The Plant Phenome Journal

Lodging in sorghum presents a significant challenge for plant breeders due to the trade-off between lodging resistance and grain yield. Manually measuring lodging across thousands of plots is time-consuming, expensive, and error-prone, making selection for lodging resistance challenging in breeding programs. Unmanned Aerial Vehicle (UAV) derived metrics offer a potential high-throughput, cost-effective alternative for lodging phenotyping. This study developed a framework for predicting plot-level lodging from UAV imagery across 2,675 sorghum breeding plots. Multi-temporal canopy height data were collected at two critical time points: maximum crop height and at manual lodging assessment. Height percentiles were extracted from UAV derived point clouds generated using photogrammetric algorithms. These data were used to develop parametric, non-parametric, and ensemble prediction models, which were evaluated using three statistical metrics. The ensemble model, averaging predictions from all models, achieved the highest accuracy with Pearson correlations of r = 0.80-0.84 and lowest residual mean square error (RMSE=16-18), explaining 64-70% of variation in manual lodging counts. Model diagnostics and iterative refinement, including inspection of UAV imagery and dataset curation, had minimal impact on model performance, demonstrating the robustness of the approach. Model performance was consistent across sites, with minimal effects of stratified sampling on accuracy, confirming the ensemble approach as optimal for plot-level lodging assessment. This study demonstrates that integrated multi-temporal UAV imagery offers a practical alternative to labor-intensive manual evaluation methods by enabling high-throughput lodging assessment suitable for implementation in sorghum breeding programs.

Potato (Solanum tuberosum L.) is a staple crop crucial to global food security, yet its production is severely threatened by late blight (LB), caused by Phytophthora infestans, one of the most destructive plant diseases worldwide. Breeding programs for LB resistance have traditionally relied on labor-intensive and subjective visual assessments, which limit scalability and consistency, particularly in early-generation trials. Unmanned aerial vehicle (UAV)-based remote sensing combined with machine learning (ML) offers a promising alternative for objective, high-throughput disease phenotyping. This study evaluated the potential of UAV-derived multispectral imagery and ML techniques to estimate LB severity across large and genetically diverse potato breeding populations, comprising 2,745 clones in one trial and 492 accessions in another, conducted in Oxapampa, Pasco, Peru. We compared vegetation index-based approaches with a machine learning framework that integrates K-means clustering and Kernel Ridge Regression (KRR) and assessed their ability to capture genotypic variation and support selection decisions. NDVI consistently showed a strong correlation with visually assessed LB severity, particularly at advanced stages of disease development, enabling objective discrimination between healthy and diseased canopy tissues. However, the KRR-based approach outperformed linear NDVI-based models by capturing nonlinear relationships between spectral responses and disease progression. Estimates of LB severity derived from NDVI and KRR models, expressed as best linear unbiased estimates (BLUEs), showed strong and biologically consistent relationships with the area under the disease progress curve (AUDPC), particularly during later UAV acquisitions. Selection coincidence between UAV-derived estimates and AUDPC-based rankings was substantially higher at intermediate to advanced stages of disease progression, suggesting that UAV assessments at these stages may capture sufficient phenotypic variation to distinguish genotypes. These findings indicate that UAV-based multispectral phenotyping, especially when integrated with ML, provides a practical and scalable approach for assessing LB severity in potato breeding programs while reducing the need for time-consuming field evaluations.

Grapevine cluster architecture is a key selection target in breeding programs because it influences disease susceptibility, yield stability and juice quality. High-throughput phenotyping offers a rapid and non-destructive approach to capture biochemical and structural variation in these traits, yet the influence of plant organ reflectance and data partitioning strategies on trait prediction remains poorly understood. In this study, we evaluated how hyperspectral reflectance from different grapevine organs contributes to the prediction of cluster architecture and juice quality traits in two clonal populations of Riesling and Pinot. Using partial least squares regression (PLSR), we assessed the prediction accuracy of eight cluster architecture and six juice quality traits under two data partitioning strategies. Models based on cluster reflectance outperformed those using dry leaf reflectance for most traits, except for pH. Partitioning the dataset by cluster type increased trait variance and improved predictions for number of berries (R{superscript 2} = 0.53), berry diameter (R{superscript 2} = 0.79), and total acidity (R{superscript 2} = 0.48). Visible, red-edge and NIR spectra were most informative regions to predict the traits studied. Together, our results highlight the importance of organ-specific data and appropriate calibration strategies to improve phenomic models for the development of scalable proxies for grapevine improvement. HighlightSpectral phenomics reveals that prediction accuracy in grapevine depends on organ spectral signatures and traits, with cluster reflectance outperforming leaves, informing new phenotyping strategies for breeding improvement.

