Investigation of the relationship between exotic blood percentage in cattle and its influence on both milk production and susceptibility to common livestock diseases across F1 and F2 generations in Bangladesh

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Abu Saif, Md. Foysal, Md. Mahbubur Rahman, Farida Yeasmin Bari, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6791952/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Crossbreeding in dairy cattle has recently become of increased interest. However, there is limited knowledge regarding the impact of crossbreeding on both the physical attributes and milk production capacity of crossbred cattle in Bangladesh. Data from 847 crossbred Holstein cattle were used to estimate the genetic effects on average milk yield. Besides, the history of 5 certain disease conditions was also collected. F1 and F2 generations were categorized by the percentage of sire blood they had and then the data were analyzed to find the point at which genetic contribution leads to the most productivity. The mean production and impact of breeding sires’ blood percentage were measured through statistical tests like ANOVA, Chi-square and Kruskal-Wallis tests. The result showed that in both F1 and F2 generations, maximum lactation yield was found in 0.9375% Holstein Friesian sires’ blood group (1265.3333 ± 15.36436 L and 1597.1803 ± 217.02733 L respectively). For the two generations, the results indicate a highly significant effect of blood percentage on overall milk production, though certain diseases were not significantly associated. The results also revealed that as the amount of local breed blood increased, the amount of milk produced consistently decreased indicating the importance of keeping the amount of exotic blood between 75% and 93.75%. In conclusion, productive performance of crossbred cattle relies heavily on strategic breeding programs and the fast crossbreeding had a thoughtful impact on milk production in the study area. Animal Science Crossbreeding Dairy cattle Holstein Friesian Milk yield Blood percentage Genetic effect Disease incidence Exotic blood level Figures Figure 1 Figure 2 Introduction Livestock, particularly cattle for milk, are the keystone species of the agriculture-dependent economy of Bangladesh and make significant contributions to rural income, food security, and national GDP (Alam et al 2009 ). Over the past decades, high demand for milk and dairy products has led to the implementation of genetic improvement schemes to enhance productivity levels among the local cow populations (Oltenacu, P. A., & Broom;2010). One of the most fascinating approaches applied in such programmes is cross-breeding the local cows with the imported cows such as Holstein Friesian and Jersey in order to produce more milk and general genetic superiority (Mekonnen et al;2020). But although there is widely documented productivity benefit from such crossbreeding, there is a similar great requirement to consider the broader implications of exotic blood introgression, i.e., for animal health and environmental adaptability (Naskar et al;2012). This study investigates the complex correlation between cattle ratios of exotic origin and their correlation with milk production and susceptibility to general livestock disease like Foot-and-Mouth Disease (FMD), Mastitis, Hemorrhagic Septicemia (HS), Reproductive Tract (RT) disease, and Dystocia. F1 and F2 generations of cross-bred cows under routine farm management systems of Bangladesh, where shortage of resources, disease stress, and environmental pathogens have a crucial effect on livestock performance, are stressed(Biswas;2018). The basis for conducting this study is that while higher percentages of exotics in genetics might pose a challenge to milk production, at the same time they might compromise disease resistance and adaptability to local climatic and management factors (Leroy G et al;2016). Higher proportion of local blood-composited animals, however, were found to be more resistant but less productive (Abera;2016). It is therefore important to bring effective equilibrium between productivity and flexibility to sustain crossbreeding schemes in Bangladesh. It examines the effect of breed upgradation rate to such performance. Based on the analysis of F1 and F2, this research will attempt to identify the most optimal gene combinations giving the highest amount of milk production with disease resistance and environmental tolerance. Lastly, the outcome of this research should direct breeding programs, policy, and decision-making at the farm level towards the optimization of dairy farm productivity and sustainability in Bangladesh. Materials and Methods Collection of data The present study data were collected from a total of 27 districts of Bangladesh by 5 veterinary graduates. The information on the milk production performance and the 5 most common disease cases (HS, FMD, Dystocia, Mastitis, and Reproductive Tract Diseases) of 1694 cows (F1 and F2 generation) of Friesian crossbred with different blood levels was collected from the marginal level farmers. The veterinarians gave the identification number of each cow for further information. Breed and genetic groups of sire considered were 50% local − 50% Friesian, 37.5% local − 62.50% Friesian, 25% Local − 75% Friesian, 12.50% local − 87.50% Friesian, 6.25% Local − 93.75% Friesian, 100% Local Friesian and Pure Friesian from the USA. Animal management This study particularly focused on the production level of Friesian crossbreed cattle at a marginal scale. Thus, there was an insufficiently balanced ration to feed the cows due to the impoverishment of the root-level farmers. However, a combination of concentrate feed of rice polish, wheat husk, mustard oil cake, molasses, khesari, salt, and vitamin-mineral premix was often supplied. Straw and green grasses (Napier/German/Para/Jumbo) are supplied as roughages. Normally, two times a day milking is performed. Commercially available sire semen (ACI/Brac/ADL) was used to breed the animals. Analysis of data Collected data were processed, tabulated, analyzed and visualized through Microsoft Excel, IBM SPSS Statistics (Version 22), and R software. Breeding upgradation speed was fixed by calculating the difference in Holstein Friesian (HF) blood percentage between the sires used to produce the F1 and F2 generations. If the difference was < 13%, it was considered Normal; if ≥ 13% and < 26%, it was classified as Moderate; if ≥ 26% and < 38%, it was classified as Fast; and if ≥ 38%, it was considered Very Fast. Disease occurrences in both F1 and F2 generations were cross-tabulated with the sires’ blood percentages to find the comparative proportion between disease incidence and disease free survival. Results and Discussion Data were cleaned and organized through statistical test like ANOVA and Chi-square, classifying continuous variables (such as blood percentage levels), and computing summary statistics (means, standard errors) to find out the influence of blood percentage on disease prone and lactation yield. Kruskal-Wallis and other non-parametric tests were used, indicating checks for possible transformations and normality. The results of ANOVA and cross-tabulations show aggregated lactation metrics and encoded categorical variables, ensuring data integrity and compatibility with analytical techniques. The result indicates significant effect of blood percentage on lactation milk yield in sire of F1 (p < 0.001) (One-Way ANOVA: F (12,834) = 112.87 F (12,834) = 112.87; df = 12,8,34) (Kruskal-Wallis-H Test: χ2(11) = 371.82 χ 2(11) = 371.82; df = 11) and F2 generations(p < 0.001) (One-Way ANOVA: F (20,826) = 38.42 F (20,826) = 38.42:df = 20,8,26) (Kruskal-WallisHTest: χ2(12) = 381.28 χ 2(12) = 381.28; df = 12). Table:1 statistical Analysis Category Test Statistic df p-value Significance Milk Yield (F1) One-Way ANOVA F(12,834) = 112.87 F (12,834) = 112.87 12, 834 < 0.001 Highly Significant Kruskal-Wallis H Test χ2(11) = 371.82 χ 2(11) = 371.82 11 < 0.001 Highly Significant Milk Yield (F2) One-Way