Effects of region, Holstein Friesian inheritance level, parity, body condition score, and age at first service on productive and reproductive performance of Holstein Friesian crossbred dairy cows in Bangladesh

Effects of region, Holstein Friesian inheritance level, parity, body condition score, and age at first service on productive and reproductive performance of Holstein Friesian crossbred dairy cows in Bangladesh | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effects of region, Holstein Friesian inheritance level, parity, body condition score, and age at first service on productive and reproductive performance of Holstein Friesian crossbred dairy cows in Bangladesh Nushrat Nourin Lisa, Md Nahid Hassan Chawdhury, Mohammad Mahbubul, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9124650/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract This study evaluated the associations of region, Holstein Friesian (HF) inheritance level, parity, Body Condition Score (BCS), and Age at First Service (AFS) with the productive and reproductive performance of Holstein Friesian local crossbred (HFLC) cows in Bangladesh. Data were collected from 158 lactating HFLC cows across Gaibandha, Dhaka Metropolitan City, and Mymensingh from January 2021 to January 2022. Productive traits included Average Daily Milk Yield (ADMY) and Lactation Length (LL), while reproductive traits included Service Per Conception (SPC) and Calving Interval (CI). Region significantly influenced CI, ADMY, and LL, with urban farms in Dhaka producing more milk and experiencing longer lactations, while rural farms in Gaibandha had shorter CI. HF inheritance level significantly influenced all productive and reproductive traits. Cows with 50-<62.5% and ≥ 62.5- 75% HF inheritance level showed higher ADMY and longer LL. Parity did not influence CI, ADMY, or LL but significantly influenced SPC, with the lowest SPC observed in first-parity cows. BCS was associated with SPC and LL but not with CI or ADMY. Cows with moderate BCS exhibited lower SPC, while those with higher BCS had longer LL. AFS significantly influenced SPC, CI, and ADMY, emphasizing the importance of first breeding age on reproductive and productive outcomes. Overall, higher HF inheritance levels were linked to increased milk production but reduced reproductive efficiency, whereas moderate HF inheritance levels were associated with better reproductive performance under field conditions in Bangladesh. Body condition score Holstein Friesian Inheritance level Milk production Parity Reproductive performance Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Increasing milk production while maintaining satisfactory reproductive efficiency remains a major challenge in dairy systems, particularly in tropical and subtropical regions. In many developing countries like Bangladesh, dairy development programs heavily rely on crossbreeding native cattle with high-yielding temperate breeds such as Holstein Friesian (HF) to boost milk production and increase farm income. Local cattle are well adapted to the regional climate but generally produce less milk, whereas exotic breeds possess higher genetic potential for milk production (Mamun et al. 2015 ; Islam et al. 2017 ). As a result, crossbreeding has become a common strategy for rapid genetic improvement of dairy cattle in tropical areas. In Bangladesh, the dairy sector plays a crucial role in supporting rural livelihoods and ensuring national food security. However, the country still faces a substantial gap between milk demand and production. Recent estimates indicate that the national milk demand is 16.233 million tons, while domestic production stands at 15.538 million tons (DLS, 2025 ). This highlights the need to boost dairy productivity through genetic improvements and improved management practices (Shahjahan 2017 ). Crossbreeding programs utilizing Holstein Friesian, Jersey, and other Bos taurus breeds have been promoted to increase milk yield and improve the reproductive efficiency of the national herd (Samad 2020 ; Azad et al. 2023 ). Among these, Holstein Friesian crossbred cows are particularly vital in smallholder and commercial dairy systems in Bangladesh (Mamun et al. 2015 ). Although HF crossbreeding has resulted in substantial increases in milk production, the performance of crossbred cows is heavily influenced by the level of exotic inheritance and its interaction with environmental factors (Galukande et al. 2013 ; Tsegaye et al. 2014 ). Several studies have demonstrated that a higher proportion of Holstein Friesian inheritance generally correlates with increased milk yield and longer lactation periods in crossbred dairy cattle (Ngodigha and Etokeren 2009 ; Mamun et al. 2015 ; Azad et al. 2023 ). However, higher levels of exotic inheritance can also reduce adaptability to tropical climates, increase susceptibility to diseases, and negatively impact reproductive performance when animals are managed under resource-limited conditions (Tsegaye et al. 2014 ; Adhikary et al. 2020 ). Therefore, several studies recommend maintaining moderate levels of HF inheritance level, typically between 50% and 75%, to achieve a balance between productive and reproductive efficiency in tropical production environments (Hossain et al. 2002 ; Galukande et al. 2013 ; Azad et al. 2023 ). Reproductive performance is crucial for dairy herd profitability because traits such as age at first calving (AFC), SPC, and CI directly influence lifetime productivity and replacement costs. Although crossbreeding has enhanced milk production potential, many studies indicate that HF crossbred cows frequently experience reproductive issues, longer calving intervals, and lower conception rates under poor management conditions (Kabir and Kisku 2013 ; Ihsanullah et al. 2020 ). Environmental stress, heat load, inadequate nutrition, and disease pressure further worsen these reproductive challenges in tropical dairy systems (Azad et al. 2023 ; Habib et al. 2024 ). These findings highlight the need to understand genotype-environment interactions to optimize both production and reproduction in crossbred dairy cattle. Besides genetic factors, several non-genetic elements significantly affect dairy cow productivity and fertility. Among these, parity and BCS are widely recognized as key influences on both milk production and reproductive efficiency (Berry et al. 2003; Islam and Kundu 2011; Roche et al. 2015 ; Qureshi et al. 2020). Parity reflects the animal's physiological maturity and the cumulative metabolic demands across successive lactations, which can impact milk yield and reproductive success (Islam and Kundu 2011; Qureshi et al. 2020). Evidence indicates that milk production and reproductive performance often peak during middle parities, typically between the second and fourth lactations, before gradually declining (Haque et al. 2013 ). BCS serves as a key indicator of nutritional status and energy balance, both vital for lactation and reproduction. Adequate body reserves are linked to higher conception rates, shorter calving intervals, and consistent milk production, while too low or too high BCS can cause metabolic and reproductive issues (Roche et al. 2015 ; Khaton 2020 ). Studies on HF crossbred cattle have also shown positive links between optimal BCS, improved fertility, and higher lactation performance in field conditions (Saha et al. 2024 ; Habib et al. 2024 ). Additionally, regional and agro-ecological differences can significantly influence dairy cattle performance in Bangladesh. Variations in climate, feed availability, management practices, and access to veterinary services across regions can cause notable differences in the productive and reproductive traits of crossbred cows (Miah et al. 2018 ; Miah et al. 2021 ). Studies in various districts have demonstrated that these regional differences, combined with genetic makeup and management methods, create diverse performance outcomes among HF crossbred cattle populations. Despite growing research on HF crossbred cattle in Bangladesh, comprehensive information about the combined effects of Holstein Friesian inheritance level, parity, body condition score, age at first service, and regional production environments remains limited. Many previous studies have concentrated on individual factors or specific locations, resulting in a fragmented understanding of how these variables interact to influence dairy cow productivity and reproductive efficiency in the field. The lack of integrated data hinders the development of effective breeding strategies and management practices to enhance the performance of crossbred dairy cattle in Bangladesh (Habib et al. 2024 ). Therefore, the current study aimed to assess the effects of Holstein Friesian inheritance level, parity, age at first service, and body condition score on the productive and reproductive performance of HF crossbred dairy cows managed under field conditions in selected regions of Bangladesh. Materials and methods Study area and production systems The study was conducted in three dairy production environments in Bangladesh. These included rural Gaibandha (Saghata Upazila), urban Dhaka Metropolitan City, and peri-urban Mymensingh (Fig. 1 ). These regions were selected to capture differences in agro-ecological conditions, management practices, and levels of dairy production. Gaibandha district (25.33°N, 89.54°E), located in northern Bangladesh within Rangpur Division, has a subtropical monsoon climate with seasonal temperatures ranging from approximately 11 to 34°C. Dairy farming here is primarily smallholder-based, featuring low to moderate input systems. Farmers typically rely on locally available feed resources such as crop residues, roadside grasses, and limited concentrate supplements. Dhaka Metropolitan City (23.81°N, 90.41°E) is characterized by a highly developed urban dairy production system. Farms in this area usually utilize improved housing facilities, higher levels of concentrate feeding, and regular veterinary and breeding services. Artificial insemination (AI) is common, and animals on these farms often have a higher proportion of exotic genetic material. Mymensingh district (24.75°N, 90.40°E), situated in north-central Bangladesh, represents a peri-urban dairy system characterized by moderate management levels and comparatively better access to veterinary care, extension services, and improved feeding practices than rural areas. Study design and animal selection The study was carried out as a field-based observational cross-sectional study from January 2021 to January 2022. Data were collected from lactating Holstein Friesian local crossbred (HFLC) cows maintained under smallholder dairy farming systems in the selected regions. A total of 158 lactating HFLC cows were included in the study. Animals were selected based on the availability of reliable production and reproductive records and the farmers' willingness to take part. Only cows with complete information on Holstein Friesian inheritance level, parity, age at first service, and body condition score were included in the final analysis. Data were collected through structured questionnaires, direct farm observations, and verification of available farm records. Each animal was individually identified, and relevant information was recorded at the cow level. Multiple farm visits were carried out throughout the study period to confirm the recorded data and reduce potential recall bias. Classification of independent variables Holstein Friesian inheritance level Cows were divided into four genetic groups based on the percentage of Holstein Friesian inheritance level from breeding records and farmer-reported breeding history. • 50-<62.5% HF • ≥ 62.5-<75% HF • 75% HF • ˃75% HF This classification was used to evaluate how exotic inheritance levels influence dairy performance, as crossbreeding with Bos taurus breeds like Holstein Friesian has been widely used to boost milk production in tropical dairy systems. Parity Parity was classified into six groups (1–6) based on the number of completed calvings. Parity is a vital physiological factor that influences milk production and reproductive performance in dairy cattle. Body Condition Score (BCS) BCS was visually assessed using a five-point scale with 0.5-point increments, following the standard dairy cattle body condition scoring system described by Edmonson et al. ( 1989 ). This system evaluates body fat reserves and indicates the nutritional and metabolic status of dairy cows. For statistical analysis, cows were categorized into three BCS groups. • 2-<3 • ≥ 3-<4 • ≥ 4 Age at First Service (AFS) Age at first service was divided into four categories: 12–16 months 17–21 months 22–30 months 30 months This variable was included to evaluate its potential connection to reproductive performance. Traits studied The following productive and reproductive traits were evaluated: Service per conception (SPC): The number of artificial inseminations required to achieve a successful conception. Calving interval (CI): The time between two consecutive calvings, measured in months. Average daily milk yield (ADMY): The average amount of milk produced per day during the lactation period, measured in liters. Lactation length (LL): The total duration of milk production