Priority sources identification and risks assessment of heavy metal(loid)s in agricultural soils of a typical antimony mining watershed.

Heavy metal(loid) (HM) pollution in agricultural soils has become an environmental concern in antimony (Sb) mining areas. However, priority pollution sources identification and deep understanding of environmental risks of HMs face great challenges due to multiple and complex pollution sources coexist. Herein, an integrated approach was conducted to distinguish pollution sources and assess human health risk (HHR) and ecological risk (ER) in a typical Sb mining watershed in Southern China. This approach combines absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models with ER and HHR assessments. Four pollution sources were distinguished for both models, and APCS-MLR model was more accurate and plausible. Predominant HM concentration source was natural source (39.1%), followed by industrial and agricultural activities (23.0%), unknown sources (21.5%) and Sb mining and smelting activities (16.4%). Although natural source contributed the most to HM concentrations, it did not pose a significant ER. Industrial and agricultural activities predominantly contributed to ER, and attention should be paid to Cd and Sb. Sb mining and smelting activities were primary anthropogenic sources of HHR, particularly Sb and As contaminations. Considering ER and HHR assessments, Sb mining and smelting, and industrial and agricultural activities are critical sources, causing serious ecological and health threats. This study showed the advantages of multiple receptor model application in obtaining reliable source identification and providing better source-oriented risk assessments. HM pollution management, such as regulating mining and smelting and implementing soil remediation in polluted agricultural soils, is strongly recommended for protecting ecosystems and humans.

Combining slow-release fertilizer and plastic film mulching reduced the carbon footprint and enhanced maize yield on the Loess Plateau.

Agricultural practices significantly contribute to greenhouse gas (GHG) emissions, necessitating cleaner production technologies to reduce environmental pressure and achieve sustainable maize production. Plastic film mulching is commonly used in the Loess Plateau region. Incorporating slow-release fertilizers as a replacement for urea within this practice can reduce nitrogen losses and enhance crop productivity. Combining these techniques represents a novel agricultural approach in semi-arid areas. However, the impact of this integration on soil carbon storage (SOCS), carbon footprint (CF), and economic benefits has received limited research attention. Therefore, we conducted an eight-year study (2015-2022) in the semi-arid northwestern region to quantify the effects of four treatments [urea supplied without plastic film mulching (CK-U), slow-release fertilizer supplied without plastic film mulching (CK-S), urea supplied with plastic film mulching (PM-U), and slow-release fertilizer supplied with plastic film mulching (PM-S)] on soil fertility, economic and environmental benefits. The results revealed that nitrogen fertilizer was the primary contributor to total GHG emissions (≥71.97%). Compared to other treatments, PM-S increased average grain yield by 12.01%-37.89%, water use efficiency by 9.19%-23.33%, nitrogen accumulation by 27.07%-66.19%, and net return by 6.21%-29.57%. Furthermore, PM-S decreased CF by 12.87%-44.31% and CF per net return by 14.25%-41.16%. After eight years, PM-S increased SOCS (0-40 cm) by 2.46%, while PM-U decreased it by 7.09%. These findings highlight the positive effects of PM-S on surface soil fertility, economic gains, and environmental benefits in spring maize production on the Loess Plateau, underscoring its potential for widespread adoption and application.

Analysis of energy consumption and change structure in major economic sectors of Pakistan.

Studying and analyzing energy consumption and structural changes in Pakistan's major economic sectors is crucial for developing targeted strategies to improve energy efficiency, support sustainable economic growth, and enhance energy security. The logarithmic mean Divisia index (LMDI) method is applied to find the factors' effects that change sector-wise energy consumption from 1990 to 2019. The results show that: (1) the change in mixed energy and sectorial income shows a negative influence, while energy intensity (EI) and population have an increasing trend over the study period. (2) The EI effects of the industrial, agriculture and transport sectors are continuously rising, which is lowering the income potential of each sector. (3) The cumulative values for the industrial, agricultural, and transport sectors increased by 57.3, 5.3, and 79.7 during 2019. Finally, predicted outcomes show that until 2035, the industrial, agriculture, and transport incomes would change by -0.97%, 13%, and 65% if the energy situation remained the same. Moreover, this sector effect is the most crucial contributor to increasing or decreasing energy consumption, and the EI effect plays the dominant role in boosting economic output. Renewable energy technologies and indigenous energy sources can be used to conserve energy and sectorial productivity.