ANOVA F(20,826) = 38.42 F (20,826) = 38.42 20, 826 < 0.001 Highly Significant Kruskal-Wallis H Test χ2(12) = 381.28 χ 2(12) = 381.28 12 < 0.001 Highly Significant Disease Association Pearson Chi- Square χ2 = 3.563 χ 2 = 3.563 5 0.614 Non-Significant Impact of Blood Percentage on Milk Production The study found a significant relationship between blood percentage of sire and total lactation yield in both F1 and F2 generation (P < 0.01). In both F1 and F2 generation maximum lactation yield was found in 93.75% exotic blood were 6326.667 L and 4791.54 L respectively. In F1 generation cattle with 93.75% exotic blood found highest milk yield (6,326.67 L) and 50%, 62.5%, 75%, 87.5% and 100% Local produced 3823.171L, 6146.310L, 3,854.27L, 3750.000L, 4681.818L milk respectively. Our findings align with Siddiquee et al. ( 2014 ), who reported higher milk yield in 62.5% HF crossbreds compared to 75%. According to Al-Amin and Nahar (2013), the maximum yearly lactation recorded in 50% HF was 1837 ± 18 kg. For 50% HF, 62.5%, and 75% HF crossbreds, the 180-day milk yield (180DMY) was 1500.33 ± 76.46 kg, 1549.79 ± 73.86 kg, and 1339.00 ± 75.83 kg, respectively. In contrast, the present study, conducted roughly five years later, recorded nearly double the yearly lactation yield in 50% HF crossbreds. This considerable increase may be attributed not only to advancements in management and nutrition but also to improved adaptability of 50% HF crossbreds over time. Also, higher milk yields in the current study might be explained by the fact that crossbreds have adapted well to local conditions and now have better traits that help them produce more milk. Furthermore, whereas milk yield was found to decrease in cattle with low exotic blood percentage with 87.5 group produced only 3,750 L, 100% Local breed showed moderate performance (4,681.82 L) which indicates retained adaptability but lower genetic potential. Similar trend was found in case of F2 with 93.75% exotic blood remaining superior (4,791.54 L) while Pure USA sire (3,008.52L) and 100% local breed lagged. Moreover, 50% exotic group in F2 generation showed better productivity performance than mid-range groups (4,147.23L vs 0.625,2.267.25 L) which highlight hybrid vigor effect of variability. In both generations with increase of local breed content, the overall lactation yield decreased (F1 total = 4,171.471; F2 total = 2,655.04 L). The decrease of lactation yield with increase of local blood percentage highlights the role of exotic genetic introgression in maximizing milk production. Also, this underscores the need for estimating important breed traits, this relationship points out the need to use the best records for age at puberty, age at first calving, postpartum heat period, service per conception, calving intervals, lactation length, lactation yield, and milk yield (Sarder et al., 2007 ). However, these findings emphasize that optimal exotic blood levels (75-93.75) balance productivity and genetic diversity, while pure local or low exotic crosses prioritize resilience over yield. Table 2 Mean Milk Yield across different Blood Percentage Blood Percentage F1 Mean Yield (L) F2 Mean Yield (L) 50 3,823.17 4,147.23 62.5 6,146.31 2,267.25 75 3,854.27 2,321.17 87.5 3,750.00 2,353.02 93.75 6,326.67 4,791.54 100% LOCAL 4,681.82 2,468.84 PURE USA N/A 3,008.52 Total 4,171.47 2,655.04 Breed Upgradation Speed and Milk Yield In both F1 and F2 generation Fast and very Fast breed upgradation speed showed higher mean per lactation (Fig. 3 ), whereas between normal and moderate breed upgradation speed, better performance was found in cattle with normal breed upgraded and comparatively poorer milk yield in moderate group. But it was notable that breeds that are upgraded very fast showed more prominent difference of mean per lactation between F1 and F2 generations than other groups. Most interestingly, cattle with moderate and normal breed upgradation speed showed approximately consistent mean per lactation yield in both F1 and F2 generations. Rapid crossbreeding can cause animals to lose vital local characteristics, such as resistance to disease and heat tolerance, which reduces their suitability for the local environment. It may boost milk production, but it also necessitates improved management, feed, and healthcare, all of which are frequently out of smallholder farmers' price range. As a result, the anticipated advantages might not always come about. According to (Sarder et al., 2007 ), accurate estimation of productive and reproductive traits in dairy cattle—such as age at puberty, age at first calving, postpartum heat period, service per conception, calving interval, lactation length, and milk yield—requires the best performance records of each breed. Moreover, maintaining the right genotype in the right environment is essential for the full expression of genetic potential (Haque et al., 2011 ). However, according to our research, productivity can be increased through rapid crossbreeding, while there was no noticeable improvement from normal crossbreeding. It could be that the rapid improvement of upgraded animals brings out hybrid vigor which causes them to produce more milk. Breeding more rapidly could have allowed exotic genes to enter more quickly and raise performance. In contrast, moderate or normal level crossbreeding may not have made much difference or lacked the strong genes needed to boost production. Impact of Blood Percentage on Diseases Whereas prone to certain diseases such as FMD, Mastitis, HS, RT, Dystocia were not significantly associated with blood percentage alone (p = 0.614) (Pearson Chi-Square χ² = 3.563) (Likelihood Ratio χ² = 3.438) (Table 1). Despite that, it was surprising to find that optimal exotic blood levels (75–93.75) balance productivity and genetic diversity, while pure local or low-exotic crosses prioritize resilience over yield. Inherited illnesses are a natural part of life, and selective breeding has been shown to increase resistance to diseases (Nicholas, 2005 ). In crossbred cattle, the reported incidence of reproductive tract (RT) disorders such as abortion varies widely between 0.4% and 10.6% (Paisley et al., 1978 ; López et al., 2002 ; Gröhn et al., 1990 ; Markusfeld-Nir, 1997 ). The estimated incidence of dystocia was 0.27% (Khair et al., 2013 ), with 0.81% for anoestrus, 0.34% for metritis, and 5.5–33.3% for repeat breeding (Kumaresan et al., 2009 ). Mastitis was reported at 2.12% (Islam et al., 2010 ). These findings indicate that crossbreeding percentage may influence disease incidence directly or indirectly. Our study investigated this relationship in greater depth to identify actual patterns between disease incidence and blood percentage. Observable differences in disease incidence across different exotic blood levels in both F1 and F2 generations (Tables 3 and 4 ). These differences were not statistically significant indicating that blood percentage may not only be a determining factor for FMD, Mastitis, HS, RT, Dystocia alone. Rather environmental factor, management practice and individual animal susceptibility also play significant role in susceptibility of these diseases. Similar conclusions were drawn by Manikkam et al. ( 2012 ) and Ihsanullah et al. (2020), who reported that environmental factors also significantly affect disease occurrence along with others. Table 3 Disease-Free Survival (%) vs. Disease Incidence (%) by Blood Percentage (F1 Generation) Blood % FMD-Free FMD-Diseased HS-Free HS-Diseased Mastitis-Free Mastitis-Diseased RT-Free RT-Diseased Dystocia-Free Dystocia-Diseased 50 48.1% 50.3% 49.8% 44.2% 50.9% 47.1% 48.8% 50.0% 50.2% 44.7% 62.5 8.7% 7.7% 8.3% 8.7% 7.2% 9.5% 8.4% 7.8% 8.0% 9.7% 75 30.1% 27.2% 28.8% 30.4% 28.5% 29.5% 28.9% 31.3% 27.6% 33.5% 87.5 0.4% - 0.3% - 0.5% - 0.1 1.6% 0.3% - 93.75 1.5% 2.3% 1.6% 2.9% 1.7% 1.8% 1.7% 3.1% 2.0% 1.0% 100% L 11.3% 12.4% 11.3% 13.8% 11.2% 12.2% 12.1% 6.3% 11.9% 11.2% Table 4 Disease-Free Survival (%) vs. Disease Incidence (%) by Blood Percentage (F2 Generation) Blood % FMD-Free FMD-Diseased HS-Free HS-Diseased