20 × % crude protein) + (0.49 × % ash); ME = 30.42 - (0.11 × % hemicellulose) + (0.31 × % ether extract) - (0.81 × MJ/kg gross energy). Conclusion Our results indicated that the chemical compositions, but not the meteorological conditions or physical characteristics could explain the variation of energy contents in yellow dent corn sourced from southwestern China fed to growing pigs.Objective Investigate the differences in several serum adipokines in perinatal dairy cows with type I and II ketosis, and the correlations between these adipokines and the two types of ketosis. Methods Serum adiponectin (ADP), leptin (LEP), resistin, tumor necrosis factor-α (TNF-α), and interleukin-6 (IL-6) levels, and energy balance indicators related to ketosis were measured. Type I and II ketosis were distinguished by serum Glucose (Glu) and Y values and the correlations between adipokines in the two types of ketosis were analyzed.  https://www.selleckchem.com/PI3K.html  Results β-hydroxybutyric acid of type I ketosis cows was significantly negatively correlated with Insulin (INS) and LEP and had a significant positive correlation with serum ADP. In type II ketosis cows, ADP and LEP were significantly negatively correlated, and INS and resistin were significantly positively correlated. Revised quantitative insulin sensitivity check index (RQUICKI) values had a significantly positive correlation with ADP and had a very significant and significant negative correlation with resistin, TNF-α, and IL-6. ADP was significantly negatively correlated with resistin and TNF-α, LEP had a significantly positive correlation with TNF-α, and a significantly positive correlation was shown among resistin, IL-6, and TNF-α. There was also a significant positive correlation between IL-6 and TNF-α. Conclusion INS, ADP, and LEP might exert biological influences to help the body recover from negative energy balance, whereas resistin, TNF-α, and IL-6 in type II ketosis cows exacerbated insulin resistance and inhibited the production and secretion of ADP, weakened INS sensitivity, and liver protection function, and aggravated ketosis.Objectives The objective of this study was to develop a model for estimating the carcass weight of Hanwoo cattle as a function of body measurements using three different modeling approaches 1) multiple regression analysis, 2) partial least square regression analysis, and 3) a neural network. Methods Data from a total of 134 Hanwoo cattle were obtained from the National Institute of Animal Science (NIAS) in South Korea. Among the 372 variables in the raw data, 20 variables related to carcass weight and body measurements were extracted to use in multiple regression, partial least square regression, and an artificial neural network to estimate the cold carcass weight of Hanwoo cattle by any of seven body measurements significantly related to carcass weight or by all 19 body measurement variables. For developing and training the model, 100 data points were used, whereas the 34 remaining data points were used to test the model estimation. Results The R2 values from testing the developed models by multiple regression, partial least square regression, and an artificial neural network with seven significant variables were 0.91, 0.91, and 0.92, respectively, whereas all the methods exhibited similar R2 values of approximately 0.93 with all 19 body measurement variables. In addition, relative errors were within 4%, suggesting that the developed model was reliable in estimating Hanwoo cattle carcass weight. The neural network exhibited the highest accuracy. Conclusion The developed model was applicable for estimating Hanwoo cattle carcass weight using body measurements. Because the procedure and required variables could differ according to the type of model, it was necessary to select the best model suitable for the system with which to calculate the model.Objective This study determined the optimal ratio of whole plant corn silage (WPCS) to corn stover (stems+leaves) silage (CSS) (WPCSCSS) to reach the greatest profit of dairy farmers and evaluated its consequences with corn available for other purposes, enteric methane production and milk nitrogen efficiency (MNE) at varying milk production levels. Methods An optimization model was developed. Chemical composition, rumen undegradable protein and metabolizable energy (ME) of WPCS and CSS from 4 cultivars were determined to provide data for the model. Results At production levels of 0, 10, 20 and 30 kg milk/cow/d, the WPCSCSS to maximize the profit of dairy farmers was 1684, 2278, 4456 and 8812, respectively, and the land area needed to grow corn plants was 4.5, 31.4, 33.4 and 30.3 ha, respectively. The amount of corn available (Mg DM/ha/yr) for other purposes saved from this land area decreased with higher producing cows. However, compared with high producing cows (30 kg/d milk), more low producing cows (10 kg/d milk) and more land area to grow corn and soybeans was needed to produce the same total amount of milk. Extra land is available to grow corn for a higher milk production, leading to more corn available for other purposes. Increasing ME content of CSS decreased the land area needed, increased the profit of dairy farms and provided more corn available for other purposes. At the optimal WPCSCSS, MNE and enteric methane production was greater, but methane production per kg milk was lower, for high producing cows. Conclusion The WPCSCSS to maximize the profit for dairy farms increases with decreased milk production levels. At a fixed total amount of milk being produced, high producing cows increase corn available for other purposes. At the optimal WPCSCSS, methane emission intensity is smaller and MNE is greater for high producing cows.Objective The aim of our study was to determine the associations of heifer reproductive performance with survival up to the first calving, first-lactation milk yield, and the probability of being culled within 50 days after first calving. Methods Data from 33 large Holstein-Friesian commercial dairy herds were gathered from the official milk recording database in Hungary. The data of heifers first inseminated between January 1, 2011 and December 31, 2014 were analyzed retrospectively, using Cox proportional hazards models, competing risks models, multivariate linear and logistic mixed-effects models. Results Heifers (n = 35,128) with younger age at conception were more likely to remain in the herd until calving, and each additional month in age at conception increased culling risk by 5.1%. Season of birth was related to first-lactation milk yield (MY1; n = 19,931), with cows born in autumn having the highest milk production (p less then 0.001). The highest MY1 was achieved by heifers that first calved between 22.