Agronomy

This study investigated the mutagenic effects of ethyl methane sulfonate (EMS) on the M{square} generation in cowpea (Vigna unguiculata (L.) Walp.) cultivar Wang Kae. A total of 275 M{square} seeds were treated with EMS concentrations of 20 mM, 40 mM, and 80 mM (75 seeds per treatment) by soaking for six hours, while 50 untreated seeds served as the control (0 mM). Phenological, yield-related and yield traits were recorded, and data were analysed using Jamovi 2.7.15 and JASP 0.95.4.0 through one-way ANOVA with post hoc contrast, principal component biplot, and cluster analyses. No optimal mutagenic concentration (LD50) was identified. Seed germination and seedling survival rates increased with increasing EMS concentration, ranging from 70.00% and 62.00% in the control (0 mM) to 89.33% and 74.67% at 80 mM, following the trend 0 mM < 20 mM < 40 mM < 80 mM. Significant differences (P < 0.05) were observed among treatments for all phenological traits, pod length, locule number, seed traits, and yield per plant. Yield was significantly higher (P = 0.047) at 20 mM (61.19 {+/-} 3.34 g) compared to the control. Contrast analysis identified genotypes B33 and D56 as the most productive mutants, with yields of 125.44 g and 111.85 g, respectively. Principal component analysis extracted eighteen components, with the first four cumulatively explaining 50.60% of total variation. Biplot analysis of PC1 and PC2 captured all phenological traits, key seed traits, and yield attributes, highlighting the superior performance of B33 and D56. Cluster analysis partitioned the 190 genotypes into six groups, with B33 and D56 constituting distinct clusters. EMS mutagenesis effectively induced heritable phenotypic variation, with putative superior genotypes identified for advancement to M{square} and evaluation in replicated multi-environment trials toward the development of farmer- and consumer-preferred cowpea varieties.

Spinach (Spinacia oleraceae) is a principal vegetable crop commercially grown in Controlled Environment Agriculture (CEA). Recent research suggests that root morphological and architectural differences among crop species influence yield, resource use efficiency, and environmental stress tolerance. These root traits may be exploited to increase yield, promote efficient nutrient use, and mitigate environmental stressors. This study measured differences between various spinach cultivars in CEA systems to reveal morphological and anatomical variation. We grew three spinach cultivars with different reported growing rates ( Income, Darkside, and El-Majestic) under NFT hydroponic and substrate-based systems in a controlled greenhouse environment over 45 days with destructive harvests at days 15, 30, and 45. Supplemental light (250 {micro}mol/m2/s) with 12-hour photoperiod and periodic fertigation was used. Harvests included the collection of leaf and root biomass, and scanning of root systems in WinRhizo software, measuring ten variables. On day 45, root cross-sections from orders 1-5 were embedded in JB-4 resin, sectioned, stained, and analyzed for diameter, vasculature, and rhizodermis characteristics. Results indicate that in spinach, differences in root system morphology are linked to cultivation systems over cultivar identity. Vascular and root anatomical alterations are minor compared to morphological differences in response to the cultivation system. Hydroponic-style growth systems are associated with the proliferation of fine-root ideotypes compared with substrate-based conditions. Such findings affirm previous studies, which suggest plastic root morphology in response to growth systems, and may be used to help create more resilient, resource-efficient cultivars. HighlightsO_LIIn spinach, root system morphology differences are linked to cultivation systems. C_LIO_LIRoot vascular and anatomical alterations are minor in response to cultivation system. C_LIO_LIHydroponic growth systems are linked to fine-root ideotype proliferation in spinach. C_LIO_LIFine-root ideotype proliferation may be a breeding target for CEA spinach. C_LI