Mastitis-Free Mastitis-Diseased RT-Free RT-Diseased Dystocia-Free Dystocia-Diseased 50 1.4% 1.8% 1.5% 1.6% 2.2% 1.2% 1.8% 0.5% 1.6% 1.5% 62.5 8.3% 10.2% 9.4% 6.4% 7.5% 9.7% 9.6% 7.1% 9.9% 7.0% 75 20.9% 19.8% 20.8% 19.2% 19.0% 21.2% 21.0% 19.2% 20.8% 19.9% 87.5 10.5% 11.7% 10.7% 12.0% 10.8% 10.9% 10.9% 10.6% 9.7% 13.3% 93.75 7.6% 6.4% 6.9% 8.8% 10.1% 5.9% 6.6% 9.1% 6.6% 8.5% 100% L 40.4% 41.7% 40.0% 45.6% 38.8% 41.8% 40.2% 42.9% 41.5% 39.5% PURE USA 10.8% 8.5% 10.7% 6.4% 11.6% 9.3% 9.9% 10.6% 9.9% 10.3% However, crossbred animals’ requirements are most often difficult for smallholder farmers to meet, which results in high mortality and financial loss (Chagunda, 2002 ; Philipsson et al., 2006 ). When genotypes do not match their environment, crossbreeding programs often take a one-size-fits-all approach, ignoring local environmental and production conditions and leading to subpar performance (Galukande et al., 2013 ). Additionally, poor health management makes cattle more susceptible to disease, which has a detrimental effect on milk production and general health (Getu, Biru, & Arbse, 2015 ). These elements demonstrate that genetic potential alone cannot ensure better health or productivity. Conclusion This study is significant in providing evidence for the intricate relationship between the percentage of exotic blood in cows and milk production and susceptibility to disease under Bangladesh's agro-climatic and management regime. It also reveals that sire blood percentage and milk production in the F1 and F2 groups have a strong connection. The research points out that effective milk production depends on balanced genetic introgression with sufficient adaptability. Even with the apparent ideal combination of productivity and toughness in F1 crossbreds, the F2 generations, although sometimes carrying enhanced lactation traits at a given moment in individual instances, are average in losing adaptability and health excellence in being exposed to field conditions. This thus brings out the necessity for proper control of breeding operations in order to achieve optimal productivity in conjunction with herd health. The results show there is no direct connection between blood fraction and disease incidence, meaning that genetics alone does not determine the vulnerability to disease. Long-term crossbreeding strategies would probably be the most realistic as well as economically viable means of optimizing Bangladesh dairy productivity without compromising the health or vitality of animals at the farm level. In general, the study shows that combining different breeds can help raise productivity, but effective management is necessary to keep the herd in good shape and performing well generation after generation. References Abera M (2016) Reproductive and productive performances of crossbred and indigenous dairy cattle under rural, peri-urban and urban dairy farming systems in West Shoa Zone, Oromia, Ethiopia. Oromia, Ethiopia Alam GM, Hoque KE, Khalifa MTB, Siraj SB, Ghani MFBA (2009) The role of agriculture education and training on agriculture economics and national development of Bangladesh. Afr J Agric Res 4(12):1334–1350 Al-Amin M, Nahar A (2007) Productive and reproductive performance of non-descript (local) and crossbred dairy cows in coastal area of Bangladesh. Asian J Anim Veterinary Adv 2:46–49 Bhuiyan A (2008) Research on characterization, conservation and improvement of red Chittagong cattle of Bangladesh (Final Technical Report). USDA Funded Project Biswas R (2018) Study on production performance, feeding management and diseases occurrence of cross-bred cows under rural condition at Shalikha upazila of Magura. Chattogram Veterinary & Animal Sciences University, Khulshi, Chattogram Chagunda MGG (2002) The importance of a national breeding policy – case for the Malawian dairy industry. In: Ojango JM, Malmfors B, Okeyo AM (eds) Animal genetics training resource, Version 2, 2006. International Livestock Research Institute, Nairobi, Kenya and Swedish University of Agricultural Sciences, Uppsala, Sweden Galukande E, Mulindwa H, Wurzinger M, Roschinsky R, Mwai AO, Sölkner J (2013) Crossbreeding cattle for milk production in the tropics: Achievements, challenges and opportunities. Anim Genetic Resour 52:111–125. https://doi.org/10.1017/S2078633612000471 Getu A, Biru G, Arbse D (2015) Cross breeding effect on the performance of indigenous cattle: Challenges and opportunities. J Agricultural Sci Food Technol 1(2):16–21 Gröhn YT, Erb HN, McCulloch CE, Saloniemi HS (1990) Epidemiology of reproductive disorders in dairy cattle: Associations among host characteristics, disease and production. Prev Vet Med 8(1):25–39. https://doi.org/10.1016/0167-5877(90)90020-I Haque MN, Haque MR, Parvin A, Hussain MM (2011) Productive and reproductive performance of different crossbred cattle at Sylhet Government Dairy Farm. Progressive Agric 22(12):47–54 Ihsanullah, Qureshi MS, Akhtar S, Suhail SM (2020) Seasonal stress affects reproductive and lactation traits in dairy cattle with various levels of exotic blood and parities under subtropical condition. Pakistan J Zool 52(1):147–155. https://doi.org/10.17582/journal.pjz/2020.52.1.147.155 Islam MA, Rahman AKMA, Rony SA, Islam MS (2010) Prevalence and risk factors of mastitis in lactating dairy cows at Baghabari milk shed area of Sirajganj. Bangladesh J Veterinary Med 8(2):157–162. https://doi.org/10.3329/bjvm.v8i2.11200 Khair A, Alam MM, Rahman AKMA, Islam MT, Azim A, Chowdhury EH (2013) Incidence of reproductive and production diseases of cross-bred dairy cattle in Bangladesh. Bangladesh J Veterinary Med 11(1):31–36. https://doi.org/10.3329/bjvm.v11i1.17730 Kumaresan A, Prabhakaran PP, Bujarbaruah KM, Pathak KA, Chhetri B, Ahmed SK (2009) Reproductive performance of crossbred dairy cows reared under traditional low input production system in the eastern Himalayas. Trop Anim Health Prod 41(1):71–78. https://doi.org/10.1007/s11250-008-9155-0 Leroy G, Baumung R, Boettcher P, Scherf B, Hoffmann I, Review (2016) Sustainability of crossbreeding in developing countries; definitely not like crossing a meadow… animal . ;10(2):262–273. 10.1017/S175173111500213X López NJ, Smith PC, Gutierrez J (2002) Higher risk of preterm birth and low birth weight in women with periodontal disease. J Dent Res 81(1):58–63. https://doi.org/10.1177/002203450208100113 Manikkam M, Guerrero-Bosagna C, Tracey R, Haque MM, Skinner MK (2012) Transgenerational actions of environmental compounds on reproductive disease and identification of epigenetic biomarkers of ancestral exposures. PLoS ONE 7(2):e31901. https://doi.org/10.1371/journal.pone.0031901 Markusfeld-Nir O (1997) Epidemiology of bovine abortions in Israeli dairy herds. Prev Vet Med 31(3–4):245–255. https://doi.org/10.1016/s0167-5877(96)01142-7 Mekonnen T, Tadesse Y, Meseret S (2020) Genetic improvement strategy of indigenous cattle breeds: Effect of cattle crossbreeding program in production performances. J Appl Life Sci Int 23(1):23–40 Naskar S, Gowane GR, Chopra A, Paswan C, Prince LLL (2012) Genetic adaptability of livestock to environmental stresses. Environ stress amelioration Livest Prod, 317–378 Nicholas FW (2005) Animal breeding and disease. Philosophical Trans Royal Soc B: Biol Sci 360(1459):1529–1536. https://doi.org/10.1098/rstb.2005.1674 Oltenacu PA, Broom DM (2010) The impact of genetic selection for increased milk yield on the welfare of dairy cows. Anim Welf 19(S1):39–49 Paisley LG, Mickelsen WD, Frost OL (1978) A survey of the incidence of prenatal mortality in cattle following pregnancy diagnosis by rectal palpation. Theriogenology 9(6):481–491. https://doi.org/10.1016/0093-691x(78)90113-9 Philipsson J, Rege JEO, Okeyo AM (2006) Sustainable breeding programs for tropical farming systemsin. In: Ojango JM, Malmfors B, Okeyo AM (eds) Animal genetics training resource, Version 2, 2006. International Livestock Research Institute, Nairobi, Kenya and Swedish University of Agricultural Sciences, Uppsala, Sweden Sarder MJU, Rahman MM, Ahmed S, Sultana MR, Alam MM, Rashid MM (2007) Consequence of dam genotypes on productive and reproductive performance of dairy cows under the rural condition in Bangladesh. Pak J Biol Sci 10(19):3341–3349 Siddiquee NU, Wadud MA, Bhuiyan MSA, Rahman AKMA, Amin MR, Bhuiyan AK F. H (2014) Suitability of temperate and tropical crossbred dairy cattle under peri-urban production system in Bangladesh. Anim Rev 1(2):26–36 Additional Declarations The authors declare no competing interests. Supplementary Files Dataanalysisresults.docx Gross result from spss Maindata.xlsx This file contains the gross data. