Litchi is a traditional tree crop grown in Southern China. Sustainable development of the litchi industry is reliant on technology adoption by farmers. The top grafting technique allows for the introduction of new, quality litchi varieties. The fact that these new varieties ripen earlier or later than the traditional ones helps stabilize litchi prices. Selling new litchi varieties can increase farmers' incomes through higher prices of quality varieties and stabilizing prices by staggering the harvest periods. However, the rate of adoption of top grafting among farmers is low, and up till now, more than half of the litchi trees in China are still traditional litchi varieties. This study explores the factors that influence top grafting adoption by litchi farmers. Using primary data gathered by the China Agriculture Research System of Litchi and Longan (CARSLL) from 567 litchi farming households, a binary logit choice model is employed to determine the factors that influence adoption of litchi top grafting among litchi farmers. The results show that farmers owning larger litchi orchards are more likely to adopt top grafting compared to ones owning smaller orchards. Litchi information accumulation, including experience and training, significantly influences farmers' technology adoption levels. Moreover, a positive attitude toward technology also significantly influences technology adoption behaviours.
China has been promoting garbage classification in its rural areas, yet it lacks financial appropriation and fiscal decentralization to support waste processing projects. Though the existing literature has suggested fiscal decentralization strategies between different local government levels, few of the studies ascertain garbage classification efficiency from a quantitative perspective. To bridge the gap, this study examines the optimal fiscal decentralization strategies for garbage classification. It uses an optimization model while considering decision makers’ requirements regarding the fund allocation amounts at different government levels and the classification ratios in villages as constraints and decisions, respectively. A three-stage heuristic algorithm is applied to determine optimal landfill locations and efficient classification ratios for the garbage processing system in rural China, with an analytical discussion on the propositions and properties of the model. Our analytical results suggest that 1) the theoretically optimal solution is conditionally achievable, 2) the applied algorithm can achieve the optimal solution faster when the relationship between governance costs and classification ratios reaches some mathematical conditions, and 3) there is always a potential for increasing the retained funds between different government levels or for reducing the total appropriation from the county government. The numerical experiment on a primary dataset from 12 towns and 143 villages in the Pingyuan county of Guangdong province, China, does not only affirm the qualitative results, but it also provides insights into the difficulties encountered during the implementation of the garbage classification policy in China’s rural areas.
The economic and environmental performances of the swine farming industry have always resulted in heated discussions in developing countries. Exploring the relationship between these features and the producers' overall performance is the focus of this paper. For constructing multi-objective features that include the above features, a compromise approach for optimization is taken into consideration. For classifying the overall performance into different levels and detecting the effect of economic and environmental features on such features, an iteration scheme is developed in which the overall performance is treated as a target label. By neglecting this target label, a k-means clustering method is then used to help predict the producer's overall performance given their economic and environmental features. In data pre-processing, correlation analysis for feature selection shows that the producer's pollution emission and received regulation intensity largely affect its overall performance, while profit is found to be negatively correlated with pollution emission as regulation intensity is neglected. The classification result derived from the Silhouette Coefficient shows that the data set can be efficiently split into different groups in terms of the producer's overall performance. The average distance between the objects in the low-performance group is larger than that of the high-performance group. The threshold position between the two groups is found to be largely dependent on the features of pollution emission and regulation intensity. The clustering result obtained by the k-means method shows good effectiveness and efficiency in separating the objects into different groups based on various features other than the overall performance. In 2-and 3-cluster cases, the result also shows evidence of the impact of economic and environmental features on the clustering result. The cross-validation analysis under a set of randomly chosen splitting points shows an increasing out-of-sample prediction quality with increases in training sample size. As one of the by-products of this paper, the geographical distribution in the clustering result is found partially consistent with the official report from Chinas central government regarding advantageous regions within the industry. In addition to current research, the ease of using the knowledge obtained in this paper for transfer learning is discussed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.