Reduce Production Risk Research Articles

RISIKO PRODUKSI USAHATANI PADI MODEL CORPORATE FARMING DI JAWA TENGAH

<p><em>Corporate farming is a strategic step to increase the production and productivity of subsistence lowland rice farming. In addition, the use of corporate farming model agriculture is a new opportunity to overcome the risk of national rice production. Especially at this time, the rice fields are getting narrower due to the conversion of functions into agro-tourism, housing, industry, and services. The purpose of this research is to estimate the production risk and farmers' behavior towards the risk of rice farming with the corporate farming model in Sukoharjo Regency, Central Java. The result of this research shows that the risk of production of the corporate farming model is high with a variability of 50.45. This means that the corporate farming model of rice farming faces the risk of major crop failure. Factors that influence the increase in production risk are SP-36 fertilizer and pesticides, while land area, Urea fertilizer, NPK fertilizer, and labor have the potential to reduce production risk but have no significant effect. Farmers' behavior towards the risk of farming with the corporate farming model is risk aversion. The attitude of reluctant farmers is shown by their behavior in facing the intermediate risk of 32 farmers</em></p>

Seasonal dynamics of fallow and cropping lands in the broadacre cropping region of Australia

Fallowing is an important strategy for enhancing soil health, water harvesting and crop yields, thus improving sustainability and reducing production risks in dryland farming systems in Australia. However, accurate data regarding the location, frequency, extent, and duration of fallow land is not readily available at high spatio-temporal resolutions before and during a cropping season. As a result, continental maps depicting seasonal dynamics of fallow and cropping lands and their responses to climate change and human activities remain missing. This study set out to develop an automated approach to discriminate fallow areas from cropping areas within Australian broadacre cropping region. This was done by applying a mathematical algorithm of turning point detection technique to a curve fitted vegetation index time series from 2016 to 2022, derived from high spatial (10 m) and temporal (5-days) resolution Sentinel-2 data. Specifically, we aimed to derive: (i) 5-day revisit fallow/cropping map layers; (ii) key attributes of the fallow and cropping periods (e.g., start, end, duration); (iii) cropping intensity and area. The proposed approach yielded a high and significant overall accuracy, surpassing 92% with substantial balanced accuracy (>0.9) against >3000 fields for individual winter and summer seasons across Australia. In addition, this study's temporal and geographical outputs, encompassing seasonal attributes, cropping intensity and cropping areas, robustly enhanced the understanding of seasonal cropping dynamics and historical shifts in dryland farming practices. Findings derived from this analysis aligned well with extreme climatic events and changes in crop activities adopted in response to such events. Future research will aim to extract key seasonal dynamics attributes influenced by cropping practices, enhancing the conversion of high-resolution earth observation data into actionable industry knowledge.

Green production planning for circular supply chain and resource management: An empirical study for high-tech textile dyeing

Effective production planning is crucial for optimizing resource management and enhancing the recycling rate of defective fabric to reduce waste and wastewater generation. Dyeing is valuable and polluting process in the textile industry. Low quality and uncertain yields increase environmental costs for handling material waste and wastewater, while little research has been done to address the present problem. This study aims to develop a robust green production planning framework to minimize wastewater production and maximize the recycling rate of defective dyed products. To validate the proposed framework, martingale models of forecast evolution were designed to consider yield uncertainty, while a simulation model was developed based on a leading textile company in Taiwan. The results have shown practical viability of the proposed model that can effectively deal with the yield rate uncertainty and reduce production risks with robust performance for both circular supply chain and total resource management.

