Evaluation of influencing factors on tea production based on random forest regression and mean impact value

https://doi.org/10.17221/399/2018-AGRICECONCitation:Chen Y., Li M. (2019): Evaluation of influencing factors on tea production based on random forest regression and mean impact value. Agric. Econ. – Czech, 65: 340-347.
download PDF

Overproduction of tea in the major producing countries is an important factor which restricts the development of tea. Therefore, the factors from the economic, social and environmental system affecting tea production have become the focus of both academia and practice. Random forest regression (RFR) and mean impact value (MIV) were applied to evaluate the weights of variables. Firstly, RFR was preliminarily used to build a well-trained model, and then the weights of variables combining with MIV were calculated. Then, a well-trained model was constructed after variable selection to evaluate the importance of tea production from 2007 to 2016. The results revealed that the economic system and the social system are the main factors that affect tea production. The net production value and total population have little negative effects on tea production, while the area harvested has a little positive effect. Based on the research findings, governments and enterprises should develop and upgrade tea production technology, promote the exchange and cooperation in the international tea trade, then ultimately achieve sustainable development of the tea industry.

Adam Elhadi, Mutanga Onisimo, Abdel-Rahman Elfatih M., Ismail Riyad (2014): Estimating standing biomass in papyrus ( Cyperus papyrus L.) swamp: exploratory of in situ hyperspectral indices and random forest regression. International Journal of Remote Sensing, 35, 693-714.  https://doi.org/10.1080/01431161.2013.870676
Breiman L. (2001): Random forests. Machine Learning, 45: 5–32. https://doi.org/10.1023/A:1010933404324
Cutler D. Richard, Edwards Thomas C., Beard Karen H., Cutler Adele, Hess Kyle T., Gibson Jacob, Lawler Joshua J. (2007): RANDOM FORESTS FOR CLASSIFICATION IN ECOLOGY. Ecology, 88, 2783-2792.  https://doi.org/10.1890/07-0539.1
Dombi George W., Nandi Partha, Saxe Jonathan M., Ledgerwood Anna M., Lucas Charles E. (1995): Prediction of Rib Fracture Injury Outcome by an Artificial Neural Network. The Journal of Trauma: Injury, Infection, and Critical Care, 39, 915-921.  https://doi.org/10.1097/00005373-199511000-00016
Fan Hailiu, Xuan Jianbang, Zhang Kaixuan, Jiang Jianlan (2018): Anticancer component identification from the extract of Dysosma versipellis and Glycyrrhiza uralensis based on support vector regression and mean impact value. Analytical Methods, 10, 371-380.  https://doi.org/10.1039/C7AY02465G
Ghasemi Jahan B., Tavakoli Hossein (2013): Application of random forest regression to spectral multivariate calibration. Analytical Methods, 5, 1863–1871.  https://doi.org/10.1039/c3ay26338j
Grömping Ulrike (2009): Variable Importance Assessment in Regression: Linear Regression versus Random Forest. The American Statistician, 63, 308-319.  https://doi.org/10.1198/tast.2009.08199
Gunathilaka R. P. Dayani, Smart James C. R., Fleming Christopher M. (2017): The impact of changing climate on perennial crops: the case of tea production in Sri Lanka. Climatic Change, 140, 577-592  https://doi.org/10.1007/s10584-016-1882-z
Gunathilaka Rajapaksha P. D., Smart James C. R., Fleming Christopher M., Hasan Syezlin (2018): The impact of climate change on labour demand in the plantation sector: the case of tea production in Sri Lanka. Australian Journal of Agricultural and Resource Economics, 62, 480-500.  https://doi.org/10.1111/1467-8489.12262
Guo Zhen-hai, Wu Jie, Lu Hai-yan, Wang Jian-zhou (2011): A case study on a hybrid wind speed forecasting method using BP neural network. Knowledge-Based Systems, 24, 1048-1056.  https://doi.org/10.1016/j.knosys.2011.04.019
