Societal Impact Statement Cereal‐legume intercropping is a promising strategy for sustainable agroecosystems, leveraging the biological complementarities between plant species to reduce the need for inputs while enhancing field biodiversity. Here, we focused on maize‐bean intercropping, which is experiencing a revival in conventional agricultural settings. In such settings, intercropping was characterized more by competition than synergy, primarily affecting yield and gene expression in beans, which, despite a reduced yield, produced larger seeds. Our findings highlight the importance of selecting suitable varieties and adjusting agricultural practices to take full advantage of the intercrop. Summary Cereal‐legume intercropping is a promising strategy for sustainable agroecosystems. The traditional intercropping of maize and bean is experiencing a revival in some modern agricultural settings, such as in southwestern France, where maize hybrids are intercropped with the commercialized Tarbais bean. We conducted on‐farm surveys and a field assay to address the following questions: How does the cropping system impact yield, nutrient uptake, and rhizosphere bacterial assemblages? Do positive or negative interactions between maize and beans dominate in intercropping? What is the effect of intercropping on plant transcriptomics? We recorded farming practices, conducted yield and nutrient measurements, and characterized soil bacterial assemblages to compare sole‐cropped maize and beans with intercropped plants. A controlled field assay was also established to extend this comparison to plant gene expression differences. Intercropping was associated with a trend toward increased bacterial diversity. The cropping system significantly influenced agronomic traits, with frequent farm‐by‐cropping system interactions underscoring the critical role of farming practices. Competition dominated maize‐bean intercropping, with 34 negative correlations among the 47 significant ones between maize and bean traits. This competition affected yield and nutrition, but primarily impacted beans, which produced fewer but bigger/heavier seeds. Transcriptomic results concurred with these findings, revealing no differentially expressed genes in maize but 5,070 in beans under competition. Overall, our findings suggest that beneficial interactions between the two crops are hindered under current field conditions, underscoring the importance of carefully considering partner varieties and farming practices to revive traditional agricultural systems.

Cereal-legume intercropping is emerging as a promising strategy for fostering more sustainable agroecosystems. The traditional farming system of maize-bean intercropping, originating from Central America, where both crops were domesticated, is experiencing a revival in modern agricultural settings. This is exemplified in southwestern France, where maize hybrids are intercropped with the recently commercialized Tarbais bean variety. Here, we combined on-farm investigations of agronomic practices, plant yield and nutrition measurements, characterization of soil and bacterial assemblages by metabarcoding, along with transcriptomic assays to compare the performance of sole cropped maize and beans to intercropped plants. Agronomic practices and soil-related variables differed among farms, with results suggesting that farmers adjust the former according to soil mineralization level. Despite this farm-specific effect, we detected a notable increase in bacterial diversity in intercropping, both in bulk and rhizosphere soil. Multivariate analyses unveiled a significant impact of cropping systems on agronomic traits in maize and beans, with frequent farm-by-cropping system interactions (11 of 22 traits in maize and 12 of 24 traits in bean). Competition dominated maize-bean intercropping, with 54 of 89 significant correlations between maize and bean traits being negative. Beans were notably impacted by this competition, yielding fewer seeds in intercropped conditions, albeit exhibiting higher seed size and weight, as well as nitrogen and carbon percentages. We set up a controlled field assay to quantify differentially expressed (DE) genes between cropping conditions for the two species. While competition between the two crops was much more severe than on-farm, we found that it primarily affected the bean and its transcriptome, with nearly 30% of DE genes vs none in maize. Overall, our findings suggest that synergies between the two crops are likely hindered, underscoring the importance of carefully considering partner varieties and agronomic practices in the revival of traditional agricultural systems.### Competing Interest StatementThe authors have declared no competing interest.

Grapevine ( Vitis vinifera ) breeding reaches a critical point. New cultivars are released every year with resistance to powdery and downy mildews. However, the traditional process remains time-consuming, taking 20 to 25 years, and demands the evaluation of new traits to enhance grapevine adaptation to climate change. Until now, the selection process has relied on phenotypic data and a limited number of molecular markers for simple genetic traits such as resistance to pathogens, without a clearly defined ideotype and was carried out on a large scale. To accelerate the breeding process and address these challenges, we investigated the use of genomic prediction, a methodology using molecular markers to predict genotypic values. In our study, we focused on two existing grapevine breeding programs: Rosé wine and Cognac production. In these programs, several families were created through crosses of emblematic and inter-specific resistant varieties to powdery and downy mildews. 30 traits were evaluated for each program, using two genomic prediction methods: GBLUP (Genomic Best Linear Unbiased Predictor) and LASSO (Least Absolute Shrinkage Selection Operator). The results revealed substantial variability in predictive abilities across traits, ranging from 0 to 0.9. These discrepancies could be attributed to factors such as trait heritability and trait characteristics. Moreover, we explored the potential of across-population genomic prediction by leveraging other grapevine populations as training sets. Integrating genomic prediction allowed us to identify superior individuals for each program, using multivariate selection index method. The ideotype for each breeding program was defined collaboratively with representatives from the wine-growing sector.