METHODOLOGICAL APPROACHES TO DETECTING UNDECLATED WORK

Negative economic phenomena such as the informal labor market and informal employment in the formal sector are becoming increasingly widespread and pose a major threat to the economy as a whole and to social security in the labor market. These phenomena have the following common features: illegal employment of an employee without entering into an employment contract, which is provided for by the current legislation of Ukraine, concealment of actual working hours and payment of wages "in an envelope", substitution of actual employment contracts with civil and commercial contracts, non-payment of taxes, social insecurity and a high probability of employees being unemployed. At the same time, in the event of an injury in the informal labor market, the injured worker must spend significant funds on treatment without any compensation payments.
 The author examines the priority areas of state policy aimed at eliminating the threats of the informal labor market and the main reasons for the spread of informal employment in Ukraine, taking into account the specifics of the current state of the economy, as well as the consequences of informal social and labor relations for both the employee and the state (a significant reduction in the volume of budget revenues, as well as the share of tax revenues). The purpose of this study is to create tools for assessing the status of formalization of labor relations in Ukraine with a view to properly organizing state supervision (control) measures to identify undeclared labor relations, and to ensure uniformity of approaches for labor inspectors to assess the risks of undeclared labor by entities. A phased expert evaluation of measures to reduce production risk at an industrial enterprise with the introduction of weighting coefficients according to certain criteria is proposed. A consistent model for managing the risks of using undeclared labor has been developed, based on a component method for assessing the risk of traumatic events and a mathematical model of management decisions. This model takes into account all available information on labor relations obtained from various sources of its accumulation in the executive authorities, stimulates preventive activities and allows to establish the dependence of the level of occupational risk on the validity of labor protection measures and reduce the influence of the subjective component of expert judgments

Efficiency, risk, and profitability of rainfed rice farming in South Sulawesi, Indonesia

Rice farming is a business that has many risks, and production risks cause fluctuations in production. Productivity influences farming income and profitability. An increase and decrease in production will affect revenue or profitability. The purpose of this study is to analyze the technical efficiency, production risk, and profitability of rainfed lowland rice farming. This research was conducted in two regencies in South Sulawesi Province, namely Maros Regency and Pangkajene Islands Regency. The sample of this study was 100 farmers in rainfed rice fields. This research used quantitative methods with a survey approach. Data were collected using observation, recording, and interview instruments, and were then analyzed using quantitative description coupled with a t-test for independent samples. The results showed that rainfed lowland rice farming in Maros Regency and Pangkajene and Archipelago Regency was technically efficient. Rainfed lowland rice farming in Maros Regency and Pangkajene Regency and Islands is at risk. Rainfed lowland rice farming in Maros Regency and Pangkajene Regency and the islands using profitability analysis generates income and profit, and is feasible to cultivate. Farmers need to use production inputs as recommended to increase production reduce production risk and efficiently use inputs.JEL Classification D10; Q00; Q12

Developing an efficient climate forecasting model for the spatiotemporal climate dynamics estimation and the prediction that fits the variable topography feature of the upper Blue Nile basin, Ethiopia

The spatiotemporal climate estimation and prediction are challenging and demanding for variable topography feature areas. Spatially, the upper Blue Nile basin (UBNB) is unsatisfactory owing to complex topographical features and the lack of accurate climate prediction and estimation models. Yet accurate information and reliable seasonal climate dynamics estimation and forecasting are essential in the region for controlling reservoir operation and flooding prevention. However, there is a lack of reports regarding the accuracy and predictability of the climate in the UBNB region. Therefore, this article aims to improve the Artificial Neural Network (ANN) model by adopting the Impulse Response Function (IRF) and comparing it to the Regional Climate Model (RCM) and European Centre of Medium-range Weather Forecast (ECMWF) models for spatiotemporal climate dynamics estimation and forecasting, which are reliant on the UBNB topography features. Different atmospheric parameter data are investigated from reanalysis models. A fast Fourier transform is applied to remove the redundancy of the data and avoid the computational cost. The RCM and ECMWF models are used to test the performance of ANN model prediction skills. The IRF model was applied to enhance the ANN model's climate prediction performance. A 12-month spatial variation of precipitation is analyzed. The ANN model showed a satisfactory prediction performance, better than the RCM and ECMWF models by 20 %. After increasing the ANN model performance by IRF, the prediction errors are reduced by 10.2 % for precipitation and by 7.9 % for temperature. Based on the model results, the temperature has increased over the past 40 years and is expected to continue for the coming three decades (30 years). In contrast, precipitation over the past 40 years has decreased, and a slight increment will be expected in the next eight years, from 2024 to 2029. Therefore, this model should be practiced across Ethiopia and the Globe for accurate prediction of climate patterns. Hence, clear awareness should be created for the local community by providing a scientific remedy for future climate conditions to reduce production risk.

