Direct and Maternal Genetic Effects and Temporal Trends of Economic Traits in Surti Buffaloes

Direct and Maternal Genetic Effects and Temporal Trends of Economic Traits in Surti Buffaloes | 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 Direct and Maternal Genetic Effects and Temporal Trends of Economic Traits in Surti Buffaloes Hari Ram Meena, Lokesh Gautam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7485713/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study evaluated the genetic parameters and decadal trends of economic traits in Surti buffaloes using 868 lactation records maintained between 1993 and 2023 at the Livestock Research Station in Udaipur, Rajasthan, India. The analyzed traits included total milk yield, 305-day milk yield, peak yield, lactation length, calving interval, dry period, and average daily milk yield per lactation length and calving interval. Variance components were estimated using restricted maximum likelihood procedures under three repeatability animal models. Heritability estimates ranged from 0.005 (CI) to 0.093 (ADMY_LL), and repeatability estimates ranged from 0.195 to 0.331. Model 1, accounting for additive genetic and permanent environmental effects, was most suitable for practical use. Strong genetic and phenotypic correlations were observed among the milk yield traits, whereas the reproductive traits exhibited more variable associations. Genetic trends revealed substantial declines per decade in TMY (−97.71%), TMY_305 (−123.82%), PY (−231.32%), ADMY_LL (−231.90%), and DP (−104.49%). In contrast, positive genetic gains were observed for CI (166.76%), ADMY_CI (111.34%), and LL, indicating improvements in reproductive efficiency and yield persistence. These findings highlight the need for a balanced selection strategy targeting both production and reproductive traits, supported by refined management practices in Surti herd. Surti buffalo heritability repeatability genetic trend Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Globally, approximately 204 million buffaloes are distributed across 40 countries, with Asia accounting for 97% of the total population. India holds the largest share, with 109.85 million buffaloes, representing 57% of the global total. Rajasthan is the second-largest buffalo-rearing state in India, with a population of 13.7 million, including 597,128 buffaloes in the Udaipur district alone, which significantly contributes to national milk production (Department of Animal Husbandry and Dairying, 2019 ). The Surti buffalo, also known as Deccani or Gujarati, is a key dairy breed native to Gujarat’s Charotar tract and southern Rajasthan. These medium-sized, docile animals feature sickle-shaped horns, a convex forehead, prominent eyes, and a well-developed udder, ideal for dairy farming. Their adaptability, efficient feed use, and high milk fat content enhance economic value (Patel et al. 2015 ). In India, buffaloes outyield indigenous cattle in per-animal milk production, with economic traits like milk yield and reproductive efficiency critical for herd profitability and sustainability, influenced by management, nutrition, genetics, and environment. Improving dairy productivity demands a comprehensive understanding of genetic and non-genetic factors affecting performance. Non-genetic factors, such as feeding, housing, healthcare, and climate shape phenotypes, and their optimization can boost milk yield and reproduction (Galsar et al. 2016 ). Genetically, quantitative traits are driven by additive and maternal effects, with mitochondrial DNA influencing milk yield via maternal inheritance (Gudex et al. 2014 ). Accurate genetic parameter estimation is vital for effective breeding, quantifying genetic and environmental contributions for precise selection, though tropical variability and data limitations can skew results (Demeke et al. 2004 ). Repeatability models help capture individual differences across lactations, but estimates may vary with population or management changes. Despite their significance, systematic genetic studies on Surti buffaloes are scarce, with research often focusing on crossbred or exotic cattle, leaving a gap in indigenous buffalo genetics. This study aimed to estimate genetic parameters, assess genetic and phenotypic trends, and evaluate non-genetic influences on key traits in Surti herds, providing evidence for selection strategies, herd management, and sustainable genetic improvement. 2 Materials and Methods 2.1 Study Location and Population This study used data from the Network Project on Buffalo Improvement (Surti) at the Livestock Research Station, Navania, Udaipur. The dataset included performance and pedigree records of Surti buffaloes over 30 years (1993–2023). Only animals with complete pedigree and economic trait records were included. Records with abnormalities (e.g., abortion, stillbirth, lactation < 100 days, yield < 500 kg) or pathological conditions were excluded. Sires with fewer than three progeny were omitted. 2.2 Herd Management Buffaloes were housed loosely, grouped by age and physiological status, with separate areas for calves, heifers, lactating, dry, pregnant animals, and breeding bulls. Green fodder was provided ad libitum, supplemented by concentrate feed (14–16% digestible protein) as required. Healthcare involved regular vaccination and deworming. Oestrus detection used vasectomized bulls, followed by frozen semen insemination; pregnancy was confirmed 60–90 days post-insemination. Milking occurred manually twice daily (6 AM and 4 PM), with yields recorded in kg. 2.3 Traits under Study Economic traits analyzed were total milk yield (TMY), 305-day milk yield (TMY_305), peak yield (PY), lactation length (LL), calving interval (CI), dry period (DP), average daily milk yield per lactation length (ADMY_LL), and per calving interval (ADMY_CI). Fixed effects included calving period (six 5-year intervals), season (summer, rainy, winter), parity (1 to ≥ 6), and age group (grouped via Sturges’ formula). 2.4 Statistical Analysis Descriptive statistics were computed using the R software (R Core Team 2023 ). The effects of non-genetic factors on economic traits were analyzed using a mixed linear model with the Least-Squares Maximum Likelihood method (Harvey 1990 ). The model included fixed effects of period, season, parity, and age group, and the random effect of sire, expressed as Y ijklmn denotes the performance record, µ is the overall population mean, A i represents the random effect of the sire, and B j , C k , D l , and E m are the fixed effects of period, season, parity, and age group at calving, respectively. The residual term e ijklmn is assumed to follow a normal distribution with mean zero and constant variance. Significance was assessed using Analysis of Variance (ANOVA), with post-hoc comparisons via Tukey’s test at P ≤ 0.05. Genetic parameters were estimated using repeatability animal models in the WOMBAT software with the Restricted Maximum Likelihood (REML) approach (Meyer 2007 ). Three models were used: $$\:Y=X\beta\:+\:{Z}_{1}a+\:{Z}_{3}pe+\:e\:with\:Cov\left(a,m\right)=0$$ 1. $$\:Y=X\beta\:+\:{Z}_{1}a+\:{Z}_{2}m+\:{Z}_{3}pe+\:e\:with\:Cov\left(a,m\right)=0$$ 2. $$\:Y=X\beta\:+\:{Z}_{1}a+\:{Z}_{2}m+\:{Z}_{3}pe+{Z}_{4}c+\:e\:with\:Cov\left(a,m\right)=0$$ 3. The variance and (co)variance structure of the random effects was as follows In the variance-covariance model, Y represents an n × 1 vector of observations for each trait, and the vectors β, a, m, pe, c, and ε represent fixed effects, direct additive genetic effects, maternal genetic effects, animal permanent environmental effects, maternal permanent environmental effects, and residual effects, respectively. Matrices X, Z₁, Z₂, Z₃, and Z₄ are incidence matrices that relate these effects to the records, respectively. A is the numerator relationship matrix among animals. The variance components are defined as follows: σ²a is the direct additive genetic variance, σ²m is the maternal genetic variance, σ²pe is the animal permanent environmental variance, σ²c is the maternal permanent environmental variance, and σ²e is the residual variance. Phenotypic variance is represented by σ²p. Identity matrices I d and I n correspond to the number of animals in their respective variance structures. Heritability (h²), maternal heritability (m²), and permanent environmental effects (c²) were calculated from the estimated variances. The formulae used were: h² = σ²a/σ²p, m² = σ²m/σ²p, and c² = σ²c/σ²p. Repeatability was estimated as R = (σ²a + σ²pe)/σ²p. The most appropriate model for each trait was determined based on the Akaike Information Criterion (AIC) (Akaike, 1973 ). The AIC is an estimator of the relative quality of statistical models for a given dataset, balancing the model fit and complexity. It was calculated as AIC = − 2 Log L + 2P, where Log L is the maximized log-likelihood of the model, and P is the number of estimated parameters. Among the competing models, the one with the lowest AIC value was selected as the most appropriate. Genetic and phenotypic correlations between traits were estimated using bivariate repeatability animal models that incorporated the best-fit, fixed, and random effects. The correlation coefficients were derived from the covariances and variances obtained using the bivariate models. Genetic trends were assessed by regressing average estimated breeding values (EBVs) on the calving period using the lm function in R. Phenotypic trends were determined by regressing the average phenotypic performance during the calving period. 3 Results The dataset’s adequacy for genetic evaluation of economic traits in Surti buffaloes was confirmed through pedigree structure, repeated records, descriptive statistics, and non-genetic factor effects (Table 1 ). For traits TMY, PY, LL, and ADMY_LL, records from 258 animals were available, while TMY_305 and CI, DP, ADMY_CI included 161 and 195 animals, respectively. The number of sires ranged from 38 (TMY_305) to 44 (TMY, PY, LL, ADMY_LL), and dams from 92 to 119. Grandparental pedigrees were traceable for 103–151 maternal grandsires and 79–108 granddams. About 28.7% of animals had one record, and over 10% had five or more records, demonstrating repeated observations. In contrast, TMY_305 showed limited repeatability with only 1.2% of animals reaching six lactations. Table 1 Characteristics of economic traits of Surti buffaloes. Traits TMY TMY_305 PY LL CI DP ADMY_LL ADMY_CI Pedigree information Total animal IDs 391 274 391 391 322 322 391 322 No. of animal 258 161 258 258 195 195 258 195 No. of sire 44 38 44 44 40 40 44 40 No. of dam 119 92 119 119 100 100 119 100 No. of animals without offspring 154 89 154 154 116 116 154 116 No. of animals with offspring 163 130 163 163 140 140 163 140 No. of animals with offspring & records 104 72 104 104 79 79 104 79 Average inbreeding coefficient % 0.21 0.085 0.21 0.21 0.21 0.21 0.21 0.21 Maternal grand sire 151 103 151 151 118 118 151 118 Maternal grand dam 108 79 108 108 86 86 108 86 Repeated records % 1 28.7 37.3 28.7 28.7 23.6 23.6 28.7 23.6 2 16.7 29.8 16.7 16.7 17.9 17.9 16.7 17.9 3 14.7 18.6 14.7 14.7 17.4 17.4 14.7 17.4 4 10.5 8.1 10.5 10.5 12.8 12.8 10.5 12.8 5 10.1 5.0 10.1 10.1 9.7 9.7 10.1 9.7 6 7.4 1.2 7.4 7.4 8.2 8.2 7.4 8.2 7–10 12.0 - 12.0 12.0 10.3 10.3 12.0 10.3 Descriptive statistics Observations 868 350 868 868 661 661 868 661 Least-Squares Mean 1466.59 1605.97 9.46 290.42 503.10 206.02 5.08 3.14 SE 14.01 15.73 0.05 2.31 5.26 4.50 0.03 0.03 SD 412.94 294.31 1.69 68.23 135.27 115.88 1.03 0.90 CV (%) 28.16 18.33 17.88 23.5 27.24 55.38 20.31 28.73 Min. 502 691 3.5 100 323 18 2.23 0.68 Max. 3399.5 2467 15.5 562 1124 800 10.01 6.14 Non-Genetic factors Age NS NS ** NS NS NS NS ** Season ** ** ** * ** ** ** * Period ** ** ** * * * ** ** Parity NS NS ** NS NS NS ** NS *TMY = total milk yield (kg); TMY_305 = 305-day milk yield (kg); PY = peak yield (g); CI = calving interval; DP = dry