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6791952","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":464607143,"identity":"f4b256c6-fda7-4f49-b28e-a3e88ec6e11d","order_by":0,"name":"Md. 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Mahbubur Rahman","email":"","orcid":"","institution":"St. Francis College, New York, USA","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Mahbubur","lastName":"Rahman","suffix":""},{"id":464610486,"identity":"c0282186-ced8-4725-83fa-43210e1f24f4","order_by":3,"name":"Farida Yeasmin Bari","email":"","orcid":"https://orcid.org/0000-0003-1358-460X","institution":"Department of Surgery and Obstetrics, Bangladesh Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Farida","middleName":"Yeasmin","lastName":"Bari","suffix":""},{"id":464610487,"identity":"912f3e45-f53e-4e47-90a4-2446c0a7f8ef","order_by":4,"name":"Moutushi Dhore","email":"","orcid":"https://orcid.org/0009-0002-7503-1429","institution":"Department of Surgery and Obstetrics, Bangladesh Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Moutushi","middleName":"","lastName":"Dhore","suffix":""},{"id":464610488,"identity":"cbb624ab-99f1-4fa4-a38d-91ea36d0e6f1","order_by":5,"name":"Mohammad Fayej Mahmud Anik","email":"","orcid":"https://orcid.org/0009-0005-1276-0167","institution":"Hajee Mohammad Danesh Science \u0026 Technology University, Dinajpur, Bangladesh","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Fayej Mahmud","lastName":"Anik","suffix":""},{"id":464610489,"identity":"b205d73b-0c91-41e3-ac00-b2b1205e94ec","order_by":6,"name":"MD.ROISUL MOMEN","email":"","orcid":"https://orcid.org/0009-0005-2139-9705","institution":"Hajee Mohammad Danesh Science \u0026 Technology University, Dinajpur, Bangladesh","correspondingAuthor":false,"prefix":"","firstName":"MD.ROISUL","middleName":"","lastName":"MOMEN","suffix":""},{"id":464610490,"identity":"d9707776-9eb9-40ad-8e93-013abae37fad","order_by":7,"name":"Radwan Islam","email":"","orcid":"https://orcid.org/0009-0005-3283-5090","institution":"Bangladesh Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Radwan","middleName":"","lastName":"Islam","suffix":""},{"id":464610491,"identity":"cc325d35-d432-46b4-a804-9a4cdf16dd04","order_by":8,"name":"Md. Dipu Sultan","email":"","orcid":"https://orcid.org/0009-0009-3372-4797","institution":"Patuakhali Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Dipu","lastName":"Sultan","suffix":""},{"id":464610492,"identity":"7b5604a9-e085-4bc7-aafa-e2db6ee39079","order_by":9,"name":"Mst. Mamuna Sharmin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIiWNgGAWjYDACCRBhAETMjA0HGBsYGPgZGNiI1MLOfBCsRbKBKC0gXfxsyWAtBgcIaJGf3fzs042CO/LmzDwGBxh32CVuvpH87MGHCgZ5frEDWLUY3DlmPDvH4JnhzmaQljPJidtupJkbzjjDYDhzdgJ2LRIJxsw5BocZNxwGaWljBmpJMJPmbWNIMLiNXYv8jPTPIC32UC31iZtnpH/Dq4XhRg7YlsQNh9kSgFqADIkc/LYY3MgpBmlJ3nCY+QDQL8eNZ5x5UyY544wETr8AHbaZOefPYdsN5w82f2DcUS3b356+TeJDhY08vzQOhyED5j8gUgCsUgKvSjTAf4AU1aNgFIyCUTACAAB/ymUKkhVqcgAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Dairy \u0026 Poultry Science, Hajee Mohammad Danesh Science \u0026 Technology University, Dinajpur, Bangladesh","correspondingAuthor":true,"prefix":"","firstName":"Mst.","middleName":"Mamuna","lastName":"Sharmin","suffix":""}],"badges":[],"createdAt":"2025-05-31 16:06:44","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-6791952/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6791952/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83804985,"identity":"52e124bb-5e8c-4802-adc2-31c7a1259749","added_by":"auto","created_at":"2025-06-03 04:38:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":87970,"visible":true,"origin":"","legend":"\u003cp\u003eMean per lactation yield(F1) vs sire blood %\u003c/p\u003e\n\u003cp\u003eMean per lactation yield(F2) vs sire blood %\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6791952/v1/c7e3ac3a92d5d3de108a29f0.png"},{"id":83804717,"identity":"a2340e38-5de0-423a-85f1-3761fffdd754","added_by":"auto","created_at":"2025-06-03 04:30:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":33816,"visible":true,"origin":"","legend":"\u003cp\u003eFig 3: Breed Upgradation Speed Vs Mean per Lactation Yield (F1 and F2 generation)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6791952/v1/3a3384f23519c71a9f25f28e.png"},{"id":83805354,"identity":"eb0b34f3-9c9a-45f8-a339-b3627bae05f6","added_by":"auto","created_at":"2025-06-03 04:46:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":867320,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6791952/v1/20d1f9dc-114d-4945-8839-4f0df65e8bd0.pdf"},{"id":83804716,"identity":"745d595c-c689-485e-83b7-9143ff24cd14","added_by":"auto","created_at":"2025-06-03 04:30:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":62772,"visible":true,"origin":"","legend":"\u003cp\u003eGross result from spss\u003c/p\u003e","description":"","filename":"Dataanalysisresults.docx","url":"https://assets-eu.researchsquare.com/files/rs-6791952/v1/1f5b5df631256e6111177fef.docx"},{"id":83804723,"identity":"4c7ad87d-8d49-48e3-bdd0-305070d2fdca","added_by":"auto","created_at":"2025-06-03 04:30:20","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":244420,"visible":true,"origin":"","legend":"\u003cp\u003eThis file contains the gross data.\u003c/p\u003e","description":"","filename":"Maindata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6791952/v1/ae90400808fae7a6e1a262ba.xlsx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eInvestigation of the relationship between exotic blood percentage in cattle and its influence on both milk production and susceptibility to common livestock diseases across F1 and F2 generations in Bangladesh\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLivestock, particularly cattle for milk, are the keystone species of the agriculture-dependent economy of Bangladesh and make significant contributions to rural income, food security, and national GDP (Alam et al \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Over the past decades, high demand for milk and dairy products has led to the implementation of genetic improvement schemes to enhance productivity levels among the local cow populations (Oltenacu, P. A., \u0026amp; Broom;2010). One of the most fascinating approaches applied in such programmes is cross-breeding the local cows with the imported cows such as Holstein Friesian and Jersey in order to produce more milk and general genetic superiority (Mekonnen et al;2020). But although there is widely documented productivity benefit from such crossbreeding, there is a similar great requirement to consider the broader implications of exotic blood introgression, i.e., for animal health and environmental adaptability (Naskar et al;2012).\u003c/p\u003e \u003cp\u003eThis study investigates the complex correlation between cattle ratios of exotic origin and their correlation with milk production and susceptibility to general livestock disease like Foot-and-Mouth Disease (FMD), Mastitis, Hemorrhagic Septicemia (HS), Reproductive Tract (RT) disease, and Dystocia. F1 and F2 generations of cross-bred cows under routine farm management systems of Bangladesh, where shortage of resources, disease stress, and environmental pathogens have a crucial effect on livestock performance, are stressed(Biswas;2018).