Search Results (90)

The adoption of green pest control technologies (GPCTs) has emerged as a critical factor in the pursuit of sustainable agricultural practices, particularly in improving farm efficiency and mitigating environmental impacts. This study investigates the effect of GPCT adoption on the technical efficiency of apple farmers in Shandong Province, China, using survey data collected in 2022. Applying advanced econometric techniques, including stochastic frontier analysis (SFA) to measure technical efficiency and endogenous switching regression model (ESR) to address endogeneity and selection bias, the findings indicate that GPCT adoption significantly enhances farmers’ technical efficiency. Specifically, under the counterfactual scenario of adoption, non-adopters’ technical efficiency would increase by 18.2% (from 0.669 to 0.851), whereas adopters would experience a 3.9% efficiency gain attributable to adoption (from the counterfactual 0.700 to the observed 0.739). The analysis further reveals that lower-income farmers benefit disproportionately from GPCT adoption, suggesting that the technology offers greater potential to enhance the productivity of resource-constrained farmers. These results underscore the importance of targeted policy interventions, such as subsidies and agricultural extension programs, to foster the widespread adoption of GPCTs, particularly among lower-income groups. This study contributes to the literature by providing empirical evidence of the dual benefits of GPCT adoption: improving farm efficiency while promoting environmental sustainability, with important implications for policy formulation in developing economies. Full article

Technology-driven agriculture, or precision agriculture (PA), is indispensable in the contemporary world due to its advantages and the availability of technological innovations. Particularly, early disease detection in agricultural crops helps the farming community ensure crop health, reduce expenditure, and increase crop yield. Governments have mainly used current systems for agricultural statistics and strategic decision-making, but there is still a critical need for farmers to have access to cost-effective, user-friendly solutions that can be used by them regardless of their educational level. In this study, we used four apple leaf diseases (leaf spot, mosaic, rust and brown spot) from the PlantVillage dataset to develop an Automated Agricultural Crop Disease Identification System (AACDIS), a deep learning framework for identifying and categorizing crop diseases. This framework makes use of deep convolutional neural networks (CNNs) and includes three CNN models created specifically for this application. AACDIS achieves significant performance improvements by combining cascade inception and drawing inspiration from the well-known AlexNet design, making it a potent tool for managing agricultural diseases. AACDIS also has Region of Interest (ROI) awareness, a crucial component that improves the efficiency and precision of illness identification. This feature guarantees that the system can quickly and accurately identify illness-related areas inside images, enabling faster and more accurate disease diagnosis. Experimental findings show a test accuracy of 99.491%, which is better than many state-of-the-art deep learning models. This empirical study reveals the potential benefits of the proposed system for early identification of diseases. This research triggers further investigation to realize full-fledged precision agriculture and smart agriculture. Full article

The codling moth Cydia pomonella (L.) represents a substantial threat to the apple tree industry, with its cellular content being agronomically vital as it serves as the final immunological and toxicological barrier of the pest. Key hemocyte types identified in the hemolymph include plasmatocytes, granulocytes, spherulocytes, and oenocytoids. Hemolymph samples were in vitro suspended in various salt buffers (physiological saline, phosphate saline buffer (PBS) and Galleria mellonella anticoagulant buffer) to determine the most suitable one for agricultural monitoring purposes. The pH influenced the total hemocyte counts and the type of cells that adhered to the slides. PBS (pH 6.5) was found to be optimal for such studies due to its high levels of cellular attachment, cell viability, absence of melanization, and cellular degeneration effects. The supplementation of 5% CaCl₂ to PBS did not enhance the functional utility of the buffer. The in vivo bacterial challenge of larval hemolymph with 4 × 10⁸ sp/mL Bacillus subtilis provided complete clearance from the microbial invader within 30 min. Hemocytes released antimicrobial lysozyme as part of their innate immune responses. Hemocytic examination of larvae as an agricultural practice is strongly recommended for baseline insecticide resistance avoidance and predictive efficiency of integrated pest management in the apple farm. Full article