Extreme events associated with climate change increasingly threaten agriculture. Experimenting on a grapevine diversity panel suddenly exposed to a record heatwave in South France, we observed varietal responses ranging from complete tolerance to severe burning. We uncovered a handful of genomic regions associated with extreme heat tolerance, showing that we may leverage genetic diversity for breeding perennial fruit crops capable of withstanding heatwaves. Extreme heatwaves are proliferating with climate change, yet their impacts on plants are barely documented, especially in perennials that populate orchards and forests (Teskey et al., 2015; Breshears et al., 2021). As breeding programmes for perennial plants typically take several decades, we urgently need to explore whether the current genetic diversity contains alleles that could be mined to improve tolerance to extreme heat (Wolkovich et al., 2018). However, conducting genetic analyses for heat tolerance in perennials is challenging. On the one hand, experimental facilities with controlled conditions are generally not designed for studying perennial plants at the genetic diversity scale. Moreover, controlled conditions rarely reproduce the short-wave radiation of the sun that critically contributes to raising tissue temperature, driving the damaging production of reactive oxygen species (Jagadish et al., 2021). On the other hand, extreme heatwaves have remained erratic episodes so far, making it difficult to have plant diversity panels ready to be exposed to natural episodes and free of confounding effects like plant phenology (Driedonks et al., 2016), intra-field heterogeneity (Costa et al., 2018) or co-occurrence of drought (Tricker et al., 2018). Hence, the genetic analyses for extreme heat tolerance are currently limited to a few annual crop species grown in natural (Chen et al., 2017; McNellie et al., 2018) or glasshouse (Wu et al., 2022) conditions. On 28 June 2019, we had the 'chance' to go through a record heatwave while we were growing a grapevine diversity panel of 279 cultivars (Nicolas et al., 2016) in a common garden experiment in Montpellier, at the heart of the Mediterranean area in South France. An incoming mass of hot air from the Sahara desert originated the all-time highest temperature observed in France, and overheated a large part of Europe (Sousa et al., 2020). This heatwave generated c. 35% yield losses on damaged farms in the viticultural region around Montpellier (Reluy et al., 2022). In our experimental vineyard with young potted vines sharing a similar vegetative stage, air temperature peaked at 45.2°C (Fig. 1a; Supporting Information Table S1), 10.8°C above the mean maximal temperature of the period over the 2009–2023 records. Three-dimensional modelling of energy balance suggests that canopy temperature reached 47.3°C in the shade and up to 53.8°C in the sun (Figs 1b,c, S1a). A few hours later, part of the vines was literally burnt – a symptom termed 'leaf firing', whereby tissue death quickly follows exposure to heat (Chen et al., 2017; McNellie et al., 2018). Leaf firing was noticeably cultivar-dependent: while some cultivars displayed severe leaf firing (especially the sunlit leaves close to the heated soil, consistent with the leaf temperature profile obtained from the model), others showed no visible symptoms (Figs 1d,e, S2). This suggests that current grapevine diversity holds a genetic potential useful to breed for plants adapted to extreme heat. We scored the proportion and intensity of leaf firing of each genotype, obtained the firing magnitude as proportion × intensity and performed genome-wide association studies (GWAS) on each trait using one single-locus (MM4LMM) and two multi-locus (MLMM, varbvs) methods. We found six regions with at least one associated single-nucleotide polymorphism (SNP; Figs 1f, S3; Table S2), which we named the Burned Leaves After heatwave and Zonal Sun Exposure (BLAZE) loci. Most associations were significant both with MM4LMM and MLMM, and among the top SNPs by inclusion probability with varbvs. Each individual SNP explained between 7% and 10% of the genotypic variation, while the key set of SNPs jointly explained up to 22% for the firing magnitude. BLAZE5.1 was significant for all traits. BLAZE13.1 and BLAZE13.2 were separated by only 1.1 Mb and were significant only for the magnitude, with both components being close to the significance threshold. The three remaining loci, BLAZE6.1, BLAZE10.1 and BLAZE14.1, contained associated SNPs that were significant or almost significant for the proportion and/or the magnitude. Cumulating favourable alleles made it more likely for a cultivar to achieve thermotolerance, yet there was large variation in the relationship (Fig. S4a). Moreover, BLAZE10.1, BLAZE13.1, BLAZE13.2 and BLAZE14.1 were most frequently found together in their homozygous favourable form (85% of cultivars bearing homozygous favourable alleles for at least three of the loci), making it difficult to quantify their individual effect (Fig. S4). We then tested whether some of these SNPs co-localized with associations for morphological traits known to correlate with heat tolerance across species. For instance, small leaf size and high leaf mass per area (LMA, closely linked to leaf thickness) have been reported to correlate with enhanced heat tolerance/avoidance across 20 broadleaf evergreen tree or shrub species (Marchin et al., 2022). This is in line with biophysical analyses showing that small leaves have a thinner boundary layer, favouring heat dissipation (Leigh et al., 2017), while thicker leaves have a higher capacity to buffer temperature variations (Leigh et al., 2012). Are these traits reliable proxies of heat tolerance at the intra-specific level? Here, while leaf size loosely correlated with the firing proportion, LMA positively correlated with all firing symptoms (Fig. S2), at odds with inter-specific correlations. Moreover, the associations we detected for leaf size and LMA did not co-localize with BLAZE associations (Fig. S5). This suggests that genetic variation