Agriculture In China Research Articles

Machine Learning-based Prediction of African Swine Fever (ASF) in Pigs

African Swine Fever (ASF) is a contiguous viral disease of the pig with serious economic threats to the pork industry. Early identification of ASF infection is important to support sustainable developments in the ASF industry. There is also a need for a solution to identify the ASF infection as early as possible based on apparent symptoms of ASF to screen the infected animals, that are not targeted in the existing literature. Many machine learning (ML) solutions have been proposed in recent years for the prediction and identification of human, animal, and plant diseases. To deal with ASF in pigs ML-assisted model is proposed for the early identification of ASF infection without medical diagnosis and expert opinion. The data regarding apparent symptoms are collected from Chinese small pig farms. The loss of appetite, weakness, diarrhea, vomiting, coughing, skin redness, and breathing difficulty levels are taken as major apparent symptoms of ASF infection. Moreover, different ML models are also evaluated for their performance in the prediction of ASF infection based on selected apparent symptoms of ASF infection. In this regard, Support Vector Machine (SVM), K-Nearest Neighbor (k-NN), Decision Tree (DT), Random Forests (RF), and Gaussian Naïve Bayes ML models are evaluated for ASF infection prediction. The implementation of the proposed solution reveals that the GNB model is more accurate as compared to the other evaluated models for the identification of ASF infection from the apparent ASF symptoms in infected pig animals, with 94.31\% accuracy. The proposed solution would be very effective in the early screening of ASF-infected pig animals without medical diagnosis and expert judgment.

A framework for cost-effectiveness analysis of greenhouse gas mitigation measures in dairy industry with an application to dairy farms in China

The dairy industry is a significant contributor to global greenhouse gas emissions (GHG). Although much effort has been directed to explore the cost-effective measures for many sectors such as electricity, building infrastructure, transportation, research on mitigation measures within dairy industry remains limited. A notable obstacle is the absence of a cost-effectiveness analysis (CEA) framework to guide decision-makers and practitioners in this sector. In response, we propose a comprehensive CEA framework tailored to mitigate GHG emissions in the dairy industry. Our conceptual framework consists of six steps: defining the system boundary to determine the activities generating GHG emissions; identifying GHG emission sources within the system boundary; identifying potential mitigation measures; determining methods to quantify GHG emissions; collecting data to estimate both GHG emissions and mitigation costs; and applying general econometric methodologies to analyze the cost-effectiveness of mitigation measures. We further conducted a case study focusing on dairy farms in China, analyzing three categories of mitigation measures: feed, energy, and manure management. The results indicate that implementing effective feed and energy measures is a cost-saving strategy, reducing the cost per unit of milk production. Conversely, adopting effective manure management measures may lead to increased costs for dairy farms. The findings offer strategic recommendations for reducing GHG emissions from dairy production in China and provide analytical insights and strategic references applicable to other developing countries.

Mulched drip irrigation: a promising practice for sustainable agriculture in China’s arid region

The long-term practice of mulched drip irrigation (MDI) has significantly advanced cotton production in China’s arid regions. In this study, we conducted an evaluation of the changes to key soil environmental factors within cotton fields under MDI, providing a holistic assessment of MDI sustainability. Specifically, long-term MDI demonstrated numerous benefits including improved soil salinity management, nutrient retention, soil structure, and microbial functions. Collectively, these changes contribute to enhanced overall soil quality. Our research also confirms MDI as an effective integrated water-saving technology approach. Affirming the sustainability of widespread MDI promotion, our findings firmly support its potential as a sustainable and viable solution for oasis agriculture in arid lands, conferring long-term benefits to agricultural production in these regions.

Identifying the spatio-temporal distribution characteristics of offshore wind turbines in China from Sentinel-1 imagery using deep learning

