Determining the Best Predictive Function for mathematically describing the Lactation Curve in Murcia Goats in Iran

Determining the Best Predictive Function for mathematically describing the Lactation Curve in Murcia Goats in Iran | 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 Determining the Best Predictive Function for mathematically describing the Lactation Curve in Murcia Goats in Iran Somayeh Barani, Rostam Pahlavan, Behrouz Mohammad-Nazari, Amir Taheri-Yeganeh, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6204557/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Aug, 2025 Read the published version in Tropical Animal Health and Production → Version 1 posted 4 You are reading this latest preprint version Abstract This study compared Wood, Wilmink, Pollott, and Dijkstra functions to fit lactation curves using 38,030 test-day (TD) milk records from first-parity Murcia goats (2018-2024). Two scenarios were analyzed: one included all animals (TD greater than 3) using Wood, Wilmink, and Pollott, while the other utilized complete records (8 TD) and all four functions. In the first scenario, Pollott demonstrated the highest percentage of typical lactation curves (81.2%) among the tested functions, followed by Wood (71.4%) and Wilmink (61.5%). Additionally, Pollott exhibited the lowest correlation among its parameters, reducing the potential for multicollinearity and overfitting. Although Pollott performed well, it had higher Akaike's Information Criterion (AIC) and Bayesian Information Criterion (BIC) values compared to the others. However, the corrected AIC (AICc) value for Pollott was lower, suggesting superior performance in smaller datasets. In contrast, Wood consistently showed the lowest AIC and BIC values across both scenarios, indicating that it was the most robust in terms of function fit. Despite this, a comparison of the predicted lactation curves revealed that Pollott more accurately predicted daily milk production, especially during the middle of lactation, although it slightly overpredicted milk yield at the start of lactation. Furthermore, the analysis of parameter correlations and variance inflation factors (VIF) highlighted that Pollott avoided significant multicollinearity issues, which were more prominent in other functions. Therefore, Pollott, due to its flexibility and biological characteristics, may be preferable for datasets with fewer records; however, the choice of an appropriate function depends on the dataset characteristics. Lactation curve mathematical functions Murcia goat evaluated criteria appropriate function Full Text Cite Share Download PDF Status: Published Journal Publication published 05 Aug, 2025 Read the published version in Tropical Animal Health and Production → Version 1 posted Reviewers agreed at journal 03 Apr, 2025 Reviewers invited by journal 02 Apr, 2025 Editor assigned by journal 20 Mar, 2025 First submitted to journal 14 Mar, 2025 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. 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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-6204557","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":437428703,"identity":"cf4d04fb-f767-49cb-9609-af34ab3e7a2e","order_by":0,"name":"Somayeh 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