Agronomy

In this study, the critical gap in understanding how fonio responds to contrasting pedoclimatic conditions, both within and outside its traditional production areas was addressed. A multi-environment trial was carried out to identify high-yielding genotypes with either broad stability or specific adaptation, thereby enabling targeted varietal recommendations to support the expansion of fonio cultivation into new areas. Randomized complete block design was used in six environments with eleven genotypes to evaluate flowering and maturity times, and grain yield. The Additive Main effect and Multiplicative Interaction and the Genotype main effect and Genotype x Environment interaction biplots revealed a significant effect of the genotype-by-environment interactions on traits, with genotypes B12 and G31 identified as high-yielding, while genotypes M5 and M14 were revealed as early-flowering and maturing. Genotypes M14 and M15 were adapted to all environments and early maturing. Boukoumbe, known as the fonio production area in Benin, was the most desirable for earliness, while Ina was the most ideal for grain yield, proving that fonio could be cultivated in Sudanian and Sudano-Guinean areas. Factor analysis revealed precipitation, C:N ratio, soil pH and texture as the main environmental variables influencing the grain yield in fonio. Our findings contributed to selecting stable, adapted genotypes.

Ralstonia pseudosolanacearum (Rps) belongs to the Ralstonia solanacearum species complex (RSSC). It is a vascular pathogen that causes lethal bacterial wilt disease in many plants, including tomato and eggplant. In this study, we infiltrated tomato leaves with the phytopathogenic bacterium at 109 CFU/mL and observed the development of necrotic scars in the infiltrated area at 48 hours post-infiltration. Interestingly, this response was followed by petiole bending toward the ground of the compound leaf. This was followed by the gradual senescence of the infiltrated leaflet only. In addition, the terminal leaflet infiltrated with the pathogen exhibited epinasty. None of the above symptoms were observed in leaves infiltrated with the known virulent deficient hrpB::{Omega} mutant. Surprisingly, all of the above symptoms were observed in leaves infiltrated with another well-known virulence-deficient mutant phcA::{Omega}. It indicated that the necrotic lesion caused in tomato leaves was hrp-dependent. Infiltration in eggplant leaves caused necrotic scarring and leaf senescence, which were relatively delayed. Necrotic scarring without petiole bending or senescence in tomato leaves was also observed due to infiltration of Pseudomonas aeruginosa SPT08, a tomato endophyte having plant growth promotion activity. The patho-phenotypes such as petiole bending, epinasty, and senescence observed in the case of tomato in this study were not reported earlier. We believe these phenotypes produced in tomato after leaf infiltration may be useful to study the virulence of this pathogen.

Seaweed cultivation has recently gained increased attention in North-West Europe as a sustainable source of biomass for biobased products. However, yields need to increase to make the seaweed sector economically viable. To achieve this, higher yielding varieties can be bred but this requires variation for yield and yield-related traits among genotypes. To reliably select high-yielding genotypes, an understanding is required of how both within-farm and between-farm environmental differences affect phenotypes and how to identify simple and reliable proxies for yield. In this study we evaluated growth of nine Saccharina latissima genotypes on two farms, 12 km apart, within the same season. We observed a threefold difference in yield among genotypes, demonstrating the potential for improvement through selection and breeding. Blade thickness and blade size-related traits were strongly correlated with yield, highlighting their potential to serve as rapid and non-destructive proxies for yield, thereby accelerating selection. Furthermore, we demonstrated the importance of adequate replication in farm trials to improve genotype performance estimation by correcting for within-farm spatial variation. Moreover, phenotypic variation was most explained by the genotype and environment, highlighting the importance of both genotype and site selection. Although genotype by environment interactions (GxE) were significant, its contributions were small, indicating stable genotype ranking across farms. Overall, these results are promising for breeding improved S. latissima as it indicates that genotype performance is consistent across close by locations and that local S. latissima populations harbour substantial phenotypic variation that can be used to breed for increased yield. Highlights- Local genetic resources harbour substantial variation in yield and morphology for breeding. - Minor GxE allows for breeding across farms. - Blade thickness and blade size related traits are good predictors of yield. - Correction for on-farm spatial variation improves genotype performance estimation.