Hong N.B., Takahashi Y., Yabe M. (2016): Environmental efficiency and economic losses of Vietnamese tea production: implications for cost savings and environmental protection. Journal of the Faculty of Agriculture Kyushu University, 61: 383–390.
Hong Nguyen Bich, Yabe Mitsuyasu (2017): Improvement in irrigation water use efficiency: a strategy for climate change adaptation and sustainable development of Vietnamese tea production. Environment, Development and Sustainability, 19, 1247-1263.  https://doi.org/10.1007/s10668-016-9793-8
Hutengs Christopher, Vohland Michael (2016): Downscaling land surface temperatures at regional scales with random forest regression. Remote Sensing of Environment, 178, 127-141.  https://doi.org/10.1016/j.rse.2016.03.006
Ishak A.B. (2016): Variable selection using support vector regression and random forests: A comparative study. Intelligent Data Analysis, 20, 83-104.  https://doi.org/10.3233/IDA-150795
Kouchaki-Penchah H., Nabavi-Pelesaraei A., O’Dwyer J., Sharifi M. (2017): Environmental management of tea production using joint of life cycle assessment and data envelopment analysis approaches. Environmental Progress & Sustainable Energy, 36: 1116–1122.
Luo Songrong, Cheng Junsheng, Wei Kexiang (2016): A Fault Diagnosis Model Based on LCD-SVD-ANN-MIV and VPMCD for Rotating Machinery. Shock and Vibration, 2016, 1-10.  https://doi.org/10.1155/2016/5141564
Mendez G., Lohr S. (2011): Estimating residual variance in random forest regression. Computational Statistics & Data Analysis, 55: 2937–2950.
Peng Yaohao, Albuquerque Pedro Henrique Melo, Camboim de Sá Jader Martins, Padula Ana Julia Akaishi, Montenegro Mariana Rosa (2018): The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with Support Vector Regression. Expert Systems with Applications, 97, 177-192.  https://doi.org/10.1016/j.eswa.2017.12.004
Qiao Yuhui, Halberg Niels, Vaheesan Saminathan, Scott Steffanie (2016): Assessing the social and economic benefits of organic and fair trade tea production for small-scale farmers in Asia: a comparative case study of China and Sri Lanka. Renewable Agriculture and Food Systems, 31, 246-257.  https://doi.org/10.1017/S1742170515000162
Ren Chao, An Ning, Wang Jianzhou, Li Lian, Hu Bin, Shang Duo (2014): Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting. Knowledge-Based Systems, 56, 226-239.  https://doi.org/10.1016/j.knosys.2013.11.015
UNFAO (2018): Database of Food and Agriculture Organization of the United Nations. UNFAO.
Wang Huiyi, Kong Chunli, Li Dapeng, Qin Na, Fan Hongbing, Hong Hui, Luo Yongkang (2015): Modeling Quality Changes in Brined Bream (Megalobrama amblycephala) Fillets During Storage: Comparison of the Arrhenius Model, BP, and RBF Neural Network. Food and Bioprocess Technology, 8, 2429-2443.  https://doi.org/10.1007/s11947-015-1595-8
Wu Hongyan, Cai Yunpeng, Wu Yongsheng, Zhong Ren, Li Qi, Zheng Jing, Lin Denan, Li Ye (2017): Time series analysis of weekly influenza-like illness rate using a one-year period of factors in random forest regression. BioScience Trends, 11, 292-296.  https://doi.org/10.5582/bst.2017.01035
Xiao Zhi, Huang Xianjin, Zang Zheng, Yang Hong (2018): Spatio-temporal variation and the driving forces of tea production in China over the last 30 years. Journal of Geographical Sciences, 28, 275-290.  https://doi.org/10.1007/s11442-018-1472-2
Zhang Z., Jin X. (2018): Prediction of peak velocity of blasting vibration based on artificial neural network optimized by dimensionality reduction of FA-MIV. Mathematical Problems in Engineering, 2018: 1–12.
Zhu Bangzhu, Han Dong, Wang Ping, Wu Zhanchi, Zhang Tao, Wei Yi-Ming (2017): Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression. Applied Energy, 191, 521-530.  https://doi.org/10.1016/j.apenergy.2017.01.076
download PDF

© 2020 Czech Academy of Agricultural Sciences | Prohlášení o přístupnosti