during a lactation cycle, measured in days. These parameters are common indicators of productive and reproductive efficiency in dairy cattle. Statistical analysis Data were initially organized using Microsoft Excel and then analyzed with R software version 4.4.2. A General Linear Model (GLM) was used to evaluate the fixed effects of region, Holstein Friesian inheritance level, parity, body condition score, and age at first service on productive and reproductive traits. The statistical model used was as follows: Yijklm = µ + Ri + Hj + Bk + Pl + Sm + eijklm Where, Yijklm = observed value of the dependent variable µ = overall population mean Ri = fixed effect of region (i = 1–3) Hj = fixed effect of HF inheritance level (j = 1–4) Bk = fixed effect of body condition score (k = 1–3) Pl = fixed effect of parity (l = 1–6) Sm = fixed effect of age at first service (m = 1–4) eijklm = random residual error, assumed to be normally distributed with mean 0 and variance σe 2 ​ Least squares means were compared using Tukey’s multiple comparison test, and statistical significance was declared at P < 0.05. Additionally, Pearson correlation coefficients were calculated to evaluate relationships among productive and reproductive traits. Results Productive and reproductive performance across regions The effects of region on productive and reproductive traits of HFLC cows are shown in Fig. 2, which clearly shows distinct regional patterns. The region significantly influenced CI, ADMY, and LL ( P < 0.001), but SPC was not significantly affected ( P = 0.079). Cows in Dhaka Metropolitan City had the longest CI (16.56 ± 0.299 months), followed by Mymensingh (13.69 ± 0.338 months), and Gaibandha (12.95 ± 0.281 months). ADMY showed significant variation among regions, with cows in Dhaka producing the highest yield (17.92 ± 1.027 L). Cows in Gaibandha and Mymensingh produced 12.55 ± 0.963 L and 11.73 ± 1.152 L, respectively. LL also varied significantly across regions, with Dhaka having the longest LL (380.97 ± 9.451 days), followed by Mymensingh (304.29 ± 10.669 days), and Gaibandha (296.34 ± 8.856 days). SPC remained fairly consistent across all regions, ranging from 1.76 ± 0.112 to 1.95 ± 0.099. Figure 2 Productive and reproductive performance of Holstein Friesian local crossbred (HFLC) cows across three regions of Bangladesh (Gaibandha, Mymensingh, and Dhaka Metropolitan City). SPC: Service per conception; CI: Calving interval; ADMY: Average daily milk yield; LL: Lactation length. Points represent mean values, and error bars indicate standard error of mean (SEM) Productive and reproductive performance by Holstein Friesian (HF) inheritance level The effects of Holstein Friesian (HF) inheritance level on productive and reproductive traits are shown in Fig. 3 . HF inheritance level significantly influenced SPC, CI, ADMY, and LL ( P < 0.001). Cows with ≥ 62.5- 75% HF inheritance had the highest SPC (2.14 ± 0.093). Calving interval increased as HF inheritance level rose, with the longest CI observed in cows with > 75% HF (15.97 ± 0.297 months). Average daily milk yield also increased significantly with higher HF inheritance levels. The highest ADMY was seen in cows with > 75% HF (17.61 ± 0.966 L), followed by cows with 75% HF (14.45 ± 1.061 L), ≥ 62.5-<75% HF (12.52 ± 1.530 L), and 50- 75% HF (368.45 ± 9.139 days). Overall, a higher HF inheritance level was linked to increased milk production and longer lactation, but it was also associated with lower reproductive efficiency, as shown by higher SPC and longer CI. Cows with moderate HF inheritance (≥ 62.5-<75%) appeared to balance productive and reproductive performance in field conditions. Productive and reproductive performance by parity The effects of parity on productive and reproductive traits are shown in Table 1 . Parity significantly influenced SPC ( P = 0.01), while CI, ADMY, and LL were not significantly affected ( P = 0.58). SPC increased with rising parity. The lowest SPC was observed in first-parity cows (1.54 ± 0.111), and the highest was in sixth-parity cows (2.67 ± 0.389). CI showed a slight upward trend with parity, ranging from 13.87 months in first and second-parity cows to 15.57 months in fifth-parity cows. ADMY was highest in third and fourth-parity cows (15.35 ± 1.299 L and 15.72 ± 1.797 L, respectively). LL varied from 282.22 ± 39.905 days in sixth-parity cows to 345.16 ± 12.348 days in third-parity cows. SPC decreased with increasing parity, while productive traits (ADMY and LL) remained relatively stable across parities under field conditions. Table 1 Least squares means with standard errors for reproductive and productive traits by different parities of HFLC cows Parity n SPC CI (months) ADMY (L) LL (days) 1 110 1.54 a ±0.111 13.87 ± 0.371 11.56 ± 1.199 319.00 ± 11.414 2 138 1.73 ab ±0.099 13.87 ± 0.331 15.08 ± 1.071 321.02 ± 10.191 3 94 2.09 abc ±0.120 14.94 ± 0. 401 15.35 ± 1.2988 345.16 ± 12.348 4 49 2.14 abc ±0.167 15.5 ± 0.555 15.72 ± 1.797 338.53 ± 17.102 5 21 2.38 bc ±0.254 15.57 ± 0.849 14.25 ± 2.745 327.05 ± 26.124 6 09 2.67 c ±0.389 15.00 ± 1.296 15.28 ± 4.194 282.22 ± 39.905 P -value 0.01 0.06 0.20 0.58 SPC: Service per conception; CI: Calving interval; ADMY: Average daily milk yield; LL: Lactation length; SEM: Standard error of mean; n: number of observations. Values are expressed as least squares means ± SEM. Means within a column with different superscripts differ significantly ( P < 0.05) Productive and reproductive performance by Body Condition Score (BCS) The effects of BCS on productive and reproductive traits are shown in Table 2 . BCS significantly influenced SPC and LL ( P = 0.02), while CI and ADMY were not significantly affected ( P > 0.05). Cows with BCS ≥ 4 had the highest SPC (2.39 ± 0.224), whereas cows with BCS 2-<3 had the lowest SPC (1.79 ± 0.089). CI ranged from 14.18 ± 0.293 to 15.43 ± 0.741 months among BCS groups. ADMY was not significantly different among BCS groups. However, LL was longest in cows with BCS ≥ 4 (360.36 ± 22.443 days) and shortest in cows with BCS 3–4 (312.93 ± 8.118 days). Overall, higher BCS was associated with decreased reproductive efficiency (higher SPC) but longer LL, while ADMY remained relatively consistent across BCS groups. Table 2 Least squares means with standard errors for reproductive and productive traits by different BCS groups of HFLC cows (combined parity) BCS n SPC CI (months) ADMY (L) LL (days) 2-<3 179 1.79 a ±0.089 14.18 ± 0.293 14.19 ± 0.944 340.69 ab ±8.876 ≥ 3–4 214 1.85 ab ±0.081 14.43 ± 0.268 14.11 ± 0.863 312.93 b ±8.118 ≥ 4 28 2.39 b ±0.224 15.43 ± 0.741 14.69 ± 2.387 360.36 a ±22.443 P -value 0.02 0.27 0.98 0.02 SPC: Service per conception; CI: Calving interval; ADMY: Average daily milk yield; LL: Lactation length; SEM: Standard error of the mean; n: number of observations. Values are expressed as least squares means ± SEM. Means within a column with different superscripts differ significantly ( P < 0.05) Productive and reproductive performance by Age at First Service (AFS) The effects of AFS on productive and reproductive traits are shown in Table 3 . AFS significantly influenced SPC, CI, and ADMY, except LL ( P = 0.13). The lowest SPC was observed in cows with AFS of 12–16 months (1.61 ± 0.095) and > 30 months (1.53 ± 0.132), while the highest SPC was in cows with AFS of 22–30 months (2.31 ± 0.115). CI was longest for cows with AFS of 22–30 months (15.99 ± 0.381 months). ADMY was highest in cows with AFS of 22–30 months (15.93 ± 1.243 L) and lowest in cows with AFS of > 30 months (10.16 ± 1.423 L). Table 3 Least squares means with standard errors for reproductive and productive traits by Age at First Service (AFS) of HFLC cows (combined parity) AFS (months) n SPC CI (months) ADMY (L) LL (days) 12–16 150 1.61 a ±0.095 13.90 a ±0.313 14.27 a ±1.019 330.63 ± 9.733 17–21 93 2.09 b ±0.120 13.69 a ±0.397 15.52 a ±1.295 302.12 ± 12.361 22–30 101 2.31 b ±0.115 15.99 b ±0.381 15.93 a ±1.243 340.06 ± 11.862 > 30 77 1.53 a ±0.132 14.09 a ±0.437 10.16 b ±1.423 335.14 ± 13.585 P -value < .0001 < .0001 0.01 0.13 SPC: Service per conception; CI: Calving interval; ADMY: Average daily milk yield; LL: Lactation length; SEM: Standard error of mean; n: number of observations. Values are expressed as least squares means ± SEM. Means within a column with different superscripts differ significantly ( P < 0.05) Correlation among productive and reproductive traits The correlations among productive and reproductive traits of HFLC cows are shown in Fig. 4 . The correlation matrix revealed several highly significant relationships regarding Holstein Friesian inheritance level (HFIL). HFIL showed a strong positive and highly significant correlation with ADMY (r = 0.55, P < 0.001), as well as significant positive correlations with CI (r = 0.28, P < 0.001) and SPC (r = 0.24, P < 0.01). HFIL had a significant negative correlation with AFS and AFC (r = -0.20, P < 0.05). CI showed a strong positive relationship with ADMY (r = 0.41, P < 0.001) and LL (r = 0.52, P < 0.001). Similarly, SPC was positively correlated with CI (r = 0.35, P < 0.001), ADMY (r = 0.23, P < 0.01), and LL (r = 0.18, P < 0.05). ADMY showed a significant positive correlation with LL (r = 0.36, P < 0.001). Both AFS and AFC were perfectly correlated with each other (r = 1.00, P < 0.001) and showed a significant negative correlation with ADMY (r = -0.28, P < 0.001). However, BCS did not show any significant correlations with other productive and reproductive traits in the matrix. Discussion The present study showed that region significantly influenced CI, ADMY, and LL, while SPC remained unaffected (Fig. 2). The lack of regional differences in SPC suggests that estrus detection efficiency and artificial insemination practices were fairly consistent across the studied production systems. Similar findings have been reported in Bangladesh by Zohara et al. ( 2019 ) and Miah et al. ( 2021 ), who found that SPC of Holstein Friesian crossbred cows did not vary significantly among regions or farm categories. This indicates that reproductive management practices, especially insemination services, may be relatively uniform across different dairy environments. In contrast, CI varied significantly among regions, with cows in Dhaka Metropolitan City showing longer calving intervals compared to those in Gaibandha and Mymensingh. Similar regional differences in reproductive performance have been documented in tropical dairy systems, where environmental factors, nutrition management, and genetics interact to influence fertility outcomes. Urban dairy farms often keep cows with more exotic genetics and higher milk production potential, which may cause increased metabolic stress during early lactation and delay ovarian activity recovery. Studies on crossbred dairy cattle in tropical environments indicate that high-producing cows often have longer postpartum anestrus and extended calving intervals when nutritional management is poor (Azad et al. 2023 ; Habib et al. 2024 ). Regional differences in milk production were also observed, with cows in Dhaka producing significantly higher ADMY than those in Gaibandha and Mymensingh. This aligns with previous studies showing that crossbred dairy cow milk production varies greatly across environments due to variations in feeding, housing, and veterinary access (Miah et al. 2018 ; Zohara et al. 2019 ). Urban farms typically provide higher concentrate supplementation and better healthcare, which may enhance milk production. Additionally, cows in urban farms often have a higher proportion of Holstein Friesian germplasm, further boosting milk yield. Lactation length also followed a similar regional trend, with the longest lactation observed in Dhaka. Extended lactation may reflect both genetic potential and management decisions related to breeding and culling. In intensive dairy systems, farmers might intentionally extend lactation to maintain a steady milk supply, especially when cows experience delayed conception. Previous studies in Bangladesh and other tropical countries also show significant differences in lactation length across farming systems, highlighting the strong influence of management and environmental factors on lactation persistence (Galukande et al. 2013 ; Miah et al. 2021 ). Holstein Friesian (HF) inheritance level significantly influenced both productive and reproductive traits in this study (Fig. 3 ). Cows with higher HF inheritance produced more milk but had poorer reproductive performance, while those with moderate HF inheritance showed better reproductive efficiency. These results highlight the common trade-off between productivity and adaptability in crossbred dairy cattle raised in tropical conditions. The increase in SPC and CI with greater HF inheritance suggests decreased reproductive efficiency in cows with more exotic germplasm. Similar patterns have been documented in several studies conducted in tropical and subtropical regions (Tsegaye et al. 2014 ; Adhikary et al. 2020 ). A high level of Bos taurus inheritance may impair heat tolerance, disease resistance, and overall adaptability to tropical environments, negatively affecting reproductive performance. Heat stress, nutritional deficiencies, and increased metabolic demands related to higher milk production are known to disrupt endocrine function