Agriculture In China Research Articles

Transformation toward precision large-scale operations for sustainable farming: A review based on China’s pig industry

This review evaluates the current situation of pig farming, identifies challenges, and projects for the sustainable development of the Chinese pig industry. A literature review using keyword searches was conducted on Google Scholar for articles from 2017–2023. The review included studies focused on pig farming in China, covering prospects, challenges, quantitative data on pro-duction, marketing, and consumption, automation in livestock farming, and publications from peer-reviewed journals, credible websites, government reports, and conference proceedings. Pork consumption in China is increasing, and the country imports a sizable amount of pork annually. Even though small-scale farms still account for most operations, the pig industry is undergoing a critical stage of modernization and transition towards large-scale farming. The major challenges identified were feed, disease, antimicrobial resistance, environmental pollution, and pork prices. Smart technologies, such as cameras, Internet of Things, and sensors, integrated into precision pig farming can improve productivity and animal health through real-time data collection and decision-making. To solve the problems we face now, we need to put a lot of money into large-scale transformation, the creation of new animal precision tools, the automation of manure treatment, and the research and development of long-lasting alternative energy sources like photovoltaics and wind. By implementing these strategies, large-scale precision pig farming in China can become economically and environmentally sustainable, which can ultimately benefit consumers by supplying wholesome pork products.

Prediction of individual total amino acids and free amino acids in Chinese Holstein cows milk using mid-infrared spectroscopy and their phenotypic variability

Establishing a high-throughput detection technology for amino acid (AA) content in milk using mid-infrared (MIR) spectroscopy has profound implications for enhancing nutritional value of milk, identifying superior milk sources, producing specialty dairy products, and expanding Dairy Herd Improvement (DHI) metrics. The aim of this study was to evaluate the effectiveness of MIR spectroscopy in predicting the content of 15 individual total AA (TAAs) and 16 free AA (FAAs) in bovine milk as well as to investigate the major factors affecting the phenotypic variability of AA content. From March 2023 to March 2024, 513 milk samples were collected from 10 Holstein dairy farms in China and analyzed using Bentley spectrometers for MIR measurements. Their TAAs and FAAs concentrations were assessed through an AA autoanalyzer. Separate quantitative prediction models were developed for each AA using partial least squares regression; accuracy of prediction was assessed using Cow-independent external validation (CEV) and Farm-independent external validation (FEV) set. In CEV, the ratio of performance to deviation (RPD) of the TAAs models ranged from 1.45 (Ser) to 2.19 (Leu), while the FAA models ranged from 1.15 (Ser) to 2.44 (Met). In FEV, the RPD of the TAAs models ranged from 0.98 (Met) to 1.76 (Asp, Glu, and Ala), and the FAAs models ranged from 0.33 (Phe) to 1.23 (Asp and Tyr). For farms included in the calibration set, MIR spectroscopy provided a rough quantitative estimation for 4 individual TAAs (Ile, Leu, Glu, and Tyr) and 2 FAAs (Met and His), as well as a qualitative determination for high and low values in 9 individual TAAs (Phe, Met, Val, Lys, Thr, Asp, Ala, His, and Arg). For farms outside the calibration set, MIR spectroscopy could only distinguish between high and low contents for 5 individual TAAs (Glu, Asp, Ala, Leu, and Arg). Phenotypically, the variation pattern in TAAs contents mirrored that of protein, while FAAs did not show a clear trend, though mastitis led to a significant elevation of FAAs in milk (p < 0.05). Overall, the application of MIR spectroscopy can be considered very promising for a low-cost, rapid, large-scale assessment of individual TAAs and FAAs contents in milk. After refinement, some models could potentially be incorporated into DHI, which would greatly benefit the milk production and food industries.

Direct analysis and identification of the intestinal microflora of shrimps for their geographical traceability via mass spectrometry and bacterial library searching.