period; ADMY_LL = average daily milk yield per lactation; ADMY_CI = average daily milk yield per calving interval. Significance: **P < 0.01; P 0.05). Least-squares means (± SE) for TMY, TMY_305, PY, LL, CI, DP, ADMY_LL, and ADMY_CI were 1466.59 ± 14.01, 1605.97 ± 15.73, 9.46 ± 0.05, 290.42 ± 2.31, 503.10 ± 5.26, 206.02 ± 4.50, 5.08 ± 0.03, and 3.14 ± 0.03, respectively. The standard deviation ranged from 0.90 (ADMY_CI) to 412.94 (TMY), whereas the coefficient of variation (CV) was highest for DP (55.38%), followed by ADMY_CI (28.73%), TMY (28.16%), and CI (27.24%). The minimum and maximum values also reflected wide trait ranges, with TMY ranging from 502 to 3399.5 kg and CI from 323 to 1124 days. The season of calving had a highly significant effect (P < 0.01) on all traits, except for LL and ADMY_CI, where the effect was significant at the 5% level. The calving period had a strong influence, being highly significant (P < 0.01) for TMY, TMY_305, PY, and ADMY_LL, and moderately significant (P < 0.05) for LL, CI, and DP. Parity had a significant effect on PY and ADMY_LL (P < 0.01), whereas no significant effect was observed on the remaining traits. Animal age significantly influenced only PY and ADMY_CI (P < 0.01), with no detectable effect on TMY, LL, CI, or DP. 3.1 Variance and co(variance) components and genetic parameters Estimates of (co)variance components and genetic parameters under three models are presented in Table 2 . For TMY, heritability ranged from 0.054 to 0.070, with corresponding repeatability between 0.318 and 0.349. TMY_305 showed slightly higher heritability (0.065–0.116) and repeatability (0.239–0.381). PY had moderate heritability (~ 0.112) and repeatability (~ 0.280). Table 2 Estimates of (co)variance components, genetic parameters, maternal effects, and model fit statistics for economic traits in Surti buffaloes. Traits Items a σ 2 a σ 2 pe σ 2 m σ 2 c σ 2 e σ 2 p h 2 a h 2 m c 2 R Max. Log L AIC TMY Model-1 11750.4 47144.1 - - 109962 168857 0.070 ± 0.05 - - 0.3487 -5558.9 11123.8 Model-2 11060.6 47597.6 207.43 - 109962 168827 0.066 ± 0.003 0.001 ± 0.04 - 0.3474 -5558.9 11125.8 Model-3 9171.21 44559.2 18.39 4826.54 110028 168603 0.054 ± 0.003 NE 0.029 ± 0.04 0.3186 -5558.7 11127.5 TMY_305 Model-1 9852.3 22440.0 - - 52384.1 84676.4 0.116 ± 0.069 - - 0.3814 -2128.8 4263.5 Model-2 6993.1 21481.8 3971.3 - 52330 84776.2 0.082 ± 0.007 0.047 ± 0.09 - 0.3359 -2128.6 4265.3 Model-3 5540.9 14744.4 839.9 11491.1 52205.4 84821.6 0.065 ± 0.006 0.010 ± 0.08 0.135 ± 0.01 0.2392 -2128.0 4266.1 PY Model-1 0.291 0.434 - - 1.86 2.589 0.112 ± 0.07 - - 0.2799 -811.7 1629.3 Model-2 0.292 0.434 0.001 - 1.86 2.591 0.113 ± 0.08 0.000 ± 0.06 - 0.2800 -811.7 1631.3 Model-3 0.291 0.429 0.001 0.007 1.86 2.592 0.112 ± 0.08 0.000 ± 0.07 0.003 ± 0.08 0.2779 -811.7 1633.3 LL Model-1 118.09 719.17 - - 3783.04 4620.30 0.026 ± 0.05 - - 0.1812 -4056.9 8119.7 Model-2 95.52 717.75 24.01 - 3782.88 4620.16 0.021 ± 0.06 0.005 ± 0.04 - 0.1760 -4056.9 8121.7 Model-3 95.02 717.56 24.35 0.001 3783.07 4619.99 0.021 ± 0.06 0.005 ± 0.05 0.000 ± 0.06 0.1759 -4056.9 8123.7 CI Model-1 161.13 1666.94 - - 16227.3 18055.3 0.009 ± 0.04 - - 0.1012 -3556.1 7118.2 Model-2 158.61 1670.89 0.001 - 16226.2 18055.7 0.009 ± 0.05 0.000 ± 0.05 - 0.1013 -3556.1 7120.2 Model-3 158.09 1671.51 0.001 0.009 16226.1 18055.7 0.009 ± 0.05 0.000 ± 0.06 0.000 ± 0.07 0.1013 -3556.1 7122.2 DP Model-1 260.53 512.82 - - 12551.1 13324.5 0.020 ± 0.04 - - 0.05804 -3459.3 6924.6 Model-2 260.36 513.91 0.04 - 12550.4 13324.7 0.020 ± 0.05 0.000 ± 0.04 - 0.05811 -3459.3 6926.6 Model-3 265.92 550.84 0.14 0.0 12530.1 13347 0.020 ± 0.05 0.000 ± 0.07 0.000 ± 0.08 0.06119 -3459.3 6928.6 ADMY_LL Model-1 0.097 0.248 - - 0.698 1.04 0.093 ± 0.07 - - 0.3308 -406.4 818.7 Model-2 0.091 0.240 0.016 - 0.698 1.04 0.087 ± 0.08 0.016 ± 0.06 - 0.3166 -406.3 820.7 Model-3 0.090 0.220 0.001 0.035 0.698 1.04 0.086 ± 0.08 0.001 ± 0.07 0.033 ± 0.08 0.2971 -406.2 822.5 ADMY_CI Model-1 0.005 0.118 - - 0.495 0.619 0.008 ± 0.06 - - 0.1994 -181.3 368.6 Model-2 0.017 0.111 0.001 - 0.495 0.624 0.028 ± 0.07 0.002 ± 0.06 - 0.2055 -181.4 370.9 Model-3 0.003 0.119 0.001 0.001 0.502 0.626 0.005 ± 0.06 0.002 ± 0.09 0.002 ± 0.10 0.1954 -181.4 372.9 σ 2 a , σ 2 pe , σ 2 m , σ 2 c , σ 2 e , and σ 2 p are additive direct, animal permanent environment, maternal genetic, maternal permanent environmental, residual variance, and phenotypic variance, respectively; h 2 a is direct heritability; h 2 m is maternal heritability; c 2 is maternal environment component; R is repeatability; log L is Log likelihood; and AIC is Akaike information criteria with row bold representing estimates from the most appropriate model. Traits like LL, CI, and DP exhibited low heritability (< 0.03) and low repeatability (< 0.18). ADMY_LL showed moderate heritability (0.086–0.093) and repeatability (0.297–0.331), whereas ADMY_CI had negligible heritability (0.005–0.028) with moderate repeatability (~ 0.20). Maternal genetic and permanent maternal environmental variances were minimal across traits and models. 3.2 Correlation Among the Economic Traits Phenotypic correlations (lower triangle) and additive genetic correlations (upper triangle) are presented in Fig. 1, and detailed correlation coefficients are provided in Supplementary Table 1. TMY exhibited strong phenotypic associations with TMY_305 (0.92), LL (0.73), and ADMY_LL (0.54). TMY_305 also showed strong positive correlations with ADMY_LL (0.94) and PY (0.74), while its correlation with CI was moderately negative (− 0.32). A strong positive phenotypic correlation was observed between CI and DP (0.89), whereas both traits were negatively correlated with ADMY_CI. Additive genetic correlations were consistently high. TMY and TMY_305 displayed a near-perfect genetic correlation (0.99) and were also strongly associated with CI, DP, PY, and LL (0.87–1.00). In contrast, ADMY_CI showed weak or negative genetic correlations with TMY (− 0.24), PY (− 0.10), and LL (− 0.02). A moderate positive genetic correlation (0.91) was observed between ADMY_LL and ADMY_CI. 3.3 Trends of economic traits The phenotypic and genetic trends of economic traits in Surti buffaloes from 1993 to 2023 are presented in Table 3 and illustrated in Figs. 2–5. TMY and TMY_305 showed significantly declining genetic trends of − 1.78 and − 1.91 kg/year, respectively, indicating reductions of 98–124% per decade. Phenotypic trends were also negative and significant. Table 3 T rends of economic traits in Surti buffaloes Traits Phenotypic Trends R 2 P % % Phenotypic change per decade Genetic trends R 2 G % % genetic change per decade TMY -10.30 ± 3.38** 24 -7.04 -1.78 ± 0.24** 66 -97.71 TMY_305 -9.83 ± 3.02** 27 -7.47 -1.91 ± 0.25** 66 -123.82 LL -0.75 ± 0.49 7 -2.54 0.02 ± 0.01 5 19.16 CI -1.78 ± 1.31 6 -3.55 -0.12 ± 0.01** 62 166.76 PY -0.016 ± 0.009 2 -1.75 -0.02 ± 0.002** 68 -231.32 ADMY_LL -0.02 ± 0.009* 15 -4.06 -0.01 ± 0.007** 87 -231.90 DP -1.22 ± 0.03 5 -5.94 -0.22 ± 0.008** 67 104.49 ADMY_CI -0.009 ± 0.008 4 -2.96 0.0004 ± 0.0001** 40 111.34 R 2 G , R 2 P : Genetic and Phenotypic Coefficient of determination CI exhibited a highly significant genetic improvement (− 0.12 days/year; 167% per decade), while LL trends were negligible. PY and ADMY_LL showed substantial genetic declines (− 0.02 and − 0.01 kg/day/year), with > 230% reduction per decade. In contrast, ADMY_CI improved genetically (0.0004 kg/day/year; 111% per decade), despite a weak negative phenotypic trend. DP demonstrated a notable genetic decline (− 0.22 days/year), reflecting 104% improvement per decade. 4 Discussion 4.1 Least-Squares Means and Non-Genetic Factor Effects The least-squares means, standard deviations, coefficients of variation, and non-genetic effects observed in this study were consistent with those reported for various breeds of buffalo. Similar trends were reported in Surti buffaloes (Shashikant et al. 2021 ), Murrah buffaloes (Jamal et al. 2018 ; Sharma et al. 2024a ), and Mehsana buffaloes (Parmar et al. 2017 ) Among the non-genetic factors, age had a non-significant effect on most traits, except for peak yield and ADMY_CI, where mature buffaloes performed better, likely due to enhanced physiological development (Kumar, 2018 ). Season significantly influenced all traits except ADMY_CI, with higher yields observed in winter owing to favorable climatic conditions (Shashikant et al. 2021 ). The calving period had a highly significant influence on all traits, reflecting long-term environmental shifts and genetic progress or decline (Gandhi et al. 2009 ; Gaikwad et al. 2022 ). Parity significantly influenced PY and ADMY_LL, with maximum performance observed at the fifth parity, followed by a decline, likely due to age-related metabolic and reproductive stresses (Pawar et al. 2012 ). 4.2 Heritability The heritability estimates for production traits in this study ranged from low to moderate, indicating that environmental factors had a dominant influence and that genetic gains through direct selection may be gradual. For TMY, heritability estimates ranged from 0.054 to 0.070, aligning with previous values reported in Surti (Nagda, 2005 ; Jatolia, 2008 ), Bhadawari (Sachan et al. 2007 ), and Murrah buffaloes (Sharma et al. 2024a ). In contrast, higher estimates have been reported in Mehsana (Galsar et al. 2016 ; 0.18–0.21), suggesting more exploitable genetic variability in that breed. Heritability for TMY_305 ranged from 0.065 to 0.116, which is lower than the estimates reported for Mehsana (Galsar et al. 2016 ; 0.22) and exotic breeds such as Holstein (VanRaden et al. 2009 ; 0.25; Cole et al. 2011 ; 0.37), highlighting the role of intensive selection and management in improving additive genetic variance. Peak yield heritability in the present study (0.112–0.113) was comparable to estimates in Murrah (Parmar et al. 2017 ), though Sharma et al. ( 2024a ) reported higher values (0.48), potentially due to breed-specific selection programs. The heritability estimates for ADMY_LL ranged from 0.086 to 0.093, consistent with findings by Ismael et al. ( 2021 ; 0.182). Accounting for maternal genetic (σ²ₘ) and maternal permanent environmental (σ²c) effects in Models 2 and 3 resulted in a slight decrease in heritability for TMY and TMY_305, underscoring minor maternal influences. The maternal variance component remained modest (≤ 3.5%), in agreement with Miglior et al. ( 2007 ; 2.8%) for Jersey cattle. In contrast, reproductive traits such as LL, DP, CI, and ADMY_CI exhibited notably lower heritability estimates, confirming their strong environmental dependency. Heritability for LL ranged from 0.027 to 0.041, consistent with earlier reports in Murrah (Bashir et al. 2017 ), Holstein (Kadarmideen et al. 2003 ), and Sahiwal (Ilatsia et al. 2007 ). The estimate for DP (0.020) was also in line with Bashir et al. ( 2017 ) and Kadarmideen et al. ( 2003 ), highlighting the predominant role of feeding and health management practices. Calving interval had negligible heritability (0.009), corroborating findings in Indian buffaloes (Nagda, 2005 ; Kaur and Narang, 2021 ; Sharma et al. 2024b ). However, higher estimates were reported by Jatolia ( 2008 ), indicating that reproductive trait heritability may vary based on management intensity and population structure. Similarly, heritability of ADMY_CI remained very low (0.005–0.028), reinforcing its limited genetic determinism under field conditions. 