Impact of Farm Structure on Estrus Detection and Reproductive Performance in Dairy Herds: Evidence from Turkey

Impact of Farm Structure on Estrus Detection and Reproductive Performance in Dairy Herds: Evidence from Turkey | 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 Impact of Farm Structure on Estrus Detection and Reproductive Performance in Dairy Herds: Evidence from Turkey Un Huseyin, Alper Kocyigit This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8448296/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Medium-sized, family-run dairy farms predominate in Turkey, yet field-based evidence from developing-market settings remains limited regarding how farm structure and estrus detection quality relate to reproductive performance. This study examined associations between structural farm characteristics, estrus detection scores, and pregnancy rates across 92 dairy enterprises, using data from 210 cows selected through purposive sampling based on active estrus signs. Farm-level variables (e.g., herd size, housing type, bull presence, personnel) were recorded, and estrus intensity was assessed prior to artificial insemination using a modified Eerdenburg scoring approach; a post-hoc power analysis indicated high statistical power (> 95%). Results Structural characteristics showed no statistically significant association with pregnancy rate (p > 0.05), a pattern plausibly consistent with limited between-farm structural variability within the study region. In contrast, estrus detection scores demonstrated a strong positive relationship with pregnancy outcomes (p < 0.001), indicating that higher clinical estrus intensity scores were associated with improved conception success. Conclusions Within this relatively structurally homogeneous farm context, reproductive outcomes appear to be more strongly aligned with the accuracy and quality of biological estrus detection than with physical infrastructure differences. These findings support the inference that prioritizing workforce training focused on clinical estrus scoring may represent a more cost-effective route to improving reproductive efficiency and sustaining economic viability than immediate, capital-intensive structural investments, while acknowledging that the purposive sampling design may limit generalizability beyond cows presenting overt estrus signs. Dairy cattle Estrus detection Farm structure Pregnancy rate Artificial insemination Figures Figure 1 Background Reproductive management is essential for the economic efficiency and sustainability of dairy enterprises [ 1 ]. Implementing effective reproductive strategies increases the calving rate per cow and shortens the calving interval, helping to maintain stable milk production [ 2 ]. A critical component of this success is the accurate and timely detection of estrus. Because the effectiveness of artificial insemination relies heavily on identifying the physiological and behavioral signs of the peri-ovulatory period, missed estrus is widely recognized as a key constraint in practice [ 3 ]. Detecting estrus has become increasingly difficult in modern high-yielding herds. Cows often show shorter duration of estrus and less obvious clinical signs, which can contribute to missed heats and mistimed inseminations [ 4 ]. Issues such as subestrus, limited observation time, and a lack of trained staff further contribute to detection errors. Consistent with this, poor estrus detection rates are associated with lower pregnancy rates and higher infertility, with implications for farm-level economic performance [ 3 , 5 ]. This supports the view that reproductive success depends not just on biology, but also on the structural characteristics of the farm [ 6 ]. Factors such as farm size, housing systems, and workforce quality are key determinants of success. In smaller herds, individual monitoring is easier, often resulting in better detection rates compared to large-scale operations where the workload limits visual observation [ 7 , 8 ]. Housing plays a similar role; cows in loose housing systems can display natural behaviors more freely, facilitating observation. In contrast, tie-stall systems restrict movement and may attenuate behavioral signs [ 9 , 10 ]. The presence of bulls is another factor often discussed in the literature. Some evidence suggests that biostimulation from bulls can enhance estrus signs and shorten the postpartum anestrus period [ 11 , 12 ]. However, results are inconsistent; other studies report limited effects, particularly in high-producing cows, suggesting that the benefit depends heavily on specific management conditions [ 4 , 5 , 13 , 14 ]. In addition, the "human factor" is critical but may receive less systematic attention in routine herd management. The number of staff, their training levels, and the farmer's experience influence how accurately estrus signs are interpreted and whether postpartum cows receive timely intervention [ 15 , 16 , 17 ]. Inexperienced or untrained personnel are more likely to misclassify signs, leading to lower pregnancy outcomes. This highlights the practical importance of regular training, especially in larger herds [ 16 , 17 ]. While reproductive physiology is universal, management practices vary significantly by region. In Turkey, the dairy sector consists mainly of medium-sized, family-run enterprises. However, most existing research comes from large-scale, intensive systems or controlled experiments that do not fully reflect field realities. There is a lack of data specifically evaluating how farm structure interacts with reproductive performance in this local context. Bridging this gap is necessary to provide veterinarians and breeders with practical, evidence-based recommendations. Therefore, this study examines the relationship between the structural characteristics of dairy enterprises, such as farm size, housing type, bull presence, and personnel experience, and pregnancy rates following artificial insemination. The aim is to reveal the current status of reproductive management in the region and compare these field findings with the broader literature. Based on previous studies, we hypothesized that favorable conditions, such as loose housing, smaller herd sizes, and trained staff, would be associated with higher estrus detection scores and improved pregnancy rates. Results Descriptive Characteristics of the Farms Among the 92 farms included in the study, most were medium-sized (10 to 50 head; 50.5%), and the tie-stall system was the predominant housing type (76.2%). Bulls were present in 29.5% of farms, and 53.3% reported employing supporting personnel. Most farmers (93.3%) had more than five years of experience, and cows constituted 83.3% of the evaluated animals. The overall pregnancy rate was 72.9%, and the mean estrus score was 126.40 ± 45.58. Relationships Between Estrus Score and Farm Parameters Analysis of estrus score in relation to farm characteristics indicated a statistically significant difference by pregnancy status (p < 0.05). Animals diagnosed as pregnant had higher estrus scores than those that did not conceive (Table 1 ). By contrast, estrus scores did not differ significantly by farm size, housing type, bull presence, availability of supporting personnel, animal category, or farmer experience (p > 0.05). Table 1 Distribution of the relationship between