reproductive tissues and yield of major crop plants are largely unknown. We subjected soybean (Glycine max) plants to a MFSC of up to five different stresses (water deficit, salinity, low phosphate, acidity, and cadmium), in an increasing level of complexity, and conducted integrative transcriptomic‐phenotypic analysis of their reproductive and vegetative tissues. We reveal that MFSC has a negative cumulative effect on soybean yield, that each set of MFSC condition elicits a unique transcriptomic response (that is different between flowers and leaves), and that selected genes expressed in leaves or flowers of soybean are linked to the effects of MFSC on different vegetative, physiological, and/or reproductive parameters. Our study identified networks and pathways associated with reactive oxygen species, ascorbic acid and aldarate, and iron/copper signaling/metabolism as promising targets for future biotechnological efforts to augment the resilience of reproductive tissues of major crop plants to MFSC. In addition, we provide unique phenotypic and transcriptomic datasets for dissecting the mechanistic effects of MFSC on the vegetative, physiological, and reproductive processes of a crop plant. [ABSTRACT FROM AUTHOR]

Soybean (Glycine max) production is greatly affected by persistent and/or intermittent droughts in rainfed soybean-growing regions worldwide. Symbiotic N2 fixation (SNF) in soybean can also be significantly hampered even under moderate drought stress. The objective of this study was to identify genomic regions associated with shoot carbon isotope ratio (δ13C) as a surrogate measure for water use efficiency (WUE), nitrogen isotope ratio (δ15N) to assess relative SNF, N concentration ([N]), and carbon/nitrogen ratio (C/N). Genome-wide association mapping was performed with 105 genotypes and approximately 4 million single-nucleotide polymorphism markers derived from whole-genome resequencing information. A total of 11, 21, 22, and 22 genomic loci associated with δ13C, δ15N, [N], and C/N, respectively, were identified in two environments. Nine of these 76 loci were stable across environments, as they were detected in both environments. In addition to the 62 novel loci identified, 14 loci aligned with previously reported quantitative trait loci for different C and N traits related to drought, WUE, and N2 fixation in soybean. A total of 58 Glyma gene models encoding for different genes related to the four traits were identified in the vicinity of the genomic loci. [ABSTRACT FROM AUTHOR]

SUMMARY: Global warming and climate change are driving an alarming increase in the frequency and intensity of extreme climate events, such as droughts, heat waves, and their combination, inflicting heavy losses to agricultural production. Recent studies revealed that the transcriptomic responses of different crops to water deficit (WD) or heat stress (HS) are very different from that to a combination of WD + HS. In addition, it was found that the effects of WD, HS, and WD + HS are significantly more devastating when these stresses occur during the reproductive growth phase of crops, compared to vegetative growth. As the molecular responses of different reproductive and vegetative tissues of plants to WD, HS, or WD + HS could be different from each other and these differences could impact many current and future attempts to enhance the resilience of crops to climate change through breeding and/or engineering, we conducted a transcriptomic analysis of different soybean (Glycine max) tissues to WD, HS, and WD + HS. Here we present a reference transcriptomic dataset that includes the response of soybean leaf, pod, anther, stigma, ovary, and sepal to WD, HS, and WD + HS conditions. Mining this dataset for the expression pattern of different stress response transcripts revealed that each tissue had a unique transcriptomic response to each of the different stress conditions. This finding is important as it suggests that enhancing the overall resilience of crops to climate change could require a coordinated approach that simultaneously alters the expression of different groups of transcripts in different tissues in a stress‐specific manner. [ABSTRACT FROM AUTHOR]

The GRAS transcription factors play an indispensable role in plant growth and responses to environmental stresses. The GRAS gene family has extensively been explored in various plant species; however, the comprehensive investigation of GRAS genes in white lupin remains insufficient. In this study, bioinformatics analysis of white lupin genome revealed 51 LaGRAS genes distributed into 10 distinct phylogenetic clades. Gene structure analyses revealed that LaGRAS proteins were considerably conserved among the same subfamilies. Notably, 25 segmental duplications and a single tandem duplication showed that segmental duplication was the major driving force for the expansion of GRAS genes in white lupin. Moreover, LaGRAS genes exhibited preferential expression in young cluster root and mature cluster roots and may play key roles in nutrient acquisition, particularly phosphorus (P). To validate this, RT‐qPCR analysis of white lupin plants grown under +P (normal P) and −P (P deficiency) conditions elucidated significant differences in the transcript level of GRAS genes. Among them, LaGRAS38 and LaGRAS39 were identified as potential candidates with induced expression in MCR under −P. Additionally, white lupin transgenic hairy root overexpressing OE‐LaGRAS38 and OE‐LaGRAS39 showed increased root growth, and P concentration in root and leaf compared to those with empty vector control, suggesting their role in P acquisition. We believe this comprehensive analysis of GRAS members in white lupin is a first step in exploring their role in the regulation of root growth, tissue development, and ultimately improving P use efficiency in legume crops under natural environments. [ABSTRACT FROM AUTHOR]