4.3 Repeatability The repeatability estimates derived for Surti buffaloes indicated moderate stability for milk production traits across lactations and low consistency for reproductive traits. For TMY, repeatability ranged from 0.32 to 0.35, which aligns closely with earlier reports in Surti and Bhadawari buffaloes (Nagda, 2005 ; Sachan et al. 2007 ) using sire model, and reflects the cumulative influence of additive genetics and permanent environmental factors. Comparable estimates were also noted for TMY_305, ranging from 0.2392 to 0.3814, similar to those reported in Mehsana (Galsar et al. 2016 ) and lower than those in Holstein-Friesians (VanRaden et al. 2009 ; Cole et al. 2011 ). Peak yield exhibited repeatability estimates of 0.2779 to 0.2800, consistent with results in Murrah (Parmar et al. 2017 ), and Holstein (Cole et al. 2011 ) cattle. The moderate repeatability of these traits suggests that individual animals tend to maintain their production levels across lactations, supporting their use in early selection strategies. Reproductive traits, however, demonstrated lower repeatability. Calving interval and dry period had estimates ranging from 0.101 and 0.058–0.061, respectively, consistent with studies in Sahiwal (Dash et al. 2015 ). These low values highlight the predominant role of non-genetic influences such as reproductive management, nutrition, and disease control. Repeatability for ADMY_LL was moderate (0.2971–0.3308), indicating fair consistency, while that for ADMY_CI was lower (0.1954–0.2055), reflecting the complex and extended nature of the calving interval. The magnitude of repeatability for these traits is supported by reports on indigenous breeds under similar management systems (Miglior et al. 2007 ). Permanent environmental variance (σ²pe) played a considerable role in shaping phenotypic expression. Its contribution to TMY reached 26.27%, with TMY_305 ranging between 13.5% and 26.5%, and ADMY_LL from 21.10–23.78%. Reproductive traits like CI and DP also showed notable permanent environmental influence (9.23–9.26% and 3.85–4.13%, respectively), which may be attributed to consistent herd-level practices and seasonal factors. Given the moderate repeatability and the substantial contribution of permanent environmental effects, Model 1 is considered suitable for routine evaluation of performance traits in Surti buffaloes. However, enhancement of reproductive efficiency may depend more on management interventions than on genetic progress. Thus, for traits with low repeatability and heritability, prioritizing health care, nutrition, and reproductive management remains crucial to improving lifetime productivity. 4.4 Genetic and Phenotypic Correlations of Economic Traits Strong positive genetic correlations were observed among key production traits, suggesting that they are influenced by common genetic factors and could be effectively improved through correlated response to selection. Total milk yield showed near-perfect genetic correlations with TMY_305 (0.99), PY (0.90), and LL (0.87), indicating a shared genetic influence. These findings are consistent with earlier reports in Surti buffaloes (Kumar, 2018 ), where genetic correlations between TMY and LL ranged from 0.81 to 0.99. Similarly, high correlations between TMY and TMY_305 have been documented in Murrah buffaloes (Sharma et al. 2024a ). The correlation between TMY and PY (0.92) is also in agreement with previous studies (Kumar, 2018 ; Shashikant et al. 2021 ), while the genetic correlation between PY and TMY_305 (0.80) supports joint selection strategies (Kumar et al. 2018). Furthermore, the correlation between TMY_305 and LL (0.99) exceeds estimates reported in Murrah buffaloes (0.63–0.79; Sigdel et al. 2015 ; Sharma et al. 2024a ), potentially reflecting differences in breed characteristics or management intensity. Moderate genetic correlation between LL and CI (0.54) suggests a genetic link between lactation duration and reproductive rhythm, consistent with results in Mehsana buffaloes (Galsar et al. 2016 ). A strong genetic correlation between CI and DP (0.99) contrasts with the weaker or negative estimates reported in other breeds (Galsar et al. 2016 ). Similarly, the high genetic correlation between TMY and CI (0.99) exceeds values previously reported in Indian buffalo populations (0.30; Jain & Tailor, 1994 ). With regard to persistency traits, TMY displayed high genetic correlations with ADMY_LL (0.77) and ADMY_CI (0.60), aligning with earlier reports in Bhadawari buffaloes (Sachan et al. 2007 ). However, LL and ADMY_LL exhibited a weak genetic correlation (0.09), indicating that prolonged lactation does not necessarily result in higher daily yield. The genetic correlation between CI and ADMY_CI was moderate (0.25), lower than the 0.53 reported in Bhadawari buffaloes (Sachan et al. 2007 ). Phenotypic correlations among traits generally reflected moderate to strong positive relationships, shaped by shared environmental influences. TMY was positively correlated with LL (0.69), TMY_305 (0.96), and PY (0.57), consistent with previously reported estimates in Surti buffaloes (Nagda, 2005 ; Jatolia, 2008 ). The phenotypic association between LL and CI (0.69) supports the genetic relationship observed and agrees with prior studies (Galsar et al. 2016 ). Despite the high genetic correlation between CI and DP, their phenotypic association was modest (0.29), highlighting the effect of environmental variation during the dry period. The phenotypic correlation between TMY and CI (0.27) was slightly higher than the 0.17 reported by Jain and Tailor ( 1994 ). TMY also showed strong phenotypic correlations with ADMY_LL (0.66) and ADMY_CI (0.71), suggesting that animals with higher total yields tend to sustain higher daily production. LL and ADMY_LL had a moderate correlation (0.33), in line with previous findings (Sachan et al. 2007 ), while CI and ADMY_CI showed a strong phenotypic correlation (0.73), exceeding earlier reports in Bhadawari buffaloes, potentially due to breed differences or improved reproductive management. 4.5 Trends The temporal genetic and phenotypic trends observed in this study highlight a concerning pattern of decline in milk production traits in Surti buffaloes. Total milk yield and TMY_305 exhibited substantial negative genetic trends of − 97.71% and − 123.82% per decade, respectively, accompanied by corresponding negative phenotypic shifts. These findings suggest that past selection efforts and management interventions have been largely ineffective in sustaining or enhancing milk yield. This contrasts with the favorable genetic trends reported in Murrah and Karan Fries breeds (Dash et al. 2015 ; Kour et al. 2020 ), underscoring breed-specific differences in selection intensity, environmental adaptability, and herd management efficiency. A sharp genetic decline was also observed for peak yield (− 231.32%) and ADMY_LL (− 231.90%), reflecting declining daily productivity despite improvements in data recording systems and general herd care. In contrast, reproductive traits showed encouraging genetic gains. Calving interval improved markedly (166.76% per decade), whereas DP declined by 104.49%, indicating enhanced reproductive control. Notably, ADMY_CI increased by 111.34%, suggesting genetic gains in daily milk yield adjusted for reproductive performance. 5 Conclusion This study demonstrated that economic traits in Surti buffaloes are largely shaped by environmental and management factors, as evidenced by low to moderate heritability and repeatability estimates. Strong genetic and phenotypic correlations among production traits such as total milk yield, 305-day yield, and peak yield suggest potential for improvement through correlated selection. However, the unfavorable genetic trends for milk production traits highlight the limitations of previous selection strategies and emphasize the need for revision. In contrast, reproductive traits including calving interval and dry period showed encouraging genetic progress, reflecting improved reproductive management. Although maternal genetic and permanent environmental variances contributed minimally, their inclusion enhanced model accuracy. Among the models evaluated, Model 1 was found to be the most appropriate for field-level evaluation. To achieve sustainable genetic gains, selection programs should balance milk production and reproductive efficiency while integrating sound herd management practices. Declarations Acknowledgements The authors acknowledge the Network Project on Surti Buffalo Improvement at the Livestock Research Station, CVAS, Navania, Vallabhnagar, for providing infrastructure, resources, and access to performance records. We appreciate the cooperation of all staff involved in data collection and record maintenance. Ethics Statement The authors confirm that the ethical policies of the journal, as outlined in the journal’s author guidelines, have been followed. No ethical approval was required, as this study involved a retrospective analysis of herd performance data routinely recorded under the Network Project on Surti Buffalo Improvement at the Livestock Research Station, College of Veterinary and Animal Science, Navania, Vallabhnagar, with no experimental interventions or invasive procedures performed on animals. All research activities at this institution are conducted under the oversight of the Institutional Animal Ethics Committee (IAEC), registered under CPCSEA Registration No. 2143/GO/Re/SL/22/CPCSEA, Ministry of Fisheries, Animal Husbandry and Dairying, Government of India. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contributions Hari Ram Meena: Data curation, Formal analysis, Methodology, Writing – original draft, and editing. Lokesh Gautam: Conceptualization, Formal analysis, investigation, Resources, Supervision, Validation, Writing – review, and editing. Funding Information This research did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors. Data Availability Statement The data supporting the findings of this study are maintained by the Network Project on Surti Buffalo Improvement at the Livestock Research Station, CVAS, Navania, Vallabhnagar, India. Access to the data can be granted upon reasonable request and with the permission of the Principal Investigator of the project. Consent for Publication The authors have reviewed the final version of the manuscript and provided their consent for the publication of the results presented in this study. References Akaike H (1973) Information theory and an extension of the maximum likelihood principle. In: Petrov BN, Csáki F (eds) Second international symposium on information theory. 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Indian J Anim Sci 92:456–460 Galsar NS, Shah RR, Gupta JP, Pandey DP, Prajapati KB, Patel JB (2016) Genetic estimates of reproduction and production traits in Mehsana buffalo. Indian J Dairy Sci 69:698–701 Gandhi RS, Raja TV, Ruhil AP (2009) Non-genetic factors affecting milk production in buffaloes. Indian J Dairy Sci 62:200–205 Gudex BW, Johnson DL, Singh D (2014) Maternal and direct genetic effects on milk production in New Zealand dairy cattle. N Z J Agric Res 57:173–181 Harvey WR (1990) User’s guide for LSMLMW and MIXMDL: PC-2 version mixed model least-squares and maximum likelihood computer program. Ohio State University Ilatsia ED, Muasya TK, Muhuyi WB, Kahi AK (2007) Genetic and phenotypic parameters for test day milk yield of Sahiwal cattle in the semi-arid tropics. Animal 1:185–192 Ismael H, Janković D, Stanojević D, Bogdanović V, Trivunović S, Djedović R (2021) Estimation of heritability and genetic correlations between milk yield and linear type traits in primiparous Holstein-Friesian cows. Rev Bras Zootec 50:e20200121 Jain A, Tailor SP (1994) Genetic and phenotypic correlations among economic traits in Surti buffaloes. Indian J Dairy Sci 47:456–460 Jamal I, Dhaka SS, Patil CS (2018) Non-genetic factors affecting milk yield in Murrah buffaloes. Indian Vet J 95:45–48 Jatolia PK (2008) Genetic analysis of reproductive traits in Surti buffaloes. Indian J Anim Sci 78:970–973 Kadarmideen HN, Thompson R, Simm G (2003) Linear and threshold model genetic parameters for disease, fertility and milk production in dairy cattle. Anim Sci 77:411–419 Kaur M, Narang R (2021) Genetic parameters of reproductive traits in Indian buffaloes. Buffalo Bull 40:123–130 Kour A, Dhaka SS, Kumar A (2020) Genetic trends in milk production traits of Karan Fries cattle. Indian