Analysis of York Pigs Feeding Behavior Using Stepwise Regression and Principal Component Regression

A statistical analysis was conducted on the feeding behavior of 106 York breeding pigs.Pearson correlation analysis,principal component correlation analysis and multiple stepwise regression equation methods were applied to establish regression equations of the York breeding pigs total feed intake per time and average feed intake per time with corrected fat thickness,feed conversion rate,and corrected daily gain.The results showed that:①there were three peak feed intake periods for the pigs,and the correlation coefficient between the feed intake and the corrected fat thickness of the pigs in the 24 h period was positive or negative,that is,increasing the number of feeding times and the feed intake was not necessarily conducive to the fat thickness accumulation,but the breeding goal of fat thickness could be achieved by controlling the feeding times and feed intake;②the average feed intake of pigs in the 60-90 kg body weight stage was 30%-50%higher than that of the 30-60 kg body weight stage,but the number of feeding times decreased,the peak feeding time was more concentrated,and the feeding duration per time was 3.0 min longer,indicating that as the weight of pigs increased,the feed intake increased significantly;and③the stepwise regression equations and the principal component equations showed that the feeding behavior of York pigs in the 30-90 kg growth stage was not only affected by the feeding time within 24 h,but also by environmental factors such as temperature and humidity.The feeding behavior of York pigs is a complex process of interaction between environmental factors and animal factors.

Using Moving North Pacific Index to Improve Rainy Season Rainfall Forecast over the Yangtze River Basin by Analog Error Correction

A new analog error correction （AEC） scheme based on the moving North Pacific index （MNPI） is designed in this study. This scheme shows obvious improvement in the prediction skill of the operational coupled general circulation model （CGCM） of the National Climate Center of China for the rainy season rainfall （RSR） anomaly pattern correlation coefficient （ACC） over the mid-to-lower reaches of the Yangtze River （MLRYR）. A comparative analysis indicates that the effectiveness of the new scheme using the MNPI is better than the system error correction scheme using the North Pacific index （NPI）. A Euclidean distance- weighted mean rather than a traditional arithmetic mean, is applied to the integration of the analog year＇s prediction error fields. By using the MNPI AEC scheme, independent sample hindcasts of RSR during the period 2003-2009 are then evaluated. The results show that the new scheme exhibited a higher forecast skill during 2003-2009, with an average ACC of 0.47; while the ACC for the NPI case was only 0.19. Furthermore, the forecast skill of the RSR over the MLRYR is examined. In the MNPI case, empirical orthogonal function （EOF） was used in the degree compression of the prediction error fields from the CCCM, whereas the AEC scheme was applied only to its first several EOF components for which the accumulative explained variance accounted for 80% of the total variance. This further improved the ACC of the independent sample hindcasts to 0.55 during the 7-yr period.