Genomic prediction of seed nutritional traits in biparental families of oat (Avena sativa).

Selection for more nutritious crop plants is an important goal of plant breeding to improve food quality and contribute to human health outcomes. While there are efforts to integrate genomic prediction to accelerate breeding progress, an ongoing challenge is identifying strategies to improve accuracy when predicting within biparental populations in breeding programs. We tested multiple genomic prediction methods for 12 seed fatty acid content traits in oat (Avena sativa L.), as unsaturated fatty acids are a key nutritional trait in oat. Using two well-characterized oat germplasm panels and other biparental families as training populations, we predicted family mean and individual values within families. Genomic prediction of family mean exceeded a mean accuracy of 0.40 and 0.80 using an unrelated and related germplasm panel, respectively, where the related germplasm panel outperformed prediction based on phenotypic means (0.54). Within family prediction accuracy was more variable: training on the related germplasm had higher accuracy than the unrelated panel (0.14-0.16 and 0.05-0.07, respectively), but variability between families was not easily predicted by parent relatedness, segregation of a locus detected by a genome-wide association study in the panel, or other characteristics. When using other families as training populations, prediction accuracies were comparable to the related germplasm panel (0.11-0.23), and families that had half-sib families in the training set had higher prediction accuracy than those that did not. Overall, this work provides an example of genomic prediction of family means and within biparental families for an important nutritional trait and suggests that using related germplasm panels as training populations can be effective.

Genome-Wide Association Mapping of Bacterial Leaf Streak Resistance in Two Elite Barley Breeding Panels.

Bacterial leaf streak (BLS), caused chiefly by the pathogen Xanthomonas translucens pv. translucens, is becoming an increasingly important foliar disease of barley in the Upper Midwest. The deployment of resistant cultivars is the most economical and practical method of control. To identify sources of BLS resistance, we evaluated two panels of breeding lines from the University of Minnesota (UMN) and Anheuser-Busch InBev (ABI) barley improvement programs for reaction to strain CIX95 in the field at St. Paul and Crookston, MN, in 2020 and 2021. The percentage of resistant lines in the UMN and ABI panels with mid-season maturity was 1.8% (6 of 333 lines) and 5.2% (13 of 251 lines), respectively. Both panels were genotyped with the barley 50K iSelect SNP array, and then a genome-wide association study was performed. A single, highly significant association was identified for BLS resistance on chromosome 6H in the UMN panel. This association was also identified in the ABI panel. Seven other significant associations were detected in the ABI panel: two each on chromosomes 1H, 2H, and 3H and one on chromosome 5H. Of the eight associations identified in the panels, five were novel. The discovery of resistance in elite breeding lines will hasten the time needed to develop and release a BLS-resistant cultivar.

Multi-scale modelling predicts plant stem bending behaviour in response to wind to inform lodging resistance.

Lodging impedes the successful cultivation of cereal crops. Complex anatomy, morphology and environmental interactions make identifying reliable and measurable traits for breeding challenging. Therefore, we present a unique collaboration among disciplines for plant science, modelling and simulations, and experimental fluid dynamics in a broader context of breeding lodging resilient wheat and oat. We ran comprehensive wind tunnel experiments to quantify the stem bending behaviour of both cereals under controlled aerodynamic conditions. Measured phenotypes from experiments concluded that the wheat stems response is stiffer than the oat. However, these observations did not in themselves establish causal relationships of this observed behaviour with the physical traits of the plants. To further investigate we created an independent finite-element simulation framework integrating our recently developed multi-scale material modelling approach to predict the mechanical response of wheat and oat stems. All the input parameters including chemical composition, tissue characteristics and plant morphology have a strong physiological meaning in the hierarchical organization of plants, and the framework is free from empirical parameter tuning. This feature of our simulation framework reveals the multi-scale origin of the observed wide differences in the stem strength of both cereals that would not have been possible with purely experimental approach.

Sources of Bacterial Leaf Streak Resistance Identified in a Diverse Collection of Barley Germplasm.

