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Genotype of PRNP: Buffaloes

Yalçın YAMAN1*, Cemal ÜN2, Orhan KARADAÄž1
As a direct public health threatening disease, Bovine Spongiform Encephalopathy (BSE) of the cattleprobablymost important disease among other Transmissible Spongiform Encephalopathies (TSE). It can be transmitted to human and cause to a new variant of the Creutzfeldt–Jakob disease (CJD). Unlike CJD, variant CJD (vCJD) has unusually affected younger people. It is known that prion protein coding gene (PRNP) plays a major role in the TSE susceptibility or resistance. Recent researches demonstrated that insertion/ deletion (indel) polymorphisms within promoter and intron 1 region of thePRNPrelated to BSE susceptibility in cattle. In contrast to cattle, BSE never been reported in water buffalo, hence, PRNP polymorphisms may be an explanation for buffalo resistance to BSE. The aim of this study was to evaluate allele, genotype and haplotype frequencies of the PRNP promoter and intron 1 indel polymorphism in Anatolian, Murrah and Anatolian X Murrah crossbred buffaloes. According to the our findings,there were no deletion alleles at two mentioned loci. All studied animals were monomorphic and have carried ins/ins haplotypes which are considered as the most resistant genotype to BSE.
Key words: Buffalo, BSE, PRNP, Promoter, Intron 1, Indel
Normal cellular prion proteins (PrPC) whichare coded by PRNP gene (McKintosh 2003) have some known functions in the organism such as “cellular trafficking, metabolism of the copper uptake, protection against oxidative stress, cell adhesion, differentiation, signaling and cell survival”(reviewed by Martins et al. 2002). After a posttranslational process PrPC converted into abnormal isoform called “prion”or PrPSC during which it acquires high beta sheet content and gain partly resistance to protease. Prions are defined as transmissible infectious particles that are lack of nucleic acid and composed ofmisfolded cellular prion protein. Prions cause incurable neurodegenerative disorders, also known as Transmissible Spongiform Encephalopathy (TSE) include Creutzfeldt–Jakob disease (CJD), Gerstmann– Straussler–Scheinker (GSS) disease, kuru and fatal familial insomnia in humans, Bovine Spongiform Encephalopathy (BSE) in cattle and scrapie insheep(Prusiner 1991, Prusiner 1997, Weissmann et al. 2002). Like other TSEs, BSE cause to fatal, irreversible neurodegeneration in the Central Nervous System (CNS) and the disease manifested by dementia and/or ataxia in cattle (Sander et all. 2004). BSE can be transmitted to humans and causes a new variant of CJD (vCJD) which have affected to unusually youngerpeople. Furthermore, it is shown that BSE also transmitted to sheep by whole blood transfusion. Similarity ofsheep-BSE experimental model and human vCJD pathogenesisget raised the concerns about thatBSE canspread between species(Hill et al. 1997, Bruce et al. 1997, Houston et al. 2000).
PRNPgene is of a key role in the pathogenesis of prion diseases, for example PRNP polymorphism at codon 129 for familial CJD and Gerstmann-Sträussler syndrome (GSS) in humans (Collinge et al. 1991, Palmers et al. 1991) also polymorphisms at codons 136, 154 and 171 for scrapie in sheep are related to disease susceptibility(Goldmann et al. 1990, Hunter et al. 1994, Clouscard et al. 1995, Hunter et al. 1997). More recently, significant relation with PRNP polymorphisms and BSEsusceptibility was defined in cattle. It is reported 23 bp indel within the promoter and 12bp indel within the intron1 of the PRNP gene associated with varying susceptibility to BSE (Sander et al. 2004, Sander et al. 2005, Juling et al. 2006).
To morphological and geographical aspect Anatolian water buffalo considered belonging to river type and Mediterranean breed as all European buffaloes included same type and breed (Soysal et al. 2005, Borghese and Mazzi 2005). Recently a karyotyping analysis study revealed that having25 pairs ofchromosome number (2n=50), Anatolian water buffalo is the same type with the river buffalo as genetically too (Ün et al. 2014). Since 1999 a improving project has been conducted by crossbreeding with Murrah buffaloes imported from Bulgaria and currently in progress at Sheep Research Station of Bandirma district in the Turkish province of Balikesir.
Up to now noTSE was identified in Buffaloes(Di Guardo 2014). It is not clear if the buffaloes are genetically resistant to the TSEsor not. Although numerous studies have been conducted in order to determine promoter and intron 1 polymorphism of cattle PRNP(Haase et al. 2007, Kerber et al. 2008, Muramatsu et al. 2008, Ün et al. 2008) there are fewreports regarding water buffalo PRNP Indel polymorphism. There is one other research conducted on Anatolian buffalo PRNP indel polymorphism, but as we know no study on Murrah buffalo.