The expansion of the seafood market has led to an increased probability of food fraud. The development of rapid and reliable traceability methods for aquatic food products is of utmost importance. In this study, direct analysis and identification of the intestinal microbiota of aquatic foods were conducted. The validity of using BacteriaMS database searching for the identification of bacteria was assessed and demonstrated through analyzing prepared bacterial mixtures. We focused on shrimp as a model for aquatic food products and utilized matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to analyze the intestinal microflora of Chinese shrimp (Fenneropenaeus chinensis) collected from three different aquaculture farms in China. It was found that the most dominant bacteria found in shrimps' intestines could serve as a basis for distinguishing shrimps' geographical origin. The most dominant bacteria in the intestines varied among shrimps from different origins but remained identical for shrimps from the same origin. The reliability of the method in tracing the geographic origin of aquatic products was further validated by analysis of black tiger shrimp (Penaeus monodon) from different origins. The findings show that the utilization of MALDI-TOF MS for the analysis of the microbial community in the intestines of shrimp samples combined with bacterial library searching can offer a rapid, accurate, and feasible method that can be employed for determining shrimps' geographical origin. The present protocol was successfully utilized for the traceability of origins of Chinese shrimp (Fenneropenaeus chinensis) and black tiger shrimp (Penaeus monodon). It is promising to extend the present protocol to other aquatic products with regional characteristics to help combat food fraud in the aquatic product market.

Heterogeneous Impacts of Aging and Feminization of Agriculture on Production Factor Inputs: Evidence From Rice Farmers in China

ABSTRACTIn contrast to other countries, China's agricultural production faces the challenges of aging and feminization, which have a tremendous impact on food security. The purpose of this research was to reveal the correlations between aging and feminization and agricultural production factor input and its heterogeneity. This was evaluated using the ordinary least squares (OLS) model. The research found that the aging of farming did not correlate with the input of land factor and employment input. However, the feminization of farming significantly correlated with the input of land factor and employment input. In particular, an increase in the feminization of farming by one standard deviation reduced the land factor input by 18.9% and employment input by 31.3%. Second, agricultural aging and feminization had a positive and significant effect on agricultural machinery input but a negative and significant effect on material input. The instrumental variable method test results were consistent with those of the main regression. Third, heterogeneity analysis showed that the correlations between aging and feminization and the input of land, employment, agricultural machinery, and materials were significantly different with different educational levels of house heads and the proportion of agricultural income. Therefore, this study provides a reference for adjusting the rural labor structure and accumulating advanced rural production factors.

Wildlife policy, the food system and One Health: a complex systems analysis of unintended consequences for the prevention of emerging zoonoses in China, the Democratic Republic of the Congo and the Philippines

IntroductionEvolving human–wildlife interactions have contributed to emerging zoonoses outbreaks, and pandemic prevention policy for wildlife management and conservation requires enhanced consideration from this perspective. However, the risk of unintended consequences...

Export competitiveness network of Chinese provincial agricultural products: E volution, performance, and influencing factors.

The development of top-tier agricultural product bases and demonstration zones aims to propel Chinese agriculture into the global market, form international competitiveness, and advance to the high end of the global industrial and value chain. This study contributes to the existing literature by investigating the performance and factors influencing the competitiveness of Chinese agricultural product exports at the provincial level, using a complex network perspective, and offers recommendations for improvement. In this regard, the article uses agricultural product export data from 2015 to 2022 to create export competitiveness networks of Chinese provincial agricultural products. Our analysis revealed four primary observations. Firstly, significant opportunities exist to improve the competitive edge of agricultural product exports across various Chinese provinces. Secondly, with the exception of Guangdong Province, the types of competitively robust agricultural products exported by each province are inconsistent. Thirdly, international sister-city relationships can foster the development of an export competitiveness network of Chinese provincial agricultural products, while geographic distance may hinder this network. These findings are consistent. Lastly, the development of this competition-focused export network is also influenced by province-level and national characteristics, and these influences have unique differences. All provinces should strive to produce distinctive and globally competitive agricultural goods to build their competitive advantage in international trade. They should also implement strategic regional planning to support the global expansion of their agricultural products.

Evaluation of the genetic diversity and population structure of 5 pheasant breeds in Shanghai.