China holds the largest apple cultivation area globally, yet yields per hectare remain relatively low. Despite substantial government investment in modern orchard technologies, adoption remains limited among farmers. This study investigates the economic and sociological drivers of technology uptake, focusing on how information sources shape adoption behavior. Based on 382 farmer surveys across major apple-producing provinces, the study examines (1) farmers’ preferences for agricultural information sources, (2) the influence of demographic characteristics on those preferences, and (3) the differential effects of specific sources on the adoption of key technologies, including dwarf rootstocks and virus-free seedlings. Results show that agri-chemical dealers (ACDs) and farmer peers (FPs) are the most commonly used information channels. Access to advice from local experts (EXPs) significantly increases the likelihood of adopting dwarf rootstocks, while information from ACDs promotes the use of virus-free seedlings. In contrast, reliance on personal farming experience is negatively associated with technology uptake. These findings highlight the need to strengthen formal information dissemination systems and better integrate trusted local actors like ACDs and EXPs into agricultural extension. Targeted information delivery can improve adoption efficiency, promote evidence-based decision-making, and support the modernization and sustainability of China’s apple sector. Full article

With the increasing importance of green transformation in agricultural production, green pest control technologies (GPCTs), defined as a set of eco-friendly methods aimed at managing agricultural pests with reduced reliance on synthetic chemical pesticides, play a key role in improving agricultural production efficiency, ensuring product quality, and protecting the ecological environment. Based on field survey data from apple farmers in Yantai and Linyi cities, Shandong Province, collected in 2022, this paper employs endogenous treatment effects regression (ETR) and instrumental variable quantile regression (IVQR) models to analyze the impact of adopting green pest control technologies on household income and explores the heterogeneity of this effect across different income levels. The results show that the adoption of green pest control technologies significantly increases apple farmers’ net apple income and household income, confirming their income-boosting effect. Moreover, the income-boosting effect is more significant for lower-income farmers, suggesting that these farmers benefit more from the adoption of green pest control technologies by improving pest management and thus enhancing apple production efficiency. This study provides empirical evidence for the promotion of green pest control technologies and offers valuable references for policymakers, especially in supporting technology adoption among lower-income farmers. Full article

Based on a sample of 412 apple farmer households across Gansu, Shaanxi, Shanxi, and Shandong provinces in China, this study estimates production efficiency and its determinants for apple growers. The stochastic frontier analysis model estimates technical efficiency while the Tobit model identifies influencing factors. Results show that the average production efficiency of smallholder apple farmers is relatively low at 0.45, indicating significant room for improvement. Production efficiency exhibits an inverted “U” relationship with farm scale, and excessive pesticide inputs have a significant negative impact on efficiency. Computer use to search for information among farmers was found to significantly improve apple production efficiency, indicating the potential benefits of ICT adoption. However, membership in cooperatives had no significant effect on efficiency. Overall, these findings suggest approaches to enhance the productivity of China’s apple growers through improved resource allocation, optimized farm scale, and the promotion of information technology. Full article

Farmer differentiation has led to significant differences in input behaviors, presenting new challenges for agricultural environmental governance. However, previous studies evaluating agricultural production systems often overlook the impact of farmer heterogeneity, and the relationship between farmer differentiation and environmental impacts remains unclear. This study takes the apple production system as a case and employs life cycle assessment (LCA) using the IMPACT2002+ model to establish environmental impact evaluation indicators for agricultural products. The environmental impacts of different types of farmers are analyzed. The findings are as follows: Overall, orchard systems under Type II part-time farmer (PTF(II)) management show the highest environmental impacts, whereas Type I part-time farmer (PTF(I)) systems exhibit the lowest, with pure farmer (PF) systems falling in between. Endpoint assessments reveal that human health is the most affected, with resource impacts being the least significant. Further analysis reveals that fertilizers are the primary environmental hotspot in the apple production system. For PFs and PTFs(I), the second-largest source of pollution in the orchard system is the purchase of storage services, whereas for PTFs(II), it is irrigation. Therefore, the government should strengthen the management of fertilizers and irrigation, and promote measures such as eco-friendly fertilizers and water-saving technologies, thereby reducing the environmental burden of production. Full article

