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The purpose of the study is to analyze changes in land use, specifically residential area expansion, in South Tangerang City and identify the factors that influence land use change.

The study used remote sensing methods in ArcGIS 10.8 for data analysis and processing, including spatial analysis and identification of land use changes. The study analyzed satellite images from 2010 and 2020 to identify changes in land use in South Tangerang City over the ten-year period.

The study found that the most significant land use changes in South Tangerang City between 2010 and 2020 were the reduction of mixed plantation area and the expansion of residential areas. The study identified the development of small townships by private developers as the main factor that influenced land use change in South Tangerang City.

The study has several limitations, including a focus on only one aspect of land use change (i.e. residential area expansion), limited scope of the study area (South Tangerang City) and a reliance on remote sensing methods for data analysis.

The findings of the study can be used by policymakers and city planners to develop sustainable land use planning strategies that balance the need for urban development with environmental and social concerns. By understanding the factors that drive land use changes in South Tangerang City, policymakers can develop policies that encourage sustainable urban growth and development while preserving natural resources and protecting the environment.

The study has social implications as the expansion of residential areas in South Tangerang City indicates a growing demand for housing in the area. The study highlights the importance of developing affordable and sustainable housing solutions to meet the needs of the growing population in South Tangerang City. Additionally, the study emphasizes the importance of understanding the social and economic factors that drive land use change and their implications for the well-being of local communities.

The residential area development in South Tangerang City is driven by private developers who make small independent cities that have all facilities in one area. These small cities attract people to reside and also drive high population growth in South Tangerang City, considering it is a buffer city of Jakarta that has good infrastructure development.

Numerical literature shows that agricultural insurance can affect pesticide investments, but few of them are devoted to explain how agricultural insurance affects farmers’ selection on green or traditional pesticides. This paper aims to develop a theoretical model about how agricultural insurance influences on green pesticides selections and tests our conclusions by using the data from China land economic survey (CLES) from 2020 to 2021.

We employ probit model to capture the effects of agricultural insurance on green pesticides adoption.

We indicate that green pesticides have a stronger effect on stabilizing yield and increasing income than traditional pesticides, but there are still risks disturbing farmers’ decisions on green pesticides usage. By providing premium subsidies after the farmers are affected by natural risk, agricultural insurance improves the farmers’ expected income and encourages farmers to use green pesticides. Further, we further confirm these conclusions by considering different scenarios such as climate risks, farmers’ entrepreneurship and credit constraints. We find that the effects are more salient if croplands are under higher natural risks and, farmers are equipped with entrepreneurship and formal credit. This paper implies that the agricultural insurance decoupled with green technologies also have salient positive effects on agricultural pollution control.

The potential contributions of this paper can be outlined in three aspects in detail. Firstly, this paper aims to revel the effects of agricultural insurance on pesticide selection by structuring a general theoretical model. By using the CLES data from 2020 to 2021, we confirm that agricultural insurance increases the probability for adopting green pesticides. Secondly, this paper discusses the effects of farmers’ characteristics on the results and finds that if farmers have entrepreneurship, the effects of agricultural insurance on green pesticide usage will be more salient. Thirdly, it uncovers some practices in China, which will supply experiences for other developing countries. For example, this paper further demonstrates that “insurance + credit” plan the present Chinese government carried out will be an important measure for strengthening effects of agricultural insurance on green pesticides usage. Moreover, it shows that decouple agricultural policies will also guide farmers to use green technologies eventually if the technologies are reliable and farmers can afford.

Managing weeds and pests in cropland is one of the major concerns in agriculture that greatly affects the quantity and quality of the produce. While the success of preventing potential weeds and pests is not guaranteed, early detection and diagnosis help manage them effectively to ensure crops’ growth and health

We propose a diagnostic framework for crop management with automatic weed and pest detection and identification in maize crops using residual neural networks. We train two models, one for weed detection with a labeled image dataset of maize and commonly occurring weed plants, and another for leaf disease detection using a labeled image dataset of healthy and infected maize leaves. The global and local explanations of image classification are obtained and presented

Weed and disease detection and identification can be accurately performed using deep-learning neural networks. Weed detection is accurate up to 97%, and disease detection up to 95% is made on average and the results are presented. Further, using this crop management system, we can detect the presence of weeds and pests in the maize crop early, and the annual yield of the maize crop can potentially increase by 90% theoretically with suitable control actions

The proposed diagnostic models can be further used on farms to monitor the health of maize crops. Images obtained from drones and robots can be fed to these models, which can then automatically detect and identify weed and disease attacks on maize farms. This offers early diagnosis, which enables necessary treatment and control of crops at the early stages without affecting the yield of the maize crop

The proposed crop management framework allows treatment and control of weeds and pests only in the affected regions of the farms and hence minimizes the use of harmful pesticides and herbicides and their related health effects on consumers and farmers.