Switchgrass, a potential biofuel crop, is a genetically diverse species with phenotypic plasticity enabling it to grow in a range of environments. Two primary divergent ecotypes, uplands and lowlands, exhibit trait combinations representative of acquisitive and conservative growth allocation strategies, respectively. Whether these ecotypes respond differently to various types of environmental drivers remains unclear but is crucial to understanding how switchgrass varieties will respond to climate change. We grew two upland, two lowland, and two intermediate/hybrid cultivars of switchgrass at three sites along a latitudinal gradient in the central United States. Over a 4‐year period, we measured plant functional traits and biomass yields and evaluated genotype‐by‐environment (G × E) interaction effects by analyzing switchgrass responses to soil and climate variables. We found substantial evidence of G × E interactions on biomass yield, primarily due to deviations in the response of the southern lowland cultivar Alamo, which produced more biomass in hotter and drier environments relative to other cultivars. While lowland cultivars had the highest potential for yield, their yields were more variable year‐to‐year compared to other cultivars, suggesting greater sensitivity to environmental perturbations. Models comparing soil and climate principal components as explanatory variables revealed soil properties, especially nutrients, to be most effective at predicting switchgrass biomass yield. Also, positive correlations between biomass yield and conservative plant traits, such as high stem mass and tiller height, became stronger at lower latitudes where the climate is hotter and drier, regardless of ecotype. Lowland cultivars, however, showed a greater predisposition to exhibit these conservative traits. These results suggest switchgrass trait allocation trade‐offs that prioritize aboveground biomass production are more tightly associated in hot, dry environments and that lowland cultivars may exhibit a more specialized strategy relative to other cultivars. Altogether, this research provides essential knowledge for improving the viability of switchgrass as a biofuel crop. [ABSTRACT FROM AUTHOR]

Urea is the most common N fertilizer worldwide, but N from urea breakdown can be easily lost through ammonia (NH3) volatilization, ammonium (NH4+) nitrification, nitrate (NO3−) denitrification or NO3−–leaching processes. Enhanced efficiency fertilizers (EEFs) are formulated to minimize N losses and maximize plant N uptake and crop yields. The capacity of urea EEFs (urea with urease inhibitor, nitrification inhibitor, both urease and nitrification inhibitors, and polymer‐coated) to maintain N in the soil profile throughout the growing season was determined by the analysis of [NH4+–N] and [NO3−–N] in three top‐soil depth increments at three dates in three seasons of no‐till maize (Zea mays L.). Surface‐applied EEFs, untreated urea, and injected urea ammonium nitrate (UAN) were compared at an application rate of 168 kg N ha−1. Within and across years, soil [NO3−–N] and [NH4+–N] typically decreased with depth and time since urea application. Among the EEFs, only a polymer‐coated urea produced greater [NO3−–N] and [NH4+–N] at several sampling dates and depths, and greater grain yields than untreated urea across the 3 yr. Nonetheless, the polymer‐coated urea resulted in significantly greater fertilizer use efficiency (FUE) than untreated urea in only 1 of 3 yr. Several EEFs increased grain yields compared to untreated urea each year, but FUE generally was similar to that with untreated urea. Environmental conditions influenced the effectiveness of EEFs in improving soil mineral N availability and increasing grain yield. Core Ideas: Environmental conditions affect enhanced efficiency fertilizer (EEF) performance and their impact on yield and fertilizer use efficiency.Most of the EEFs evaluated were efficient at reducing N losses up to 46 d after N application.Polymer‐coated EEF duration and injected urea ammonium nitrate often produced greater grain yields and fertilizer use efficiency than untreated urea. [ABSTRACT FROM AUTHOR]

