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.

This study used an integrated approach to mainly assess the water quality of paddy field during cultivation and quantify its equivalent ecological damages. Accordingly, an isolated pilot area with 0.6 ha and subsurface drainage pipes was prepared for flow measurement and multiple pollutant examination (DO, EC, pH, COD, TKN, TN, TP, NO3, butachlor) under controlled condition during 94 days of rice cultivation. Based on life cycle impact assessment (LCIA) database, the indices of ReCiPe (2016) were used to convert the examined nutrient and herbicide pollution. Results showed that TKN and TP were significant pollutants and reached the maximum concentrations of 7.2 and 4.9 mg/L in pilot outflow, respectively. Here, their average discharged loads were 56.2 gN/day and 45.3 gP/day. These loads equal leaching 8.5% and 9.4% of applied urea and phosphate fertilizers, respectively. The nutrient export coefficients were 8.4 kgN/ha and 6.8 kgP/ha. Nevertheless, the majority of this pollution was transferred by inflow. The net export coefficients were 0.3 kgN/ha and 2.6 kgP/ha while net leaching rates were 0.3%TN and 3.3%TP. The trend of combined ecological damages also showed that the 11-17th day of cultivation imposed the highest ecological risks. The state-of-the-art index of ecological footprint per food production estimates the equivalent ratio of lost lives by impaired ecosystem against lives saved from starvation. This index showed that 7% of the potential of produced paddy rice in this area for saving lives would be spoiled by releasing pollution to the terrestrial ecosystem in the long term. Yet, it can be enhanced as a matter of direct discharge to the freshwater. Therefore, using suitable agricultural operations or improving farm management practices for pollution abatement or assimilation potential is highly recommended.

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.

OBJECTIVE: Farming is a high-risk, physically challenging occupation. Considering farmers report high stress and barriers to seeking healthcare, it is important to understand factors influencing alcohol use to tailor interventions and healthcare resources for alcohol use in rural areas.
METHODS: An online survey was distributed to the agricultural community in the United States (n = 1045). Data was collected through QualtricsXM, and SPSS 28.0 was used for data analysis.
RESULTS: Both formal healthcare challenges (β = 0.112, p = .004) and stigma (β = 0.328, p < .001) were identified as predictors of increased perceived stress, while resilience (β = -0.137, p < .001) was identified as a protective factor against perceived stress. Higher perceived stress was identified as a predictor of binge drinking behavior (β = 0.151, p < .001), and formal healthcare challenges were associated with higher drinking volume (β = 0.174, p < .001), and engaging in more frequent alcohol consumption (β = 0.123, p = .004) over the last three months. Resilience was identified as a protective factor against increased alcohol consumption (β = -0.084, p = .032). Stigmatization of help-seeking for mental health challenges was associated with fewer instances of alcohol consumption over the last three months (β = -0.169, p < .001).
CONCLUSIONS: Interventions to address stress and alcohol consumption should focus on promoting resilience, reducing stigma, and encouraging peer support to address cultural norms around mental health and alcohol use. Rural practitioners should develop cultural competence to better serve agricultural communities to prevent alcohol use disorders. To discuss ways to reduce stigma and encourage peer support to address alcohol and mental health disorders in rural farming populations.

The intensification of livestock farming can pose risks to the environment due to the increased use of veterinary products and the generation of waste in confined areas. The quality of water bodies near livestock establishments (Areco River (A) and Doblado stream (D), San Antonio de Areco, Buenos Aires, Argentina) was studied by physicochemical parameters, metals, pesticides, emerging contaminants, and lethal and sublethal toxicity (neurotoxicity and oxidative stress) in larvae of the native amphibian Rhinella arenarum. Six sites were selected: upstream (S1A and S1D), at the level (S2A and S2D), and downstream (S3A and S3D) from the establishments. A low concentration of dissolved oxygen was observed in Doblado stream (< 2.34 mg/L). Cu, Mn, V, and Zn exceeded the limits for the protection of aquatic life at various sites. Between 24 and 34 pesticides were detected in all sites, with 2,4-D, atrazine, and metolachlor being the most recurrent. In water and sediment, the concentrations of ivermectin (S2A, 1.32 μg/L and 58.18 μg/kg; S2D, 0.8 μg/L and 85.22 μg/kg) and oxytetracycline (S2A, < 1 mg/L and < 1 mg/kg; S2D, 11.8 mg/L and 39 mg/kg) were higher at sites near the establishments. All sites caused between 30 and 38.3% of lethality and produced neurotoxicity and alterations in the reduced glutathione content. Moreover, larvae exposed to samples from all sites incorporated ivermectin. These results demonstrate the degradation of the studied sites in relation to the agricultural activities of the area, highlighting the need to take measures to protect and preserve aquatic ecosystems.