Prosopis juliflora is an invasive alien plant species and a problematic weed that poses significant ecological and socio-economic challenges in Ethiopia, particularly in the Afar rangelands. The study explored the diversity and effects of insect herbivores communities feeding on the flowers and pods of P. juliflora to determine their role in limiting reproductive success across three selected ecological sites: Amibara, Gewanne, and Aysayita. A total of 118 adult insect specimens were collected between January and November 2021 using a sweep net and hand collection methods. Community structure, analysis via the Shannon Wiener diversity index, strongly influenced damage pattern. Amibara exhibited the highest insect diversity resulting in significant reproductive damage, including 5.98% of flower loss and 10.39% pods tunneling, primarily caused by Chrysomelidae and Pyralidae. Conversely, Gewanne was showed lower diversity, but higher sap-sucking (13.39 % shriveled pods; 5.11 % flower curling) were caused by Aphididae. Overall, 18.41 % of the pods, and 11.59 % of the flowers were exhibited insect related injury. These finding confirm that more internal seed predation and nutrient depletion were revealed significantly reduce viable seed production. The result was suggested that natural insect communities currently function as partial biological control agents. This indicates strong potential for developing integrated biological control strategies to manage P. juliflora invasion in Ethiopia rangelands.

Whitebark pine (Pinus albicaulis), a wide-ranging high-elevation conifer in western North America, is listed as threatened in the U.S. and as endangered in Canada. A major threat to whitebark pine is the non-native, invasive white pine blister rust disease, caused by the fungal pathogen Cronartium ribicola. In many pathosystems (including white pine blister rust), seedling inoculation trials are used to identify parent trees with genetic resistance. However, many of these trials use only one spore density for inoculation, and little information exists on the effectiveness of quantitative disease resistance (QDR) under varying spore densities and the corresponding implications for field performance. In this study, we examine the levels of infection and survival present within six whitebark pine seedling families previously rated for QDR (three susceptible and three resistant families) under six widely varying inoculum densities. The susceptible families showed very high infection and mortality at all inoculum densities, while performance of the resistant families varied with spore density treatment. The information gathered from the study will be useful in updating the projections of the future of whitebark pine populations under field conditions in areas of different rust hazard. The results also serve as a caution to those working in other pathosystems where seedling inoculation trials based on one spore density level are used to rate the resistance level of parent trees and their associated progeny.

Though currently a minor crop, faba bean is a promising source of plant-based protein as global diets shift towards more plant-based nutrition. To realise this potential, advances in breeding and cultivation are crucial. To exploit heterosis, faba bean breeding frequently utilises synthetic cultivars, which involves open pollination of inbred lines to produce a mixture of F1 hybrid seeds and self-pollinated offspring. Pure F1 hybrid cultivars are currently unavailable due to unstable cytoplasmic male sterility (CMS) systems. An ability to distinguish F1 seeds from their parental inbreds via characteristics associated with xenia effects could change this. The xenia effect refers to the influence of paternal pollen on seed traits, for example seed weight and cotyledon cells in faba bean. In this study, we exploited the xenia effect captured in hyperspectral imaging data to develop machine learning scenarios for discriminating between parental and F1 seeds of open pollinated synthetic combinations (Syn-1). The hyperspectral data were pre-processed using Savitzky-Golay filtering to reduce noise and smooth the spectra. Various machine learning algorithms were applied, incorporating Bayesian hyperparameter optimisation. The scenarios achieved up to 98.9 % accuracy in separating parental components of Syn-1. When including all seeds, the model achieved 40.7 %, indicating moderate detection and classification performance. As the harmonic mean of precision and recall, the F1 score accounts for both the correctness of F1 seed detections and the completeness with which F1 seeds were detected. While this approach does not yet enable the development of full hybrid cultivars, it paves the way for hybrid-enriched cultivars. These could help to streamline breeding for synthetic cultivars and potentially increase yields, for example by increasing the proportion of F1 hybrid seeds in synthetic cultivars. This study extends knowledge of the xenia effect in faba bean and provides a basis for further research aimed at enhancing breeding methods and productivity.