This study presents an integrated weed and disease diagnostic framework, which is scarcely reported in the literature

The study aims to identify the possible risk factors for electricity grids operational disruptions and to determine the most critical and influential risk indicators.

A multi-criteria decision-making best-worst method (BWM) is employed to quantitatively identify the most critical risk factors. The grey causal modeling (GCM) technique is employed to identify the causal and consequence factors and to effectively quantify them. The data used in this study consisted of two types – quantitative periodical data of critical factors taken from their respective government departments (e.g. Indian Meteorological Department, The Central Water Commission etc.) and the expert responses collected from professionals working in the Indian electric power sector.

The results of analysis for a case application in the Indian context shows that temperature dominates as the critical risk factor for electrical power grids, followed by humidity and crop production.

The study helps to understand the contribution of factors in electricity grids operational disruptions. Considering the cause consequences from the GCM causal analysis, rainfall, temperature and dam water levels are identified as the causal factors, while the crop production, stock prices, commodity prices are classified as the consequence factors. In practice, these causal factors can be controlled to reduce the overall effects.

From the results of the analysis, managers can use these outputs and compare the risk factors in electrical power grids for prioritization and subsequent considerations. It can assist the managers in efficient allocation of funds and manpower for building safeguards and creating risk management protocols based on the severity of the critical factor.

The research comprehensively analyses the risk factors of electrical power grids in India. Moreover, the study apprehends the cause-consequence pair of factors, which are having the maximum effect. Previous studies have been focused on identification of risk factors and preliminary analysis of their criticality using autoregression. This research paper takes it forward by using decision-making methods and causal analysis of the risk factors with blend of quantitative and expert response based data analysis to focus on the determination of the criticality of the risk factors for the Indian electric power grid.

In the process of making agricultural production decisions in rural households, severe weather conditions, either extreme cold or heat, may squeeze the labor input in the agricultural sector, leading to a reallocation of labor between the agricultural and non-agricultural sectors. By applying a dataset with a wide latitude range, this study empirically confirms the influence of extreme temperatures on the agricultural labor reallocation, reveal the mechanism of farmers’ adaptive behavioral decision and therefore enriches the research on the impact of climate change on rural labor markets and livelihood strategies.

This study utilizes data from Chinese meteorological stations and two waves of China Household Income Project to examine the impact and behavioral mechanism of extreme temperatures on rural labor reallocation.

(1) Extremely high and low temperatures had led to a reallocation of labor force from agricultural activities to non-farm employment, with a more pronounced effect from extreme high temperature events. (2) Extreme temperatures influence famers’ decision in abandoning farmland and reducing investment in agricultural machinery, thus creating an interconnected impact on labor mobility. (3) The reallocation effect of rural labor induced by extreme temperatures is particularly evident for males, persons that perceives economic hardship or labor in economically active areas.

By applying a dataset with a wide latitude range, this study empirically confirms the influence of extreme temperatures on the agricultural labor reallocation, and reveals the mechanism of farmers’ adaptive behavioral decision and therefore enriches the research on the impact of climate change on rural labor markets and livelihood strategies.

Food waste is generated along the entire agricultural supply chain. From farm overproduction to lack of cold chain infrastructure, waste occurs for multiple reasons and negatively impacts the environment and society while generating economic losses. Although various supply chain actors and institutions have made attempts to reduce it, the activity is often confined to a single farm or to a retailer and charity dyad, without a systematic resolution of the problem. The environment is not only negatively impacted by the reduction of soil, water and biodiversity but also human beings suffer from malnutrition and food insecurity and finally, the entire supply chain faces considerable economic losses. Various supply chain actors have attempted to reduce this waste, but the results are often limited. The purpose of this paper is to consider systematic resolution by proposing a reconceptualisation from an alternative Operations and Supply Chain Management (O&SCM) perspective.

The proposed paper is problem-based research, which merges the research and industry perspectives derived from the authors’ field experience interviewing different supply chain stakeholders in Italy, the UK, the USA and France with an analysis of O&SCM literature related to food loss and waste.

In order to address the food waste problem, we propose a new perspective in dealing with food loss and waste through the lens of O&SCM. By reconceptualising O&SCM theories and methods with the unique aspects of food loss and waste and taking into account the multitude of stakeholders involved, we propose five research avenues.