The genetics of pheasant breeds in Chinese farms has not been investigated yet. Understanding their genetic diversity and population structure is important for future advancements in pheasant breeding. In this study, the whole-genome resequencing was used to analyze a total of 352 samples from 5 pheasant species (American pheasant, White pheasant, Green pheasant, Shenhong pheasant, and Fengxian blue pheasant). The average effective population size (Ne) was 45.82. The average of expected heterozygosity (He) and observed heterozygosity (Ho) was 0.28514 and 0.27938, respectively. The Green pheasant had the lowest values of He (0.2730) and Ho (0.2692), whereas Fengxian blue pheasant had the highest values of He (0.2885) and Ho (0.2937), respectively. In addition, the 5 pheasant breeds could be divided into four different genetic populations. A similar genetic structure was also observed between American pheasant and Shenhong pheasant, whereas the other three pheasant breeds (White pheasant, Green pheasant, and Fengxian blue pheasant) exhibited obviously different genetic structures. Further analysis of population structure showed that some individuals among all 5 pheasant breeds had a high genetic distance and weak genetic relationships. A certain degree of inbreeding might exist in the population of White pheasant. Thus, effective breeding and conservation plans should be conducted to retain the genetic distinctiveness for White pheasant. Our data is of great significance for promoting the conservation and development of pheasant genetic resources.

Morphological and molecular characteristics of swine coccidia based on single oocyst isolation

Swine coccidiosis is a host-specific protozoan disease caused by Cystoisospora suis and various Eimeria species, leading to diarrhea or subclinical signs in pigs. In this study, 3296 fecal samples from 55 farms across six provinces in China were collected and examined to determine the prevalence and molecular characteristics of swine coccidia. The single oocyst isolation technique (SOIT) and molecular characterization identified nine coccidian species, with an overall infection prevalence of 13.83%. Infection rates varied by locations, host age groups, and sampling seasons. Among the positive swine coccidia samples, Cystoisospora suis showed the highest prevalence at 58.77%, followed by eight Eimeria spp. species. SOIT along with molecular characterization of C. suis at the SSU rRNA and ITS rRNA gene loci revealed the sequence homology from 99.8 to 100.0%. Additionally, the eight species of Eimeria. exhibited a high degree of sequence homology at the SSU rRNA and COX I gene loci. Due to the limitation of single molecular marker genes, this study introduced SOIT for DNA extraction, followed by nested PCR amplification of the SSU rRNA, ITS rRNA, and COX I gene loci to identify swine coccidia. This study is the first to systematically evaluate the prevalence and genetic diversity of coccidia in Chinese pig farms by SOIT, offering a method for accurately identifying swine coccidiosis and a scientific foundation for its effective prevention and control.Graphical

Organophosphorus Pesticides Management Strategies: Prohibition and Restriction Multi-Category Multi-Class Models, Environmental Transformation Risks, and Special Attention List.

Organophosphorus pesticides (OPs) have become one of the most widely used pesticides in Chinese agriculture; however, methods to identify potential restrictions on OPs molecules are lacking. Therefore, this study retrieved the OPs restriction list and constructed eight multi-class, multi-category machine learning models for OPs restrictions. Among these, the random forest (RF) model demonstrated excellent predictive performance, as it was successfully validated and applied. Potential environmental transformation products of OPs were obtained using EAWAG-BBD software, while toxicity indicators for the parent OPs and their transformation products were predicted with ADMETlab 3.0 software. This study found that unrestricted OPs, such as phorate, parathion, and chlorpyrifos, exhibited a high probability of toxicity. Additionally, the environmental transformation products of OPs posed similar comprehensive toxicity risks as the parent compounds. A special attention list for OPs was created based on the toxicity risks of unrestricted parent OPs and their transformation products, using standard deviation classification. Phorate and parathion were identified as OPs requiring special attention. This paper aims to provide an effective method for identifying the potential restriction levels of OPs and to propose an evaluation system that comprehensively considers the health risk, thereby supporting the improvement and optimization of management and usage strategies for OPs.