Study on the multifunctional spatial-temporal evolution and coupling coordination of cultivated land: A case study of Hebei Province, China.

The rational use of cultivated land can guarantee food security and thus is highly important for ensuring social stability, economic development and national security. The current study investigated the multifunctional temporal and spatial variation characteristics of cultivated land and explored the spatial and temporal characteristics of the multifunction and coupling coordination degrees of cultivated land throughout Hebei Province. Based on the administrative division data, statistical yearbook data and land use status data of the impacted areas, a multifunctional evaluation index system of cultivated land was established. The CRITIC weight method and entropy weight method were used to determine the weight of the index, the comprehensive index model was used to determine the production, social security, ecology and landscape functions of cultivated land of Hebei Province in different periods, the coupling coordination model was used to explore the multifunctional coupling coordination degree of cultivated land in each county, and spatial autocorrelation analysis was performed to determine the correlation of the multifunctional coupling coordination degrees. From 2000 to 2020, the production, social security and landscape function of cultivated land in Hebei Province trended upward; the ecological function trended slightly downward. The multifunctional coupling coordination degree of cultivated land in Hebei Province trended significantly upward and changed from limited coordination to intermediate coordination. Furthermore, it exhibited strong agglomeration and a significant positive spatial correlation, forming a 'V'-type change rule of first decreasing and then increasing. Hebei Province exhibited remarkable spatial and temporal characteristics of the multifunction and coupling coordination degrees of cultivated land. Regions could thus customize different cultivated land functions to maximize the benefits of cultivated land use. The findings of this study may provide a scientific basis and theoretical support for sustainably using and managing cultivated land resources in areas with similar human geographical environments.

The potential climate benefits of seaweed farming in temperate waters.

Seaweed farming is widely promoted as an approach to mitigating climate change despite limited data on carbon removal pathways and uncertainty around benefits and risks at operational scales. We explored the feasibility of climate change mitigation from seaweed farming by constructing five scenarios spanning a range of industry development in coastal British Columbia, Canada, a temperate region identified as highly suitable for seaweed farming. Depending on growth rates and the fate of farmed seaweed, our scenarios sequestered or avoided between 0.20 and 8.2 Tg CO2e year-1, equivalent to 0.3% and 13% of annual greenhouse gas emissions in BC, respectively. Realisation of climate benefits required seaweed-based products to replace existing, more emissions-intensive products, as marine sequestration was relatively inefficient. Such products were also key to reducing the monetary cost of climate benefits, with product values exceeding production costs in only one of the scenarios we examined. However, model estimates have large uncertainties dominated by seaweed production and emissions avoided, making these key priorities for future research. Our results show that seaweed farming could make an economically feasible contribute to Canada's climate goals if markets for value-added seaweed based products are developed. Moreover, our model demonstrates the possibility for farmers, regulators, and researchers to accurately quantify the climate benefits of seaweed farming in their regional contexts.

A Proportionate Mortality Study of Mississippi's Agricultural Industry, 2017-2021.

OBJECTIVES: Individuals employed in the agricultural industry encounter hazards in their work that could lead to injury or illness. Furthermore, the mental stress of being involved in the agricultural industry could lead to negative health-related outcomes for workers. This study evaluates the causes of deaths among employees in Mississippi's agricultural industry from 2017 to 2021. METHODS: Data are provided by the Mississippi Department of Health. Proportionate mortality ratios (PMRs) are calculated to determine if agricultural industry employees show an elevated mortality in comparison to the general population for any cause of death. RESULTS: Agricultural industry employees show a statistically significant elevated mortality for circulatory disease (PMR 107, 95% confidence interval [CI] 103-110) and coronavirus disease 2019 (PMR 122, 95% CI 111-134). They also show a significant excess mortality for deaths caused by transport accidents (PMR 117, 95% CI 101-136) and exposure to inanimate mechanical forces (PMR 274, 95% CI 183-396). CONCLUSIONS: The causes of death for which agricultural employees show an excess mortality can be explained by the hazards associated with working in the agricultural industry. These findings can be used to create targeted future public health programs for individuals who are employed in agriculture.