Bacterial leaf streak (BLS) is a sporadic yet damaging disease of cereals that is growing in importance across the Upper Midwest production region. In barley (Hordeum vulgare ssp. vulgare), this disease is caused primarily by the bacterium Xanthomonas translucens pv. translucens. Accessions resistant to BLS have been reported in past studies, but few have been rigorously validated in the field. To identify accessions carrying diverse resistance alleles to BLS, a largescale germplasm screening study was undertaken against strain CIX95 of X. translucens pv. translucens in St. Paul and Crookston, Minnesota, in 2020 and 2021. The germplasm screened was diverse and included adapted breeding lines from two improvement programs, two landrace panels (one global and one from Ethiopia/Eritrea), introgression lines from wild barley (H. vulgare ssp. spontaneum) in the genetic background of barley cultivar 'Rasmusson', and an assemblage of accessions previously reported to carry BLS resistance. Of the 2,094 accessions evaluated in this study, 32 (1.5%) exhibited a consistently high level of resistance across locations and years and had heading dates similar to standard cultivars grown in the region. Accessions resistant to BLS were identified from all germplasm panels tested, providing genetically diverse sources for barley improvement programs focused on breeding for resistance to this important bacterial disease.

Effects of interrupting daily sedentary behavior on children's glucose metabolism: A 6-day randomized controlled trial.

BACKGROUND: Metabolic disease risk in youth is influenced by sedentary behaviors. Acute in-lab studies show that, during a single day, interrupting a sedentary period with short bouts of physical activity improves glucometabolic outcomes. OBJECTIVE: To determine if acutely improved glucose metabolism persists after multi-day interruptions of sitting with walking brief bouts. We hypothesized that children who underwent interrupting sitting on multiple days would demonstrate lower insulin area under the curve during an oral glucose tolerance test compared to uninterrupted sitting. METHODS: Healthy, normoglycemic children (N = 109) ages 7-11 years were randomized to one of two conditions: Control (3 h of daily Uninterrupted Sitting) or Interrupted Sitting (3-min of moderate-intensity walking every 30 min for 3 h daily); with dietary intake controlled through provision of foodstuffs for the entire experiment. Participants attended six consecutive daily visits at a research ambulatory unit. The primary outcome was insulin area under the curve during the oral glucose tolerance test on day 6 during interrupted or uninterrupted sitting; secondary outcomes included glucose and c-peptide area under the curve, energy intake at a buffet meal on day 6, and free-living activity. RESULTS: Among 93 children (42 uninterrupted sitting, 51 interrupted sitting), daily interrupted sitting resulted in 21% lower insulin (ß = 0.102 CI:0.032-0.172, p = 0.005) and a 10% lower C-peptide (ß = 0.043, CI:0.001-0.084, p = 0.045) area under the curve. Matsuda and Glucose Effectiveness Indices were also improved (p's < 0.05). There were no group differences in energy intake or expenditure. CONCLUSIONS: Sustained behavioral change by interrupting sedentary behaviors is a promising intervention strategy for improving metabolic risk in children.

The evolutionary patterns of barley pericentromeric chromosome regions, as shaped by linkage disequilibrium and domestication.

The distribution of recombination events along large cereal chromosomes is uneven and is generally restricted to gene-rich telomeric ends. To understand how the lack of recombination affects diversity in the large pericentromeric regions, we analysed deep exome capture data from a final panel of 815 Hordeum vulgare (barley) cultivars, landraces and wild barleys, sampled from across their eco-geographical ranges. We defined and compared variant data across the pericentromeric and non-pericentromeric regions, observing a clear partitioning of diversity both within and between chromosomes and germplasm groups. Dramatically reduced diversity was found in the pericentromeres of both cultivars and landraces when compared with wild barley. We observed a mixture of completely and partially differentiated single-nucleotide polymorphisms (SNPs) between domesticated and wild gene pools, suggesting that domesticated gene pools were derived from multiple wild ancestors. Patterns of genome-wide linkage disequilibrium, haplotype block size and number, and variant frequency within blocks showed clear contrasts among individual chromosomes and between cultivars and wild barleys. Although most cultivar chromosomes shared a single major pericentromeric haplotype, chromosome 7H clearly differentiated the two-row and six-row types associated with different geographical origins. Within the pericentromeric regions we identified 22 387 non-synonymous SNPs, 92 of which were fixed for alternative alleles in cultivar versus wild accessions. Surprisingly, only 29 SNPs found exclusively in the cultivars were predicted to be 'highly deleterious'. Overall, our data reveal an unconventional pericentromeric genetic landscape among distinct barley gene pools, with different evolutionary processes driving domestication and diversification.

