Entomopathogenic nematodes for the control of oriental fruit fly Bacterocera dorsalis (Diptera: Tephritidae)

Background: Fruit fly species are most damaging pests around the globe which reduced the commercial value of fruits at maturity. Entomopathogenic nematodes (EPNs) from genera Heterorhabditis and Steinernema cause death by inducing septicimia in insect pests in the soil, moreover, endemic nearly all soils. Current study was planned to manage the oriental fruit fly, Bacterocera dorsalis (Hendel.) (Diptera: Tephritidae) hazards by using indigenous EPNs (Heterorhabditis bacteriophora, H. indica, Steinernema asiticum, S. corpocapsae and S. glasseri) as they have host finding ability and recognise as potential eco-friendly biocontrol agent over synthetic chemicals. Methods: Study for the assessment of EPNs concentrations, various temperatures, soil type and soil moisture levels against mortality (%) of fruit fly (B. dorsalis) larvae was conducted in completely randomized design (CRD) under factorial arrangements. Results: EPNs concentrations (70 IJs/ml, 110 IJs/ml, 150 IJs/ml) showed that S. asiaticum (150 IJs/ml) showed maximum mortality (94.97 %) of B. dorsalis as compared to other used EPNs along with their respective concentrations. Impact of various temperatures (20 °C, 24 °C, 28 °C, 32 °C, 36 °C) with respect to used EPNs exhibited that H. indica (36 °C) showed maximum mortality (94.33 %) of B. dorsalis as compared to all other treatment. Various soil types (Sandy, Sandy loam, Loam, Clay) impacted the infectivity of EPNs against fruit fly, In case of sandy loam soil, S. asiaticum showed maximum mortality (98.05 %) of B. dorsalis followed by all used treatments. Soil moisture level (12 %, 18 %, 24 %, 30 %) also significantly influenced the infectivity of EPNs against mortality of fruit fly. In case of 12% moisture level, S. asiaticum showed maximum mortality (99.06 %) of B. dorsalis over all the applied treatments along with respective moisture levels. Conclusion: In crux, Steinernema asiaticum higher concentration exhibited efficient control of fruit fly larvae in sandy loam soil with 12 % moisture level at 36 °C over the used EPN species. While application of EPNs against fruit fly is most suitable strategy to manage the fruit fly hazards and it should be included as a part of integrated pest management control programme

sGenotypes, Epidemiological Variables and Fungicides Application Associated With Wheat Leaf Rust Development and Grain Yield

The present study was carried out at the Plant Pathology Hafizabad Research Station, the University of Layyah, during the crop seasons 2021–2022 and 2022–2023 to evaluate the response of various wheat genotypes against leaf rust severity (%), environmental conditions favourable for disease development and grain yield. Except for minimum temperature and minimum relative humidity, which had a negative association with disease development, there was a significant correlation between leaf rust severity (%) and all environmental conditions such as maximum temperature, maximum relative humidity, rainfall, and wind speed. All epidemiological variables such as maximum temperature, minimum temperature, minimum relative humidity, rainfall and wind speed significantly affect the disease progression. The disease predictive model accounted for 48-69% variability in leaf rust severity. The model performance was evaluated using the coefficient of determination (R2 = 0.69) and RMSE, both demonstrated acceptable predictive results for leaf rust severity (%) management. Leaf rust severity (%) increased with an increase in maximum temperature (17.8 to 30 °C), maximum relative humidity (76.3 to 85%), rainfall (2.2 to 10.85 mm) and wind speed 1.1-2.7 km/h and decreased with the increase of minimum temperature (7.91 to 16.71 °C) minimum relative humidity (47.15 to 56.45%) during both rating seasons 2021–2022 and 2022–2023. The single and two applications of fungicides at the Zadok's scale 3, ZS 4.3, and ZS 5.4 stages led to a significant reduction in grain yield losses caused by leaf rust severity (%) in both the 2021-2022 and 2022-2023 crop seasons. Single and two sprays of prothioconazole, were found to be the first choice among all treatments to reduce the disease severity and increase grain production and maximum gross revenue (513.1-777.8$/ha), as compared to followed by single and two sprays of propiconazole (Progress), tebuconazole + trifloxystrobin, tebuconazole, bixafen + tebuconazole, and propiconazole (Tilt), respectively. These findings recommend the involvement of genotype resistance and weather predictors in wheat leaf rust development, along with fungicide application studies, to improve the predictability of host resistance to disease, future models, and the sustainability of disease control methods

sGenotypes, Epidemiological Variables and Fungicides Application Associated With Wheat Leaf Rust Development and Grain Yield