estrus score and farm-related parameters Variable Category n Median estrus score (Min-Max) Test statistic p Herd size (no. of animals) 50 13 140(75–175) a Housing type Tie/stall 161 140(35–175) a 1.199 2 0.549 Loose/free 14 140(75–175) a Semi-open 35 140(40–175) a Bull Present 61 140(35–175) 4.600 3 0.974 Absent 149 140(35–175) Supporting staff Present 12 140(35–175) 5.491 3 0.993 Absent 98 140(35–175) Animal type Heifer 35 140(35–175) 3.487 3 0.182 Cow 175 140(35–175) Farmer experience 5 years 196 140(35–175) Pregnancy Pregnant 153 140(40–175) 2.983 3 < 0.001* Non-pregnant 57 135(35–175) ²Kruskal–Wallis H test, ³Mann–Whitney U test, Min–Max: Minimum–Maximum. Different superscript letters indicate statistically significant differences. *p < 0.05 Relationships Between Pregnancy Rate and Farm Parameters Associations between pregnancy rate and farm size, housing type, bull presence, availability of supporting personnel, animal category, and farmer experience are summarized in Table 2 . None of these variables showed a statistically significant association with pregnancy rate in the present dataset (p > 0.05). Table 2 Distribution of the relationship between pregnancy rate and farm-related parameters Pregnant Non-pregnant p n % n % Herd size (no. of animals) 50 9 5.9 4 7.0 Housing type Tie/stall 120 78.4 40 70.2 0.130 Loose/free 7 4.6 7 12.3 Semi-open 26 17.0 10 17.5 Bull Present 44 28.8 18 31.6 0.690 Absent 109 71.2 39 68.4 Supporting staff Present 85 55.6 27 47.4 0.290 Absent 68 44.4 30 52.6 Animal type Heifer 23 15.0 12 21.1 0.298 Cow 130 85.0 45 78.9 Farmer experience 5 years 144 94.1 52 91.2 The overall pregnancy rate observed in the farms was 72.9%, and estrus score was significantly associated with pregnancy outcome. In contrast, farm structural characteristics, including size, housing type, bull presence, supporting personnel, and farmer experience, were not significantly associated with pregnancy rate (Fig. 1 ). Discussion This study evaluated whether selected structural characteristics of dairy enterprises in the Suluova district of Amasya province were associated with artificial insemination outcomes. In contrast to our a priori hypothesis, none of the assessed structural parameters showed a statistically significant association with pregnancy rate. Instead, estrus score was the variable most clearly associated with pregnancy status, with higher scores observed among animals that conceived. Taken together, these findings are compatible with the view that, within the production context represented by the present dataset, the quality and timing of estrus identification may be more influential for insemination success than between-farm differences in physical infrastructure. The importance of estrus detection for reproductive performance is well described. Previous work has linked inadequate estrus detection with reduced pregnancy rates and increased infertility [ 3 , 5 ]. Roelofs et al. (2010) also estimated that estrus detection errors contribute substantially to economic losses in the dairy sector. In this context, the positive association observed here between estrus score and pregnancy outcome aligns with published evidence and supports the practical relevance of accurate estrus recognition and timely insemination. Farm size has been proposed as an indirect determinant of reproductive performance, largely through its effect on observation intensity and labor allocation [ 18 , 19 ]. Smaller herds may allow more consistent individual monitoring, potentially improving estrus detection [ 20 ], whereas larger operations can face constraints in observation time per animal, which may reduce detection efficiency [ 21 ]. The lack of a measurable association in the present study may reflect the distribution of herd sizes in the region, where most participating farms were medium-scale (10 to 50 head) and very large herds were relatively uncommon. A related consideration is the limited structural variability across farms, as the sample largely reflects the regional, medium-sized family enterprise model. In addition, the purposive sampling strategy, which enrolled only animals already exhibiting clinical signs of estrus and judged suitable for insemination, may have reduced the ability to detect structural effects that operate earlier in the pathway, such as factors influencing estrus expression or the probability of heat being noticed. Under this sampling frame, once estrus was expressed and recognized, conception outcome may have depended more on animal-level readiness than on farm-level constraints. Housing system is another structural feature often discussed in relation to estrus expression. Loose housing, by enabling greater freedom of movement, is commonly considered to facilitate natural estrus behaviors and thus improve detection [ 13 , 17 ]. In the present study, pregnancy rate did not differ significantly between housing types. This finding could be partly explained by the small number of farms using loose housing systems, which limits statistical contrast, or by compensatory management in tie-stall farms. For example, experienced farmers in tie-stall settings may increase observation frequency or rely on additional cues, which could mitigate disadvantages related to restricted movement. The influence of bull presence remains debated. While some studies suggest that bull-associated stimuli, including pheromonal cues, may enhance estrus behaviors [ 11 ], other reports have not demonstrated consistent effects [ 13 ]. In our dataset, bull presence was not significantly associated with pregnancy rate. One plausible explanation is that bulls were not routinely used for natural service in most farms, where artificial insemination was the primary breeding method. Under such conditions, any potential biostimulatory effect may be modest or context-dependent, and may not translate into measurable differences in pregnancy outcome. Human resources and farmer experience are frequently cited as determinants of reproductive performance, particularly through their effects on estrus detection accuracy and insemination timing [ 5 , 15 ]. The absence of a significant association in the present analysis may reflect the limited variability in this domain, as most farmers reported more than five years of experience. When experience level is relatively homogeneous, between-group differences can be difficult to detect even if human factors are important in principle. Conclusion Within the context represented by these medium-sized dairy enterprises in Turkey, structural characteristics such as herd size, housing type, and bull presence were not statistically associated with pregnancy rate following artificial insemination. In contrast, estrus score showed a significant association with pregnancy outcome, indicating that the intensity and clarity of estrus signs, as captured by the scoring approach, tracked insemination success in this dataset. These results suggest that management effectiveness, particularly accurate estrus detection and appropriate timing of insemination, may offset certain structural constraints in comparable medium-scale systems. From a practical standpoint, strategies focused on strengthening clinical observation skills and routine training of veterinarians and breeders may be at least as relevant as investments targeting structural expansion, although