The aim of this study was to genotype of PRNP according to 23bp and 12 bp indel polymorphism of Anatolian, Murrah and crossbred buffaloes, expand the data for the PRNP genotype of the Anatolian water buffalo and compare allele/genotype frequencies with those in other buffalo breeds.
Materials and Methods:
2. 1. Animals
Total 195water buffalo consisted 89 Anatolian, 20 Murrah and 86 Murrah X Anatolian crossbreds were studied. Because of the number of the purebred Murrah buffaloes were not sufficientrelatively, the F1, G1 and G2 level Murrah x Anatolian crossbred which some of their ancestors culled before the sample collection were included the study due to the detection of all possible in/del polymorphisms. These animals have been raised in the Sheep Research Station of General Directorate of Agricultural Research and Policies (TAGEM) of Republic of Turkey Ministry of Food, Agriculture and Livestock.
2. 2. DNA isolation and PCR
10 ml sterilized tubes with EDTA were used for whole blood collection. Blood samples were collected under aseptic conditions from V. jugulars and stored in -20 0C until laboratory working. The genomic DNA was extracted from whole blood samples using commercial kits. DNA quantification was made using Qubit device. To amplify promoter and intron regions of the PRNP gene two separate PCR reaction was conducted using the primers designed for cattle (Juling et al. 2006).
The PCR mixture was consisted 1 U Taq DNA polymerase (Fermantas Life Sciences, Burlington, Canada), 2. 5 mÓŽ10X PCR buffer [750 mÓŽ Tris-HCl (pH 8. 0), 200 mÓŽ (NH4)2SO4, 0. 1% Tween 20)], 1. 5 mÓŽ MgCl2, 50–100 ng genomic DNA, 100 µÓŽ dNTP (Fermantas Life Sciences) and 10 pmol of each primer and H2O to a final volume of the 25 µl. Reaction condition was as follows; 95 C for 5 min; 32 cycles of 94 0C for 45 s, 58 0C for 45 s, 72 0C for 45 s; and a final extension at 72 0C for 7 min. PCR products were separated on 3% agarose gel, stained with safe stain (NBS Biologicals, England)and visualized under UV light. Gel electrophoresisbanding patterns were expected to be either 191 ( ) or 168 (-) for indel 23bp of promoter and either 215 ( ) or 203bp (-) for 12bp indel of the intron 1 region.
2. 3. Statistical Analysis
Genotypic and allelic frequencies of the PRNP promoter and intron 1 variants were estimated via direct counting. In order to check whether the population in Hardy–Weinberg equilibrium or not the PopGene32 software (Yeh et al. 2000) was used.
Results and discussion
In the study, all buffaloes were detected as monomorphic for both loci. There were no deletion allele and both of two loci were mixed as / 23bp and / 12bp for all buffaloes. Neither 23bp nor 12 indel allele frequencies were consistent Hardy-Weinberd equation (HWE, df=1, P< 0,001). From 1970 to 2008, there has been a dramatic decrease in the buffalo population of approximately 93 percent in Turkey (Sarıözkan 2011). The loss of genetic diversity and deviation from HWE might result from such a population bottleneck.
In another study carried out on Anatolian Water buffaloes PRNP indel allele frequencies were found to be ? and %8 for 23 bpindel, ? and for 12bp indel respectively (Oztabak et al. 2009). Sample collection for that research was made from entirely unrelated buffaloes which reared in different provinces in Turkey. When comparing with those results obtained by Oztabak et. al, it can be considered that the inbreeding coefficient of our animals might have increased along the years. It may be another additional reason whydeletion alleles have disappeared.
It was demonstrated that nucleotide changes in non-coding regions of the PRNP gene have an influence on prion protein expression levels(Sander et al. 2005, Kashkevich et al. 2007, Msalya et al. 2011) and it has been hypothesized that PRNP promoter polymorphisms including intron 1 region, which is contributing to promoter activity might change PRNP expression level, thus, could modify the susceptibility and/or incubation timeof the BSE disease(Sander et al. 2005).
