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The study was aimed to evaluate the effect of essential oils against fungal pathogens including the chemical analysis (GC-MS) of the most effective essential oil.

Climate variability affects the phenological, physiological, and morphological adaptability of Kiwifruit. Hence, five Kiwifruit varieties were evaluated under variable rainfed climatic conditions in Pothowar. Phenological data of all five varieties was observed at both study sites, including bud initiation, bud swelling, and bud burst. Goldflesh (GDF) and Green Flesh 1 (GF1) were observed to be late in bud burst and to have poor physio-morphological responses and plant survival rates. In contrast, Hayward (H), Bruno (B), and Greenflesh 2 (GF2) exhibited superior performance in terms of plant physiological and morphological traits (e.g., heat damage percentage, photosynthetic activity, shoot length, t runk diameter, d isease incidence percentage, and survival rate). Greenflesh 2 (GF2) plants at both study locations exhibited a lower percentage of heat damage (4.25 and 5.83) than Greenflesh 1 (6.2 and 9.87) and Goldflesh (6.11 and 8.83), respectively. Bruno had more photosynthetic activity (627.31 and 773.5 mol m-2 sec-1) than Goldflesh (330.49 and 343.71 mol m-2 sec-1) and Greenflesh 1 (346.96 and 354.25 mol m-2 sec-1, respectively). Hayward (112.56 and 183.44 cm) and Bruno (109.98 and 160.39 cm) had longer shoots compared to Greenflesh 1 (44.23 and 71.26 cm) and Goldflesh (62.49 and 111.16 cm). Bruno (1.02 and 1.6 cm) and Hayward (0.93 and 1.56 cm) had a significantly larger trunk diameter than Greenflesh 1 (0.74 and 1.3 cm) and Goldflesh (0.78 and 1.3 cm), respectively. Hayward, Bruno, and Greenflesh 2 were significantly more resistant to disease incidence (0.27, 0.15, and 0.41) and plant survival was high (80%, 78%, and 79%) in all three varieties at both locations. Bruno and Greenflesh 2 proved to be the most productive varieties under variable climate conditions; thus, their material can be used to develop future Kiwifruit ideotypes in rainfed regions. [ABSTRACT FROM AUTHOR]

Blue mold caused by Penicillium species is a major fungal disease threatening the viticulture industry in Pakistan, responsible for deteriorating the quality of grapes during handling, transportation, and distribution. Identification-based approaches of Penicillium species provide a better strategy on accurate diagnosis and effective management. In this study, 13 isolates were recovered from symptomatic grape bunches at five main fruit markets of Rawalpindi district, Punjab province. Based on morphological data and multi-loci phylogenetic analysis, the isolates were identified as two distinct species viz. Penicillium expansum (eight isolates) and Penicillium crustosum (five isolates). Meanwhile, the pathogenicity test of Penicillium isolates presented by the inoculation of grape bunches showed various degrees of severity. For improving the fruit quality and eliminating the needs of synthetic fungicides, botanicals such as thyme (Thymus vulgaris L.), fennel (Foeniculum vulgare L.), garlic (Allium sativum L.), ginger (Zingiber officinale L.), and carum (Carum capticum L.) essential oils (EOs) at concentrations of 0.6, 0.9, 1.2, and 1.5 mg/mL were evaluated. In vitro studies indicated that thyme EO showed a highly significant reduction of fungal growth. Furthermore, the experiments related to reducing the decay development and average weight loss percentage of grapes revealed similar findings. Based on the results of this study, it is recommended that thyme EO can be used as an eco-friendly botanical fungicide against Penicillium spp. causing blue mold disease.