This study explores the acoustic, mechanical and sensory characteristics of Royal Gala dried apples, with a special focus on the potential of solar drying as a sustainable processing method. Apple samples were subjected to different drying techniques, being solar dried (SDA) or oven dried (ODA), with two industrially processed commercial products (CCA—commercial apples C and CFA—commercial apples F) included. The samples were analyzed using acoustic measurements, X-ray diffraction (XRD) and sensory evaluation to assess textural properties and consumer perception. Acoustic analysis revealed that crispier samples produced louder and higher-frequency sounds upon fracture, showing strong alignment with sensory assessments. X-ray diffraction indicated an increase in crystallinity during dehydration, with a shift in the amorphous peak toward lower angles, and reduced intensity, reflecting progressive water removal. Sensory evaluation showed varying degrees of crispiness among the samples, in the following order: CFA > SDA > CCA > ODA. Consumer testing highlighted greater acceptance and consensus for SDA and ODA samples in terms of texture and overall appeal, whereas CCA and CFA received more polarized opinions. These findings demonstrate how different drying methods influence the structural and textural properties of dried apples. Solar drying was shown to be a promising sustainable alternative; as it uses a renewable energy source, it has a low operating cost and simple maintenance. It allows farmers and small producers to process their own food, adding value and reducing post-harvest losses, preserving desirable textural attributes and achieving high consumer acceptance. Full article

The present study explores the impact of farm scale on environmental efficiency to provide theoretical support and policy reference for the modernization and sustainable development of the apple industry. The study is based on research data from apple farmers in three counties of the Shaanxi and Gansu provinces in 2021. Firstly, the life cycle approach is applied to assess the comprehensive environmental pollution emissions in apple production and to clarify the non-desired outputs. Secondly, the environmental efficiency of apple production is measured using the SBM model, based on which the Tobit model is utilized to explore the impact of operation scale on the environmental efficiency of apple production and its potential mechanism of action. The results of the study show the following: (1) The mean environmental efficiency of the farmers in the sample is 0.278, indicating that the overall environmental efficiency of apple production is low; (2) there is an inverted U-shaped relationship between the scale of operation and the environmental efficiency of apple production, and the results are robust. This analysis was conducted after addressing endogeneity. Thirdly, the study found that the intensity of the adoption of green technology and farmers’ environmental awareness play a significant mediating role in the influence of business scale on the environmental efficiency of apple production. The potential mechanism of the effect of the scale of operation on the environmental efficiency of apple production was also investigated. Consequently, it is recommended to expedite the promotion of moderate-scale orchard operations, to proactively cultivate new management entities, and to enhance the adoption level of green technology and environmental cognition among farmers. These measures are proposed to encourage sustainable and high-quality development in the apple industry. Full article

The olive tree (Olea europaea subsp. europaea L.) is one of the most important perennial crops in the Mediterranean Basin. Olive Scab, caused by the fungal species Spilocaea oleagina, a member of the Venturiaceae family, is among the most significant diseases affecting olive cultivation, prompting farmers to spend millions of euros annually on fungicides for its control. The fungal genus Venturia includes highly specialized species responsible for diseases in other crops, such as Apple Scab, caused by V. inaequalis. One of the most effective control strategies for Apple Scab has been developing and using resistant varieties. However, in the case of Olive Scab, genetic resistance remains relatively underexplored. In apples, breeders have identified approximately 20 resistance genes against V. inaequalis, known as Rvi genes, over recent decades. In this study, we identified and characterized four homologous genes to the Rvi family in olive, analyzing their genomic organization and expression profiles in silico. A total of 14 homologous sequences were identified in the olive genome, all sharing conserved domains typical of the leucine-rich repeat (LRR) superfamily, widely associated with plant immune responses. Functional annotation using gene ontology indicated enrichment in categories related to stimulus response and diverse biological processes. Notably, homologous sequences corresponding to apple proteins linked to V. inaequalis resistance exhibited high expression levels in response to biotic and abiotic stresses. These results indicate that olive trees may harbor resistance mechanisms analogous to those observed in apples, providing a foundation for further research into olive disease resistance and breeding programs. Full article