The perspective of O&SCM management is missing when dealing systematically with food loss and waste, as researchers neglect its unique characteristics.

The study aims to analyze the impact of market participation on small paddy farmers' income and consumption expenditure. The study also estimates various determinants affecting the market participation of smallholders. Further, the study computes the efficiency of different paddy marketing channels and identifies the determinants that impact the marketing channel selection of paddy growers in Eastern India.

The study used the propensity score matching (PSM) approach to measure the impact of market participation on farm income and per capita consumption. Further, the study employed Acharya and Aggarwal's composite index approach to estimate the marketing efficiency of various paddy marketing channels. Further, a multinomial logit model was used to determine the marketing channel selection constraints.

The outcomes indicate that market participation positively impacts farm income and consumption expenditure. Education, membership in farmers' organizations, price information and distance to the marketplace significantly affect farmers' market participation. The results show that the producer–retailer marketing channel is the most efficient compared to others. However, most paddy farmers sell paddy to farmgate collectors due to a lack of market information, vehicle ownership, storage system, and inability to take the risk of venturing out of the farmgate into markets.

The study uses primary data and captures only farmers' perspectives to measure the impact of market participation, marketing channel efficiency and determinants for market channel selection. The other stakeholder's perceptions can be included in future studies.

Rarely does any study identifies the efficiency of different marketing channels for paddy farmers in India and includes cognitive factors like risk perception and trust in buyers as constraints for market channel selection.

This paper aimed to examine the impacts of agricultural services on welfare of rural farmers in Ghana.

Using data from 1431 rural maize farmers, we employ multinomial endogenous switching regression and multivalued inverse probability weighted regression adjustment to assess the impacts.

Results show that 19.8%, 9.7% and 3.42% of farmers adopted solely irrigation, extension and mechanization, respectively. Furthermore, utilizing a range of agricultural services significantly improves maize yields, gross income and per capita food consumption.

This study recommends strategies that target the adoption of combinations of agricultural services to enhance rural farmers’ welfare in Ghana and other developing countries.

While agricultural services are claimed to improve agricultural production and peasants’ welfare, their impacts are not studied exhaustively. This paper contributes by providing empirical evidence of the impacts of agricultural services on farmers’ welfare.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2022-0745.

This study investigates whether, how and under what circumstances off-farm work induces farmland abandonment, which is of great importance for developing countries to cope with food security.

Exploiting large-scale panel data from the newly released Chinese Family Database, this study employs a two-way fixed effects model to empirically estimate the causal relationship between off-farm work and farmland abandonment.

In the context of large-scale labor migration in rural China, current off-farm work leads to an increase in the probability and area of farmland abandoned due to insufficient agricultural labor. However, off-farm work does not harm farm households in plain areas, or villages with land rental markets, abundant agricultural labor, and agricultural machinery, while it harms others. Moreover, farmers who work off-farm in the local area are less likely to abandon their farmland than those in other areas. Additionally, when the number of off-farm workers in a household exceeds two, the probability and area of farmland abandonment will miraculously decline, as the household will no longer live entirely on agriculture.

This study may fill the gap in clarifying the relationship between off-farm work and farmland abandonment, and identify scenarios where off-farm work may not cause farmland abandonment through multiple dimensions, providing insights into the governance of farmland abandonment during rural-urban transformation in developing countries.

Climate change negatively affects agriculture and food security, and jeopardizes Myanmar's agriculture, which is vital to ensure food security, rural livelihoods, and the economy. This study explores the asymmetric impacts of climate change on Myanmar's agricultural sector.

We utilize the nonlinear autoregressive distributed lag (NARDL) approach for the years 1991–2020, the Wald test to validate the asymmetric relationship between climate change and agriculture, and the FMOLS and DOLS approaches to confirm the validity of the outcomes.

Our findings reveal that temperature has a positive impact on Myanmar's agriculture, whereas rainfall and CO2 have negative effects over the long and short terms. Evidently, decreasing temperatures more favorably impact agriculture than increasing temperatures, while increasing rainfall more negatively impacts agriculture than decreasing rainfall. Increasing carbon emissions have a more detrimental effect on agriculture than decreasing them.

We gathered data over periods longer than 30 years to provide more robust findings. However, owing to data limitations, such as missing values or unavailability, the study period spans from 1991 to 2020.

This study contributes to the existing literature on the asymmetric effects of climatic and non-climatic factors on agriculture. It is the first study in Myanmar to use the NARDL approach to measuring the effects of climate change on both the agricultural gross production index and value, providing robust findings.