Bioavailability of iron, an essential micronutrient to plants, is low in alkaline or calcareous soils, which are prevalent across semi-arid production regions. Breeding efforts to increase tolerance to iron deficiency chlorosis (IDC) in sorghum, a major crop of semi-arid regions, are confounded by spatial variation of stress severity in field trials. Here we developed and validated two high-throughput phenotyping approaches to address this challenge, with multi-spectral aerial imaging in the field and a controlled-environment assay to isolate the effects of iron bioavailability. In the field, severity and uniformity of stress are highly predictive of genetic signals for IDC tolerance (R2 > 0.6 for soil pH metrics and H2). Plot-level data filtering for stress conditions based on control genotypes successfully addresses field spatial variation (unfiltered H2 = 0.18 vs. filtered H2 = 0.4). The controlled-environment assay proxies field stress using iron sources with differential bioavailability, evidenced by high heritability ( H2 = 0.98) and phenotypic differential for hybrid control genotypes that matches field performance. Finally, we show that assay phenotypes are suitable for genome-wide association studies in global germplasm. Together, these field and lab phenomic approaches can be deployed to understand genetics of IDC tolerance and develop crops resilient to alkaline soils. HIGHLIGHTStress severity and uniformity greatly impact detection of genetic signals underlying iron deficiency chlorosis tolerance in sorghum. A controlled-environment assay reduces spatial heterogeneity and improves assessment of tolerance genetics.

Climate change increasingly threatens global Capsicum (pepper) production. Accelerating the deployment of climate-resilient cultivars requires effective use of genetic diversity conserved in genebanks. We implement a "turbocharging" strategy in Capsicum by integrating genome-wide association studies and genomic prediction in a core collection (n = 423), followed by genomic prediction across the global collection (n = 10,250) using the core as a training population. We generated genomic estimated breeding values (GEBVs) for 31 high-accuracy traits (r > 0.5) encompassing hyperspectral phenotypes (heat/control), agronomic performance (heat/control) and fruit quality. To enhance accessibility and decision-making, we developed a large language model (LLM) integrated application that enables flexible, preference-based selection of candidates. By narrowing the parental decision space, this framework streamlines screening of large germplasm collections while balancing climate resilience, quality attributes and market demands. Our approach provides a scalable decision-support system to accelerate climate-resilient Capsicum breeding and maximize global genetic resources.

Potato common scab (Streptomyces sp.) is an economically important disease that reduces the quality and market value of tubers. A key aspect in developing management strategies involves accurately quantifying the disease. Due to the three-dimensional nature of the tuber and the heterogeneous distribution of lesions across its surface, visual estimates of severity can be challenging. Therefore, the objectives of this study were to develop and validate a standard area diagram (SAD) for estimating common scab severity on potato tubers and to compare validation outcomes obtained using real tubers and digital images. A SAD comprising six severity levels (from 1.3 to 66.8%) was developed based on image analysis of naturally infected tubers. Validation was conducted using two complementary approaches in which inexperienced raters evaluated either real potato tubers or digital images of the same tubers under unaided and aided conditions. Accuracy, bias components, and inter-rater reliability were quantified using absolute error metrics, Lins concordance correlation coefficient, intraclass correlation coefficients, and overall concordance correlation coefficients. Use of the SAD significantly improved accuracy, reduced systematic bias, and increased inter-rater reliability across both validation approaches. No significant differences were detected between assessments conducted on real tubers and images, although image-based evaluations showed a slight, non-significant tendency toward reduced scale and location bias under aided conditions. These results demonstrate that a dimension-aware SAD integrating information across the full tuber surface enhances the reliability and reproducibility of visual severity assessments and supports the use of image-based evaluations for training, large-scale surveys, and remote or collaborative applications involving three-dimensional plant organs.

