Search Results (10)

Farmers’ participation in sustainable forest management plays a significant role in increasing their income and contributing to the comprehensive advancing of the rural revitalization strategy. This study focuses on farmers living near existing national forest health bases in Inner Mongolia. Using the endogenous switching regression model (ESRM), we empirically examine the income effects of farmers’ participation in sustainable forest management through employment and land leasing. The robustness of the model estimation is tested through various methods, including replacing the dependent variable. Furthermore, heterogeneity analysis is conducted using quantile regression. The results show the following: (1) Participation in sustainable forest management through employment (p < 0.001) and land leasing (p < 0.001) significantly increases annual household income by 4.28% and 1.44%, respectively. The income effect for farmers participating through employment is 2.84% higher than for those participating through land leasing. (2) For farmers who did not participate in sustainable forest management, the counterfactual scenario indicates a reduction in annual household income by 5.87% and 2.55%, respectively, highlighting a greater potential income improvement for non-participating farmers if they were to engage in sustainable forest management. (3) Heterogeneity analysis reveals that the income effects of the two participation forms vary across income levels. Employment participation in forest health bases has a more significant impact on low-income (QR_10) farmers, while land leasing participation has a greater impact on high-income (QR_90) farmers. Full article

The threat posed by adversarial examples (AEs) to deep learning applications has garnered significant attention from the academic community. In response, various defense strategies have been proposed, including adversarial example detection. A range of detection algorithms has been developed to differentiate between benign samples and adversarial examples. However, the detection accuracy of these algorithms is significantly influenced by the characteristics of the adversarial attacks, such as attack type and intensity. Furthermore, the impact of image preprocessing on detection robustness—a common step before adversarial example generation—has been largely overlooked in prior research. To address these challenges, this paper introduces a novel adversarial example detection algorithm based on high-level feature differences (HFDs), which is specifically designed to improve robustness against both attacks and preprocessing operations. For each test image, a counterpart image with the same predicted label is randomly selected from the training dataset. The high-level features of both images are extracted using an encoder and compared through a similarity measurement model. If the feature similarity is low, the test image is classified as an adversarial example. The proposed method was evaluated for detection accuracy against four comparison methods, showing significant improvements over FS, DF, and MD, with a performance comparable to ESRM. Therefore, the subsequent robustness experiments focused exclusively on ESRM. Our results demonstrate that the proposed method exhibits superior robustness against preprocessing operations, such as downsampling and common corruptions, applied by attackers before generating adversarial examples. It is also applicable to various target models. By exploiting semantic conflicts in high-level features between clean and adversarial examples with the same predicted label, the method achieves high detection accuracy across diverse attack types while maintaining resilience to preprocessing, providing a valuable new perspective in the design of adversarial example detection algorithms. Full article

With the market-driven approach to agricultural technical services and the application of digital technology, digital agro-technical services have gradually emerged as a novel service model. However, there is a lack of empirical research on the effectiveness of this service in the academic literature. To address this research gap, this study measured the impact of this service on the technical efficiency and income levels of mango farmers, using data collected from 131 mango farmers in Hainan Province, China, from 2022 to 2024. This study employed the endogenous switching regression model (ESRM) and the inverse probability-weighted regression adjustment model (IPWRA) to analyze the data, addressing endogeneity through the instrumental variable method by replacing core explanatory variables and conducting sub-regional regression for robustness testing. The main research conclusions are as follows: Under the counterfactual assumption of ESRM, farmers who adopt this service would have experienced a decrease in technical efficiency by 0.025 (a decline of 3.6%) if they had not adopted the service. Conversely, farmers who did not adopt it would have seen an increase in technical efficiency by 0.047 (an increase of 7.3%) if they had chosen to do so. Additionally, under the post-treatment income effect estimation using IPWRA, compared to farmers who did not receive the service, those who did so saw an income increase of 15.6%. The analysis results from methods such as K-nearest neighbors matching also confirm this conclusion. Therefore, it is evident that digital agro-technology services play a significant role in improving the technical efficiency and income levels of small-scale farmers. Full article