Faktor-Faktor Yang Memengaruhi Risiko Produksi Padi Pada Sistem Bagi Hasil Di Kabupaten Bone

The sharecropping system has been used by Indonesian people for generations. Sharecropping agreements are made to expand the land cultivated by farmers to increase agricultural production. Specifically in Bone Regency, South Sulawesi, there are two sharecropping types: Type 1 and Type 2. In Type 1, the tenant gets two shares, and the landowner receives one share of output, while the tenant bears almost all production input costs. In Type 2, the landowner and tenant get an equal share of the output because both parties share the costs of production inputs. In addition to sharing production inputs and outputs, sharecropping also involves sharing production risks. This study aimed to analyze the factors that affect the risk of sharecropping systems in type 1 and type 2. The research sample was determined using the snowball sampling method with a sample size of 65 type 1 farmers and 15 type 2 farmers. The data analysis method used was multiple linear regression analysis with the OLS model using SPP software. The production risk function model uses the Just and Pope risk model with the Cobb-Douglas production function. The results of this study concluded that in Type 1 and Type 2, there are differences in factors that significantly affect production risk. In type 1, the factors that significantly influence production risk are pesticides and age as a risk-reducing factor, while land area is a risk-inducing factor. As for type 2, pesticides and labour have a significant effect on inducing production risk, and there are no factors that have a significant impact on reducing production risk. The results of this study suggest that the use of type 1 can be done if tenants have capital, whereas if tenants do not have capital and only rely on their ability, they should choose type 2.

Независимый аудит как новое направление оценки промышленной безопасности на опасных производственных объектах горнодобывающего комплекса

The paper discusses a new key trend used to assess the state of industrial safety at mining operations as well as the cooperation efficiency. The experience GEOEXPERT LLC has gained in carrying out independent technical auditing and the relevance of this line of activity has been demonstrated. Taking into account current challenges and the experience of technical auditing, as well as the prospects for the development of this activity, the article and the data presented in it will help the top management to make their decisions, while the description of real auditing cases will provide the mining companies with an insight into the use of this new method of co-operation. Independent technical auditing is one of the tools to control the state of industrial safety at a mining operation, which provides a general picture of the company, classification of hazardous production facilities according to their hazard characteristics, independent verification of compliance of the production process organization and the production patterns with the regulatory requirements in order to improve the safety of operation, enhance the quality of products and reduce production risks. Three decades of experience gained by GEOEXPERT LLC and studying various aspects of challenges in the mining industry help to identify the method of independent technical auditing as one of the main tools for solving industrial safety problems and preventing emergencies at the operations.

Integrating Field Data and a Modeling Approach to Inform Optimum Planting Date × Maturity Group for Soybeans under Current and Future Weather Conditions in Kansas

Optimizing planting date by maturity group (PD × MG) is critical to increase productivity and reduce production risks. Understanding the effect of management, not only under current, but also future weather conditions, is even more relevant for developing effective mitigation strategies. This paper provides an analysis of the optimum combinations of soybean PD × MG management in the central-eastern region of Kansas (United States) for both current and future weather conditions. Three geographical clusters illustrating the main environmental and management characteristics were defined within the central-eastern region of Kansas. The Agricultural Production Systems Simulator platform was employed to explore PD × MG combinations (PD from mid-April to mid-July; MG from III to VI) comparing current (2011–2021) and future (2042–2052) weather conditions. Overall, early planting dates produce greater yields, but reduce their stability over time (with a 15% increase in yield variation relative to late planting) across the clusters. Late planting dates resulted in a reduction close to 27% for soybean yields relative to those obtained by planting at early dates under current weather conditions. Furthermore, longer maturity groups (IV, V, and VI) resulted in a reduced yield penalty when planting time was delayed under the current weather conditions. However, this combination did not always represent the strategy that maximized yields.