Particle morphology and soil properties affect the retention of copper oxide nanoparticles in agricultural soils.

The interaction between nanoscale copper oxides (nano-CuOs) and soil matrix significantly affects their fate and transport in soils. This study investigates the retention of nano-CuOs and Cu2+ ions in ten typical agricultural soils by employing the Freundlich adsorption model. Retention of nano-CuOs and Cu2+ in soils was well fitted by the Freundlich model. The retention parameters (KD, KF, and N) followed an order of CuO NTs > CuO NPs > Cu2+, highlighting significant impact of nano-CuOs morphology. The KF and N values of CuO NPs/Cu2+ were positively correlated with soil pH and electrical conductivity (EC), but exhibited a weaker correlation for CuO NTs. Soil pH and/or EC could be used to predict KF and N values of CuO NPs or CuO NTs, with additional clay content should be included for Cu2+.The different relationship between retention parameters and soil properties may suggest that CuO NTs retention mainly caused by agglomeration, whereas adsorption and agglomeration were of equal importance to CuO NPs. The amendment of Ca2+ at low and medium concentration promoted retention of nano-CuOs in alkaline soils, but reduced at high concentration. These findings provided critical insights into the fate of nano-CuOs in soil environments, with significant implications for environmental risk assessment and soil remediation strategies.

Redesigning and validation of fertilizer use in maize for variable plant densities in central rift valley and Jimma in Ethiopia.

Due to low adoption and sub-optimal fertilizer use and planting density recommendation in maize, redesigning and testing these technologies are required. The study was conducted to evaluate redesigned fertilizer use of maize in two pant densities (32,443 and 53,333 plants ha-1 in Central Rift Valley (CRV); 27724 and 62,000 plants ha-1 in Jimma) on farmers' fields in contrasting agro-ecologies of Ethiopia. The on-farm study was conducted in the 2017 and 2018 cropping seasons with 3 × 2 fertilizer and plant density, factors in both regions of Ethiopia. In redesigned fertilizer use, nutrients were estimated based on the target yield. In this study, 40.8, 0.0, and 12.2 kg ha-1 N, P, and K were estimated for the redesigned fertilizer use in CRV (50% of water-limited potential yield (Yw) = 3.1 t ha-1) whereas in Jimma (50% of Yw = 7.5 t ha-1) 149.8, 9, 130.6 kg ha-1 N, P and K were estimated to produce the 50% of Yw. Linear mixed modeling was used to assess the effect of fertilizer-plant density treatments on maize yield and nutrient use efficiency. The result revealed that the average estimated maize yield for WOF, FFU, and RDFU fertilizer treatments were 2.6, 3.6, and 4.5 t ha-1 under current plant density (32,443 plants ha-1) in CRV whereas the average yields of these treatments were 3.2, 4.5 and 4.5 t ha-1 respectively when maize was grown with redesigned plant density (53,333 plants ha-1) in the same location. The average maize yield with WOF, FFU, and RDFU were 3.0, 4.6, and 4.6 t ha-1 with 27,774 plants ha-1 plant density in Jimma whereas the average maize yields over the two seasons with the same treatments were 4.3, 6.0 and 8.0 t ha-1 respectively when the crop is planted with 62,000 plants ha-1 plant density. The RDFU and redesigned plant density resulted in significantly higher yield compared to their respective control CRV but RDFU significantly increased maize yield when it was planted at redesigned (62,000 plant ha-1) in Jimma. FFU and RDFU were economically viable and redesigned plant density was also a cheaper means of improving maize productivity, especially in the Jimma region. Soil organic carbon and N were closely related to the grain yield response of maize compared to other soil factors. In conclusion, this investigation gives an insight into the importance of redesigned fertilizer use and redesigned plant density for improving maize productivity and thereby narrowing the yield gaps of the crop in high maize potential regions in Ethiopia like Jimma.

A critical systematic review on spectral-based soil nutrient prediction using machine learning.