Development of effective prevention and mitigation strategies for marine plastic pollution requires a better understanding of the pathways and transport mechanisms of plastic waste. Yet the role of estuaries as a key interface between riverine inputs of plastic pollution and delivery to receiving marine environments remains poorly understood. This study quantified the concentration and distribution of microplastics (MPs) (50-3175 μm) in surface waters of the St. Lawrence Estuary (SLE) in eastern Canada. Microplastics were identified and enumerated based on particle morphology, colour, and size class. Fourier Transform Infrared (FTIR) spectroscopy was used on a subset of particles to identify polymers. Generalized linear models (Gamma distribution with log-link) examined the relationship between MP concentrations and oceanographic variables and anthropogenic sources. Finally, a risk assessment model, using MP concentrations and chemical hazards based on polymer types, estimated the MP pollution risk to ecosystem health. Mean surface MP concentration in the SLE was 120 ± 42 SD particles m-3; MP concentrations were highest in the fluvial section and lowest in the Northwest Gulf of St. Lawrence. However, MP concentrations exhibited high heterogeneity along the length and width of the SLE. Microplastics were elevated at stations located closer to wastewater treatment plant outflows and downstream sites with more agricultural land. Black, blue, and transparent fibers and fragments ≤250 μm were most commonly encountered. Predominant polymer types included polyethylene terephthalate, regenerated cellulose, polyethylene, and alkyds. While the overall risk to ecosystem health in the entire estuary was considered low, several stations, particularly near urban centres were at high or very high risk. This study provides new insights into the quantification and distribution of MPs and first estimates of the risk of MP pollution to ecosystem health in one of the world\'s largest estuaries.

OBJECTIVE: 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.

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.

Several crops depend on both managed and wild bees to produce fruits and/or seeds, and the efficiency of numerous wild bees is higher than that of some managed species. Therefore, knowing and understanding the required resources for wild bees could enabled the establishment of management practices to increase their populations. Here, we provide information about the nesting biology of Megachile (Chrysosarus) jenseni, a Faboideae-specialist bee species. Based on observations from two populations occurring in contrasting agroecosystems, this bivoltine species showed common behavioral features shared with other species of subgenus Chrysosarus, such as the use of petal pieces and mud as nesting materials and the utilization of pre-existing cavities. Both studied populations showed a bivoltine life cycle with a rapid early-summer generation and a second generation, with most individuals overwintering. Main causes of mortality were unknown diseases (or other factors), causing the death of preimaginal stages. Moreover, this species was attacked by a cleptoparasite megachilid (Coelioxys remissa), a parasitic eulophid wasp (Melittobia sp.), and a bee fly (Anthrax oedipus). Finally, we discussed the potential use of this leaf-cutter bee species for alfalfa pollination.

Agricultural nutrients nitrogen and phosphorus can subsequently be transported to waterways and are often managed through the adoption of best management practices (BMPs). However, we have a poor understanding of how the use of BMPs varies spatially and how BMP adoption might be related to nutrient yields in surface waters. To address this, we performed a survey of agricultural landowners across New York State and compared this with estimates of annual incremental nitrogen and phosphorus yields of agricultural origin from the Spatially Referenced Regressions On Watershed attributes (SPARROW) model. Using these socio-behavioral data and SPARROW predictions, we perform colocation analysis to identify areas where watersheds with high nutrient yield from agriculture are collocated with non-use of agricultural BMPs. This colocation analysis offers a novel methodology for identifying areas where monitoring of waterways and promotion of best management practices could be targeted to achieve the greatest benefits.