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.

Soil salinity and sodicity are among the major challenges threatening agricultural productivity in the Central Rift Valley of Ethiopia. A column experiment was conducted in laboratory on saline-sodic soils of Metehara Sugar Estate to evaluate the effectiveness of phosphogypsum and leaching in reclaiming these soils. The treatments comprised of five rates of phosphogypsum equivalent to 50, 75, 100, 150, and 200% gypsum requirement, 100% gypsum requirement of natural gypsum, and an absolute control with no amendments applied, and five volumes of leaching water. The treatments were arranged in Complete Randomized Design with three replications. The leaching water was applied to the columns in an intermittent ponding mode. Leachates and soil samples collected from the columns after termination of the leaching process were analyzed for selected soil properties. Results showed that applying phosphogypsum at a rate of 100% gypsum requirement or higher (which is equivalent to [&ge;] 13 tons/ha) along with 3-4 pore volume of leaching water was found to be the most effective combination to reduce salinity and sodicity to levels that are suitable for most crops (ECe <4 dS/m and ESP < 10%,). The efficiency of phosphogypsum equivalent to 200% gypsum requirement was 81% and 75% in soluble salt removal and Na reduction, respectively. Results of the study suggest that phosphogypsum is a promising reclamation material for saline-sodic soils. However, a field experiment has to be conducted to evaluate the effectiveness of these amendments under natural conditions and come-up with implementable rate recommendations.

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

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.

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.

Global demand for pseudocereals, including buckwheat, has surged in recent years due to their higher nutritional and pharmaceutical value than cereals and also due to them being a climate-resilient, gluten-free, and potential crop for combating cancer, type ll diabetes, and overcoming micronutrients hidden hunger problems that lack in cereals. Major efforts are needed to make its cultivation more popular by improving its quantitative and qualitative traits through crop genetics by adopting modern genetic, molecular, and mutational approaches, which also necessitate the induction of genetic variation for better yielding and improved varieties. In this experimental study, the induced mutant populations of widely recommended VL-7 and PRB-1 varieties of buckwheat were generated using different concentrations treatments of ethyl methane sulfonate (EMS). Investigation on induced phenotypical and genotypical variations in individual plants of M1 population of different treatments resulted in morphological and cytological mutant types affecting plant germination, survival, height and morphology, leaf morphology, flower morphology, growth period, chlorophyll and pigments abnormalities in leaves, leaf growth pattern, plant fertility, yield, and cytological aberrations. This experiment showed that plant survival decreased with the concentration of the mutagen doses. The lower doses resulted in dwarf varieties suitable for cultivation as they increased yield by having higher breaking force and lower lodging index over the tall plants. Studies on various quantitative parameters revealed the general effectiveness of intermediate doses and stimulatory effectiveness of lower and higher concentrations in M1 generation.

BackgroundControlled-environment cultivation of medicinal cannabis (Cannabis sativa L.) typically optimizes light conditions to enhance the biosynthesis of pharmaceutically important metabolites like cannabinoids. Such experimental strategies may also influence other specialized metabolites like terpenoids, flavonoids, alkaloids, among others. Previous untargeted metabolomics studies testing short wavelength conditions like UV and blue light have shown that terpenoids and prenylated flavonoids in cannabis leaves respond differentially. However, since metabolomic studies in cannabis have so far mostly focused on floral cannabinoids, a comprehensive untargeted study into cannabis floral metabolome response to short wavelengths is currently lacking. ObjectivesOur study investigates the impact of short wavelength usage on cannabis specialized metabolism, and in particular the influence of UVB, UVA, and blue light on the cannabis floral flavonoid metabolome and associated glycosylation moieties. MethodsCannabis plants were grown under a white background light and exposed to supplemental UVB, UVA, or blue light during the generative phase of the cultivation cycle. Treatments were compared to a reference white background light without UV or blue light. Metabolites from floral tissue were extracted and analyzed via ultra-performance liquid chromatography-tandem mass spectrometry. A comparative metabolomics workflow was designed and used to characterize the floral flavonoid metabolome and associated glycosylation moieties. ResultsOur results demonstrate how short wavelengths differentially affect the metabolism of natural product compound classes including polyketides and phenylpropanoids/shikimates. Blue light induced flavonoids similarly to how UVB did, while both UVA and blue light specifically induced flavanones accumulation. UVB showed the strongest regulatory effect on flavonoids production and glycosylation patterns. ConclusionsUVB reshapes the cannabis floral flavonoid metabolome by selectively stimulating the accumulation and structural modification of flavonoids. Therefore, UVB application in cannabis cultivation represents a useful horticultural strategy to increase inflorescence medicinal quality without affecting cannabinoid levels.

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.

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.