Fitness costs of plant disease defence are often subtle and difficult to quantify. In this study, we therefore used comparative high-throughput phenotyping in two independent facilities to assess growth, morphology and physiology of potato (cv. Desiree) with high time-resolution monitoring different defence mechanisms under pathogen-free conditions. Plants were either treated weekly with the resistance inducers {beta}-aminobutyric acid (BABA; 10 mM) or potassium phosphite (KPhi; 36 mM) or comprised six transgenic lines expressing late blight resistance genes (single Rpi genes or a three-gene stack) or reduced jasmonate perception (StCOI1-RNAi). Over four weeks, image-derived traits revealed consistent cross-facility effects for plant height and colour: BABA treatment increased plant height but reduced canopy area and induced a paler greenness signature, whereas KPhi caused minimal and transient growth effects. Chlorophyll fluorescence at the NaPPI facility indicated reduced vitality (Rfd_Lss) in BABA-treated plants and increased Rfd_Lss following KPhi, while maximum PSII efficiency was largely unchanged. Several transgenic lines showed somewhat reduced above-ground biomass. Enzyme activity profiling produced distinct treatment and genotype signatures, but was strongly modulated by facility conditions that overrode these specificities. Overall, high-throughput phenotyping robustly detected subtle growth-defence trade-offs across platforms. HighlightHigh-throughput optical phenotyping validated across two independent research facilities reveals that stacked resistance genes and resistance inducers in potato trigger subtle growth trade-offs. Graphical abstracts O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/713143v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@89df47org.highwire.dtl.DTLVardef@1a1ce64org.highwire.dtl.DTLVardef@1f52f0dorg.highwire.dtl.DTLVardef@1e41c35_HPS_FORMAT_FIGEXP M_FIG C_FIG Experimental timeline for high-throughput plant phenotyping platforms. Created in BioRender. Poque, S. (2026) https://BioRender.com/nmkve7g

Smart irrigation management is essential for improving crop resilience under increasing drought frequency driven by climate change. Although satellite-based remote sensing provides valuable tools for monitoring crop water status at large spatial scales, its accuracy is often limited in mountainous and heterogeneous agricultural landscapes. In this study, we investigated drought-induced metabolic responses in potato (Solanum tuberosum L.) to identify biochemical biomarkers that could complement satellite-based irrigation advisories in the mid-Himalayan region of India. A field experiment was conducted using a gradient of soil moisture regimes corresponding to moderate (50% field capacity), critical (25% field capacity), and extreme drought stress (5-8% field capacity). Satellite-derived evapotranspiration-based irrigation advisories were validated against in situ soil moisture measurements, revealing discrepancies attributed to the inability of satellite estimates to capture actual water loss under drought stress conditions, highlighting the need for additional ground-truth biomarkers across heterogeneous field conditions. To capture plant-level physiological responses, untargeted metabolite profiling of potato leaves was performed using gas chromatography-mass spectrometry (GC-MS). Approximately fifty metabolites belonging to amino acids, organic acids, sugars, and sugar alcohols were detected. Multivariate statistical analyses revealed distinct metabolic signatures associated with progressive drought stress. Notably, accumulation of proline, serine, isoleucine, sucrose, fructose, glucose, and polyols such as mannitol and myo-inositol reflected key metabolic reprogramming associated with osmoprotection, redox homeostasis, and energy metabolism under drought conditions. Collectively, this ensemble of stress-responsive metabolites represents a robust panel of drought stress biomarkers. As a proof of concept, proline was validated as a qualitative biomarker of plant water status through a rapid and cost-effective colorimetric biochemical assay, demonstrating its practical applicability for field-level irrigation management. These findings demonstrate that metabolomics-derived biomarkers can provide sensitive plant-level indicators of drought stress that complement satellite-based monitoring systems. The integration of biochemical diagnostics with remote sensing platforms offers a promising approach for improving drought detection and developing low-cost, field-deployable tools for smart irrigation advisories in heterogeneous agricultural landscapes. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=93 SRC="FIGDIR/small/712810v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@59919corg.highwire.dtl.DTLVardef@66ce49org.highwire.dtl.DTLVardef@17143dcorg.highwire.dtl.DTLVardef@11e2769_HPS_FORMAT_FIGEXP M_FIG C_FIG