for heat tolerance in this grapevine panel was driven by other traits. Alternatively, leaf temperature could be reduced by minimizing the absorption of solar radiation (through leaf optical properties, leaf orientation or shoot architecture), or by enhancing latent heat loss through leaf transpiration, also known as evaporative cooling. Evaporative cooling is a powerful strategy to limit leaf temperature under hot environments (Drake et al., 2018). Simulating an enhancement or reduction in evapotranspiration within biologically relevant ranges (Materials and Methods) resulted in a −2.9°C or +1.9°C change in mean leaf temperature during the heatwave compared with the default simulation, respectively (Fig. S1), highlighting evaporative cooling as a potent lever for reducing leaf firing. Evapotranspiration occurs at the leaf surface through the stomatal pores and, to a lesser extent, the cuticle. Surprisingly, however, we found no candidate gene obviously linked to stomata or cuticle properties around the detected SNPs (Fig. 2; Table S3): We only noticed the E3 ubiquitin ligase COP1 (93 kb from BLAZE10.1) and the ras-related small GTP-binding protein RabE1C (50 kb from BLAZE13.2), both involved in abscisic acid-induced stomatal closure in Arabidopsis (Chen et al., 2021a, 2021b), but they were not the best candidates in their region (to be described later). This unexpected output may yet be explained by the high risk of hydraulic failure during extreme heatwaves, as the extensive water flow required to support evaporative cooling could be readily disrupted if soil water becomes limited or if the hydraulic conductances on the path for leaf water supply are not large enough to meet evaporative demand (Cochard, 2021). Beyond temperature regulation, a myriad of processes potentially underlie heat tolerance such as membrane stabilization, scavenging of reactive oxygen species or osmoprotection (Pettenuzzo et al., 2022). Heat shock proteins (HSP) and heat shock transcription factors (HSF) are known as central actors in plant response to heat. Recently in grapevine, allelic variations at HSFA2 and HSFB1 from the species Vitis davidii and Vitis quinquangularis were found to confer higher thermotolerance compared with that of the cultivated Vitis vinifera (Chen et al., 2023; Liu et al., 2023). Here, within the list of candidate genes born from our pure Vitis vinifera panel, we found a 15.4 kDa class V HSP (69 kb from BLAZE10.1, Fig. 2c) and one HSF named HSFB4b (55 kb from BLAZE6.1, Fig. 2b). The latter, however, was also close (50 kb) to a cluster of flavonoid 3′,5′-hydroxylases (F3′5′Hs; Falginella et al., 2010). These cytochrome P450 enzymes (CYP75A family) are involved in the synthesis of anthocyanins, which have sunscreen and antioxidant properties. This cytochrome P450 cluster on chromosome 6 is the largest in grapevine, gathering 35 members of the CYP75 and CYP79 families (Ilc et al., 2018). Interestingly, on the ohnologous chromosome 13, BLAZE13.1 localized 5-kb upstream from another cluster of cytochromes P450 (Fig. 2d), mostly from the CYP79 family that is responsible for the production of oxime derivatives precursors of cyanogenic glucosides (Ilc et al., 2018). They were annotated as tryptophan N-monooxygenase CYP79A68 or phenylalanine N-monooxygenase CYP79D16-like. In Prunus mume, CYP79D16 produces phenylacetaldoxime, a toxic intermediate in the amygdalin pathway (Yamaguchi et al., 2014). We also noticed that the cluster at BLAZE13.1 included one CYP716B1, tandem of which was found at BLAZE10.1 as well. The CYP716 family is involved in the biosynthesis of triterpenoid saponins (Miettinen et al., 2017), which are generally able to permeabilize and perforate plasma membranes (Mugford & Osbourn, 2013). Whether toxic compounds accumulate upon heat stress in grapevine leaves of sensitive cultivars to induce apoptosis remains an avenue to be explored. The additional regions revealed other promising candidate genes (Fig. 2; Table S3). Noticeably, BLAZE5.1, associated with all firing traits (Fig. 2a), was located within a large cluster of 24 ankyrin repeat-containing proteins orthologues of Arabidopsis INCREASED TOLERANCE TO NaCl 1 (ITN1) and ANKYRIN-LIKE1 (ANK1). Arabidopsis ITN1 promotes the production of reactive oxygen species under salt stress (Sakamoto et al., 2008), making it credible that one of its grapevine orthologues plays a similar role during heat shocks. The two remaining BLAZE loci also mapped to genomic regions containing gene clusters, but other genes therein were more likely candidates for firing traits. First, BLAZE13.2 (Fig. 2e) fell a few kb away from ZAT12, a zinc finger protein that prevents leaf burning upon excessive light in Arabidopsis (Iida et al., 2000) and shows a pivotal role in many stresses, including heat and oxidative stress (Davletova et al., 2005). Second, BLAZE14.1 (Fig. 2f) was 56-kb downstream of a gene coding for phosphatidylinositol 4-kinase gamma 5 (PI4Kγ5), which engages in the generation of phosphatidylinositol 4,5-bisphosphate (PIP2), a messenger involved in the signalling of heat stress (Mishkind et al., 2009). Our real-life study demonstrates that cultivated grapevine possesses strong genetic variation for its canopy response to extreme heat, which could not have been detected using morphological proxies, and discloses a suite of promising markers to breed a perennial plant for extreme heat tolerance. The prioritized candidate genes deserve further investigation to decipher the underlying mechanisms. The relevance of BLAZE loci should also be assessed in productive vineyards where berries are subjected to sunburn (Gambetta et al., 2021), and under conditions of water deficit that could reveal genetic links between leaf firing and transpiration, which were not apparent here. A diversity panel of 279 cultivars of Vitis vinifera L. was designed to maximize genetic diversity and minimize relatedness among cultivated grapevine while capturing the main low structure in three genetic pools (Nicolas et al., 2016). In winter 2018, cuttings of each cultivar were obtained from