A Review Study on ML-based Methods for Defect-Pattern Recognition in Wafer Maps

The identification of defects plays a key role in the semiconductor industry as it can reduce production risks, minimize the effects of unexpected downtimes and optimize the production process. A literature review protocol is implemented and latest advances are reported in defect detection considering wafer maps towards quality control. In particular, the most recent works are outlined to demonstrate the use of AI-technologies in wafer maps defect detection. The popularity of these technologies is then presented in the form of visualizing graphs. This enables the identification of the most popular and most prominent ML-methods that can be exploited for the purposes of Industry 4.0.

СТАТИСТИЧЕСКИЙ АНАЛИЗ АВАРИЙНОСТИ НА ПРОИЗВОДСТВЕ В РЕЗУЛЬТАТЕ НЕКОРРЕКТНОГО ФУНКЦИОНИРОВАНИЯ СИСТЕМЫ ЭНЕРГООБЕСПЕЧЕНИЯ ПРЕДПРИЯТИЯ

This article presents an analysis of industrial accidents in order to ensure the continuous operation of the enterprise. The impact of potentially hazardous production factors on a person is considered on the example of the chemical industry. The analysis of statistical data on emergency situations in the electric power industry over the past seven years has been carried out. A set of measures to prevent accidents is proposed, consisting of five main groups of measures: legislative, technical, organizational, economic, medical and preventive. One of the best and promising options for reducing production risks is an automated control system (ACS), the advantages of which include reducing unproductive costs and energy losses, obtaining reliable information about the progress of the technological process, the condition of equipment and controls in order to quickly eliminate abnormal emergency and post-emergency modes.

A systematic review on machine learning methods for root cause analysis towards zero-defect manufacturing

The identification of defect causes plays a key role in smart manufacturing as it can reduce production risks, minimize the effects of unexpected downtimes, and optimize the production process. This paper implements a literature review protocol and reports the latest advances in Root Cause Analysis (RCA) toward Zero-Defect Manufacturing (ZDM). The most recent works are reported to demonstrate the use of machine learning methodologies for root cause analysis in the manufacturing domain. The popularity of these technologies is then summarized and presented in the form of visualizing graphs. This enables us to identify the most popular and prominent methods used in modern industry. Although artificial intelligence gains more and more attraction in smart manufacturing, machine learning methods for root cause analysis seem to be under-explored. The literature survey revealed that only limited reviews are available in the field of RCA towards zero-defect manufacturing using AI and machine learning; thus, it attempts to fill this gap. This work also presents a set of open challenges to determine future developments.

Pengaruh Kemitraan Terhadap Risiko Produksi Usaha Ternak Ayam Broiler di Kabupaten Serang Provinsi Banten

Serang Regency is one of the broiler producing regions in Banten Province. Broiler’s production in Serang Regency faces a high risk of chicken production and mortality. Production risk also has an impact on the breeder’s income and decisions to breed broilers. This research aimed to analyze the risk of the broiler breeder’s production and analyze the effect of partnerships on the risk of broiler breeding in Serang Regency. This research was conducted in Serang Regency, Banten Province. The selection of respondents was done by purposive sampling to collect 64 broiler breeders. The number of samples is divided into 32 partnered breeders and 32 independent breeders. The Just and Pope model is used to analyze production risk. The results of the analysis showed that the production risks faced by partnered breeders are higher than independent breeders. Besides that, partnerships do not have a significant influence to reduce the risk of production of broiler breeding. The results of the analysis showed that the factors of production that are increasing production risk in broiler are vaccines, labor, medicines, husks, dummy partners, and dummy seasons. Factors that are reducing production risk in the business of broiler are feed, heat, and vitamins.

