Genomic prediction of regional-scale performance in switchgrass (Panicum virgatum) by accounting for genotype-by-environment variation and yield surrogate traits.

Switchgrass is a potential crop for bioenergy or carbon capture schemes, but further yield improvements through selective breeding are needed to encourage commercialization. To identify promising switchgrass germplasm for future breeding efforts, we conducted multi-site and multi-trait genomic prediction with a diversity panel of 630 genotypes from 4 switchgrass subpopulations (Gulf, Midwest, Coastal, and Texas), which were measured for spaced plant biomass yield across 10 sites. Our study focused on the use of genomic prediction to share information among traits and environments. Specifically, we evaluated the predictive ability of cross-validation (CV) schemes using only genetic data and the training set, (cross validation 1: CV1), a subset of the sites (cross validation 2: CV2), and/or with two yield surrogates (flowering time and fall plant height). We found that genotype-by-environment interactions were largely due to the north-south distribution of sites. The genetic correlations between yield surrogates and biomass yield were generally positive (mean height r=0.85; mean flowering time r=0.45) and did not vary due to subpopulation or growing region (North, Middle, South). Genomic prediction models had cross-validation predictive abilities of -0.02 for individuals using only genetic data (CV1) but 0.55, 0.69, 0.76, 0.81, and 0.84 for individuals with biomass performance data from one, two, three, four and five sites included in the training data (CV2), respectively. To simulate a resource-limited breeding program, we determined the predictive ability of models provided with: one site observation of flowering time (0.39), one site observation of flowering time and fall height (0.51), one site observation of fall height (0.52), one site observation of biomass (0.55), and five site observations of biomass yield (0.84). The ability to share information at a regional scale is very encouraging but further research is required to accurately translate spaced plant biomass to commercial-scale sward biomass performance.

Spectral enhancement of PlanetScope using Sentinal-2 images to estimate soybean yield and seed composition.

Soybean is an essential crop to fight global food insecurity and is of great economic importance around the world. Along with genetic improvements aimed at boosting yield, soybean seed composition also changed. Since conditions during crop growth and development influences nutrient accumulation in soybean seeds, remote sensing offers a unique opportunity to estimate seed traits from the standing crops. Capturing phenological developments that influence seed composition requires frequent satellite observations at higher spatial and spectral resolutions. This study introduces a novel spectral fusion technique called multiheaded kernel-based spectral fusion (MKSF) that combines the higher spatial resolution of PlanetScope (PS) and spectral bands from Sentinel 2 (S2) satellites. The study also focuses on using the additional spectral bands and different statistical machine learning models to estimate seed traits, e.g., protein, oil, sucrose, starch, ash, fiber, and yield. The MKSF was trained using PS and S2 image pairs from different growth stages and predicted the potential VNIR1 (705 nm), VNIR2 (740 nm), VNIR3 (783 nm), SWIR1 (1610 nm), and SWIR2 (2190 nm) bands from the PS images. Our results indicate that VNIR3 prediction performance was the highest followed by VNIR2, VNIR1, SWIR1, and SWIR2. Among the seed traits, sucrose yielded the highest predictive performance with RFR model. Finally, the feature importance analysis revealed the importance of MKSF-generated vegetation indices from fused images.

Pre-silking water deficit in maize induced kernel loss through impaired silk growth and ovary carbohydrate dynamics.

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.

Identification of QTLs for symbiotic nitrogen fixation and related traits in a soybean recombinant inbred line population.

KEY MESSAGE: The genetic architecture of symbiotic N fixation and related traits was investigated in the field. QTLs were identified for percent N derived from the atmosphere, shoot [N] and C to N ratio. Soybean [Glycine max (L.) Merr.] is cultivated worldwide and is the most abundant source of plant-based protein. Symbiotic N2 fixation (SNF) in legumes such as soybean is of great importance; however, yields may still be limited by N in both high yielding and stressful environments. To better understand the genetic architecture of SNF and facilitate the development of high yielding cultivars and sustainable soybean production in stressful environments, a recombinant inbred line population consisting of 190 lines, developed from a cross between PI 442012A and PI 404199, was evaluated for N derived from the atmosphere (Ndfa), N concentration ([N]), and C to N ratio (C/N) in three environments. Significant genotype, environment and genotype × environment effects were observed for all three traits. A linkage map was constructed containing 3309 single nucleotide polymorphism (SNP) markers. QTL analysis was performed for additive effects of QTLs, QTL × environment interactions, and QTL × QTL interactions. Ten unique additive QTLs were identified across all traits and environments. Of these, two QTLs were detected for Ndfa and eight for C/N. Of the eight QTLs for C/N, four were also detected for [N]. Using QTL × environment analysis, six QTLs were detected, of which five were also identified in the additive QTL analysis. The QTL × QTL analysis identified four unique epistatic interactions. The results of this study may be used for genomic selection and introgression of favorable alleles for increased SNF, [N], and C/N via marker-assisted selection.