Promoter-23 bp
Ä°ntron 1-12 bp
Ä°n ( )
Del (-)
Ä°n ( )
River Buffalo
Uchida et al. (2014)
River Buffalo
Nili Buffalo
Imrana et al. (2012)
Ravi Buffalo
Azikheli Buffalo
Kundhi Buffalo
Nili Ravi Buffalo
Nehir Buffalo
Kobak et al. (2014)
Nehir Buffalo
Anatolian Buffalo
Oztabak et al. (2009)
Anatolian Buffalo
This study
Murrah Buffalo
Anatolian X Murrah
It is reported that the cattle which are carrying 23bp ins / 12 bp ins haplotype are clearly the most resistant individuals (Sander et al. 2004, Sander et al. 2005, Juling et al. 2006). While some researchers have found the stronger effect at 23 bp promoter polymorphism (Sander et al. 2004 and Haase et al. 2007), however, others have argued that 12bp intron 1 polymorphism has a major effect on BSE susceptibility(Juling et al. 2006, Kashkevich et al. 2007). As shown in the table 1, 23bp promoter deletion allele frequencies of other buffalo breeds found between 0 to 0,08 even 0,47 in Thai water buffaloes, likewise 12bp intron 1 deletion allele frequencies varying between 0 to 0. 17. In the animals included the our study there was no deletion allele at the both regions. Whether promoter or intron1 deletions have stronger association with BSE susceptibility, in both case Anatolian and Murrah water buffaloes seem to be more resistant to BSE comparing most of the other buffaloes breed
Water buffaloPRNPindel frequencies mostly different from cattle (Oztabak et al. 2009, Imrana et al. 2012, Uchida et al. 2014, Kobak et al. 2014). Since 1986 when the first recognition of BSE in UK, it is estimated that several million cattle exposed to infection (SMÄ°TH, P. G. and BRADLEY, R. 2003),beside this, buffaloes have likely consumed the prion contaminated feed. (Aidaros 2003). Taking into account that there is no report about buffalo TSE, these genetic differences may be the probable reason of the resistance of water buffaloes to BSE (Di Guardo 2014). On the other hand, aside from relation between PRNP indel polymorphism and BSE susceptibility in cattle it is reported that breed specific differences could be exist (Haase et al. 2007). It can be assumed that same differences could be exist between buffalo breeds too. This and other relevant studies(Oztabak et al. 2009, Imrana et al. 2012, Kobak et al. 2014,Uchida et al. 2014) indicate that PRNP promoter and intron 1 region in water buffaloes mostly consists of insertion alleles therefore further investigations on PRNP gene, including ORF and 3’ UTR regions will contribute to our understanding of genetic fundamentals of the Bovidae Prion diseases.

Production Capacity of Jersey Cows

The aim of this study to look up at production capacity of Jersey cows in Malaysia including environment factors effecting milk production Jersey cows. This study to estimated production capacity Jersey cows at state of Keratong, Muadzam, Pahang at longitude 103.4833°T and latitude 2.8167°U in closed house system with temperature 24°C. The milk production of Jersey cows not only referring to genetic makeup but also another environment factors must be consider. The patent climate in Malaysia is hot and humid and Malaysia in tropical region because of this kind of climate the performance Jersey cows not equivalent as Jersey cow’s performance at temperate country. A good and practical farm management effluence performance of Jersey cows, if a good farm management practice not seriously consider it effect performance Jersey cow’s milk yield decreases or cannot reach as usual. Because of differences between Malaysia climate and temperate country resulting Jersey cows performance and milk production. Data collecting from farm record which is Makmur Dairy Sdn Bhd under LKPP (Lembaga Kemajuan Perusahaan Pertanian) corporation. Data collecting including milk production milk yield, lacataion length, dry off period, animal identification and date of giving birth. In this study, research done in the area on milk production was reviewed. Constraints to increase production were summarized and proposals are made for the sustainable development of the dairy sector in Malaysia.
CHAPTER 1: INTRODUCTION 1.1 Malaysia’s Geography
Malaysia in the south east part of Asia has a geographic coordinate that reads 2° 30′ north latitude and 112° 30′ east longitude. Malaysia country classified as hot and high humid climate and has tropical weather influenced by monsoonal climate because of its latitude and longitude. But the weather in general in Malaysia is without extremities. Monsoon comes twice a year, during the summer season and the other during winter. Summer monsoon brings lots of downpour in Malaysia. Winter monsoon does not cause that much rain and is generally dry.
1.2 Malaysia Self-Sufficiency
In Malaysia agriculture history, Malaysia produce milk product at insufficient level and cannot supporting demand from consumer because this may attributed to poor production local dairy animals. The demand for milk from consumers increased each year. In last decade, Malaysia almost depends on milk importation from other countries to fulfill the demands from consumers, about 95% is imported. The statistics from the Department Veterinary of Services in year 2008 for milk product in Malaysia recorded 56.49 million liters of milk was produced in that year and the self-sufficiency in livestock recorded 4.89%. In year 2010, the government target output of milk is 65 million liters and at least 10% self-sufficiency.
Malaysia Dairy Industries
The cattle population in Malaysia home 900,000 cattle and the large population at northern state of Kelantan 141,502 of cattle and in the southern state of Johor 111,000 of cattle. From the total population only 4% are dairy cattle (DVS, 2008). The total dairy farmer in Malaysia categorize to three structure; smallholder, semi-commercial and commercial. Dairy farm at smallholder level in Malaysia is recorded 519 and has least than 30 number of breeder (DVS, 2008). Meanwhile, 32 semi-commercial dairy farm levels have recorded and have 30 to 50 numbers of breeders (DVS, 2008). The commercial level dairy farms were recorded 28 of them and have more than 50 numbers of breeders (DVS, 2008). Reported milk yield of Malaysia 2631.3 kg per year (DVS, 2008) compared from central of Thailand produced yield from Malaysia that is 3500 kg per year (Kasetsart J, 2009).
