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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.

The focus of this study was to evaluate the relationship between research publications in the pesticide field, a country’s gross domestic product (GDP) and GDP per capita. The study aims to analyze pesticide use in association with a country’s population and research publications. The purpose of this study is to uncover the country’s contribution to pesticide research and assess the financial resources allocated to it as a percentage of their GDP by exploring these factors.

The Web of Science database was used to retrieve data for the period of 2001–2020. The use of scientometric indicators allowed for the analysis of the collaborative patterns and active performance of countries in pesticide research. Socio-economic analysis was used to determine the contribution of countries toward pesticide research.

This study demonstrated a strong association (0.952%) between a country’s GDP and its research publications in the field of pesticide research. Countries, such as Denmark, Belgium and Australia, have benefited from global collaboration, which has enhanced their research efforts. Despite ranking lower in pesticide utilization, India focused on pesticide research, as indicated by its high publication/GDP per capita ratio (0.26).

Research on pesticides directly impacts agricultural practices, which, in turn, influence the economic production of the agricultural sector. Changes in pesticide usage can have inference for crop yields, food price and, eventually, the GDP. Comparative analysis can assist in evaluating the efficiency of regulatory policies in balancing ecological concerns with economic interests. Changes in regulations may impact both pesticide usage and economic outcomes.

This study aims to examine the effects of climate change and agricultural technologies on crop production in Vietnam for the period 1990–2018.

Several econometric techniques – such as the augmented Dickey–Fuller, Phillips–Perron, the autoregressive distributed lag (ARDL) bounds test, variance decomposition method (VDM) and impulse response function (IRF) are used for the empirical analysis.

The results of the ARDL bounds test confirm the significant dynamic relationship among the variables under consideration, with a significance level of 1%. The primary findings indicate that the average annual temperature exerts a negative influence on crop yield, both in the short term and in the long term. The utilization of fertilizer has been found to augment crop productivity, whereas the application of pesticides has demonstrated the potential to raise crop production in the short term. Moreover, both the expansion of cultivated land and the utilization of energy resources have played significant roles in enhancing agricultural output across both in the short term and in the long term. Furthermore, the robustness outcomes also validate the statistical importance of the factors examined in the context of Vietnam.

This study provides persuasive evidence for policymakers to emphasize advancements in intensive agriculture as a means to mitigate the impacts of climate change. In the research, the authors use average annual temperature as a surrogate measure for climate change, while using fertilizer and pesticide usage as surrogate indicators for agricultural technologies. Future research can concentrate on the impact of ICT, climate change (specifically pertaining to maximum temperature, minimum temperature and precipitation), and agricultural technological improvements that have an impact on cereal production.

To the best of the authors’ knowledge, this study is the first to examine how climate change and technology effect crop output in Vietnam from 1990 to 2018. Various econometrics tools, such as ARDL modeling, VDM and IRF, are used for estimation.

Climate change-related weather events significantly affect rice production. In this paper, we investigate the impact of and interrelationships between agriculture inputs, climate change factors and financial variables on rice production in India from 1970–2021.

This study is based on the time series analysis; the unit root test has been employed to unveil the integration order. Further, the study used various econometric techniques, including vector autoregression estimates (VAR), cointegration test, autoregressive distributed lag (ARDL) model and diagnostic test for ARDL, fully modified least squares (FMOLS), canonical cointegrating regression (CCR), impulse response functions (IRF) and the variance decomposition method (VDM) to validate the long- and short-term impacts of climate change on rice production in India of the scrutinized variables.

The study's findings revealed that the rice area, precipitation and maximum temperature have a significant and positive impact on rice production in the short run. In the long run, rice area (ß = 1.162), pesticide consumption (ß = 0.089) and domestic credit to private sector (ß = 0.068) have a positive and significant impact on rice production. The results show that minimum temperature and direct institutional credit for agriculture have a significant but negative impact on rice production in the short run. Minimum temperature, pesticide consumption, domestic credit to the private sector and direct institutional credit for agriculture have a negative and significant impact on rice production in the long run.

