Context. Tomato productivity is increasingly threatened by viral pathogens, particularly tomato brown rugose fruit virus (ToBRFV) and tomato spotted wilt virus (TSWV), which overcome classical resistance genes and cause severe yield losses. Aims. This study evaluated the leaf-level tolerance responses of 10 commercially grown tomato cultivars from the Czech Republic to ToBRFV and TSWV under controlled greenhouse conditions, on the basis of leaf symptom expression and virus accumulation in leaves. Methods. Viral accumulation in leaf tissue was quantified by reverse-transcription quantitative polymerase chain reaction. Cultivars were screened for resistance-associated markers (Tm-1, Tm-2 and Tm-22 for ToBRFV; Sw5, Sw5b and Sw7 for TSWV). Defence responses were assessed by expression analysis of PAL, HQT, NBR1a, WRKY33a, PR-1 and ATG7 at 21 days post-inoculation. Results. Viral titres varied markedly among cultivars. Stupick & eacute; poln & iacute; ran & eacute; and Vilma consistently exhibited reduced ToBRFV accumulation (2.3-4.2 & times; 10(6) copies), whereas Gallant and Odat showed high ToBRFV accumulation in leaves (>2.0 & times; 10(7) copies). For TSWV, Stupick & eacute; poln & iacute; ran & eacute; exhibited a more tolerant leaf response, associated with the presence of the Sw7 allele, whereas Start, Gallant and Perun showed the least favourable leaf-level responses. None of the cultivars carried classical Tm resistance alleles for ToBRFV. Reduced viral accumulation was associated with enhanced induction of phenylpropanoid (PAL, HQT) and immune-related (NBR1a, WRKY33a, PR-1 and ATG7) genes. Conclusions. Stupick & eacute; poln & iacute; ran & eacute; and Vilma displayed the highest leaf-level tolerance (reduced symptoms and/or reduced leaf virus accumulation). Cultivar responses and defence activation varied widely among commercial cultivars. Implications. These results support molecularly informed breeding strategies, focusing on gene stacking and novel resistance sources to achieve durable, improved outcomes against both viruses.
Context Harvest weed seed control (HWSC) targets weed seeds as they pass through the harvester. Seed impact mills are aftermarket modifications that are mounted onto the back of the harvester and are one way to implement HWSC. These mills process the chaff fraction as it exits the harvester and kills the weed seeds therein. Aims Two experiments were conducted to evaluate the integrated Harrington Seed Destructor (iHSD) in soybean and the Redekop Seed Control Unit (SCU) in soybean and wheat. The purpose was to determine weed seed kill by the mill (weed seed kill) and seed loss in the straw (weed seed fate) during commercial harvest. Methods During harvest, a known amount of weed seeds was fed into the harvester front or directly into the mill and captured in the chaff or straw of harvester outputs. Six weed species were tested in soybean with the iHSD, and three species each were tested in soybean and wheat with the SCU. Key results The mills delivered >98% seed kill on all tested species during soybean harvest. In wheat, seed kill was >91% for Italian ryegrass and >99% for the other two species tested. During soybean and wheat harvest, <7% of seeds escaped HWSC in the straw fraction.Conclusions These data indicate that both mills generate high seed kill during grain crop harvest and very few seeds escape HWSC in the straw fraction. Implications Seed impact mills could be used during harvest to reduce the return of problematic species to the soil seedbank.