Crop Production Risk Implication on Intensity of Technology Adoption in Ethiopia: Multivariate Probit Model Analysis

The main objective of the study is investigating the impact of production risks on adoption of improved production technologies (such as improved seed, inorganic fertilizer and chemical inputs) and the level of adoption of multiple improved production technologies in Ethiopia particularly in Amhara region. The study used two years cross sectional data collected in 2011 and 2013. The study applied two stage regression analysis, first, I regressed the production function in order to drive expected output and risk factors (variance and skewness). In the second stage, I have regressed multivariate probit and order probit models of adoption including expected output, variances and skewness of output as risk measure. The result revealed that expected output, variance and skewness of output are major determinant for adoption decisions of improved seed, inorganic fertilizer, chemical inputs and the level of adoption of multiple improved production technologies. Moreover, credit access, membership of farm cooperatives, ownership of livestock, land size and percentage of irrigated land size positively and significantly determine the adoption decisions of improved seed, inorganic fertilizer and chemical inputs and the level of adoption of multiple improved production technologies. To increase adoption of modern production technology consequently increase production and reduce production risk. Policy makers should put emphasis on overcoming credit market failures, to create strong and sustainable farm cooperatives and production risk and risk aversion farmers should be considered when designing economic policies. Key words: production risk, modern farm technology adoption, multivariate probit model and Ethiopia. DOI: 10.7176/RJFA/13-5-02 Publication date: March 31 st 2022

Lowering soil greenhouse gas emissions without sacrificing yields by increasing crop rotation diversity in the North China Plain

Intensive double cropping in the North China Plain has been challenged by serious environmental problems. Crop rotation is one of the important strategies for sustainable agriculture, which can reduce production risk and environmental impact. Yet, few studies have assessed enhanced crop rotation diversity on GHG emissions in the North China Plain. Carbon dioxide (CO 2 ), methane (CH 4 ), and nitrous oxide (N 2 O) emissions and yields were measured from four cropping systems: (1) wheat ( Triticum aestivum L.)-maize ( Zea mays L.) double cropping (WM); (2) peanut ( Arachis hypogaea L.) and WM rotation (PWM); (3) ryegrass ( Lolium perenne L.)-cotton ( Gossypium hirsutum L.), peanut and WM rotation (RCPWM); and (4) continuous cotton (C), to determine if enhanced crop rotation diversity can reduce greenhouse gas (GHG) emissions and improve yields. The RCPWM, PWM and WM cropping system produced 11.9%, 15.7% and 32.4% higher net greenhouse gas emissions (GHGE), respectively, than the C cropping system each year by the soil-based method assessing the field productive process. From 2017 to 2018, in the same crop planting seasons, RCPWM had lower cumulative N 2 O and CO 2 emissions, and produced similar peanut, wheat, and maize yields when compared to PWM. In 2018, PWM had higher cumulative N 2 O than WM during the wheat period, and they had similar seasonal cumulative N 2 O emissions during the maize period. Meanwhile, PWM produced lower cumulative CO 2 emissions and higher wheat and maize yields. In 2019, RCPWM did not have significantly different seasonal cumulative N 2 O emissions and yield, but slightly higher soil CO 2 emissions than C during the cotton season. The GHG emission intensity (GHGI) of the same crops was lower in RCPWM than in PWM during the peanut, wheat, and maize period or slightly higher than in the C treatment during cotton period. The results demonstrate that RCPWM could reduce soil N 2 O emissions when the same crops in PWM were compared due to less soil CO 2 emissions that the previous cotton brought about. The RCPWM system did not show a lower soil organic carbon (SOC) sequestration rate than the PWM and C systems. Therefore, increasing cotton into the peanut and wheat-maize rotation system (RCPWM) for more diversified system can help reduce soil GHG emissions without sacrificing yields. • PWM, RCPWM and C cropping systems reduced indirect GWP compared to WM. • PWM did not decrease soil N 2 O emissions but improved wheat and maize yield. • The previous cotton lowered soil CO 2 and N 2 O emissions for next crop growing season. • Increasing crop species in rotation could reduce soil GHG emissions without sacrificing yields.