1.4 Fact of Jersey Cows
The term production indicates something going to be produced or an output product. Meanwhile, capacity is describing the total amount of production at maximum or optimum level. Production capacity of Jersey cow can be measured from the total amount of milk yield. In dairy cattle, Jersey is one of the popular breeds. The origin Jersey cows are from the small British Island of Jersey in the English Channel. A Jersey also known as Alderney cattle at island of Jersey. The colour of Jersey varies, usually light gray to a dark fawn, being darker around the head and hips. Jerseys are noted for the highest milk fat of all dairy breeds. The body of Jersey is at medium ranking categories. An average weight excellent Jersey cows is around 408 to 544 kg. The milk yields of Jersey cow be able to categorize under intermediate milk producer can reach up to 13 times from their own body weight in milk.
1.5 Factor Affecting Production Capacity of Jersey Cows
There are two major factors which can influence production of dairy cows, the main quantitative factor is genetics of the breed and the second qualitative factor is environmental. Under normal situations, milk production increasing during the first six week of lactation and then gradually decreases. Breed of cow effluence milk yield, in North America Holstein cow has the highest volume of milk production and total production 7073 kg per year and Jersey cows 4444 kg per year. A dry off period usually practices for two months to the next calving. Milk yield usually reduced when dry period is less than 40-60 days (25-40% less milk). Meanwhile, dry period longer than 60 days in length does not result in a significant increase in milk production. For effective management is accurate quantitative knowledge of factors affecting productive performance of animal (Bagnato and Oltenacu, 1994). The actual amount of milk production affected by several factors of parity, geographic region and management factors. The environment factor is in terms of nutrition intake of cows in the feed ration. Stage of lactation and persistency can be determined by different stages of lactation phase. Management factors consider as feed and water supply the cows consume, insufficient water supply for few hours will result in a rapid drop in milk yield. Milking intervals practices at farm may result milk production. An unequal milking interval produces less milk than those milked at equal intervals. Milking frequency twice a day yields at 40% more milk than once a day.
1.6 Objectives
Information on production capacity Jersey cows in Malaysia is still unpublished therefore the present of study was designated by following objective:
To measure production capacity Jersey cows at state of Keratong, Muadzam, Pahang at longitude 103.4833°T and latitude 2.8167°U in closed house system with temperature 24°C is .
Study various environmental factors affecting performance of Jersey cows in Malaysia
Adaptability of Jersey cows in Malaysia climate
CHAPTER 2: LITERATURE REVIEW A research regarding Jersey cow’s performance has been conducted in various part of the world. There are two major factor affecting milk productions in dairy cattle. First are quantitative factors referring to genetic of dairy breed and the second is qualitative factors referring to environmental factors such as nutrition, stage of lactation and persistency, milking practices, age and size of cow, dry period, estrous cycle and pregnancy, environment. An environmental factor has been stated are known to exert influence on the performance dairy cattle (Javed et al, 2000).
A study of factors affecting milk yield in dairy cattle has conducted many people for many years and until now. For next paragraph is the review of studies regarding factors affecting milk yield from various part of the world. These reviews are indicated large differences in performance different of breed in different environment. These variations may be either due to breed, herd, location and environmental differences.
2.1.1 Genetics
Differences of dairy breed may reflex on different of milk yield. Comparison between two breed dairy cattle Holstein and Jersey, Holstein has ability to produce more milk higher than Jersey breed. The review of literature revealed that lactation milk yield in Holstein cows can produce up to 5,902 kg and Jersey cows 3,797kg (Gacula et al, 1968). The fat contain in milk composition of Jersey cow is higher than Holstein cows also higher than others dairy breed. Fat percentage in milk composition of Jersey cows is 5.01% and Holstein 3.35% (Sharma et al, 1988). Jerseys had the highest fat content, followed by Guernseys, Brown Swiss, Ayrshires, and Holsteins (Gacula et al, 1968).