The present study was carried out at the Plant Pathology Hafizabad Research Station, the University of Layyah, during the crop seasons 2021–2022 and 2022–2023 to evaluate the response of various wheat genotypes against leaf rust severity (%), environmental conditions favourable for disease development and grain yield. Except for minimum temperature and minimum relative humidity, which had a negative association with disease development, there was a significant correlation between leaf rust severity (%) and all environmental conditions such as maximum temperature, maximum relative humidity, rainfall, and wind speed. All epidemiological variables such as maximum temperature, minimum temperature, minimum relative humidity, rainfall and wind speed significantly affect the disease progression. The disease predictive model accounted for 48-69% variability in leaf rust severity. The model performance was evaluated using the coefficient of determination (R2 = 0.69) and RMSE, both demonstrated acceptable predictive results for leaf rust severity (%) management. Leaf rust severity (%) increased with an increase in maximum temperature (17.8 to 30 °C), maximum relative humidity (76.3 to 85%), rainfall (2.2 to 10.85 mm) and wind speed 1.1-2.7 km/h and decreased with the increase of minimum temperature (7.91 to 16.71 °C) minimum relative humidity (47.15 to 56.45%) during both rating seasons 2021–2022 and 2022–2023. The single and two applications of fungicides at the Zadok's scale 3, ZS 4.3, and ZS 5.4 stages led to a significant reduction in grain yield losses caused by leaf rust severity (%) in both the 2021-2022 and 2022-2023 crop seasons. Single and two sprays of prothioconazole, were found to be the first choice among all treatments to reduce the disease severity and increase grain production and maximum gross revenue (513.1-777.8$/ha), as compared to followed by single and two sprays of propiconazole (Progress), tebuconazole + trifloxystrobin, tebuconazole, bixafen + tebuconazole, and propiconazole (Tilt), respectively. These findings recommend the involvement of genotype resistance and weather predictors in wheat leaf rust development, along with fungicide application studies, to improve the predictability of host resistance to disease, future models, and the sustainability of disease control methods

Stepwise Regression Models-Based Prediction for Leaf Rust Severity and Yield Loss in Wheat

Leaf rust is a devastating disease in wheat crop. The disease forecasting models can facilitate the economic and effective use of fungicides and assist in limiting crop yield losses. In this study, six wheat cultivars were screened against leaf rust at two locations, during three consecutive growing seasons. Subsequently, the stepwise regression analysis was employed to analyze the correlation of six epidemiological variables (minimum temperature, maximum temperature, minimum relative humidity, maximum relative humidity, rainfall and wind speed) with disease severity and yield loss (%). Disease predictive models were developed for each cultivar for final leaf rust severity and yield loss prediction. Principally, all epidemiological variables indicated a positive association with leaf rust severity and yield loss (%) except minimum relative humidity. The effectiveness of disease predictive models was estimated using coefficient of determination (R2) values for all models. Then, these predictive models were validated to forecast disease severity and yield loss at another location in Faisalabad. The R2 values of all disease predictive models for each of the tested cultivars were high, evincing that our regression models could be effectively employed to predict leaf rust disease severity and anticipated yield loss. The validation results explained 99% variability, suggesting a highly accurate prediction of the two variables (leaf rust severity and yield loss). The models developed in this research can be used by wheat farmers to forecast disease epidemics and to make disease management decisions accordingly

Stepwise Regression Models-Based Prediction for Leaf Rust Severity and Yield Loss in Wheat

Leaf rust is a devastating disease in wheat crop. The disease forecasting models can facilitate the economic and effective use of fungicides and assist in limiting crop yield losses. In this study, six wheat cultivars were screened against leaf rust at two locations, during three consecutive growing seasons. Subsequently, the stepwise regression analysis was employed to analyze the correlation of six epidemiological variables (minimum temperature, maximum temperature, minimum relative humidity, maximum relative humidity, rainfall and wind speed) with disease severity and yield loss (%). Disease predictive models were developed for each cultivar for final leaf rust severity and yield loss prediction. Principally, all epidemiological variables indicated a positive association with leaf rust severity and yield loss (%) except minimum relative humidity. The effectiveness of disease predictive models was estimated using coefficient of determination (R2) values for all models. Then, these predictive models were validated to forecast disease severity and yield loss at another location in Faisalabad. The R2 values of all disease predictive models for each of the tested cultivars were high, evincing that our regression models could be effectively employed to predict leaf rust disease severity and anticipated yield loss. The validation results explained 99% variability, suggesting a highly accurate prediction of the two variables (leaf rust severity and yield loss). The models developed in this research can be used by wheat farmers to forecast disease epidemics and to make disease management decisions accordingly