The impact of multifactorial stress combination on reproductive tissues and grain yield of a crop plant.

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.

The effects of multifactorial stress combination on rice and maize.

The complexity of environmental factors affecting crops in the field is gradually increasing due to climate change-associated weather events, such as droughts or floods combined with heat waves, coupled with the accumulation of different environmental and agricultural pollutants. The impact of multiple stress conditions on plants was recently termed "multifactorial stress combination" (MFSC) and defined as the occurrence of 3 or more stressors that impact plants simultaneously or sequentially. We recently reported that with the increased number and complexity of different MFSC stressors, the growth and survival of Arabidopsis (Arabidopsis thaliana) seedlings declines, even if the level of each individual stress is low enough to have no significant effect on plants. However, whether MFSC would impact commercial crop cultivars is largely unknown. Here, we reveal that a MFSC of 5 different low-level abiotic stresses (salinity, heat, the herbicide paraquat, phosphorus deficiency, and the heavy metal cadmium), applied in an increasing level of complexity, has a significant negative impact on the growth and biomass of a commercial rice (Oryza sativa) cultivar and a maize (Zea mays) hybrid. Proteomics, element content, and mixOmics analyses of MFSC in rice identified proteins that correlate with the impact of MFSC on rice seedlings, and analysis of 42 different rice genotypes subjected to MFSC revealed substantial genetic variability in responses to this unique state of stress combination. Taken together, our findings reveal that the impacts of MFSC on 2 different crop species are severe and that MFSC may substantially affect agricultural productivity.

Identification of loci associated with water use efficiency and symbiotic nitrogen fixation in soybean.

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.

Overexpression of LaGRAS enhances phosphorus acquisition via increased root growth of phosphorus-deficient white lupin.

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.

The transcriptome of soybean reproductive tissues subjected to water deficit, heat stress, and a combination of water deficit and heat stress.

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.

Genetic determinants of switchgrass-root-associated microbiota in field sites spanning its natural range.

A fundamental goal in plant microbiome research is to determine the relative impacts of host and environmental effects on root microbiota composition, particularly how host genotype impacts bacterial community composition. Most studies characterizing the effect of plant genotype on root microbiota undersample host genetic diversity and grow plants outside of their native ranges, making the associations between host and microbes difficult to interpret. Here, we characterized the root microbiota of a large diversity panel of switchgrass, a North American native C4 bioenergy crop, in three field locations spanning its native range. Our data, composed of 1,961 samples, suggest that field location is the primary determinant of microbiome composition; however, substantial heritable variation is widespread across bacterial taxa, especially those in the Sphingomonadaceae family. Despite diverse compositions, relatively few highly prevalent taxa make up the majority of the switchgrass root microbiota, a large fraction of which is shared across sites. Local genotypes preferentially recruit/filter for local microbes, supporting the idea of affinity between local plants and their microbiota. Using genome-wide association, we identified loci impacting the abundance of >400 microbial strains and found an enrichment of genes involved in immune responses, signaling pathways, and secondary metabolism. We found loci associated with over half of the core microbiota (i.e., microbes in >80% of samples), regardless of field location. Finally, we show a genetic relationship between a basal plant immunity pathway and relative abundances of root microbiota. This study brings us closer to harnessing and manipulating beneficial microbial associations via host genetics.

De novo transcriptome assembly from the nodal root growth zone of hydrated and water-deficit stressed maize inbred line FR697.