2.2.1 Nutrition
A nutrient value content for dairy performance in dry matter intake is 89.5%, crude protein 16.6%, crude fiber 10.1%, energy expenditure 3.8%, ash 6.1%, nitrogen free extract 63.4%, total digestible nutrients 72.8%, metabolism energy 11.10%, calcium 0.77% and 0.66% of phosphorus (DVS, 2000). The recommended rates of concentrate feeding for lactating cows from department of veterinary services ministry of agriculture Malaysia suggest for uniformity a concentrate feeding is assumed to be a mixture of feed materials containing a minimum of 15% crude protein, 10.6 MJ/kg metabolize energy, 0.60% calcium, 0.4% phosphorus and 1.0% salt show at table 2.2.1, appendix. Knowledge of feed quality and intake, the extent of cows nutrient reserves that can be used to support milk production can now estimated (Alderman and Corttrill, 1993)
Several nutritional factors can influence milk composition. These include plane of nutrition or underfeeding affect milk composition. Under feeding dairy cows can reduces lactose percentage and increasing milk fat percentage. Negative energy balance increasing milk fat Imbalance rations on animal feed (low energy and protein) resulting milk fat decrease and protein percentages (Petersen et al, 1986). Effects on ISO (isometric) on milk fat can probably explain by two factors. First, primiparous cows of high groups of lost substantial body weight during lactation induce negative energy balance and second factors high: moderate of groups produce less milk content higher milk fat because milk production and milk fat are negatively correlated (Pierre et al, 2003).
As the proportion of the concentrate in ration increases above 50-60% milk fat percentage tends to decline. These conditions happen because of lower ruminal production of acetate and butyrate (function to synthesis milk fatty acid in mammary gland) associated with high concentration diets in feeding rations. A feeding system may effect on mammary gland health have been studies recently (Ouweltjes et al., 2007). Furthermore, there are interactions between breed and feeding system in dairy cattle may contributed mammary gland health status and milking characteristics (Ouweltjes et al., 2007). A grass feeding system and different level of concentrate offered to animals, (Turner et al., 2003; McCarthy et al., 2007) not effecting lactation stage and somatic cell count. A grazing system applied on dairy cattle (McCarthy et al., 2007), with high concentrate diet have influence average milk flow and milk duration. The extent of lactation period of milk depression is subjective by other feeding regime such as rate of feeding and feeding system. In wide-ranging, the impact of feeding high levels of concentrates on milk fat percentage will be less where total mix rations are fed and if feed is offered three or more times daily.
There are about 30 nutrients essential for dairy cattle performance, maintenance, reproduction, growth, and lactation. The good quality of feedstuff in ration is to make sure the animal get enough supply for those 30 nutrients, lacking with nutrient in feed can corrected in giving additional supplement to animal. With intensive herd management, deficient nutrients such as those providing energy, protein, minerals or vitamins can be supplied by forage and supplements of adequate quality to produce acceptable gains in milk yield and weight gain (National Research Council, 1971; Bath et al., 1978; Ranjhan, 1981). Miller and Dickinson (1968) and Miller (1969) in their studies has found that management practices related to feeding, particularly amounts of concentrate, and reproductive efficiency (percentage days in milk) have the greatest value in predicting herd average milk production and are the most important characteristics common to higher producing herds. McCullough (1969), Verité and Journet (1971), Ekern and Sundstol (1974), Ekern, Save and Vik-Mo (1975) and Wilkinson (1983) have also shown that intensive feeding of ensiled forages and hay, containing appropriate grain and protein requirements and fed free choice, increases and/or maintains milk production at a higher level by providing the opportunity for animals to be fed the conserved products with minimal loss of nutrients during periods of inadequate forage supplies.
2.2.2 Stage of Lactation and Persistency
The animals become profitable when the animal can produce milk at maximum level of lifetime. An earlier selection of animals for their productivity should lead to maximum output in total lifetime (Murdia and Tripathi, 1993). Milk production increases with lactation number and is maximized in the fourth or the fifth lactation. According to Walsh et al, (2007) reported all breed reached average milk flow at week fifth to eight lactation, followed by a gradual decline until lowest point average milk flow at the end of lactation. This is result of the increasing development and size of the udder and the increasing body size over that of the first lactation dairy animal. The expected mature yield (mature equivalent) of primiparous cow calving at two years of age can be estimated by multiplying yield of first lactation by 1.3.