Evolving agricultural practices and contexts invite to reconsider the way crop and plant models represent agroecosystem processes. Crop models assume spatial homogeneity, which reduces confidence in their predictions for structurally heterogeneous systems, whereas FSPMs complexity limits their application to field-scale or large-scale studies. To benefit from strengths of both approaches, we introduce ArchiCrop, a parametric 3D architectural model of cereals that generates plant geometries constrained by crop model dynamics and coordination rules. Inspired by the concept of equifinality, ArchiCrop generates a morphospace of architecturally diverse morphotypes which remain equivalent at crop scale in terms of LAI and height. This multiscale approach enables the comparison of processes computed at different scales on wheat, rice, maize and sorghum. We demonstrate its application evaluating light interception Beers formalism in STICS soil-crop model relying on the leaf-resolved radiosity model Caribu for the 3D reference simulations, for a sorghum monocrop. We show that the consideration of the variability of only two plant architectural traits, leaf insertion angle and leaf number, introduces up to 27% of uncertainty in the cumulated absorbed light at the end of the season. A possible outcome from this method is also the definition of metamodels for crop model processes, as exemplified for extinction coefficient of Beers law. ArchiCrop can support a range of applications, including crop model uncertainty analysis, model-assisted phenotyping, and ideotype design. HighlightsO_LIArchiCrop is the first 3D+t botany-based parametric generative model for cereals. C_LIO_LIArchiCrop downscales crop model dynamics to 3D+t architecture canopies efficiently. C_LIO_LIArchiCrop compares big leaf versus leaf-resolved light interception. C_LIO_LIPlant architectures with same leaf area intercept light with up to 27% variability. C_LIO_LIArchiCrop helps ideotyping, crop model evaluation and error propagation analysis. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/716970v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@18d2783org.highwire.dtl.DTLVardef@1d49daorg.highwire.dtl.DTLVardef@dba711org.highwire.dtl.DTLVardef@b58907_HPS_FORMAT_FIGEXP M_FIG C_FIG

In cereal-legume intercrops, weed suppression is primarily driven by cereals, whose competitiveness is shaped by trait plasticity--morphological adjustments in response to the intercrop environment. However, how individual cereal traits respond plastically and contribute to system performance remains unclear, hampering improvements through breeding or system design. We combined field experiments with functional-structural plant modelling to quantify plastic responses of four cereal traits (tiller number, tiller angle, specific leaf area (SLA), and specific internode length (SIL)) and their effects on weed suppression and crop productivity. Field measurements revealed plasticity in tiller number, tiller angle, and SIL between sole crops and intercrops, while SLA showed minimal differences. Simulations showed that intermediate tiller numbers resulted in the strongest weed suppression and highest productivity, indicating an optimum, while more horizontal tillers suppressed weeds slightly better than vertical ones. Weed suppression increased with higher SLA values, while SIL showed a saturating response, increasing to intermediate SIL values and plateauing thereafter. In simulations with short-statured cereal phenotypes (low SIL), the reduction in cereal weed suppression was compensated by the legume component. This study demonstrates how FSP modelling can be used to investigate trait plasticity mechanisms and generate testable hypotheses about trait effects in complex intercrop systems. HighlightCereal trait plasticity shapes weed suppression in cereal-legume intercrops, with distinct response patterns per trait, while legumes can compensate for weakly competitive cereals, suggesting balanced competition over cereal dominance.

Understanding crop genetic diversity is essential for conservation and breeding, yet farmer-maintained germplasm remains largely underrepresented in genomic studies. Theobroma cacao L. has a complex domestication history and extensive global diversity, and cacao currently cultivated in Central America, particularly in Costa Rica, has been understudied compared to South American and Mexican cultivars despite cultural and historical importance. In this study, we investigate the genetic diversity of cacao from farmer-managed systems across Costa Rica to search for Criollo germplasm and identify and characterize any unique local genetic groups. Ninety-four trees were sampled from 17 farms across four regions of the country and sequenced using whole genome resequencing. Farmer materials were analyzed alongside 166 previously characterized reference accessions representing major cacao genetic groups. Population structure analyses, phylogenetic reconstruction, and network approaches revealed that Costa Rican cacao encompasses multiple known genetic groups, including Criollo-derived lineages, while also harboring locally distinct diversity not fully represented in current global reference collections. Analyses revealed close kinship between many accessions with no clear geographic patterns corresponding to the observed population differentiation, reflecting the effects of farmers in creating dominant patterns of gene flow through seed-saving, clonal propagation, and sharing genotypes among farms. Heterozygosity levels varied substantially among individuals, consistent with a mixture of highly inbred Criollo trees and more heterozygous, admixed genotypes. We find that farmer-managed cacao systems are reservoirs of genetic diversity, including possibly rare or historically important lineages, underscoring the value of these farming systems for effective conservation and management of genomic resources for cacao resilience and improvement.