Soybean (Glycine max [L.] Merr.) productivity is frequently compromised by soil-borne pathogens. Macrophomina phaseolina (Mp), the causal agent of charcoal rot, can produce important soybean yield losses especially when hot and dry weather prevails. Integrating biological control agents with chemical seed treatments represents a promising strategy for improving disease management. This study aimed to (i) assess the in vitro compatibility of Trichoderma koningiopsis with commercial fungicide seed treatments, and (ii) evaluate the field performance of T. koningiopsis, alone or combined with compatible fungicides, across three soybean growing seasons. Compatibility assays revealed fungicide-specific effects, with Acronis(R) classified as non-fungitoxic and Topseed Extra as moderately fungitoxic. Across field seasons, Mp inoculation reduced seedling emergence, while several seed treatments improved emergence compared to the inoculated control, however, treatment effects varied markedly among years. Disease severity did not differ significantly among treatments in any season, and yield responses were strongly modified by environmental conditions rather than treatment effects. Temperature-response assays showed that T. koningiopsis exhibited optimal growth between 28 to 30{degrees}C and complete inhibition above 40{degrees}C, indicating high thermal sensitivity. The results demonstrate that T. koningiopsis can be integrated with compatible fungicides and may enhance early stand establishment under favorable conditions, but its field performance is strongly limited by high temperatures. These findings highlight the importance of environmental conditions when biological seed treatments are used.

Pennycress (Thlaspi arvense L.) is an intermediate winter oilseed crop that has only recently been domesticated for agronomic use. Improving agronomic traits requires sources of genetic variation, and mutagenesis is frequently used to help overcome the limitations of natural populations. We investigate the impact of Ethyl methanesulfonate (EMS) on genetically effective cells (GECs) to characterize the intra-individual genetic variation of EMS mutagenesis in pennycress. We identified that pennycress contains at least 4 GECs which, when treated with EMS, create unique mutations across different branches within the same individual plant. We then propagated the M2 plants for whole genome sequencing, providing extensive characterization of the EMS mutation profile and developing a gene index as a resource for future reverse genetic screenings. Article SummaryPennycress is an emerging winter oil seed crop in the American Midwest. Domestication efforts have advanced rapidly through a combination of genetic techniques. One of the most successful methods has been the use of a mutant gene index, a large collection of pennycress seed where new genetic variation has been created through Ethyl methanesulfonate (EMS). EMS mutations are not uniform however, and a single treated seed can have wide genetic variation within the resulting plant. We investigate the role of genetically effective cells on EMS variation, and present the full EMS population as a resource for further pennycress domestication efforts.

The persistence of specialised survival spores produced by microbial pathogens represents a primary bottleneck in the management of plant diseases. In oomycetes, these spores (known as oospores) are largely impervious to chemical control, allowing them to persist in soil and initiate new infection cycles over many years. A prominent example is the soil-borne pathogen Phytophthora agathidicida, the causal agent of kauri dieback disease, where long-lived oospores hinder conservation efforts in native forests. The resilience of oospores is attributed to their thick wall composed of complex {beta}-glucan layers that render the oospores impermeable to most conventional biocides. Here we have investigated an enzyme-based approach for weakening the oospore cell wall. We searched enzyme databases to select {beta}-glucanases targeting a variety of linkages found in Phytophthora oospore walls. Eight of these {beta}-glucanases were successfully purified and tested for their digestive activity against intact oospores in vitro using a phenol-sulfuric acid assay. We showed that combining these enzymes was crucial to achieve significant digestion through synergies and additive effects. The optimal combination, comprising 1,3-, 1,6-, and 1,3(4)-{beta}-glucanases, was evaluated for its ability to permeabilise oospores to five biocides typically effective only on other, more sensitive lifecycle stages of the pathogen. Using a live/dead fluorescence assay, we observed that the effects of the membrane-targeting biocides were potentiated in oospores that were pre-treated with the {beta}-glucanase mixture. Our results highlight enzymatic cell wall permeabilisation as a promising strategy toward improved management of oospore persistence in kauri forest soils and against broader oomycete threats. KeypointsO_LIOur phenol-sulfuric acid assay can be used to screen for oospore-degrading enzymes. C_LIO_LISynergistic enzyme combinations are essential for effective oospore wall digestion. C_LIO_LIEnzyme pre-treatment sensitises oospores to membrane-targeting biocides. C_LI