and delay ovarian activity in high-producing cows. Conversely, cows with moderate HF inheritance (around 50–75%) exhibited shorter CI and lower SPC, indicating improved reproductive efficiency under field conditions. Previous research suggests that intermediate levels of exotic inheritance often strike an optimal balance between productivity and environmental adaptability in tropical dairy systems (Galukande et al. 2013 ). Crossbreeding strategies that maintain HF inheritance at moderate levels may be better suited for smallholder dairy systems where feed resources and management are limited. As expected, milk production increased significantly with higher HF inheritance. Cows with > 75% HF inheritance produced the highest ADMY, reflecting the strong genetic potential of Holstein Friesian cattle for milk production. Similar links between HF inheritance level and milk yield have been observed in Bangladesh and other tropical countries (Mamun et al. 2015 ; Rahman et al. 2017; Azad et al. 2023 ). The increased milk production in cows with more HF inheritance may result from better mammary gland development, higher metabolic capacity, and superior genetic potential for lactation. Lactation length also increased with higher HF inheritance levels, with cows possessing more HF blood showing longer lactation periods. This aligns with earlier reports indicating that high-producing crossbred cows tend to sustain longer lactation cycles due to improved milk production (Hossen et al. 2012 ; Miah et al. 2021 ). However, prolonged lactation may also be linked to delayed conception, leading to longer calving intervals. The findings highlight a clear trade-off between productive and reproductive performance related to increasing HF inheritance. While higher levels of exotic germplasm boost milk yield, moderate inheritance levels seem to offer a better balance between productive and reproductive efficiency under the current environmental and management conditions in Bangladesh. Parity had a significant influence on SPC in this study, while CI, ADMY, and LL were not significantly affected (Table 1 ). The consistent increase in SPC with higher parity suggests declining reproductive efficiency in older cows. Age-related physiological changes, accumulated metabolic stress, and increased susceptibility to reproductive problems may lead to reduced fertility in later parities. Similar patterns have been observed in dairy cattle within tropical production systems, where reproductive efficiency tends to decrease as cows age (Haque et al. 2013 ; Qureshi et al. 2020). The lack of a significant parity effect on CI contrasts with some earlier studies that identified parity as a key factor in reproductive success (Islam and Kundu 2011; Wassie and Mekuriaw 2015). However, in smallholder farming systems, the effect of parity on CI may be masked by differences in nutrition, management, and breeding practices across farms. Although parity did not significantly affect ADMY, milk yield generally increased up to middle parities before stabilizing or slightly declining in later parities. This pattern aligns with the natural development of dairy cows, as milk production typically rises with maturity and peaks around the third or fourth lactation (Berry et al. 2003; Qureshi et al. 2020). The lack of statistical significance in this study may be due to variability in feeding practices and the relatively small sample sizes in higher parity groups. Similarly, LL did not differ significantly among parities, although slight variations were observed. Previous research indicates that LL can be influenced by parity, reproductive management, and environmental factors (Das et al. 2011 ). The relatively consistent LL observed here suggests that management practices may have a greater influence than physiological factors in determining LL under field conditions. Body condition score (BCS) significantly influenced SPC and LL but did not affect CI or ADMY (Table 2 ). The increase in SPC observed in cows with higher BCS suggests reduced reproductive efficiency in over-conditioned animals. Excess fat accumulation may impair ovarian function and disturb hormonal balance, potentially decreasing conception rates. Previous studies have shown that both very low and very high BCS can negatively impact fertility in dairy cows (Roche et al. 2015 ; Khaton 2020 ). The absence of a significant BCS effect on CI in this study aligns with findings by Rahman et al. (2017), who highlighted that calving interval is influenced by multiple factors, including nutrition, health, management practices, and genetics. Although cows with higher BCS tended to have slightly longer CI, this difference was not significant. Similarly, ADMY remained relatively stable among BCS groups, although cows with higher BCS exhibited slightly increased milk production. Adequate energy reserves are essential for maintaining lactation, but excessive body condition may raise the risk of metabolic disorders that can reduce productivity (Berry et al. 2003). LL was significantly longer in cows with higher BCS, indicating that better nutritional status may support sustained milk production. Sufficient body reserves supply the energy needed to maintain lactation during periods of negative energy balance, especially in early lactation. However, excessively high BCS may also be linked to delayed conception, which can indirectly prolong lactation. Age at first service (AFS) significantly influenced SPC in HFLC cows (Table 3 ). Although SPC tended to be lower in cows with AFS at 12–16 months and > 30 months, and relatively higher at 17–21 and 22–30 months, the pattern varied and was not consistent. This indicates that SPC may not depend solely on AFS but could also be influenced by management, nutrition, and environmental factors. Similar results have been observed in crossbred dairy cattle, where no clear relationship between AFS and the number of SPC was found. Abera et al. ( 2016 ) reported an overall mean SPC of 1.35 ± 0.42 in crossbred cows, with no significant link to AFS. Furthermore, studies with Holstein Friesian heifers showed that earlier breeding does not necessarily impair fertility during the first lactation (Sakaguchi et al. 2005 ). AFS also significantly influenced CI. In this study, the longest CI was observed in cows with AFS at 22–30 months, while shorter CIs were recorded in other groups. The absence of a clear trend suggests that CI may be affected by multiple interacting factors rather than AFS alone. Abera et al. ( 2016 ) documented an average CI of 14.64 ± 2.2 months in crossbred cows, with no clear connection to AFS. Similarly, controlled research with Holstein Friesian heifers reported comparable CIs among early and late bred animals, implying that earlier breeding may not extend CI if animals are well managed, including proper nutrition (Sakaguchi et al. 2005 ). Kabir and Kisku ( 2013 ) noted that variations in CI are often linked to environment, nutrition, reproductive management, and estrous irregularities. A significant effect of AFS on ADMY was also observed. Cows bred between 12 and 30 months produced higher ADMY compared to those bred after 30 months. This difference may partly relate to genetic factors, especially Holstein Friesian (HF) inheritance levels. Rahman et al. (2017) found that higher HF inheritance level increases milk production, but Adhikary et al. ( 2020 ) reported no significant association between age at puberty and ADMY in HF crossbred cows. Nonetheless, existing evidence from crossbred populations does not consistently support either earlier or later AFS for higher milk yield. Abera et al. ( 2016 ) reported an average yearly milk yield of 3386 ± 898 L and a lactation length of 11.96 ± 2.5 months in crossbred cows, with no significant effect of AFS. Similarly, studies with Holstein Friesian heifers indicated that early first breeding did not reduce first-lactation milk production or overall productivity when animals were properly managed (Sakaguchi et al. 2005 ). AFS also significantly influenced LL. The longest LL was observed in cows with AFS at 22–30 months, while cows serviced at 17–21 months had shorter LL. Variations in LL can be related to breed traits, feeding practices during lactation, and herd management strategies. Qureshi (2000) suggested that management, nutrition, and farmer decisions about lactation persistence can significantly influence LL. However, as with other production traits, previous research shows limited consistent evidence linking AFS to LL in crossbred dairy cattle. The correlation analysis revealed several highly significant relationships among productive and reproductive traits of HFLC cows (Fig. 4 ). The relationship between CI and ADMY showed a strong positive and highly significant correlation (r = 0.41, P < 0.001). This finding contrasts with Qureshi et al. (2000), who reported a significant negative correlation (-0.67) between CI and ADMY in dairy cattle in Pakistan. This may be due to differences in genetic makeup, feeding management, and production environment between the two populations. The strong positive relationship indicates that high-yielding cows tend to experience delayed conception, leading to longer CIs. A moderate and significant positive correlation was found between CI and LL (r = 0.52). Uddin et al. ( 2008 ) also reported a positive association between these traits. This suggests that cows with longer CIs generally have longer lactation periods, possibly because delayed conception extends the current lactation cycle. HFIL showed a strong positive correlation with ADMY (r = 0.55, P < 0.001). Similar findings have been observed in crossbred dairy cattle, where increasing the proportion of Holstein Friesian genetics significantly improves milk production (Hossain et al. 2002 ; Haque et al. 2013 ). These results support the benefit of incorporating Holstein Friesian genetics to increase milk yield in crossbred cattle within tropical production systems. However, HFIL also exhibited significant positive correlations with reproductive intervals, specifically CI (r = 0.28, P < 0.001) and SPC (r = 0.24, P < 0.01). This indicates that, while increased exotic inheritance boosts productive traits, it may also reduce reproductive performance. Similar findings were reported by Islam and Kundu (2012), who observed that higher exotic inheritance improved productivity but often led to declined reproductive performance under certain management and environmental conditions. A perfect positive correlation was found between AFS and AFC (r = 1.00), indicating that delayed first service directly results in delayed first calving. Both AFS and AFC showed a significant negative correlation with ADMY (r = -0.28, P < 0.001). Overall, these results demonstrated that productive traits, especially ADMY, are positively linked to HF inheritance level, while reproductive traits like higher CI and SPC also tend to increase with exotic genetics. The findings highlight a clear biological trade-off between milk production and reproductive efficiency, influenced by factors such as genotype, body condition, parity, and management environment. Urban farms with high HF inheritance and higher BCS tend to produce more milk but have reduced reproductive performance. Conversely, rural and peri-urban systems with moderate HF inheritance level support better fertility but lower milk yields. For sustainable smallholder dairy farming in tropical environments, maintaining a moderate HF inheritance level (50–75%), optimal BCS (around 3), and proper parity management can balance productivity and reproduction, maximizing both economic returns and herd health. Declarations Competing interests The authors declare that they have no conflicts of interest concerning this publication. Ethical approval The study involved non-invasive field data collection from dairy farms. Farmers were informed about the study's objectives before participation, and verbal consent was obtained prior to data collection. This is an observational study. Bangladesh Agricultural University Research Ethics Committee has confirmed that no ethical approval is necessary. Funding The study was funded by the National Science and Technology Fellowship, Ministry of Science and Technology, Government of Bangladesh. Author Contributions Nushrat Nourin Lisa: Writing - original draft, methodology, investigation, data curation, formal analysis, review, and editing. Md Nahid Hassan Chawdhury: Methodology, investigation, visualization, data interpretation, review, and editing. Mohammad Mahbubul: Review and editing. Md Ruhul Amin: Conceptualization, project design, methodology, review, and editing. Acknowledgments The authors sincerely thank the dairy farmers from Gaibandha, Dhaka Metropolitan City, and Mymensingh for their cooperation in this study and for granting access to their farm records. They also appreciate the field assistants and technical staff who supported data collection throughout the study period. Data availability The datasets generated and/or analyzed during this study are not publicly available but can be obtained from the corresponding author upon reasonable request. References Abera Z, Mengiste B, Demise T (2016) Reproductive and lactation performance of crossbreed dairy cows in