Generalizable approaches for genomic prediction of metabolites in plants.

Plant metabolites are important traits for plant breeders seeking to improve nutrition and agronomic performance yet integrating selection for metabolomic traits can be limited by phenotyping expense and degree of genetic characterization, especially of uncommon metabolites. As such, developing generalizable genomic selection methods based on biochemical pathway biology for metabolites that are transferable across plant populations would benefit plant breeding programs. We tested genomic prediction accuracy for >600 metabolites measured by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) in oat (Avena sativa L.) seed. Using a discovery germplasm panel, we conducted metabolite genome-wide association study (mGWAS) and selected loci to use in multikernel models that encompassed metabolome-wide mGWAS results or mGWAS from specific metabolite structures or biosynthetic pathways. Metabolite kernels developed from LC-MS metabolites in the discovery panel improved prediction accuracy of LC-MS metabolite traits in the validation panel consisting of more advanced breeding lines. No approach, however, improved prediction accuracy for GC-MS metabolites. We ranked model performance by metabolite and found that metabolites with similar polarity had consistent rankings of models. Overall, testing biological rationales for developing kernels for genomic prediction across populations contributes to developing frameworks for plant breeding for metabolite traits.

Development of first linkage map for Silphium integrifolium (Asteraceae) enables identification of sporophytic self-incompatibility locus.

Silphium integrifolium (Asteraceae) has been identified as a candidate for domestication as a perennial oilseed crop and is assumed to have sporophytic self-incompatibility system-the genetic basis of which is not well understood in the Asteraceae. To address this gap, we sought to map the genomic location of the self-recognition locus (S-locus) in this species. We used a biparental population and genotyping-by-sequencing to create the first genetic linkage map for this species, which contained 198 SNP markers and resolved into the correct number of linkage groups. Then we developed a novel crossing scheme and set of analysis methods in order to infer S-locus genotypes for a subset of these individuals, allowing us to map the trait. Finally, we evaluated potential genes of interest using synteny analysis with the annual sunflower (Helianthus annuus) and lettuce (Lactuca sativa) genomes. Our results confirm that S. integrifolium does indeed have a sporophytic self-incompatibility system. Our method is effective and efficient, allowed us to map the S. integrifolium S-locus using fewer resources than existing methods, and could be readily applied to other species.

Oat Crown Rust Disease Severity Estimated at Many Time Points Using Multispectral Aerial Photos.

All plant breeding programs are dependent on plant phenotypic and genotypic data, but the development of phenotyping technology has been slow relative to that of genotyping. Crown rust (Puccinia coronata f. sp. avenae Erikss.) is the most important disease of cultivated oat (Avena sativa L.), making the development of disease-resistant oat cultivars an important breeding objective. Visual observation is the most common scoring method, but it can be laborious and subjective. We visually scored a diverse collection of 256 oat lines at a total of 27 time points in three disease nursery environments. Multispectral aerial photos were collected using an unmanned aerial vehicle at the same time points as the visual observations. The photos were analyzed, and subsets of the spectral properties of each plot were measured. Random forest modeling was used to model the relationship between the spectral properties of the plots and visually observed disease severity. The ability of the photo data and the random forest model to estimate visually observed disease severity was evaluated using three different cross-validation analyses. We specifically addressed the issue of assessing phenotyping accuracy across and within time points. The accuracy of the photo estimates was greatest for adult plants shortly before they began to senesce. Accuracy outside of that time frame was generally low but statistically significant. Unmanned aerial vehicle-mounted sensors could increase disease scoring efficiency, but additional investigation into the spectral signature of disease severity at all plant growth stages may be necessary to automate accurate full-season measurements.

Selection for seed size has uneven effects on specialized metabolite abundance in oat (Avena sativa L.).

