The primary goal of Chinese agricultural development is to guarantee national food security and supply of major agricultural products. Hence, the scientific work on agricultural monitoring and early warning as well as agricultural outlook must be strengthened. In this study, we develop the China Agricultural Monitoring and Early-warning System (CAMES) on the basis of a comparative study of domestic and international agricultural outlook models. The system is a dynamic and multi-market partial equilibrium model that integrates biological mechanisms with economic mechanisms. This system, which includes 11 categories of 953 kinds of agricultural products, could dynamically project agricultural market supply and demand, assess food security, and conduct scenario analysis at different spatial levels, time scale levels, and macro-micro levels. Based on the CAMES, the production, consumption, and trade of the major agricultural products in China over the next decade are projected. The following conclusions are drawn: i) The production of major agricultural products will continue to grow steadily, mainly because of the increase in yield. ii) The growth of agricultural consumption will be slightly higher than that of agricultural production. Meanwhile, a high self-sufficiency rate is expected for cereals such as rice, wheat, and maize, with the rate being stable at around 97%. iii) Agricultural trade will continue to thrive. The growth of soybean and milk imports will slow down, but the growth of traditional agricultural exports such as vegetables and fruits is expected to continue.

This paper establishes a short-term prediction model of weekly retail prices for eggs based on chaotic neural network with the weekly retail prices of eggs from January 2008 to December 2012 in China. In the process of determining the structure of the chaotic neural network, the number of input layer nodes of the network is calculated by reconstructing phase space and computing its saturated embedding dimension, and then the number of hidden layer nodes is estimated by trial and error. Finally, this model is applied to predict the retail prices of eggs and compared with ARIMA. The result shows that the chaotic neural network has better nonlinear fitting ability and higher precision in the prediction of weekly retail price of eggs. The empirical result also shows that the chaotic neural network can be widely used in the field of short-term prediction of agricultural prices.

This paper studies how the price movements of pork, chicken and egg respond to those of related cost factors in short terms in Chinese market. We employ a linear quantile approach not only to explore potential data heteroscedasticity but also to generate confidence bands for the purpose of price stability study. We then evaluate our models by comparing the prediction intervals generated from the quantile regression models with in-sample and out-of-sample forecasts. Using monthly data from January 2000 to October 2010, we observed these findings: (i) the price changes of cost factors asymmetrically and unequally influence those of the livestock across different quantiles; (ii) the performance of our models is robust and consistent for both in-sample and out-of-sample forecasts; (iii) the confidence intervals generated from 0.05th and 0.95th quantile regression models are good methods to forecast livestock price fluctuation.

The paper studies the relationship, adjustment ability, path, efficiency and intensity of price transmission in the swine industry chain in China, which consists of the prices of corn, compound feed for fattening pig, piglet, pig and pork. Monthly prices covering a period of 18 yr (1994-2011) are analyzed using a Market-Chain Cooperated Model (MCM). The empirical results show that there exists a stable long-term cointegration and short-term dynamic relationship in the price system. First, the adjustment speed of each price series is very slow and the transmission path is top-down and one-way significantly. Second, the price from upstream to downstream lags about 2 mon, while there is no lag in price transmission from midstream to downstream. Third, in terms of price transmission intensity, the price of pig impacted greatly on pork price, not only in the current period but also through the whole period. Besides, the price of corn has the largest lagged effects on pork price. According to the above empirical results, we suggest that government should strengthen monitoring and early warning of the swine industry chain, especially the upstream and midstream, attach great importance to the timely adjustment of feed prices and perfect the measures of price subsidy.

This paper examines the vertical price relationship between upstream and downstream products in China’s layer industry chain by estimating elasticity coefficients of the price transmission. We use cointegration tests, error correction models and finite distributed lag models to analyze vertical price shifts between corn, layer feed, egg-laying chicken and egg prices. With monthly data from 1994 to 2010, our results show that various prices in China’s layer industry chain are well integrated. In addition, our results indicate that both long-run and short-run price relationships between upstream and downstream products in layer industry chain exist and the long-run equilibrium, to a certain extent, may eliminate the short-run dynamics price deviation. We also found that the influence of corn and feed prices on egg prices is still more remarkable than egg-laying chicken prices. Our findings imply that governments should pay more attention to corn and layer feed prices in order to stabilize egg prices under the conditions of market integration.