IntroductionThe environmental impacts of pesticides have raised increasing concern, prompting the development of indicators to assess associated risks across ecosystems. Two main categories are generally distinguished: score-based indicators, which aggregate variables into scores, and risk-based indicators, grounded in the definition of risk as the product of hazard and exposure. Although more data-intensive and more complex to implement, risk-based indicators are recognized to better preserve proportionality with actual risk levels. ObjectivesThis study presents Phytorisque, a model based on the exposure-toxicity ratio to monitor risks associated with pesticide use in Walloon agriculture, from farm to regional scales, and to identify the most contributing active substances in support of risk-reduction policies MethodPhytorisque is a hybrid model that combines mechanistic, empirical, and statistical approaches, integrating quantities of active substances, their ecotoxicological characteristics, and their mobility, persistence, and bioaccumulation properties to generate indices specific to different environmental compartments. ResultsThe indices obtained enable comparison across substances, agricultural sectors, years, and management scenarios. The Phytorisque model provides an integrated assessment of risk across environmental compartments. It can monitor risk evolution over the years for policy impacts evaluation, diagnose the most problematic substances and prospect environmental risks associated with the use of chemical phytoproducts. ConclusionsPhytorisque provides an integrated risk assessment approach adapted to temporal monitoring, diagnosis, and forecasting. It is a relevant operational tool for supporting regional strategies aimed at reducing pesticide-related risks. The model is also transferable to other regions through the adaptation of parameters to local conditions and context.

Maximum utilization of existing genetic variability in a breeding program depends on the efficient classification of the inbred lines into heterotic groups, particularly under stress conditions. This study applied practical breeding approaches to determine the mode of genetic inheritance for Striga resistance and proposes a weighted heterotic grouping method based on the general combining ability of multiple traits (WHGCAMT) and compares its effectiveness with other existing methods in classifying the inbred lines into heterotic groups in Striga-infested and optimum environments. Using Diallel design IV, 300 crosses were generated from 21 inbred lines and 4 standard testers. The crosses, along with six checks, were evaluated in an 18 x 17 alpha lattice design with two replications at two locations, in both artificial Striga-infested and Striga-free environments. The inbred lines were genotyped using DArTtag SNP markers. Phenotypic and genotypic data were analyzed using R. Analysis of variance revealed significant mean squares for hybrid, general combining ability (GCA), specific combining ability (SCA) and their interactions with environment. Significant positive and negative GCA and SCA effects were detected for grain yield and other measured traits. However, a larger proportion of additive gene action than non-additive gene action was observed for grain yield and most measured traits. The analysis of molecular variance also showed substantial genetic differences within and between clusters. Except for HSCA, the mean grain yield between the inter-group and intra-group hybrids was significant for each method. Pairwise comparison of the inter- and intra-group hybrids of all the methods showed significant differences between the WHGCAMT and all other methods in most cases. WHGCAMT consistently produced higher-yielding inter-group hybrids and lower-yielding intra-group hybrids, achieving breeding efficiency improvements of 55.8%, 4.3%, 15.7%, and 11.4% over the HSCA, HSGCA, HGCAMT and molecular marker methods, respectively, under Striga infestation. Thus, WHGCAMT offers more precise, reliable and biologically meaningful heterotic groups among early-maturing maize inbred lines.

