Lentil wilt root rot complex remains a major lentil productivity globally including lentil growing of central parts of highlands of Ethiopia. A single control method has been evaluated, while integrated control measures under field conditions were limited. This study evaluated the effectiveness of integrated management in reducing wilt root rot disease incidence and severity and improving grain yield. The objective was to develop effective integrated management strategies for the lentil wilt root rot complex using host resistance, seed treatment, and mustard cake biofumigation. Field experiments were conducted at Bishoftu and Chefe Donsa in 2023 and 2024 cropping seasons under natural disease conditions. The treatment consisted of three lentil varieties, three fungicides seed treatment at recommended rate and Pseudomonas fluorescens bioagent, soil incorporated biofumigation. The total of 18 factorial treatments combinations in randomized complete block design with three replications were used. Disease progress rate was modeled using monomolecular growth, selected based on its biological suitability for the monocyclic nature of lentil wilt and its superior fit to the data. Analysis of variance revealed that a variety × seed treatment fungicides interaction significantly different at (P < 0.05) on affected the disease incidence, severity, AUDPC and seed yield at Bishoftu and Chefe Donsa. A significant (P < 0.05) variety × soil incorporated biofumigation interaction was observed for disease incidence, severity, and AUDPC, with Furi plus mustard cake biofumigation showing the greatest disease suppression. The application of mustard cake at 2 t ha−1 resulted in grain yields of 2.21 and 2.74 t ha−1 for Furi, and 1.71 and 2.10 t ha−1 for Derso, at Bishoftu and Chefe Donsa, respectively. The highest grain yields were therefore recorded for Furi (2.74 t ha−1) and Derso (2.10 t ha−1) at Chefe Donsa. Moreover, the benefit cost ratios up to 10.5, boosting economic returns as well as indicating both yield and economic advantages. The results demonstrated that integrating resistant lentil varieties, seed treatment fungicides (proceed plus 63
The present study conducted during 2022–2023, evaluated the efficacy of the biocontrol agents Photorhabdus luminescens, and Trichoderma harzianum, applied alone and in combination with the nematicide Fluopyram, against the root-knot nematode Meloidogyne incognita infecting tomato. Bioassay experiments under laboratory conditions and a pot experiments in polyhouse were conducted using nematode-inoculated Petri plate’s and tomato plants to assess the effects of individual and combined treatments on nematode suppression, plant growth, and disease severity. A prominent progress was made in managing M. incognita and strong nematicidal properties of Fluopyram were demonstrated in laboratory testing, wherein 86.40
Anthracnose symptoms were observed on Nandina domestica in ornamental plant nurseries and landscape areas of Sakarya Province, Türkiye, in June 2025, affecting 10–15
Cluster bean (Cyamopsis tetragonoloba) is a globally significant crop, known for its culinary, industrial, and health benefits. Early and precise identification of foliar diseases in cluster beans is critical for reducing crop losses. Recently, deep learning techniques have revolutionized crop disease detection, offering more efficient and accurate alternatives to traditional methods. Despite the availability of various public datasets for many other crops, there is a scarcity of specialized datasets for cluster bean crop. To address this gap, the presented study introduces a new dataset, named ClusterBean-FD, comprising 9,249 real-world field-condition images of cluster bean plants across 13 foliar disease classes from the Gwalior-Chambal region of Madhya Pradesh, India. This is introduced as the first publicly available dataset specifically designed for the detection and classification of foliar diseases in cluster bean, a crop of considerable industrial and nutritional importance due to its role in guar gum production. To enhance the robustness of disease detection models, the dataset includes images capturing various environmental conditions, such as different lighting, angles, and stages of leaf damage. This dataset uniquely contributes to the field and paves the way for more accurate and reliable disease prediction models. To assess the efficacy and applicability of the curated dataset, meta-analyses for multiple deep learning models based on CNNs architecture were performed. The analysis demonstrated that, among the evaluated CNN models, EfficientNet-B0 achieved the best performance due to its efficient multi-scale feature extraction and a good trade-off between computation and accuracy.
Fusarium ear rot, primarily associated with Fusarium verticillioides, and the resulting fumonisin contamination represent major sanitary constraints to maize (Zea mays L.) production worldwide. Previous evidence from a single growing season under Argentine conditions indicated that late planting was associated with increased culturable F. verticillioides strains and fumonisin content in maize grains. Here, that previously characterized growing season was incorporated as a comparative baseline, together with subsequent growing seasons to evaluate whether these planting-date-associated patterns persisted across a broader multi-season framework (2020–2024). F. verticillioides strains in grains, fumonisin contamination, grain yield, and nutritional composition were assessed under contrasting planting dates (early vs. late). Across growing seasons, late plantings generally exhibited higher F. verticillioides strain counts and fumonisin content, together with lower yields than early plantings. However, the magnitude of these differences and the relationship between F. verticillioides strain counts and fumonisin accumulation varied among growing seasons. Grain nutritional composition remained comparatively stable across planting dates and seasons. Overall, these multi-season observations identify late-planted maize as a production scenario associated with greater sanitary risk and lower productivity under the evaluated field conditions, with planting dates also exposing crops to contrasting weather conditions across reproductive stages.