Bishoftu, Ada’a District of East Shoa, Eastern Ethiopia. Sci Technol Arts Res J 4:113–119. https://doi.org/10.4314/STAR.V4I4.16 Adhikary K, Roy K, Barua K, Akter N, Bhowmik P, Sultana N, Hossain ME (2020) Performance of crossbred dairy cattle under commercial farming conditions in the Chattogram district of Bangladesh. Bangl J Vet Anim Sci 8:141–150 Azad A, Mou M, Hridoy M, Azam S, Safa M, Bhuiyan A, Bhuiyan M (2023) Production performance, breeding practices and challenges of Holstein-local crossbred cattle in some selected areas of Bangladesh. Bangl J Anim Sci 52:69–77. https://doi.org/10.3329/bjas.v52i3.69207 Das A, Das Gupta M, Khan MKI, Miah G (2011) Effect of non-genetic factors on the productive and reproductive traits of Friesian crossbred dairy cows. Wayamba J Anim Sci 1:62–64 DLS (2025) Annual progress report 2024-25. Department of Livestock Services, Ministry of Fisheries and Livestock, Dhaka Edmonson AJ, Lean IJ, Weaver LD, Farver T, Webster G (1989) A body condition scoring chart for Holstein dairy cows. J Dairy Sci 72:68–78. https://doi.org/10.3168/jds.S0022-0302(89)79081-0 Galukande E, Mulindwa H, Wurzinger M, Roschinsky R, Mwai AO, Sölkner J (2013) Cross-breeding cattle for milk production in the tropics: achievements, challenges and opportunities. Anim Genet Resour 52:111–125. https://doi.org/10.1017/S2078633612000471 Habib MA, Rahman MM, Islam MR, Hossain MM (2024) Reproductive performance and management factors affecting fertility of crossbred dairy cows in Bangladesh. Trop Anim Health Prod 56:1–10 Haque MN, Husain SS, Khandoker MAMY, Miazi OF, Bhuiyan AKFH (2013) Productive and reproductive performance of dairy cattle in Bangladesh. Bangl J Anim Sci 42:55–60 Hossain MM, Bhuiyan AKFH, Bhuiyan MSA (2002) Performance evaluation of different grades of Holstein Friesian crossbred cows under farm conditions in Bangladesh. Asian-Australas J Anim Sci 15:777–782 Hossen MS, Hossain SS, Bhuiyan AKFH, Hoque MA, Talukder MAS (2012) Comparison of some important dairy traits of crossbred cows at Baghabarighat milk shed area of Bangladesh. Bangl J Anim Sci 41:13–18 Ihsanullah M, Khan MAS, Rahman MM, Islam MS (2020) Factors affecting reproductive performance of dairy cows in tropical production systems. Vet World 13:1012–1018 Islam MR, Alam MJ, Rahman MM (2017) Comparative productive performance of Holstein Friesian crossbred and indigenous dairy cows in Bangladesh. Bangl J Anim Sci 46:181–188 Kabir F, Kisku JJ (2013) Reproductive performance of crossbred dairy cows under smallholder farming systems. Int J Livest Prod 4:150–156 Khaton R (2020) Relationship between body condition score and reproductive performance of dairy cattle. Bangl J Vet Med 18:23–29 Mamun MJA, Khan MAS, Bhuiyan AKFH, Islam MN (2015) Productive and reproductive performance of Holstein Friesian crossbred and indigenous cows under smallholder farming systems. Bangl J Anim Sci 44:164–170 Miah MAM, Rashid MA, Bhuiyan MSA (2018) Dairy farming systems and milk production performance in different regions of Bangladesh. Bangl J Anim Sci 47:102–110 Miah MAM, Rashid MA, Bhuiyan MSA (2021) Regional variation in dairy production systems and productivity of crossbred cows in Bangladesh. Trop Anim Health Prod 53:1–9 Ngodigha EM, Etokeren E (2009) Evaluation of milk production traits in Holstein Friesian crossbred dairy cattle. J Anim Vet Adv 8:610–613 Roche JR, Friggens NC, Kay JK, Fisher MW, Stafford KJ, Berry DP (2015) Body condition score and its association with dairy cow productivity, health, and welfare. J Dairy Sci 98:5801–5812 Saha S, Hasan MN, Uddin MN, Rahman BMM, Khan MMH, Ahmed SSU, Kitazawa H (2024) A comparison between crossbred (Holstein × local cattle) and Bangladeshi local cattle for body and milk quality traits. Dairy 5:1–12. https://doi.org/10.3390/dairy5010012 Sakaguchi M, Suzuki T, Sasamoto Y, Takahashi Y, Nishiura A, Aoki M (2005) Effects of first breeding age on the production and reproduction of Holstein heifers up to the third lactation. Anim Sci J 76:419–426. https://doi.org/10.1111/j.1740-0929.2005.00285.x Samad MA (2020) A six-decade review: research on cattle production, management and dairy products in Bangladesh. J Vet Med OH Res 2:0021. https://doi.org/10.36111/JVMOHR.2020.2(2).0021 Shahjahan M (2017) High yielding dairy cattle husbandry and their production performance at Baghabari Milk Vita areas of Bangladesh. Asian-Australas J Biosci Biotechnol 2:60–67 Tsegaye M, Dessie T, Rege JEO (2014) Genotype by environment interaction for milk production traits of crossbred dairy cattle in tropical production systems. Trop Anim Health Prod 46:459–467 Uddin MK, Wadud A, Begum D, Siddiki MSR, Rashid MH (2008) Productive and reproductive performance of indigenous and crossbred cattle in Comilla district. Bangl J Anim Sci 37:39–43 Wassie T, Mekuriaw G, Mekuriaw Z (2015) Reproductive performance for Holstein Friesian × Arsi and Holstein Friesian × Boran crossbred cattle. Iran J Appl Anim Sci 5:35–40 Zohara BF, Rita SA, Rahman SML, Ali MA, Alam MS, Islam MF (2019) Reproductive and productive performance of dairy cows in different upazila of Dinajpur district. Bangl Livest J 1:51–55 Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 06 Apr, 2026 Reviewers invited by journal 01 Apr, 2026 Editor assigned by journal 20 Mar, 2026 First submitted to journal 17 Mar, 2026 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-9124650","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":615908528,"identity":"30a3fcd9-7b8e-434d-8bc5-4a60636a24df","order_by":0,"name":"Nushrat Nourin Lisa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIie3PsQqCQBjA8S8OzuUT14PEZwiELHqZInDSN4jQxTafoIdoclY+8hkc3ASHpiIIJ0mdmvTagu4/3PHB94M7AJXqJ2NBCpCawFh3gzAlyGwgCIxve4JSBAYCuOjHaeLMwzBrDiU6J3zeisMKQaPrZYysz1lAmNdokp5svLx7GLpuMUYWxS4g4ISC6Ynt8Y4IXE6SrGl7grXttZIk1aOBsMqPJAnpcd0RvmR+LJBP/2VPj+ZVWsKg6um9jpahUT5KPuNiOGXX+9j9m22VSqX6n975iUidGCVNqwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0007-6947-0771","institution":"Bangladesh Livestock Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Nushrat","middleName":"Nourin","lastName":"Lisa","suffix":""},{"id":615908529,"identity":"2166d4f6-66f3-49ec-9531-8115f26b8216","order_by":1,"name":"Md Nahid Hassan Chawdhury","email":"","orcid":"","institution":"Bangladesh Livestock Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Md","middleName":"Nahid Hassan","lastName":"Chawdhury","suffix":""},{"id":615908530,"identity":"2faff70f-e173-4059-beba-746a949ceb5f","order_by":2,"name":"Mohammad Mahbubul","email":"","orcid":"","institution":"Bangladesh Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Mahbubul","suffix":""},{"id":615908531,"identity":"7ec085dc-fb8b-4a15-bcaf-ec1d508027d5","order_by":3,"name":"Md Ruhul Amin","email":"","orcid":"","institution":"Bangladesh Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Md","middleName":"Ruhul","lastName":"Amin","suffix":""}],"badges":[],"createdAt":"2026-03-14 19:23:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9124650/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9124650/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106359880,"identity":"88d6ca3c-2e31-46ce-a620-479ad65de670","added_by":"auto","created_at":"2026-04-07 19:55:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55404,"visible":true,"origin":"","legend":"\u003cp\u003eMap showing the geographical locations of the study areas: Saghata Upazila in Gaibandha, Mymensingh Sadar, and Dhaka Metropolitan Area\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9124650/v1/656e67f6f614db720b21ee16.png"},{"id":106359863,"identity":"c3fd7d0b-0e0b-4bb2-8055-9fb648295404","added_by":"auto","created_at":"2026-04-07 19:55:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24576,"visible":true,"origin":"","legend":"\u003cp\u003eProductive and reproductive performance of Holstein Friesian local crossbred (HFLC) cows across three regions of Bangladesh (Gaibandha, Mymensingh, and Dhaka Metropolitan City). SPC: Service per conception; CI: Calving interval; ADMY: Average daily milk yield; LL: Lactation length. Points represent mean values, and error bars indicate standard error of mean (SEM)\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9124650/v1/78d23a9838ce65d42278fcfa.png"},{"id":106359841,"identity":"23dfc71f-8a22-4ac4-afc4-b0c3fb8a3052","added_by":"auto","created_at":"2026-04-07 19:55:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":27635,"visible":true,"origin":"","legend":"\u003cp\u003eProductive and reproductive performance of Holstein Friesian local crossbred (HFLC) cows across different Holstein Friesian (HF) inheritance levels. SPC: Service per conception; CI: Calving interval; ADMY: Average daily milk yield; LL: Lactation length. Points represent mean values, and error bars indicate standard error of mean (SEM)\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9124650/v1/76159e499eb5985c9595d18b.png"},{"id":106359842,"identity":"92bcafc9-7e43-4172-a32d-7beebf308426","added_by":"auto","created_at":"2026-04-07 19:55:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":20107,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrix showing relationships among productive and reproductive traits of Holstein Friesian local crossbred (HFLC) cows. SPC: Service per conception; CI: Calving interval; BCS: Body condition score; ADMY: Average daily milk yield; LL: Lactation length; HFIL: Holstein Friesian inheritance level; AFC: Age at first calving; AFS: Age at first service. Values represent Pearson correlation coefficients among these traits\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9124650/v1/71b3a93b2a762961574fffed.png"},{"id":106415101,"identity":"ca4105c6-754c-4fda-8858-0db36bd7e5e2","added_by":"auto","created_at":"2026-04-08 10:32:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1113311,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9124650/v1/f75dfecf-5510-4ac6-a8ae-b5f97f957c7a.pdf"}],"financialInterests":"","formattedTitle":"Effects of region, Holstein Friesian inheritance level, parity, body condition score, and age at first service on productive and reproductive performance of Holstein Friesian crossbred dairy cows in Bangladesh","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIncreasing milk production while maintaining satisfactory reproductive efficiency remains a major challenge in dairy systems, particularly in tropical and subtropical regions. In many developing countries like Bangladesh, dairy development programs heavily rely on crossbreeding native cattle with high-yielding temperate breeds such as Holstein Friesian (HF) to boost milk production and increase farm income. Local cattle are well adapted to the regional climate but generally produce less milk, whereas exotic breeds possess higher genetic potential for milk production (Mamun et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Islam et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). As a result, crossbreeding has become a common strategy for rapid genetic improvement of dairy cattle in tropical areas. In Bangladesh, the dairy sector plays a crucial role in supporting rural livelihoods and ensuring national food security. However, the country still faces a substantial gap between milk demand and production. Recent estimates indicate that the national milk demand is 16.233\u0026nbsp;million tons, while domestic production stands at 15.538\u0026nbsp;million tons (DLS, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This highlights the need to boost dairy productivity through genetic improvements and improved management practices (Shahjahan \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Crossbreeding programs utilizing Holstein Friesian, Jersey, and other Bos taurus breeds have been promoted to increase milk yield and improve the reproductive efficiency of the national herd (Samad \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Azad et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Among these, Holstein Friesian crossbred cows are particularly vital in smallholder and commercial dairy systems in Bangladesh (Mamun et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Although HF crossbreeding has resulted in substantial increases in milk production, the performance of crossbred cows is heavily influenced by the level of exotic inheritance and its interaction with environmental factors (Galukande et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Tsegaye et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Several studies have demonstrated that a higher proportion of Holstein Friesian inheritance generally correlates with increased milk yield and longer lactation periods in crossbred dairy cattle (Ngodigha and Etokeren \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Mamun et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Azad et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, higher levels of exotic inheritance can also reduce adaptability to tropical climates, increase susceptibility to diseases, and negatively impact reproductive performance when animals are managed under resource-limited conditions (Tsegaye et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Adhikary et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, several studies recommend maintaining moderate levels of HF inheritance level, typically