The widespread use of pesticides has long been a cornerstone of modern agriculture, but their overuse has led to several challenges, including increased production costs, food safety risks, and environmental damage. Green pest control technologies (GPCTs) have emerged as a promising alternative to traditional chemical methods, although their widespread adoption is still in progress, and their environmental and economic impacts require further examination. This study aims to evaluate the adoption of GPCT in apple orchards by employing a rigorous framework to measure pesticide intensity per unit, assess the impact of GPCT on pesticide reduction, and analyze the associated environmental effects in large-scale apple farming systems in China. Based on survey data collected from apple farmers across key production regions in China, we apply an Endogenous Treatment Effect Regression (ETR) model to evaluate the effects of these technologies on pesticide usage and concentration. This model allows for more accurate estimates of the treatment effects by addressing selection bias and accounting for both observable and unobservable factors. Our results show that the adoption of GPCT leads to a significant reduction in pesticide use intensity. Notably, the reductions are more pronounced among specific groups of farmers, particularly those who are less risk-averse and those with larger or more fragmented landholdings. These findings underscore the dual ecological and economic benefits of GPCT, providing strong support for policy initiatives that promote sustainable agricultural practices and encourage land consolidation. Full article

Agriculture’s sustainable growth necessitates the application of advanced science and technology to ensure the sensible use of resources and improve the agricultural economy’s long-term stability. In this study, apple trees were employed as research objects throughout the spring (NSS) and autumn shoot stop-growing stage (ASS), and the data source was canopy hyperspectral data of fruit trees collected using ASD near-earth sensors, which was then combined with multiple sensitive wavelength screening algorithms and machine learning models to create an efficient and accurate apple yield estimation system. This is critical for guiding fruit farmers’ production, maintaining market supply and demand balances, fostering stable agricultural economy development, and providing a scientific basis and technical support for agricultural sustainability. Firstly, the fruit tree canopy hyperspectral data and apple tree yield data were collected, and the Savitsky–Golay convolution smoothing method (SG) was used to preprocess the canopy hyperspectral data. Secondly, six algorithms—Competitive Adaptive Re-weighting Sampling (CARS), Genetic Algorithm (GA), Successive Projections Algorithm (SPA), Uninformative Variable Elimination Algorithm (UVE), Variable Iteration Spatial Shrinking Algorithm (VISSA), and Variable Combination Population Algorithm (VCPA)—were employed to screen for the sensitive wavelengths related to apple tree yield, then preferring three methods for two-by-two combinations to determine the optimal algorithm combinations. Finally, using the best algorithm combinations, we built the apple yield linear model partial least squares regression (PLSR) and three machine learning models, Random Forest (RF), Cubist, and XGBoost, to screen for the best estimation model. The results demonstrated that ASS was the best fertility period for estimating yield; the validation set of the model constructed using each algorithm in ASS had a higher R² of 0.05–0.51 and a lower RMSE of 0.21–5.33 than those in NSS. The three algorithms preferred were CARS, GA, and VISSA. After combining the three algorithms in two combinations, the best combination of VISSA-CARS was found. The RF model established based on the best VISSA-CARS combination algorithm is the best model for apple yield estimation, with a validation set R² = 0.78 and RMSE = 6.03. The findings of this study may provide a new concept for accurately and quickly estimating apple yield, allowing fruit growers to improve production efficiency and promote agricultural sustainability. Full article