J Anim Sci 90:456–460 Kumar M (2018) Genetic and non-genetic factors affecting milk production in Surti buffaloes. Indian J Dairy Sci 71:145–150 Meyer K (2007) WOMBAT—a tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML). J Zhejiang Univ Sci B 8:815–821 Miglior F, Muir BL, Van Doormaal BJ (2007) Selection indices in Holstein cattle of various countries. J Dairy Sci 90:891–902 Nagda RK (2005) Genetic studies on production and reproduction traits in Surti buffaloes. Indian J Anim Sci 75:920–925 Parmar GA, Gupta JP, Chaudhari JD (2017) Genetic parameters of production traits in Mehsana buffaloes. Indian J Anim Sci 87:756–760 Patel AK, Patel JB, Patel MP (2015) Characteristics and performance of Surti buffaloes in Gujarat. Indian J Anim Sci 85:769–773 Pawar HR, Kumar G, Narang R (2012) Effect of parity on milk production in Murrah buffaloes. Indian J Dairy Sci 65:315–319 R Core Team (2023) R: a language and environment for statistical computing (version 4.3.0). R Foundation for Statistical Computing Sachan CB, Kushwaha BP, Kundu SS (2007) Evaluation of production performance of Bhadawari buffaloes. Indian J Anim Sci 77:781–783 Sharma S, Dhaka SS, Patil CS (2024a) Estimation of additive and maternal covariance of production traits in Murrah buffalo. J Anim Breed Genet 141:415–424 Sharma S, Dhaka SS, Patil CS, Rathi P (2024b) Estimation of direct and maternal covariance along with genetic and phenotypic trends of reproduction traits in Murrah buffalo. Reprod Domest Anim 59:e14526 Shashikant K, Nagda RK, Kumar V, Meena VK (2021) Effect of non-genetic factors on calving interval in Surti buffaloes. Pharma Innov J 10:984–986 Sigdel A, Bhatti SA, Khan MS (2015) Genetic analysis of milk production traits in Murrah buffaloes. Pak J Agric Sci 52:723–728 VanRaden PM, Van Tassell CP, Wiggans GR, Sonstegard TS, Schnabel RD, Taylor JF, Schenkel FS (2009) Invited review: reliability of genomic predictions for North American Holstein bulls. J Dairy Sci 92:16–24 Supplementary Files SupplementaryTable1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7485713","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":511691882,"identity":"d33bc6f4-3864-4ff2-b117-7fbea35171e2","order_by":0,"name":"Hari Ram Meena","email":"","orcid":"","institution":"Rajasthan University of Veterinary and Animal Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hari","middleName":"Ram","lastName":"Meena","suffix":""},{"id":511691883,"identity":"1af93833-ca40-4561-9ca0-ee9b6a6b8030","order_by":1,"name":"Lokesh 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06:54:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7485713/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7485713/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91344628,"identity":"6f02459e-a320-46ea-86d6-2560f71dfd83","added_by":"auto","created_at":"2025-09-15 13:39:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":172101,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelations among economic traits in Surti buffaloes.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7485713/v1/e28fb36e8c1eed1178edc448.png"},{"id":91343276,"identity":"7b60593c-566e-4536-8995-b81545db5925","added_by":"auto","created_at":"2025-09-15 13:31:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":17836,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrends for TMY and TMY_305.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7485713/v1/be429ad74ebb9eb4dc9b1be5.png"},{"id":91344629,"identity":"a2fed681-77c3-4ddd-af6a-9494b37c7522","added_by":"auto","created_at":"2025-09-15 13:39:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":17189,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrends for LL and CI.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7485713/v1/bfd9de73ecbfe7bec31406a0.png"},{"id":91343277,"identity":"a21cb251-2884-4555-8a7c-7b6fb8a151c6","added_by":"auto","created_at":"2025-09-15 13:31:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":17326,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrends for PY and ADMY_LL.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7485713/v1/373c6e2b3d088f4539cb3c13.png"},{"id":91343281,"identity":"b3632bc9-e5f2-4dc9-988b-dc998201e81a","added_by":"auto","created_at":"2025-09-15 13:31:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":18427,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrends for DP and ADMY_CI.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7485713/v1/1fdec8b5b7f1ad23e27e20bd.png"},{"id":95802573,"identity":"13b5a214-2627-45d7-a914-ceb2a378d525","added_by":"auto","created_at":"2025-11-13 08:27:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1662156,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7485713/v1/5f6f3f44-c45a-42b7-8d1b-0adff8c856c6.pdf"},{"id":91343292,"identity":"c6624288-a4f1-438a-9569-332a3563cde1","added_by":"auto","created_at":"2025-09-15 13:31:55","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":16167,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7485713/v1/db052efd58430ced74fb160f.docx"}],"financialInterests":"","formattedTitle":"Direct and Maternal Genetic Effects and Temporal Trends of Economic Traits in Surti Buffaloes","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eGlobally, approximately 204\u0026nbsp;million buffaloes are distributed across 40 countries, with Asia accounting for 97% of the total population. India holds the largest share, with 109.85\u0026nbsp;million buffaloes, representing 57% of the global total. Rajasthan is the second-largest buffalo-rearing state in India, with a population of 13.7\u0026nbsp;million, including 597,128 buffaloes in the Udaipur district alone, which significantly contributes to national milk production (Department of Animal Husbandry and Dairying, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The Surti buffalo, also known as Deccani or Gujarati, is a key dairy breed native to Gujarat\u0026rsquo;s Charotar tract and southern Rajasthan. These medium-sized, docile animals feature sickle-shaped horns, a convex forehead, prominent eyes, and a well-developed udder, ideal for dairy farming. Their adaptability, efficient feed use, and high milk fat content enhance economic value (Patel et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In India, buffaloes outyield indigenous cattle in per-animal milk production, with economic traits like milk yield and reproductive efficiency critical for herd profitability and sustainability, influenced by management, nutrition, genetics, and environment.\u003c/p\u003e\u003cp\u003eImproving dairy productivity demands a comprehensive understanding of genetic and non-genetic factors affecting performance. Non-genetic factors, such as feeding, housing, healthcare, and climate shape phenotypes, and their optimization can boost milk yield and reproduction (Galsar et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Genetically, quantitative traits are driven by additive and maternal effects, with mitochondrial DNA influencing milk yield via maternal inheritance (Gudex et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Accurate genetic parameter estimation is vital for effective breeding, quantifying genetic and environmental contributions for precise selection, though tropical variability and data limitations can skew results (Demeke et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Repeatability models help capture individual differences across lactations, but estimates may vary with population or management changes.\u003c/p\u003e\u003cp\u003eDespite their significance, systematic genetic studies on Surti buffaloes are scarce, with research often focusing on crossbred or exotic cattle, leaving a gap in indigenous buffalo genetics. This study aimed to estimate genetic parameters, assess genetic and phenotypic trends, and evaluate non-genetic influences on key traits in Surti herds, providing evidence for selection strategies, herd management, and sustainable genetic improvement.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Study Location and Population\u003c/h2\u003e\n \u003cp\u003eThis study used data from the Network Project on Buffalo Improvement (Surti) at the Livestock Research Station, Navania, Udaipur. The dataset included performance and pedigree records of Surti buffaloes over 30 years (1993\u0026ndash;2023). Only animals with complete pedigree and economic trait records were included. Records with abnormalities (e.g., abortion, stillbirth, lactation\u0026thinsp;\u0026lt;\u0026thinsp;100 days, yield\u0026thinsp;\u0026lt;\u0026thinsp;500 kg) or pathological conditions were excluded. Sires with fewer than three progeny were omitted.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Herd Management\u003c/h2\u003e\n \u003cp\u003eBuffaloes were housed loosely, grouped by age and physiological status, with separate areas for calves, heifers, lactating, dry, pregnant animals, and breeding bulls. Green fodder was provided ad libitum, supplemented by concentrate feed (14\u0026ndash;16% digestible protein) as required. Healthcare involved regular vaccination and deworming. Oestrus detection used vasectomized bulls, followed by frozen semen insemination; pregnancy was confirmed 60\u0026ndash;90 days post-insemination. Milking occurred manually twice daily (6 AM and 4 PM), with yields recorded in kg.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Traits under Study\u003c/h2\u003e\n \u003cp\u003eEconomic traits analyzed were total milk yield (TMY), 305-day milk yield (TMY_305), peak yield (PY), lactation length (LL), calving interval (CI), dry period (DP), average daily milk yield per lactation length (ADMY_LL), and per calving interval (ADMY_CI). Fixed effects included calving period (six 5-year intervals), season (summer, rainy, winter), parity (1 to \u0026ge;\u0026thinsp;6), and age group (grouped via Sturges\u0026rsquo; formula).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e\n \u003cp\u003eDescriptive statistics were computed using the R software (R Core Team \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). The effects of non-genetic factors on economic traits were analyzed using a mixed linear model with the Least-Squares Maximum Likelihood method (Harvey \u003cspan class=\"CitationRef\"\u003e1990\u003c/span\u003e). The model included fixed effects of period, season, parity, and age group, and the random effect of sire, expressed as\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003eijklmn\u003c/em\u003e\u003c/sub\u003e denotes the performance record, \u0026micro; is the overall population mean, \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e represents the random effect of the sire, and \u003cem\u003eB\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eD\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e are the fixed effects of period, season, parity, and age group at calving, respectively. The residual term \u003cem\u003ee\u003c/em\u003e\u003csub\u003e\u003cem\u003eijklmn\u003c/em\u003e\u003c/sub\u003e is assumed to follow a normal distribution with mean zero and constant variance. Significance was assessed using Analysis of Variance (ANOVA), with post-hoc comparisons via Tukey\u0026rsquo;s test at P\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/p\u003e\n \u003cp\u003eGenetic parameters were estimated using repeatability animal models in the WOMBAT software with the Restricted Maximum Likelihood (REML) approach (Meyer \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). Three models were used:\u003c/p\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:Y=X\\beta\\:+\\:{Z}_{1}a+\\:{Z}_{3}pe+\\:e\\:with\\:Cov\\left(a,m\\right)=0$$\u003c/div\u003e\n \u003c/div\u003e1.\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e$$\\:Y=X\\beta\\:+\\:{Z}_{1}a+\\:{Z}_{2}m+\\:{Z}_{3}pe+\\:e\\:with\\:Cov\\left(a,m\\right)=0$$\u003c/div\u003e\n \u003c/div\u003e2.