Certain cultivars of maize show increased tolerance to water deficit conditions by maintenance of root growth. To better understand the molecular mechanisms related to this adaptation, nodal root growth zone samples were collected from the reference inbred line B73 and inbred line FR697, which exhibits a relatively greater ability to maintain root elongation under water deficits. Plants were grown under various water stress levels in both field and controlled environment settings. FR697-specific RNA-Seq datasets were generated and used for a de novo transcriptome assembly to characterize any genotype-specific genetic features. The assembly was aided by an Iso-Seq library of transcripts generated from various FR697 plant tissue samples. The Necklace pipeline was used to combine a Trinity de novo assembly along with a reference guided assembly and the Viridiplantae proteome to generate an annotated consensus "SuperTranscriptome" assembly of 47,915 transcripts with a N50 of 3152 bp in length. The results were compared by Blastn to maize reference genes, a Benchmarking Universal Single-Copy Orthologs (BUSCO) genome completeness report and compared with three maize reference genomes. The resultant 'SuperTranscriptome' was demonstrated to be of high-quality and will serve as an important reference for analysis of the maize nodal root transcriptomic response to environmental perturbations.

Differential transpiration between pods and leaves during stress combination in soybean.

Climate change is causing an increase in the frequency and intensity of droughts, heat waves, and their combinations, diminishing agricultural productivity and destabilizing societies worldwide. We recently reported that during a combination of water deficit (WD) and heat stress (HS), stomata on leaves of soybean (Glycine max) plants are closed, while stomata on flowers are open. This unique stomatal response was accompanied by differential transpiration (higher in flowers, while lower in leaves) that cooled flowers during a combination of WD + HS. Here, we reveal that developing pods of soybean plants subjected to a combination of WD + HS use a similar acclimation strategy of differential transpiration to reduce internal pod temperature by approximately 4 °C. We further show that enhanced expression of transcripts involved in abscisic acid degradation accompanies this response and that preventing pod transpiration by sealing stomata causes a significant increase in internal pod temperature. Using an RNA-Seq analysis of pods developing on plants subjected to WD + HS, we also show that the response of pods to WD, HS, or WD + HS is distinct from that of leaves or flowers. Interestingly, we report that although the number of flowers, pods, and seeds per plant decreases under conditions of WD + HS, the seed mass of plants subjected to WD + HS increases compared to plants subjected to HS, and the number of seeds with suppressed/aborted development is lower in WD + HS compared to HS. Taken together, our findings reveal that differential transpiration occurs in pods of soybean plants subjected to WD + HS and that this process limits heat-induced damage to seed production.

Identification of genomic regions associated with the plasticity of carbon 13 ratio in soybean.

Improving water use efficiency (WUE) for soybean [Glycine max (L.) Merr.] through selection for high carbon isotope (C13) ratio may increase drought tolerance, but increased WUE may limit growth in productive environments. An ideal genotype would be plastic for C13 ratio; that is, be able to alter C13 ratio in response to the environment. Our objective was to identify genomic regions associated with C13 ratio plasticity, C13 ratio stability, and overall C13 ratio in two panels of diverse Maturity Group IV soybean accessions. A second objective was to identify accessions that differed in their C13 ratio plasticity. Panel 1 (205 accessions) was evaluated in seven irrigated and four drought environments, and Panel 2 (373 accessions) was evaluated in four environments. Plasticity was quantified as the slope from regressing C13 ratio of individual genotypes against an environmental index calculated based on the mean within and across environments. The regression intercept was considered a measure of C13 ratio over all environments, and the root mean square error was considered a measure of stability. Combined over both panels, genome-wide association mapping (GWAM) identified 19 single nucleotide polymorphisms (SNPs) for plasticity, 39 SNPs for C13 ratio, and 16 SNPs for stability. Among these SNPs, 71 candidate genes had annotations associated with transpiration or water conservation and transport, root development, root hair elongation, and stomatal complex morphogenesis. The genomic regions associated with plasticity and stability identified in the current study will be a useful resource for implementing genomic selection for improving drought tolerance in soybean.

Regional analysis of planting date and cultivar maturity recommendations that improve soybean oil yield and meal protein concentration.

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.

The genetic basis for panicle trait variation in switchgrass (Panicum virgatum).

