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Anthropogenic activities are increasing the amount of heavy metals and metalloids in the environment on a global scale, harming all living things and necessitating the employment of bioremediation procedures. Metal-resistant bacteria were used to clean polluted soil and promote plant growth; this approach has gained attention in recent years for bioremediation of heavy metal-contaminated systems. We studied the effects of chromium and lithium in Oryza sativa under controlled conditions. In the present study, lithium concentration was applied 50 ppm to 200 ppm according to the dose tolerance level, while the concentration of chromium was 10 ppm throughout the experimental setup due to its concentration observed up to 10 ppm in the targeted soil, which is present in Kasur area Punjab, Pakistan, for rice crop production in future perspective. The results reflect that plants with high lithium concentration have shown decreased plant growth and development, but due to bacterial presence, they thrived until harvesting stage. Due to increase in stress concentration up to 200 ppm, decline in plant growth was observed, but after bacterial inoculation, better growth was seen (chlorophyll content increased to 40, and panicle numbers were more than 13). Our findings reveal that lithium and chromium have a direct negative impact on Oryza sativa, which can be minimized by utilizing halophilic microbes (Klebsiella pneumonia and Enterobacter cloacae) through soil–plant system. [ABSTRACT FROM AUTHOR]

Agriculture sector is uncertain and susceptible to climatic risks. Farmer's risk attitude and risk perceptions play important roles in managing the risks at farm level. Precise and timely risk perception may help to evaluate the incidence and severity of risks while farmers' attitude determines the response of the farmers toward risk. Therefore, farmers' risk attitude and perceptions are significant in farmers' decision-making during the uncertain conditions. The present study was designed to examine the farmers' risk attitude and risk perceptions related to climatic factors. The study also focused on socioeconomic factors affecting the farmers' risk attitude and risk perceptions. The study was conducted in three districts of Punjab such as Bahawalpur, Layyah and Rahim Yar Khan. Using a multistage sampling technique, a sample of 382 respondents was interviewed from all districts. The study employed the Equally Likely Certainty Equivalent and Cubic Utility Function to assess the risk attitude. Risk matrix was used to estimate the risk perceptions related to climate factors. The study used probit model to analyze the factors affecting the risk attitude and risk perceptions among farmers. Findings of the study indicate that most of the farmers have risk averse behavior. Farmers perceive rise in temperature as high risk followed by erratic rains, wind storms and pest breakouts. The results show that farmer's age, education, farming experience and off-farm income significantly determine the farmers' risk attitude and perceptions. The study results will be helpful for policy makers and researchers to design risk management strategies. [ABSTRACT FROM AUTHOR]

Pakistan is known to be a climate vulnerable country in the world. Being an agrarian country, floods poses higher threat to rural livelihood. The study assessed the underline causes of livelihood vulnerability in a flood prone rural area surrounded by riverine Jhok Reserve forest of Punjab Province. Using Livelihood Vulnerability Index (LVI), eight potential components were inquired from two hundred natural-resource dependent households. Two approaches of LVI were customized to incorporate indigenous knowledge into 42 sub-components. The findings of the study showed that local people had poor socio-economic conditions, which led to have a wide networking gap in the community. People have become highly vulnerable to climatic changes because of higher environmental degradation in form of riverbank erosion, deforestation, etc. It was revealed that community in Jhok Reserve Forest had low adaptive capacities mainly due to higher poverty, low education level and lack of awareness about future disasters. And this has exposed them being unable to adapt to environmental changes. The findings of the study are significant for the policy makers to analyze the root causes of livelihood vulnerability and formulate agricultural adaptations for other rural communities. Overall, it is suggested that a reliable vulnerability assessment approach is presented in the study which can be replicated in other settings and regions of the world. [ABSTRACT FROM AUTHOR]

The current investigation examines the effectiveness of various approaches in predicting the soil texture class (clay, silt, and sand contents) of the Rawalpindi district, Punjab province, Pakistan. The employed techniques included artificial neural networks (ANNs), kriging, co-kriging, and inverse distance weighting (IDW). A total of 44 soil specimens from depths of 10–15 cm were gathered, and then the hydrometer method was adopted to measure their texture. The map of soil grain sets was formulated in the ArcGIS environment, utilizing distinct interpolation approaches. The MATLAB software was used to evaluate soil texture. The gradient fraction, latitude and longitude, elevation, and soil texture fragments of points were proposed to an ANN. Several statistical values, such as correlation coefficient (R), geometric mean error ratios (GMER), and root mean square error (RMSE), were utilized to evaluate the precision of the intended techniques. In assessing grain size and spatial dissemination of clay, silt, and sand, the effectiveness and precision of ANN were superior compared to kriging, co-kriging, and inverse distance weighting. Still, less than a 50% correlation was observed using the ANN. In this examination, the IDW had inferior precision compared to the other approaches. The results demonstrated that the practices produced acceptable results and can be used for future research. Soil texture is among the most central variables that can manipulate agriculture plans. The prepared maps exhibiting the soil texture groups are imperative for crop yield and pastoral scheduling. • To predict the soil texture class comparing the performance of ANN, kriging, co-kriging and IDW. • The soil samples were collected from Rawalpindi District of Punjab province, Pakistan. • The results showed that the performance of ANN was better than the other methods. [ABSTRACT FROM AUTHOR]