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The purpose of this paper is to investigate the determinants and impacts of outsourcing pest and disease management on rice production in China.

A multinomial endogenous treatment effects model which accounts for selection bias was used.

The results show that outsourcing decisions are driven mainly by the size of the farm, the age of the household head and other household characteristics. Further, the authors find that outsourcing labor for pest and disease control has no significant effect on pest control cost and rice yields, though it reduces the number of pesticide applications. Conversely, outsourcing of professional services can increase rice yields by 4.1 percent, and at the same time it increases pest and disease control costs by 50.6 percent. However, it is found that outsourcing of professional services exerts no significant impact on the farm profitability.

This study suggests that households with large farm size are more likely to outsource professional services and, therefore, service providers and governments should target those farmers to provide incentives and create greater awareness of the benefits from the outsourcing of professional services. Moreover, the increase in yields along with the government subsidy justifies the outsourcing of professional services by farmers. However, service providers and policy makers have a lot of leeway to come up with cheaper methods for pest and disease management in rice production.

This study is the first attempt to simultaneously evaluate the determinants and impacts of outsourcing pest and disease management on rice production in China.

This study aims to identify the most relevant causal factors and the feedback loops of the dynamics between Tuta absoluta incidence in tomato crops and farmers' reactions to the problem. The authors seek to develop a conceptual model based on farmers' know-how to address crop damage by T. absoluta at a local and regional levels in order to determine how to confront this problem in the tomato-growing region of Sáchica, Colombia.

Community-Based System Dynamics (CBSD) is a participatory research methodology in which a group of stakeholders identifies relevant variables and the cause-effect relations among them which are then arranged into a causal loop diagram. The authors implemented this methodology in a workshop, focused on the farmers' insights related to the pest situation at the local and regional level, to achieve a causal loop diagram that explained pest dynamics and their potential management.

The relevant factors for the presence of T. absoluta, seen in the causal loop diagram, vary regionally and locally. At the local level, the pest impacts tomato production, farmers' well-being and their cash flow, while at the regional level, it affects market dynamics and environment and promotes regional coordination among farmers. Farmers propose product innocuity as a key regional objective. They also proposed establishing a planting calendar and census of greenhouses to control the pest throughout the region and the tomato supply.

First, the synthesized model could not be validated with the farmers due to the COVID 19 epidemic. However, the authors held sessions with experts to analyze each result. Second, decision-makers from the local government did not participate in the workshop. Nevertheless, the approach of the workshop was aimed at understanding the mental models of the farmers since they are the ones who decide how pests are managed. Finally, even though farmers showed interest in projects aimed at proposing area-wide, long-term and wide pest control strategies, there is a risk that they will not adopt the proposed changes, due to risk aversion.

CBSD has not been applied to agricultural systems to analyze impacts from pests at the local and regional levels. The results of this study contribute to designing future interventions for pest control in the region, along with the factors which may turn out to be “side effects” or unwanted results. To design pest control interventions at a regional level, a sound understanding of the variables or factors that control the system dynamics at various levels is required. This study represents the first step towards that end.

Since the advent of agriculture, man has suffered losses of his crops to various pests. In 1859, in the introduction to his book Farm Insects, John Curtis wrote ‘… little attention has, comparatively, been paid to those animals which annually consume an amount of produce that sets calculation at defiance, and indeed if an approximation could be made to the quantity thus destroyed, the world would remain sceptical of the result obtained, considering it too marvellous to be received as truth’.

Using the remarkable olfaction ability, insects can sense trace amounts of host plant volatiles that are notorious for causing severe damage to fruits and vegetables and in consequence the industry. The purpose of the paper is to investigate the interactions between olfactory proteins, odorant-binding proteins (OBPs) and host plant volatiles through the developed olfactory biosensors. It might be helpful to develop novel pest control strategies.

Using the successfully expressed and purified OBPs of the oriental fruit fly Bactrocera dorsalis, a biosensor was developed by immobilizing the proteins on interdigitated electrodes through nitrocellulose membrane. Based on electrochemical impedance sensing, benzaldehyde emitted by the host plants, such as Beta vulgaris, was detected, which could be used to investigate and analyze the mechanisms of pests’ sense of chemical signals. The relative decreases of charge transfer resistances of the sensor were proportional to the odorant concentrations from 10⁻⁷ M to 10⁻³ M. Meanwhile, the interactions between OBPs and benzaldehyde were studied through the process of molecular docking.

The paper provides a pest OBPs-based biosensor that could sensitively detect the host odorants benzaldehyde. Meanwhile, the most related amino acids of OBPs that bind to host plant volatiles can be distinguished with molecular docking.