The present study makes valuable and original contributions to the literature by examining the short- and long-term impacts of climate change on rice production in India over 1970–2021. To the best of the authors’ knowledge, The majority of the studies examined the impact of climate change on rice production with the consideration of only “mean temperature” as one of the climatic variables, while in the present study, the authors have considered both minimum as well as maximum temperature. Furthermore, the authors also considered the financial variables in the model.

Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of agricultural products. Pesticides can be used to improve agricultural land products. This study aims to make the spraying of cherry trees more effective and efficient with the designed artificial intelligence (AI)-based agricultural unmanned aerial vehicle (UAV).

Two approaches have been adopted for the AI-based detection of cherry trees: In approach 1, YOLOv5, YOLOv7 and YOLOv8 models are trained with 70, 100 and 150 epochs. In Approach 2, a new method is proposed to improve the performance metrics obtained in Approach 1. Gaussian, wavelet transform (WT) and Histogram Equalization (HE) preprocessing techniques were applied to the generated data set in Approach 2. The best-performing models in Approach 1 and Approach 2 were used in the real-time test application with the developed agricultural UAV.

In Approach 1, the best F1 score was 98% in 100 epochs with the YOLOv5s model. In Approach 2, the best F1 score and mAP values were obtained as 98.6% and 98.9% in 150 epochs, with the YOLOv5m model with an improvement of 0.6% in the F1 score. In real-time tests, the AI-based spraying drone system detected and sprayed cherry trees with an accuracy of 66% in Approach 1 and 77% in Approach 2. It was revealed that the use of pesticides could be reduced by 53% and the energy consumption of the spraying system by 47%.

An original data set was created by designing an agricultural drone to detect and spray cherry trees using AI. YOLOv5, YOLOv7 and YOLOv8 models were used to detect and classify cherry trees. The results of the performance metrics of the models are compared. In Approach 2, a method including HE, Gaussian and WT is proposed, and the performance metrics are improved. The effect of the proposed method in a real-time experimental application is thoroughly analyzed.

This study aimed to the attributes, consequences and personal values that motivate the behavior of consumers of fruits and vegetables (FV) at local markets and how these elements are associated with food safety.

This is a qualitative research that used the laddering in-depth interview technique for data collection. Fifty interviews were conducted with consumers from Minas Gerais, Brazil. From the codification of the interview content, a hierarchical value map was constructed, showing the relationships between the attributes, consequences and values involved in the consumers’ purchasing decision.

Consumers value characteristics related to the origin of the product and the way it is produced and marketed. They seek particular benefits – such as satisfaction with the purchase, care for their health/well-being and safety when consuming food – and social benefits, such as the possibility of contributing to the local economy. Issues related to hygiene, organization, exposure and handling of products were some of the concerns reported by respondents with regard to food safety.

It is worth highlighting the application of the laddering technique itself. Analyzing the predictive validity of the method, there is a propensity for biases linked to possible interference by the researcher, especially in the coding stage of the elements.

This study can be used by producers, marketing professionals and public policymakers to promote FV sold at local markets and to encourage the improvement of food safety practices.

The research points to five consumer segments according to the different motivations that guide their purchase behavior for local FV. In addition, a focus is given to food safety, revealing its importance in the investigated context.

Food systems research is typically focused on productivity and efficiency. But in the face of impending challenges of climate, investment, markets, and incomes small holders may do well to shift to diversity and sufficiency. The transition requires institutions such as Farmer Producer Organisations (FPOs) to play the role of intermediaries. This paper aims to understand this challenging phenomenon using a case from India.

In this article, drawing from the emerging literature of PO as a sustainability transition intermediary, this paper uses the case study of a women-owned FPO and explores its role in contributing to sustainable food systems through practices of non-pesticide management of agriculture. This paper explores, through non-participant observer methods, focus group discussions and interviews with multiple stakeholders how an FPO embeds sustainability in its purpose and the challenges faced in transforming producer and consumers towards sustainable food systems.