\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e$$\\:Y=X\\beta\\:+\\:{Z}_{1}a+\\:{Z}_{2}m+\\:{Z}_{3}pe+{Z}_{4}c+\\:e\\:with\\:Cov\\left(a,m\\right)=0$$\u003c/div\u003e\n \u003c/div\u003e3.\u003cp\u003eThe variance and (co)variance structure of the random effects was as follows\u003c/p\u003e\n \u003cdiv id=\"Eque\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eIn the variance-covariance model, Y represents an n \u0026times; 1 vector of observations for each trait, and the vectors \u0026beta;, a, m, pe, c, and \u0026epsilon; represent fixed effects, direct additive genetic effects, maternal genetic effects, animal permanent environmental effects, maternal permanent environmental effects, and residual effects, respectively. Matrices X, Z₁, Z₂, Z₃, and Z₄ are incidence matrices that relate these effects to the records, respectively. A is the numerator relationship matrix among animals. The variance components are defined as follows: \u0026sigma;\u0026sup2;a is the direct additive genetic variance, \u0026sigma;\u0026sup2;m is the maternal genetic variance, \u0026sigma;\u0026sup2;pe is the animal permanent environmental variance, \u0026sigma;\u0026sup2;c is the maternal permanent environmental variance, and \u0026sigma;\u0026sup2;e is the residual variance. Phenotypic variance is represented by \u0026sigma;\u0026sup2;p. Identity matrices \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003ed\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003en\u003c/em\u003e\u003c/sub\u003e correspond to the number of animals in their respective variance structures.\u003c/p\u003e\n \u003cp\u003eHeritability (h\u0026sup2;), maternal heritability (m\u0026sup2;), and permanent environmental effects (c\u0026sup2;) were calculated from the estimated variances. The formulae used were: h\u0026sup2; = \u0026sigma;\u0026sup2;a/\u0026sigma;\u0026sup2;p, m\u0026sup2; = \u0026sigma;\u0026sup2;m/\u0026sigma;\u0026sup2;p, and c\u0026sup2; = \u0026sigma;\u0026sup2;c/\u0026sigma;\u0026sup2;p. Repeatability was estimated as R = (\u0026sigma;\u0026sup2;a\u0026thinsp;+\u0026thinsp;\u0026sigma;\u0026sup2;pe)/\u0026sigma;\u0026sup2;p. The most appropriate model for each trait was determined based on the Akaike Information Criterion (AIC) (Akaike, \u003cspan class=\"CitationRef\"\u003e1973\u003c/span\u003e). The AIC is an estimator of the relative quality of statistical models for a given dataset, balancing the model fit and complexity. It was calculated as AIC = \u0026minus;\u0026thinsp;2 Log L\u0026thinsp;+\u0026thinsp;2P, where Log L is the maximized log-likelihood of the model, and P is the number of estimated parameters. Among the competing models, the one with the lowest AIC value was selected as the most appropriate.\u003c/p\u003e\n \u003cp\u003eGenetic and phenotypic correlations between traits were estimated using bivariate repeatability animal models that incorporated the best-fit, fixed, and random effects. The correlation coefficients were derived from the covariances and variances obtained using the bivariate models. Genetic trends were assessed by regressing average estimated breeding values (EBVs) on the calving period using the \u003cem\u003elm\u003c/em\u003e function in R. Phenotypic trends were determined by regressing the average phenotypic performance during the calving period.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003eThe dataset\u0026rsquo;s adequacy for genetic evaluation of economic traits in Surti buffaloes was confirmed through pedigree structure, repeated records, descriptive statistics, and non-genetic factor effects (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). For traits TMY, PY, LL, and ADMY_LL, records from 258 animals were available, while TMY_305 and CI, DP, ADMY_CI included 161 and 195 animals, respectively. The number of sires ranged from 38 (TMY_305) to 44 (TMY, PY, LL, ADMY_LL), and dams from 92 to 119. Grandparental pedigrees were traceable for 103\u0026ndash;151 maternal grandsires and 79\u0026ndash;108 granddams. About 28.7% of animals had one record, and over 10% had five or more records, demonstrating repeated observations. In contrast, TMY_305 showed limited repeatability with only 1.2% of animals reaching six lactations.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of economic traits of Surti buffaloes.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTraits\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTMY\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTMY_305\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePY\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eADMY_LL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eADMY_CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003ePedigree information\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal animal IDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e322\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. of animal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. of sire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. of dam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. of animals without offspring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. of animals with offspring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. of animals with offspring \u0026amp; records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage inbreeding coefficient %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaternal grand sire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaternal grand dam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003eRepeated records %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u0026ndash;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003eDescriptive statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e661\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeast-Squares Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1466.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1605.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e290.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e503.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e206.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e412.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e294.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCV (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMin.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMax.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3399.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003eNon-Genetic factors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeason\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeriod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e*TMY\u0026thinsp;=\u0026thinsp;total milk yield (kg); TMY_305\u0026thinsp;=\u0026thinsp;305-day milk yield (kg); PY\u0026thinsp;=\u0026thinsp;peak yield (g); CI\u0026thinsp;=\u0026thinsp;calving interval; DP\u0026thinsp;=\u0026thinsp;dry period; ADMY_LL\u0026thinsp;=\u0026thinsp;average daily milk yield per lactation; ADMY_CI\u0026thinsp;=\u0026thinsp;average daily milk yield per calving interval. Significance: **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; NS, non-significant (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eLeast-squares means (\u0026plusmn;\u0026thinsp;SE) for TMY, TMY_305, PY, LL, CI, DP, ADMY_LL, and ADMY_CI were 1466.59\u0026thinsp;\u0026plusmn;\u0026thinsp;14.01, 1605.97\u0026thinsp;\u0026plusmn;\u0026thinsp;15.73, 9.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05, 290.42\u0026thinsp;\u0026plusmn;\u0026thinsp;2.31, 503.10\u0026thinsp;\u0026plusmn;\u0026thinsp;5.26, 206.02\u0026thinsp;\u0026plusmn;\u0026thinsp;4.50, 5.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03, and 3.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03, respectively. The standard deviation ranged from 0.90 (ADMY_CI) to 412.94 (TMY), whereas the coefficient of variation (CV) was highest for DP (55.38%), followed by ADMY_CI (28.73%), TMY (28.16%), and CI (27.24%). The minimum and maximum values also reflected wide trait ranges, with TMY ranging from 502 to 3399.5 kg and CI from 323 to 1124 days. The season of calving had a highly significant effect (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) on all traits, except for LL and ADMY_CI, where the effect was significant at the 5% level. The calving period had a strong influence, being highly significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) for TMY, TMY_305, PY, and ADMY_LL, and moderately significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for LL, CI, and DP. Parity had a significant effect on PY and ADMY_LL (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), whereas no significant effect was observed on the remaining traits. Animal age significantly influenced only PY and ADMY_CI (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with no detectable effect on TMY, LL, CI, or DP.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Variance and co(variance) components and genetic parameters\u003c/h2\u003e\n \u003cp\u003eEstimates of (co)variance components and genetic parameters under three models are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. For TMY, heritability ranged from 0.054 to 0.070, with corresponding repeatability between 0.318 and 0.349. TMY_305 showed slightly higher heritability (0.065\u0026ndash;0.116) and repeatability (0.239\u0026ndash;0.381). PY had moderate heritability (~\u0026thinsp;0.112) and repeatability (~\u0026thinsp;0.280).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEstimates of (co)variance components, genetic parameters, maternal effects, and model fit statistics for economic traits in Surti buffaloes.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTraits\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eItems\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026sigma;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026sigma;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003e\u003cem\u003epe\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026sigma;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026sigma;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026sigma;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026sigma;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ec\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMax. Log L\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAIC\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eTMY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel-1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e11750.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e47144.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e109962\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e168857\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.070\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.3487\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-5558.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e11123.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11060.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47597.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e207.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.066\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5558.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11125.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9171.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44559.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4826.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.054\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5558.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11127.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eTMY_305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel-1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e9852.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e22440.