No abstract

The Attappady Black goat is a native goat breed of Kerala in India and is mainly known for its valuable meat and skin. In this work, a comparative study of connectionist network [also known as artificial neural network (ANN)] and multiple regression is made to predict the body weight from body measurements in Attappady Black goats. A multilayer feed forward network with backpropagation of error learning mechanism was used to predict the body weight. Data collected from 824 Attappady Black goats in the age group of 0-12 months consisting of 370 males and 454 females were used for the study. The whole data set was partitioned into two data sets, namely training data set comprising of 75 per cent data (277 and 340 records in males and females, respectively) to build the neural network model and test data set comprising of 25 per cent (93 and 114 records in males and females, respectively) to test the model. Three different morphometric measurements viz. chest girth, body length and height at withers were used as input variables, and body weight was considered as output variable. Multiple regression analysis (MRA) was also done using the same training and testing data sets. The prediction efficiency of both models was compared using the R 2 value and root mean square error (RMSE). The correlation coefficients between the actual and predicted body weights in case of ANN were found to be positive and highly significant and ranged from 90.27 to 93.69%. The low value of RMSE and high value of R 2 in case of connectionist network (RMSE: male-1.9005, female-1.8434; R 2 : male-87.34, female-85.70) in comparison with MRA model (RMSE: male-2.0798, female-2.0836; R 2 : male-84.84, female-81.74) show that connectionist network model is a better tool to predict body weight in goats than MRA.

A total of 1,367 first lactation records of daughters of 81 sires, having 5 or more progeny were used to evaluate sires by 3 different methods viz., least squares (LS), best linear unbiased prediction (BLUP) and derivative free restricted maximum likelihood (DFREML) method. The highest and lowest overall average breeding value of sires for first lactation 305 days or less milk yield was obtained by BLUP (1,520.72 kg) and LS method (1,502.22 kg), respectively. The accuracy, efficiency and stability of different sire evaluation methods were compared to judge their effectiveness. The error variance of DFREML method was lowest (191,112 kg 2 ) and its coefficient of determination of fitting the model was highest (33.39%) revealing that this method of sire evaluation was most efficient and accurate as compared to other methods. However, the BLUP method was most stable amongst all the methods having coefficient of variation (%) very near to unadjusted data (18.72% versus 19.89%). The higher rank correlations (0.7979 to 0.9568) between different sire evaluation methods indicated that there was higher degree of similarity of ranking sires by different methods ranging from about 80 to 96 percent. However, the DFREML method seemed to be the most effective sire evaluation method as compared to other methods for the present set of data.

SummaryThe Sahiwal cattle, one of the best dairy breeds of Zebu cattle in India and Pakistan, originate from the Montgomery district of Pakistan and is distributed on farmer herds in certain pockets of the bordering districts of Punjab and Rajsthan in India. The animals of this breed are also available in Kenya and are used for crossing with local East African Zebu types to improve milk production. Sahiwal cattle have deep body, loose skin, short legs, stumpy horns and a broad head with pale red to dark brown body colour. The average body weight in adult females and males is around 350 and 500 kg, respectively. The animals of this breed are maintained on various State and Central Government farms, privately owned farms, charitable trusts and a small proportion of animals are also available with the farmers. More than 1 200 breedable females are available at various farms in the country. The average lactation milk yield of Sahiwal cattle on organized farms ranges between 1 500 to 2 500 kg. However, in well-managed herds, the highest lactation milk production in certain cows is more than 4 500 kg. The overall weighted average milk yield, age at first calving, lactation length and calving interval based on the performance at various herds is around 1 900 kg, 36 months, 315 days and 420 days, respectively. The fat and Solid Non Fat (SNF) percent ranges from 4.6 to 5.2 percent and 8.9 to 9.3 percent, respectively. Quite a large proportion of pure-bred Sahiwal cattle maintained on organized breeding farms has been used for the production of cross-bred cattle. As a result, different cross-bred strains of dairy cattle viz Karan Swiss, Karan Fries and Frieswal have evolved at the National Dairy Research Institute, Karnal and Military Dairy Farms. The breed has also been utilized for the production of synthetic strains like Jamaica Hope (JH), Australian Milking Zebu (AMZ) and Australian Friesian Sahiwal (AFS) in other countries. Currently, efforts are being made to characterize, evaluate and conserve the breed in field conditions. More than 0.10 million doses of frozen semen of this breed are cryopreserved at various semen banks in the country. The frozen semen is being utilized for strengthening and genetically improving the existing herds of the breed through progeny testing programmes of sires associating various herds of Sahiwal in the country.

No abstract

Gandhi, R.S., Raja, T.V., Ruhil, A.P. and Kumar, A. 2010. Artificial neural network versus multiple regression analysis for prediction of lifetime milk production in Sahiwal cattle. J. Appl. Anim. Res., 38: 233-237. First lactation records (1493) of Sahiwal cows comprising of age at first calving, first lactation 305-d or less milk yield, first lactation length, first service period and first dry period were analyzed to predict the lifetime milk production using multiple regression analysis (MRAJ and artificial neural network (ANN). The data was divided into three sets i.e. cows retained in the herd up to first three parities (set

The first lactation production records of 351 Sahiwal cows maintained at National Dairy Research Institute, Karnal over a period of 52 years (1961-2012) were used to estimate the persistency measures using different methods viz. Rao and Sundaresan method, 1982 (P₁), P_TOMAX2 (P₂), P_TOMAX3 (P₃), P_SD2 (P₄), P_SD3 (P₅), P_YV (P₆) and Wood's function (P₇). The average persistency of milk yield estimated by above different methods viz. was 194±1.10, 145.10±0.81, 156.71±1.01, 2.29±0.04, 2.20±0.03, 1.00±0.03 and 0.14±0.02, respectively. The T_OMAX2 method was found to be most efficient out of all ratio methods and P_SD3 method was observed to be most efficient amongst variation of yields method. However, mathematical lactation curve model was found to be least efficient as compared to other methods for estimation of persistency.

No abstract