\u003c/p\u003e \u003cp\u003eThe basis for conducting this study is that while higher percentages of exotics in genetics might pose a challenge to milk production, at the same time they might compromise disease resistance and adaptability to local climatic and management factors (Leroy G et al;2016). Higher proportion of local blood-composited animals, however, were found to be more resistant but less productive (Abera;2016). It is therefore important to bring effective equilibrium between productivity and flexibility to sustain crossbreeding schemes in Bangladesh. It examines the effect of breed upgradation rate to such performance. Based on the analysis of F1 and F2, this research will attempt to identify the most optimal gene combinations giving the highest amount of milk production with disease resistance and environmental tolerance. Lastly, the outcome of this research should direct breeding programs, policy, and decision-making at the farm level towards the optimization of dairy farm productivity and sustainability in Bangladesh.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eCollection of data\u003c/p\u003e \u003cp\u003eThe present study data were collected from a total of 27 districts of Bangladesh by 5 veterinary graduates. The information on the milk production performance and the 5 most common disease cases (HS, FMD, Dystocia, Mastitis, and Reproductive Tract Diseases) of 1694 cows (F1 and F2 generation) of Friesian crossbred with different blood levels was collected from the marginal level farmers. The veterinarians gave the identification number of each cow for further information. Breed and genetic groups of sire considered were 50% local − 50% Friesian, 37.5% local − 62.50% Friesian, 25% Local − 75% Friesian, 12.50% local − 87.50% Friesian, 6.25% Local − 93.75% Friesian, 100% Local Friesian and Pure Friesian from the USA.\u003c/p\u003e \u003cp\u003eAnimal management\u003c/p\u003e \u003cp\u003eThis study particularly focused on the production level of Friesian crossbreed cattle at a marginal scale. Thus, there was an insufficiently balanced ration to feed the cows due to the impoverishment of the root-level farmers. However, a combination of concentrate feed of rice polish, wheat husk, mustard oil cake, molasses, khesari, salt, and vitamin-mineral premix was often supplied. Straw and green grasses (Napier/German/Para/Jumbo) are supplied as roughages. Normally, two times a day milking is performed. Commercially available sire semen (ACI/Brac/ADL) was used to breed the animals.\u003c/p\u003e \u003cp\u003eAnalysis of data\u003c/p\u003e \u003cp\u003eCollected data were processed, tabulated, analyzed and visualized through Microsoft Excel, IBM SPSS Statistics (Version 22), and R software. Breeding upgradation speed was fixed by calculating the difference in Holstein Friesian (HF) blood percentage between the sires used to produce the F1 and F2 generations. If the difference was \u0026lt; 13%, it was considered Normal; if ≥ 13% and \u0026lt; 26%, it was classified as Moderate; if ≥ 26% and \u0026lt; 38%, it was classified as Fast; and if ≥ 38%, it was considered Very Fast. Disease occurrences in both F1 and F2 generations were cross-tabulated with the sires’ blood percentages to find the comparative proportion between disease incidence and disease free survival.\u003c/p\u003e \n\n \n\n \n\n "},{"header":"Results and Discussion","content":"\u003cp\u003eData were cleaned and organized through statistical test like ANOVA and Chi-square, classifying continuous variables (such as blood percentage levels), and computing summary statistics (means, standard errors) to find out the influence of blood percentage on disease prone and lactation yield. Kruskal-Wallis and other non-parametric tests were used, indicating checks for possible transformations and normality. The results of ANOVA and cross-tabulations show aggregated lactation metrics and encoded categorical variables, ensuring data integrity and compatibility with analytical techniques. The result indicates significant effect of blood percentage on lactation milk yield in sire of F1 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (One-Way ANOVA: F (12,834)\u0026thinsp;=\u0026thinsp;112.87\u003cem\u003eF\u003c/em\u003e (12,834)\u0026thinsp;=\u0026thinsp;112.87; df\u0026thinsp;=\u0026thinsp;12,8,34) (Kruskal-Wallis-H Test: \u0026chi;2(11)\u0026thinsp;=\u0026thinsp;371.82\u003cem\u003e\u0026chi;\u003c/em\u003e2(11)\u0026thinsp;=\u0026thinsp;371.82; df\u0026thinsp;=\u0026thinsp;11) and F2 generations(p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (One-Way ANOVA: F (20,826)\u0026thinsp;=\u0026thinsp;38.42\u003cem\u003eF\u003c/em\u003e (20,826)\u0026thinsp;=\u0026thinsp;38.42:df\u0026thinsp;=\u0026thinsp;20,8,26) (Kruskal-WallisHTest: \u0026chi;2(12)\u0026thinsp;=\u0026thinsp;381.28\u003cem\u003e\u0026chi;\u003c/em\u003e2(12)\u0026thinsp;=\u0026thinsp;381.28; df\u0026thinsp;=\u0026thinsp;12).\u003c/p\u003e\n\u003cp\u003eTable:1 statistical Analysis\u003c/p\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTest\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStatistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSignificance\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMilk Yield (F1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOne-Way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF(12,834)\u0026thinsp;=\u0026thinsp;112.87\u003cem\u003eF\u003c/em\u003e(12,834)\u0026thinsp;=\u0026thinsp;112.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12, 834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHighly Significant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKruskal-Wallis H Test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;2(11)\u0026thinsp;=\u0026thinsp;371.82\u003cem\u003e\u0026chi;\u003c/em\u003e2(11)\u0026thinsp;=\u0026thinsp;371.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHighly Significant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMilk Yield (F2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOne-Way ANOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF(20,826)\u0026thinsp;=\u0026thinsp;38.42\u003cem\u003eF\u003c/em\u003e(20,826)\u0026thinsp;=\u0026thinsp;38.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20, 826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHighly Significant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKruskal-Wallis H Test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;2(12)\u0026thinsp;=\u0026thinsp;381.28\u003cem\u003e\u0026chi;\u003c/em\u003e2(12)\u0026thinsp;=\u0026thinsp;381.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHighly Significant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease Association\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePearson Chi- Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;2\u0026thinsp;=\u0026thinsp;3.563\u003cem\u003e\u0026chi;\u003c/em\u003e2\u0026thinsp;=\u0026thinsp;3.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Significant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003eImpact of Blood Percentage on Milk Production\u003c/h3\u003e\n\u003cp\u003eThe study found a significant relationship between blood percentage of sire and total lactation yield in both F1 and F2 generation (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In both F1 and F2 generation maximum lactation yield was found in 93.75% exotic blood were 6326.667 L and 4791.54 L respectively. In F1 generation cattle with 93.75% exotic blood found highest milk yield (6,326.67 L) and 50%, 62.5%, 75%, 87.5% and 100% Local produced 3823.171L, 6146.310L, 3,854.27L, 3750.000L, 4681.818L milk respectively. Our findings align with Siddiquee et al. (\u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e), who reported higher milk yield in 62.5% HF crossbreds compared to 75%. According to Al-Amin and Nahar (2013), the maximum yearly lactation recorded in 50% HF was 1837\u0026thinsp;\u0026plusmn;\u0026thinsp;18 kg. For 50% HF, 62.5%, and 75% HF crossbreds, the 180-day milk yield (180DMY) was 1500.33\u0026thinsp;\u0026plusmn;\u0026thinsp;76.46 kg, 1549.79\u0026thinsp;\u0026plusmn;\u0026thinsp;73.86 kg, and 1339.00\u0026thinsp;\u0026plusmn;\u0026thinsp;75.83 kg, respectively. In contrast, the present study, conducted roughly five years later, recorded nearly double the yearly lactation yield in 50% HF crossbreds. This considerable increase may be attributed not only to advancements in management and nutrition but also to improved adaptability of 50% HF crossbreds over time. Also, higher milk yields in the current study might be explained by the fact that crossbreds have adapted well to local conditions and now have better traits that help them produce more milk.