Isolation, identification and genetic variation analysis of avian orthoreovirus in commercial broilers in China from 2016 to 2021

ABSTRACT In the last decade, the emergence of variant strains of avian orthoreovirus (ARV) has caused an enormous economic impact on the poultry industry across China and other countries. This study aimed to evaluate the molecular evolution of the ARV lineages detected in Chinese commercial broiler farms. Firstly, ARV isolation and identification of commercial broiler arthritis cases from different provinces in China from 2016 to 2021 were conducted. A total of 51 pure ARV isolates were obtained. Sequencing results showed that there were five genotypes of the strains isolated in this study, of which genotype 1 ARV predominated, accounting for 56.9% (29/51). The whole gene sequences of 19 ARV representative isolates were successfully obtained. The genetic evolution analysis of 10 genome segments of 19 ARV isolates showed that the σC-encoding gene had evolved into six different lineages, while the other genome segments only differentiated into two to four different lineages. The results of recombination analysis showed that recombination events were present in the L3, M1 and S1 genome segments. Analysis of the variation of the key factor σC protein showed that the nucleotide and amino acid homologies of the σC were low among the different genotypes. Three-dimensional structural visualization analysis showed that all the structural changes of σC protein were concentrated in the spherical domain at the C-terminal, which is associated with host receptor binding.

Epidemiological surveillance and economic impact analysis of different porcine reproductive and respiratory syndrome virus infection statuses in 23 breeding pig farms in Hubei, China

Porcine reproductive and respiratory syndrome virus (PRRSV) has emerged as a significant threat to the pig farming industry worldwide, resulting in considerable economic losses. However, few reports detail its economic impact on the pig farming sector. A study was conducted on 23 breeding pig farms in Hubei Province from January 2021 to December 2023, and the PRRSV infection status and associated economic losses were monitored to address this gap. PRRSV antigens and antibodies were identified through enzyme-linked immunosorbent assay (ELISA) and quantitative reverse transcription polymerase chain reaction (qRT-PCR). Additional monthly production data and weaning costs were gathered. The Kruskal-Wallis nonparametric test was used to assess the differences in production efficiency and weaning costs across various PRRSV infection statuses. Dunn’s test was used to compare multiple groups. The parameter distributions of various variables were determined via @RISK (V. 8.5.2) software. Models were developed to evaluate the economic impact of PRRSV infection status on breeding pig farms and assess the losses from a PRRSV outbreak in either provisional PRRSV-negative or PRRSV-positive stable farms. A total of 754 months of monitoring was conducted across 23 breeding pig farms, which included 131 months (17.37%) classified as PRRSV provisional negative, 298 months (39.52%) as PRRSV-positive stable, and 325 months (43.11%) as PRRSV-positive unstable. The production efficiency and weaning costs were similar between provisional PRRSV-negative farms and PRRSV-positive stable farms, revealing no significant differences. However, these metrics varied significantly compared with those of PRRSV-positive unstable farms. With respect to provisional PRRSV-negative farms, PRRSV-positive stable farms and unstable farms faced additional annual losses of ¥3,135.17 and ¥4,898.79 per sow, respectively. Compared with PRRSV-positive stable farms, PRRSV-positive unstable farms incurred an extra annual loss of ¥1,763.62 per sow. Upon a PRRSV outbreak on a swine farm, followed by a return to preoutbreak conditions, the average economic loss per sow on provisional PRRSV-negative farms is approximately ¥3,061.21. Conversely, PRRSV-positive stable farms face an average loss of approximately ¥508.42 per sow. This study provides a systematic evaluation of the economic impact of PRRSV on Chinese pig farms, offering data to support the quantitative assessment of economic losses stemming from PRRSV within the domestic pig farming industry.

