Analyzing Agricultural Agglomeration in China

Li, Erling; Coates, Ken; Li, Xiaojian; Ye, Xinyue; Leipnik, Mark R.

doi:10.3390/su9020313

Cited by 31 publications

(27 citation statements)

References 36 publications

(35 reference statements)

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“…Villages are the bases for implementing development strategies in China [50]. Peasants who live in the same village communicate more with each other, live in more similar environments, and own more similar natural resources than peasants in other villages [28]. These factors lead to the similarity of rural livelihood strategies within a village.…”

Section: Discussionmentioning

confidence: 99%

“…However, individuals are nested in households, which can belong to a certain village. Thus, the livelihood strategies of individuals within the same group may have strong similarities [27,28]. Understanding why some peasants choose to engage in only on-farm enterprises, while others participate in pluriactivity or only off-farm employment, will contribute to the design of reasonable policies to sustain agricultural production and safeguard rural livelihoods for vulnerable peasants.…”

Section: Introductionmentioning

confidence: 99%

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Multilevel Modeling of Rural Livelihood Strategies from Peasant to Village Level in Henan Province, China

et al. 2018

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Abstract:With the rapid development of China's economy, Chinese peasants now have a growing number of livelihood choices. Rural livelihood strategies are primarily a matter of choice, while the characteristics of the household and village may affect the decision-making process. However, until now, there has been a lack of empirical studies that have been carried out for the identification of the multi-level determinants of rural livelihood strategies. To fill this gap, this paper applies multi-level modeling approach to model rural livelihood strategies in Henan Province, China. The results show that rural livelihood strategies have insignificant between-group variability at the household level, and significant between-group variability at the village level, with the variance at the peasant level accounting for the largest proportion of the total variance. Younger peasants who are male and have a higher education level are more likely to engage in only off-farm work, while peasants with the opposite characteristics are more likely to engage in only on-farm work. Pluriactive peasants integrate the characteristics of the other two groups, and generally live closer to urban areas than the others. In order to reduce rural income inequality and sustain agricultural production, the Chinese government should design effective policies to make farming an appealing livelihood choice, particularly for the young generation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multilevel Modeling of Rural Livelihood Strategies from Peasant to Village Level in Henan Province, China

et al. 2018

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“…Xiaojun et al [23] propose a multi-input and multioutput AQ prediction model based on ANN (Artificial Neural Network). Based on the relationship between current and past 24 hours of pollutant concentration, a 24 hour prediction network is Although the existing studies have proposed a very mature and extensive AQ monitoring program, in China, most agriculture areas are in remote locations and in harsh environments [10]. Data analysis and processing through the cloud computing solution at the network layer cannot meet the requirements of low latency [7].…”

Section: Prediction Modelmentioning

confidence: 99%

“…Sci. 2019, 9, Although the existing studies have proposed a very mature and extensive AQ monitoring program, in China, most agriculture areas are in remote locations and in harsh environments [10]. Data analysis and processing through the cloud computing solution at the network layer cannot meet the requirements of low latency [7].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, in a traditional IoT-based AQ monitoring system, the data collected by the sensing layer needs to be uploaded, analyzed and processed in the cloud computing platform at the network layer [9]. However, in China, most agricultural areas are in remote locations and harsh environments limited by bandwidth and network connectivity [10]. The timely uploading of monitoring data and the analysis of prediction results cannot be guaranteed, which affects the timeliness of decision-making.…”