Plant breeding strategies to optimize metabolite profiles are necessary to develop health-promoting food crops. In oats (Avena sativa L.), seed metabolites are of interest for their antioxidant properties, yet have not been a direct target of selection in breeding. In a diverse oat germplasm panel spanning a century of breeding, we investigated the degree of variation of these specialized metabolites and how it has been molded by selection for other traits, like yield components. We also ask if these patterns of variation persist in modern breeding pools. Integrating genomic, transcriptomic, metabolomic, and phenotypic analyses for three types of seed specialized metabolites-avenanthramides, avenacins, and avenacosides-we found reduced heritable genetic variation in modern germplasm compared with diverse germplasm, in part due to increased seed size associated with more intensive breeding. Specifically, we found that abundance of avenanthramides increases with seed size, but additional variation is attributable to expression of biosynthetic enzymes. In contrast, avenacoside abundance decreases with seed size and plant breeding intensity. In addition, these different specialized metabolites do not share large-effect loci. Overall, we show that increased seed size associated with intensive plant breeding has uneven effects on the oat seed metabolome, but variation also exists independently of seed size to use in plant breeding. This work broadly contributes to our understanding of how plant breeding has influenced plant traits and tradeoffs between traits (like growth and defense) and the genetic bases of these shifts.

Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations.

KEY MESSAGE: Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.

Genetic dissection of a pericentromeric region of barley chromosome 6H associated with Fusarium head blight resistance, grain protein content and agronomic traits.

KEY MESSAGE: Fine mapping of barley 6H pericentromeric region identified FHB QTL with opposite effects, and high grain protein content was associated with increased FHB severity. Resistance to Fusarium head blight (FHB), kernel discoloration (KD), deoxynivalenol (DON) accumulation and grain protein content (GPC) are important traits for breeding malting barley varieties. Previous work mapped a Chevron-derived FHB QTL to the pericentromeric region of 6H, coinciding with QTL for KD resistance and GPC. The Chevron allele reduced FHB and KD, but unfavorably increased GPC. To determine whether the correlations are caused by linkage or pleiotropy, a fine mapping approach was used to dissect the QTL underlying these quality and disease traits. Two populations, referred to as Gen10 and Gen10/Lacey, derived from a recombinant near-isogenic line (rNIL) were developed. Recombinants were phenotyped for FHB, KD, DON, GPC and other agronomic traits. Three FHB, two DON and two KD QTLs were identified. One of the three FHB QTLs, one DON QTL and one KD QTL were coincident with the GPC QTL, which contains the Hv-NAM1 locus affecting grain protein accumulation. The Chevron allele at the GPC QTL increased GPC and FHB and decreased DON and KD. The other two FHB QTL and the other DON and KD QTL were identified in the regions flanking the Hv-NAM1 locus, and the Chevron alleles decreased FHB, DON and KD. Our results suggested that the QTL associated with FHB, KD, DON and GPC in the pericentromeric region of 6H was controlled by both pleiotropy and tightly linked loci. The rNILs identified in this study with low FHB severity and moderate GPC may be used for breeding malting barley cultivars.

Measurements of lethal and nonlethal inbreeding depression inform the de novo domestication of Silphium integrifolium.

PREMISE: Inbreeding depression, or the reduction in fitness of progeny with related parents, has the potential to adversely affect the long-term viability of both wild and captive plant populations. Silphium integrifolium, a prairie plant native to the central United States, has been identified as a potential candidate for domestication as a perennial oilseed crop. Little is known about the potential for inbreeding depression in this species, but it is expected to be nonnegligible because S. integrifolium is both perennial and self-incompatible. Here, we measure lethal inbreeding depression expressed through embryo deaths, and nonlethal inbreeding depression expressed through changes in vigor and fitness phenotypes of progeny. METHODS: First, we made controlled crosses among related and unrelated individuals to determine the effect of two different levels of inbreeding on seed production. Then, we grew inbred and outbred progeny from this population to reproductive maturity and measured 11 key traits. RESULTS: We found that within an improved S. integrifolium population, individuals carried an average of slightly less than one lethal allele per gamete. In progeny, significant inbreeding depression was observed in at least one family for eight of the 11 measured traits. CONCLUSIONS: Inbreeding depression is likely to be an important challenge to S. integrifolium domestication, reducing overall population fecundity and values for important phenotypes. These effects may grow worse as selection reduces effective population size. We recommend several strategies for S. integrifolium breeding to help mitigate these problems.