The study argues for early articulation of the “sustainability transition intermediary” role in the FPO’s vision and mission. Second, FPOs’ role of being a transition intermediary is impacted by the key stakeholders and the durability of relationship with them.

By studying FPOs in India, from the framework of sustainability transitions, this article adds to the limited literature that looks as POs as sustainability transition intermediaries.

There is an urgent need to develop climate-smart agrosystems capable of mitigating climate change and adapting to its effects. Conventional agricultural practices prevail in Mauritius, whereby synthetic chemical fertilizers, pesticides and insecticides are used. It should be noted that Mauritius remains a net-food importing developing country of staple food such as cereals and products, roots and tubers, pulses, oil crops, vegetables, fruits and meat (FAO, 2011). In Mauritius, the agricultural sector faces extreme weather conditions like drought or heavy rainfall. Moreover, to increase the crop yields, farmers tend to use 2.5 times the prescribed amount of fertilizers in their fields. These excess fertilizers are washed away during heavy rainfall and contaminate lakes and river waters. By using smart irrigation and fertilization system, a better management of soil water reserves for improved agricultural production can be implemented. Soil Nitrogen, Phosphorus and Potassium (NPK) content, humidity, pH, conductivity and moisture data can be monitored through the cloud platform. The data will be processed at the level of the cloud and an appropriate mix of NPK and irrigation will be used to optimise the growth of the crops. Machine learning algorithms will be used for the control of the land drainage, fertilization and irrigation systems and real time data will be available through a mobile application for the whole system. This will contribute towards the Sustainable Development Goals (SDGs): 2 (Zero Hunger), 11 (Sustainable cities and communities), 12 (Responsible consumption and production) and 15 (Life on Land). With this project, the yield of crops will be boosted, thus reducing the hunger rate (SDG 2). On top of that, this will encourage farmers to collect the waters and reduce fertilizer consumption thereafter sustaining the quality of the soil on which they are cultivating the crops, thereby increasing their yields (SDG 15).

The ongoing transition in the attitude of consumers toward health and environment has a direct implication on the organic food industries, making it necessary to examine the drivers of the purchase intention of organic food items, specifically in developing economies like India. Therefore, this study tries to frame and validate the instrumental scale by collecting data from 574 organic food item consumers to examine the determinants of purchase intention among consumers in India.

This study has employed advanced statistical tools i.e. Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), Harmon’s single factor test and other statistical measures using SPSS and AMOS 23, for framing and validating the instrumental scale for this study.

The results of EFA explain 67.714% variance of total research variable variance with six major constructs. Moreover, the result of the CFA confirms the six factors and the proposed instrumental scale. The finding explicates that health consciousness, ecological trustworthiness and functional value are the major drivers of the purchase intention of organic food items.

This study has major policy implications for organic producers, processor and marketers for understanding the complex phenomenon of organic consumer behavior. The result explains that marketers and producers should adopt ad hoc marketing strategies that aim to promote the organic food items as healthy and safe.

There is hardly any study that has proposed and validated an instrumental scale with these factors collectively in India for studying the purchase intention of organic food consumers in India.

The purpose of this study is to offer a better and more effective hexacopter design for a 3 kg payload using finite element analysis (FEA), facilitating the use of different materials for different components that too without compromising strength.

A 3D computer-aided design (CAD) model of a hexacopter with a regular hexagonal frame is presented. Furthermore, a finite element model is developed to perform a structural analysis and determine Von Mises stress and strain values along with deformations of different components of the proposed hexacopter design.

The results establish that carbon fibre outperforms acrylonitrile butadiene (ABS) with respect to deformations. Within the permissible limits of the stress and strain values, both carbon fiber and ABS are suggested for different components. Thus, a proposed hexacopter offers lighter weight, high strength and low cost.

The use of different materials for different components is suggested by making use of static structural analysis. This encourages new research work and helps in developing new applications of hexacopter, and it has never been reported in literature. The suggested materials for the components of the hexacopter will prove to be suitable considering weight, strength and cost.