between 50% and 75%, to achieve a balance between productive and reproductive efficiency in tropical production environments (Hossain et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Galukande et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Azad et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eReproductive performance is crucial for dairy herd profitability because traits such as age at first calving (AFC), SPC, and CI directly influence lifetime productivity and replacement costs. Although crossbreeding has enhanced milk production potential, many studies indicate that HF crossbred cows frequently experience reproductive issues, longer calving intervals, and lower conception rates under poor management conditions (Kabir and Kisku \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ihsanullah et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Environmental stress, heat load, inadequate nutrition, and disease pressure further worsen these reproductive challenges in tropical dairy systems (Azad et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Habib et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These findings highlight the need to understand genotype-environment interactions to optimize both production and reproduction in crossbred dairy cattle. Besides genetic factors, several non-genetic elements significantly affect dairy cow productivity and fertility. Among these, parity and BCS are widely recognized as key influences on both milk production and reproductive efficiency (Berry et al. 2003; Islam and Kundu 2011; Roche et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Qureshi et al. 2020). Parity reflects the animal's physiological maturity and the cumulative metabolic demands across successive lactations, which can impact milk yield and reproductive success (Islam and Kundu 2011; Qureshi et al. 2020). Evidence indicates that milk production and reproductive performance often peak during middle parities, typically between the second and fourth lactations, before gradually declining (Haque et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). BCS serves as a key indicator of nutritional status and energy balance, both vital for lactation and reproduction. Adequate body reserves are linked to higher conception rates, shorter calving intervals, and consistent milk production, while too low or too high BCS can cause metabolic and reproductive issues (Roche et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Khaton \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Studies on HF crossbred cattle have also shown positive links between optimal BCS, improved fertility, and higher lactation performance in field conditions (Saha et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Habib et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, regional and agro-ecological differences can significantly influence dairy cattle performance in Bangladesh. Variations in climate, feed availability, management practices, and access to veterinary services across regions can cause notable differences in the productive and reproductive traits of crossbred cows (Miah et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Miah et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Studies in various districts have demonstrated that these regional differences, combined with genetic makeup and management methods, create diverse performance outcomes among HF crossbred cattle populations.\u003c/p\u003e\u003cp\u003eDespite growing research on HF crossbred cattle in Bangladesh, comprehensive information about the combined effects of Holstein Friesian inheritance level, parity, body condition score, age at first service, and regional production environments remains limited. Many previous studies have concentrated on individual factors or specific locations, resulting in a fragmented understanding of how these variables interact to influence dairy cow productivity and reproductive efficiency in the field. The lack of integrated data hinders the development of effective breeding strategies and management practices to enhance the performance of crossbred dairy cattle in Bangladesh (Habib et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, the current study aimed to assess the effects of Holstein Friesian inheritance level, parity, age at first service, and body condition score on the productive and reproductive performance of HF crossbred dairy cows managed under field conditions in selected regions of Bangladesh.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area and production systems\u003c/h2\u003e \u003cp\u003eThe study was conducted in three dairy production environments in Bangladesh. These included rural Gaibandha (Saghata Upazila), urban Dhaka Metropolitan City, and peri-urban Mymensingh (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These regions were selected to capture differences in agro-ecological conditions, management practices, and levels of dairy production. Gaibandha district (25.33\u0026deg;N, 89.54\u0026deg;E), located in northern Bangladesh within Rangpur Division, has a subtropical monsoon climate with seasonal temperatures ranging from approximately 11 to 34\u0026deg;C. Dairy farming here is primarily smallholder-based, featuring low to moderate input systems. Farmers typically rely on locally available feed resources such as crop residues, roadside grasses, and limited concentrate supplements. Dhaka Metropolitan City (23.81\u0026deg;N, 90.41\u0026deg;E) is characterized by a highly developed urban dairy production system. Farms in this area usually utilize improved housing facilities, higher levels of concentrate feeding, and regular veterinary and breeding services. Artificial insemination (AI) is common, and animals on these farms often have a higher proportion of exotic genetic material. Mymensingh district (24.75\u0026deg;N, 90.40\u0026deg;E), situated in north-central Bangladesh, represents a peri-urban dairy system characterized by moderate management levels and comparatively better access to veterinary care, extension services, and improved feeding practices than rural areas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy design and animal selection\u003c/h3\u003e\n\u003cp\u003eThe study was carried out as a field-based observational cross-sectional study from January 2021 to January 2022. Data were collected from lactating Holstein Friesian local crossbred (HFLC) cows maintained under smallholder dairy farming systems in the selected regions. A total of 158 lactating HFLC cows were included in the study. Animals were selected based on the availability of reliable production and reproductive records and the farmers' willingness to take part. Only cows with complete information on Holstein Friesian inheritance level, parity, age at first service, and body condition score were included in the final analysis. Data were collected through structured questionnaires, direct farm observations, and verification of available farm records. Each animal was individually identified, and relevant information was recorded at the cow level. Multiple farm visits were carried out throughout the study period to confirm the recorded data and reduce potential recall bias.\u003c/p\u003e\n\u003ch3\u003eClassification of independent variables\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eHolstein Friesian inheritance level\u003c/h2\u003e \u003cp\u003eCows were divided into four genetic groups based on the percentage of Holstein Friesian inheritance level from breeding records and farmer-reported breeding history.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e• 50-\u003c62.5% HF\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e\u0026bull; \u0026ge;\u0026thinsp;62.5-\u0026lt;75% HF\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e\u0026bull; 75% HF\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section4\"\u003e \u003ch2\u003e\u0026bull; ˃75% HF\u003c/h2\u003e \u003cp\u003eThis classification was used to evaluate how exotic inheritance levels influence dairy performance, as crossbreeding with Bos taurus breeds like Holstein Friesian has been widely used to boost milk production in tropical dairy systems.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eParity\u003c/h2\u003e \u003cp\u003eParity was classified into six groups (1\u0026ndash;6) based on the number of completed calvings. Parity is a vital physiological factor that influences milk production and reproductive performance in dairy cattle.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBody Condition Score (BCS)\u003c/h2\u003e \u003cp\u003eBCS was visually assessed using a five-point scale with 0.5-point increments, following the standard dairy cattle body condition scoring system described by Edmonson et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). This system evaluates body fat reserves and indicates the nutritional and metabolic status of dairy cows. For statistical analysis, cows were categorized into three BCS groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e\u0026bull; 2-\u0026lt;3\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e\u0026bull; \u0026ge;\u0026thinsp;3-\u0026lt;4\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section4\"\u003e \u003ch2\u003e\u0026bull; \u0026ge;\u0026thinsp;4\u003c/h2\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eAge at First Service (AFS)\u003c/h3\u003e\n\u003cp\u003eAge at first service was divided into four categories:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e12\u0026ndash;16 months\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e17\u0026ndash;21 months\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e22\u0026ndash;30 months\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e30 months\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThis variable was included to evaluate its potential connection to reproductive performance.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eTraits studied\u003c/h2\u003e \u003cp\u003eThe following productive and reproductive traits were evaluated:\u003c/p\u003e \u003cp\u003eService per conception (SPC): The number of artificial inseminations required to achieve a successful conception.\u003c/p\u003e \u003cp\u003eCalving interval (CI): The time between two consecutive calvings, measured in months.\u003c/p\u003e \u003cp\u003eAverage daily milk yield (ADMY): The average amount of milk produced per day during the lactation period, measured in liters.\u003c/p\u003e \u003cp\u003eLactation length (LL): The total duration of milk production during a lactation cycle, measured in days. These parameters are common indicators of productive and reproductive efficiency in dairy cattle.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData were initially organized using Microsoft Excel and then analyzed with R software version 4.4.2. A General Linear Model (GLM) was used to evaluate the fixed effects of region, Holstein Friesian inheritance level, parity, body condition score, and age at first service on productive and reproductive traits. The statistical model used was as follows:\u003c/p\u003e \u003cp\u003eYijklm\u0026thinsp;=\u0026thinsp;\u0026micro;\u0026thinsp;+\u0026thinsp;Ri\u0026thinsp;+\u0026thinsp;Hj\u0026thinsp;+\u0026thinsp;Bk\u0026thinsp;+\u0026thinsp;Pl\u0026thinsp;+\u0026thinsp;Sm\u0026thinsp;+\u0026thinsp;eijklm\u003c/p\u003e \u003cp\u003eWhere,\u003c/p\u003e \u003cp\u003eYijklm\u0026thinsp;=\u0026thinsp;observed value of the dependent variable\u003c/p\u003e \u003cp\u003e\u0026micro;\u0026thinsp;=\u0026thinsp;overall population mean\u003c/p\u003e \u003cp\u003eRi\u0026thinsp;=\u0026thinsp;fixed effect of region (i\u0026thinsp;=\u0026thinsp;1\u0026ndash;3)\u003c/p\u003e \u003cp\u003eHj\u0026thinsp;=\u0026thinsp;fixed effect of HF inheritance level (j\u0026thinsp;=\u0026thinsp;1\u0026ndash;4)\u003c/p\u003e \u003cp\u003eBk\u0026thinsp;=\u0026thinsp;fixed effect of body condition score (k\u0026thinsp;=\u0026thinsp;1\u0026ndash;3)\u003c/p\u003e \u003cp\u003ePl\u0026thinsp;=\u0026thinsp;fixed effect of parity (l\u0026thinsp;=\u0026thinsp;1\u0026ndash;6)\u003c/p\u003e \u003cp\u003eSm\u0026thinsp;=\u0026thinsp;fixed effect of age at first service (m\u0026thinsp;=\u0026thinsp;1\u0026ndash;4)\u003c/p\u003e \u003cp\u003eeijklm\u0026thinsp;=\u0026thinsp;random residual error, assumed to be normally distributed with mean 0 and variance σe\u003csup\u003e2\u003c/sup\u003e​\u003c/p\u003e \u003cp\u003eLeast squares means were compared using Tukey\u0026rsquo;s multiple comparison test, and statistical significance was declared at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Additionally, Pearson correlation coefficients were calculated to evaluate relationships among productive and reproductive traits.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eProductive and reproductive performance across regions\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe effects of region on productive and reproductive traits of HFLC cows are shown in Fig.\u0026nbsp;2, which clearly shows distinct regional patterns. The region significantly influenced CI, ADMY, and LL (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but SPC was not significantly affected (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.079). Cows in Dhaka Metropolitan City had the longest CI (16.