the present findings should be interpreted in light of the study’s sampling frame and the limited variability in some farm-level characteristics. Methods Study Area and Animals The study was conducted in privately owned commercial dairy farms affiliated with the Amasya Cattle Breeders’ Association in the Suluova district of Amasya province (Turkey). Farms were visited upon owners’ request for routine veterinary reproductive management services; animals were not purchased, transferred, or housed for research purposes. Within the scope of the research, 92 farms were visited and a total of 210 female animals were evaluated. Farms were selected to reflect the regional production model, which is predominantly composed of medium-sized enterprises. Selection also considered broadly similar structural features to limit heterogeneity across holdings. The study did not involve the creation of a dedicated animal group or the use of experimental animals. The animals were privately owned dairy cattle maintained in commercial farms whose owners requested routine veterinary services. Written informed consent was obtained from farm owners/managers for participation and for the use of farm and animal records/observations in this research. No animals were euthanised or sacrificed for the purposes of this study, and no anaesthesia, injections, or medications were administered as part of the research protocol. Animal selection followed a purposive sampling approach. Rather than enrolling all animals within each herd, only cows that displayed clinical signs of estrus and were considered suitable for artificial insemination during the study period were included. Accordingly, the sample size (n = 210) represents the subset of actively cycling animals inseminated within the defined observation window, not the total herd population. Semen doses used for artificial insemination were evaluated at the Andrology Laboratory of the Department of Reproduction and Artificial Insemination, Faculty of Veterinary Medicine, Ondokuz Mayis University. Only semen samples with motility greater than 60% were included in the study. Estrus Scoring and Artificial Insemination Estrus detection was performed using a revised scoring system based on the Eerdenburg Scale [ 22 ]. Following estrus notification by the farmer, a clinical history was obtained and the estrus scoring chart for the relevant animal was completed. The scoring framework incorporated locomotor activity (35 points), mucus discharge (5 points), standing heat (100 points), and rectal examination findings (35 points). Animals receiving a total score of 35 or higher were inseminated using the rectovaginal method. Pregnancy diagnosis was performed by a veterinarian via rectal palpation starting on day 60 after insemination. Evaluation of Farm Structure After estrus scoring and insemination, structural characteristics of each farm were recorded using a standardized farm information form. Farm size, housing type, bull presence, availability of supporting personnel, animal category (cow or heifer), and farmer experience were documented based on researcher observations and farmer statements. Statistical Analysis Data were analyzed using IBM SPSS Statistics 26. Descriptive statistics included the mean, standard deviation, median, minimum, and maximum values. Normality of distribution was assessed using the Shapiro-Wilk test. For normally distributed variables, one-way analysis of variance (one-way ANOVA) was applied. For non-normally distributed variables, the Mann-Whitney U test was used when comparisons involved two categories, and the Kruskal-Wallis test was used for comparisons involving more than two categories. Multiple comparisons were conducted using Tukey and Dunn tests, as appropriate. Associations between categorical variables were assessed using Pearson’s chi-square test, whereas relationships between continuous variables were examined using Pearson correlation analysis. Statistical significance was set at p < 0.05 for all tests. A post hoc power analysis was conducted to assess sample size adequacy. Based on the total of 210 animals (153 pregnant and 57 non-pregnant), the analysis indicated power greater than 0.95 to detect a medium effect size (Cohen’s d = 0.5) at a Type I error rate of α = 0.05. This suggests that the sample size was sufficient to detect statistically significant differences in estrus scores between groups under the stated assumptions. Declarations Acknowledgements The authors gratefully acknowledge the invaluable contributions of the research fellows in the Department of Reproduction and Artificial Insemination. Authors’ contributions U.H. and K.A. conceived and designed the study; U.H. conducted the field work; K.A. performed the statistical analysis and edited the manuscript. All authors interpreted the data, critically reviewed the manuscript for important intellectual contents and approved the final version. Fundings: This study was supported by Ondokuz Mayis University Scientific Research Projects Coordination Unit with project number 4994. Data availability Data are provided within the manuscript or supplementary information files. Ethics approval and consent to participate This study was based on routine clinical veterinary practice conducted for diagnostic and therapeutic purposes and on the analysis of routine farm/clinical records and observations. No animal experimentation and no research-driven invasive procedures were performed. The study was conducted within the Turkish national framework governing animal experiments ethics committees (Regulation on the Working Procedures and Principles of Animal Experiments Ethics Committees, Official Gazette: 15 February 2014, No. 28914), under which non-experimental clinical veterinary practice is addressed outside the remit of animal experimentation review. Given the non-experimental nature of the work, formal IACUC/HADYEK approval was not applicable under this framework. Written informed consent was obtained from farm owners/managers for participation and for the use of farm and animal records/observations in this research. All clinical procedures were carried out in line with applicable legislation and within the framework of Good Veterinary Practices (GVP). No animals were euthanised or sacrificed for the purposes of this study, and no anaesthesia, injections, or medications were administered as part of the research protocol. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Giordano J. Economic impact of reproductive performance in dairy herds and approaches for program selection. Clin Theriogenol. 2019;11:329–35. Belandria G. Reproductive management strategies and efficiency in dairy herds: a review. Vet Sci. 2023;10:215. Roelofs JB, López-Gatius F, Hunter RHF, et al. When is a cow in estrus? Clinical and practical aspects. Theriogenology. 2010;74:327–44. Tippenhauer M. Estrus expression in high-yielding dairy cows: behavioral and endocrine changes. Reprod Domest Anim. 2021;56:1215–24. Crowe MA, Hostens M, Opsomer G. Reproductive management in dairy cows-the future. Ir Vet J. 2018;71:1–1. Wang Z, Liu H, Li X, et al. Effects of herd size and management practices on reproductive performance in dairy cattle. Animals. 