The United Nations (UN) emphasizes the pivotal role of sustainable agriculture in addressing persistent starvation and working towards zero hunger by 2030 through global development. Intensive agricultural practices have adversely impacted soil quality, necessitating soil nutrient analysis for enhancing farm productivity and environmental sustainability. Researchers increasingly turn to Artificial Intelligence (AI) techniques to improve crop yield estimation and optimize soil nutrition management. This study reviews 155 papers published from 2014 to 2024, assessing the use of machine learning (ML) and deep learning (DL) in predicting soil nutrients. It highlights the potential of hyperspectral and multispectral sensors, which enable precise nutrient identification through spectral analysis across multiple bands. The study underscores the importance of feature selection techniques to improve model performance by eliminating redundant spectral bands with weak correlations to targeted nutrients. Additionally, the use of spectral indices, derived from mathematical ratios of spectral bands based on absorption spectra, is examined for its effectiveness in accurately predicting soil nutrient levels. By evaluating various performance measures and datasets related to soil nutrient prediction, this paper offers comprehensive insights into the applicability of AI techniques in optimizing soil nutrition management. The insights gained from this review can inform future research and policy decisions to achieve global development goals and promote environmental sustainability.

Agronomic performance and technological quality of sugarcane submitted to different poultry litter dosages.

Sugarcane is a central crop for sugar and ethanol production. Investing in sustainable practices can enhance productivity, technological quality, mitigate impacts, and contribute to a cleaner energy future. Among the factors that help increase the productivity of sugarcane, the physical, chemical and biological parameters of the soil are amongst the most important. The use of poultry litter has been an important alternative for soil improvement, as it acts as a soil conditioner. Therefore, this work aimed to verify the best doses of poultry litter for the vegetative, reproductive and technological components of sugarcane. The experiment was carried out at Usina Denusa Destilaria Nova União S/A in the municipality of Jandaia, GO. The experimental design used was a complete randomized block design with four replications: 5 × 4, totaling 20 experimental units. The evaluated factor consisted of four doses of poultry litter plus the control (0 (control), 2, 4, 6 and 8 t ha-1). In this study, were evaluated the number of tillers, lower stem diameter, average stem diameter, upper stem diameter, plant height, stem weight and productivity. The technological variables of total recoverable sugar, recoverable sugar, Brix, fiber, purity and percentage of oligosaccharides were also evaluated. It was observed, within the conditions of this experiment, that the insertion of poultry litter did not interfere significantly in most biometric, productive and technological variables of the sugarcane. But it can also be inferred that there was a statistical trend toward better results when the sugarcane was cultivated with 4 t ha-1 of poultry litter.

Disability and labour market participation among smallholder farmers in Western Kenya.

BACKGROUND: Despite the importance of labour market participation and the high number of people with disabilities in rural Africa who rely on subsistence agriculture to survive, very few studies have documented labour market outcomes among farmers with and without disabilities in Africa. OBJECTIVE: We examined how labour market participation differed by disability and other factors among smallholder farmers in Western Kenya. METHODS: We use cross-sectional data collected between January and April 2022 from sorghum farmers enrolled in a trial evaluating the impact of a programme designed to improve labour market participation among sorghum farmers in rural Western Kenya. Disability and Labour market outcomes were assessed using questions from the Washington Group /ILO Labor Force Survey Disability Module the ILO Labour Force Survey module respectively. Univariate and multiple regression analyses were conducted to identify socio-demographic characteristics and other related factors associated with labour market participation. RESULTS: Among 4459 participants, disability was reported by 20.3% of women and 12.3% of men. Labour market participation was reported by 77.1% and 81.3% of women and men, respectively. Adjusting for demographic confounders, having a disability was associated with a lower likelihood of labour market participation (odds ratio 0.59, 95% confidence interval, 0.42-0.83, P = 0.001). These findings were similar in a modified model that looked at functional difficulties separately from anxiety and depression. Women, older participants, and those who were dependent on others were also more likely not to report participation in the labour market. CONCLUSIONS: Increased recognition and understanding of functional limitations among smallholder farmers is vital for the success of economic empowerment programmes aimed at increasing labour market participation among the most vulnerable populations.

The effect of environmental variations on the production of the principal agricultural products in Colombia.