the Vassal-Montpellier Grapevine Biological Resource Center (Marseillan-Plage, France). In spring 2018, 20 own-rooted plants per cultivar were then transplanted into individual 3-l pots containing a 30 : 70 (v/v) mixture of loamy soil and organic compost and cultivated at the Pierre Galet experimental vineyard of Institut Agro Montpellier (France), fitted with a drip fertigation system and a weather station. Plants were organized along eight double rows (Fig. 1d). For each cultivar, all 20 plants were grouped, with 10 plants facing south-west and 10 plants facing north-east. While a randomized design would have been ideal, we chose to group the plants by cultivar for practical reasons (i.e. to facilitate plant management and phenology assessment). Each row had the same geometry and both extreme rows (1 and 8) were surrounded by several rows of other potted vines with similar architecture, thus limiting spatial effects. Plants were well-irrigated, and one leafy axis was selected on each plant. Inflorescences were removed if present, and the leafy axis was topped at a height of 2 m. In winter 2019, plants were spur-pruned and then managed as in the previous season. Ten plants of each genotype were scored for the presence of inflorescences, and only 20% of the genotypes carried inflorescences on at least one plant. In these genotypes, flowering occurred between 12 and 27 May. Flowers were then eliminated to avoid potential bias. By the end of June, all plants had been topped at c. 2 m and had started growing lateral shoots. Thus, all plants shared similar vegetative stage, which was far from bud break and unaffected by reproductive development of the current year or by autumn senescence. This contrasts with Monocot species, where it is virtually impossible to synchronize phenological stages. Moreover, in Monocots, developing leaves are known to be more susceptible to leaf firing than mature leaves (Chen et al., 2017; McNellie et al., 2018), making phenology a critical factor. In our grapevine system, young leaves were less prone to heating (due to their increased distance to the soil), and leaf firing was observed predominantly on proximal, mature leaves. Thus, phenology was unlikely to be a confounding factor here. Irrigation was adjusted weekly based on meteorological conditions and shoot development. On the day of the heatwave, each plant received 880 ml of drip irrigation in three deliveries (morning, midday and evening). Phenotyping was carried out in the summer of 2019 on 2-yr-old plants. The proportion and intensity of leaf firing were scored for all 279 cultivars of the diversity panel on 2 July, 3 d after the heatwave. Scoring was based on a visual scale (Fig. S6). One score for leaf firing proportion and one score for leaf firing intensity were attributed for the group of 10 plants of each cultivar in each orientation (south-west or north-east). Proportion of leaf firing was scored from 0% to 100% as the proportion of leaves at least partly burnt within the canopy. Intensity of leaf firing was scored as 0, 1, 2, 3, 4 or 5 to describe the extent of the firing within one representative fired leaf (0 when no symptoms, 1 when only leaf margins were affected, up to 5 when the whole leaf was affected). We further calculated the magnitude of symptoms at the canopy level as the product of proportion and intensity. From mid-June to the end of July, we sampled 10–14 leaves for each cultivar in order to measure their area. Leaves were chosen as fully expanded and well-irradiated, generally between the tenth and fifteenth phytomers from the apex. They were flattened and cut if necessary to avoid overlap, and then immediately scanned using a flatbed scanner (Epson 4990; Seiko Epson Corp., Nagano, Japan). Leaf area was then obtained through a segmentation procedure in Python. Leaf dry weight was measured after 1 wk in a drying oven (60°C), and LMA was calculated as the ratio between dry weight and area. Data analyses were handled in R (R Development Core Team, 2020). For leaf area and LMA, the best linear unbiased predictors (BLUPs) of genetic values were estimated for use in GWAS. To this end, different mixed-effect models were tested (R/lme4) with a random effect of the genotype and two optional fixed effects, sampling date and orientation (south-west or north-east). For both traits, analysing the Akaike information criterion showed that models accounting for orientation only were similarly performant as models accounting for both orientation and sampling date. Moreover, genotypic ranking was strictly maintained whatever sampling date was included or not. Thus, we selected the same model including genotype and orientation effects for extracting BLUPs of both traits (Table S4). Variance components were used to estimate broad-sense heritability as: H2 = σ G 2 / σ G 2 + σ Res 2 / n with σ G 2 the genotypic variance, σ Res 2 the residual variance, and n the mean number of replicates (leaves) per cultivar. H2 values were high for both morphological traits (> 0.85, Table S4). For leaf firing proportion and intensity, each score already consisted in a single average observation for each cultivar and orientation. Because phenotypes were much more contrasted on the south-west side of rows (i.e. the one exposed to the radiation when the temperature peak was reached; Fig. 1d), we focused on scores for this side in GWAS that were directly used as genotypic values. Hence, for these traits, broad-sense heritability could not be estimated. Since proportion of leaf firing and proportion × intensity showed non-normal distribution, we also performed the genetic analyses using log-transformed data. Population structure did not show any significant effect on the symptoms of leaf firing (ANOVA, Pval > 0.15). To detect associations between genotypic values and genomic markers, GWAS was performed using a set of 197 885 SNPs issued from microarray and genotyping-by-sequencing (Flutre et al., 2022) and reduced to 82 868 SNPs after excluding SNPs that did not vary across the panel, SNPs with a minor allelic frequency (MAF) below 