Persistency of lactation refers to the ability of the cow to maintain production after peak milk yield; persistent cows consider when the cow tends to maintain their peak yield within a lactation period (Togashi and Lin, 2003; Kamidi, 2005). The cost of the production system can reduce if practice persistency of lactation these is associated with feeding and health costs, reproductive performance, resistance to diseases and the return from milk considering a 305 days production cycle (Solkner and Fuchs, 1987; Dekkers et al., 1996, 1998). Persistent animals require less energy in early lactation, allowing greater utilization of cheap roughage (Solkner and Fuchs, 1987). All farmer desires their animal given profit in return, according to Dekkers et al. (1997) dairy cow with greater lactation persistency tend to be more profitable than average dairy cows when yield and lactation persistency are correlated, even though the differences are relatively small unless reproductive performance is very poor. Other studies however have identified a greater difference in production between once and twice daily herds during late lactation than in early lactation (Claesson et al., 1959), indicating that the persistency of cows milked once daily is less than that of cows milked twice daily. Muir et al. (2004) reported favorable relationships among lactation persistency and some measures of fertility and poor relationships with others (e.g., calving interval). Appuhamy et al. (2007, 2009) reported that diseases tend to significantly affect lactation persistency, rather than persistency affecting disease occurrence, and that there are undesirable genetic correlations among persistency of milk and fat yields and several metabolic diseases. Harder et al. (2006) also reported unfavorable genetic correlations among persistency and metabolic diseases. Lactation persistency as defined by Cole and Van- Raden (2006) is useful as a measure of the shape of the lactation curve independent of 305-d yield. The results of Jamrozik et al. (1998) and van der Linde et al. (2000) suggest that lactation curves and persistency differ between lactations, and differences probably exist between early- and late-maturing breeds. This was confirmed by Cole et al. (2009), who estimated lactation curves for first and later parities in 6 breeds of dairy cattle and found that parameters describing the shapes of the curves can vary considerably.
2.2.3 Milking practices
Milking Jersey cows three times daily results in an average increase. The increase in yield due to milking three times daily varies greatly with the producing ability of the animal on twice a day milking and is inversely proportional to such ability. High producing cows show a smaller increase in yield when milked three times daily than do cows with a lower inherited producing ability. Milking heifers three times daily on test results in a slightly greater development than when the initial records are made on twice a day milking of approximately 19 per cent in butterfat and 21 per cent in milk yield. In addition, positive correlations have been reported between milk yield and MD (Petersen et al, 1986; Weiss et al., 2004). Similarly, results from this study indicate positive correlations for milk yield with average milk flow and milk duration suggest that these correlations differ depending on breed.
2.2.4 Dry period
Dry period is non-lactating days between lactations. This non lactation time is generally a 5 to 6 weeks rest period before freshening. The duration of dairy cattle stop from lactating. This duration is needed for regression of mammary gland. During this period, mammary gland starts to repair or develop back imbalance tissues to normal. To induce this process of repairing a special treatment is applied to cow called dry cow’s therapy. The dry cow’s therapy is a treatment for controlling bovine mastitis from intact to mammary gland.
Usually the recommended dry period practices for about 45-50 days. A practices dry period is less than 40 days, can effect on decreasing of next lactation (Swanson 1965; Coppock et al. 1974; Dias and Allaire, 1982). According to Capuco et al. (1997) the cows given 60 days dry period can increasing mammary DNA synthesis begin occurring about
2.2.6 Calving Interval and Parity
Breeding efficiency can be measured by looking to calving interval and age at first calving, although they are not the only measures of herd performance. Speicher and Meadows (1967) have suggested the correct calving interval is 12 months (365 days), the longer calving interval more than 12 months can lose the profit in overdue of feed cost. McDowell (1971) considers the following factors adequate for successful dairying: a calving interval between lactations of less than 450 days; an excess of 200 days in lactation; less than 30 percent female mortality; and proper management.
According to Murdia and Tripathi, 1993 a good calving interval for cows at range of 360 to 390 day (12 month to 13 month), if the calving interval shorten than 360 days it can give impact on milk production decline 3.7 to 9 percent in lactation length. If the condition vice versa having longer calving interval more than 450 days (15 months) it will increasing milk yield production for 3.5 per cent. Although, the longer calving interval more 15 months increased milk production this situation may reflect on farmer profit because the milk has been produced not equivalent to feed has been giving to the cows.
A study from I.R. Bajwa et al, 2004 the effect of average lactation length on parity is decreased begin at parity 4th and 5th, after parities 5th the milk yield and lactation length start to increased back until parties 8. Both breed and parity effects have been shown to exist on lactation curves (e.g. Wood, 1980; Collins-Lusweti, 1991; Friggens et al., 1999; Rekaya et al., 2001) and can now easily be included as fixed factors in test-day models and other linear models that incorporate time trends (Van der Werf et al., 1998; Macciotta et al., 2005).
2.2.7 Environment
Results to characterize effects of climate on milk production are important for commercial milk production, perhaps particularly under circumstances where climate is a limiting factor (Sharma et al, 1988). Major benefits of shade management on milk yield appear to be related to indirect effects such as feed intake and digestive tract performance rather than direct effects on ability of the mammary gland to synthesize milk (Collier et al, 1981). Cows calving from November through March yielded more milk and fat than cows that calved in July and August. These results are similar to those reported by Blanehard etal. (3), Frick et al. (6), Gaunt et al. (7), and Wunder and McGilliard (22). Seasonal variation in animal performance in tropics is expected to be primarily a manifestation of variation in feed quality and quantity (Javed et al., 2000). The animals of temperate regions maintained in tropical conditions cannot behave similarly in both the environments (Javed et al., 2002). This indicated that the animals of temperate zone did not adapt to the harsh environments of tropics and could not perform satisfactorily (Javed et al., 2004).