Improving Genomic Prediction for Seed Quality Traits in Oat (Avena sativa L.) Using Trait-Specific Relationship Matrices.

The observable phenotype is the manifestation of information that is passed along different organization levels (transcriptional, translational, and metabolic) of a biological system. The widespread use of various omic technologies (RNA-sequencing, metabolomics, etc.) has provided plant genetics and breeders with a wealth of information on pertinent intermediate molecular processes that may help explain variation in conventional traits such as yield, seed quality, and fitness, among others. A major challenge is effectively using these data to help predict the genetic merit of new, unobserved individuals for conventional agronomic traits. Trait-specific genomic relationship matrices (TGRMs) model the relationships between individuals using genome-wide markers (SNPs) and place greater emphasis on markers that most relevant to the trait compared to conventional genomic relationship matrices. Given that these approaches define relationships based on putative causal loci, it is expected that these approaches should improve predictions for related traits. In this study we evaluated the use of TGRMs to accommodate information on intermediate molecular phenotypes (referred to as endophenotypes) and to predict an agronomic trait, total lipid content, in oat seed. Nine fatty acids were quantified in a panel of 336 oat lines. Marker effects were estimated for each endophenotype, and were used to construct TGRMs. A multikernel TRGM model (MK-TRGM-BLUP) was used to predict total seed lipid content in an independent panel of 210 oat lines. The MK-TRGM-BLUP approach significantly improved predictions for total lipid content when compared to a conventional genomic BLUP (gBLUP) approach. Given that the MK-TGRM-BLUP approach leverages information on the nine fatty acids to predict genetic values for total lipid content in unobserved individuals, we compared the MK-TGRM-BLUP approach to a multi-trait gBLUP (MT-gBLUP) approach that jointly fits phenotypes for fatty acids and total lipid content. The MK-TGRM-BLUP approach significantly outperformed MT-gBLUP. Collectively, these results highlight the utility of using TGRM to accommodate information on endophenotypes and improve genomic prediction for a conventional agronomic trait.

Translating insights from the seed metabolome into improved prediction for lipid-composition traits in oat (Avena sativa L.).

Oat (Avena sativa L.) seed is a rich resource of beneficial lipids, soluble fiber, protein, and antioxidants, and is considered a healthful food for humans. Little is known regarding the genetic controllers of variation for these compounds in oat seed. We characterized natural variation in the mature seed metabolome using untargeted metabolomics on 367 diverse lines and leveraged this information to improve prediction for seed quality traits. We used a latent factor approach to define unobserved variables that may drive covariance among metabolites. One hundred latent factors were identified, of which 21% were enriched for compounds associated with lipid metabolism. Through a combination of whole-genome regression and association mapping, we show that latent factors that generate covariance for many metabolites tend to have a complex genetic architecture. Nonetheless, we recovered significant associations for 23% of the latent factors. These associations were used to inform a multi-kernel genomic prediction model, which was used to predict seed lipid and protein traits in two independent studies. Predictions for 8 of the 12 traits were significantly improved compared to genomic best linear unbiased prediction when this prediction model was informed using associations from lipid-enriched factors. This study provides new insights into variation in the oat seed metabolome and provides genomic resources for breeders to improve selection for health-promoting seed quality traits. More broadly, we outline an approach to distill high-dimensional "omics" data to a set of biologically meaningful variables and translate inferences on these data into improved breeding decisions.

Genetic loci mediating circadian clock output plasticity and crop productivity under barley domestication.