\u003c/p\u003e\n\u003cp\u003eFurthermore, whereas milk yield was found to decrease in cattle with low exotic blood percentage with 87.5 group produced only 3,750 L, 100% Local breed showed moderate performance (4,681.82 L) which indicates retained adaptability but lower genetic potential. Similar trend was found in case of F2 with 93.75% exotic blood remaining superior (4,791.54 L) while Pure USA sire (3,008.52L) and 100% local breed lagged. Moreover, 50% exotic group in F2 generation showed better productivity performance than mid-range groups (4,147.23L vs 0.625,2.267.25 L) which highlight hybrid vigor effect of variability. In both generations with increase of local breed content, the overall lactation yield decreased (F1 total\u0026thinsp;=\u0026thinsp;4,171.471; F2 total\u0026thinsp;=\u0026thinsp;2,655.04 L). The decrease of lactation yield with increase of local blood percentage highlights the role of exotic genetic introgression in maximizing milk production. Also, this underscores the need for estimating important breed traits, this relationship points out the need to use the best records for age at puberty, age at first calving, postpartum heat period, service per conception, calving intervals, lactation length, lactation yield, and milk yield (Sarder et al., \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). However, these findings emphasize that optimal exotic blood levels (75-93.75) balance productivity and genetic diversity, while pure local or low exotic crosses prioritize resilience over yield.\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMean Milk Yield across different Blood Percentage\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBlood Percentage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF1 Mean Yield (L)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF2 Mean Yield (L)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,823.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,147.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e62.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,146.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,267.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e75\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,854.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,321.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e87.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,750.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,353.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e93.75\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6,326.67\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e4,791.54\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e100% LOCAL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,681.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,468.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePURE USA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,008.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,171.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,655.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ch3\u003eBreed Upgradation Speed and Milk Yield\u003c/h3\u003e\n\u003cp\u003eIn both F1 and F2 generation Fast and very Fast breed upgradation speed showed higher mean per lactation (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), whereas between normal and moderate breed upgradation speed, better performance was found in cattle with normal breed upgraded and comparatively poorer milk yield in moderate group. But it was notable that breeds that are upgraded very fast showed more prominent difference of mean per lactation between F1 and F2 generations than other groups. Most interestingly, cattle with moderate and normal breed upgradation speed showed approximately consistent mean per lactation yield in both F1 and F2 generations.\u003c/p\u003e\n\u003cp\u003eRapid crossbreeding can cause animals to lose vital local characteristics, such as resistance to disease and heat tolerance, which reduces their suitability for the local environment. It may boost milk production, but it also necessitates improved management, feed, and healthcare, all of which are frequently out of smallholder farmers\u0026apos; price range. As a result, the anticipated advantages might not always come about. According to (Sarder et al., \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e), accurate estimation of productive and reproductive traits in dairy cattle\u0026mdash;such as age at puberty, age at first calving, postpartum heat period, service per conception, calving interval, lactation length, and milk yield\u0026mdash;requires the best performance records of each breed. Moreover, maintaining the right genotype in the right environment is essential for the full expression of genetic potential (Haque et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, according to our research, productivity can be increased through rapid crossbreeding, while there was no noticeable improvement from normal crossbreeding. It could be that the rapid improvement of upgraded animals brings out hybrid vigor which causes them to produce more milk. Breeding more rapidly could have allowed exotic genes to enter more quickly and raise performance. In contrast, moderate or normal level crossbreeding may not have made much difference or lacked the strong genes needed to boost production.\u003c/p\u003e\n\u003ch3\u003eImpact of Blood Percentage on Diseases\u003c/h3\u003e\n\u003cp\u003eWhereas prone to certain diseases such as FMD, Mastitis, HS, RT, Dystocia were not significantly associated with blood percentage alone (p\u0026thinsp;=\u0026thinsp;0.614) (Pearson Chi-Square \u0026chi;\u0026sup2; = 3.563) (Likelihood Ratio \u0026chi;\u0026sup2; = 3.438) (Table\u0026nbsp;1). Despite that, it was surprising to find that optimal exotic blood levels (75\u0026ndash;93.75) balance productivity and genetic diversity, while pure local or low-exotic crosses prioritize resilience over yield.\u003c/p\u003e\n\u003cp\u003eInherited illnesses are a natural part of life, and selective breeding has been shown to increase resistance to diseases (Nicholas, \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e). In crossbred cattle, the reported incidence of reproductive tract (RT) disorders such as abortion varies widely between 0.4% and 10.6% (Paisley et al., \u003cspan class=\"CitationRef\"\u003e1978\u003c/span\u003e; L\u0026oacute;pez et al., \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e; Gr\u0026ouml;hn et al., \u003cspan class=\"CitationRef\"\u003e1990\u003c/span\u003e; Markusfeld-Nir, \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e). The estimated incidence of dystocia was 0.27% (Khair et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e), with 0.81% for anoestrus, 0.34% for metritis, and 5.5\u0026ndash;33.3% for repeat breeding (Kumaresan et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). Mastitis was reported at 2.12% (Islam et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). These findings indicate that crossbreeding percentage may influence disease incidence directly or indirectly. Our study investigated this relationship in greater depth to identify actual patterns between disease incidence and blood percentage. Observable differences in disease incidence across different exotic blood levels in both F1 and F2 generations (Tables \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). These differences were not statistically significant indicating that blood percentage may not only be a determining factor for FMD, Mastitis, HS, RT, Dystocia alone. Rather environmental factor, management practice and individual animal susceptibility also play significant role in susceptibility of these diseases. Similar conclusions were drawn by Manikkam et al. (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) and Ihsanullah et al. (2020), who reported that environmental factors also significantly affect disease occurrence along with others.