Environmental factors affect crop growth and development thus their consideration across sites and years become essential for genotypic evaluation. Genomic selection (GS) has been broadly implemented to accelerate breeding cycles by skipping field evaluations thus allowing early identification of outperforming genotypes. In this study, 7,740 phenotypic records corresponding to 516 Miscanthus sacchariflorus genotypes evaluated in five locations across three years were considered for analysis. Additionally, environmental data on six weather covariates was implemented to characterize similarities between locations. Different sets of locations of variable sizes were used for model calibration based on two cross-validations (CV00 and CV0) schemes leaving out one location at a time. Predictive ability across locations of the best model varied between 0.45 and 0.90 for both schemes. These results were compared to associate predictive ability in function of weather patterns between training and testing sets to allow models calibration optimization. We found it is feasible to optimize resource allocation by considering environmentally correlated sets. In most cases, the information from only one and, at most, two locations were enough to deliver better results than using all four locations, reducing training sets by up to 75%. The results obtained shed light on helping breeders make informed decisions considering weather data when designing evaluations.

Genomic selection holds the potential to serve as a strategic tool to enhance the genetic gain of complex traits in Miscanthus breeding programs. The development of improved cultivars requires their assessment for various traits across diverse environments to ensure suitable overall performance. Hence, the multi-trait multi-environment (MTME) genomic prediction (GP) models offer an opportunity to improve selection accuracy. This study aims to evaluate the potential of five GP models: (1) three MTME models including genotype-by-trait-by-environment interaction (GxExT) and (2) two single-trait multi-environment (STME) models (with and without GxE interaction). A Miscanthus sacchariflorus population comprising 336 genotypes evaluated in three environments and scored for four traits (biomass yield YDY, total culm number TCM, average internode length AIL, and culm node number CNN) was analyzed. The predictive ability of the models was evaluated considering three cross-validation schemes resembling realistic scenarios (CV1: predicting new genotypes, CVP: predicting missing traits in a given environment, and CV2: predicting partially observed genotypes). On average, in all cross-validation schemes compared to the STME the predictive ability of the MTME models was 10% to 70% higher for TCM and AIL. On the other hand, for YDY and CNN, both STME models performed similarly or slightly better (between 5 to 64%) than the MTME models in most environments. While the MTME models were not successful for all traits when compared to their STME counterparts, MTME models improved the prediction of the performance of genotypes that were untested across environments or lacked trait information in a specific environment. Overall, our study suggests that MTME GP models can be implemented in Miscanthus breeding programs to improve the predictive ability of the complex traits, shorten breeding cycles, and accelerate selection decisions.

Phenotyping high-biomass perennial crops is laborious and the rate of genetic gain in perennial crop breeding programs is typically low. So, it is especially important to identify methods that produce efficiency gains in the breeding process. Miscanthus is a C4 perennial grass with favorable characteristics for producing biomass as a feedstock for biofuels and diverse biobased products. Increasing biomass yield will increase profitability and environmental benefits, so is a key target for Miscanthus breeding. In addition, the identification of well-adapted genotypes across a wide range of environmental conditions requires the establishment of multi-environment trials (METs). Sparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations. A Miscanthus sacchariflorus (MSA) population comprising 336 genotypes observed across three environments was analyzed. Three prediction models considering main effects (environments, genotypes, genomic) and interaction effects (genotype-by-environment; GxE interaction) were implemented for forecasting dry biomass yield (YDY), total culm (TCM), average internode length (AIL), and culm node number (CNN). Multiple calibration sets based on different compositions and sizes were considered to evaluate performance in terms of the predictive ability (PA) and the mean square error (MSE) for a fixed testing set size. The training set size ranged from 52 to 112 to predict a fixed set of 224 unobserved genotypes across all three environments. The results showed that the model accounting for GxE interaction presented the highest PA and the lowest MSE for CNN (PA: [~]0.77, MSE: [~]0.5) and YDY (PA: [~]0.70, MSE: [~]1.3) while for TCM and AIL these ranged from [~]0.28 to 0.41 and [~]1.3 to 4.3, respectively. Overall, varying training sets and allocation strategies did not affect PA and MSE, with 52 non-overlapping and 0 overlapping genotypes per environment as the optimal cost-effective allocation framework. This suggests that implementing sparse testing designs could significantly reduce phenotyping costs by fivefold, without compromising PA in breeding programs for perennial crops such as Miscanthus.