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.299 months), followed by Mymensingh (13.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.338 months), and Gaibandha (12.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.281 months). ADMY showed significant variation among regions, with cows in Dhaka producing the highest yield (17.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.027 L). Cows in Gaibandha and Mymensingh produced 12.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.963 L and 11.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.152 L, respectively. LL also varied significantly across regions, with Dhaka having the longest LL (380.97\u0026thinsp;\u0026plusmn;\u0026thinsp;9.451 days), followed by Mymensingh (304.29\u0026thinsp;\u0026plusmn;\u0026thinsp;10.669 days), and Gaibandha (296.34\u0026thinsp;\u0026plusmn;\u0026thinsp;8.856 days). SPC remained fairly consistent across all regions, ranging from 1.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.112 to 1.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.099.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;2\u003c/b\u003e Productive and reproductive performance of Holstein Friesian local crossbred (HFLC) cows across three regions of Bangladesh (Gaibandha, Mymensingh, and Dhaka Metropolitan City). SPC: Service per conception; CI: Calving interval; ADMY: Average daily milk yield; LL: Lactation length. Points represent mean values, and error bars indicate standard error of mean (SEM)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eProductive and reproductive performance by Holstein Friesian (HF) inheritance level\u003c/h2\u003e \u003cp\u003eThe effects of Holstein Friesian (HF) inheritance level on productive and reproductive traits are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e. HF inheritance level significantly influenced SPC, CI, ADMY, and LL (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Cows with \u0026ge;\u0026thinsp;62.5-\u0026lt;75% HF inheritance had the lowest SPC (1.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.147), while cows with \u0026gt;\u0026thinsp;75% HF inheritance had the highest SPC (2.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.093). Calving interval increased as HF inheritance level rose, with the longest CI observed in cows with \u0026gt;\u0026thinsp;75% HF (15.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.297 months). Average daily milk yield also increased significantly with higher HF inheritance levels. The highest ADMY was seen in cows with \u0026gt;\u0026thinsp;75% HF (17.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.966 L), followed by cows with 75% HF (14.45\u0026thinsp;\u0026plusmn;\u0026thinsp;1.061 L), \u0026ge;\u0026thinsp;62.5-\u0026lt;75% HF (12.52\u0026thinsp;\u0026plusmn;\u0026thinsp;1.530 L), and 50-\u0026lt;62.5% HF (7.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.462 L). Similarly, lactation length varied significantly among inheritance groups, with the longest LL recorded in cows with \u0026gt;\u0026thinsp;75% HF (368.45\u0026thinsp;\u0026plusmn;\u0026thinsp;9.139 days). Overall, a higher HF inheritance level was linked to increased milk production and longer lactation, but it was also associated with lower reproductive efficiency, as shown by higher SPC and longer CI. Cows with\u003c/p\u003e \u003cp\u003emoderate HF inheritance (\u0026ge;\u0026thinsp;62.5-\u0026lt;75%) appeared to balance productive and reproductive performance in field conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eProductive and reproductive performance by parity\u003c/h2\u003e \u003cp\u003eThe effects of parity on productive and reproductive traits are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Parity significantly influenced SPC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), while CI, ADMY, and LL were not significantly affected (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.58). SPC increased with rising parity. The lowest SPC was observed in first-parity cows (1.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.111), and the highest was in sixth-parity cows (2.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.389). CI showed a slight upward trend with parity, ranging from 13.87 months in first and second-parity cows to 15.57 months in fifth-parity cows. ADMY was highest in third and fourth-parity cows (15.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.299 L and 15.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.797 L, respectively). LL varied from 282.22\u0026thinsp;\u0026plusmn;\u0026thinsp;39.905 days in sixth-parity cows to 345.16\u0026thinsp;\u0026plusmn;\u0026thinsp;12.348 days in third-parity cows. SPC decreased with increasing parity, while productive traits (ADMY and LL) remained relatively stable across parities under field conditions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLeast squares means with standard errors for reproductive and productive traits by different parities of HFLC cows\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSPC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCI (months)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eADMY (L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLL (days)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.54\u003csup\u003ea\u003c/sup\u003e\u0026plusmn;0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.56\u0026thinsp;\u0026plusmn;\u0026thinsp;1.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e319.00\u0026thinsp;\u0026plusmn;\u0026thinsp;11.414\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.73\u003csup\u003eab\u003c/sup\u003e\u0026plusmn;0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.08\u0026thinsp;\u0026plusmn;\u0026thinsp;1.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e321.02\u0026thinsp;\u0026plusmn;\u0026thinsp;10.191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.09\u003csup\u003eabc\u003c/sup\u003e\u0026plusmn;0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0. 401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e345.16\u0026thinsp;\u0026plusmn;\u0026thinsp;12.348\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.14\u003csup\u003eabc\u003c/sup\u003e\u0026plusmn;0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e338.53\u0026thinsp;\u0026plusmn;\u0026thinsp;17.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.38\u003csup\u003ebc\u003c/sup\u003e\u0026plusmn;0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.25\u0026thinsp;\u0026plusmn;\u0026thinsp;2.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e327.05\u0026thinsp;\u0026plusmn;\u0026thinsp;26.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.67\u003csup\u003ec\u003c/sup\u003e\u0026plusmn;0.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.28\u0026thinsp;\u0026plusmn;\u0026thinsp;4.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e282.22\u0026thinsp;\u0026plusmn;\u0026thinsp;39.905\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eSPC: Service per conception; CI: Calving interval; ADMY: Average daily milk yield; LL: Lactation length; SEM: Standard error of mean; n: number of observations. Values are expressed as least squares means\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM. Means within a column with different superscripts differ significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eProductive and reproductive performance by Body Condition Score (BCS)\u003c/h2\u003e \u003cp\u003eThe effects of BCS on productive and reproductive traits are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. BCS significantly influenced SPC and LL (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02), while CI and ADMY were not significantly affected (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Cows with BCS\u0026thinsp;\u0026ge;\u0026thinsp;4 had the highest SPC (2.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.224), whereas cows with BCS 2-\u0026lt;3 had the lowest SPC (1.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.089). CI ranged from 14.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.293 to 15.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.741 months among BCS groups. ADMY was not significantly different among BCS groups. However, LL was longest in cows with BCS\u0026thinsp;\u0026ge;\u0026thinsp;4 (360.36\u0026thinsp;\u0026plusmn;\u0026thinsp;22.443 days) and shortest in cows with BCS 3\u0026ndash;4 (312.93\u0026thinsp;\u0026plusmn;\u0026thinsp;8.118 days). Overall, higher BCS was associated with decreased reproductive efficiency (higher SPC) but longer LL, while ADMY remained relatively consistent across BCS groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLeast squares means with standard errors for reproductive and productive traits by different BCS groups of HFLC cows (combined parity)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSPC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCI (months)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eADMY (L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLL (days)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2-\u0026lt;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.79\u003csup\u003ea\u003c/sup\u003e\u0026plusmn;0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e340.69\u003csup\u003eab\u003c/sup\u003e\u0026plusmn;8.876\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.85\u003csup\u003eab\u003c/sup\u003e\u0026plusmn;0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e312.93\u003csup\u003eb\u003c/sup\u003e\u0026plusmn;8.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.39\u003csup\u003eb\u003c/sup\u003e\u0026plusmn;0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.69\u0026thinsp;\u0026plusmn;\u0026thinsp;2.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e360.36\u003csup\u003ea\u003c/sup\u003e\u0026plusmn;22.443\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eSPC: Service per conception; CI: Calving interval; ADMY: Average daily milk yield; LL: Lactation length; SEM: Standard error of the mean; n: number of observations. Values are expressed as least squares means\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM. Means within a column with different superscripts differ significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eProductive and reproductive performance by Age at First Service (AFS)\u003c/h2\u003e \u003cp\u003eThe effects of AFS on productive and reproductive traits are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. AFS significantly influenced SPC, CI, and ADMY, except LL (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.13). The lowest SPC was observed in cows with AFS of 12\u0026ndash;16 months (1.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.095) and \u0026gt;\u0026thinsp;30 months (1.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.132), while the highest SPC was in cows with AFS of 22\u0026ndash;30 months (2.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.115). CI was longest for cows with AFS of 22\u0026ndash;30 months (15.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.381 months). ADMY was highest in cows with AFS of 22\u0026ndash;30 months (15.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.243 L) and lowest in cows with AFS of \u0026gt;\u0026thinsp;30 months (10.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.423 L).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLeast squares means with standard errors for reproductive and productive traits by Age at First Service (AFS) of HFLC cows (combined parity)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFS (months)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSPC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCI (months)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eADMY (L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLL (days)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u0026ndash;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.61\u003csup\u003ea\u003c/sup\u003e\u0026plusmn;0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.90\u003csup\u003ea\u003c/sup\u003e\u0026plusmn;0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.27\u003csup\u003ea\u003c/sup\u003e\u0026plusmn;1.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e330.63\u0026thinsp;\u0026plusmn;\u0026thinsp;9.733\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u0026ndash;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.09\u003csup\u003eb\u003c/sup\u003e\u0026plusmn;0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.69\u003csup\u003ea\u003c/sup\u003e\u0026plusmn;0.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.52\u003csup\u003ea\u003c/sup\u003e\u0026plusmn;1.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e302.12\u0026thinsp;\u0026plusmn;\u0026thinsp;12.361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.31\u003csup\u003eb\u003c/sup\u003e\u0026plusmn;0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.99\u003csup\u003eb\u003c/sup\u003e\u0026plusmn;0.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.93\u003csup\u003ea\u003c/sup\u003e\u0026plusmn;1.