ABSTRACT Offshore wind power is a crucial clean energy source for coastal countries and advances blue economies. Accurate spatial mapping of offshore wind turbines supports energy assessment and the sustainable development of marine resources. Offshore wind power has developed rapidly in China, but the spatiotemporal distribution characteristics of offshore wind turbines and farms remain poorly understood. Extracting nearshore small targets from Sentinel-1 synthetic aperture radar images remain challenging due to noise from the rough sea surface background and the diverse substrate types in offshore wind turbine (OWT) distributions. Here, to address these issues, we first created a dataset of China’s offshore wind turbines (COWTs) that contained typical substrate types (e.g. seawater and tidal flats). Then, a deep learning model integrating an attention mechanism and receptive fields was used to accurately detect COWTs. The well-trained model was used to detect the changes in COWTs from 2015 to 2022. Our model detected an increase in the number of offshore wind farms in China from 7 to 114 (clusters counted as an offshore wind farm), while COWTs increased from 305 to 6451 from 2015 to 2022. The Taiwan Strait exhibited the highest wind speeds, yet the Jiangsu and Guangdong regions had the most installed turbines. Over 90% of COWTs were located in areas with offshore wind energy resources less than 500 W/m3, indicating a mismatch between COWT installation and wind resource distribution. This study demonstrated temporal and geographical generalizability, which is promising for global wind power detection. Our analysis of spatiotemporal variation in COWTs facilitates informed decision-making for future field establishment and sustainable marine planning.

Farmers’ Adoption of Agricultural Nature-Based Solutions in Northeast China: An Extended Theory of Planned Behavior Approach

The academic field has limited studies on implementing nature-based solutions (NbSs) in Chinese agriculture, especially regarding households’ and farmers’ attitudes toward the acceptance of agricultural NbS technologies. Based on this, this study aims to fill this knowledge gap by examining the factors behind farmers’ agricultural practices of NbS adoption in Northeast China. Accordingly, this paper constructs an extended theoretical framework of planned behavior (TPB) and empirically tests it using partial least squares structural equation modeling (PLS-SEM) on data from 298 farmers in Inner Mongolia. It was found that incorporating institutional trust (IT) into TPB improves the prediction of Chinese farmers’ intention to de-farm behavior. Unlike previous studies, this study identified two indirect pathways influencing farmers’ intention to adopt agricultural NbS technologies: from institutional trust through attitude to intention, and from institutional trust through perceived behavioral control to intention. The findings broaden the research perspective in this area, provide empirical evidence for the application of NbSs in agricultural practices in China and other developing countries, and demonstrate in practice that NbS has been promoted as an essential tool to maximize nature’s ability to provide ecosystem services and create direct economic benefits for farmers through increased yields and reduced costs.

Analysis of the General Situation of Organic Fertilizer in China

Organic fertilizers are crucial to China's agriculture, significantly enhancing soil fertility and sustainability. Derived from sources like livestock manure, crop residues, and green manure, these fertilizers improve soil structure, water retention, and nutrient availability, thus boosting crop yields and soil health. Despite their benefits, challenges such as variability in quality, application efficiency, and balancing with inorganic fertilizers remain. Government policies and ongoing research are addressing these issues to optimize the use of organic fertilizers and support sustainable agricultural practices.

Impact of Ecological Cognitive Bias on Pesticide Reduction by Natural Rubber Farmers in China: Insight from Price Insurance Satisfaction

Some natural rubber farmers mistakenly equate the ecological functions of rubber plantations with those of primary forests. This cognitive bias can hinder pesticide reduction efforts. Meanwhile, natural rubber farmers gain security through price insurance, which helps them adopt a long-term perspective on environmental protection, mitigating the negative impact of cognitive biases on pesticide use decisions. However, existing research often overlooks the influence of ecological cognitive bias on pesticide reduction and the moderating role of price insurance satisfaction. This study utilizes field survey data from Hainan and employs logit models and double machine learning models to empirically analyze the impact of ecological cognitive bias on pesticide reduction among natural rubber farmers. It further tests the underlying mechanisms using moderation models. The results indicate that (1) ecological cognitive bias negatively affects pesticide reduction among natural rubber farmers, and (2) price insurance satisfaction mitigates the impact of ecological cognitive bias on pesticide reduction. Based on these findings, the government should enhance education and training to raise ecological awareness among natural rubber farmers and reduce ecological cognitive biases. Disseminating knowledge about price insurance and establishing a comprehensive insurance system can secure farmers’ income and promote the rational use of pesticides.