Net radiation (Rn) can be estimated using models that apply the Brunt equation for the incoming longwave radiation and air temperature (Tair) for the outgoing longwave radiation under reference conditions. This study aimed to estimate Rn using two previously regionally calibrated Brunt model, thereby eliminating the need site-specific calibration, and to assess whether Tair can be used as a substitute for canopy temperature (Tc) under well-watered crop conditions. Measurements were conducted in sesame and cotton fields during the first year and in a cotton field during the second year. Canopy temperature was measured during the second year, and the calculations were performed at hourly and daily time scales. Regardless of the method used to estimate sky emissivity or whether Tc or Tair was used, errors were greater at hourly time scale. The overall RMSE, MAE, Bias and KGE values at the daily time scales were 11.88, 9.13, 2.53, and 0.91, in the first year, and 13.45, 10.56, 0.10 and 0.74, in the second year, respectively. When using both regionally calibrated Brunt model, Rn simulation performance was superior to that of the Allen/FAO method. The comparison between Rn estimated using Tair and Tc, indicated statistical differences. Nevertheless, linear regression and error metrics showed that these differences were modest, especially at daily time scale. Thus, for practical purposes both regionally calibrated Brunt equations can be used to calculate clear-sky emissivity and improve Rn estimations, and Tair can be used as a substitute for Tc at the daily time scale under well-watered conditions.

Enset (Ensete ventricosum), a multipurpose crop domesticated exclusively in Ethiopia, serves as a staple food for millions of smallholder farmers. It is primarily cultivated as a monocrop in homegardens, leaving it vulnerable to climate change risks. One potential nature-based solution involves agroforestry systems; however, ensets response to canopy cover remains unclear. This study examined how scattered trees in enset farms affected microclimate and enset morpho-physiology in South Ethiopia. Trees significantly modified the microclimate conditions in enset homegardens. The average daily reductions in air, soil surface, and soil temperatures ranged from -0.5 to -1.9 {degrees}C, -0.4 to -2.1 {degrees}C, and +0.4 to -1.0 {degrees}C, respectively. The minimum soil moisture offset ranged from +0.8% to +5.7%. Although the tree identity effect on enset growth was negligible, planting position relative to the overstory trees significantly influenced enset responses. Most morphophysiological traits were higher under tree canopies, with progressively lower values at the edge and outside the tree canopy. In contrast, leaf dry matter content exhibited an inverse trend, aligning with the leaf economics spectrum. These results demonstrate ensets adaptability to canopy shade, suggesting potential for agroforestry expansion. Cultivar-specific shade tolerance and ideal shade levels to maintain enset productivity should be investigated further.

CoverCress is a new winter annual oilseed crop developed from field pennycress within the past 20 years. Field pennycress is commonly considered to be self-pollinated but little basic research has been published and there is some misalignment of conclusions. Our experience working with pennycress plant growth in greenhouse and field conditions over the past 13 years suggests that outcrossing is uncommon. We conducted lab, greenhouse, and field experiments to strengthen the body of work. Pollen viability kinetics analysis showed that longevity of pollen viability is negatively impacted by increasing temperatures and by direct exposure to light. Samples treated at 4C declined to 50% viability in 12 hours while it took just 2.5 hrs at 37C, and 1.6 hrs in full sunlight on a cool early April day. Cross-pollination was absent among greenhouse-grown plants flowering inside an agitated plastic pollen-containment covering. Across greenhouse tests, high rates of cross-pollination occurred only in an emasculation treatment that rendered flowers male sterile and opened the pistil to cross-fertilization. Field trials designed to measure pollen flow distance using a trackable fae1 knockout reporter gene failed to show detectable movement of pollen under field conditions in two locations. This data strongly suggests that domesticated field pennycress may be considered a self-pollinated crop and managed as such.