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e52384.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e84676.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.116\u0026thinsp;\u0026plusmn;\u0026thinsp;0.069\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.3814\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2128.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e4263.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6993.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21481.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3971.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84776.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.082\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2128.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4265.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5540.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14744.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e839.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11491.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52205.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84821.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.065\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.135\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2128.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4266.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003ePY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel-1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.291\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.434\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.86\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.589\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.112\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.2799\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-811.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e1629.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.113\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-811.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1631.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.112\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-811.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1633.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eLL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel-1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e118.09\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e719.17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3783.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4620.30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.026\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.1812\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-4056.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e8119.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e717.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3782.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4620.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.021\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4056.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8121.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e717.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3783.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4619.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.021\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4056.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8123.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel-1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e161.13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e1666.94\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e16227.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e18055.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.1012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-3556.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e7118.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e158.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1670.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16226.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18055.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3556.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7120.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e158.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1671.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16226.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18055.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3556.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7122.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel-1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e260.53\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e512.82\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e12551.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e13324.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.020\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.05804\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-3459.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e6924.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e260.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e513.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12550.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13324.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.020\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3459.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6926.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e265.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e550.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12530.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.020\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3459.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6928.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eADMY_LL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel-1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.097\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.248\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.698\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.093\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.3308\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-406.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e818.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.087\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-406.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e820.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.086\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-406.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e822.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eADMY_CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel-1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.118\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.495\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.619\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.1994\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-181.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e368.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.028\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-181.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e370.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-181.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e372.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"14\"\u003e\u003cem\u003e\u0026sigma;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003ea\u003c/em\u003e, \u003cem\u003e\u0026sigma;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003epe\u003c/em\u003e, \u003cem\u003e\u0026sigma;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003em\u003c/em\u003e, \u003cem\u003e\u0026sigma;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003ec\u003c/em\u003e, \u003cem\u003e\u0026sigma;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003ee\u003c/em\u003e, and \u003cem\u003e\u0026sigma;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e are additive direct, animal permanent environment, maternal genetic, maternal permanent environmental, residual variance, and phenotypic variance, respectively; \u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003ea\u003c/em\u003e is direct heritability; \u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003em\u003c/em\u003e is maternal heritability; \u003cem\u003ec\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e is maternal environment component; R is repeatability; log L is Log likelihood; and \u003cem\u003eAIC\u003c/em\u003e is Akaike information criteria with row bold representing estimates from the most appropriate model.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTraits like LL, CI, and DP exhibited low heritability (\u0026lt;\u0026thinsp;0.03) and low repeatability (\u0026lt;\u0026thinsp;0.18). ADMY_LL showed moderate heritability (0.086\u0026ndash;0.093) and repeatability (0.297\u0026ndash;0.331), whereas ADMY_CI had negligible heritability (0.005\u0026ndash;0.028) with moderate repeatability (~\u0026thinsp;0.20). Maternal genetic and permanent maternal environmental variances were minimal across traits and models.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Correlation Among the Economic Traits\u003c/h2\u003e\n \u003cp\u003ePhenotypic correlations (lower triangle) and additive genetic correlations (upper triangle) are presented in Fig.\u0026nbsp;1, and detailed correlation coefficients are provided in Supplementary Table\u0026nbsp;1. TMY exhibited strong phenotypic associations with TMY_305 (0.92), LL (0.73), and ADMY_LL (0.54). TMY_305 also showed strong positive correlations with ADMY_LL (0.94) and PY (0.74), while its correlation with CI was moderately negative (\u0026minus;\u0026thinsp;0.32). A strong positive phenotypic correlation was observed between CI and DP (0.89), whereas both traits were negatively correlated with ADMY_CI.\u003c/p\u003e\n \u003cp\u003eAdditive genetic correlations were consistently high. TMY and TMY_305 displayed a near-perfect genetic correlation (0.99) and were also strongly associated with CI, DP, PY, and LL (0.87\u0026ndash;1.00). In contrast, ADMY_CI showed weak or negative genetic correlations with TMY (\u0026minus;\u0026thinsp;0.24), PY (\u0026minus;\u0026thinsp;0.10), and LL (\u0026minus;\u0026thinsp;0.02). A moderate positive genetic correlation (0.91) was observed between ADMY_LL and ADMY_CI.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Trends of economic traits\u003c/h2\u003e\n \u003cp\u003eThe phenotypic and genetic trends of economic traits in Surti buffaloes from 1993 to 2023 are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and illustrated in Figs. 2\u0026ndash;5. TMY and TMY_305 showed significantly declining genetic trends of \u0026minus;\u0026thinsp;1.78 and \u0026minus;\u0026thinsp;1.91 kg/year, respectively, indicating reductions of 98\u0026ndash;124% per decade. Phenotypic trends were also negative and significant.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003erends of economic traits in Surti buffaloes\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTraits\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePhenotypic Trends\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eP\u003c/sub\u003e %\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% Phenotypic change per decade\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGenetic trends\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eG\u003c/sub\u003e %\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% genetic change per decade\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTMY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-10.30\u0026thinsp;\u0026plusmn;\u0026thinsp;3.38**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-7.