This study examines the economic and environmental impacts of green production practices among farmers. It aims to contribute to sustainable agricultural development, mitigate agricultural non-point source (NPS) pollution, and align environmental protection with economic growth. This paper utilizes survey data from 1345 farm households in the main rice production areas of Jiangxi Province, China, using the example of reduced fertilizer application (RFA) among rice farmers. This study constructs a slack-based measure data envelopment analysis (DEA—SBM) model with undesirable outputs to measure environmental effects and applies an endogenous switching regression model (ESRM) to test the economic and environmental effects of farmers’ adoption of green production technologies. We found the following: (1) The RFA behavior of farmers has a significant positive impact on their net profit per hectare (NPH), helping farmers increase their income, with the increase ranging from 2.05% to 6.54%. (2) Farmers’ RFA behavior has a significant positive impact on agricultural green productivity (AGP), contributing to the improvement of the environment, ranging from 44.09% to 45.35%. (3) A heterogeneity analysis found inconsistencies in the income-enhancing and environmental-enhancing effects at different quantiles of NPH and AGP. Therefore, attention should be placed on improving the agricultural product quality supervision system under the market circulation mechanism, creating land scale conditions conducive to the promotion and application of fertilizer reduction technologies and promoting the implementation of externality internalization compensation systems. Full article

In the past decades, indigenous leafy vegetables (ILVs) have played a significant role in household food security, especially in poor rural households. However, ILVs have been replaced by exotic cash crops in the contemporary world. This study was conducted to assess the consumption of indigenous leafy vegetables and their contribution to household food security of households in Limpopo and Mpumalanga provinces. The study used secondary data collected by the South African Vulnerability Assessment Committee in 2016. A total of 1520 respondents were selected using a multistage sampling method. The results from descriptive statistics revealed that most consumers did not produce ILVs but consumed them. Meanwhile, a small number of people produced ILVs yet did not consume them. The results from the Household Food Insecurity Access Scale (HFIAS) showed that a large proportion of the population experienced moderate food security while some of the individuals within the population experienced severe food insecurity. An endogenous switching regression model (ESRM) was employed to analyze the impact of the consumption of ILVs on household food security. The results revealed that only a few variables of the consumption of ILVs were significant and positive (household size, wealth index, and ‘if the disabled person receives grants’). As a result, the consumption of ILVs had a minimal impact on the household food security of the Limpopo and Mpumalanga provinces. The findings further revealed that age, gender, and education variables negatively influenced the consumption of ILVs. Thus, the recommended programs must be established to educate people about the importance of consuming ILVs. Agricultural extension services must equally promote the consumption of exotic cash crops and ILVs. Lastly, policies can contribute by increasing the diversity of ILVs left at retail outlets through diverse production. Full article

Amid recent climate difficulties, integrated soil fertility management (ISFM) strategies are vital in restoring soil fertility, enhancing yield, and achieving the farmer community’s well-being. This study examines ISFM’s adoption and impact on wheat yields in Punjab, Pakistan, by employing an endogenous switching regression model (ESRM). The selection equation highlights the multiple factors such as age, gender, education, extension access, credit access, and social influence as essential predictors of ISFM adoption. Treatment effects showed that the average wheat yield is higher for adopters. The findings suggest refining the current institutional system will enhance adoption and food security by improving agricultural production. Full article

A lack of adequate exercise threatens human health, weakening human capital accumulation. The relationship between exercise and income has become the focus of attention in health economics. In terms of reducing body weight and improving physical fitness, diet and physical exercise are intertwined and become effective ways to shape a healthy state. Based on individual-level survey data from China, this study quantified the economic returns of habitual exercise behavior by using an endogenous switching regression model (ESRM) to eliminate selection bias. The study shows that (1) participants in the group with regular exercise behavior increased their income by 3.79% compared with those not exercising regularly; (2) for the group with no regular exercise behavior, regular exercise increased their income by 13.36% compared with those not exercising regularly. Additionally, empirical evidence shows that both drinking and smoking can significantly increase individual income, despite unhealthy habits. These results suggest that the habit of regular physical activity plays a vital role in increasing individual income and improving overall national health, and the effect of individual behavior on income is affected by national culture. The outcomes are empirical evidence for the Chinese government to promote Healthy China Action and support developing countries worldwide to enable habitual exercise, stimulating a policy of exercise behavior. Full article