The data on 233 performance records of 170 Jersey heifers, five bulls for natural mating purpose and 58 are in calves’ stages during the period of 2007 through 2009 were utilized for present study. All Jersey breed at this farm are pure and imported from Australia. After editing
3.2 Background and Location of Farm
The data sources are from farm dairy at state of Keratong, Muadzam, Pahang at longitude 103.4833°T and latitude 2.8167°U in closed house system with temperature 24°C. The sources of data are from Makmur Dairy Sdn Bhd. The Makmur Dairy Sdn Bhd was established under LKPP (Lembaga Kemajuan Perusahaan Pertanian) Corporation Sdn Bhd in May 2006. At beginning farm opening, 300 Jersey animals were imported from Australia. This farm not only focusing on dairy animal but also doing multiple ranches including feedlot cattle, dual purpose goat Shami breed.
3.3 General Management and Feeding Practice
This farm practices cut and carry feeding (Guinea and Napier grass) system 40kg/head and concentrate 5-10kg/ head. This farm practices machine milking two daily routine, first at 7.00am and 4.00pm at evening. The entire animal at this farm are under intensive system. After milking process, the animal allow to free-range at paddock from 9.30 am until 10.00am. In this farm consist with two animal barns, barn A and barn B. Inside barn B; it completely closed house system provided with cooling pad and seven fans for cooling purpose. At this farm, they practices animal bedding using sand.
3.4 Data Record Extraction and Editing
The following data will be collected: animal identification, date of calving, lactation milk yield, lactation length, lactation stage, parity, dry period, calving interval and nutrition value on concentrate feed.
Following performance traits will be recorded/analysis and utilized in the present study. The data analyzed starting from year 2007 until 2009.
3.4.1 Lactation Milk Yield and Milk Yield per Day
Total milk produced during a given lactation which terminated normally was considered as lactation milk yield. Lactation milk yield Effects of lactation length; calving interval and service period on lactation milk yield also estimated. Two lactation milk yields was grouped to three classes, this is because the farm newly established for about four years. The mean value, average and standard deviation is counted for lactation milk yield and milk yield per day beginning from 2007 until 2009.
3.4.2 Lactation Length
Lactation period each cows is counted start from calving until the cows has dried up. Each of lactation length of cows is counting on average and mean value. The length of lactation period depends on production level and open period of each cow.
3.4.3 Lactation Stage
In analyzed the data lactation stage has categorized to three sub classes the three sub class are ‘1’ early lactation stage between 14 to 100 days, ‘2’ mid lactation stage between 100 to 200 days and ‘3’ late lactation stage between 200-350 days. The data regarding from 2007 to 2009 record farm keeping.
3.4.4 Parity
The date of calving from first calving at years 2007 until 2009 is record for analyzing the data. Parity if a one major factors effect on lactation milk yield. More parity indicated lower of milk yield in dairy cattle especially in parities 4th and 5th (I.R Bajwa, 2004). The parities are divided into three group first is one for first parity, second is two for second parity and third is three for third parity.
3.4.5 Dry Period
The right and proper management of dry period is one of keywords to make sure the milk production can stand in a high amount production. The data regarding dry period is going to make three sub class ‘1’ dry period for 0 to 40days, ‘2’ dry period 40 to 70 days and ‘3’ dry period above 70 days.
3.4.6 Calving Interval
The data from date of parturition is used for calculating calving interval, the means, standard deviation and standard error are applied to summarize the data. Because of the cows only have three parities, the calving interval is calculated based on first parities and second parities. The calving interval is divided into four categories that is zero represented for no calving interval, one is below than 360days, two for calving interval at range 360 to 390days and three is above 390.
3.4.7 Nutrition Value on Concentrate feed
Proximate analysis of concentrate feed from the farm record is taking up for comparing the nutrition value concentrate feed at farm with recommended nutrient requirements for dairy cattle in different lactation stage. Nutrition is a factors effecting milk production of dairy animal. In different year the nutrition value is different in each year. The lactating ration and the basal ration nutrition value are taking up for comparison on effecting milk yield.
3.5 Data Analysis
Analyzing data with unequal parameters and disproportionate sub class analyzed using statistical analysis software SAS 9.0. The data will be analyzed using SAS software with analysis of variance (ANOVA). The two independent variables in ANOVA are called factors, the factors are genetic and environment which can influence milk yield of Jersey cows.
Mean values of several factors effecting on milk production capacity of Jersey cows were analyzed on analysis of variance (ANOVA):
Production Capacity Based on Year 2007-2009 relating nutrient value on concentrated feed.