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-97.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTMY_305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-9.83\u0026thinsp;\u0026plusmn;\u0026thinsp;3.02**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-7.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-123.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e166.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.016\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-231.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADMY_LL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-231.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADMY_CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.009\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0004\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eR\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eG\u003c/sub\u003e, R\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eP\u003c/sub\u003e: Genetic and Phenotypic Coefficient of determination\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eCI exhibited a highly significant genetic improvement (\u0026minus;\u0026thinsp;0.12 days/year; 167% per decade), while LL trends were negligible. PY and ADMY_LL showed substantial genetic declines (\u0026minus;\u0026thinsp;0.02 and \u0026minus;\u0026thinsp;0.01 kg/day/year), with \u0026gt;\u0026thinsp;230% reduction per decade. In contrast, ADMY_CI improved genetically (0.0004 kg/day/year; 111% per decade), despite a weak negative phenotypic trend. DP demonstrated a notable genetic decline (\u0026minus;\u0026thinsp;0.22 days/year), reflecting 104% improvement per decade.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Least-Squares Means and Non-Genetic Factor Effects\u003c/h2\u003e\u003cp\u003eThe least-squares means, standard deviations, coefficients of variation, and non-genetic effects observed in this study were consistent with those reported for various breeds of buffalo. Similar trends were reported in Surti buffaloes (Shashikant et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Murrah buffaloes (Jamal et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sharma et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e), and Mehsana buffaloes (Parmar et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eAmong the non-genetic factors, age had a non-significant effect on most traits, except for peak yield and ADMY_CI, where mature buffaloes performed better, likely due to enhanced physiological development (Kumar, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Season significantly influenced all traits except ADMY_CI, with higher yields observed in winter owing to favorable climatic conditions (Shashikant et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The calving period had a highly significant influence on all traits, reflecting long-term environmental shifts and genetic progress or decline (Gandhi et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Gaikwad et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Parity significantly influenced PY and ADMY_LL, with maximum performance observed at the fifth parity, followed by a decline, likely due to age-related metabolic and reproductive stresses (Pawar et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Heritability\u003c/h2\u003e\u003cp\u003eThe heritability estimates for production traits in this study ranged from low to moderate, indicating that environmental factors had a dominant influence and that genetic gains through direct selection may be gradual. For TMY, heritability estimates ranged from 0.054 to 0.070, aligning with previous values reported in Surti (Nagda, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Jatolia, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), Bhadawari (Sachan et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), and Murrah buffaloes (Sharma et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). In contrast, higher estimates have been reported in Mehsana (Galsar et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; 0.18\u0026ndash;0.21), suggesting more exploitable genetic variability in that breed. Heritability for TMY_305 ranged from 0.065 to 0.116, which is lower than the estimates reported for Mehsana (Galsar et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; 0.22) and exotic breeds such as Holstein (VanRaden et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; 0.25; Cole et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; 0.37), highlighting the role of intensive selection and management in improving additive genetic variance. Peak yield heritability in the present study (0.112\u0026ndash;0.113) was comparable to estimates in Murrah (Parmar et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), though Sharma et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e) reported higher values (0.48), potentially due to breed-specific selection programs.\u003c/p\u003e\u003cp\u003eThe heritability estimates for ADMY_LL ranged from 0.086 to 0.093, consistent with findings by Ismael et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; 0.182). Accounting for maternal genetic (σ\u0026sup2;ₘ) and maternal permanent environmental (σ\u0026sup2;c) effects in Models 2 and 3 resulted in a slight decrease in heritability for TMY and TMY_305, underscoring minor maternal influences. The maternal variance component remained modest (\u0026le;\u0026thinsp;3.5%), in agreement with Miglior et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; 2.8%) for Jersey cattle. In contrast, reproductive traits such as LL, DP, CI, and ADMY_CI exhibited notably lower heritability estimates, confirming their strong environmental dependency. Heritability for LL ranged from 0.027 to 0.041, consistent with earlier reports in Murrah (Bashir et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Holstein (Kadarmideen et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), and Sahiwal (Ilatsia et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The estimate for DP (0.020) was also in line with Bashir et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Kadarmideen et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), highlighting the predominant role of feeding and health management practices. Calving interval had negligible heritability (0.009), corroborating findings in Indian buffaloes (Nagda, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Kaur and Narang, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sharma et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). However, higher estimates were reported by Jatolia (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), indicating that reproductive trait heritability may vary based on management intensity and population structure. Similarly, heritability of ADMY_CI remained very low (0.005\u0026ndash;0.028), reinforcing its limited genetic determinism under field conditions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Repeatability\u003c/h2\u003e\u003cp\u003eThe repeatability estimates derived for Surti buffaloes indicated moderate stability for milk production traits across lactations and low consistency for reproductive traits. For TMY, repeatability ranged from 0.32 to 0.35, which aligns closely with earlier reports in Surti and Bhadawari buffaloes (Nagda, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Sachan et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) using sire model, and reflects the cumulative influence of additive genetics and permanent environmental factors. Comparable estimates were also noted for TMY_305, ranging from 0.2392 to 0.3814, similar to those reported in Mehsana (Galsar et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and lower than those in Holstein-Friesians (VanRaden et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Cole et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Peak yield exhibited repeatability estimates of 0.2779 to 0.2800, consistent with results in Murrah (Parmar et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and Holstein (Cole et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) cattle. The moderate repeatability of these traits suggests that individual animals tend to maintain their production levels across lactations, supporting their use in early selection strategies.\u003c/p\u003e\u003cp\u003eReproductive traits, however, demonstrated lower repeatability. Calving interval and dry period had estimates ranging from 0.101 and 0.058\u0026ndash;0.061, respectively, consistent with studies in Sahiwal (Dash et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These low values highlight the predominant role of non-genetic influences such as reproductive management, nutrition, and disease control. Repeatability for ADMY_LL was moderate (0.2971\u0026ndash;0.3308), indicating fair consistency, while that for ADMY_CI was lower (0.1954\u0026ndash;0.2055), reflecting the complex and extended nature of the calving interval. The magnitude of repeatability for these traits is supported by reports on indigenous breeds under similar management systems (Miglior et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePermanent environmental variance (σ\u0026sup2;pe) played a considerable role in shaping phenotypic expression. Its contribution to TMY reached 26.27%, with TMY_305 ranging between 13.5% and 26.5%, and ADMY_LL from 21.10\u0026ndash;23.78%. Reproductive traits like CI and DP also showed notable permanent environmental influence (9.23\u0026ndash;9.26% and 3.85\u0026ndash;4.13%, respectively), which may be attributed to consistent herd-level practices and seasonal factors.\u003c/p\u003e\u003cp\u003eGiven the moderate repeatability and the substantial contribution of permanent environmental effects, Model 1 is considered suitable for routine evaluation of performance traits in Surti buffaloes. However, enhancement of reproductive efficiency may depend more on management interventions than on genetic progress. Thus, for traits with low repeatability and heritability, prioritizing health care, nutrition, and reproductive management remains crucial to improving lifetime productivity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Genetic and Phenotypic Correlations of Economic Traits\u003c/h2\u003e\u003cp\u003eStrong positive genetic correlations were observed among key production traits, suggesting that they are influenced by common genetic factors and could be effectively improved through correlated response to selection. Total milk yield showed near-perfect genetic correlations with TMY_305 (0.99), PY (0.90), and LL (0.87), indicating a shared genetic influence. These findings are consistent with earlier reports in Surti buffaloes (Kumar, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), where genetic correlations between TMY and LL ranged from 0.81 to 0.99. Similarly, high correlations between TMY and TMY_305 have been documented in Murrah buffaloes (Sharma et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). The correlation between TMY and PY (0.92) is also in agreement with previous studies (Kumar, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Shashikant et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), while the genetic correlation between PY and TMY_305 (0.80) supports joint selection strategies (Kumar et al. 2018). Furthermore, the correlation between TMY_305 and LL (0.99) exceeds estimates reported in Murrah buffaloes (0.63\u0026ndash;0.79; Sigdel et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sharma et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e), potentially reflecting differences in breed characteristics or management intensity.