Use of artificial neural networks to rescue agrochemical-based health hazards: A resource optimisation method for cleaner crop production. (20th November 2019)

• Record Type: Journal Article
• Authors: Elahi, Ehsan; Weijun, Cui; Zhang, Huiming; Abid, Muhammad
• Is Part Of: Journal of cleaner production. Volume 238(2019)
• Publication Date: 2019-11-20
• Subjects: Rice -- Agrochemicals -- Artificial neural network -- Efficiency -- Human health exposures -- Cleaner production; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2019.117900
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: The main aim of the study was to estimate the target values of agrochemical use, and its impact on crop productivity, and human health. Adoptions of protective measures and their impacts on human health during pesticide application were also evaluated. To fulfil the study objectives, using a standardised questionnaire, cross-sectional data of 480 rice growers were collected from September to October, 2017 from the Hafizabad and Sheikhupura districts of Punjab, Pakistan. Various econometric methods were used for data analysis. The artificial neural network (ANN) found an indiscriminate use of agrochemicals in the study area and suggested reducing the applied quantity of Pure N and pesticides by 43.6 and 52.6%, respectively, at a given level of rice yield, while the quantity of Pure P, Pure K, and farmyard manure (FYM) need to increase by 67.6, 15, and 21%, respectively, to obtain an efficient production level. The Cobb-Douglas (CD) production function estimated a positive and significant impact of Pure P, Pure K, FYM, education, and farming experience on rice yield. Results also showed that an indiscriminate use of pesticide not only compromised rice efficiency, it impinged on field worker health. Poisson regression found that occurrence of eye irritation, dizziness, cough, and nausea were significantly related with chemical application. Logistic regression found that education, farming experience, and extension services significantly increased in the adoption of various protective measures. Furthermore, cases of primary health exposures were significantly less among those who were used protective measures during pesticide application. Results of Poison regression function also confirmed that use of protective clothing, goggles, mask, gloves and boots during pesticide spraying significantly reduced human health exposures. It is recommended to apply the quantity of nutrients and chemicals suggested by ANN method. Use of biochemicals is a more sustainable and environmentally friendly strategy. Provision of educational programs and trainings to farmers on safe use of pesticides is required to avert occupational health exposures. Furthermore, governmental actions such as restriction and/or interdiction on use of highly toxic pesticides and enforcement to adopt safety measures during pesticide application, are needed to reduce pesticide exposures. Graphical abstract: Structure of ANN method.Image 1 Highlights: Unsafe use of agrochemicals increased the virulence of occupational health exposures. Artificial neural network suggested to reduce overuse of agrochemicals in study area. Use of personal protective devices was not common practice among farmers. Education and farming experience significantly reduced occupational health exposures. Adoption of safety measures significantly reduced the pesticide exposures.