Circadian clock rhythms are shown to be intertwined with crop adaptation. To realize the adaptive value of changes in these rhythms under crop domestication and improvement, there is a need to compare the genetics of clock and yield traits. We compared circadian clock rhythmicity based on Chl leaf fluorescence and transcriptomics among wild ancestors, landraces, and breeding lines of barley under optimal and high temperatures. We conducted a genome scan to identify pleiotropic loci regulating the clock and field phenotypes. We also compared the allelic diversity in wild and cultivated barley to test for selective sweeps. We found significant loss of thermal plasticity in circadian rhythms under domestication. However, transcriptome analysis indicated that this loss was only for output genes and that temperature compensation in the core clock machinery was maintained. Drivers of the circadian clock (DOC) loci were identified via genome-wide association study. Notably, these loci also modified growth and reproductive outputs in the field. Diversity analysis indicated selective sweep in these pleiotropic DOC loci. These results indicate a selection against thermal clock plasticity under barley domestication and improvement and highlight the importance of identifying genes underlying for understanding the biochemical basis of crop adaptation to changing environments.

Multiscale characterization and micromechanical modeling of crop stem materials.

An essential prerequisite for the efficient biomechanical tailoring of crops is to accurately relate mechanical behavior to compositional and morphological properties across different length scales. In this article, we develop a multiscale approach to predict macroscale stiffness and strength properties of crop stem materials from their hierarchical microstructure. We first discuss the experimental multiscale characterization based on microimaging (micro-CT, light microscopy, transmission electron microscopy) and chemical analysis, with a particular focus on oat stems. We then derive in detail a general micromechanics-based model of macroscale stiffness and strength. We specify our model for oats and validate it against a series of bending experiments that we conducted with oat stem samples. In the context of biomechanical tailoring, we demonstrate that our model can predict the effects of genetic modifications of microscale composition and morphology on macroscale mechanical properties of thale cress that is available in the literature.

Cold Conditioned: Discovery of Novel Alleles for Low-Temperature Tolerance in the Vavilov Barley Collection.

Climate changes leading to higher summer temperatures can adversely affect cool season crops like spring barley. In the Upper Midwest region of the United States, one option for escaping this stress factor is to plant winter or facultative type cultivars in the autumn and then harvest in early summer before the onset of high-temperature stress. However, the major challenge in breeding such cultivars is incorporating sufficient winter hardiness to survive the extremely low temperatures that commonly occur in this production region. To broaden the genetic base for winter hardiness in the University of Minnesota breeding program, 2,214 accessions from the N. I. Vavilov Institute of Plant Industry (VIR) were evaluated for winter survival (WS) in St. Paul, Minnesota. From this field trial, 267 (>12%) accessions survived [designated as the VIR-low-temperature tolerant (LTT) panel] and were subsequently evaluated for WS across six northern and central Great Plains states. The VIR-LTT panel was genotyped with the Illumina 9K SNP chip, and then a genome-wide association study was performed on seven WS datasets. Twelve significant associations for WS were identified, including the previously reported frost resistance gene FR-H2 as well as several novel ones. Multi-allelic haplotype analysis revealed the most favorable alleles for WS in the VIR-LTT panel as well as another recently studied panel (CAP-LTT). Seventy-eight accessions from the VIR-LTT panel exhibited a high and consistent level of WS and select ones are being used in winter barley breeding programs in the United States and in a multiparent population.

Transpiration increases under high-temperature stress: Potential mechanisms, trade-offs and prospects for crop resilience in a warming world.

The frequency and intensity of high-temperature stress events are expected to increase as climate change intensifies. Concomitantly, an increase in evaporative demand, driven in part by global warming, is also taking place worldwide. Despite this, studies examining high-temperature stress impacts on plant productivity seldom consider this interaction to identify traits enhancing yield resilience towards climate change. Further, new evidence documents substantial increases in plant transpiration rate in response to high-temperature stress even under arid environments, which raise a trade-off between the need for latent cooling dictated by excessive temperatures and the need for water conservation dictated by increasing evaporative demand. However, the mechanisms behind those responses, and the potential to design the next generation of crops successfully navigating this trade-off, remain poorly investigated. Here, we review potential mechanisms underlying reported increases in transpiration rate under high-temperature stress, within the broader context of their impact on water conservation needed for crop drought tolerance. We outline three main contributors to this phenomenon, namely stomatal, cuticular and water viscosity-based mechanisms, and we outline research directions aiming at designing new varieties optimized for specific temperature and evaporative demand regimes to enhance crop productivity under a warmer and dryer climate.