The agricultural sector of Colombia supports the national economy and food security due to the rich lands for cultivation. Although Colombia has a vast hydrological basin, climate change can impact agricultural productivity, generating economic and social adverse effects. For this, we evaluated the impact of some environmental variables on the production of the most sold crops using production, climatic, and hydrological data of the 1121 municipalities from 2007 to 2020. We modeled the production of coffee, rice, palm oil, sugarcane, and corn, adopting a Bayesian spatio-temporal model that involved a set of environmental variables: average temperature, minimum temperature, maximum temperature, evapotranspiration, precipitation, runoff, soil moisture, vapor pressure, radiation, and wind speed. We found that increases in the average temperatures can affect coffee (-0.2% per °C), rice (-3.76% per °C), and sugarcane (-0.19% per °C) production, meanwhile, these increases can boost palm oil (+2.55% per °C) and corn (+1.28% per °C) production in Colombia. This statement implies that the agricultural sector needs to substitute land use, promoting the production of palm oil and corn. Although our results did not find a significant effect of hydrological variables in any crop, suggesting that the abundance of water in Colombia might balance the impact of these variables. The increases in vapor pressure impact all the crops negatively (between -11.2% to -0.43% per kPa), except rice, evidencing that dry air conditions affect agricultural production. Colombia must manage the production location of the traditional products and implement agro-industrial technologies to avoid the climate change impact on crops.

Geospatial insights into Alphonso mango cultivation: a comprehensive land suitability study in the coastal belt of Maharashtra, India.

Land suitability assessment is integral to the advancement of precision agriculture. This inquiry is focused on identifying optimal regions for cultivating Alphonso mango in the coastal belt of Maharashtra, spanning across Palghar, Raigad, Thane, Ratnagiri, and Sindhudurg districts. Employing a GIS-based Analytic Hierarchy Process (AHP) methodology, 10 crucial parameters have been considered, encompassing climatic, physical, and chemical soil characteristics: cation exchange capacity, organic carbon, slope, rainfall, soil pH, soil texture, mean annual soil temperature, base saturation, soil drainage, and soil depth. Weights are assigned to these parameters based on expert opinions and existing literature to determine their significance in developing a soil suitability map. The study reveals distinct land suitability zones for Alphonso mango cultivation. The land suitability map designates 25.78% of the study area as highly suitable, while 9.18% is considered unsuitable for Alphonso mango cultivation. To validate the study, the Receiver Operating Characteristic (ROC) curve has been employed, indicating an 83% approval rate for the reliability and performance of the soil suitability. The results categorise soil suitability classes, providing valuable insights for farmers and agricultural planners to make informed decisions regarding Alphonso mango cultivation in similar geoenvironmental regions.

Early agriculture and crop transitions at Kakapel Rockshelter in the Lake Victoria region of eastern Africa.

The histories of African crops remain poorly understood despite their contemporary importance. Integration of crops from western, eastern and northern Africa probably first occurred in the Great Lakes Region of eastern Africa; however, little is known about when and how these agricultural systems coalesced. This article presents archaeobotanical analyses from an approximately 9000-year archaeological sequence at Kakapel Rockshelter in western Kenya, comprising the largest and most extensively dated archaeobotanical record from the interior of equatorial eastern Africa. Direct radiocarbon dates on carbonized seeds document the presence of the West African crop cowpea (Vigna unguiculata (L.) Walp) approximately 2300 years ago, synchronic with the earliest date for domesticated cattle (Bos taurus). Peas (Pisum sativum L. or Pisum abyssinicum A. Braun) and sorghum (Sorghum bicolor (L.) Moench) from the northeast and eastern African finger millet (Eleusine coracana (L.) Gaertn.) are incorporated later, by at least 1000 years ago. Combined with ancient DNA evidence from Kakapel and the surrounding region, these data support a scenario in which the use of diverse domesticated species in eastern Africa changed over time rather than arriving and being maintained as a single package. Findings highlight the importance of local heterogeneity in shaping the spread of food production in sub-Saharan Africa.

Positive public attitudes towards agricultural robots.

Robot technologies could lead to radical changes in farming. But what does the public know and think about agricultural robots? Recent experience with other agricultural technologies-such as plant genetic engineering-shows that public perceptions can influence the pace and direction of innovation, so understanding perceptions and how they are formed is important. Here, we use representative data from an online survey (n = 2269) to analyze public attitudes towards crop farming robots in Germany-a country where new farming technologies are sometimes seen with skepticism. While less than half of the survey participants are aware of the use of robots in agriculture, general attitudes are mostly positive and the level of interest is high. A framing experiment suggests that the type of information provided influences attitudes. Information about possible environmental benefits increases positive perceptions more than information about possible food security and labor market effects. These insights can help design communication strategies to promote technology acceptance and sustainable innovation in agriculture.