\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDisease-Free Survival (%) vs. Disease Incidence (%) by Blood Percentage (F1 Generation)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBlood %\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFMD-Free\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFMD-Diseased\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHS-Free\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHS-Diseased\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMastitis-Free\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMastitis-Diseased\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRT-Free\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRT-Diseased\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDystocia-Free\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDystocia-Diseased\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100% L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDisease-Free Survival (%) vs. Disease Incidence (%) by Blood Percentage (F2 Generation)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBlood %\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFMD-Free\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFMD-Diseased\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHS-Free\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHS-Diseased\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMastitis-Free\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMastitis-Diseased\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRT-Free\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRT-Diseased\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDystocia-Free\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDystocia-Diseased\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100% L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePURE USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eHowever, crossbred animals\u0026rsquo; requirements are most often difficult for smallholder farmers to meet, which results in high mortality and financial loss (Chagunda, \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e; Philipsson et al., \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). When genotypes do not match their environment, crossbreeding programs often take a one-size-fits-all approach, ignoring local environmental and production conditions and leading to subpar performance (Galukande et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). Additionally, poor health management makes cattle more susceptible to disease, which has a detrimental effect on milk production and general health (Getu, Biru, \u0026amp; Arbse, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). These elements demonstrate that genetic potential alone cannot ensure better health or productivity.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study is significant in providing evidence for the intricate relationship between the percentage of exotic blood in cows and milk production and susceptibility to disease under Bangladesh's agro-climatic and management regime. It also reveals that sire blood percentage and milk production in the F1 and F2 groups have a strong connection. The research points out that effective milk production depends on balanced genetic introgression with sufficient adaptability. Even with the apparent ideal combination of productivity and toughness in F1 crossbreds, the F2 generations, although sometimes carrying enhanced lactation traits at a given moment in individual instances, are average in losing adaptability and health excellence in being exposed to field conditions. This thus brings out the necessity for proper control of breeding operations in order to achieve optimal productivity in conjunction with herd health. The results show there is no direct connection between blood fraction and disease incidence, meaning that genetics alone does not determine the vulnerability to disease. Long-term crossbreeding strategies would probably be the most realistic as well as economically viable means of optimizing Bangladesh dairy productivity without compromising the health or vitality of animals at the farm level. In general, the study shows that combining different breeds can help raise productivity, but effective management is necessary to keep the herd in good shape and performing well generation after generation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbera M (2016) Reproductive and productive performances of crossbred and indigenous dairy cattle under rural, peri-urban and urban dairy farming systems in West Shoa Zone, Oromia, Ethiopia. \u003cem\u003eOromia, Ethiopia\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlam GM, Hoque KE, Khalifa MTB, Siraj SB, Ghani MFBA (2009) The role of agriculture education and training on agriculture economics and national development of Bangladesh. Afr J Agric Res 4(12):1334\u0026ndash;1350\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Amin M, Nahar A (2007) Productive and reproductive performance of non-descript (local) and crossbred dairy cows in coastal area of Bangladesh. Asian J Anim Veterinary Adv 2:46\u0026ndash;49\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhuiyan A (2008) \u003cem\u003eResearch on characterization, conservation and improvement of red Chittagong cattle of Bangladesh\u003c/em\u003e (Final Technical Report). USDA Funded Project\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBiswas R (2018) Study on production performance, feeding management and diseases occurrence of cross-bred cows under rural condition at Shalikha upazila of Magura. Chattogram Veterinary \u0026amp; Animal Sciences University, Khulshi, Chattogram\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChagunda MGG (2002) The importance of a national breeding policy \u0026ndash; case for the Malawian dairy industry. In: Ojango JM, Malmfors B, Okeyo AM (eds) Animal genetics training resource, Version 2, 2006. International Livestock Research Institute, Nairobi, Kenya and Swedish University of Agricultural Sciences, Uppsala, Sweden\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalukande E, Mulindwa H, Wurzinger M, Roschinsky R, Mwai AO, S\u0026ouml;lkner J (2013) Crossbreeding cattle for milk production in the tropics: Achievements, challenges and opportunities. Anim Genetic Resour 52:111\u0026ndash;125. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S2078633612000471\u003c/span\u003e\u003cspan address=\"10.1017/S2078633612000471\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGetu A, Biru G, Arbse D (2015) Cross breeding effect on the performance of indigenous cattle: Challenges and opportunities. J Agricultural Sci Food Technol 1(2):16\u0026ndash;21\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGr\u0026ouml;hn YT, Erb HN, McCulloch CE, Saloniemi HS (1990) Epidemiology of reproductive disorders in dairy cattle: Associations among host characteristics, disease and production. Prev Vet Med 8(1):25\u0026ndash;39. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0167-5877(90)90020-I\u003c/span\u003e\u003cspan address=\"10.1016/0167-5877(90)90020-I\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaque MN, Haque MR, Parvin A, Hussain MM (2011) Productive and reproductive performance of different crossbred cattle at Sylhet Government Dairy Farm. Progressive Agric 22(12):47\u0026ndash;54\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIhsanullah, Qureshi MS, Akhtar S, Suhail SM (2020) Seasonal stress affects reproductive and lactation traits in dairy cattle with various levels of exotic blood and parities under subtropical condition. Pakistan J Zool 52(1):147\u0026ndash;155. