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.

Wheat (Triticum aestivum L.) is a staple and the most important food crop around the world. The growth and productivity of wheat are influenced by different factors, viz., sowing time, tillage system and nutrient application. The current field experiment consists of different boron (B) application rates, viz., B₀ = No application (Control), B₁ = soil applied (2 kg ha⁻¹), B₂ = foliar applied (2 kg ha⁻¹), B₃ = water spray; two tillage systems, viz., zero tillage (ZT) and conventional tillage (CT); and three sowing dates (S₁ = 15 November; S₂ = 5 December and S₃ = 25 December). It was conducted during the years 2019–2020 and 2020–2021 under a split-split plot arrangement. The results showed that sowing dates and boron had beneficial impacts on the growth and productivity of wheat. The wheat crop sown on 15 November showed the highest plant height, chlorophyll contents, grains per spike, and grains’ boron content. Similarly, the application of boron under late sown conditions also improved the plant height (83.8 cm), chlorophyll contents (45.6), biological (5418 kg ha⁻¹) and grain (4018 kg ha⁻¹) yield as compared to control during both years. Furthermore, the higher crop growth and yield parameters were noted with the foliar application of boron at 2 kg ha⁻¹. However, wheat crop growth and yield characteristics were not significantly affected by tillage techniques, h. In conclusion, the application of boron @ 2 kg ha⁻¹ could be a suitable option for achieving higher wheat grain yield and productivity under late-sown conditions.

The present study was designed to develop a disease predictive model based upon meteorological variables, that is, minimum and maximum temperatures, rainfall, and relative humidity to predict lentil wilt severity. Correlation and regression analyses were performed to determine the relationship of meteorological variables with disease severity. A significant correlation was found between all meteorological variables and lentil wilt severity. Maximum temperature showed negative correlation, while minimum temperature, rainfall, and relative humidity exhibited positive correlation with lentil wilt severity. Environmental variables and disease severity data of 2 years (2017–2018) were used to develop a disease predictive model using a stepwise multiple regression analysis. Maximum and minimum temperatures, rainfall, and relative humidity significantly contributed to disease development and explained 94.39% variability in disease severity. This model, based on 2 years data, was then validated with 5 years (2012–2016) meteorological variables and disease severity data set. Homogeneity of regression line in the multiple regression equations of 2 years (2017–2018) model and 5 years (2012–2016) model indicated that they validated each other. Lentil wilt severity was high at maximum (17–25°C) and minimum temperature (9.5–15.1°C), rainfall (4.5–6.5 mm), and relative humidity (55%–85%), respectively. The lentil wilt disease predictive model developed for four lentil varieties, namely M‐85, NL‐2, Mansehra‐89, and NARC‐08‐1, during the present investigation will be useful to predict disease severity before epidemic occurrence and the time of fungicide application. Hence, the model is helpful to reduce the use of fungicides, lessen environmental pollution, and help limit the cost of production of lentil growers.

Leaf and stripe rust caused byPuccinia reconditaandPuccinia striiformisare important diseases of wheat in Pakistan and worldwide. The survey was carried out to monitor virulence pattern among the pathogen populations on rust trap nurseries planted in four different locations of Pakistan in 2016 and 2017 years. The rust trap nurseries consisted of 39 leaf and 27 stripe rust near isogenic lines and data were recorded following modified Cobb's scale. No virulence was found in the leaf and stripe rust genes ofLr18, Lr19, Lr23+(GAZA), Lr28, Lr29, Lr32, Lr34, Lr35, Lr36andLr37andYr1, Yr3, Yr4, Yr5, Yr8, Yr9, Yr10, Yr15, Yr17, Yr18, Yr26, Yr28, Yr29, Opata, Yrcv, Yrsp, Super Kauz, PBW-343andAoc + Yra,respectively. Faisalabad-08, Punjab-11, AARI-2011, Millat-11, AAS-11, and Ujala-16 varieties exhibited effective resistance to both rust pathotypes. Moderately stripe rust resistance was noticed on Galaxy-13 ranging from 20-30 MRMS. Thus, the use of resistant genes and their effective combination with other genes had paramount importance to combat both leaf and stripe rust effectively.