The decline and transformation of smallholders in Chinese agriculture: national trends

ABSTRACT Using data from official statistical sources and a nationally representative survey, we find that since 2001, China’s agricultural labor force has declined by over 50 percent—a loss of over 200 million smallholders, probably the largest in human history. This trend is the most pronounced in livestock sectors: from 2012 to 2021, 62.5 million smallholder units have disappeared. The 150 million remaining smallholders are mostly commodity producers, participating widely in commodity relations to access agrochemicals, machinery, rental land, and wage labor to produce for markets. The ageing smallholder population also face challenges in intergenerational reproduction, which will accelerate its decline.

How does digital technology application empower specialty agricultural farmers? Evidence from Chinese litchi farmers

IntroductionSpecialty agricultural farmers have widely adopted digital technologies in all aspects of their specialty agricultural operations. However, the effect of digital technology application (DTA) on specialty agricultural farmers’ income has not been fully evaluated. Moreover, it remains unclear whether DTA enhances farmers’ income (FI) by improving their varied capabilities. To fill this gap, we analyzed the intrinsic relationship and mechanism between DTA, farmers’ capability (FC), and specialty agricultural farmers’ income.MethodsUsing field survey data from 635 litchi farmers in China, we employed OLS regression models and mediation effect models to empirically investigate DTA’s direct and indirect impacts on litchi farmers’ income. Additionally, we examined the group heterogeneity and regional heterogeneity.Results and discussionThe empirical results show that DTA can promote specialty agricultural farmers’ income by enhancing their production capacity (PC) and transaction capacity (TC). Heterogeneity analysis showed that the empowering effect of DTA is especially significant for disadvantaged farmers, and farmers who are in specialty agricultural resources-rich regions.ConclusionThis study contributes to the body of research on the empowerment of specialty agricultural farmers by examining the effect of DTA from a capability perspective. Consequently, to better play the empowering effect of DTA for specialty agricultural farmers, policymakers should account for regional disparities in promoting digital agriculture, and enhance heterogeneous farmers’ DTA capability.

Green Development of Chinese Agriculture from the Perspective of Bidirectional Correlation

China’s agricultural green development exhibits spatial imbalances. To promote the coordinated green development of agriculture across various regions, this paper explores the evolution of spatial association networks and driving factors of agricultural green development from a bidirectional correlation perspective, using social network analysis and spatial econometric methods. This research indicates that China’s agricultural green efficiency has steadily improved, with a complex multi-threaded network structure. Although the spatial interaction and spillover effects of the overall network structure have increased, they remain relatively weak. The individual network structure shows significant regional imbalances. The spatial association network of agricultural green efficiency forms four major blocks, with sparse connections within the blocks but close connections between blocks, demonstrating strong spillover effects. Regarding the driving factors, the proportion of the primary industry, labor level, and R&D investment have significant spatial impacts, while the spatial impacts of human capital level, degree of openness, economic development level, and new quality productivity level are not significant. Therefore, we believe that it is necessary to establish the concept of coordinated green development in agriculture, fully leverage regional associations and spillover effects, and formulate differentiated policies to improve agricultural green efficiency.

Quantitative detection of <i>Pythium porphyrae</i> and <i>Pythium chondricola</i> (Oomycota), the causative agents of red rot disease in <i>Pyropia farms</i> in China

Red rot disease is one of the notorious algal diseases that threaten the cultivation of <i>Pyropia</i> in China, and two <i>Pythium pathogens</i>, i.e., <i>Pythium porphyrae</i> and <i>P. chondricola</i>, have been reported as causative agents. To monitor the pathogens, a fluorescent quantitative polymerase chain reaction (PCR) method was developed to quantitatively detect their abundance. Using overlapping PCR and pathogen-specific primer pairs, two pathogen-specific fragments were concatenated to construct an internal standard plasmid, which was used for quantification. For zoospores of known numbers, the results showed that this method can detect as less as 100 and 10 zoospores mL<sup>-1</sup> in a 200 mL solution for <i>P. porphyrae</i> and <i>P. chondricola</i>, respectively. Using monthly collected seawater at 10 sites in Haizhou Bay, a typical aquaculture farm in China, a significantly higher temperature and a significantly lower salinity were determined in December 2021. <i>P. porphyrae</i> was determined to be more abundant than <i>P. chondricola</i>, though with similar temporal distribution patterns from December 2021 to February 2022. When a red rot disease occurred in December 2021, the two pathogens were significantly more abundant at two infected sub-sites than the uninfected sub-site within both seawater and sediment, though they were all significantly more enriched in sediment than in seawater. The present method provides the capability to quantify and compare the abundance of two pathogens and also has the potential to forecast the occurrence of red rot disease, which is of much significance in managing and controlling the disease.

