In vitro and in vivo evaluation of different measures to control Ascochyta blight in chickpea

Ascochyta blight, an infection caused by Ascochyta rabiei is a destructive disease in many chickpea growing regions and it caused significant yield losses. To minimize the impact of Ascochyta blight, 5 fungicides viz., Aliette, Cabrio Top, Thiovit Jet, Cymoxanil and Difenoconazole, 5 plants extracts namely Azadirachta indica, Azadirachta azedarach, Datura stramonium, Chenopodium album and Allium sativum L. and two strains T-22 and E58 of bio-control agents (BCAs) Trichoderma viride and Aspergillus flavus were evaluated on the growth of A. rabiei under in vitro conditions by using the food poison technique. The colony growth of Ascochyta rabiei was inhibited at all concentrations of fungicides @ 0.07, 0.15, 0.21%, plants extracts @ 4, 6, 9% and bio-control agents @ 105, 106 and 107 conidia ml-1 respectively. Among all applied treatments, maximum inhibition colony growth of pathogen was recorded in the case of Aliette (83.4%), followed by Cabrio Top (74.3%), Azadirachta indica (50.3%) and Trichoderma viride (60.3%) at their high concentrations. Field trials showed that Aliette and Cabario Top significantly reduced the disease severity to 10 % and 24% respectively, followed by Azadirachta indica and Allium sativum which reduced the disease severity to 40% and 50% respectively. Bio-control agent Trichoderma viride proved less effective in controlling Ascochyta bight severity under field conditions. The present study showed that systemic and sulphur containing fungicides, plant extracts and bio-control agents (BCAs) have the potential to control Ascochyta blight in both in vitro and in vivo conditions

Temperature-dependent effects on some biological aspects of two ectoparasitoids of Phyllocnistis citrella (Lepidoptera: Gracillariidae)

Abstract Background Temperature alters host suitability for the development of parasitoids through direct (thermal effect) and indirect (parental effect) pathways. The effects of three temperature regimes on the development and survival of two parasitoid species, Citrostichus phyllocnistoides (Narayanan) and Cirrospilus ingenuus Gahan (Eulophidae: Hymenoptera) of the citrus leafminer, Phyllocnistis citrella Stainton (Lepidoptera: Gracillariidae) was evaluated. The experiment was conducted at 20, 25, and 30°C temperatures with 65 ± 2% relative humidity (R.H.) and 16h: 8h (L: D) photoperiod. Results In C. phyllocnistoides, the pre-ovipositional period was longer at 20°C, while non- significant difference was observed in the pre-ovipositional period of C. ingenuus under the effect of different temperatures (P > 0.05). The ovipositional period of C. phyllocnistoides and C. ingenuus was higher at 20°C and gradually decreased by increasing the temperature. Non- significant (P > 0.05) difference was found in post-ovipositional period of both parasitoid species. Both species exhibited the maximum fecundity at 25°C, while, the minimum fecundity was recorded at 30°C. However, the adult longevity of both parasitoid species was highest at 20°C and gradually decreased by increasing the temperature. In both parasitoids, the parasitism rate was highest at 25°C. Conclusion This study highlighted the importance of thermal effects on some parasitoid species of insect pests to predict the future of trophic dynamics in global warming situations

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

Regression Modeling Strategies to Predict and Manage Potato Leaf Roll Virus Disease Incidence and Its Vector

The potato leaf roll virus (PLRV) disease is a serious threat to successful potato production and is mainly controlled by integrated disease management; however, the use of chemicals is excessive and non-judicious, and it could be rationalized using a predictive model based on meteorological variables. The goal of the present investigation was to develop a disease predictive model based on environmental responses viz. minimum and maximum temperature, rainfall and relative humidity. The relationship between epidemiological variables and PLRV disease incidence was determined by correlation analysis, and a stepwise multiple regression was used to develop a model. For this purpose, five years (2010–2015) of data regarding disease incidence and epidemiological variables collected from the Plant Virology Section Ayub Agriculture Research Institute (AARI) Faisalabad were used. The model exhibited 94% variability in disease development. The predictions of the model were evaluated based on two statistical indices, residual (%) and root mean square error (RMSE), which were ≤±20, indicating that the model was able to predict disease development. The model was validated by a two-year (2015–2017) data set of epidemiological variables and disease incidence collected in Faisalabad, Pakistan. The homogeneity of the regression equations of the two models, five years (Y = −47.61 − 0.572x1 + 0.218x2 + 3.78x3 + 1.073x4) and two years (Y = −28.93 − 0.148x1 + 0.510x2 + 0.83x3 + 0.569x4), demonstrated that they validated each other. Scatter plots indicated that minimum temperature (5–18.5 °C), maximum temperature (19.1–34.4 °C), rainfall (3–5 mm) and relative humidity (35–85%) contributed significantly to disease development. The foliar application of salicylic acid alone and in combination with other treatments significantly reduced the PLRV disease incidence and its vector population over control. The salicylic acid together with acetamiprid proved the most effective treatment against PLRV disease incidence and its vector M. persicae

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

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

Predicting Stripe Rust Severity in Wheat Using Meteorological Data with Environmental Response Modeling

Objective: The main objective of current investigation was to develop a predictive disease model based upon meteorological data, viz., maximum temperature, minimum temperature, rainfall, relative humidity, and wind speed to predict stripe rust severity (%). Methods: Five years' data of stripe rust severity on three wheat varieties, namely SA-42, Sandal-73, and Barani-70, continuously cultivated for five years (2013–2017), were collected from experimental trials of Deputy Director of Agriculture Extension Layyah to develop a predictive disease model. For validation of the model, a research trial was conducted in the Research Area of the Department of Plant Pathology, Bahadar Sub-Campus Layyah, during the crop seasons of 2018–2019, following procedures similar to those utilized in five years investigation. The data on epidemiological variables used in the present investigation was collected from the Pakistan Meteorological Observatory at Karor-Layyah. To evaluate the association between meteorological factors and disease severity correlation and regression analysis was performed. Results: All meteorological variables contributed significantly in disease development and showed 89 % variability in stripe rust severity (%). Root means square error (RMSE) and residual (%) were used to evaluate the model's predictions. Both indices were below 20, showing that the model could accurately predict the progression of disease. The regression equations of 5 years model (Y = -63.11 + 0.96x1 + 1.72x2 + 3.72x3 + 0.43x4) and 2 years model (Y = -40.2 + 1.80x1 + 1.18x2 + 2.29x3 + 0.39x4) validated each other. Scatter plots indicated that environmental factors such as maximum temperature (12.8–22.5 °C), minimum temperature (8.7–14.8 °C), relative humidity (50–85 %), and wind speed (1.3–4.5) influenced the progression of stripe rust epidemic. Conclusion: Understanding the epidemiology of stripe rust will help us to forecast its progression, allowing wheat growers to more precisely adapt plant protection measures