Effects of adding sodium dichloroacetate to low-protein diets on nitrogen balance and amino acid metabolism in the portaldrained viscera and liver of pigs

Background:Identifying regulatory measures to promote glucose oxidative metabolism while simultaneously reducing amino acid oxidative metabolism is one of the foremost challenges in formulating low-protein(LP)diets designed to reduce the excretion of nitrogen-containing substances known to be potential pollutants.In this study,we investigated the effects of adding sodium dichloroacetate(DCA)to a LP diet on nitrogen balance and amino acid metabolism in the portal-drained viscera(PDV)and liver of pigs.To measure nitrogen balance,18 barrows(40±1.0 kg)were fed one of three diets(n=6 per group):18%crude protein(CP,control),13.5%CP(LP),and 13.5%CP+100 mg DCA/kg dry matter(LP-DCA).To measure amino acid metabolism in the PDV and liver,15 barrows(40±1.0 kg)were randomly assigned to one of the three diets(n=5 per group).Four essential amino acids(Lys,Met,Thr,and Trp)were added to the LP diets such that these had amino acid levels comparable to those of the control diet.Results:The LP-DCA diet reduced nitrogen excretion in pigs relative to that of pigs fed the control diet(P<0.05),without any negative effects on nitrogen retention(P>0.05).There were no differences between the control and LP-DCA groups with respect to amino acid supply to the liver and extra-hepatic tissues in pigs(P>0.05).The net release of ammonia into the portal vein and production rate of urea in the liver of pigs fed the LP-DCA diet was reduced relative to that of pigs fed the control and LP diets(P<0.05).Conclusion:The results indicated that addition of DCA to a LP diet can efficiently reduce nitrogen excretion in pigs and maximize the supply of amino acids to the liver and extra-hepatic tissues.

Predicting the excretion of feces,urine and nitrogen using support vector regression:A case study with Holstein dry cows

Predicting the excretion of feces,urine and nitrogen(N)from dairy cows is an effective way to prevent and control the environmental pollution caused by scaled farming.The traditional prediction methods such as pollutant generation coefficient(PGC)and mathematical model based on linear regression(LR)may be limited by prediction range and regression function assumption,and sometimes may deviate from the actual condition.In order to solve these problems,the support vector regression(SVR)was applied for predicting the cows'feces,urine and N excretions,taking Holstein dry cows as a case study.SVR is a typical non-parametric machine learning model that does not require any specific assumptions about the regression function in advance and only by learning the training sample data,and also it can fit the function closest to the actual in most cases.To evaluate prediction accuracy effectively,the SVR technique was compared with the LR and radial basis function artificial neural network(RBF-ANN)methods,using the required sample data obtained from actual feeding experiments.The prediction results indicate that the proposed technique is superior to the other two conventional(especially LR)methods in predicting the main indicators of feces,urine,and N excretions of Holstein dry cows.