Advancing common bean (Phaseolus vulgaris L.) disease detection with YOLO driven deep learning to enhance agricultural AI.

Common beans (CB), a vital source for high protein content, plays a crucial role in ensuring both nutrition and economic stability in diverse communities, particularly in Africa and Latin America. However, CB cultivation poses a significant threat to diseases that can drastically reduce yield and quality. Detecting these diseases solely based on visual symptoms is challenging, due to the variability across different pathogens and similar symptoms caused by distinct pathogens, further complicating the detection process. Traditional methods relying solely on farmers' ability to detect diseases is inadequate, and while engaging expert pathologists and advanced laboratories is necessary, it can also be resource intensive. To address this challenge, we present a AI-driven system for rapid and cost-effective CB disease detection, leveraging state-of-the-art deep learning and object detection technologies. We utilized an extensive image dataset collected from disease hotspots in Africa and Colombia, focusing on five major diseases: Angular Leaf Spot (ALS), Common Bacterial Blight (CBB), Common Bean Mosaic Virus (CBMV), Bean Rust, and Anthracnose, covering both leaf and pod samples in real-field settings. However, pod images are only available for Angular Leaf Spot disease. The study employed data augmentation techniques and annotation at both whole and micro levels for comprehensive analysis. To train the model, we utilized three advanced YOLO architectures: YOLOv7, YOLOv8, and YOLO-NAS. Particularly for whole leaf annotations, the YOLO-NAS model achieves the highest mAP value of up to 97.9% and a recall of 98.8%, indicating superior detection accuracy. In contrast, for whole pod disease detection, YOLOv7 and YOLOv8 outperformed YOLO-NAS, with mAP values exceeding 95% and 93% recall. However, micro annotation consistently yields lower performance than whole annotation across all disease classes and plant parts, as examined by all YOLO models, highlighting an unexpected discrepancy in detection accuracy. Furthermore, we successfully deployed YOLO-NAS annotation models into an Android app, validating their effectiveness on unseen data from disease hotspots with high classification accuracy (90%). This accomplishment showcases the integration of deep learning into our production pipeline, a process known as DLOps. This innovative approach significantly reduces diagnosis time, enabling farmers to take prompt management interventions. The potential benefits extend beyond rapid diagnosis serving as an early warning system to enhance common bean productivity and quality.

Enhancing agriculture through real-time grape leaf disease classification via an edge device with a lightweight CNN architecture and Grad-CAM.

Crop diseases can significantly affect various aspects of crop cultivation, including crop yield, quality, production costs, and crop loss. The utilization of modern technologies such as image analysis via machine learning techniques enables early and precise detection of crop diseases, hence empowering farmers to effectively manage and avoid the occurrence of crop diseases. The proposed methodology involves the use of modified MobileNetV3Large model deployed on edge device for real-time monitoring of grape leaf disease while reducing computational memory demands and ensuring satisfactory classification performance. To enhance applicability of MobileNetV3Large, custom layers consisting of two dense layers were added, each followed by a dropout layer, helped mitigate overfitting and ensured that the model remains efficient. Comparisons among other models showed that the proposed model outperformed those with an average train and test accuracy of 99.66% and 99.42%, with a precision, recall, and F1 score of approximately 99.42%. The model was deployed on an edge device (Nvidia Jetson Nano) using a custom developed GUI app and predicted from both saved and real-time data with high confidence values. Grad-CAM visualization was used to identify and represent image areas that affect the convolutional neural network (CNN) classification decision-making process with high accuracy. This research contributes to the development of plant disease classification technologies for edge devices, which have the potential to enhance the ability of autonomous farming for farmers, agronomists, and researchers to monitor and mitigate plant diseases efficiently and effectively, with a positive impact on global food security.

Exposure of Syrian refugee agricultural workers to pesticides in Lebanon: a socio-economic and political lens.