An olfactory biosensor was developed to explore interactions and mechanism between the pest OBPs and benzaldehyde, which showed promising potentials for small organic molecule sensing. Simultaneously, it might be helpful for novel pest control strategies.

The purpose of this paper is to analyse factors affecting the use of pesticides in intensive vegetable farming in Java, Indonesia. Evaluating such factors is expected to provide appropriate policies to reduce pesticides, and eventually, mitigates the adverse impacts of pesticides on human health and the environment.

Data were compiled from a farm survey of randomly selected 240 vegetable farmers in three regions of Java in 2014. A concept of economic threshold was employed to analyse the pesticide use determinants, which were estimated using econometric models.

Factors leading to the increase in the application of pesticides were the number of observed insect pests, prices of vegetables, use of local varieties, and use of mixed pesticides. Conversely, factors lowering the use of pesticides were the number of observed diseases, the cost of pesticides, and area planted to vegetables. The most important factor in influencing pesticide use was farmers’ perception on the correct prediction of yield losses associated with pests and diseases.

The sample for this research is somewhat low and the analysis was based on one-year data of the quantity of pesticides in a formulation.

The use of pesticides can be reduced by training farmers on crop protection practices, which provide correct information on pests and diseases. Policies related to the price of pesticides would be ineffective, as farmers still highly relied on pesticides. These findings will be useful for reducing the use of pesticides in intensive vegetable farming in Indonesia, and in tropical countries in general.

Pesticides have two opposite properties: to increase income on the one side and to cause devastation of life on the other side. Because pesticides are generally less selectively toxic than would be desired, non-targets including humans and the environment must be protected from contamination by these agrochemicals. This study found the most important determinants for reducing pesticide exposures in Indonesian intensive farming.

Mental budgeting, as a part of mental accounting theory, is expected to impact a household's budgetary management in terms of expenses. The purpose of this paper is to study whether and how mental budgeting can explain differences in farmers' reactions to different incentives of low-toxicity pesticide use.

Based on data from a survey of 393 vegetable farmers in the Sichuan Province, this analysis, using a Likert Scale approach, first explores whether farmers utilize mental budgeting. Secondly, using a Probit model, this paper analyzes how mental budgeting affects farmers' intentions to switch to low-toxicity pesticide use when faced with different incentives.

The results show that the majority of farmers categorize agricultural inputs into different groups and that 26.46% of the investigated farmers utilize mental budgeting for pest control practices. In addition, farmers who utilizing mental budgeting report a higher willingness to switch to low-toxicity pesticides when they're presented with a specific subsidy. Furthermore, if offered a price premium for quality, the willingness to switch to low-toxicity pesticides for farmers utilizing mentally budget is lower compared to other farmers.

This paper examines the existence of mental budgeting among farmers. It provides a better understanding of how farmers categorize agricultural inputs and their mental mechanisms with respect to agricultural expenses. Finally, this paper is the first to study the effects of mental budgeting on farmers' reactions to different incentives aimed at stimulating the adoption of low-toxicity pesticides.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

Integrated pest management (IPM) is a well‐known innovation that accords with modern environmental management “best practice”. In this paper it is examined as an example of the theory and practice underpinning workplace learning and andragogy. Particular attention is focused on the role of agricultural extension workers (AEWs) as learning facilitators in a non‐formal setting. As contextual background, a recent IPM diffusion project in a region of Thailand, where durian is extensively grown, as a process of innovation adoption is outlined. In sum, the intelligent way IPM knowledge was transferred, though the mediating role of AEWs reflected the current emphasis on collaborative partnerships in “real‐life” workplace learning contexts as an effective means of managing change in complex environments.

The evidence on sources of food poisoning is reviewed. Research shows that many of the problems originate in the kitchen and three lines of attack against pests – exclusion, restriction and destruction – are identified. A number of particular types of pests and the preferred treatment methods are described.

Proliferating pest problems, sharply rising pest control costs, increasing environmental pollution, rising rates of injury and death due to pesticide poisoning, and burgeoning pesticide‐related legal entanglements leave little doubt in the minds of most citizens that a crisis in chemical pest control exists. The underlying cause of today's pesticide dilemma lies in the lack of ecological consideration given the synthesis, experimental development, registration and utilisation of newly developed synthetic pesticides. The production of synthetic organic pesticides increased from an estimated 464,000 pounds in 1951 to approximately 1.4 billion pounds in 1980. Increases in production were followed by the recognition that such increased use of synthetic chemicals would be accompanied by extensive human and environmental impact. With the rapid increase in the use of pesticides, there has been a corresponding increase in public attention and public concern about this impact on human health.