Planting date and cultivar maturity group (MG) are major management factors affecting soybean [Glycine max (L.) Merr.] yield, but their effect on seed oil and protein concentration, and in particular meal protein concentration, is less understood. We quantified changes in seed oil and protein, and estimated meal protein concentration, and total oil and protein yield in response to planting date and cultivar MG ranging from 3 to 6 and across locations comprising a 8.3° range in latitude in the U.S. Midsouth. Our results show that delayed planting date and later cultivar maturity reduced oil concentration, and this was partially associated with a decrease in temperature during the seed fill phase. Thus, optimum cultivar MG recommendations to maximize total oil yield (in kg ha-1) for planting dates in May and June required relatively earlier cultivar MGs than those recommended to maximize seed yield. For planting dates in April, short-season MG 3 cultivars did not increase oil yield compared to full-season MG 4 or 5 cultivars due to a quadratic yield response to planting date at most locations. Planting date and cultivar maturity effects on seed protein concentration were not always consistent with the effects on estimated meal protein concentration after oil extraction. Meal protein concentration decreased with lower temperatures during seed fill, and when the start of seed fill occurred after August 15, but relatively short-season cultivar MGs reduced the risk of low meal protein concentration. Meal protein concentration is a trait of interest for the feed industry that would be beneficial to report in future studies evaluating genetic, management, and environmental effects on seed protein concentration. [ABSTRACT FROM AUTHOR]

The pre-harvest estimation of seed composition from standing crops is imperative for field management practices and plant phenotyping. This paper presents for the first time the potential of Unmanned Aerial Vehicles (UAV)-based high-resolution hyperspectral and LiDAR data acquired from in-season stand crops for estimating seed protein and oil compositions of soybean and corn using multisensory data fusion and automated machine learning. UAV-based hyperspectral and LiDAR data was collected during the growing season (reproductive stage five (R5)) of 2020 over a soybean test site near Columbia, Missouri and a cornfield at Urbana, Illinois, USA. Canopy spectral and texture features were extracted from hyperspectral imagery, and canopy structure features were derived from LiDAR point clouds. The extracted features were then used as input variables for automated machine-learning methods available with the H2O Automated Machine-Learning framework (H2O-AutoML). The results presented that: (1) UAV hyperspectral imagery can successfully predict both the protein and oil of soybean and corn with moderate accuracies; (2) canopy structure features derived from LiDAR point clouds yielded slightly poorer estimates of crop-seed composition compared to the hyperspectral data; (3) regardless of machine-learning methods, the combination of hyperspectral and LiDAR data outperformed the predictions using a single sensor alone, with an R2 of 0.79 and 0.67 for corn protein and oil and R2 of 0.64 and 0.56 for soybean protein and oil; and (4) the H2O-AutoML framework was found to be an efficient strategy for machine-learning-based data-driven model building. Among the specific regression methods evaluated in this study, the Gradient Boosting Machine (GBM) and Deep Neural Network (NN) exhibited superior performance to other methods. This study reveals opportunities and limitations for multisensory UAV data fusion and automated machine learning in estimating crop-seed composition. [ABSTRACT FROM AUTHOR]

Plant function arises from a complex network of structural and physiological traits. Explicit representation of these traits, as well as their connections with other biophysical processes, is required to advance our understanding of plant‐soil‐climate interactions. We used the Terrestrial Regional Ecosystem Exchange Simulator (TREES) to evaluate physiological trait networks in maize. Net primary productivity (NPP) and grain yield were simulated across five contrasting climate scenarios. Simulations achieving high NPP and grain yield in high precipitation environments featured trait networks conferring high water use strategies: deep roots, high stomatal conductance at low water potential ("risky" stomatal regulation), high xylem hydraulic conductivity and high maximal leaf area index. In contrast, high NPP and grain yield was achieved in dry environments with low late‐season precipitation via water conserving trait networks: deep roots, high embolism resistance and low stomatal conductance at low leaf water potential ("conservative" stomatal regulation). We suggest that our approach, which allows for the simultaneous evaluation of physiological traits, soil characteristics and their interactions (i.e., networks), has potential to improve our understanding of crop performance in different environments. In contrast, evaluating single traits in isolation of other coordinated traits does not appear to be an effective strategy for predicting plant performance. Our process‐based model uncovered two beneficial but contrasting trait networks for maize which can be understood by their integrated effect on water use/conservation. Modification of multiple, physiologically aligned, traits were required to bring about meaningful improvements in NPP and yield. [ABSTRACT FROM AUTHOR]