It is essential for sustainable economic development to comprehend how environmental factors impact public health. This study analyses this relationship in the context of Pakistan using long‐term data. This study aims to determine how environmental factors influence health production function in Pakistan to enlighten policy decisions that can improve human life and advance the cause of sustainable development. We hypothesize, based on prior research, that urbanization and rainfall will increase life expectancy in Pakistan, while deforestation, temperature, and CO2 emissions will decrease it. To verify our theory, we use the autoregressive distributed lag (ARDL) method to calculate the long‐run association between the variables, as well as the Zivot–Andrews and Lee–Strazicich unit root tests to identify structural breaks. Utilizing the bound and Gregory–Hansen co‐integration tests, co‐integration is confirmed. According to ARDL estimates, there are statistically significant correlations between factors that affect Pakistan's life expectancy, such as deforestation, temperature, and CO2 emissions, as well as rainfall and urbanization. The findings of this study underscore the importance of addressing environmental degradation and deforestation in Pakistan. For enhancing human life and achieving sustainable development objectives in the nation, it is essential to modernize forest laws and regulations and adopt eco‐friendly technologies. [ABSTRACT FROM AUTHOR]

This article examines the impact of government effectiveness (GE) and technological innovation on economic growth and environmental degradation in Middle East & North Africa countries. The study utilizes panel data from the period of 2002–2020. The findings indicate that there is a significant and positive relationship between innovation (INV) and CO2 emissions (0.0244), while GE has a significant and negative impact on CO2 emissions (−0.5753). In a similar vein, it can be observed that both INV and government efficacy exert a substantial and favorable influence on the process of economic growth (0.0353 and 0.0773). Furthermore, the interaction of INV and GE showed a noteworthy moderating effect on CO2 emissions (−0.1581). Conversely, it exhibited a more pronounced positive influence on economic growth (0.1122). The study highlights that synergizing INV with GE is essential for attaining sustainable economic growth in the studied area. [ABSTRACT FROM AUTHOR]

Crop growth models can be valuable tools for researchers, academia, extension educators, and policy makers/planners for the evaluation of sustainable and long-term husbandry practices. Determining the optimum sowing window, which can be determined using crop growth models, is imperative under changing climate conditions. Thus, the main objectives herein were to 1) assess the performance of the cropping system model-crop environment resource synthesis-maize model for hybrids and sowing dates in the spring and autumn, and 2) determine the optimum sowing window in 15 districts of Punjab, Pakistan. In the spring experiment, 3 hybrids (P-33M15, M-DK6525, and S-NK8441) were planted in the main plots and then on 5 different sowing dates (January 15th, February 5th, February 25th, March 15th, and April 5th), they were planted in the subplots. In the autumn experiment, 3 hybrids (P-30R50, M-DK6714, and S-NK6621) were planted in the main plots and then on 5 different sowing dates (June 15th, July 5th, July 25th, August 15th, and September 5th), they were planted in the subplots. Model calibration and evaluation results were better in the spring and autumn. Performance of the model was good for the grain yield in the autumn (mean percentage difference (MPD): 7.47% to 8.90%) compared to the spring (MPD: 9.42% to 11.72%). Model evaluation was good for the early sowing dates (January 15th and February 5th) (error range: 6.26% to 9.65%) compared to the delayed dates (February 25th to April 5th) (error range: 9.34 to 14.91%). In the autumn, the model showed better performance for the delayed sowing dates (February 25th and August 15th) (error range: 5.22% to 9.43%) compared to the early dates (June 15th and July 5th) (error range: 8.56% to 11.27%). The model simulated good growth, development, grain yield, and yield components in the spring and autumn and of both 2016 and 2017. For the model application simulation of data over the long-term (1980 to 2017), the optimum sowing window in the spring was January 15th to March 5th and for the autumn it was July 23rd to August 27th for the 15 districts in Punjab, Pakistan. Simulation of the sowing dates for the whole year indicated that the spring was better compared to the autumn for obtaining the maximum grain yield. The results of the model were in line with the recommendations of the agricultural extension department for the sowing window for spring and autumn maize. It is therefore suggested that farmers should complete the sowing of spring and autumn maize within the sowing window to attain a higher yield of maize in arid and semiarid areas of Punjab, Pakistan. [ABSTRACT FROM AUTHOR]