Understanding Chinese Farmers’ Behavioral Intentions to Use Alternative Fuel Machinery: Insights from the Technology Acceptance Model and Theory of Planned Behavior

Mitigating greenhouse gas emissions and reducing fossil fuel dependence have heightened interest in alternative fuels, particularly in agriculture, a key energy-intensive sector. This study investigates the use of the Technology Acceptance Model and the Theory of Planned Behavior to analyze Chinese farmers’ behavioral intentions to adopt alternative fuel machinery. In 2023, a total of 281 full-time farmers were sampled from Jiangsu, Sichuan, and Hebei provinces, followed by path analysis conducted using PLS-SEM. The results underline that perceived usefulness β = 0.369, p &lt; 0.001, and social norms β = 0.185, p &lt; 0.001 are strong drivers for the adoption of alternative fuel machinery, while perceived barriers to operate and infrastructure gaps act as inhibitors. Also, perceived benefits β = 0.235, p = 0.002, and perceived behavior control β = 0.157, p = 0.027 positively predict behavior intentions. This thus suggests that improvement in the technological infrastructure and increasing farmers’ awareness of the practical and economic benefits of alternative fuel machinery could result in a serious increase in adoption rates. This study does have weaknesses, as its focus on 50 mid-sized farms in the Midwest and utilization of self-reported data may limit the generalizability of its findings. This study, however, has major implications for policy and manufacturers to underscore the fact that targeted interventions through subsidies, training programs, and infrastructural development would be necessary to permit a transition toward sustainable energy in agriculture.

Assessing the impact of carbon mitigation strategies on agricultural GHG emissions: insights from a dynamic CGE model analysis

The selection of effective carbon reduction strategies and the management of agricultural greenhouse gas (GHG) emissions are critical issues in climate change mitigation. Different climate actions can lead to varied pathways for agricultural GHG emissions. This study constructs a Computable General Equilibrium (CGE) model for Chinese agriculture to identify which measures can contribute to achieving established climate governance objectives, exploring potential net emission pathways for agricultural GHG. On this basis, we provide a rationale for selecting emission reduction measures. Our findings indicate that: (1) Carbon taxation is an indispensable climate action for achieving China’s “dual carbon” goals and net-zero emissions, necessitating combination with other mitigation strategies; (2) Carbon sequestration, non-agricultural carbon taxation, and CCUS measures can alter the net emission trajectory of agricultural GHG, and carbon sequestration shows the most significant impact; (3) Based on the ‘dual carbon’ or net-zero emission goals, China’s agricultural GHG emission pathway might exhibit a flat M-shaped characteristic, whereas intensifying carbon sequestration efforts could lead to an inverted V-shaped trajectory. Our results offer decision-making support for the formulation of GHG emission reduction measures in China.

Long-Term Application of an Oil Residue Organic Fertilizer Improved Soil Physical Properties in the Root Zone of Jujube Trees

Organic fertilizers have been widely applied in Chinese agriculture. Soil aggregate composition and pore size distribution indicate the effects of fertilizer practices on soil quality. While the effects of the long-term application of organic fertilizers on total soil pore space have been reported, few studies have examined the distribution of connected pore spaces (of critical importance for solute transport in the soil). Soil physical and chemical properties influence plant growth and soil processes. We compared the effects of organic fertilizer application (10 years, 5 years, and no application) on soil connectivity pore structures and physicochemical properties within the root zone (20–40 cm depth) around date palms through CT scanning or core samples. Specifically, when the number of years of organic fertilizer application increased, the proportion of 2–0.25 mm particle size aggregates in the soil increased, and soil connectivity pore structure became more complex and stable, with higher connectivity, pore numbers, and numbers of pore throats. The connectivity of particles of the 0–300 μm size class of pores increased and the proportion of the 500–1000 μm class of pore throats increased. Soil organic matter and enzyme activity were significantly related to soil pore structure characteristics. In conclusion, the application of organic fertilizer improved soil pore structures, and the effects became more pronounced with the increased duration of application. These results provide theoretical and practical support for the application of organic fertilizer to improve soil structures in arid areas, and the findings have significant implications for sustainable agriculture in arid and semi-arid regions.