5% and duplicated positions. SNP positions refer to the 12X.v2 reference sequence (Canaguier et al., 2017). Three types of GWAS models were used. Second, we also used the multi-locus mixed model MLMM (Segura et al., 2012), which jointly analyses all SNPs to handle LD while selecting a subset of SNPs with a stepwise regression procedure. It starts with an SNP-by-SNP model, followed by inclusion, at each iteration, of the SNP with the smallest P-value as an additional fixed effect, until the proportion of variance explained by the polygenic effect is close to zero. We fitted it with R/mlmm allowing a maximum of 6 iterations. The selected model was the one with the largest number of SNPs, which all have a P-value below the multiple-testing significance threshold as previously determined with the effective number of markers (mBonf criterion). For each trait, the total percentage of variation explained by all significant SNPs identified with MLMM was extracted from MLMM output. The percentage of explained variance and the effect associated with each individual SNP was also assessed, with linear models for each SNP separately. To investigate the candidate genes in the associated regions, we first explored strict quantitative trait locus (QTL) intervals of ±50 kb around the significant SNPs based on whole-genome analyses of LD decay (Flutre et al., 2022). Because LD decay could differ locally, we further extended the intervals to ±100 kb to explore neighbouring genes. We retrieved the list of genes overlapping the intervals of our QTLs from the reference Vitis genome 12X.v2 and the VCost.v3 annotation (Canaguier et al., 2017; Table S3). We then used the correspondence between IGGP (International Grapevine Genome Program) and NCBI RefSeq gene model identifiers provided by URGI (https://urgi.versailles.inra.fr/Species/Vitis/Annotations) to get putative functions from NCBI, when available (last consulted on 08 December 2023). We searched UniProt (https://www.uniprot.org/) and TAIR (https://www.arabidopsis.org/) databases to cross-reference a number of candidate genes. In addition, we used the GREAT (GRape Expression Atlas) RNA-Seq data analysis workflow (https://great.colmar.inrae.fr/app/GREAT), which gathers published expression data, to assess the level of expression of our candidate genes in grapevine leaves, the organs relevant for the traits considered in this study. RNA-Seq data are normalized as detailed on the GREAT platform. Data were retrieved with all filters set to 'Select All' except for the organ considered that was restricted to 'Leaves'. Simulations of canopy temperature were carried out using HydroShoot, a functional–structural model programmed in Python and supported by the OpenAlea platform (Albasha et al., 2019). Five plant mock-ups corresponding to five different genotypes (Belle Denise, Plant de Chaudefonds 53, Poulsard, Raboso piave and Salicette) with contrasting leaf inclination angles were used to explore different biologically plausible scenarios. Mock-ups were created by digitizing one representative plant of each genotype using a Polhemus electromagnetic digitizer (3Space Fastrak; Polhemus Inc., Colchester, VT, USA). Seven points were obtained per internode (the petiole insertion on the stem, the petiole–blade junction, and distal extremity of the five main veins; Millan et al., 2023). Each 3D mock-up was first mirrored around the row axis to create twin pots, and the twin pots were then repeated along the axis to create a virtual scene resembling our experimental vineyard. We therefore obtained five virtual scenes corresponding to each of the five genotypes. Solar radiation, wind speed, air temperature and relative humidity recorded from the weather station of the experimental vineyard were input to the model at each time step of 1 h. Soil level temperature was forced based on an empirical relationship with air temperature measured at the 2 m reference height (Albasha et al., 2019). In the default simulations, we varied the profiles of wind speed and air temperature with depth, respectively increasing (Leuning et al., 1995) and decreasing (Heilman et al., 1994) exponentially from the soil to the top of the canopy. Further simulations were performed with either of both variables being constant with depth. As expected, setting both wind speed and air temperature constant (to their value at 2 m) reduced the mean and maximum leaf temperature (−1.6°C and −4.2°C at 15:00 h solar time on the South-West orientation, respectively). The leaves were considered in the shade if they received < 100 μmol m−2 s−1 photosynthetic radiation when estimating the maximal temperature of shaded leaves vs sunlit leaves. To estimate the contribution of evaporative cooling to leaf temperature, we simulated different biological responses. The driving force for evapotranspiration is vapour pressure deficit (VPD), a measure of air dryness that reached here more than 9 kPa (Table S1). When temperature rises, VPD increases exponentially, which accelerates evapotranspiration. However, stomata respond by closing, which feedbacks on water efflux. On the contrary, cuticle permeability could increase at high temperature, due to thermal alteration of the cuticle structure. Overall, the rate of water loss through the leaves could increase with VPD as temperature rises or else could reach a limit in case of strong stomatal closure and low cuticular permeability. To test these options, we first varied stomatal sensitivity to VPD (Fig. S1a–c) using the scaling parameter D0 of Leuning's model (Leuning, 1995), which we set to 7 kPa by default (Prieto et al., 2012), 1 kPa (strong sensitivity) or 60 kPa (weak sensitivity). We also tested the possibility that stomata may close due to inactivation of photosynthesis through photoinhibition at high temperature (Albasha et al., 2019; Fig. S1d). Leaf temperature profiles at the peaks of air temperature, VPD or radiation were largely identical if stomata were allowed to close due to stomatal sensitivity to VPD or to a feedback of photoinhibition-induced decline in photosynthesis. While