2023;13:688. Wang J, Zhang Y, Wang J, Zhao K, Li X, Liu B. Using machine-learning technique for estrus onset detection in dairy cows from acceleration and location data acquired by a neck-tag. Biosyst Eng. 2022;214:193–206. Perez Marquez HJ, Ambrose DJ, Bench CJ. Behavioral changes to detect estrus using ear-sensor accelerometer compared to in-line milk progesterone in a commercial dairy herd. Front Anim Sci. 2023;4:1149085. Sawa A, Bogucki M. Relationship between housing system and reproductive performance of dairy cows. Arch Anim Breed. 2011;11:265–75. Rios Mohar JA, López Díaz CA, Hernández Cerón J, et al. Economic analysis of different pregnancy rates in dairy herds under intensive management. Vet Mex OA. 2022;12:e401. Akhtar M. Effect of bull pheromones on estrus expression in dairy cows. J Anim Reprod Sci. 2014;149:110–5. Choudhary S, Kamboj ML, Sahu D, et al. Effect of biostimulation on growth rate and reproductive development of Bos indicus dairy heifers. Trop Anim Health Prod. 2022;54:138. Shipka MP, Ellis LC. No effects of bull exposure on expression of estrous behavior in high-producing dairy cows. Appl Anim Behav Sci. 1998;57:1–7. Orihuela A. Some factors affecting the behavioural manifestation of oestrus in cattle: a review. Appl Anim Behav Sci. 2000;70:1–16. Wicaksono A, Edwardes F, Steeneveld W, et al. The economic effect of cow-based reproductive management programs with a systematic use of reproductive hormones. J Dairy Sci. 2024;107:11016–35. Derks M, van Werven T, Hogeveen H, Kremer WD. Veterinary herd health management programs on dairy farms in the Netherlands: use, execution, and relations to farmer characteristics. J Dairy Sci. 2013;96:1623–37. Wicaksono A, Steeneveld W, van Werven T, et al. Knowledge, attitude and behaviour of farmers towards the use of reproductive hormones in dairy cattle. Animal. 2025;19:101470. Caraviello DZ, Weigel KA, Fricke PM, Wiltbank MC, Florent MJ, Cook NB, et al. Survey of management practices on reproductive performance of dairy cattle on large US commercial farms. J Dairy Sci. 2006;89:4723–35. Consentini CE, Wiltbank MC, Sartori R. Factors that optimize reproductive efficiency in dairy herds with an emphasis on timed artificial insemination programs. Animals. 2021;11:301. Fodor I, Ózsvári L. Estrus detection and its impact on reproductive and economic performance in large dairy herds. Anim Welf Etol Tartastechnol. 2019;15:18–28. Walsh DP, Fahey AG, Mulligan FJ, et al. Effects of herd fertility on the economics of sexed semen in a high-producing, pasture-based dairy production system. J Dairy Sci. 2021;104:3181–96. Van Vliet JH, Van Eerdenburg FJCM, Noordhuizen JPTM. The use of a behaviour score for oestrus detection in dairy cattle. Vet Rec. 1996;139:64–8. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 16 Feb, 2026 Reviewers agreed at journal 05 Feb, 2026 Reviewers invited by journal 22 Jan, 2026 Editor assigned by journal 14 Jan, 2026 Submission checks completed at journal 09 Jan, 2026 First submitted to journal 09 Jan, 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-8448296","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":578700605,"identity":"8ebf76b4-4e8c-4f1c-83c6-bc5837bc599b","order_by":0,"name":"Un Huseyin","email":"","orcid":"","institution":"Ondokuz Mayıs University","correspondingAuthor":false,"prefix":"","firstName":"Un","middleName":"","lastName":"Huseyin","suffix":""},{"id":578700606,"identity":"1401ddfb-fb18-4d54-b8c8-721105c66981","order_by":1,"name":"Alper Kocyigit","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYDACCTYGhgdAmh/ESSggVksCkJZsAGkxIEWLwQEQjxgtBrfbEj8k7rDJNz6/OvHDAwMGeX6xAwS03Dl2WCLxTJrlthtvN0sAHWY4c3YCfi2SM9IbJBLbDhuY3Ti7AaQlweA2YS3NPxLb/hsYzzi7+QdRWvgl0o4BbTlgYMDfu404W4Ba0iwSzyQbSNzg3WaRYCBB2C9sEmnGNz7usDPg7z+7+eaPCht5fmkCWsCAsQFISIBVShChHK6F/wCRqkfBKBgFo2DEAQBGaUWLaU+VNgAAAABJRU5ErkJggg==","orcid":"","institution":"Ondokuz Mayıs University","correspondingAuthor":true,"prefix":"","firstName":"Alper","middleName":"","lastName":"Kocyigit","suffix":""}],"badges":[],"createdAt":"2025-12-25 10:23:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8448296/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8448296/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101243253,"identity":"8609eef9-bd6f-4204-a53e-6ffe24dca9bc","added_by":"auto","created_at":"2026-01-27 16:02:45","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":80347,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation Comparison of Pregnancy Rate and Estrus Score with Farm Variables\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8448296/v1/015cbcbf1c7f6f8475333424.jpeg"},{"id":101243261,"identity":"feee2394-6643-48f9-83ef-cd91beab1dbe","added_by":"auto","created_at":"2026-01-27 16:02:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":717430,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8448296/v1/222c52a6-c6c1-4ccf-87b7-a465662e98cd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Farm Structure on Estrus Detection and Reproductive Performance in Dairy Herds: Evidence from Turkey","fulltext":[{"header":"Background","content":"\u003cp\u003eReproductive management is essential for the economic efficiency and sustainability of dairy enterprises [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Implementing effective reproductive strategies increases the calving rate per cow and shortens the calving interval, helping to maintain stable milk production [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. A critical component of this success is the accurate and timely detection of estrus. Because the effectiveness of artificial insemination relies heavily on identifying the physiological and behavioral signs of the peri-ovulatory period, missed estrus is widely recognized as a key constraint in practice [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDetecting estrus has become increasingly difficult in modern high-yielding herds. Cows often show shorter duration of estrus and less obvious clinical signs, which can contribute to missed heats and mistimed inseminations [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Issues such as subestrus, limited observation time, and a lack of trained staff further contribute to detection errors. Consistent with this, poor estrus detection rates are associated with lower pregnancy rates and higher infertility, with implications for farm-level economic performance [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This supports the view that reproductive success depends not just on biology, but also on the structural characteristics of the farm [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Factors such as farm size, housing systems, and workforce quality are key determinants of success. In smaller herds, individual monitoring is easier, often resulting in better detection rates compared to large-scale operations where the workload limits visual observation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Housing plays a similar role; cows in loose housing systems can display natural behaviors more freely, facilitating observation. In contrast, tie-stall systems restrict movement and may attenuate behavioral signs [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe presence of bulls is another factor often discussed in the literature. Some evidence suggests that biostimulation from bulls can enhance estrus signs and shorten the postpartum anestrus period [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, results