Productivity response and production risk: A study of mangrove forest effects in aquaculture in the Mekong River Delta

In Vietnam, most households that are allotted rights to mangrove forests are allowed to convert forestland into surface water to build mixed mangrove-shrimp farming systems. One result has been deforestation in mangrove forests in an effort to increase production. However, mangroves play multiple roles in shrimp yield, and their net effect is an empirical question. In addition, mangroves can reduce production risk, such that clearing mangroves can increase risk. Utilizing panel survey data of extensive and semi-intensive aquaculture farms in the Mekong River delta of Vietnam, this paper applies the Just-Pope framework to examine the impact of mangrove forests on the productivity and the volatility of output. The results show robust evidence that mangrove forests have a negative but risk-reducing effect on shrimp yield and justify the hypothesis that farmers can earn higher revenue, but at higher risk, when converting more mangrove into water surface. Therefore, a risk-averse farmer would preserve more mangrove forests than the risk-neutral farmer.

THE USE OF A RISK-ORIENTED APPROACH OF PRODUCTION FACTORS ASSESSMENT FOR INCREASING THE EFFICIENCY OF OCCUPATIONAL SAFETY MANAGEMENT SYSTEM

The article deals with important aspects of research and implementation of risk-oriented approach (ROA), including: The operation of OSMS is necessary to carry out based on the management of professional risks. Decision-making and management of safe labour are associated with probabilistic certainty. That is the concept of risk, the features of which largely depend on the methods of enterprise management as a whole.Certification of workplaces and assessment of injury safety, in particular, should be associated with risk assess-ment. It allows building a safety system based on the concept of risk. At the same time, the idea of acceptable risk (CAR) permits you to analyze and ensure the level of safety of the production environment and apply a fairly simple, objective and not contradicting the requirements of the labour protection method of a quantitative assessment of professional risks.A significant improvement of injury safety assessment methodology is the transition to the concept of acceptable risk and the rejection from the concept of absolute safety, which requires new methods of a quantitative assessment of the level of injury safety. The proposed method of risk assessment is based on statistical characteristics of injuries and economic losses of the enterprise.The article proposes a set of measures to increase the level of safety at work and improve the ROA.It is important to update the legislation on the restructuring of social policy in the field of labour relations in Ukraine, including labour protections, to improve injury prevention, OSMS reform.The prevention of occupational injuries based on improving the effectiveness of measures and means of labour protection at the sectoral, state and industrial levels should be ensured by the new OSMS developed based on the Concept and the updated Law "On labour protection" in Ukraine.The purpose of research is to develop and implement methods of increasing the efficiency of activities in the field of labour protection by developing mechanisms to prevent industrial risks.The use of the conducted analysis and research will help to prevent hard forms of injuries with on-time adoption of the necessary management decisions on occupational safety at work.The eccentricity of the study is to perform an analytical assessment of the effectiveness of activities in the field of protection and develop models of organization and planning of risk-oriented approach (ROA) for its implementation in OSH. Developed a model of production risk management, which is based on a component method of assessing the risk of traumatic events and a mathematical model of management decisions, which differs from the existing one taking into account all available information related to the risk of the enterprise.Conclusion. A step-by-step expert assessment of measures to reduce production risk at an industrial enterprise by implementing measures according to certain criteria is proposed. The use of expert assessments in the planning of measures to reduce occupational injuries is appropriate. Because such an approach links preventive measures in the field of labour protection with the results of risk assessment and reduces the subjectivity in management decisions.Validity / Consequences of research. The proposed method of expert assessment of measures to reduce occupa-tional risk is relevant for industrial enterprises. It helps to find ways to reduce occupational injuries, as well as the imple-mentation of the Concept of labour protection reform in Ukraine.