Breeding disease-resistant varieties (DRV) is a central strategy for reducing reliance on phytosanitary products. However, the successful deployment and long-term durability of these cultivars rely on acquiring field data across diverse production conditions, a step that remains frequently neglected, especially in perennial crops. Since 2018, the OSCAR observatory, a network of vineyard plots planted in France with varieties resistant to downy and powdery mildew, the two major pathogens of grapevine, has aimed to close this gap for viticulture. The observatory comprises over 199 commercial plots, covering 127 hectares across diverse agroclimatic conditions, all managed by winegrowers under their own production practices. The observatory currently monitors 30 disease-resistant grapevine varieties, tracking both their agronomic performance and the dynamics of key pathogens. Since 2018, while phytosanitary treatments have been reduced by an average of 79% compared to conventional plots, the incidence of downy and powdery mildew, remain low, even in years highly conducive to these diseases. However, the long-term survey also highlights the decline in efficacy of some resistances to downy mildew and the emergence of black rot, a disease effectively controlled by conventional phytosanitary programs. Beyond acting as a rapid warning system for resistance breakdown, the observatory promotes sustainable disease management in viticulture. It provides valuable insights to winegrowers on effective DRV management. It also delivers actionable feedback to breeders to guide more durable DRV breeding strategies. Highlights- OSCAR observatory monitors 199 plots of grapevine disease resistant varieties (DRV) - Grapevine DRV cuts fungicide uses by 79% while maintaining good disease control - Some resistances efficacy declines against downy mildew, but not powdery mildew - Black rot, a disease usually controlled by fungicide, is rising in OSCAR plots OSCAR provides useful feedback to breeders and winegrowers on DRV management

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.

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.

Russian wheat aphid (Diuraphis noxia Homoptera; Aphididae) is a major pest that significantly reduces chlorophyll content and photosynthetic capacity in wheat (Triticum aestivum L.), leading to substantial crop yield losses. Jasmonic acid (JA) is a plant signaling molecule known to activate defense mechanisms against herbivorous insects. This study examined the effectiveness of exogenous jasmonic acid application in maintaining chlorophyll content during Russian wheat aphid infestation. A pot experiment was conducted with four treatments: control (no treatment), aphid infestation only, jasmonic acid application only, and jasmonic acid with aphid infestation. Results demonstrated that aphid infestation significantly reduced chlorophyll a (F = 42.565, P = 0.0001), chlorophyll b (F = 52.565, P = 0.0001), and total chlorophyll (F = 32.565, P = 0.0002) contents compared to healthy plants. Jasmonic acid treatment at 2 mM concentration effectively preserved all forms of chlorophyll, significantly counteracting aphid-induced chlorophyll depletion (P < 0.01). The protective effect of jasmonic acid was evident through the statistically significant interaction between aphid stress and JA application for all chlorophyll parameters. These findings suggest that foliar application of jasmonic acid can serve as an effective strategy to maintain photosynthetic capacity and plant vigor under Russian wheat aphid attack, thereby contributing to sustainable crop management and improved wheat production.

Giant Miscanthus giganteus (Mxg) is one of the most promising perennial crops to generate biomass feedstock for bioenergy and biobased products. It is derived from the natural inter-species hybridization of Miscanthus sacchariflorus (Msa) and Miscanthus sinensis (Msi) species, thus population improvement within these species is crucial. Genomic selection (GS) is an attractive option to accelerate breeding of perennial grasses, such as Miscanthus, which requires up to three years of evaluation to produce reliable phenotypic data. Hence, genotypes are observed in multiple years and locations causing inconsistent response patterns from one year to the next, between location, and/or location-by-year combinations. These inconsistencies are known as the genotype-by-environment interaction effect (GxE). Although GS has been successfully implemented in multiple annual crops where straightforward cross-validation schemes exist to assess the levels of predictive ability that can be reached, for perennial crops new cross-validation schemes will help avoid data contamination. Here, we propose a series of cross-validation schemes to evaluate model performance for perennial crops. We perform a case study by analyzing one panel of each species (516 genotypes of Msa, 280 genotypes of Msi) scored for biomass yield at different locations around the world over several years. The results of the different cross-validation schemes provide insights about the usefulness of GS to accelerate the breeding process of Miscanthus species. In addition, leveraging the GxE effects of different types significantly increases predictive ability (up to 10% in Msa and 30% for Msi) compared to the conventional approaches based on main effects only.

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.

Common bean (Phaseolus vulgaris L.) is the leading grain legume consumed directly by humans and a primary source of nutrients in many communities. This study utilized common bean genotypes with diverse seed coat phenotypes to investigate genotypic and environmental effects on pigmented seed coat area and seed macronutrient (protein, starch, fat, ash, moisture), anti-nutrient (phytate), and mineral nutrient (iron, zinc, calcium, phosphorus, magnesium, potassium, sodium) profiles. Recombinant inbred lines (RILs) that comprise six phenotypic classes for seed coat patterning and nine commercial cultivars were field-evaluated for multiple years across inland, coastal, and intermountain environments in California. A custom near-infrared spectroscopy calibration improved macronutrient prediction accuracy relative to a pre-existing calibration. Environmental effects on macronutrients were pronounced; the 2022 coastal growing environment was the most distinct, characterized by significantly higher starch and moisture content and significantly lower protein content in the RILs relative to any other environments. Across growing years in the RILs, greater consistency was observed at the inland site, where only protein was significantly different; all macronutrient traits significantly differed within the intermountain site. Certain commercial cultivars largely maintained their relative rank for protein content across environments, indicating consistency of genotypic performance, and Black Nightfall ranked among the highest for iron, zinc, phosphorus, and magnesium. Percent pigmented seed coat area was significantly negatively correlated with both calcium and magnesium concentrations. These results underscore the importance of genotype-by-environment field trials for seed coat patterning, seed nutritional composition, and their interplay, to support breeding of common bean among other grain legumes. HighlightsO_LICustom near-infrared spectroscopy (NIRS) calibration improved prediction accuracies C_LIO_LIEnvironmental effects significantly influenced common bean macronutrient composition C_LIO_LICertain cultivars ranked consistently for macronutrient traits across environments C_LIO_LISeed coat pattern was significantly associated with mineral nutrient concentrations C_LI