are inconsistent; other studies report limited effects, particularly in high-producing cows, suggesting that the benefit depends heavily on specific management conditions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, the \"human factor\" is critical but may receive less systematic attention in routine herd management. The number of staff, their training levels, and the farmer's experience influence how accurately estrus signs are interpreted and whether postpartum cows receive timely intervention [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Inexperienced or untrained personnel are more likely to misclassify signs, leading to lower pregnancy outcomes. This highlights the practical importance of regular training, especially in larger herds [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile reproductive physiology is universal, management practices vary significantly by region. In Turkey, the dairy sector consists mainly of medium-sized, family-run enterprises. However, most existing research comes from large-scale, intensive systems or controlled experiments that do not fully reflect field realities. There is a lack of data specifically evaluating how farm structure interacts with reproductive performance in this local context. Bridging this gap is necessary to provide veterinarians and breeders with practical, evidence-based recommendations.\u003c/p\u003e \u003cp\u003eTherefore, this study examines the relationship between the structural characteristics of dairy enterprises, such as farm size, housing type, bull presence, and personnel experience, and pregnancy rates following artificial insemination. The aim is to reveal the current status of reproductive management in the region and compare these field findings with the broader literature.\u003c/p\u003e \u003cp\u003eBased on previous studies, we hypothesized that favorable conditions, such as loose housing, smaller herd sizes, and trained staff, would be associated with higher estrus detection scores and improved pregnancy rates.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Characteristics of the Farms\u003c/h2\u003e \u003cp\u003eAmong the 92 farms included in the study, most were medium-sized (10 to 50 head; 50.5%), and the tie-stall system was the predominant housing type (76.2%). Bulls were present in 29.5% of farms, and 53.3% reported employing supporting personnel. Most farmers (93.3%) had more than five years of experience, and cows constituted 83.3% of the evaluated animals. The overall pregnancy rate was 72.9%, and the mean estrus score was 126.40\u0026thinsp;\u0026plusmn;\u0026thinsp;45.58.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRelationships Between Estrus Score and Farm Parameters\u003c/h3\u003e\n\u003cp\u003eAnalysis of estrus score in relation to farm characteristics indicated a statistically significant difference by pregnancy status (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Animals diagnosed as pregnant had higher estrus scores than those that did not conceive (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). By contrast, estrus scores did not differ significantly by farm size, housing type, bull presence, availability of supporting personnel, animal category, or farmer experience (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\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\u003eDistribution of the relationship between estrus score and farm-related parameters\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian estrus score\u003c/p\u003e \u003cp\u003e(Min-Max)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTest statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eHerd size\u003c/p\u003e \u003cp\u003e(no. of animals)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140(35\u0026ndash;175)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e4.875\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140(40\u0026ndash;175)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140(35\u0026ndash;175)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140(75\u0026ndash;175)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHousing type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTie/stall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140(35\u0026ndash;175)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1.199\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoose/free\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140(75\u0026ndash;175)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSemi-open\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140(40\u0026ndash;175)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140(35\u0026ndash;175)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e4.600\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140(35\u0026ndash;175)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSupporting staff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140(35\u0026ndash;175)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5.491\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140(35\u0026ndash;175)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAnimal type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeifer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140(35\u0026ndash;175)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3.487\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140(35\u0026ndash;175)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFarmer experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140(40\u0026ndash;175)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.445\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140(35\u0026ndash;175)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePregnant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140(40\u0026ndash;175)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2.983\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-pregnant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135(35\u0026ndash;175)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u0026sup2;Kruskal\u0026ndash;Wallis H test, \u0026sup3;Mann\u0026ndash;Whitney U test, Min\u0026ndash;Max: Minimum\u0026ndash;Maximum. Different superscript letters indicate statistically significant differences. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eRelationships Between Pregnancy Rate and Farm Parameters\u003c/h3\u003e\n\u003cp\u003eAssociations between pregnancy rate and farm size, housing type, bull presence, availability of supporting personnel, animal category, and farmer experience are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. None of these variables showed a statistically significant association with pregnancy rate in the present dataset (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\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\u003eDistribution of the relationship between pregnancy rate and farm-related parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ePregnant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eNon-pregnant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eHerd size\u003c/p\u003e \u003cp\u003e(no. of animals)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHousing type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTie/stall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoose/free\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSemi-open\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.690\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSupporting staff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAnimal type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeifer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e78.