Micro-dwarf tomato cultivars are increasingly considered for urban and controlled-environment agriculture due to their compact architecture and suitability for high-density planting. However, optimal canopy management strategies for these cultivars remain poorly defined. In this study, we evaluated the effects of different leaf removal intensities on leaf-level physiological performance, fruit yield, and fruit quality in three micro-dwarf tomato cultivars (Mohammed, Hahms Gelbe Topftomate, and Red Robin) grown under contrasting seasonal light conditions. Plants were subjected to low (15%), moderate (30%), or severe (90%) leaf removal, and leaf-level gas exchange was measured across canopy layers, along with yield and fruit quality assessments. Severe leaf removal (90%) increased carbon assimilation, transpiration, and stomatal conductance in middle and lower canopy leaves by up to approximately twofold compared with control plants, indicating improved light availability at the leaf level. However, these physiological enhancements did not consistently translate into higher yield, reflecting reduced whole-plant source capacity under excessive leaf removal. Low to moderate leaf removal (15-30%) generally increased or maintained yield and fruit number, whereas severe leaf removal reduced yield in Hahms Gelbe and Red Robin, particularly under low seasonal radiation. In contrast, Mohammed exhibited yield increases of up to 220% under low leaf removal and maintained increased yield even under severe leaf removal under high-light conditions. Fruit quality was largely unaffected by leaf removal, except for total soluble solids, which declined by approximately 12% under severe leaf removal across cultivars, consistent with sugar dilution under source limitation. Overall, these results demonstrate that optimal leaf removal in micro-dwarf tomatoes requires balancing improved canopy light distribution with maintenance of sufficient leaf area for carbon assimilation. Leaf removal thresholds are strongly cultivar- and light-dependent, emphasizing the need for cultivar-specific canopy management strategies in compact tomato systems and controlled-environment agriculture.

Stripe rust and leaf rust, caused by Puccinia striiformis f. sp. tritici and P. triticina, respectively, are the most destructive wheat diseases in the southern Great Plains. Green Hammer is a hard red winter wheat (HRWW) cultivar released by Oklahoma State University in 2018 and has demonstrated a stable adult plant resistance to stripe rust and race-specific seedling resistance to leaf rust. To identify and map rust resistance loci, 109 doubled haploid (DH) lines derived from the cross between Green Hammer and another HRWW cultivar, Lonerider, were developed. Lonerider showed adult plant resistance to stripe rust but was susceptible to multiple P. triticina races. The DH lines were evaluated for stripe rust at the adult plant stage in greenhouse and field environments across Oklahoma, Kansas, and Washington, and for leaf rust at the seedling stage against seven U.S. P. triticina races and at the adult plant stage in Oklahoma and Texas. Genotyping-by-sequencing generated 6,078 polymorphic single-nucleotide polymorphisms used for genetic mapping. Quantitative trait loci (QTL) analysis identified 14 stripe rust and 8 leaf rust resistance QTL. For stripe rust, a major QTL in Green Hammer, QYr.osughln-2AS, was identified in the proximity of the 2NvS translocation. Three other major stripe rust resistance QTL were identified in Lonerider on chromosomes 2AL (two QTL) and 2BS (one QTL). For leaf rust, QLr.osughln-1DS and QLr.osughln-2DS.1 were the two major QTL identified in Green Hammer and most likely correspond to the all-stage resistance genes Lr21 and Lr39, respectively. In this study, we identified previously characterized genes as well as unknown genes that can be utilized in wheat breeding programs to enhance resistance to leaf rust and stripe rust.