BackgroundPlants are exposed to various environmental challenges. With ongoing climate change, droughts and insect outbreaks are expected to become more frequent. Thus, a better understanding is needed of how different plant species respond to such single and combined challenges. This study investigated common versus species-specific responses to environmental challenges in three perennial plant species of different growth forms and whether responses differ intraspecifically among accessions. Clones of different accessions of the herbaceous species Tanacetum vulgare, the woody vine Solanum dulcamara, and the tree Populus nigra were subjected to similar control, herbivory, drought, and combined (drought and herbivory) treatments for the same periods. After the exposure, concentrations of foliar phytohormones and various morphological traits were measured. ResultsAcross all species, several foliar phytohormones and one of ten morphological traits responded consistently to the environmental challenges. Jasmonoyl-isoleucine was induced by herbivory and the combined treatment, abscisic acid (ABA) by drought and the combined treatment, and indole acetic acid by the combined treatment in all species. Root mass remained unchanged in all species. However, structural equation models (SEMs) revealed a shared regulatory pathway across species in which ABA connected treatment and root mass, indicating a common hormonal response potentially linking challenges to growth responses. Despite these common patterns, species-specific responses were pronounced. In P. nigra, a unique induction of salicylic acid was found under the combined treatment, while aboveground mass and root-shoot ratio remained unaffected by any treatment, in contrast to the other two species. Species-specific SEMs further indicated distinct phytohormone-mediated pathways underlying morphological variation. Phenotypic plasticity reflected these species-specific patterns, with none of the phytohormones or morphological traits exhibiting uniform plasticity across species. Intraspecific variation further shaped responses, as phytohormone and morphological trait plasticity depended on accession, indicating substantial accession-specific plant responses. ConclusionsOur results indicate that some responses to comparable challenges may be conserved across species, while others are species-specific. The combined treatment elicited the most pronounced responses, and such complex responses may become more frequent under current global change. Our study highlights that comprehensive understanding of plant responses requires systematic comparisons at both interspecific and intraspecific scales.

Leaf diseases pose a serious threat to faba bean production. Leaf blotch of faba bean, caused by Alternaria spp., has become increasingly widespread and destructive in several countries. Leaf diseases pose a serious threat to faba bean production. The infection of plant by pathogens can be influenced by various factors associated with the host plant, environmental conditions and presence of other microorganisms. The phyllosphere and endosphere play a critical role in plant health and disease development. This study aimed to evaluate the factors shaping the structure and diversity of fungal communities associated with faba beans. Plant samples were collected in 2004 from two intensively managed faba bean production fields in the central region of Latvia. Fungal assemblages were characterized using an ITS region metabarcoding approach based on Illumina MiSeq sequencing. Among the assigned amplicon sequence variant (AVS), 65% belonged to the phylum Ascomycota, while approximately 4% were classified as Basidiomycota. Alternaria and Cladosporium were the dominant genera across samples. The alfa and beta diversities of fungal communities was higher during flowering of faba beans to compare with ripening. The higher abundance of Basidiomycota yeasts were observed during flowering, in contrast, Cladosporium genus was significantly more abundant during ripening. Alternaria DNA was found on leaves that showed no symptoms of the disease. The diversity and composition of fungal communities were significantly influenced by sampling time and presence of leaf blotch, caused by Alternaria spp.

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.

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.