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFarmer experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe overall pregnancy rate observed in the farms was 72.9%, and estrus score was significantly associated with pregnancy outcome. In contrast, farm structural characteristics, including size, housing type, bull presence, supporting personnel, and farmer experience, were not significantly associated with pregnancy rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study evaluated whether selected structural characteristics of dairy enterprises in the Suluova district of Amasya province were associated with artificial insemination outcomes. In contrast to our a priori hypothesis, none of the assessed structural parameters showed a statistically significant association with pregnancy rate. Instead, estrus score was the variable most clearly associated with pregnancy status, with higher scores observed among animals that conceived. Taken together, these findings are compatible with the view that, within the production context represented by the present dataset, the quality and timing of estrus identification may be more influential for insemination success than between-farm differences in physical infrastructure.\u003c/p\u003e \u003cp\u003eThe importance of estrus detection for reproductive performance is well described. Previous work has linked inadequate estrus detection with reduced pregnancy rates and increased infertility [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Roelofs et al. (2010) also estimated that estrus detection errors contribute substantially to economic losses in the dairy sector. In this context, the positive association observed here between estrus score and pregnancy outcome aligns with published evidence and supports the practical relevance of accurate estrus recognition and timely insemination.\u003c/p\u003e \u003cp\u003eFarm size has been proposed as an indirect determinant of reproductive performance, largely through its effect on observation intensity and labor allocation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Smaller herds may allow more consistent individual monitoring, potentially improving estrus detection [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], whereas larger operations can face constraints in observation time per animal, which may reduce detection efficiency [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The lack of a measurable association in the present study may reflect the distribution of herd sizes in the region, where most participating farms were medium-scale (10 to 50 head) and very large herds were relatively uncommon. A related consideration is the limited structural variability across farms, as the sample largely reflects the regional, medium-sized family enterprise model. In addition, the purposive sampling strategy, which enrolled only animals already exhibiting clinical signs of estrus and judged suitable for insemination, may have reduced the ability to detect structural effects that operate earlier in the pathway, such as factors influencing estrus expression or the probability of heat being noticed. Under this sampling frame, once estrus was expressed and recognized, conception outcome may have depended more on animal-level readiness than on farm-level constraints.\u003c/p\u003e \u003cp\u003eHousing system is another structural feature often discussed in relation to estrus expression. Loose housing, by enabling greater freedom of movement, is commonly considered to facilitate natural estrus behaviors and thus improve detection [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In the present study, pregnancy rate did not differ significantly between housing types. This finding could be partly explained by the small number of farms using loose housing systems, which limits statistical contrast, or by compensatory management in tie-stall farms. For example, experienced farmers in tie-stall settings may increase observation frequency or rely on additional cues, which could mitigate disadvantages related to restricted movement.\u003c/p\u003e \u003cp\u003eThe influence of bull presence remains debated. While some studies suggest that bull-associated stimuli, including pheromonal cues, may enhance estrus behaviors [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], other reports have not demonstrated consistent effects [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In our dataset, bull presence was not significantly associated with pregnancy rate. One plausible explanation is that bulls were not routinely used for natural service in most farms, where artificial insemination was the primary breeding method. Under such conditions, any potential biostimulatory effect may be modest or context-dependent, and may not translate into measurable differences in pregnancy outcome.\u003c/p\u003e \u003cp\u003eHuman resources and farmer experience are frequently cited as determinants of reproductive performance, particularly through their effects on estrus detection accuracy and insemination timing [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The absence of a significant association in the present analysis may reflect the limited variability in this domain, as most farmers reported more than five years of experience. When experience level is relatively homogeneous, between-group differences can be difficult to detect even if human factors are important in principle.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWithin the context represented by these medium-sized dairy enterprises in Turkey, structural characteristics such as herd size, housing type, and bull presence were not statistically associated with pregnancy rate following artificial insemination. In contrast, estrus score showed a significant association with pregnancy outcome, indicating that the intensity and clarity of estrus signs, as captured by the scoring approach, tracked insemination success in this dataset. These results suggest that management effectiveness, particularly accurate estrus detection and appropriate timing of insemination, may offset certain structural constraints in comparable medium-scale systems. From a practical standpoint, strategies focused on strengthening clinical observation skills and routine training of veterinarians and breeders may be at least as relevant as investments targeting structural expansion, although the present findings should be interpreted in light of the study\u0026rsquo;s sampling frame and the limited variability in some farm-level characteristics.