Climate resilience in aquaculture: A cluster analysis of Chinese coastal regional production and farming practices in the face of climatic shifts 2017–2021

This study conducts a comprehensive analysis of the aquaculture industry across 11 coastal regions in eastern China from 2017 to 2021 to assess their adaptability and resilience in the face of climate change. Cluster analysis was employed to examine regional variations in aquaculture adaptation by analyzing data on annual average temperatures, annual extreme high/low temperatures, annual average relative humidity, annual sunshine duration, and total yearly precipitation alongside various aquaculture practices. The findings reveal that southern regions, such as Fujian and Guangdong, demonstrate higher adaptability and resilience due to their stable subtropical climates and advanced aquaculture technologies. In contrast, northern regions like Liaoning and Shandong, characterized by more significant climatic fluctuations, exhibit varying degrees of cluster changes, indicating a continuous need to adjust aquaculture strategies to cope with climatic challenges. Additionally, the study explores the specific impacts of climate change on species selection, disease management, and water resource utilization in aquaculture, emphasizing the importance of developing region-specific strategies. Based on these insights, several strategic recommendations are proposed, including promoting species diversification, enhancing disease monitoring and control, improving water quality management techniques, and urging governmental support for policies and technical guidance to enhance the climate resilience and sustainability of the aquaculture sector. These strategies and recommendations aim to assist the aquaculture industry in addressing future climate challenges and fostering long-term sustainable development.

Half substitution of mineral N with fish protein hydrolysate enhancing microbial residue C and N storage and climate benefits under high straw residue return

The widespread utilization of straw return was a popular practice straw disposal for highly intensive agriculture in China, which has brought about some negative impacts such as less time for straw complete biodegradation, aggravation of greenhouse gas evolution, and lower efficient of carbon accumulation. It was urgent to find an eco-friendly N-rich organic fertilizer instead of mineral N as activator to solve the above problems and lead a carbon accumulation in long tern management. Besides, microbial necromass was considered as a crucial contributor to persistent soil carbon (C) and nitrogen (N) pool. How organic fertilizer activators influence microbial residue under different amount of crop residues input remained unclear. Thus, soils incorporating moderate and high rate of rice straw residue with additions of half and full of organic activators (fish protein hydrolysates vs. manure) were incubated for measuring carbon dioxide (CO2) and nitrous oxide (N2O) emission, microbial community and necromass. It was found that soil CO2 emission was rapidest during the first 13 days of straw decomposition but remained lowest in the treatments of 50% mineral N substituted by fish protein hydrolysate. There were that 81%–89% of total CO2 release and 59%–65% of total N2O emission occurred within 60 days of incubation period, and bacterial community and nitrate positively affected soil CO2 and N2O release respectively. Straw incorporation amount and organic activator application interactively influenced soil CO2 emission but not affected soil N2O emission. After 360 days of incubation, the difference of bacterial necromass was noticeable but fungal necromass remained almost unaltered across all treatments. All treatments showed generally comparable contribution of microbial necromass N to the total N pool. The treatment of 50% mineral N substituted by fish protein hydrolysate under high rate of straw input (HSF50) promoted the highest proportion of microbial necromass C in soil organic C because of alleviating N limitation for microorganisms. Finally, HSF50 was recommended as an eco-friendly strategy for enhancing microbial necromass C and N storage and climate benefits in agroecosystems.

Development of Ecological Low-Carbon Agriculture with Chinese Characteristics in the New Era: Features, Practical Issues, and Pathways

