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Soil cadmium (Cd) contamination has emerged as a significant global environmental concern, posing numerous risks to individual organisms and entire ecosystems. Concurrently, the global increase in pesticide usage has elevated the influx of chloride ions (Cl−) into the soil. Given Cl−’s robust ability to coordinate and complex with various heavy metal ions, understanding its influence on the migration and transformation of Cd in soil-rice systems is essential for the rational application of pesticides and the effective mitigation of soil heavy metal pollution. In this paper, we explained the effect of Cl− on the environmental behavior of Cd in the soil-rice system in terms of growth traits, Cd uptake and accumulation by rice, and Cd solid-solution phase interface behavior through pot experiments and sand culture experiments. The results showed that Cd concentrations in all parts of the rice treated with CaCl2 during the filling period were lower than those in the Ca(NO3)2-treated group, with Cd accumulation diminishing as Cl− concentration increased. This suggests that the filling period is critical for Cd uptake and accumulation in rice. Unlike the accompanying anion NO3−, exogenous Cl− reduced Cd concentrations in the soil solution but increased them in rice. Notably, when the Cd/Cl ratio ranged from 0.625 to 2.5, Cl− formed predominantly CdCl+-complexes with free Cd2+ in the soil solution, enhancing the mobilization of Cd bound to soil particles and its subsequent absorption by rice. This study aims to assess Cl−’s effect on Cd migration and transformation in soil-rice systems, providing insights for safe rice production on Cd-contaminated soils and rational use of chlorine-containing pesticides.

Cocoa is one of the major cash crops in Nigeria and its production is directly related to the effective utilization of agrochemicals. This paper analysed the factors influencing cocoa farmers’ compliance with agrochemical safety regulations. The data were collected from 326 cocoa farmers from Ogun and Osun states, using multi-stage sampling procedures. The data were analysed with Principal Component Analysis (PCA) and an Endogenous Tobit Regression model. The results showed that majority of the farmers were primarily growing cocoa and average ownership of personal protective equipment (PPE) was less than two. Awareness of manufacturers’ instructions was high for insecticides and fungicides, while majority of the farmers were not eating or drinking while handling agrochemicals. Safety compliance indicators were significantly influenced (p < 0.10) by farmers’ ownership of PPE, education, age, awareness of manufacturers’ safety instructions and health status. It was concluded that utilization of PPE was very low among the farmers and efforts to facilitate safety training on the use of different agrochemicals will facilitate safety compliance through proper understanding of manufacturers’ instructions.

Introduction: The prevalence of occupational diseases in the agricultural sector is higher than in other industries, since agricultural workers are at higher risk of exposure to different chemicals and pesticides, and are more prone to occupational accidents. Objective: To conduct a review of recent literature on occupational health and risk in agriculture. Materials and methods: A literature search was conducted in PubMed, SciencieDirect and Scopus using the following search strategy: type of articles: original research papers; publication language: English; publication period: 2006-2016; search terms: “agricultural health”, “agrarian health”, “risk factors”, “epidemiology”, “causality” and “occupational”, used in different combinations (“AND” and “OR”). Results: The search yielded 350 articles, of which 102 met the inclusion criteria. Moreover, 5 articles were found in grey literature sources and included in the final analysis. Most research on this topic has been conducted in the United States, which produced 91% (97/107) of the articles included in the review. Conclusions: Most studies on agricultural health focused primarily on the harmful effects of occupational exposure to agrochemicals and pesticides, and the consequences of occupational accidents. However, since more than 90% of these studies come from USA, a more comprehensive approach to agricultural health is required, since what is reported here may be far from the reality of other regions, especially Latin America.

This paper aimed to optimize the method of obtaining the intrinsic vulnerability index of the aquifers to the pollution (IVIAP) conferring to the variable infiltration and recharge the matrix combination between the slope intervals and the relative density of vegetation cover. In addition to the collection of hydrolytic data from the aquifer units, the Geographic Information System (GIS) was used to map 34 springs in Rondonópolis, MT, compile data from the groundwater table of tubular wells, and correct satellite and radar images from medium-high spatial resolutions. The IVIAP was identified as an alternative, simple and effective method to qualify vulnerabilities to aquifer pollution, especially in regions where there is a shortage of hydrological information

The uniform application (UA) of agrochemicals results in the over-application of harmful chemicals, increases crop input costs, and deteriorates the environment when compared with variable rate application (VA). A smart variable rate sprayer (SVRS) was designed, developed, and tested using deep learning (DL) for VA application of agrochemicals. Real-time testing of the SVRS took place for detecting and spraying and/or skipping lambsquarters weed and early blight infected and healthy potato plants. About 24,000 images were collected from potato fields in Prince Edward Island and New Brunswick under varying sunny, cloudy, and partly cloudy conditions and processed/trained using YOLOv3 and tiny-YOLOv3 models. Due to faster performance, the tiny-YOLOv3 was chosen to deploy in SVRS. A laboratory experiment was designed under factorial arrangements, where the two spraying techniques (UA and VA) and the three weather conditions (cloudy, partly cloudy, and sunny) were the two independent variables with spray volume consumption as a response variable. The experimental treatments had six repetitions in a 2 × 3 factorial design. Results of the two-way ANOVA showed a significant effect of spraying application techniques on volume consumption of spraying liquid (p-value < 0.05). There was no significant effect of weather conditions and interactions between the two independent variables on volume consumption during weeds and simulated diseased plant detection experiments (p-value > 0.05). The SVRS was able to save 42 and 43% spraying liquid during weeds and simulated diseased plant detection experiments, respectively. Water sensitive papers’ analysis showed the applicability of SVRS for VA with >40% savings of spraying liquid by SVRS when compared with UA. Field applications of this technique would reduce the crop input costs and the environmental risks in conditions (weed and disease) like experimental testing.

This paper describes a mathematical model of the learning process suitable for studies of conditioning using the proboscis extension reflex (PER) in honey bees when bees are exposed to agrochemicals. Although procedural variations exist in the way laboratories use the PER paradigm, proboscis conditioning is widely used to investigate the influence of pesticides and repellents on honey bee learning. Despite the availability of several mathematical models of the learning process, no attempts have been made to apply a mathematical model to the learning curve in honey bees exposed to agrochemicals. Our model is based on the standard transfer function in the form Y=B3 e-B2 (X-1) +B4(1-e-B2 (X-1)) where X is the trial number, Y is the proportion of correct responses, B2 is the learning rate, B3 is readiness to learn, and B4 is ability to learn. We reanalyze previously published data on the effect of several classes of agrochemicals including: (1) those that are considered harmless to bees (e.g., pymetrozine, essential oils, dicofol); (2) sublethal exposure to pesticides known to harm honey bees (e.g., coumaphos, cyfluthrin, fluvalinate, permethrin); and (3) putative repellents of honey bees (e.g., butyric acid, citronella). The model revealed additional effects not detected with standard statistical tests of significance.