Heat-tolerant rice cultivars are essential for mitigating global warming impacts. Basal anther dehiscence length (BDL) is a promising visible morphological marker for heat tolerance through stable pollination. We investigated the effects of sowing date on anther morphology, pollination, and fertility under controlled high-temperature conditions (35, 37, or 39 {degrees}C at flowering). Three japonica cultivars-- Akitakomachi (early heading), Koshihikari (medium), and Hatsushimo (late)--were sown monthly over 3 months and grown in pots. At heading, the plants were exposed to the temperature treatments for 3 days, and the proportion of florets with [&ge;]10 germinated pollen grains on the stigma (GP10) and seed set were assessed. Among anther traits, BDL showed the greatest variation, with all cultivars from the second sowing exhibiting the shortest BDL. Analysis of variance revealed significant effects of genotype, sowing date, and their interaction on anther traits and fertility. Regression analysis indicated that fertility was associated with GP10, with BDL contributing significantly to GP10 in the late-heading Hatsushimo, together with maximum temperature at flowering. Thus, both genotype and environment shape anther morphology, pollination, and fertility, indicating that BDL plasticity and genotype-specific environmental responses must be carefully considered when using BDL as a breeding marker for heat tolerance. HighlightVariation in sowing date significantly affects anther morphology and heat tolerance in rice. Genotype-specific responses to the growing environment require careful consideration for reliable breeding assessments.

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.

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.

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.

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.

Photosynthesis accounts for most of the final grain yield in rice, making improvements in radiation use efficiency (RUE) a key strategy for enhancing productivity. Agronomically, RUE is defined as the biomass produced per unit of total solar radiation or photosynthetically active radiation intercepted by the canopy. However, the interaction between carbon and nitrogen metabolism plays a critical role in determining plant growth and grain yield. Assimilated nitrogen is required for the synthesis of photosynthetic pigments and enzymes, while the reduction of nitrate (NOLL) and nitrite (NOLL), as well as the assimilation of ammonium (NHLL), depend on the reducing power and carbon skeletons generated by photosynthesis. In this study, two high-yielding rice (Oryza sativa) cultivars--an indica-type (El Paso 144) and a japonica-type (INIA Parao) were subjected to two nitrogen treatments (3 mM and 9 mM NOLL/NHLL) and two light intensities (850 and 1500 mol mL{superscript 2} sL{superscript 1}). A strong interaction between light intensity and nitrogen metabolism was observed, with contrasting responses between subspecies. These differences reflect a coordinated regulation of carbon assimilation and primary nitrogen metabolism. The results provide new insights into the metabolic strategies underlying nitrogen compound accumulation under variable irradiance. Such knowledge is essential for improving nitrogen fertilizer use efficiency and yield performance in elite rice genotypes cultivated under commercial field conditions.

Black spruce (Picea mariana [Mill.] B.S.P.) is an emblematic and ubiquitous species of the North Americas boreal forest. While conifer breeding programs have traditionally focused on growth and wood property traits, the study of climate adaptation traits is becoming increasingly prevalent, given the predicted impact of climate change on North Americas boreal zone. Through this study, we aimed to identify genes associated with climate adaptation in black spruce across Canada. A total of 254 black spruce trees from 30 populations, covering most of the species distribution range, were sampled and genotyped for SNPs located in [~]5000 gene loci. Uni- and multivariate Genotype-Environment Association (GEA) approaches, namely LFMM and RDA, as well as an outlier method based on population differentiation (FST) were used to identify genes significantly associated with climatic factors. As such, a total of 77 genes carrying significant candidate SNPs were identified, among which 14 candidates were corroborated by at least two methods. Many of these gene SNPs were also confirmed at a smaller geographic scale, across west - east partitions corresponding to the two main black spruce historical lineages. Notably, significant gene SNPs were more frequently associated to moisture/aridity factors in the western part of the range, and more to temperature factors in the eastern part. The genes carrying these SNPs were also frequently associated to abiotic and biotic stress response. In the context of rapid climate change in the Canadian boreal forest, the results obtained within the framework of this study should support implementing gene conservation efforts while assisting prediction in black spruce breeding programs, which are instrumental to producing adapted planting stock for the large-scale reforestation efforts conducted annually across the Canadian boreal forest.

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.