Agriculture is not only the source of carbon emissions, but also an important carbon sink. The development of low-carbon agriculture in China is closely related to achieving the national strategic goal of “dual carbon.” Reducing carbon emissions in China’s agricultural sector and accelerating the development of ecological low-carbon agriculture (ELA) are extremely important and urgent, both from the perspective of the long-term common welfare for mankind and from the perspective of maintaining the sustainable development of agriculture itself. However, ELA is still an emerging concept in China, and its connotation and characteristics are not yet clear. There is a lack of effective paths for orderly and scientific promotion of the development of ELA in China. Based on this, this paper focuses on the emerging concept of ELA and examines ELA with Chinese characteristics from the dimensions of connotation, features, practical issues, and pathways, aiming to provide guidance for the sustainable development of ELA in China. The research results indicate that there are regional development imbalances, low willingness of farmers to participate, insufficient technological reserves, and difficulties in realizing the value of ecological products in China’s ELA. It is necessary to improve the modern agricultural production and operation system, tap into the potential of agricultural emission reduction and carbon sequestration, build a technical system to support the development of ELA, and establish a sound mechanism for realizing the value of ecological products. By continuously improving the regional organizational capacity and performance of ELA, accelerating the advancement of ELA technology and demonstrating its regional promotion, and by establishing and continuously improving the support system for ELA, one can promote the sustainable development of ELA in China. In addition, the research findings of this paper can also provide reference for the ecological low-carbon development of the global agricultural sector, supporting the contribution of the agricultural sector to achieving high-quality global sustainable development goals.

Deep Vertical Rotary Tillage: A Sustainable Agricultural Practice to Improve Soil Quality and Crop Yields in China

Deep vertical rotary tillage (DVRT) is an innovative soil tillage technology that has been widely adopted in China and shown significant potential in enhancing soil quality, optimizing water use efficiency, and increasing crop yields across diverse ecological and agronomic conditions. DVRT utilizes a vertical spiral drill bit for deep plowing, which preserves soil structure, reduces soil compaction, and improves water retention, making it particularly effective in regions facing climatic challenges such as drought. This review synthesizes the effects of DVRT on soil’s physical and chemical properties, crop root systems, photosynthesis, and water use efficiency, demonstrating its advantages in promoting robust root development and improving nutrient utilization. Although the technology has been applied successfully across various crops and regions, its nationwide adoption remains limited. This paper emphasizes the need for further research to refine the theoretical framework of DVRT and develop tailored strategies for different local conditions. Additionally, integrating DVRT with other agronomic practices and advancing machinery design, supported by policy measures, is essential for maximizing its benefits. In conclusion, DVRT presents a promising approach for sustainable agriculture in China, contributing to improved soil quality, increased crop yields, and enhanced food security.

Identification of a highly pathogenic Riemerella anatipestifer strain causing duck spleen marble-like necrosis disease in China

Duck spleen marble-like necrosis disease (DSMND) has been prevalent in Chinese duck farms since 2016. The etiological study was carried out in this study using etiological detection, pathogen isolation, whole genome sequencing, and artificial infection test. A highly pathogenic Riemerella anatipestifer (RA) strain was determined as the etiologic agent. Phylogenomic analysis showed that this RA strain was closely related to duck origin RA strain RCAD0421 and chicken origin RA strain S63. The clinical symptoms and pathological changes of artificial infection ducks were similar to those of clinical cases. This is the first identification of RA as the pathogen of DSMND, which provides a theoretical basis for this disease’ s prevention and control.

Antimicrobial Resistance and Use on Chinese Dairy Farms: Awareness and Opinions Regarding Selective Treatments of Farm Managers.

In China's expanding dairy industry, a lack of oversight regarding antimicrobial use and increasing antimicrobial resistance are evident. Selective treatments of dairy cows for clinical mastitis or dry cow therapy are proposed to promote judicious antimicrobial use without adversely impacting cattle health. These approaches have been successfully implemented on farms in other countries. On 28 October 2023, a 2-day in-person seminar was held in Beijing, China, on selective antimicrobial treatments of dairy cows for clinical mastitis or dry cow therapy on large Chinese dairy farms. Concurrently, a qualitative study involving 15 technical managers from the 13 largest Chinese dairy groups used focus group discussions and questionnaires to explore perspectives on selective treatments of dairy cows for clinical mastitis or dry cow therapy. The main outcomes assessed were opinions and concerns regarding implementing selective antimicrobial treatments. Although there was diversity of cognition on AMR and selective treatments, the technical managers were generally positive regarding adoption of selective treatments. However, they expressed a need for more evidence and tools, including anticipated economic impacts, effects of delaying treatment until diagnosis, accurate interpretation of milk recording data, safe use of internal teat sealants, and spread of pathogens. Participants stressed the need for awareness, staff training, farm management, and China-specific standards, suggesting large-scale trials to assess efficacy of selective treatments. The findings revealed key challenges and barriers currently impeding selective AMU practices. These insights could inform efforts to promote judicious AMU on farms through targeted treatment regimens, reducing mounting selective pressure driving resistance.