Agrophotovoltaic (APV) systems provide a unique opportunity for improving agricultural land-use efficiency by combining solar energy capture via photovoltaic panels with crop production. However, in-depth information on plant growth patterns within the spatially heterogenous microclimate created by the intermittent shading of APVs is largely missing. In the present study, we implement a customized robot-mounted 3D-multispectral imaging system to closely monitor the growth and spectral reflectance patterns of a conventional soybean cultivar "Eiko" (EK) and a chlorophyll-deficient mutant variety MinnGold (MG) under an APV system. Weekly trends in canopy morphometric features revealed significant variations in plant height, 3D leaf area, light penetration, and canopy volume across the APV field depending on the proximity with the overhead solar panels for both EK and MG, with plants receiving adequate rainfall and intermittent shade performing the best. Furthermore, although spectral indices exhibited variations between EK and MG due to intrinsic differences in pigmentation, symptoms of stress could be detected for both genotypes within rain-shaded areas of the APV plot. Hence, the present investigation depicts the potential for complementary usage of robotics and machine vision for high-precision high-throughput crop monitoring under APVs, which would enable better crop management within such non-homogenous cultivation systems.

Drought is a significant factor in agricultural losses, making it imperative to understand how root system architecture (RSA) adapts to environmental condition like water deficit. HydroRoot is a functional-structural plant model (FSPM) aimed at analyzing and simulating hydraulic and solute transport of RSA. The model integrates a static hydraulic solver, a coupled water-solute transport solver, a statistical generator of RSA based on Markov model, and a dynamic hydraulic model accounting for root growth. This paper presents the model, the mathematical description of the formalism of solvers, and use cases with their associated tutorials. Five use cases illustrate capabilities of HydroRoot, which has been successfully used for phenotyping root hydraulics across various species, including Arabidopsis, maize, and millet. The model-driven phenotyping method "cut and flow" is presented to characterize axial and radial conductivities on a given root genotype. Finally, three step-by-step tutorials provide a structured way to learn how to use HydroRoot 1) to simulate hydraulic on a given architecture, 2) to simulate water and solute transport on a maize root, and 3) to simulate hydraulic on two pearl millet genotypes with varying soil conditions. Hydroroot is an open-source package of the OpenAlea platform, with the code publicly available on Github. A comprehensive documentation is available with a reproducible gallery of examples.

The narrow genetic base of cultivated tomato (Solanum lycopersicum L.) represents a major constraint on crop improvement, necessitating the exploitation of wild relatives to broaden allelic diversity. Here we present SABER (Solanum lycopersicum Allele Biodiversity Enriched Resources), a novel eight-founder Multiparent Advanced Generation Intercross (MAGIC) population that, for the first time, incorporates the Galapagos wild relative Solanum cheesmaniae as a founder alongside seven elite S. lycopersicum lines. Following a structured crossing scheme and Single Seed Descent advancement, F6 recombinant inbred lines were genotyped at 5,850 high-confidence SNP markers using Single Primer Enrichment Technology (SPET). Population structure analyses confirmed low residual heterozygosity, limited substructure among offspring, and successful introgression of S. cheesmaniae alleles across all twelve chromosomes. Mapping performance was validated through three Mendelian traits with known genetic determinants, all of which resolved to genomic positions consistent with the literature. QTL mapping for quantitative agronomic traits identified known loci for fruit epicarp and flesh color, and two novel QTL for days to flowering, number of leaves before flowering, and soluble solids content. Together, these results demonstrate that SABER is a powerful and reliable platform for high-resolution QTL mapping and candidate gene discovery, and establish a replicable framework for integrating wild germplasm into multiparental tomato breeding resources