\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eStudy Area and Animals\u003c/h2\u003e \u003cp\u003eThe study was conducted in privately owned commercial dairy farms affiliated with the Amasya Cattle Breeders\u0026rsquo; Association in the Suluova district of Amasya province (Turkey). Farms were visited upon owners\u0026rsquo; request for routine veterinary reproductive management services; animals were not purchased, transferred, or housed for research purposes. Within the scope of the research, 92 farms were visited and a total of 210 female animals were evaluated. Farms were selected to reflect the regional production model, which is predominantly composed of medium-sized enterprises. Selection also considered broadly similar structural features to limit heterogeneity across holdings. The study did not involve the creation of a dedicated animal group or the use of experimental animals. The animals were privately owned dairy cattle maintained in commercial farms whose owners requested routine veterinary services. Written informed consent was obtained from farm owners/managers for participation and for the use of farm and animal records/observations in this research. No animals were euthanised or sacrificed for the purposes of this study, and no anaesthesia, injections, or medications were administered as part of the research protocol.\u003c/p\u003e \u003cp\u003e Animal selection followed a purposive sampling approach. Rather than enrolling all animals within each herd, only cows that displayed clinical signs of estrus and were considered suitable for artificial insemination during the study period were included. Accordingly, the sample size (n\u0026thinsp;=\u0026thinsp;210) represents the subset of actively cycling animals inseminated within the defined observation window, not the total herd population.\u003c/p\u003e \u003cp\u003eSemen doses used for artificial insemination were evaluated at the Andrology Laboratory of the Department of Reproduction and Artificial Insemination, Faculty of Veterinary Medicine, Ondokuz Mayis University. Only semen samples with motility greater than 60% were included in the study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEstrus Scoring and Artificial Insemination\u003c/h3\u003e\n\u003cp\u003eEstrus detection was performed using a revised scoring system based on the Eerdenburg Scale [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Following estrus notification by the farmer, a clinical history was obtained and the estrus scoring chart for the relevant animal was completed. The scoring framework incorporated locomotor activity (35 points), mucus discharge (5 points), standing heat (100 points), and rectal examination findings (35 points). Animals receiving a total score of 35 or higher were inseminated using the rectovaginal method. Pregnancy diagnosis was performed by a veterinarian via rectal palpation starting on day 60 after insemination.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of Farm Structure\u003c/h2\u003e \u003cp\u003eAfter estrus scoring and insemination, structural characteristics of each farm were recorded using a standardized farm information form. Farm size, housing type, bull presence, availability of supporting personnel, animal category (cow or heifer), and farmer experience were documented based on researcher observations and farmer statements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData were analyzed using IBM SPSS Statistics 26. Descriptive statistics included the mean, standard deviation, median, minimum, and maximum values. Normality of distribution was assessed using the Shapiro-Wilk test.\u003c/p\u003e \u003cp\u003eFor normally distributed variables, one-way analysis of variance (one-way ANOVA) was applied. For non-normally distributed variables, the Mann-Whitney U test was used when comparisons involved two categories, and the Kruskal-Wallis test was used for comparisons involving more than two categories. Multiple comparisons were conducted using Tukey and Dunn tests, as appropriate. Associations between categorical variables were assessed using Pearson\u0026rsquo;s chi-square test, whereas relationships between continuous variables were examined using Pearson correlation analysis. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all tests.\u003c/p\u003e \u003cp\u003eA post hoc power analysis was conducted to assess sample size adequacy. Based on the total of 210 animals (153 pregnant and 57 non-pregnant), the analysis indicated power greater than 0.95 to detect a medium effect size (Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;0.5) at a Type I error rate of α\u0026thinsp;=\u0026thinsp;0.05. This suggests that the sample size was sufficient to detect statistically significant differences in estrus scores between groups under the stated assumptions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the invaluable contributions of the research fellows in the Department of Reproduction and Artificial Insemination.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eU.H. and K.A. conceived and designed the study; U.H. conducted the field work; K.A. performed the statistical analysis and edited the manuscript. All authors interpreted the data, critically reviewed the manuscript for important intellectual contents and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundings:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Ondokuz Mayis University Scientific Research Projects Coordination Unit with project number 4994.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are provided within the manuscript or supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was based on routine clinical veterinary practice conducted for diagnostic and therapeutic purposes and on the analysis of routine farm/clinical records and observations. No animal experimentation and no research-driven invasive procedures were performed. The study was conducted within the Turkish national framework governing animal experiments ethics committees (Regulation on the Working Procedures and Principles of Animal Experiments Ethics Committees, Official Gazette: 15 February 2014, No. 28914), under which non-experimental clinical veterinary practice is addressed outside the remit of animal experimentation review. Given the non-experimental nature of the work, formal IACUC/HADYEK approval was not applicable under this framework. Written informed consent was obtained from farm owners/managers for participation and for the use of farm and animal records/observations in this research. All clinical procedures were carried out in line with applicable legislation and within the framework of Good Veterinary Practices (GVP). No animals were euthanised or sacrificed for the purposes of this study, and no anaesthesia, injections, or medications were administered as part of the research protocol.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGiordano J. Economic impact of reproductive performance in dairy herds and approaches for program selection. Clin Theriogenol. 2019;11:329\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelandria G. Reproductive management strategies and efficiency in dairy herds: a review. Vet Sci. 2023;10:215.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoelofs JB, L\u0026oacute;pez-Gatius F, Hunter RHF, et al. When is a cow in estrus? Clinical and practical aspects. Theriogenology. 2010;74:327\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTippenhauer M. Estrus expression in high-yielding dairy cows: behavioral and endocrine changes. Reprod Domest Anim. 2021;56:1215\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrowe MA, Hostens M, Opsomer G. Reproductive management in dairy cows-the future. Ir Vet J. 2018;71:1\u0026ndash;1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z, Liu H, Li X, et al. Effects of herd size and management practices on reproductive performance in dairy cattle. Animals. 