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e340.06\u0026thinsp;\u0026plusmn;\u0026thinsp;11.862\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.53\u003csup\u003ea\u003c/sup\u003e\u0026plusmn;0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.09\u003csup\u003ea\u003c/sup\u003e\u0026plusmn;0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.16\u003csup\u003eb\u003c/sup\u003e\u0026plusmn;1.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e335.14\u0026thinsp;\u0026plusmn;\u0026thinsp;13.585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eSPC: Service per conception; CI: Calving interval; ADMY: Average daily milk yield; LL: Lactation length; SEM: Standard error of mean; n: number of observations. Values are expressed as least squares means\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM. Means within a column with different superscripts differ significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eCorrelation among productive and reproductive traits\u003c/h2\u003e \u003cp\u003eThe correlations among productive and reproductive traits of HFLC cows are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The correlation matrix revealed several highly significant relationships regarding Holstein Friesian inheritance level (HFIL). HFIL showed a strong positive and highly significant correlation with ADMY (r\u0026thinsp;=\u0026thinsp;0.55, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as well as significant positive correlations with CI (r\u0026thinsp;=\u0026thinsp;0.28, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and SPC (r\u0026thinsp;=\u0026thinsp;0.24, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). HFIL had a significant negative correlation with AFS and AFC (r = -0.20, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). CI showed a strong positive relationship with ADMY (r\u0026thinsp;=\u0026thinsp;0.41, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and LL (r\u0026thinsp;=\u0026thinsp;0.52, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, SPC was positively correlated with CI (r\u0026thinsp;=\u0026thinsp;0.35, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), ADMY (r\u0026thinsp;=\u0026thinsp;0.23, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and LL (r\u0026thinsp;=\u0026thinsp;0.18, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). ADMY showed a significant positive correlation with LL (r\u0026thinsp;=\u0026thinsp;0.36, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Both AFS and AFC were perfectly correlated with each other (r\u0026thinsp;=\u0026thinsp;1.00, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and showed a significant negative correlation with ADMY (r = -0.28, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, BCS did not show any significant correlations with other productive and reproductive traits in the matrix.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study showed that region significantly influenced CI, ADMY, and LL, while SPC remained unaffected (Fig.\u0026nbsp;2). The lack of regional differences in SPC suggests that estrus detection efficiency and artificial insemination practices were fairly consistent across the studied production systems. Similar findings have been reported in Bangladesh by Zohara et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Miah et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who found that SPC of Holstein Friesian crossbred cows did not vary significantly among regions or farm categories. This indicates that reproductive management practices, especially insemination services, may be relatively uniform across different dairy environments. In contrast, CI varied significantly among regions, with cows in Dhaka Metropolitan City showing longer calving intervals compared to those in Gaibandha and Mymensingh. Similar regional differences in reproductive performance have been documented in tropical dairy systems, where environmental factors, nutrition management, and genetics interact to influence fertility outcomes. Urban dairy farms often keep cows with more exotic genetics and higher milk production potential, which may cause increased metabolic stress during early lactation and delay ovarian activity recovery. Studies on crossbred dairy cattle in tropical environments indicate that high-producing cows often have longer postpartum anestrus and extended calving intervals when nutritional management is poor (Azad et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Habib et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Regional differences in milk production were also observed, with cows in Dhaka producing significantly higher ADMY than those in Gaibandha and Mymensingh. This aligns with previous studies showing that crossbred dairy cow milk production varies greatly across environments due to variations in feeding, housing, and veterinary access (Miah et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zohara et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Urban farms typically provide higher concentrate supplementation and better healthcare, which may enhance milk production. Additionally, cows in urban farms often have a higher proportion of Holstein Friesian germplasm, further boosting milk yield. Lactation length also followed a similar regional trend, with the longest lactation observed in Dhaka. Extended lactation may reflect both genetic potential and management decisions related to breeding and culling. In intensive dairy systems, farmers might intentionally extend lactation to maintain a steady milk supply, especially when cows experience delayed conception. Previous studies in Bangladesh and other tropical countries also show significant differences in lactation length across farming systems, highlighting the strong influence of management and environmental factors on lactation persistence (Galukande et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Miah et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHolstein Friesian (HF) inheritance level significantly influenced both productive and reproductive traits in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Cows with higher HF inheritance produced more milk but had poorer reproductive performance, while those with moderate HF inheritance showed better reproductive efficiency. These results highlight the common trade-off between productivity and adaptability in crossbred dairy cattle raised in tropical conditions. The increase in SPC and CI with greater HF inheritance suggests decreased reproductive efficiency in cows with more exotic germplasm. Similar patterns have been documented in several studies conducted in tropical and subtropical regions (Tsegaye et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Adhikary et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A high level of Bos taurus inheritance may impair heat tolerance, disease resistance, and overall adaptability to tropical environments, negatively affecting reproductive performance. Heat stress, nutritional deficiencies, and increased metabolic demands related to higher milk production are known to disrupt endocrine function and delay ovarian activity in high-producing cows. Conversely, cows with moderate HF inheritance (around 50\u0026ndash;75%) exhibited shorter CI and lower SPC, indicating improved reproductive efficiency under field conditions. Previous research suggests that intermediate levels of exotic inheritance often strike an optimal balance between productivity and environmental adaptability in tropical dairy systems (Galukande et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Crossbreeding strategies that maintain HF inheritance at moderate levels may be better suited for smallholder dairy systems where feed resources and management are limited. As expected, milk production increased significantly with higher HF inheritance. Cows with \u0026gt;\u0026thinsp;75% HF inheritance produced the highest ADMY, reflecting the strong genetic potential of Holstein Friesian cattle for milk production. Similar links between HF inheritance level and milk yield have been observed in Bangladesh and other tropical countries (Mamun et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Rahman et al. 2017; Azad et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The increased milk production in cows with more HF inheritance may result from better mammary gland development, higher metabolic capacity, and superior genetic potential for lactation. Lactation length also increased with higher HF inheritance levels, with cows possessing more HF blood showing longer lactation periods. This aligns with earlier reports indicating that high-producing crossbred cows tend to sustain longer lactation cycles due to improved milk production (Hossen et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Miah et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, prolonged lactation may also be linked to delayed conception, leading to longer calving intervals. The findings highlight a clear trade-off between productive and reproductive performance related to increasing HF inheritance. While higher levels of exotic germplasm boost milk yield, moderate inheritance levels seem to offer a better balance between productive and reproductive efficiency under the current environmental and management conditions in Bangladesh.\u003c/p\u003e \u003cp\u003eParity had a significant influence on SPC in this study, while CI, ADMY, and LL were not significantly affected (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The consistent increase in SPC with higher parity suggests declining reproductive efficiency in older cows. Age-related physiological changes, accumulated metabolic stress, and increased susceptibility to reproductive problems may lead to reduced fertility in later parities. Similar patterns have been observed in dairy cattle within tropical production systems, where reproductive efficiency tends to decrease as cows age (Haque et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Qureshi et al. 2020). The lack of a significant parity effect on CI contrasts with some earlier studies that identified parity as a key factor in reproductive success (Islam and Kundu 2011; Wassie and Mekuriaw 2015). However, in smallholder farming systems, the effect of parity on CI may be masked by differences in nutrition, management, and breeding practices across farms. Although parity did not significantly affect ADMY, milk yield generally increased up to middle parities before stabilizing or slightly declining in later parities. This pattern aligns with the natural development of dairy cows, as milk production typically rises with maturity and peaks around the third or fourth lactation (Berry et al. 2003; Qureshi et al. 2020). The lack of statistical significance in this study may be due to variability in feeding practices and the relatively small sample sizes in higher parity groups. Similarly, LL did not differ significantly among parities, although slight variations were observed. Previous research indicates that LL can be influenced by parity, reproductive management, and environmental factors (Das et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The relatively consistent LL observed here suggests that management practices may have a greater influence than physiological factors in determining LL under field conditions.\u003c/p\u003e \u003cp\u003eBody condition score (BCS) significantly influenced SPC and LL but did not affect CI or ADMY (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The increase in SPC observed in cows with higher BCS suggests reduced reproductive efficiency in over-conditioned animals. Excess fat accumulation may impair ovarian function and disturb hormonal balance, potentially decreasing conception rates. Previous studies have shown that both very low and very high BCS can negatively impact fertility in dairy cows (Roche et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Khaton \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The absence of a significant BCS effect on CI in this study aligns with findings by Rahman et al. (2017), who highlighted that calving interval is influenced by multiple factors, including nutrition, health, management practices, and genetics. Although cows with higher BCS tended to have slightly longer CI, this difference was not significant. Similarly, ADMY remained relatively stable among BCS groups, although cows with higher BCS exhibited slightly increased milk production. Adequate energy reserves are essential for maintaining lactation, but excessive body condition may raise the risk of metabolic disorders that can reduce productivity (Berry et al. 2003). LL was significantly longer in cows with higher BCS, indicating that better nutritional status may support sustained milk production. Sufficient body reserves supply the energy needed to maintain lactation during periods of negative energy balance, especially in early lactation. However, excessively high BCS may also be linked to delayed conception, which can indirectly prolong lactation.