Research on the Applications of Artificial Intelligence in Agriculture

With the continuous growth of the global population, agricultural production faces unprecedented challenges. Traditional agricultural methods exhibit numerous deficiencies in food production, quality control, and environmental protection, necessitating urgent intervention by modern technologies. This paper investigates the application of artificial intelligence (AI) technology in the agricultural sector, exploring its significant contributions to enhancing agricultural production efficiency, precision, and sustainability. Drawing on the research findings of several scholars, this paper demonstrates specific applications of AI in soil moisture classification, unmanned aerial vehicle (UAV) thermal imaging for nest detection, and crop health monitoring. Additionally, this paper discusses the crucial role of AI technology in the modernization of agriculture in China, particularly its potential in cultivating intelligent agricultural talents and promoting high-quality agricultural development. In summary, AI is injecting new vitality into modern agriculture with its unique advantages, driving agriculture towards green and sustainable development. Through efforts in intelligent machinery, precision agriculture, and talent development, AI presents a promising outlook for its application in agriculture, offering new perspectives and solutions to address global agricultural challenges.

The Role of Trust in Facilitating Land Transfers and Contract Formalization: A Study of Chinese Farm Households

Facilitating land transfers and contract standardization is crucial for optimizing farmland use and achieving high-quality agricultural development following the round of confirmation, registration, and certification of contracted rural land management rights by the government. Social trust is crucial for cracking the growth dilemma of land transfer, and the impact of farmers’ trust on land transfer may also change after the round of land titling. This study aims to examine the impact of social trust on farm households’ land transfer decisions and the formalization of land transfer contracts within the context of the new round of land titling in China, using models such as IV-Probit, Heckprobit, and conditional mixed process (CMP), with survey data from 2600 Chinese farmers in 2020–2021. The results show that increased social trust significantly enhances both land transfer and contract formalization, and these results remain robust after addressing potential endogeneity. Further analysis reveals that while interpersonal trust promotes land transfer, institutional trust plays a more pivotal role in the formalization of contracts. The results underscore the importance of strengthening grassroots governance to foster institutional trust, enhance institutional reliability, and support the regulated growth of the land transfer market.

Integrating traditional and biotechnological innovations for mitigating greenhouse gas emissions in dairy farming in China

Integrating traditional and biotechnological innovations for mitigating greenhouse gas emissions in dairy farming in China

Quasi-Newton Optimized Kolmogorov-Arnold Networks for Wind Farm Power Prediction

Having accurate and effective wind energy forecasting that can be easily incorporated into smart networks is important. Appropriate planning and energy generation predictions are necessary for these infrastructures. The production of wind energy is linked to instability and unpredictability. Wind energy forecasting has traditionally been performed using statistical models, but with the advent of artificial intelligence (AI), research indicates that AI is more accurate than the statical technique. In this study, the nominal power of six wind farms in China was predicted using Kolmogorov-Arnold Networks (KAN) and Multilayer Perceptron (MLP) models. KAN as an alternative to the conventional MLP, has the ability to handle problems with scalability, vanishing gradients, and interpretability associated with MLP. The KAN uses learnable B-Spline as activation functions prompting it to address the issues of the MLP. We employed the Radial Basis Function (RBF) with Gaussian kernels to approximate the 3-order B-spline basis. In most deep learning models stochastic gradient-based optimisation algorithms such as Adaptive Moment Estimation (ADAM) and Stochastic Gradient Descent (SGD) optimizer are mostly employed, a quasi-Newton optimization technique Limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm LBFGS was employed in this work to approximate the Hessian matrix and estimate the parameter space's curvature. Also, in the preprocessing of the data, the Interquartile Range (IQR) technique is used to handle outliers and a clustering-based K-Nearest Neighbor (KNN) imputer to handle missing values. Based on different sites, the KAN-LBFGS shows superior performance based on the performance evaluation metrics with site 5 achieving MSE of 0.0039, RMSE of 0.0622, MAE of 0.0352, and DC of 0.9468. The study highlights the importance of the model’s architecture, preprocessing and optimization techniques.

How does social capital influence farmers' adoption of soil testing and formulated fertilisation technology? Evidence from Chinese maize farmers

How does social capital influence farmers' adoption of soil testing and formulated fertilisation technology? Evidence from Chinese maize farmers

Implementation Of The Digital Media Course In The Pearl River College Of South China Agricultural University

Implementation Of The Digital Media Course In The Pearl River College Of South China Agricultural University