cuticular conductance was not explicit in our model, the minimal conductance was set to 10 mmol m−2 s−1 irrespective of leaf temperature. We did not implement a situation where leaf conductance increases with temperature as we did not find any evidence in the literature that it could occur, at least up to 45°C in grapevine (Luo et al., 2011; Greer & Weedon, 2012; Greer, 2018, 2019), indicating that stomatal closure in these conditions is likely to dominate over a potential increase in cuticular permeance. We are grateful to Freddy Gavanon for managing the experimental vineyard, as well as Lucille Roux, Aurélien Ausset and Romain Boulord for technical assistance during the measurements. We thank the ETAP and DAAV team members for providing help during the preparation of the grapevine panel. We thank Jean-Pierre Péros for his involvement in the early stages of the project. AC-L acknowledges the support from the Company of Biologists for a travelling fellowship (STOCOVIT project, grant DEVTF1903164). This research was supported by the ANR (French Research Agency) through the G2WAS project (ANR-19-CE20-0024) and a visiting fellowship to RA (ANR-21-PRRD-0008-01), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE) and Région Occitanie (PhD fellowships to AJW and MM), and Institut Agro Montpellier. None declared. FP, AC-L, AJW, AD, TF, PT, LT and TS designed the project. LT managed the G2WAS project. AC-L and AJW collected data. AC-L analysed data with input from AJW, AD, TF and VS. MM digitized the plants. BP wrote the scripts to reconstruct the 3D mock-ups. RA performed the HydroShoot simulations. FP supervised research. FP and AC-L wrote the manuscript with input from all authors. Genotyping data are published and freely accessible (Flutre et al., 2022). Meteorological data are provided in Table S1. Phenotypic data and scripts for genetic analyses are publicly available at doi: 10.57745/PW2UHA. The code and simulations of the 3D model are publicly available in GitHub at https://github.com/RamiALBASHA/Leaf_burn. Fig. S1 Simulations of leaf temperature using HydroShoot, a functional–structural plant model. Fig. S2 Correlation matrix of leaf firing symptoms and morphological traits. Fig. S3 Allelic effects at the BLAZE loci. Fig. S4 Accumulation of favourable alleles at different BLAZE loci. Fig. S5 Genetic architecture of leaf morphological traits. Fig. S6 Visual scale used for scoring leaf firing symptoms. Table S1 Meteorological data on early summer between 2009 and 2023. Table S2 Associations detected for leaf firing and morphological traits. Table S3 List of genes underlying the regions around the SNPs significantly associated with leaf firing symptoms or morphological traits. Table S4 Statistics of the genotypic variability for leaf firing and morphological traits. Please note: Wiley is not responsible for the content or functionality of any Supporting Information supplied by the authors. Any queries (other than missing material) should be directed to the New Phytologist Central Office. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Context Mixing cultivars is an agroecological practice of crop diversification, increasingly used for cereals. The yield of such cereal mixtures is higher on average than the mean yield of their components in pure stands, but with a large variance. The drivers of this variance are plant- plant interactions leading to different plant phenotypes in pure and mixed stands, i.e phenotypic plasticity. Objectives The objectives were (i) to quantify the magnitude of phenotypic plasticity for yield in pure versus mixed stands, (ii) to identify the yield components that contribute the most to yield plasticity, and (iii) to link such plasticities to differences in functional traits, i.e. plant height and flowering earliness. Methods A new experimental design based on a precision sowing allowed phenotyping each cultivar in mixture, at the level of individual plants, for above-ground traits throughout growth. Eight commercial cultivars of Triticum aestivum L. were grown in pure and mixed stands in field plots repeated for two years (2019-2020, 2020-2021) with contrasted climatic conditions and with nitrogen fertilization, fungicide and weed removal management strategies. Two quaternary mixtures were assembled with cultivars contrasted either for height or earliness. Results Compared to the average of cultivars in pure stands, the height mixture strongly underyielded over both years (-29%) while the earliness mixture overyielded the second year (+11%) and underyielded the first year (-8%). The second year, the magnitude of cultivar’s grain weight plasticity, measured as the difference between pure and mixed stands, was significantly and positively associated with their relative yield differences in pure stands (R 2 =0.51). When grain weight plasticity, measured as the log ratio of pure over mixed stands, was partitioned as the sum of plasticities in each yield component, its strongest contributor was the plasticity in spike number per plant (∼56% of the sum), driven by even stronger but opposed underlying plasticities in both tiller emission and regression. For both years, the plasticity in tiller emission was significantly, positively associated with the height differentials between cultivars in mixture (R 2 =0.43 in 2019-2020 and 0.17 in 2020-2021). Conclusions Plasticity in the early recognition of potential resource competitors is a major component of cultivar strategies in mixtures, as shown here for tillering dynamics. Our results also highlighted a link between plasticity in tiller emission and height differential in mixture. Both height and tillering dynamics displayed plasticities typical of the shade avoidance syndrome. Implications Both the new experimental design and decomposition of plasticities developed in this study open avenues to better study plant-plant interactions in agronomically-realistic conditions. This study also contributed a unique, plant-level data set allowing the calibration of process-based plant models to explore the space of all possible mixtures.