Improving Supply Chain Performance Through Supplier Selection and Order Allocation Problem

Suppliers play the vital role of ensuring the continuous supply of goods to the market for businesses. If businesses do not maintain a strong bond with their suppliers, they may not be able to secure a steady supply of goods and products for their customers. As a result of failure to deliver products, the production and business activities of the business can be delayed which leads to the loss of customers. Normally, each trading enterprise will have a variety of commodity supply chains with multiple suppliers. Suppliers play an important role and contribute to the value of the entire supply chain. Should any supplier encounters a problem, the whole supply chain of businesses will be affected and could lead to not guaranteeing the stable supply to the market. Thus, suppliers can be seen as a threat to businesses where they have the ability to increase input prices or decrease the quality of the required products and services they provide. The quantity of the business, and the supply lead time directly affect the operations and reduce the profitability of the business. The paper mainly focuses on the supplier selection problem under a variety of price level and product families when using a two-phase fuzzy multi-objective linear programming. The objectives of the proposed model are to minimize the total purchasing and ordering cost in order to reduce the quantity of defective materials and the late-delivery components from suppliers. Moreover, the piecewise linear membership function is applied in the model to determine an optimal solution which is based on the requirement of decision makers under their fuzzy environment. The results of this study can be applied in various business environment and provide a reliable decision tool for choosing potential suppliers relating to these objectives. Based on the results, the company can make a good decision on supplier selection; therefore, the company can improve the quality and quantity of their final product. This is because, the best supplier can supply raw material using just-in-time application and reduce production risk on the manufacturing process.

Farmer behavior towards pesticide use for reduction production risk: A Theory of Planned Behavior

Agriculture plays a critical role in economic growth, food security, poverty reduction, and rural development; however, it is a volatile business because of inherent production risks. In developing countries, farmers face bad weather, pests and disease outbreaks, and input supply and input–output price changes. We investigated the intentions of 320 rice farmers in Bangladesh with the aim of influencing their actions is as to reduce production risks and the application of pesticides. Descriptive statistics, the Delphi method, the Theory of Planned Behavior (TPB), and a Structural Equation Model (SEM) were used to achieve the research objectives. Pesticides are a vital technology and play an important role in increasing productivity, but have harmful effects on populations’ health and the environment when used in an excessive and widespread manner. Our results showed that three TPB constructs and one psychological construct mediated the relationship between production risk reduction and behavioral intentions regarding pesticide use. The results also showed that Perceived Behavioral Control (PBC), Behavioral Goals (BGs), Behavioral Attitude (BA), and Subjective Norms (SN) positively influenced the farmers’ intentions to use pesticides to minimize production risk and maximize profits. Interventions aimed at farmers' behavioral change should concentrate on growing awareness and encouraging farmers to assess their control beliefs and improve their capacity to cope with social expectations and circumstances that compete with pesticide use.

Impacts of cooperative membership on banana yield and risk exposure: Insights from China

AbstractThe yield‐increasing effects of agricultural cooperative membership have been widely examined in the literature. However, so far, little is known about whether cooperative membership has the potential to reduce farmers’ exposure to production risk. We address this gap by estimating the impacts of cooperative membership on expected yield, yield variance (variability), and yield skewness (exposure to downside risk), using data collected from a survey of 626 banana farmers in China. We employ an endogenous switching regression model to address the selectivity bias issue. The empirical results show that cooperative membership increases banana yield by 3% and reduces the variance and downside risk exposure by 60% and 114%, respectively. The results are supported by robustness check estimates, using propensity score matching and inverse probability weighted regression adjustment models. Additional analysis reveals that the treatment effects of cooperative membership vary among members with different household and farm characteristics. Our findings suggest that agricultural cooperatives can be an effective institutional arrangement for reducing production risk and crop failure and point to the need for policies and programmes in developing cooperatives and increasing membership involvements of smallholder farmers.