Context Dryland cropping systems in temperate subhumid and semi-arid regions confront increasing climate variability that threatens yield stability and long-term productivity. Optimizing crop choice and management requires understanding species-specific responses to environmental stress.Aims This study analyzed grain yield (GY) determination in four crop species (maize, sorghum, soybean, and peanut) that compete for land in temperate regions with contrasting water supply.Methods Field experiments were conducted over two seasons at three sites representing humid, subhumid, and semiarid conditions in Central Argentina, a major producing area of these crops. Early and late sowings were also evaluated. GY, shoot biomass, harvest index (HI), grain number (GN), and individual grain weight (GW) were measured and related to environmental variables.Key results GY declined with increasing aridity, higher vapor pressure deficit (VPD), and reduced water supply (WS). Biomass production was the primary GY driver, except in peanut, where HI was also critical. GN was strongly associated with crop growth rate and VPD during the critical period. GW contributed to GY variation in maize and soybean but not in sorghum and peanut. Sorghum exhibited resilience to elevated VPD and low WS, whereas peanut was sensitive to extreme aridity during the critical period and excess water or low temperatures during grain filling.Conclusions Species differed in yield formation strategies under stress. Sorghum was most resilient against dry conditions.Implications These findings emphasize the need for specific crop choice and management strategies aligned with local water regimes and evaporative demand to enhance yield stability in dryland systems.
Context Accurate prediction of phenological and morphological traits in alfalfa (Medicago sativa L.) is essential to improve yield performance and increase selection efficiency in breeding programs.Aims To identify morpho-phenological traits driving total dry matter yield in local lucerne genotypes and to evaluate the predictive ability of multiple machine-learning (ML) algorithms.Methods Field data were collected over 2 years with three replicates per trait (n = 360 observations). Correlations between yield and traits were assessed using Pearson's correlation coefficient, r. Four ML algorithms - random forest (RF), elastic net regression (ENet), extreme gradient boosting (XGBoost), and support vector regression (SVR), were trained to predict yield; performance was compared using coefficient of determination (R2) and root mean square error (RMSE). Trait importance was examined across models.Key results Yield was strongly positively associated with winter dormancy, post-harvest regrowth rate and plant resistance (r approximate to 0.67-0.68). RF achieved the best predictive performance (R2 = 0.61; RMSE = 26.49). Five traits (winter dormancy, post-harvest regrowth, resistance, plant form and root-crown bud number) consistently ranked as the most influential predictors.Conclusions RF best captured the partially non-linear, interaction-driven yield structure, and pinpointed a coherent set of morpho-phenological predictors aligned with classical correlation outcomes.Implications Integrating morpho-phenological traits with ML substantially improves yield predictability and provides a practical, data-driven framework to prioritise traits (e.g. winter dormancy, regrowth, resistance, plant form, root-crown buds) for selecting high-yielding, stress-resilient lucerne genotypes.
Context Pearl millet, a climate-resilient, nutrient-rich cereal, faces productivity challenges from environmental variability, necessitating identification of stable, high-yielding genotypes.Aims This study aimed to identify stable, high-yielding pearl millet to achieve increased grain yield per hectare (GYPH), spike length (SL), spike girth (SG), and thousand-seed weight (TSW) from a global germplasm collection of 248 pearl millet genotypes.Methods Field trials were conducted in an alpha lattice design with three replications across three environments. Genotype stability and performance were assessed using additive main effect and multiplicative interaction (AMMI) and genotype main effect plus genotype-by-environment interaction (GGE) biplot analyses. Stability indices (AMMI-based stability parameter (ASTAB), AMMI stability index (ASI), AMMI stability value (ASV), modified AMMI stability index (MASI), and modified AMMI stability value (MASV)) were integrated to quantitatively assess the stable genotypes.Key results The first two AMMI principal components explained 55% and 45% of the total variation for GYPH, whereas GGE biplots explained 84.99%, 88.89%, 77.7%, and 83.17% for GYPH, SL, SG, and TSW respectively. AMMI identified G57, G101, and G209 as stable for GYPH, whereas GGE selected G87, G242, G246, and G131. The genotype selection index (GSI) highlighted G87, G209, G242, G143, and G172 as highly stable for GYPH. Stable genotypes were also identified for SL (G57, G246, G140), SG (G218, G157, G8), and TSW (G57, G95, G149).Conclusions The integrated assessment of stability using multiple approaches was shown to be effective in identifying stable genotypes across diverse environments. Notably, G87 emerged as the most stable genotype for GYPH.Implications Integrating stable genotypes into breeding programs could enhance yield stability, disease resistance, and grain quality, ensuring adaptability across environments.