High-Resolution Wide-Swath (HRWS) is an important development direction of space-borne Synthetic Aperture Radar (SAR). The two-dimensional spatial variation of the Doppler parameters is the most significant characteristic of the sliding spotlight space-borne SAR system under the requirements of HRWS. Therefore, the compensation of the two-dimensional spatial variation is the most challenging problem faced in the imaging of HRWS situations. The compensatory approach is then proposed to address this problem in this paper. The spatial distribution of the Doppler parameters for the HRWS space-borne SAR data in the sliding spotlight working mode is firstly analyzed, based on which a Spatial-Variant Equivalent Slant Range Model (SV-ESRM) is put forward to accurately formulate the range history for the distributed target. By introducing an azimuth-varying term, the SV-ESRM can precisely describe the range history for not only central targets but also marginal targets, which is more adaptive to the HRWS space-borne SAR requirements. Based on the SV-ESRM, a Modified Hybrid Correlation Algorithm (MHCA) for HRWS space-borne SAR imaging is derived to focus the full-scene data on one single imaging processing. A Doppler phase perturbation incorporated with the sub-aperture method is firstly performed to eliminate the azimuth variation of the Doppler parameters and remove the Doppler spectrum aliasing. Then, an advanced hybrid correlation is employed to achieve the precise differential Range Cell Migration (RCM) correction and Doppler phase compensation. A range phase perturbation method is also utilized to eliminate the range profile defocusing caused by range-azimuth coupling for marginal targets. Finally, a de-rotation processing is performed to remove the azimuth aliasing and the residual azimuth-variance and obtain the precisely focused SAR image. Simulation shows that the SAR echoes for a 20 km × 20 km scene with a 0.25 m resolution in both the range and azimuth directions could be focused precisely via one single imaging processing, which validates the feasibility of the proposed algorithm. Full article

As an emerging orbital system with flexibility and brand application prospects, the highly elliptical orbit synthetic aperture radar (HEO SAR) can achieve both a low orbit detailed survey and continuous earth surface observation in high orbit, which could be applied to marine reconnaissance and surveillance. However, due to its large eccentricity, two challenges have been faced in the signal processing of HEO SAR at present. The first challenge is that the traditional equivalent squint range model (ESRM) fails to accurately describe the entire range for the whole orbit period including the perigee, the apogee, and the squint subduction section. The second one is to exploit an efficient HEO SAR imaging algorithm in the squinted case which solves the problem that traditional imaging algorithm fails to achieve the focused imaging processing of HEO SAR during the entire orbit period. In this paper, a novel imaging algorithm for HEO SAR is presented. Firstly, the signal model based on the geometric configuration of the large elliptical orbit is established and the Doppler parameter characteristics of SAR are analyzed. Secondly, due to the particularity of Doppler parameters variation in the whole period of HEO, the equivalent velocity and equivalent squint angle used in MESRM can no longer be applied, a refined fourth-order equivalent squint range model(R4-ESRM) that is suitable for HEO SAR is developed by introducing fourth-order Doppler parameter into Modified ESRM (MESRM), which accurately reconstructs the range history of HEO SAR. Finally, a novel imaging algorithm combining azimuth resampling and time-frequency domain hybrid correlation based on R4-ESRM is derived. Simulation is performed to demonstrate the feasibility and validity of the presented algorithm and range model, showing that it achieves the precise phase compensation and well focusing. Full article

This study investigated the impact of Sustainable Land Management Practices (SLMP) on the smallholder maize farmer’s welfare in the Gert Sibande District in the Mpumalanga Province of South Africa. Farmers’ welfare is paramount to agricultural development and rural vitalisation, especially in sub-Saharan Africa. The aim of the study is to identify the factors that influence the adoption of SLMP and to assess its impact on the net farm income. A multivariate-probit (MVP) model was used to analyse the determinants of SLMP adopted and an efficient endogenous switching regression model (ESRM) was used to estimate the impact of SLMP on the net farm income of the smallholder maize farmers. The MVP results show that household socio-economic characteristics and institutional factors statistically influenced the choice of SLMP. Subsequently, the pair-wise correlation matrix of the MVP model revealed complementarities among all SLMP implemented by the farmers. Similarly, the ESRM treatment effect indicated that the average net farm income of farmers who adopted SLMP were significantly higher than that of the group who did not. Consequently, the study recommended support policies on farmers’ demography, farm-based characteristics, and institutional factors to improve the welfare of the farmers and promote rural vitalisation. Full article