Production Capacity Based on Parity, Lactation Stage, Dry Period, and Calving Interval.
CHAPTER 4: RESULTS AND DISCUSSION The analysis performance groups of Jersey cow’s productivity from 2007-2009 in various responses factors is interpret.
4.1 Production Capacity Patterns
4.1.1 Production Capacity Based on Year 2007-2009 Relating with Nutrient Value on Concentrated Feed
Calculating mean of milk yields begin from year 2007 to 2009 for 126 milking cows was 906.15±63.32 liters with a coefficient of variation 78.4%. The highest yield was 949.91 liters at year 2009. In year 2007, the mean milk yield is 615.46 ±85.23liters with coefficient variations 57%. In the next year 2008 the mean milk yield is 953.27±99.44liters with coefficient variations 74%. In year 2009 the mean milk yield is 949.91±101.84 with coefficient variations 81.4%.
The mean lactation length of all cows was 166±7.71days, with a coefficient variation 52.2%. For year 2007 it was 166±13.19days of lactation length with coefficient variations 32.8%. In year 2008 the mean lactation length is 165±12.57days with 54.4% coefficient variations. In year 2009 the mean lactation length is 167±12.13days with 55.3% coefficient variations. The milk production and lactation days based on year are shown in table 4.1.1(a) and fig 4.1.1(a).
The analysis of variance on milk yield based on nutrient value of year has no significant different effect (P1.66). Nutrient value may effect on milk yield (liters), in nutrient value based on year one the NFE amount around 43-38%, for year two the amount of NFE is around 52-49% and third year the amount of NFE in range 48-52%. The requirement of nitrogen free extracts (NFE) not enough for supporting milk production as recommended value is 63.4% NFE. In year 2007-2008 the amount of total digestible nutrient (TDN) is in rage 56-68% only and it not enough for supporting milk production as recommended amount 72.8%. In year 2007-2009 value of metabolisms energy is sufficient for supporting milk yield because the recommended amount of metabolism energy in ration must be 11.10%.

4.1.2 Production Capacity Based on Parity, Lactation Stage, Dry Period, and Calving Interval.
Number of 30 heads milking cow have been observed on the means milk yield (liters) of cows in parities one is 1219.51±126.17 with coefficient variance 56.7% and means value for second parities is 1403.22±101.02 with 26.9% of coefficient variance in fourteen cows been milking. The third parities for means value of milk yield is 1153.74±162.08 liters with 34.4% with six heads milking cows. Analysis of variance (ANOVA) on factor effecting milk yield on parity one, parity two and parity three are significantly different (P<0.07) from each other. In the analysis of variance it have summarize that within three parities have effect on the amount of milk yield the cows produce. In parities three with means value 1153.74± 162.08 with coefficient variance 34.4% is decline compared to first parities which 1219.51±126.17 with coefficient variance 56.7%.
Lactation stage are divided to three sub classes, the sub classes are lactation stage one considering as early lactation for 14-100days, second lactation stage is a mid lactation in range of 100-200days and third lactation stage with range of days 200-305days. The mean value for lactation stage one is 160± 251.29 with coefficient variance 14.4%. In second lactation stage the mean value is 777.10± 92.04 with coefficient variance 45.87%. The third lactation stage the men value is 1550.80± 77.91 with coefficient variance 28.86%. The analysis of variance (ANOVA) is lactation stage one, two and three have significantly (P<0.0001) on milk yield. Each class of lactation stage can affect the amount of milk yield on Jersey cows.
Mean value in first dry period is 2266.42± 221.66 with coefficient variance 19.56% and second dry period on mean value 1362.10± 163.19 with coefficient variance 23.96%. The last, third dry period on mean value is 1158.10± 81.58 with coefficient variance 45.65%. The number one, two and three is representing on 1 for 0 to 40days, 2 for 40 to 70days and 3 for above 70days dry period. The analysis of variance (ANOVA) in dry period are significantly (P<0.0001) and the right of length dry period can cause total quantity milk yield (liters).
The mean value for calving interval on zero is 1229.70± 130.17 with 57% and second calving interval the mean value is 1605.20± 76.88 with coefficient variance 6.8%. The third calving interval is 1300.30± 129.46 of mean value and 29.86% of coefficient variance. The calving interval in one and three have not significantly (P<0.0001) different because of calving interval one and three is shorter and longer of calving interval. Meanwhile, the second calving interval is a right calving interval and can maximize the milk yield.
Relating all factor effect on milk yield from analysis of variance they are significant between lactation stage and dry period (P<0.0001). Meanwhile between parity and calving interval it have no significant effect on milk yield with different probability value (P<0.07) and (P<0.3). All the factor effect on milk yield (liters) is summarize on data in table 4.1.2(a). A review of factor effect on milk yield for individual Jersey cows is sum up in appendix, table 4.2.1(c).