Freshwater algae represent an underexplored source of naturally occurring bioactive metabolites with potential applications in pharmaceutical and biomedical research. This study investigated the phytochemical composition, antioxidant capacity, and preliminary cytotoxic potential of ethanolic and n-hexane extracts of freshwater algal species collected at Jilani Park, Lahore, Pakistan. Algal species were identified morphologically by Dr. Ghazal Yasmeen (Institute of Botany, Punjab University, Lahore). Extracts were analyzed using gas chromatography-mass spectrometry (GC-MS) and qualitative phytochemical screening. Antioxidant activity was evaluated using DPPH radical scavenging, hydrogen peroxide scavenging, and reducing power assays. Cytotoxic potential was assessed using MTT and cell adhesion assays on HeLa and SF767 cell lines as preliminary indicators of bioactivity. GC-MS analysis identified 25 compounds, including sterols, fatty acid esters, terpenoids, phenolic compounds, and volatile metabolites. Phytochemical screening confirmed the presence of flavonoids, phenolics, tannins, and terpenoids in the extracts. Among the tested extracts, the n-hexane fraction demonstrated comparatively higher antioxidant activity across multiple assays. Ethanolic extracts showed moderate reductions in HeLa cell viability, whereas limited effects were observed in SF767 cells. These findings suggest that freshwater algae are promising natural reservoirs of antioxidant metabolites with potential relevance for future isolation and characterization of bioactive compounds for biomedical applications. Further purification and mechanistic studies are required to identify specific active constituents.

(1) BackgroundSaffron crocus (Crocus sativus) is the source of saffron, the most expensive spice in the world. It evolved about 3000 years ago as a sterile triploid clone in Greece. Since then, saffron has spread across the globe, where regionally distinct practices of saffron cultivation have developed. Despite differences in morpho-physiological traits, genetic variability is low, if present at all. Here, we aim to resolve chromosomal and sequence-associated variability across saffron crocus cultivars from the crops main cultivation areas in Africa, Asia and Europe. (2) MethodsWe used genome-wide DNA polymorphisms obtained through genotyping-by-sequencing (GBS) of 33 saffron and 14 closely related Crocus accessions, which we place into a phylogenetic context. For karyotyping, we compare nine saffron accessions by multi-color fluorescent in situ hybridisation (FISH) with repetitive DNA probes. (3) Key resultsPhylogenetic analyses confirmed the single origin and clonal nature of all saffron accessions. We detected slight DNA differences among saffron crocus genotypes, which were minor compared with those in wild C. cartwrightianus populations. Still, the Iranian saffron accessions form a genetically very narrow group that differs from the other proveniences in population genetic analyses. However, chromosomes of some saffron accessions display variable FISH signals, likely resulting from gains and losses of tandemly repeated DNA. (4) Main conclusionsBased on the high genetic identity and small karyotypic differences, we confirm the clonal origin of the saffron accessions. Nevertheless, as we detected small and regional chromosomal variability, we conclude that at least four somaclonal saffron lineages emerged after saffrons origin. Societal Impact StatementFor millennia, many cultures developed cultivation practices and regional crop varieties. A notable case is saffron, the worlds most expensive spice that is harvested from stigmas of saffron crocus. This flower crop arose 3000 years ago in a singular genome triplication event and since then spread clonally across the globe. By identifying genetic and chromosomal variability in clonal saffron accessions, we highlight regional diversity, support the preservation of traditional knowledge, and underscore the risk of relying on only one clonal lineage. This informs strategies for saffron cultivation, linking cultural heritage with modern genomics to address biodiversity, evolution, and food security.

In quantitative genetics, candidate SNPs are identified through genotype-phenotype associations inferred with genome-wide association studies (GWAS). In this study, we explore an alternative approach to detect genetic variants with non-neutral effects by tracking temporal trends in allele frequency in a winter wheat (Triticum aestivum L.) breeding population over an eight-year period, from which signals of selection may be inferred. Selection signatures were inferred with a generalized linear model, where we modeled trends in allele frequency as a function of time (crossing year). These signatures of selection were used to prioritize variants. Associations between phenotypic performance and individual load of prioritized variants were then investigated. Furthermore, we assessed whether incorporating selection information into a genomic best linear unbiased prediction (GBLUP) model improves model performance in terms of quality of fit and prediction ability. Our findings indicate that the inferred signals of selection are effective in identifying non-neutral variants. Variants under strong negative selection were associated with a decrease in protein content adjusted for grain yield (p-value < 0.01), while genetic variants that had been under moderate to high levels of positive selection were associated with increased grain yield (p-value < 0.01). However, incorporating selection information did not improve prediction accuracy. In conclusion, temporal trends in allele frequency can be used to detect non-neutral variants. The proposed approach may hence complement traditional quantitative genetic methods for detecting non-neutral genetic variation. This approach may allow breeders to detect non-neutral variants earlier in the breeding cycle, without resorting to phenotypic data.

