Genetic and environmental causes of variation in milk production traits of Sahiwal cattle in Pakistan

Dahlin, A. Sigrun; Khan, U. N.; Zafar, A. H.; Saleem, Muhammad; Chaudhry, Muhammad Anwar; Philipsson, J.

doi:10.1017/s1357729800009437

Cited by 34 publications

(43 citation statements)

References 22 publications

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“…The findings of the present study are in line with earlier reports on Sahiwal cattle in Pakistan (DAHLIN et al, 1998). A higher first lactation 305-day milk yield (2,083 ± 33 kg) based on 580 observations have been reported by BHATNAGAR et al (1983) for Indian Sahiwal.…”

Section: Discussionsupporting

confidence: 93%

“…However, MOHIUDDIN et al (1991) and JAVED (1999) have reported this correlation as 0.44 and 0.60, respectively. Genetic correlation between first lactation milk yield and lactation length has been reported from 0.23 to 0.83 (DAHLIN et al, 1998;JAVED, 1999 andCHOUDHARY et al, 2003). Materials and Methods Data on performance traits of Sahiwal Cattle from five main Governmental herds i.e.…”

Section: Introductionmentioning

confidence: 99%

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Factors affecting first lactation performance of Sahiwal cattle in Pakistan

et al. 2008

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To study the environmental and genetic factors affecting productive and reproductive traits, data on 5,897 cows from five main recorded herds (for 1964-2004) of Sahiwal cattle in Pakistan were used. A general linear model was applied on the data. The 305-day milk yield, total milk yield, lactation length, age at first calving, dry period, calving interval and service period averaged 1,393 ± 12 kg, 1,429 ± 11 kg, 235 ± 2, 1,390 ± 4, 244 ± 3, 464 ± 3 and 1,78 ± 3 days, respectively. The age at first calving was effected by herd, year and season of birth. The 305-day and total milk yields were affected by herd, year, season of calving, age at first calving, service period and lactation length while all other first lactation traits were affected by herd, year, season of calving and 305-day milk yield. Animal model heritability estimates for these traits were 0.11 ± 0.029, 0.11 ± 0.028, 0.09 ± 0.027, 0.02 ± 0.019, 0.05 ± 0.019, 0.12 ± 0.027 and 0.04 ± 0.020, respectively. Rate of decline in first lactation milk yield was 7 l per year over the last 35 years with genetic trend close to zero. Lactation length and service period decreased and age at first calving increased by 2, 1 and 6 days per year, respectively. Herd differences for most of the traits were significant. Improved feeding and management may improve performance traits of Sahiwal cattle. Accurate recording of pedigrees and performance traits would help improve genetic parameters. Variation among animals suggested that genetic improvement in milk yield is possible in Sahiwal cattle. : 0,11 ± 0,029; 0,11 ± 0,028; 0,09 ± 0,027; 0,02 ± 0,019; 0,05 ± 0,019; 0,12 ± 0,027 und 0,04 ± 0,020. Der Rückgang der Erstlaktationsleistung betrug in den zurückliegenden 35 Jahren jährlich 7 l mit dem genetischen Trend nahe null. Die Verringerung der Länge für Laktationsdauer, Serviceperiode, und Erstkalbealter lagen bei zwei, einem bzw. sechs Tagen pro Jahr. Bei den meisten untersuchten Merkmalen waren die Unterschiede zwischen den Herden signifikant. Verbesserungen hinsichtlich Fütterung und Management ermöglichen Leistungsverbesserungen der Sahiwal-Rinder, ebenso wie eine genaue Abstammungs-und Leistungsdokumentation dazu beitragen wird, die genetischen Parameter zu verbessern. Die vorhandene genetische Variation bei dieser Population wird eine weitere Verbesserung der Milchleistung der SahiwalRinder ermöglichen.

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Section: Discussionsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Factors affecting first lactation performance of Sahiwal cattle in Pakistan

et al. 2008

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“…Lactation yield is usually calculated as the sum of daily milk yield a cow produces in a particular lactation. No restrictions were imposed on the minimum TD and lactation length because of the considerable variation in lactation length and daily milk yield in B. indicus breeds (Madalena, 1988;Maule, 1990;Rege et al, 1992;Dahlin et al, 1998). As a result, the number of TD records were variable throughout the eight TD (Table 1).…”

Section: Data Sourcementioning

confidence: 99%

“…The Sahiwal breed is known to have the greatest potential for milk production, growth and reproductive efficiency in tropical environments compared with other Bos indicus breeds (Mwandotto, 1994;Dahlin et al, 1998;Khan et al, 1999;Muhuyi et al, 1999). Its ability to endure and produce in harsh environments coupled with its dual-purpose role has widened the distribution of this breed in tropical and subtropical countries.…”