KEY MESSAGE: We investigate the genetic basis of panicle architecture in switchgrass in two mapping populations across a latitudinal gradient, and find many stable, repeatable genetic effects and limited genetic interactions with the environment. Grass species exhibit large diversity in panicle architecture influenced by genes, the environment, and their interaction. The genetic study of panicle architecture in perennial grasses is limited. In this study, we evaluate the genetic basis of panicle architecture including panicle length, primary branching number, and secondary branching number in an outcrossed switchgrass QTL population grown across ten field sites in the central USA through multi-environment mixed QTL analysis. We also evaluate genetic effects in a diversity panel of switchgrass grown at three of the ten field sites using genome-wide association (GWAS) and multivariate adaptive shrinkage. Furthermore, we search for candidate genes underlying panicle traits in both of these independent mapping populations. Overall, 18 QTL were detected in the QTL mapping population for the three panicle traits, and 146 unlinked genomic regions in the diversity panel affected one or more panicle trait. Twelve of the QTL exhibited consistent effects (i.e., no QTL by environment interactions or no QTL × E), and most (four of six) of the effects with QTL × E exhibited site-specific effects. Most (59.3%) significant partially linked diversity panel SNPs had significant effects in all panicle traits and all field sites and showed pervasive pleiotropy and limited environment interactions. Panicle QTL co-localized with significant SNPs found using GWAS, providing additional power to distinguish between true and false associations in the diversity panel.

Physiological trait networks enhance understanding of crop growth and water use in contrasting environments.

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.

Differential regulation of flower transpiration during abiotic stress in annual plants.

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.

A generalist-specialist trade-off between switchgrass cytotypes impacts climate adaptation and geographic range.

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.

The genetic basis of the root economics spectrum in a perennial grass.

Construction economics of plant roots exhibit predictable relationships with root growth, death, and nutrient uptake strategies. Plant taxa with inexpensively constructed roots tend to more precisely explore nutrient hotspots than do those with costly constructed roots but at the price of more frequent tissue turnover. This trade-off underlies an acquisitive to conservative continuum in resource investment, described as the "root economics spectrum (RES)." Yet the adaptive role and genetic basis of RES remain largely unclear. Different ecotypes of switchgrass (Panicum virgatum) display root features exemplifying the RES, with costly constructed roots in southern lowland and inexpensively constructed roots in northern upland ecotypes. We used an outbred genetic mapping population derived from lowland and upland switchgrass ecotypes to examine the genetic architecture of the RES. We found that absorptive roots (distal first and second orders) were often "deciduous" in winter. The percentage of overwintering absorptive roots was decreased by northern upland alleles compared with southern lowland alleles, suggesting a locally-adapted conservative strategy in warmer and acquisitive strategy in colder regions. Relative turnover of absorptive roots was genetically negatively correlated with their biomass investment per unit root length, suggesting that the key trade-off in framing RES is genetically facilitated. We also detected strong genetic correlations among root morphology, root productivity, and shoot size. Overall, our results reveal the genetic architecture of multiple traits that likely impacts the evolution of RES and plant aboveground-belowground organization. In practice, we provide genetic evidence that increasing switchgrass yield for bioenergy does not directly conflict with enhancing its root-derived carbon sequestration.

The impact of stress combination on reproductive processes in crops.

Historically, extended droughts combined with heat waves caused severe reductions in crop yields estimated at billions of dollars annually. Because global warming and climate change are driving an increase in the frequency and intensity of combined water-deficit and heat stress episodes, understanding how these episodes impact yield is critical for our efforts to develop climate change-resilient crops. Recent studies demonstrated that a combination of water-deficit and heat stress exacerbates the impacts of water-deficit or heat stress on reproductive processes of different cereals and legumes, directly impacting grain production. These studies identified several different mechanisms potentially underlying the effects of stress combination on anthers, pollen, and stigma development and function, as well as fertilization. Here we review some of these findings focusing on unbalanced reactive oxygen accumulation, altered sugar concentrations, and conflicting functions of different hormones, as contributing to the reduction in yield during a combination of water-deficit and heat stress. Future studies focused on the effects of water-deficit and heat stress combination on reproduction of different crops are likely to unravel additional mechanisms, as well as reveal novel ways to develop stress combination-resilient crops. These could mitigate some of the potentially devastating impacts of this stress combination on agriculture.