Genomic prediction models that account genotype-by-environment (GxE) have the potential to accelerate the rate of genetic gain for yield and agronomic performance, yet relatively few studies have applied GxE prediction in public soft red winter wheat (Triticum aestivum) breeding programs. In this study, we extended a reaction norm-based genomic prediction framework by integrating weather-based environmental covariates to more effectively capture genotype- environment interactions. Key agronomic traits, including seed yield, plant height, test weight, and heading date, were evaluated across 33 environments (location-year) using over 3,200 breeding lines from the North Carolina State University small grains breeding program. Multiple genomic prediction models were compared using several cross-validation (CV) schemes representing common breeding scenarios. Across traits, the reaction norm M5 model, which incorporates both GxE and genotype-by-environmental covariate interactions (GxO), achieved the highest prediction accuracy (PA) in CV2 (predicting incomplete field trials) and CV1 for yield and test weight (predicting new lines). The highest PA was observed for test weight under CV2 (0.54) and for yield under CV1 (0.41). Under CV0 (predicting new environments), the M3 model incorporating GxE produced highest PA across traits, with the greatest accuracy for plant height (0.45), although differences among M2, M3, and M4 were small. Prediction under CV00 (predicting new lines in new environments) remained more challenging, with PA values 0.10 - 0.20 across traits. Overall, our results demonstrate that integrating environmental covariates into genomic prediction models can improve predictive performance across diverse wheat-growing environments in North Carolina, supporting their utility for applied breeding efforts. CORE IDEASO_LIIntegrating genotype-by-environment (GxE) interactions with environmental covariates improves prediction accuracy across environments. C_LIO_LIModel performance varies by prediction scenario, with different approaches performing best for new lines, incomplete trials, or new environments. C_LIO_LIPrediction of new lines in new environments remains challenging. C_LI PLAIN LANGUAGE SUMMARYThis study explores how adding environmental information to genomic prediction models can improve prediction accuracy in a public winter wheat breeding program. Using data from multi-environment trials conducted across diverse conditions in North Carolina, we evaluated statistical models that capture how different wheat lines respond to changing environments. By incorporating weather data, we improved the ability to predict performance across locations and years. These findings provide practical insights for refining selection strategies and accelerating genetic gain in wheat breeding.