\u003c/p\u003e \u003cp\u003eAge at first service (AFS) significantly influenced SPC in HFLC cows (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Although SPC tended to be lower in cows with AFS at 12\u0026ndash;16 months and \u0026gt;\u0026thinsp;30 months, and relatively higher at 17\u0026ndash;21 and 22\u0026ndash;30 months, the pattern varied and was not consistent. This indicates that SPC may not depend solely on AFS but could also be influenced by management, nutrition, and environmental factors. Similar results have been observed in crossbred dairy cattle, where no clear relationship between AFS and the number of SPC was found. Abera et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) reported an overall mean SPC of 1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42 in crossbred cows, with no significant link to AFS. Furthermore, studies with Holstein Friesian heifers showed that earlier breeding does not necessarily impair fertility during the first lactation (Sakaguchi et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). AFS also significantly influenced CI. In this study, the longest CI was observed in cows with AFS at 22\u0026ndash;30 months, while shorter CIs were recorded in other groups. The absence of a clear trend suggests that CI may be affected by multiple interacting factors rather than AFS alone. Abera et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) documented an average CI of 14.64\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2 months in crossbred cows, with no clear connection to AFS. Similarly, controlled research with Holstein Friesian heifers reported comparable CIs among early and late bred animals, implying that earlier breeding may not extend CI if animals are well managed, including proper nutrition (Sakaguchi et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Kabir and Kisku (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) noted that variations in CI are often linked to environment, nutrition, reproductive management, and estrous irregularities. A significant effect of AFS on ADMY was also observed. Cows bred between 12 and 30 months produced higher ADMY compared to those bred after 30 months. This difference may partly relate to genetic factors, especially Holstein Friesian (HF) inheritance levels. Rahman et al. (2017) found that higher HF inheritance level increases milk production, but Adhikary et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported no significant association between age at puberty and ADMY in HF crossbred cows. Nonetheless, existing evidence from crossbred populations does not consistently support either earlier or later AFS for higher milk yield. Abera et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) reported an average yearly milk yield of 3386\u0026thinsp;\u0026plusmn;\u0026thinsp;898 L and a lactation length of 11.96\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5 months in crossbred cows, with no significant effect of AFS. Similarly, studies with Holstein Friesian heifers indicated that early first breeding did not reduce first-lactation milk production or overall productivity when animals were properly managed (Sakaguchi et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). AFS also significantly influenced LL. The longest LL was observed in cows with AFS at 22\u0026ndash;30 months, while cows serviced at 17\u0026ndash;21 months had shorter LL. Variations in LL can be related to breed traits, feeding practices during lactation, and herd management strategies. Qureshi (2000) suggested that management, nutrition, and farmer decisions about lactation persistence can significantly influence LL. However, as with other production traits, previous research shows limited consistent evidence linking AFS to LL in crossbred dairy cattle.\u003c/p\u003e \u003cp\u003eThe correlation analysis revealed several highly significant relationships among productive and reproductive traits of HFLC cows (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The relationship between CI and ADMY showed a strong positive and highly significant correlation (r\u0026thinsp;=\u0026thinsp;0.41, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This finding contrasts with Qureshi et al. (2000), who reported a significant negative correlation (-0.67) between CI and ADMY in dairy cattle in Pakistan. This may be due to differences in genetic makeup, feeding management, and production environment between the two populations. The strong positive relationship indicates that high-yielding cows tend to experience delayed conception, leading to longer CIs. A moderate and significant positive correlation was found between CI and LL (r\u0026thinsp;=\u0026thinsp;0.52). Uddin et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) also reported a positive association between these traits. This suggests that cows with longer CIs generally have longer lactation periods, possibly because delayed conception extends the current lactation cycle. HFIL showed a strong positive correlation with ADMY (r\u0026thinsp;=\u0026thinsp;0.55, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similar findings have been observed in crossbred dairy cattle, where increasing the proportion of Holstein Friesian genetics significantly improves milk production (Hossain et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Haque et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These results support the benefit of incorporating Holstein Friesian genetics to increase milk yield in crossbred cattle within tropical production systems. However, HFIL also exhibited significant positive correlations with reproductive intervals, specifically CI (r\u0026thinsp;=\u0026thinsp;0.28, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and SPC (r\u0026thinsp;=\u0026thinsp;0.24, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This indicates that, while increased exotic inheritance boosts productive traits, it may also reduce reproductive performance. Similar findings were reported by Islam and Kundu (2012), who observed that higher exotic inheritance improved productivity but often led to declined reproductive performance under certain management and environmental conditions. A perfect positive correlation was found between AFS and AFC (r\u0026thinsp;=\u0026thinsp;1.00), indicating that delayed first service directly results in delayed first calving. Both AFS and AFC showed a significant negative correlation with ADMY (r = -0.28, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Overall, these results demonstrated that productive traits, especially ADMY, are positively linked to HF inheritance level, while reproductive traits like higher CI and SPC also tend to increase with exotic genetics. The findings highlight a clear biological trade-off between milk production and reproductive efficiency, influenced by factors such as genotype, body condition, parity, and management environment. Urban farms with high HF inheritance and higher BCS tend to produce more milk but have reduced reproductive performance. Conversely, rural and peri-urban systems with moderate HF inheritance level support better fertility but lower milk yields. For sustainable smallholder dairy farming in tropical environments, maintaining a moderate HF inheritance level (50\u0026ndash;75%), optimal BCS (around 3), and proper parity management can balance productivity and reproduction, maximizing both economic returns and herd health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no conflicts of interest concerning this publication.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthical approval\u003c/h2\u003e \u003cp\u003eThe study involved non-invasive field data collection from dairy farms. Farmers were informed about the study's objectives before participation, and verbal consent was obtained prior to data collection. This is an observational study. Bangladesh Agricultural University Research Ethics Committee has confirmed that no ethical approval is necessary.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe study was funded by the National Science and Technology Fellowship, Ministry of Science and Technology, Government of Bangladesh.\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eNushrat Nourin Lisa: Writing - original draft, methodology, investigation, data curation, formal analysis, review, and editing. Md Nahid Hassan Chawdhury: Methodology, investigation, visualization, data interpretation, review, and editing. Mohammad Mahbubul: Review and editing. Md Ruhul Amin: Conceptualization, project design, methodology, review, and editing.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe authors sincerely thank the dairy farmers from Gaibandha, Dhaka Metropolitan City, and Mymensingh for their cooperation in this study and for granting access to their farm records. They also appreciate the field assistants and technical staff who supported data collection throughout the study period.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe datasets generated and/or analyzed during this study are not publicly available but can be obtained from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbera Z, Mengiste B, Demise T (2016) Reproductive and lactation performance of crossbreed dairy cows in Bishoftu, Ada\u0026rsquo;a District of East Shoa, Eastern Ethiopia. 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Iran J Appl Anim Sci 5:35\u0026ndash;40\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZohara BF, Rita SA, Rahman SML, Ali MA, Alam MS, Islam MF (2019) Reproductive and productive performance of dairy cows in different upazila of Dinajpur district. Bangl Livest J 1:51\u0026ndash;55\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"tropical-animal-health-and-production","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trop","sideBox":"Learn more about [Tropical Animal Health and Production](https://www.springer.com/journal/11250)","snPcode":"11250","submissionUrl":"https://submission.nature.com/new-submission/11250/3","title":"Tropical Animal Health and Production","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Body condition score, Holstein Friesian, Inheritance level, Milk production, Parity, Reproductive performance","lastPublishedDoi":"10.21203/rs.3.rs-9124650/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9124650/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study evaluated the associations of region, Holstein Friesian (HF) inheritance level, parity, Body Condition Score (BCS), and Age at First Service (AFS) with the productive and reproductive performance of Holstein Friesian local crossbred (HFLC) cows in Bangladesh. Data were collected from 158 lactating HFLC cows across Gaibandha, Dhaka Metropolitan City, and Mymensingh from January 2021 to January 2022. Productive traits included Average Daily Milk Yield (ADMY) and Lactation Length (LL), while reproductive traits included Service Per Conception (SPC) and Calving Interval (CI). Region significantly influenced CI, ADMY, and LL, with urban farms in Dhaka producing more milk and experiencing longer lactations, while rural farms in Gaibandha had shorter CI. HF inheritance level significantly influenced all productive and reproductive traits. Cows with 50-\u0026lt;62.5% and \u0026ge;\u0026thinsp;62.5-\u0026lt;75% HF inheritance level had lower SPC and shorter CI, whereas cows with \u0026gt;\u0026thinsp;75% HF inheritance level showed higher ADMY and longer LL. Parity did not influence CI, ADMY, or LL but significantly influenced SPC, with the lowest SPC observed in first-parity cows. BCS was associated with SPC and LL but not with CI or ADMY. Cows with moderate BCS exhibited lower SPC, while those with higher BCS had longer LL. AFS significantly influenced SPC, CI, and ADMY, emphasizing the importance of first breeding age on reproductive and productive outcomes. Overall, higher HF inheritance levels were linked to increased milk production but reduced reproductive efficiency, whereas moderate HF inheritance levels were associated with better reproductive performance under field conditions in Bangladesh.\u003c/p\u003e","manuscriptTitle":"Effects of region, Holstein Friesian inheritance level, parity, body condition score, and age at first service on productive and reproductive performance of Holstein Friesian crossbred dairy cows in Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-07 19:54:49","doi":"10.21203/rs.3.rs-9124650/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-04-06T06:01:13+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-01T11:47:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-20T12:20:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Tropical Animal Health and Production","date":"2026-03-17T13:48:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"tropical-animal-health-and-production","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trop","sideBox":"Learn more about [Tropical Animal Health and Production](https://www.springer.com/journal/11250)","snPcode":"11250","submissionUrl":"https://submission.nature.com/new-submission/11250/3","title":"Tropical Animal Health and Production","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f0f58609-5ee1-4faa-937e-20ac74f5511f","owner":[],"postedDate":"April 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T19:54:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-07 19:54:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9124650","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9124650","identity":"rs-9124650","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}