Prediction of pork supply via the calculation of pig population based on population prediction model

Fan Zhang, Fulin Wang

Abstract


Based on the perspective of pig population system prediction, and accorded to principle of pig months transfer, this paper refers to the modeling principle and method of discrete population quantity prediction model. Then the prediction model of pork supply is derived and established: Firstly, the recursive model of pig population system and estimation model of pork supply was established. Then this study estimated the sum of monthly mortality and culling rate of breeding sows. Furthermore, the method for new left gilts in each month and estimation of breeding sows at each month of age was established. Last, this research established the estimation method model of the initial state of pig population. On this basis, an example calculation is made to predict the monthly pork supply in Heilongjiang Province from January 2016 to March 2018 in the future. The results showed that the prediction model of pork supply based on the prediction of pig population system is an effective perspective to study the forecast of pork supply. In the prediction stage, the prediction accuracy of the number of slaughtered fattened hogs was 96.36% and 97.54%, and the prediction accuracy of pork supply was 98.08% and 93.82%.This study not only lay a theoretical foundation for further study on the balance between pork supply and demand, but also helps to guide pork producers and governments at all levels to make relevant production decisions and plans.
Keywords: pig population, breeding sow, pig production, pork supply prediction, population prediction model
DOI: 10.25165/j.ijabe.20201302.5303

Citation: Zhang F, Wang F L. Prediction of pork supply via the calculation of pig population based on population prediction model. Int J Agric & Biol Eng, 2020; 13(2): 208–217.

Keywords


pig population, breeding sow, pig production, pork supply prediction, population prediction model

Full Text:

PDF

References


Ezekiel M. The cobweb theorem. Quarterly Journal of Economics, 1938; 52(2): 255–280.

Kaldor N. A Classificatory note on the determinateness of equilibrium. Review of Economic Studies, 1934; 1(2): 122–136.

Jaile-Benitez J M, Ferrer-Comalat J C, Linares-Mustarós S. Determining the influence variables in the pork price, based on expert systems. Scientific Methods for the Treatment of Uncertainty in Social Sciences. Springer, Cham, 2015: pp.81–92.

Aydogdu M H. General analysis of recent changes in poultry meat consumptions in Turkey. International Journal of Advances in Agriculture Sciences, 2018; 12(3): 6–11

Wu H, Qi Y, Chen D. A dynamic analysis of influencing factors in price fluctuation of live pigs-based on statistical data in Sichuan province, China. Asian Social Science, 2012; 8(7): 256.

Jia W, Yang Y, Qin F. The study on China's pork industry chain price transmission mechanism: Based on province comparison. Statistics & Information Forum, 2013; 3: 49–55.

Zhou D, Yu X, Herzfeld T. Dynamic food demand in urban China. China Agricultural Economic Review, 2015; 7(1): 27–44.

Xu S, Li Z, Cui L, Dong X, Kong F, Li G. Price transmission in China's swine industry with an application of MCM. Journal of Integrative Agriculture, 2012; 11(12): 2097–2106.

Felipe I, Mól A, Almeida V. Application of ARIMA models in soybean series of prices in the north of Paraná. Custos e@ gronegócio Online, 2012; 8.

Adanacioglu H, Yercan M. An analysis of tomato prices at wholesale level in Turkey: An application of SARIMA model. Custos e@ gronegócio on line, 2012; 8(4): 52–75.

Li G, Xu S, Li Z, Sun Y, Dong X. Using quantile regression approach to analyze price movements of agricultural products in china. Journal of Integrative Agriculture, 2012; 11(4): 674–683.

Saengwong S, Jatuporn C, Roan S W. An analysis of Taiwanese livestock prices: empirical time series approaches. Journal of Animal and Veterinary Advances, 2012; 11(23): 4340–4346.

Martín-Rodríguez G, Cáceres-Hernández J J. Forecasting pseudo-periodic seasonal patterns in agricultural prices. Agricultural Economics, 2012; 43(5): 531–544.

Paul R K, Gurung B, Paul A. Modelling and forecasting of retail price of arhar dal in Karnal, Haryana. The Indian Journal of Agricultural Sciences, 2015; 85(1): 69-72.