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17582/journal.pjz/2020.52.1.147.155\u003c/span\u003e\u003cspan address=\"10.17582/journal.pjz/2020.52.1.147.155\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIslam MA, Rahman AKMA, Rony SA, Islam MS (2010) Prevalence and risk factors of mastitis in lactating dairy cows at Baghabari milk shed area of Sirajganj. Bangladesh J Veterinary Med 8(2):157\u0026ndash;162. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3329/bjvm.v8i2.11200\u003c/span\u003e\u003cspan address=\"10.3329/bjvm.v8i2.11200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhair A, Alam MM, Rahman AKMA, Islam MT, Azim A, Chowdhury EH (2013) Incidence of reproductive and production diseases of cross-bred dairy cattle in Bangladesh. Bangladesh J Veterinary Med 11(1):31\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3329/bjvm.v11i1.17730\u003c/span\u003e\u003cspan address=\"10.3329/bjvm.v11i1.17730\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumaresan A, Prabhakaran PP, Bujarbaruah KM, Pathak KA, Chhetri B, Ahmed SK (2009) Reproductive performance of crossbred dairy cows reared under traditional low input production system in the eastern Himalayas. Trop Anim Health Prod 41(1):71\u0026ndash;78. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11250-008-9155-0\u003c/span\u003e\u003cspan address=\"10.1007/s11250-008-9155-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeroy G, Baumung R, Boettcher P, Scherf B, Hoffmann I, Review (2016) Sustainability of crossbreeding in developing countries; definitely not like crossing a meadow\u0026hellip; \u003cem\u003eanimal\u003c/em\u003e. ;10(2):262\u0026ndash;273. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/S175173111500213X\u003c/span\u003e\u003cspan address=\"10.1017/S175173111500213X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL\u0026oacute;pez NJ, Smith PC, Gutierrez J (2002) Higher risk of preterm birth and low birth weight in women with periodontal disease. J Dent Res 81(1):58\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/002203450208100113\u003c/span\u003e\u003cspan address=\"10.1177/002203450208100113\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManikkam M, Guerrero-Bosagna C, Tracey R, Haque MM, Skinner MK (2012) Transgenerational actions of environmental compounds on reproductive disease and identification of epigenetic biomarkers of ancestral exposures. PLoS ONE 7(2):e31901. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0031901\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0031901\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarkusfeld-Nir O (1997) Epidemiology of bovine abortions in Israeli dairy herds. Prev Vet Med 31(3\u0026ndash;4):245\u0026ndash;255. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s0167-5877(96)01142-7\u003c/span\u003e\u003cspan address=\"10.1016/s0167-5877(96)01142-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMekonnen T, Tadesse Y, Meseret S (2020) Genetic improvement strategy of indigenous cattle breeds: Effect of cattle crossbreeding program in production performances. J Appl Life Sci Int 23(1):23\u0026ndash;40\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaskar S, Gowane GR, Chopra A, Paswan C, Prince LLL (2012) Genetic adaptability of livestock to environmental stresses. Environ stress amelioration Livest Prod, 317\u0026ndash;378\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNicholas FW (2005) Animal breeding and disease. Philosophical Trans Royal Soc B: Biol Sci 360(1459):1529\u0026ndash;1536. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1098/rstb.2005.1674\u003c/span\u003e\u003cspan address=\"10.1098/rstb.2005.1674\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOltenacu PA, Broom DM (2010) The impact of genetic selection for increased milk yield on the welfare of dairy cows. Anim Welf 19(S1):39\u0026ndash;49\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaisley LG, Mickelsen WD, Frost OL (1978) A survey of the incidence of prenatal mortality in cattle following pregnancy diagnosis by rectal palpation. Theriogenology 9(6):481\u0026ndash;491. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0093-691x(78)90113-9\u003c/span\u003e\u003cspan address=\"10.1016/0093-691x(78)90113-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhilipsson J, Rege JEO, Okeyo AM (2006) Sustainable breeding programs for tropical farming systemsin. In: Ojango JM, Malmfors B, Okeyo AM (eds) Animal genetics training resource, Version 2, 2006. International Livestock Research Institute, Nairobi, Kenya and Swedish University of Agricultural Sciences, Uppsala, Sweden\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarder MJU, Rahman MM, Ahmed S, Sultana MR, Alam MM, Rashid MM (2007) Consequence of dam genotypes on productive and reproductive performance of dairy cows under the rural condition in Bangladesh. Pak J Biol Sci 10(19):3341\u0026ndash;3349\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiddiquee NU, Wadud MA, Bhuiyan MSA, Rahman AKMA, Amin MR, Bhuiyan AK F. H (2014) Suitability of temperate and tropical crossbred dairy cattle under peri-urban production system in Bangladesh. Anim Rev 1(2):26\u0026ndash;36\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Bangladesh Agricultural University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Crossbreeding, Dairy cattle, Holstein Friesian, Milk yield, Blood percentage, Genetic effect, Disease incidence, Exotic blood level","lastPublishedDoi":"10.21203/rs.3.rs-6791952/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6791952/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCrossbreeding in dairy cattle has recently become of increased interest. However, there is limited knowledge regarding the impact of crossbreeding on both the physical attributes and milk production capacity of crossbred cattle in Bangladesh. Data from 847 crossbred Holstein cattle were used to estimate the genetic effects on average milk yield. Besides, the history of 5 certain disease conditions was also collected. F1 and F2 generations were categorized by the percentage of sire blood they had and then the data were analyzed to find the point at which genetic contribution leads to the most productivity. The mean production and impact of breeding sires\u0026rsquo; blood percentage were measured through statistical tests like ANOVA, Chi-square and Kruskal-Wallis tests. The result showed that in both F1 and F2 generations, maximum lactation yield was found in 0.9375% Holstein Friesian sires\u0026rsquo; blood group (1265.3333\u0026thinsp;\u0026plusmn;\u0026thinsp;15.36436 L and 1597.1803\u0026thinsp;\u0026plusmn;\u0026thinsp;217.02733 L respectively). For the two generations, the results indicate a highly significant effect of blood percentage on overall milk production, though certain diseases were not significantly associated. The results also revealed that as the amount of local breed blood increased, the amount of milk produced consistently decreased indicating the importance of keeping the amount of exotic blood between 75% and 93.75%. In conclusion, productive performance of crossbred cattle relies heavily on strategic breeding programs and the fast crossbreeding had a thoughtful impact on milk production in the study area.\u003c/p\u003e","manuscriptTitle":"Investigation of the relationship between exotic blood percentage in cattle and its influence on both milk production and susceptibility to common livestock diseases across F1 and F2 generations in Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-03 04:30:15","doi":"10.21203/rs.3.rs-6791952/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"76aecc9b-06df-4ba5-8537-13b2d480be6b","owner":[],"postedDate":"June 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49331938,"name":"Animal Science"}],"tags":[],"updatedAt":"2025-06-03T04:30:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-03 04:30:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6791952","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6791952","identity":"rs-6791952","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}