Summary: Heat waves occurring during droughts can have a devastating impact on yield, especially if they happen during the flowering and seed set stages of the crop cycle. Global warming and climate change are driving an alarming increase in the frequency and intensity of combined drought and heat stress episodes, critically threatening global food security.Because high temperature is detrimental to reproductive processes, essential for plant yield, we measured the inner temperature, transpiration, sepal stomatal aperture, hormone concentrations and transcriptomic response of closed soybean flowers developing on plants subjected to a combination of drought and heat stress.Here, we report that, during a combination of drought and heat stress, soybean plants prioritize transpiration through flowers over transpiration through leaves by opening their flower stomata, while keeping their leaf stomata closed. This acclimation strategy, termed 'differential transpiration', lowers flower inner temperature by about 2–3°C, protecting reproductive processes at the expense of vegetative tissues.Manipulating stomatal regulation, stomatal size and/or stomatal density of flowers could serve as a viable strategy to enhance the yield of different crops and mitigate some of the current and future impacts of global warming and climate change on agriculture. [ABSTRACT FROM AUTHOR]

Polyploidy results from whole-genome duplication and is a unique form of heritable variation with pronounced evolutionary implications. Different ploidy levels, or cytotypes, can exist within a single species, and such systems provide an opportunity to assess how ploidy variation alters phenotypic novelty, adaptability, and fitness, which can, in turn, drive the development of unique ecological niches that promote the coexistence of multiple cytotypes. Switchgrass, Panicum virgatum, is a widespread, perennial C4 grass in North America with multiple naturally occurring cytotypes, primarily tetraploids (4×) and octoploids (8×). Using a combination of genomic, quantitative genetic, landscape, and niche modeling approaches, we detect divergent levels of genetic admixture, evidence of niche differentiation, and differential environmental sensitivity between switchgrass cytotypes. Taken together, these findings support a generalist (8×)-specialist (4×) trade-off. Our results indicate that the 8× represent a unique combination of genetic variation that has allowed the expansion of switchgrass' ecological niche and thus putatively represents a valuable breeding resource. [ABSTRACT FROM AUTHOR]

Nitrogen supplied by [N.sub.2] fixation to soybean [Glycine may (L.) Merr.] may not be sufficient to maximize yield. Field studies were conducted in 2002, 2003, and 2004 on Sharkey clay soil (very-fine, smectitic, thermic Chromic Epiaquert) at Stoneville, MS (33°26' N lat). The objective was to determine the effect of high rates of N applied as a replacement for [N.sub.2] fixation in nonirrigated and irrigated environments. Eight cultivars ranging from Maturity Group II to IV were planted on 17 Apr. 2002, 2 Apr. 2003, and 25 Mar. 2004. Not all cultivars were evaluated in all 3 yr. Glyphosate herbicide was used in all 3 yr and a non-glyphosate herbicide treatment was applied in 2002. Cultivars grown in 2003 were also evaluated under an application of 21.3 kg [ha.sup.-1] of Mn. All cultivar, herbicide, and Mn treatments were evaluated in irrigated and nonirrigated environments with fertilizer N (PlusN treatment) or without fertilizer N (ZcroN treatment). In the PlusN treatment, granular N[H.sub.4]N[O.sub.3] was surface applied at soybean emergence at rates of 290 kg [ha.sup.-1] in 2002, 310 kg [ha.sup.-1] in 2003, and 360 kg [ha.sup.-1] in 2004. When analyzed over all management practices (years, cultivars, herbicide, and Mn treatments), the PlusN treatment resulted in significantly decreased ureide concentration (57.2 and 53.5% reduction) and significantly increased biomass accumulation (14.1 and 16.7%), N accumulation (12.8 and 28.1%), and seed yield (7.7 and 15.5%) for the irrigated and nonirrigated environments, respectively. The majority of the yield increase in each environment resulted from increased number of seed (9.5% irrigated and 16.2% nonirrigated). These results confirm the sensitivity of [N.sub.2] fixation to drought and indicate that Ns fixation may limit yield of soybean grown in both irrigated and nonirrigated environments of the midsouthern USA, and that [N.sub.2] fixation deficiencies occur before the beginning of processes that determine number of seed., WATER DEFICIT STRESS (drought) is difficult to define and quantify because the magnitude of its effect depends on numerous crop (species, cultivar, phenology, etc.) and environmental factors (intensity, duration, evaporative [...]