Improving photosynthesis is a promising approach to enhance sugar beet productivity. However, genetic variation in leaf photosynthesis and its relationship with disease resistance remain underexplored. We evaluated 98 sugar beet genotypes representing different breeding categories, including commercial F1 hybrids, seed-parent lines, and pollinator lines, in Hokkaido, northern Japan. Leaf gas exchange was measured during early growth under field conditions around the infection period of Cercospora leaf spot (CLS). To account for fluctuating irradiance during large-scale phenotyping, we applied a multilevel mixed-effects light-response model to estimate genotype-specific photosynthetic characteristics. Substantial genotypic variations in photosynthetic characteristics were detected. F1 hybrids exhibited higher photosynthetic capacity than breeding lines, whereas differences among breeding categories were unclear due to large within-category variation. Some breeding lines exhibited photosynthetic rates higher than those of hybrids, indicating exploitable genetic resources within the present genetic panel. We did not detect statistically significant trade-off between leaf photosynthesis and CLS resistance among 98 genotypes; in a subset of 19 genotypes analysed in detail, the relationship was even synergistic. Our results highlight the genetic diversity of leaf photosynthesis and its category-dependent structure, and suggest that selection for enhanced photosynthesis can proceed without substantial trade-off with CLS resistance. HighlightLeaf photosynthesis of 98 sugar beet genotypes showed significant genetic variation and dependence on breeding category. Active photosynthesis incurred minimal trade-off with Cercospora leaf spot resistance.

Manual inoculation of plant roots is labor-intensive, spatially imprecise, and limits experimental throughput in plant-microbe interaction studies. Here, we present an integrated computer vision and robotics pipeline for automated, landmark-based root inoculation in Arabidopsis thaliana. Seedlings grown on Gelrite plates were imaged using the HADES automated phenotyping platform at the Netherlands Plant Eco-Phenotyping Centre, Utrecht University. A U-Net-based segmentation model (RootNet, F1 = 0.80) identified root structures, from which primary root tips were localized with a mean error of 0.25 mm. An affine coordinate transformation (mean target registration error: 1.09 mm) mapped image coordinates to the workspace of an Opentrons OT-2 liquid handling robot for targeted dispensing of 10 {micro}L volumes. The system achieved successful inoculation in all 17 benchmark seedlings, corresponding to 100% accuracy (95% CI: 80-100%, Clopper-Pearson), and biological validation with fluorescent bacteria confirmed successful colonization along the root axis in 9 of 10 seedlings. To our knowledge, this is the first reported demonstration of automated, landmark-based root inoculation, extending the concept of automated phenotyping from passive measurement to active robotic intervention in real-time. The pipeline is generalizable to other root landmarks and organisms, enabling precise and reproducible delivery of microorganisms to specific root locations for systematic investigation of localized plant-microbe interactions.

The genetics of interspecific groups remains largely unexplored, despite the central role of social (or indirect) genetic effects in shaping phenotypic expression within communities. Intercropping, i.e. the simultaneous cultivation of multiple crop species in the same field, offers a powerful model to harness these interspecific social effects. Such species mixtures provide well-documented agricultural benefits, yet few breeding frameworks have integrated the genetics of social interactions. Here, we address this gap by extending quantitative genetic theory to interspecific groups, with intercropping as a concrete and applied model case. We propose a quantitative genetic model that jointly analyzes intra and interspecific interactions within a unifying framework. Breeding values are decomposed into a direct component, shared in mono and mixed-crops, an interspecific social component corresponding to the effect of one species on another, and an intraspecific component that captures the social effects within a mono-genotypic stand of cloned plants. Statistically, this consists in simultaneously fitting several linear mixed models, one per stand type, all having direct breeding values in common. As no open-source software can fit such a complex mixed model, we provide such an implementation in R/C++. Simulations across various genetic (co)variance structures and sparse experimental designs showed accurate estimation of all genetic (co)variances and breeding values. With an incomplete, yet balanced design combining sole crops and intercrops, genetic gains in both systems were achievable simultaneously, enabling breeding strategies that progressively integrate intercropping into existing, sole-crop-only schemes. More broadly, this framework allows dissecting direct and social genetic effects when genotypes are observed in mono- and mixed-species situations, cultivated or not.