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confidence: 99%

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IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction

et al. 2019

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In order to obtain high-accuracy measurements, traditional air quality monitoring and prediction systems adopt high-accuracy sensors. However, high-accuracy sensors are accompanied with high cost, which cannot be widely promoted in Internet of Things (IoT) with many sensor nodes. In this paper, we propose a low-cost air quality monitoring and real-time prediction system based on IoT and edge computing, which reduces IoT applications dependence on cloud computing. Raspberry Pi with computing power, as an edge device, runs the Kalman Filter (KF) algorithm, which improves the accuracy of low-cost sensors by 27% on the edge side. Based on the KF algorithm, our proposed system achieves the immediate prediction of the concentration of six air pollutants such as SO 2 , NO 2 and PM2.5 by combining the observations with errors. In the comparison experiments with three common predicted algorithms including Simple Moving Average, Exponentially Weighted Moving Average and Autoregressive Integrated Moving Average, the KF algorithm can obtain the optimal prediction results, and root-mean-square error decreases by 68.3% on average. Taken together, the results of the study indicate that our proposed system, combining edge computing and IoT, can be promoted in smart agriculture. of industry digitalization in agile connectivity, real-time business, data optimization, application intelligence, security and privacy protection, etc. Edge computing features are like human nerve endings, which can self-process simple stimuli and feedback the processed features to the cloud brain.Smart agriculture makes the application of IoT technology in traditional agricultural production more "intelligent" by using sensors and software to control agricultural production through mobile platforms or computer platforms. In smart agriculture, establishing a real-time monitoring and prediction system for air quality (AQ) is the most basic and most important solution [5]. The prediction for AQ is based on the analysis of the monitoring data. In other words, the accuracy of the monitoring data affects the accuracy of the prediction to a certain extent [6]. At the same time, the Environment Agency also has specified specific values for AQ [7]. Once current AQ exceeds the threshold, people should take appropriate countermeasures. However, when the prediction is inaccurate, it will lead to decision errors. In order to obtain high-precision monitoring data, many AQ monitoring schemes currently existing use high-precision sensors. However, high-precision sensors are often accompanied by higher costs. A complete system consists of multiple sets of sensors [8], so there is a trade-off between cost and accuracy. In addition, in a traditional IoT-based AQ monitoring system, the data collected by the sensing layer needs to be uploaded, analyzed and processed in the cloud computing platform at the network layer [9]. However, in China, most agricultural areas are in remote locations and harsh environments limited by bandwidth and network connectivity [10]. The timely ...

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Evolution of rural multifunction and its natural and socioeconomic factors in coastal China

Wang,

Duan,

Wang

et al. 2023

J. Geogr. Sci.

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Rural decline has become a global problem. To address this issue, the division of rural functions and identification of driving factors are important means of rural revitalization. Taking the town area as a unit, this study conducts a division and evolution analysis of rural regional functions in Jiangsu province in coastal China by constructing an evaluation system using the spatial econometric model to diagnose endogenous and exogenous driving factors of rural multifunction formation. The results show that the functions of agricultural supply and ecological conservation have decreased, while the functions of economic development and social security have increased. Agricultural production functions are concentrated in northern and central Jiangsu. The economic development function is mainly based on industrial development, and is the strongest in southern Jiangsu. Social security functions are concentrated in suburban area, county centers, and key towns. High-value areas of ecological conservation are concentrated along lakes, the coast, and hilly areas of southern Jiangsu. The multifunctional development of villages and towns is affected by endogenous and exogenous factors, including economic geographic location, natural resources, economic foundation, human capital, traffic conditions, market demand, infrastructure, and environmental governance. Natural factors have a significant impact on the supply of agricultural products and the formation of ecological conservation functions. The effects of socioeconomic factors on these four functions differ significantly. This study expands the theory of rural development functions, the classification and zoning paradigm, and the quantitative study of driving mechanisms. The results provide a reference for practical value and policy significance for the reconstruction of rural functions and rural revitalization.

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Analyzing Agricultural Agglomeration in China

Cited by 31 publications

References 36 publications

Multilevel Modeling of Rural Livelihood Strategies from Peasant to Village Level in Henan Province, China

Multilevel Modeling of Rural Livelihood Strategies from Peasant to Village Level in Henan Province, China

IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction

Evolution of rural multifunction and its natural and socioeconomic factors in coastal China

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