Wu L, Liu S, Yang Y. Grey double exponential smoothing model and its application on pig price forecasting in China. Applied Soft Computing, 2016; 39: 117-123.

Tian F, Peng Y. Machine vision system of nondestructive real-time prediction of live-pig meat yield. Transactions of the CSAE, 2016; 32(2): 230–235. (in Chinese)

Luo C G, Zhang M Z, Xue J L. Short-term forecasts of hog price based on ARIMA model, World Agriculture, 2010; 10: 45–48.

Zheng L, Duan D, Lu F, Xu W, Yang C, Wang S. Integration forecast of Chinese pork consumption demand-Empirical based on ARIMA, VAR and VEC models. Systems Engineering-Theory & Practice, 2013; 33(4): 918–925. (in Chinese)

Zhang J H, Li Z M, Kong F T, Dong X X, Chen W, Wang S W. Prediction of pork prices based on SVM. Proceedings of 2013 World Agricultural Outlook Conference. Springer, Berlin, Heidelberg, 2014; pp.173–178.

Ma Z, Chen Z, Chen T, et al. Application of machine learning methods in pork price forecast. Proceedings of the 2019 11th International Conference on Machine Learning and Computing. ACM, 2019; pp.133–136.

Jurkėnaitė N, Djuric I. Impact of the Russian trade bans on Lithuanian pork sector. Management Theory and Studies for Rural Business and Infrastructure Development, 2018; 40(4): 481–491

Meng Z, Nan Z. Lean meat percentage prediction of pig carcass based on radial basis function neural network. Journal of Agricultural Mechanization Research, 2017; 6: 38. (in Chinese)

Liu Y, Duan Q, Wang D, Zhang Z, Liu C. Prediction for hog prices based on similar sub-series search and support vector regression. Computers and Electronics in Agriculture, 2019; 157: 581–588.

Shih M L, Huang B W, Chiu N H, Chiu C, Hu W Y. Farm price prediction using case-based reasoning approach—A case of broiler industry in Taiwan. Computers and electronics in agriculture, 2009; 66(1): 70–75.

Ribeiro C O, Oliveira S M. A hybrid commodity price-Forecasting model applied to the sugar–alcohol sector. Australian Journal of Agricultural and Resource Economics, 2011; 55(2): 180–198.

Jha G K, Sinha K. Time-delay neural networks for time series prediction: An application to the monthly wholesale price of oilseeds in India. Neural Computing and Applications, 2014; 24(3-4): 563–571.

Su X, Wang Y, Duan S, Ma J et al. Detecting chaos from agricultural product price time series. Entropy, 2014; 16(12): 6415–6433.

Pechrova M, Šimpach O. Modelling the development of the consumer price of sugar. Proceedings of the 35th International Conference Mathematical Methods in Economics (MME), 2017; pp.527–531.

Wang B, Liu P, Zhang C, Wang J, Liu P. Prediction of garlic price based on ARIMA model. International Conference on Cloud Computing and Security. Springer, Cham, 2018; pp.731–739.

Li Z M, Xu S W, Cui L G, Li G, Dong X, Wu J. The short-term forecast model of pork price based on CNN-GA. Advanced Materials Research. Trans Tech Publications, 2013; 628: 350–358.

Liu T, Li Z, Teng G, Luo C. Prediction of pig weight based on radical basis function neural network. Journal of agricultural machinery, 2013; 44(8): 245–249.

Tao X, Chongguang L I, Yukun B. An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China. Agricultural Economics, 2017; 63(3): 136–148.

Song J. Population prediction and population control. People's Publishing House, 1982. (in Chinese)

Harlow A A. The hog cycle and the cobweb theorem. Journal of Farm Economics, 1960; 42(4): 842–853.

Ministry of Agriculture and Rural Affairs in China. Technical specifications for ear tags for breeding sows. http://www.moa.gov.cn/nybgb/2010/dyq/201805/t20180529_6145222.htm. (in Chinese)




Copyright (c) 2020 International Journal of Agricultural and Biological Engineering

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

2023-2026 Copyright IJABE Editing and Publishing Office