This article adopts a socio-economic and political lens to elucidate the interplay of factors that heighten the vulnerability of Syrian refugee agricultural workers and their exposure to pesticides in Lebanon. It provides a comprehensive understanding for the interconnected social, political and economic factors at the global, regional, national and local levels and how they increase the vulnerability of Syrian refugee agricultural workers, particularly their exposure to pesticides. The global factors highlight the shifts from colonialism to state-controlled economies to neoliberal policies. These changes have prioritized the interests of large agricultural schemes and multinationals at the expense of small and medium-sized agriculture. Consequently, there has been a boost in pesticides demand, coupled with weak regulations and less investment in agriculture in the countries of the Global South. The article explains how the dynamic interaction of climate change and conflicts in the Middle East and North Africa region has negatively impacted the agriculture sector and food production, which led to an increased potential for pesticide use. At the national and local levels, Lebanon's social, political and economic policies have resulted in the weakening of the agricultural sector, the overuse of pesticides, and the intensification of the Syrian refugee agricultural workers' vulnerability and exposure to pesticides. The article recommends that researchers, policymakers, and practitioners adopt a political-economic-social lens to analyze and address the full dynamic situation facing migrant and refugee workers in Lebanon and other countries and promote equity in the agricultural sector globally.

The antecedents of customer satisfaction in the live-streaming commerce of green agricultural products.

Live-streaming technology has been widely adopted to promote the sale of green agricultural products. Based on the literature regarding electronic commerce and customer satisfaction, this article integrates expectation-disconfirmation theory and the SERVQUAL model to investigate the antecedents of customer satisfaction and the routes along which the former drives the latter in the live-streaming commerce of green agricultural products. Our results demonstrate that most consumers are satisfied with the live-streaming commerce of green agricultural products, with an overall satisfaction degree of medium to high. In addition, a total of four antecedents are identified, namely commodity, live-streaming platforms, live-streaming contents and supporting services. Among the variables relevant to commodity, "commodity brand building" has the highest weight. Meanwhile, the corresponding variables for live-streaming platforms, live-streaming contents and supporting services are "interface design", "live-streaming atmosphere" and "privacy protection", respectively. Furthermore, live-streaming platforms are found to have the strongest direct influence on customer satisfaction, while commodity is found to have the strongest indirect and total influence on customer satisfaction. The theoretical and managerial implications are discussed at the conclusion of this article.

Improving groundwater vulnerability assessment in structurally controlled hard rock aquifer: insight from lineament density and land use/land cover pattern.

A comprehensive seasonal assessment of groundwater vulnerability was conducted in the weathered hard rock aquifer of the upper Swarnrekha watershed in Ranchi district, India. Lineament density (Ld) and land use/land cover (LULC) were integrated into the conventional DRASTIC and Pesticide DRASTIC (P-DRASTIC) models and were extensively compared with six modified models, viz. DRASTIC-Ld, DRASTIC-Lu, DRASTIC-LdLu, P-DRASTIC-Ld, P-DRASTIC-Lu, and P-DRASTIC-LdLu, to identify the most optimal model for vulnerability mapping in hard rock terrain of the region. Findings were geochemically validated using NO3- concentrations of 68 wells during pre-monsoon (Pre-M) and post-monsoon (Post-M) 2022. Irrespective of the applied model, groundwater vulnerability shows significant seasonal variation, with > 45% of the region classified as high to very high vulnerability in the pre-M, increasing to Ì´67% in post-M season, highlighting the importance of seasonal vulnerability assessments. Agriculture and industries' dominant southern region showed higher vulnerability, followed by regions with high Ld and thin weathered zone. Incorporating Ld and LULC parameters into DRASTIC-LdLu and P-DRASTIC-LdLu models increases the 'Very High' vulnerability zones to 17.4% and 17.6% for pre-M and 29.4% and 27.9% for post-M, respectively. Similarly, 'High' vulnerable zones increase from 32.5% and 25% in pre-M to 33.8% and 35.3% in post-M for respective models. Model output comparisons suggest that modified DRASTIC-LdLu and P-DRASTIC-LdLu perform better, with accurate estimations of 83.8% and 89.7% for pre-M and post-M, respectively. However, results of geochemical validation suggest that among all the applied modified models, DRASTIC-LdLu performs best, with accurate estimations of 34.4% and 20.6% for pre-M and post-M, respectively.