Scholarlink

<p>Milk composition yield dynamics reveal production efficiency, nutritional and economic importance and adaptation of cows to their surrounding environment. To optimize farm management, analyzing test day data from organized herds has become integral to precision animal farming. With the growing human population, development and urbanization the market trend shifted with more preference to cheese and other processed milk products and understanding milk composition yield dynamics is crucial for identifying persistency and production efficiency, which can guide genetic selection and sustainable herd management in tropical dairy systems. In this context, the temporal patterns of milk composition traits fat yield (FY), Solid-not-fat yield (SNFY), protein yield (PY) and lactose yield (LY) were analyzed in primiparous Sahiwal cows using 7,046 longitudinal test-day records from 863 cows spanning over four decades (1980–2024) from an organized herd at ICAR-NDRI, Karnal, India. Fifteen empirical lactation curve functions were evaluated using non-linear regression to assess their fitting performance. Model performance was assessed using adjusted R<sup>2</sup>, root mean square error (RMSE), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and the Durbin-Watson (DW) statistic. The parabolic exponential function consistently demonstrated superior fit, with the highest adjusted R<sup>2</sup> (89.41–91.57%), lowest RMSE (2.27–5.78 kg/test day) and minimum AIC (2.65–20.60) and BIC (2.58–20.85) values across all traits. All DW values ranged from 1.32 to 2.74, indicating minimal residual autocorrelation. In contrast, the polynomial regression function exhibited the poorest fit for all traits. These findings emphasize the practical utility of the PX function in modelling milk composition dynamics in Sahiwal cattle and highlight its valuable insight for breeding programs with precision herd management in tropical environments. The study provides a valuable reference point for future research on lactation modelling in other indigenous cattle breeds.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Characterization, modelling and evaluation of various lactation curve functions for historical milk composition traits in Sahiwal cattle

  • 期刊论文

  • 发表日期：

    2026年1月21日

查看全文
  • Jayesh Vyas,
  • Mohit Khatri,
  • Sushil Kumar Sharma,
  • Ishmeet Kumar,
  • Anil Chitra,
  • Asad Khan,
  • T. V. Raja,
  • Sabyasachi Mukherjee,
  • Anupama Mukherjee

摘要

Milk composition yield dynamics reveal production efficiency, nutritional and economic importance and adaptation of cows to their surrounding environment. To optimize farm management, analyzing test day data from organized herds has become integral to precision animal farming. With the growing human population, development and urbanization the market trend shifted with more preference to cheese and other processed milk products and understanding milk composition yield dynamics is crucial for identifying persistency and production efficiency, which can guide genetic selection and sustainable herd management in tropical dairy systems. In this context, the temporal patterns of milk composition traits fat yield (FY), Solid-not-fat yield (SNFY), protein yield (PY) and lactose yield (LY) were analyzed in primiparous Sahiwal cows using 7,046 longitudinal test-day records from 863 cows spanning over four decades (1980–2024) from an organized herd at ICAR-NDRI, Karnal, India. Fifteen empirical lactation curve functions were evaluated using non-linear regression to assess their fitting performance. Model performance was assessed using adjusted R2, root mean square error (RMSE), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and the Durbin-Watson (DW) statistic. The parabolic exponential function consistently demonstrated superior fit, with the highest adjusted R2 (89.41–91.57%), lowest RMSE (2.27–5.78 kg/test day) and minimum AIC (2.65–20.60) and BIC (2.58–20.85) values across all traits. All DW values ranged from 1.32 to 2.74, indicating minimal residual autocorrelation. In contrast, the polynomial regression function exhibited the poorest fit for all traits. These findings emphasize the practical utility of the PX function in modelling milk composition dynamics in Sahiwal cattle and highlight its valuable insight for breeding programs with precision herd management in tropical environments. The study provides a valuable reference point for future research on lactation modelling in other indigenous cattle breeds.