Field-based monitoring of tree species in forests is often sparse due to logistical constraints. Remote sensing enables repeated, spatially contiguous collection of reflectance data across large areas. Tree species classification accuracy using such data is variable, likely because most studies use observational datasets where species occurrence correlates with environmental variation. We used two sites of a tree biodiversity experiment in Germany (BIOTREE: Kaltenborn and Bechstedt), where different species have been planted with high replication under controlled diversity levels, to assess how well tree species could be classified using reflectance data from airborne imaging spectroscopy and different classification methods (linear discriminant analysis, LDA, and a non-linear support vector machine, SVM). Reflectance data for 589 wavelengths between 400-2400 nm were acquired at 1 m spatial resolution during peak growing season. Reflectance spectra showed large and significant variation between taxonomic classes, orders, and species, and weak, but still significant, interactions between classes or orders and diversity levels. Classification accuracy reached 100% in training datasets, 77%-83% for the four species in Kaltenborn prediction datasets, and 31%-49% for the 16 species in Bechstedt prediction datasets. LDA provided more accurate predictions than SVM; and using similarly-spaced original wavelengths with LDA was as efficient as using principal components derived from the original data. While airborne imaging spectroscopy effectively distinguished up to four tree species in our datasets, classification accuracy was lower in more species-rich plots. In these cases, the methodology may be more useful for functional diversity monitoring than for tree species classification.

During drought, numerous compounds accumulate in plant tissues, but their physiological roles remain unclear - they may function as osmolytes, osmoprotectants, or merely arise as by-products of stress-induced metabolic shifts. We developed an experimental approach to link accumulation patterns with specific functions, using Scots pine (Pinus sylvestris L.) saplings subjected to water deprivation and subsequent rewatering as a model system. We monitored changes in relative water content (RWC) and osmotic adjustment dynamics, employed untargeted primary metabolite profiling for preliminary screening of compounds correlated with water status, and performed quantitative GC-MS and LC-MS analyses of selected metabolites. Major inorganic cations (K, Ca{superscript 2}, Mg{superscript 2}) were also quantified to assess their potential roles. Our results revealed that tryptophan, valine, and lysine - though generally present in low abundance - exhibited selective accumulation under severely reduced RWC ([≤] 70%), suggesting their involvement as osmoprotectants. Major organic acids, particularly shikimic acid, showed trends consistent with osmotic adjustment. Notably, neither sucrose nor inorganic cations appeared to function as primary osmolytes in this context. The proposed approach offers a viable strategy for identifying compounds involved in plant adaptation to water deficit, with potential applications in breeding programs aimed at improving drought tolerance. HighlightsAn approach to identify osmolytes and osmoprotectants was implemented Accumulation of Trp, Val and Lys was consistent with their role in osmoprotection Osmotic adjustment relied predominantly on organic acids than on inorganic ions Monosaccharides but not sucrose correlates with changes in needle water status

Grapevine Trunk diseases (GTDs) represent a major threat for the wine industry. Despite several break-through, their etiology remains unclear and no curative treatment is currently available. Wood anatomy and water transport contribute to the symptoms of young plant decline. This study investigates wood anatomical alterations in two Alsatian grapevine cultivars presenting different susceptibility to GTDs, focusing on wood structure over six months of vegetative growth and in response to infection. Using a validated FasGa staining protocol, wood sections from transverse, tangential, and radial directions were stained to differentiate lignified and cellulosic tissues. Microscopic analysis was performed at x4, x10, and x40 magnifications, yielding a dataset of 4771 images. To support this high-throughput quantitative analysis of microscopy images, a computational model was developed, enabling reliable and efficient assessment of anatomical traits. Pre-established woody tissues presented higher xylem vessels diameter in Gewurztraminer than Riesling, with a dorsoventral arrangement whereas the number of vessels remained the same all over the cross section. No significant anatomical changes were observed in established woody tissues, whereas newly formed xylem anatomy showed a possible rearrangement during infection, especially in Gewurztraminer cultivar. Furthermore, colorimetric analysis quantified the lignification of woody tissues in response to wounding damage compared to un-treated plants. While definitive conclusions remain limited due to the experimental timeframe and sample variability, the findings highlight the need for longer-term studies and broader cultivar evaluation. Code and microscopy images have been made publicly available, providing a scalable digital tool for future research in plant vascular systems.