Background and Aims Functional-structural plant models are increasingly being used by plant scientists to address a wide variety of questions. However, the calibration of these complex models is often challenging, mainly because of their high computational cost. In this paper, we applied an automatic method to the calibration of WALTer: a functional-structural wheat model that simulates the plasticity of tillering in response to competition for light. Methods We used a Bayesian calibration method to estimate the values of 5 parameters of the WALTer model by fitting the model outputs to tillering dynamics data. The method presented in this paper is based on the Efficient Global Optimisation algorithm. It involves the use of Gaussian process metamodels to generate fast approximations of the model outputs. To account for the uncertainty associated with the metamodels approximations, an adaptive design was used. The efficacy of the method was first assessed using simulated data. The calibration was then applied to experimental data. Key Results The method presented here performed well on both simulated and experimental data. In particular, the use of an adaptive design proved to be a very efficient method to improve the quality of the metamodels predictions, especially by reducing the uncertainty in areas of the parameter space that were of interest for the fitting. Moreover, we showed the necessity to have a diversity of field data in order to be able to calibrate the parameters. Conclusions The method presented in this paper, based on an adaptive design and Gaussian process metamodels, is an efficient approach for the calibration of WALTer and could be of interest for the calibration of other functional-structural plant models.

Crop breeding involves two selection steps: choosing progenitors and selecting individuals within progenies. Genomic prediction, based on genome-wide marker estimation of genetic values, could facilitate these steps. However, its potential usefulness in grapevine (Vitis vinifera L.) has only been evaluated in non-breeding contexts mainly through cross-validation within a single population. We tested across-population genomic prediction in a more realistic breeding configuration, from a diversity panel to ten bi-parental crosses connected within a half-diallel mating design. Prediction quality was evaluated over 15 traits of interest (related to yield, berry composition, phenology and vigour), for both the average genetic value of each cross (cross mean) and the genetic values of individuals within each cross (individual values). Genomic prediction in these conditions was found useful: for cross mean, average per-trait predictive ability was 0.6, while per-cross predictive ability was halved on average, but reached a maximum of 0.7. Mean predictive ability for individual values within crosses was 0.26, about half the within-half-diallel value taken as a reference. For some traits and/or crosses, these across-population predictive ability values are promising for implementing genomic selection in grapevine breeding. This study also provided key insights on variables affecting predictive ability. Per-cross predictive ability was well predicted by genetic distance between parents and when this predictive ability was below 0.6, it was improved by training set optimization. For individual values, predictive ability mostly depended on trait-related variables (magnitude of the cross effect and heritability). These results will greatly help designing grapevine breeding programs assisted by genomic prediction.

Interactions among plants have been long recognized as a major force driving plant community dynamics and crop yield. Surprisingly, our knowledge of the ecological genetics associated with variation of plant-plant interactions remains limited. In this opinion article by scientists from complementary disciplines, the international PLANTCOM network identified four timely questions to foster a better understanding of the mechanisms mediating plant assemblages. We propose that by identifying the key relationships among phenotypic traits involved in plant-plant interactions and the underlying adaptive genetic and molecular pathways, while considering environmental fluctuations at diverse spatial and time scales, we can improve predictions of genotype-by-genotype-by-environment interactions and modeling of productive and stable plant assemblages in wild habitats and crop fields.

To cope with the challenges facing agriculture, speeding-up breeding programs is a worthy endeavor, especially for perennial species such as grapevine, but requires understanding the genetic architecture of target traits. To go beyond the mapping of quantitative trait loci in bi-parental crosses, we exploited a diversity panel of 279 Vitis vinifera L. cultivars planted in 5 blocks in the vineyard. This panel was phenotyped over several years for 127 traits including yield components, organic acids, aroma precursors, polyphenols, and a water stress indicator. The panel was genotyped for 63k single nucleotide polymorphisms by combining an 18K microarray and genotyping-by-sequencing. The experimental design allowed to reliably assess the genotypic values for most traits. Marker densification via genotyping-by-sequencing markedly increased the proportion of genetic variance explained by single nucleotide polymorphisms, and 2 multi-single nucleotide polymorphism models identified quantitative trait loci not found by a single nucleotide polymorphism-by-single nucleotide polymorphism model. Overall, 489 reliable quantitative trait loci were detected for 41% more response variables than by a single nucleotide polymorphism-by-single nucleotide polymorphism model with microarray-only single nucleotide polymorphisms, many new ones compared with the results from bi-parental crosses. A prediction accuracy higher than 0.42 was obtained for 50% of the response variables. Our overall approach as well as quantitative trait locus and prediction results provide insights into the genetic architecture of target traits. New candidate genes and the application into breeding are discussed.