Integrative transcriptomics and proteomics analysis provide a deep insight into goose astrovirus-host interactions during GAstV infection

Goose astrovirus (GAstV) is a newly discovered astrovirus. GAstV causes gout and death in 4- to -16-day-old goslings. For the past few years, fatal gout, the cardinal clinical symptom of gosling infected with GAstV, has been spreading rapidly in some goose Chinese farms, which caused continuous economic losses to the goose breeding industry in China. Currently, several underlying mechanisms involved in viral replication, inflammatory reaction, virions release, and viral pathogenesis of GAstV remain to be elucidated. In this study, we explored the mechanisms of GAstV-host interactions, the transcriptome and proteome profiles of GAstV-infected LMH cells were sequenced by RNA-seq and data-independent acquisition (DIA) techniques, respectively, and followed using an integrative analysis. Compared with uninfected LMH cells, a total of 322 differentially expressed genes (DEG) (195 up-regulated, 127 down-regulated) and 36 differentially expressed proteins (DEP) (31 up-regulated, 5 down-regulated) were detected. Nine DEGs were randomly selected for further validation by quantitative real-time polymerase chain reaction (qPCR). Through GO and KEGG enrichment analysis, DEG and DEP were significantly enriched in several important cellular signaling pathways, including MAPK, PI3K-Akt, cAMP, chemokine, calcium, phospholipase D, Ras, TNF, IL-17, Rap1, NF-kappa B signaling pathways, indicating that GAstV affects cell growth and immune signaling. This study provided an overview of changes in transcriptome and proteome profiles of GAstV-infected LMH cells, therefore, providing a crucial basis to further explore the mechanisms of GAstV-host interactions.

The Role of Fiscal Support in Promoting the Application of Green Technologies in Sustainable Agriculture in China

This paper aims to explore the relationship between fiscal support and the promotion of sustainable agriculture, with a specific focus on development in China. Also, this paper aims to explore and research challenges during the process of carrying out fiscal support. It also examines how fiscal support can be effectively utilized to encourage and incentivize the adoption of innovative green agriculture technologies in sustainable agriculture. This research is meaningful to the development of sustainable agriculture on the future and enlightening to the future research on cooperation among governments, farmers, and agricultural technology companies. This paper gathers information from China and the author conducts field visit to get the result in this paper. The author reviews the findings of the literature of fiscal support and sustainable agriculture. The author finds that the combination of subsidies, tax incentives, loan programs, and research funding is widely and effectively utilized in fiscal support of sustainable agriculture in China generally. However, there are challenges during the process of carrying out fiscal support and the author analyzed out several suggestions for them. The result of this research of this paper can foster the development of fiscal support and broaden this field.

Impact of heavy metal hazard perceptions on pollution control intentions: Empirical evidence from rice farmers in China

Heavy metal pollution presents significant challenges to global sustainable development. The treatment of such pollution predominantly relies on government policies, often neglecting farmers' crucial role in the remediation process. Therefore, this study investigates the driving mechanisms behind farmers' willingness to control heavy metal pollution in farmland. By analyzing survey data from 718 rice farmers in China, this study employs the Probit model to quantitatively assess the impact of farmers' perceptions of heavy metal hazards on their inclination to control pollution in farmland. Additionally, it examines the heterogeneity of these impacts between cultivated rice for domestic consumption and rice for export. The findings reveal the following: (1) Approximately 70% of farmers recognize the harmful effects of heavy metals, but only 66.2% of them express willingness to engage in pollution control measures. (2) More awareness regarding heavy metal hazards corresponds to a stronger intention to control pollution. (3) In addition to hazard perception, households with village cadres demonstrate a significantly positive influence on pollution control intention. (4) There are notable disparities in farmers' attitudes towards retained rice (for domestic consumption) and exported rice, with a greater willingness to implement pollution control measures for retained rice. As such, this study provides valuable insights for informing policy development to address the challenges associated with heavy metal pollution control's complex and limited effects. The findings contribute to a better understanding of the Chinese context as a typical developing country, offering analytical cases that promote global sustainable development while leveraging Chinese wisdom.