Phenomic prediction (PP) is a breeding value prediction method using near infrared spectroscopy (NIRS). Spectra pre-processing is a key step in the analysis pipeline of PP and generally involves chemometrics methods. However, there is still little understanding in the genetics community of what pre-processing does and why it increases performances. Consequently, the choice of pre-processing is done either arbitrarily or through a search of the optimal set of methods and associated parameters. In this study, we propose a PCA-based pre-processing method where genetic values of spectra are estimated on a set of principal components instead of individual wavelengths. This way, estimations are based on a few informative and orthogonal features of spectra instead of many correlated, uninformative wavelengths. We tested this new pre-processing method on five data sets representing four plant species (maize, rice, sorghum and grapevine). Results show that it performs as good, or better than the best classical chemometric pre-processing methods in almost all cases. Combining PCA-based and classical chemometric pre-processing methods maximizes predictive ability. Moreover, this pre-processing method opens up possibilities of better understanding and selecting parts of the spectral information that are relevant for the prediction of breeding values. Indeed, components representing together about 1% of spectral variability were found to be responsible for most of PP predictive ability. Plain language summaryCultivated plants are the result of a breeding process during which their genetic values are used to select those to breed. Estimation of breeding values requires heavy experimental means and is time consuming. Phenomic prediction is a low cost and high throughput genetic value estimation method that is increasingly being used. It often uses near infrared spectroscopy measurements as predictors of genetic values that are easy to collect and thus routinely used in many species. However, near infrared spectra generally require pre-processing before being used in prediction. Currently used pre-processing methods arise from the chemometrics community, and still deserve a better in-depth appropriation by geneticists. In this study, we propose a new pre-processing approach that performs as good as or better than the best chemometric pre-processing generally used, reduces computation time, and allows for a better understanding of what parts of spectral information are relevant for prediction. Core IdeasO_LIWorking on principal components of spectra instead of wavelengths increases predictive ability of phenomic prediction and performs as good as or better than classical chemometrics pre-processing C_LIO_LIWorking on principal components of spectra requires less optimization of parameters than chemometrics pre-processing C_LIO_LIAbout 1% of spectral variance is responsible for most of the predictive power of phenomic prediction C_LIO_LIWorking on principal components of spectra pre-processed with classical chemometrics pre-processing can increase predictive ability even more C_LIO_LIPCA-based methods are valuable to optimize predictive ability of phenomic prediction and could be used more widely in the quantitative genetics field C_LI

The production of reactive oxygen species (ROS) in response to cadmium (Cd) has been extensively studied, demonstrating that they play a key role in the plants response to this heavy metal. While the role of enzymes like RBOHs has been thoroughly studied, the function of other ROS-producing enzymes, such as peroxisomal glycolate oxidase (GOX), remains largely overlooked. Peroxisomal GOX is a core metabolic enzyme of the photorespiratory pathway occurring in chloroplasts, mitochondria and peroxisomes. Using Arabidopsis (Arabidopsis thaliana) mutants lacking the main peroxisomal GOX genes, GOX1 (gox1-1) and GOX2 (gox2-1) we explored their function in plant response to Cd. Although photosynthetic capacity appears to be affected to the same extent in both mutants under control and Cd stress conditions, GOX2 seems to play a greater role in ROS production in response to the metal. Transcriptomic analyses on WT and gox2-1 pointed to the mitochondrial electron transport chain (mETC) as a target of Cd stress. We further investigated the individual GOX1 and GOX2 functions in mETC regulation and redox state. Although oxidative ratio of mitochondria was higher in both mutants, it was more pronounced in the absence of GOX1. Furthermore, the mETC is affected in both mutants but the regulation of its components differs in each mutant. These results point out the different functions of the two photorespiratory GOX isoforms in Arabidopsis, leading to a better understanding of the photorespiratory pathway.

The chemical constituents and cytoprotective potential of Cyathea podophylla, a Vietnamese fern, remain poorly investigated. This study aimed to isolate its compounds and evaluate their in vitro cytoprotective activity against 6-hydroxydopamine (6-OHDA)-induced toxicity in F11 cells. Compounds were chromatographically isolated and structurally characterized using NMR and HR-ESI-MS. Seven compounds were identified: five phenolics (trans-cinnamic acid, (E)-4-(3,4-dihydroxyphenyl)but-3-en-2-one, p-coumaric acid, 3,4-dihydroxybenzoic acid, 4-O-acetyl-caffeic acid), 5-hydroxymethylfurfural, and butyl-{beta}-D-fructofuranoside. Six of these are newly reported for the Cyathea genus. In MTT assays, butyl-{beta}-D-fructofuranoside exhibited the strongest cytoprotective effect (69.6% cell protection at 10 {micro}M, p < 0.001), followed by (E)-4-(3,4-dihydroxyphenyl)but-3-en-2-one (39.2% at 10 {micro}M). The remaining compounds lacked significant activity. These findings expand the phytochemical profile of Cyathea podophylla and provide preliminary evidence of its cytoprotective properties against 6-OHDA-induced injury, warranting further mechanistic and in vivo validation.