Section: Introductionmentioning

confidence: 99%

Genetic and phenotypic parameters for test day milk yield of Sahiwal cattle in the semi-arid tropics

Ilatsia

Muasya

Muhuyi

et al. 2007

Animal

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A total of 19 376 test day (TD) milk yield records from the first three lactations of 1618 cows daughters of 162 sires were used to estimate genetic and phenotypic parameters and determine the relationship between daily milk yield and lactation milk yield in the Sahiwal cattle in Kenya. Variance components were estimated using animal models based on a derivative free restricted maximum likelihood procedure. Variance components were estimated using various univariate and multi-trait fixed regression test day models (TDM) that defined contemporary groups either based on the year-season of calving (YSCV) or on the year-season of TD milk sampling (YSTD). Variance components were influenced by CG which resulted in differences in heritability and repeatability estimates between TDM. Models considering YSTD resulted in higher additive genetic variances and lower residual variances compared with models in which YSCV was considered. Heritability estimates for daily yield ranged from 0.28 to 0.46, 0.38 to 0.52 and 0.33 to 0.52 in the first, second and third lactation, respectively. In the first and second lactation, the heritability estimates were highest between TD 2 and TD 4. Genetic correlations among daily milk yields ranged from 0.41 to 0.93, 0.50 to 0.83 and 0.43 to 86 in the first, second and third lactation, respectively. The phenotypic correlations were correspondingly lower. Genetic correlations were different from unit when fitting multi-trait TDM. Therefore, a multiple trait model would be more ideal in determining the genetic merit of dairy sires and bulls based on daily yield records. Genetic and phenotypic correlations between daily yield and lactation yields were high and positive. Genetic correlations ranged from 0.84 to 0.99, 0.94 to 1.00 and 0.94 to 0.97 in the first, second and third lactations, respectively. The corresponding phenotypic correlation estimates ranged from 0.50 to 0.85, 0.50 to 0.83 and 0.53 to 0.87. The high genetic correlation between daily yield and lactation yield imply that both traits are influenced by similar genes. Therefore daily yields records could be used in genetic evaluation in the Sahiwal cattle breeding programme.

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“…For example, in Pakistan there are~25 million dairy cattle, but the most established progeny testing system in the country records <1000 milking animals per year. Furthermore, in developing dairy sectors, problems can be exacerbated due to the large environmental effects, poor management due to limited resources and poor data quality (Dahlin, 1998;Ilatsia et al, 2007). These problems have their implications and potentially reduce genetic gain.…”

Section: Introductionmentioning

confidence: 99%

Selecting an appropriate genetic evaluation model for selection in a developing dairy sector

McGill

Mulder

Thomson

et al. 2014

Animal

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This study aimed to identify genetic evaluation models (GEM) to accurately select cattle for milk production when only limited data are available. It is based on a data set from the Pakistani Sahiwal progeny testing programme which includes records from five government herds, each consisting of 100 to 350 animals, with lactation records dating back to 1968. Different types of GEM were compared, namely: (1) multivariate v. repeatability model when using the first three lactations, (2) an animal v. a sire model, (3) different fixed effects models to account for effects such as herd, year and season; and (4) fitting a model with genetic parameters fixed v. estimating the genetic parameters as part of the model fitting process. Two methods were used for the comparison of models. The first method used simulated data based on the Pakistani progeny testing system and compared estimated breeding values with true breeding values. The second method used cross-validation to determine the best model in subsets of actual Australian herd-recorded data. Subsets were chosen to reflect the Pakistani data in terms of herd size and number of herds. Based on the simulation and the cross-validation method, the multivariate animal model using fixed genetic parameters was generally the superior GEM, but problems arise in determining suitable values for fixing the parameters. Using mean square error of prediction, the best fixed effects structure could not be conclusively determined. The simulation method indicated the simplest fixed effects structure to be superior whereas in contrast, the cross-validation method on actual data concluded that the most complex one was the best. In conclusion it is difficult to propose a universally best GEM that can be used in any data set of this size. However, some general recommendations are that it is more appropriate to estimate the genetic parameters when evaluating for selection purposes, the animal model was superior to the sire model and that in the Pakistani situation the repeatability model is more suitable than a multivariate.

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Genetic and environmental causes of variation in milk production traits of Sahiwal cattle in Pakistan

Cited by 34 publications

References 22 publications

Factors affecting first lactation performance of Sahiwal cattle in Pakistan

Factors affecting first lactation performance of Sahiwal cattle in Pakistan

Genetic and phenotypic parameters for test day milk yield of Sahiwal cattle in the semi-arid tropics

Selecting an appropriate genetic evaluation model for selection in a developing dairy sector

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