2023;13:688.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Zhang Y, Wang J, Zhao K, Li X, Liu B. Using machine-learning technique for estrus onset detection in dairy cows from acceleration and location data acquired by a neck-tag. Biosyst Eng. 2022;214:193\u0026ndash;206.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerez Marquez HJ, Ambrose DJ, Bench CJ. Behavioral changes to detect estrus using ear-sensor accelerometer compared to in-line milk progesterone in a commercial dairy herd. Front Anim Sci. 2023;4:1149085.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSawa A, Bogucki M. Relationship between housing system and reproductive performance of dairy cows. Arch Anim Breed. 2011;11:265\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRios Mohar JA, L\u0026oacute;pez D\u0026iacute;az CA, Hern\u0026aacute;ndez Cer\u0026oacute;n J, et al. Economic analysis of different pregnancy rates in dairy herds under intensive management. Vet Mex OA. 2022;12:e401.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkhtar M. Effect of bull pheromones on estrus expression in dairy cows. J Anim Reprod Sci. 2014;149:110\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoudhary S, Kamboj ML, Sahu D, et al. Effect of biostimulation on growth rate and reproductive development of Bos indicus dairy heifers. Trop Anim Health Prod. 2022;54:138.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShipka MP, Ellis LC. No effects of bull exposure on expression of estrous behavior in high-producing dairy cows. Appl Anim Behav Sci. 1998;57:1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrihuela A. Some factors affecting the behavioural manifestation of oestrus in cattle: a review. Appl Anim Behav Sci. 2000;70:1\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWicaksono A, Edwardes F, Steeneveld W, et al. The economic effect of cow-based reproductive management programs with a systematic use of reproductive hormones. J Dairy Sci. 2024;107:11016\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDerks M, van Werven T, Hogeveen H, Kremer WD. Veterinary herd health management programs on dairy farms in the Netherlands: use, execution, and relations to farmer characteristics. J Dairy Sci. 2013;96:1623\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWicaksono A, Steeneveld W, van Werven T, et al. Knowledge, attitude and behaviour of farmers towards the use of reproductive hormones in dairy cattle. Animal. 2025;19:101470.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaraviello DZ, Weigel KA, Fricke PM, Wiltbank MC, Florent MJ, Cook NB, et al. Survey of management practices on reproductive performance of dairy cattle on large US commercial farms. J Dairy Sci. 2006;89:4723\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConsentini CE, Wiltbank MC, Sartori R. Factors that optimize reproductive efficiency in dairy herds with an emphasis on timed artificial insemination programs. Animals. 2021;11:301.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFodor I, \u0026Oacute;zsv\u0026aacute;ri L. Estrus detection and its impact on reproductive and economic performance in large dairy herds. Anim Welf Etol Tartastechnol. 2019;15:18\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalsh DP, Fahey AG, Mulligan FJ, et al. Effects of herd fertility on the economics of sexed semen in a high-producing, pasture-based dairy production system. J Dairy Sci. 2021;104:3181\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Vliet JH, Van Eerdenburg FJCM, Noordhuizen JPTM. The use of a behaviour score for oestrus detection in dairy cattle. Vet Rec. 1996;139:64\u0026ndash;8.\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":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Dairy cattle, Estrus detection, Farm structure, Pregnancy rate, Artificial insemination","lastPublishedDoi":"10.21203/rs.3.rs-8448296/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8448296/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMedium-sized, family-run dairy farms predominate in Turkey, yet field-based evidence from developing-market settings remains limited regarding how farm structure and estrus detection quality relate to reproductive performance. This study examined associations between structural farm characteristics, estrus detection scores, and pregnancy rates across 92 dairy enterprises, using data from 210 cows selected through purposive sampling based on active estrus signs. Farm-level variables (e.g., herd size, housing type, bull presence, personnel) were recorded, and estrus intensity was assessed prior to artificial insemination using a modified Eerdenburg scoring approach; a post-hoc power analysis indicated high statistical power (\u0026gt;\u0026thinsp;95%).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eStructural characteristics showed no statistically significant association with pregnancy rate (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), a pattern plausibly consistent with limited between-farm structural variability within the study region. In contrast, estrus detection scores demonstrated a strong positive relationship with pregnancy outcomes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that higher clinical estrus intensity scores were associated with improved conception success.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eWithin this relatively structurally homogeneous farm context, reproductive outcomes appear to be more strongly aligned with the accuracy and quality of biological estrus detection than with physical infrastructure differences. These findings support the inference that prioritizing workforce training focused on clinical estrus scoring may represent a more cost-effective route to improving reproductive efficiency and sustaining economic viability than immediate, capital-intensive structural investments, while acknowledging that the purposive sampling design may limit generalizability beyond cows presenting overt estrus signs.\u003c/p\u003e","manuscriptTitle":"Impact of Farm Structure on Estrus Detection and Reproductive Performance in Dairy Herds: Evidence from Turkey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-27 16:02:40","doi":"10.21203/rs.3.rs-8448296/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-16T14:02:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43083293263795728269256257196451980580","date":"2026-02-06T04:55:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-22T10:55:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-14T09:10:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-09T12:41:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Veterinary Research","date":"2026-01-09T12:32:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b7561711-32e0-4bba-9198-b82917ed4718","owner":[],"postedDate":"January 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-27T16:02:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-27 16:02:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8448296","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8448296","identity":"rs-8448296","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}