Freeze-drying fresh vegetables and fruits may not only prevent post-harvest losses but also provide a concentrated source of nutrients and phytochemicals. This study focused on the phenolic composition of different freeze-dried products derived from horticultural crop remains (HCRs) in the vegetable and fruit production chain. These products may be considered as a potential health-promoting solution for preventing post-harvest fruit spoiling and losses. The total polyphenolic content (TPC) and the main phenolics were studied using high-performance liquid chromatography (HPLC) with a diode array detector (DAD). Additionally, an in vitro chemical screening of the antioxidant capacity was carried out using the Ferric Reducing Antioxidant Power (FRAP) assay. These analyses were performed together with an investigation of the correlations among phenolics and their antioxidant properties, and a bioinformatic approach was used to estimate the main potential bio-targets in human beings. Furthermore, a statistical approach using Principal Component Analysis (PCA) was carried out for a multivariate characterization of these products. Catechins, flavonols, and phenolic acids were the predominant and most discriminating classes in different products. The TPC values obtained in this study ranged from 366.86 ± 71.30 mg GAE/100 g DW (apple, MD) to 1077.13 ± 35.47 mg GAE/100 g DW (blueberry, MID) and 1102.25 ± 219.71 mg GAE/100 g DW (kaki, KD). The FRAP values ranged from 49.28 ± 2.88 mmol Fe²⁺/kg DW (apple, MD) to 80.43 ± 0.02 mmol Fe²⁺/kg DW (blueberry, MID) and 79.05 ± 0.21 mmol Fe²⁺/kg DW (kaki, KD). The proposed approach may be an effective tool for quality control and valorization of these products. This study showed that the utilization of crop remains can potentially lead to the development of new functional foods, providing additional economic benefits for farmers. Finally, the use of freeze-drying may potentially be a sustainable and beneficial solution for growers who may directly utilize this technology to produce dried products from the crop remains of their fruit productions. Full article

Apple proliferation is among the most important diseases in European fruit production. Early and reliable detection enables farmers to respond appropriately and to prevent further spreading of the disease. Traditional phenotyping approaches by human observers consider multiple symptoms, but these are difficult to measure automatically in the field. Therefore, the potential of hyperspectral imaging in combination with data analysis by machine learning algorithms was investigated to detect the symptoms solely based on the spectral signature of collected leaf samples. In the growing seasons 2019 and 2020, a total of 1160 leaf samples were collected. Hyperspectral imaging with a dual camera setup in spectral bands from 400 nm to 2500 nm was accompanied with subsequent PCR analysis of the samples to provide reference data for the machine learning approaches. Data processing consists of preprocessing for segmentation of the leaf area, feature extraction, classification and subsequent analysis of relevance of spectral bands. The results show that imaging multiple leaves of a tree enhances detection results, that spectral indices are a robust means to detect the diseased trees, and that the potentials of the full spectral range can be exploited using machine learning approaches. Classification models like rRBF achieved an accuracy of 0.971 in a controlled environment with stratified data for a single variety. Combined models for multiple varieties from field test samples achieved classification accuracies of 0.731. Including spatial distribution of spectral data further improves the results to 0.751. Prediction of qPCR results by regression based on spectral data achieved RMSE of 14.491 phytoplasma per plant cell. Full article

The adoption of organic fertilizers is essential for advancing China’s green agricultural transformation, ensuring food security, and supporting agricultural adaptations. However, several challenges hinder its widespread use in rural areas. This study examines how part-time farming and agricultural service provision influences organic fertilizer use, employing fixed and random effects models on data from 523 households in Shaanxi Province, one of China’s main apple-producing regions. The results reveal: (1) Part-time farming reduces organic fertilizer use by 7.6%, primarily due to labor shortages; (2) Higher non-farm income exacerbates this decline, particularly for Type II part-time farmers; and (3) Mechanized fertilization services help mitigate this reduction. These findings offer valuable policy insights for promoting organic fertilizer adoption in the context of shifting rural labor dynamics and highlight the complex interactions between farming practices and labor migration in the broader trajectory of organic fertilizer use. Moreover, this study highlights the role of organic fertilizer use in enhancing food security while also helping to reduce the carbon footprint of the crop sector in China. Full article