Summary Agroecosystem diversification through increased crop genetic diversity could provide multiple services such as improved disease control or increased productivity. However, we still poorly understand how genetic diversity affects agronomic performance. We grew 179 inbred lines of durum wheat in pure stands and in 202 binary mixtures in field conditions. We then tested the effect of allelic richness between genotypes and genotype richness on grain yield and Septoria tritici blotch disease. Allelic richness was tested at 19K single nucleotide polymorphisms distributed along the durum wheat genome. Both genotype richness and allelic richness could be equal to 1 or 2. Mixtures were overall more productive and less diseased than their pure stand components. Yet, we identified one locus at which allelic richness between genotypes was associated with increased disease severity and decreased grain yield. The effect of allelic richness at this locus was stronger than the effect of genotype richness on grain yield (−7.6% vs +5.7%). Our results suggest that positive effects of crop diversity can be reversed by unfavourable allelic associations. This highlights the need to integrate genomic data into crop diversification strategies. More generally, investigating plant–plant interactions at the genomic level is promising to better understand biodiversity–ecosystem functioning relationships.

Mixed cropping has been suggested as a resource-efficient approach to meet high produce demands while maintaining biodiversity and minimizing environmental impact. Current breeding programs do not select for enhanced general mixing ability (GMA) and neglect biological interactions within species mixtures. Clear concepts and efficient experimental designs, adapted to breeding for mixed cropping and encoded into appropriate statistical models, are lacking. Thus, a model framework for GMA and SMA (specific mixing ability) was established. Results of a simulation study showed that an incomplete factorial design combines advantages of two commonly used full factorials, and enables to estimate GMA, SMA, and their variances in a resource-efficient way. This model was extended to the Producer (Pr) and Associate (As) concept to exploit additional information based on fraction yields. It was shown that the Pr/As concept allows to characterize genotypes for their contribution to total mixture yield, and, when relating to plant traits, allows to describe biological interaction functions (BIF) in a mixed crop. Incomplete factorial designs show the potential to drastically improve genetic gain by testing an increased number of genotypes using the same amount of resources. The Pr/As concept can further be employed to maximize GMA in an informed and efficient way. The BIF of a trait can be used to optimize species ratios at harvest as well as to extend our understanding of competitive and facilitative interactions in a mixed plant community. This study provides an integrative methodological framework to promote breeding for mixed cropping.

Abstract Viticulture has to cope with climate change and to decrease pesticide inputs, while maintaining yield and wine quality. Breeding is a key lever to meet this challenge, and genomic prediction a promising tool to accelerate breeding programs. Multivariate methods are potentially more accurate than univariate ones. Moreover, some prediction methods also provide marker selection, thus allowing quantitative trait loci (QTLs) detection and the identification of positional candidate genes. To study both genomic prediction and QTL detection for drought-related traits in grapevine, we applied several methods, interval mapping (IM) as well as univariate and multivariate penalized regression, in a bi-parental progeny. With a dense genetic map, we simulated two traits under four QTL configurations. The penalized regression method Elastic Net (EN) for genomic prediction, and controlling the marginal False Discovery Rate on EN selected markers to prioritize the QTLs. Indeed, penalized methods were more powerful than IM for QTL detection across various genetic architectures. Multivariate prediction did not perform better than its univariate counterpart, despite strong genetic correlation between traits. Using 14 traits measured in semi-controlled conditions under different watering conditions, penalized regression methods proved very efficient for intra-population prediction whatever the genetic architecture of the trait, with predictive abilities reaching 0.68. Compared to a previous study on the same traits, these methods applied on a denser map found new QTLs controlling traits linked to drought tolerance and provided relevant candidate genes. Overall, these findings provide a strong evidence base for implementing genomic prediction in grapevine breeding.

ABSTRACTViticulture has to cope with climate change and decrease pesticide inputs, while maintaining yield and wine quality. Breeding is a potential key to meet this challenge, and genomic prediction is a promising tool to accelerate breeding programs, multivariate methods being potentially more accurate than univariate ones. Moreover, some prediction methods also provide marker selection, thus allowing quantitative trait loci (QTLs) detection and allowing the identification of positional candidate genes. We applied several methods, interval mapping as well as univariate and multivariate penalized regression, in a bi-parental grapevine progeny, in order to compare their ability to predict genotypic values and detect QTLs. We used a new denser genetic map, simulated two traits under four QTL configurations, and re-analyzed 14 traits measured in semi-controlled conditions under different watering conditions. Using simulations, we recommend the penalized regression method Elastic Net (EN) as a default for genomic prediction, and controlling the marginal False Discovery Rate on EN selected markers to prioritize the QTLs. Indeed, penalized methods were more powerful than interval mapping for QTL detection across various genetic architectures. Multivariate prediction did not perform better than its univariate counterpart, despite strong genetic correlation between traits. Using experimental data, penalized regression methods proved as very efficient for intra-population prediction whatever the genetic architecture of the trait, with accuracies reaching 0.68. These methods applied on the denser map found new QTLs controlling traits linked to drought tolerance and provided relevant candidate genes. These methods can be applied to other traits and species.

File S1 contains the raw phenotypic data. Descriptive statistics of phenotypic data are presented in File S2 .File S3 contains the estimations of trait heritability. The genotypic data are available in File S4. The results of QTL detection are summarized in File S5 and the two genetic maps are provided in File S6. Pairwise genetic correlation between the traits are available in File S7. R scripts are provided in File S8.

File S1 contains the raw phenotypic data. Descriptive statistics of phenotypic data are presented in File S2 .File S3 contains the estimations of trait heritability. The genotypic data are available in File S4. The results of QTL detection are summarized in File S5 and the two genetic maps are provided in File S6. Pairwise genetic correlation between the traits are available in File S7. R scripts are provided in File S8.