\u003c/p\u003e\u003cp\u003eModerate genetic correlation between LL and CI (0.54) suggests a genetic link between lactation duration and reproductive rhythm, consistent with results in Mehsana buffaloes (Galsar et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A strong genetic correlation between CI and DP (0.99) contrasts with the weaker or negative estimates reported in other breeds (Galsar et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Similarly, the high genetic correlation between TMY and CI (0.99) exceeds values previously reported in Indian buffalo populations (0.30; Jain \u0026amp; Tailor, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWith regard to persistency traits, TMY displayed high genetic correlations with ADMY_LL (0.77) and ADMY_CI (0.60), aligning with earlier reports in Bhadawari buffaloes (Sachan et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). However, LL and ADMY_LL exhibited a weak genetic correlation (0.09), indicating that prolonged lactation does not necessarily result in higher daily yield. The genetic correlation between CI and ADMY_CI was moderate (0.25), lower than the 0.53 reported in Bhadawari buffaloes (Sachan et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePhenotypic correlations among traits generally reflected moderate to strong positive relationships, shaped by shared environmental influences. TMY was positively correlated with LL (0.69), TMY_305 (0.96), and PY (0.57), consistent with previously reported estimates in Surti buffaloes (Nagda, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Jatolia, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The phenotypic association between LL and CI (0.69) supports the genetic relationship observed and agrees with prior studies (Galsar et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Despite the high genetic correlation between CI and DP, their phenotypic association was modest (0.29), highlighting the effect of environmental variation during the dry period.\u003c/p\u003e\u003cp\u003eThe phenotypic correlation between TMY and CI (0.27) was slightly higher than the 0.17 reported by Jain and Tailor (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). TMY also showed strong phenotypic correlations with ADMY_LL (0.66) and ADMY_CI (0.71), suggesting that animals with higher total yields tend to sustain higher daily production. LL and ADMY_LL had a moderate correlation (0.33), in line with previous findings (Sachan et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), while CI and ADMY_CI showed a strong phenotypic correlation (0.73), exceeding earlier reports in Bhadawari buffaloes, potentially due to breed differences or improved reproductive management.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Trends\u003c/h2\u003e\u003cp\u003eThe temporal genetic and phenotypic trends observed in this study highlight a concerning pattern of decline in milk production traits in Surti buffaloes. Total milk yield and TMY_305 exhibited substantial negative genetic trends of \u0026minus;\u0026thinsp;97.71% and \u0026minus;\u0026thinsp;123.82% per decade, respectively, accompanied by corresponding negative phenotypic shifts. These findings suggest that past selection efforts and management interventions have been largely ineffective in sustaining or enhancing milk yield. This contrasts with the favorable genetic trends reported in Murrah and Karan Fries breeds (Dash et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kour et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), underscoring breed-specific differences in selection intensity, environmental adaptability, and herd management efficiency. A sharp genetic decline was also observed for peak yield (\u0026minus;\u0026thinsp;231.32%) and ADMY_LL (\u0026minus;\u0026thinsp;231.90%), reflecting declining daily productivity despite improvements in data recording systems and general herd care. In contrast, reproductive traits showed encouraging genetic gains. Calving interval improved markedly (166.76% per decade), whereas DP declined by 104.49%, indicating enhanced reproductive control. Notably, ADMY_CI increased by 111.34%, suggesting genetic gains in daily milk yield adjusted for reproductive performance.\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study demonstrated that economic traits in Surti buffaloes are largely shaped by environmental and management factors, as evidenced by low to moderate heritability and repeatability estimates. Strong genetic and phenotypic correlations among production traits such as total milk yield, 305-day yield, and peak yield suggest potential for improvement through correlated selection. However, the unfavorable genetic trends for milk production traits highlight the limitations of previous selection strategies and emphasize the need for revision. In contrast, reproductive traits including calving interval and dry period showed encouraging genetic progress, reflecting improved reproductive management. Although maternal genetic and permanent environmental variances contributed minimally, their inclusion enhanced model accuracy. Among the models evaluated, Model 1 was found to be the most appropriate for field-level evaluation. To achieve sustainable genetic gains, selection programs should balance milk production and reproductive efficiency while integrating sound herd management practices.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the Network Project on Surti Buffalo Improvement at the Livestock Research Station, CVAS, Navania, Vallabhnagar, for providing infrastructure, resources, and access to performance records. We appreciate the cooperation of all staff involved in data collection and record maintenance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that the ethical policies of the journal, as outlined in the journal’s author guidelines, have been followed. No ethical approval was required, as this study involved a retrospective analysis of herd performance data routinely recorded under the Network Project on Surti Buffalo Improvement at the Livestock Research Station, College of Veterinary and Animal Science, Navania, Vallabhnagar, with no experimental interventions or invasive procedures performed on animals. All research activities at this institution are conducted under the oversight of the Institutional Animal Ethics Committee (IAEC), registered under CPCSEA Registration No. 2143/GO/Re/SL/22/CPCSEA, Ministry of Fisheries, Animal Husbandry and Dairying, Government of India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHari Ram Meena: Data curation, Formal analysis, Methodology, Writing – original draft, and editing.\u003c/p\u003e\n\u003cp\u003eLokesh Gautam: Conceptualization, Formal analysis, investigation, Resources, Supervision, Validation, Writing – review, and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are maintained by the Network Project on Surti Buffalo Improvement at the Livestock Research Station, CVAS, Navania, Vallabhnagar, India. Access to the data can be granted upon reasonable request and with the permission of the Principal Investigator of the project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have reviewed the final version of the manuscript and provided their consent for the publication of the results presented in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkaike H (1973) Information theory and an extension of the maximum likelihood principle. In: Petrov BN, Cs\u0026aacute;ki F (eds) Second international symposium on information theory. Akad\u0026eacute;miai Kiad\u0026oacute;, pp 267\u0026ndash;281\u003c/li\u003e\n\u003cli\u003eBashir MK, Khan MS, Bhatti SA (2017) Genetic and phenotypic parameters of some economic traits in Murrah buffaloes. Pak J Agric Sci 54:683\u0026ndash;689\u003c/li\u003e\n\u003cli\u003eCole JB, Wiggans GR, Ma L, Sonstegard TS, Lawlor TJ, Crooker BA, VanRaden PM (2011) Genome-wide association analysis of thirty-one production, health, reproduction and body conformation traits in contemporary U.S. Holstein cows. BMC Genomics 12:408\u003c/li\u003e\n\u003cli\u003eDash SK, Singh A, Chakravarty AK (2015) Genetic analysis of some reproductive traits in Sahiwal cattle. Indian J Anim Sci 85:1133\u0026ndash;1136\u003c/li\u003e\n\u003cli\u003eDemeke S, Neser FWC, Schoeman SJ (2004) Variance components and genetic parameters for early growth traits in South African Boer goats. Small Rumin Res 53:15\u0026ndash;21\u003c/li\u003e\n\u003cli\u003eDepartment of Animal Husbandry and Dairying (2019) 20th livestock census. Ministry of Fisheries, Animal Husbandry \u0026amp; Dairying, Government of India\u003c/li\u003e\n\u003cli\u003eGaikwad SS, Patil CS, Dhaka SS (2022) Non-genetic factors affecting milk production traits in Murrah buffaloes. Indian J Anim Sci 92:456\u0026ndash;460\u003c/li\u003e\n\u003cli\u003eGalsar NS, Shah RR, Gupta JP, Pandey DP, Prajapati KB, Patel JB (2016) Genetic estimates of reproduction and production traits in Mehsana buffalo. Indian J Dairy Sci 69:698\u0026ndash;701\u003c/li\u003e\n\u003cli\u003eGandhi RS, Raja TV, Ruhil AP (2009) Non-genetic factors affecting milk production in buffaloes. Indian J Dairy Sci 62:200\u0026ndash;205\u003c/li\u003e\n\u003cli\u003eGudex BW, Johnson DL, Singh D (2014) Maternal and direct genetic effects on milk production in New Zealand dairy cattle. N Z J Agric Res 57:173\u0026ndash;181\u003c/li\u003e\n\u003cli\u003eHarvey WR (1990) User\u0026rsquo;s guide for LSMLMW and MIXMDL: PC-2 version mixed model least-squares and maximum likelihood computer program. Ohio State University\u003c/li\u003e\n\u003cli\u003eIlatsia ED, Muasya TK, Muhuyi WB, Kahi AK (2007) Genetic and phenotypic parameters for test day milk yield of Sahiwal cattle in the semi-arid tropics. Animal 1:185\u0026ndash;192\u003c/li\u003e\n\u003cli\u003eIsmael H, Janković D, Stanojević D, Bogdanović V, Trivunović S, Djedović R (2021) Estimation of heritability and genetic correlations between milk yield and linear type traits in primiparous Holstein-Friesian cows. Rev Bras Zootec 50:e20200121\u003c/li\u003e\n\u003cli\u003eJain A, Tailor SP (1994) Genetic and phenotypic correlations among economic traits in Surti buffaloes. Indian J Dairy Sci 47:456\u0026ndash;460\u003c/li\u003e\n\u003cli\u003eJamal I, Dhaka SS, Patil CS (2018) Non-genetic factors affecting milk yield in Murrah buffaloes. Indian Vet J 95:45\u0026ndash;48\u003c/li\u003e\n\u003cli\u003eJatolia PK (2008) Genetic analysis of reproductive traits in Surti buffaloes. Indian J Anim Sci 78:970\u0026ndash;973\u003c/li\u003e\n\u003cli\u003eKadarmideen HN, Thompson R, Simm G (2003) Linear and threshold model genetic parameters for disease, fertility and milk production in dairy cattle. Anim Sci 77:411\u0026ndash;419\u003c/li\u003e\n\u003cli\u003eKaur M, Narang R (2021) Genetic parameters of reproductive traits in Indian buffaloes. Buffalo Bull 40:123\u0026ndash;130\u003c/li\u003e\n\u003cli\u003eKour A, Dhaka SS, Kumar A (2020) Genetic trends in milk production traits of Karan Fries cattle. Indian J Anim Sci 90:456\u0026ndash;460\u003c/li\u003e\n\u003cli\u003eKumar M (2018) Genetic and non-genetic factors affecting milk production in Surti buffaloes. Indian J Dairy Sci 71:145\u0026ndash;150\u003c/li\u003e\n\u003cli\u003eMeyer K (2007) WOMBAT\u0026mdash;a tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML). J Zhejiang Univ Sci B 8:815\u0026ndash;821\u003c/li\u003e\n\u003cli\u003eMiglior F, Muir BL, Van Doormaal BJ (2007) Selection indices in Holstein cattle of various countries. J Dairy Sci 90:891\u0026ndash;902\u003c/li\u003e\n\u003cli\u003eNagda RK (2005) Genetic studies on production and reproduction traits in Surti buffaloes. Indian J Anim Sci 75:920\u0026ndash;925\u003c/li\u003e\n\u003cli\u003eParmar GA, Gupta JP, Chaudhari JD (2017) Genetic parameters of production traits in Mehsana buffaloes. Indian J Anim Sci 87:756\u0026ndash;760\u003c/li\u003e\n\u003cli\u003ePatel AK, Patel JB, Patel MP (2015) Characteristics and performance of Surti buffaloes in Gujarat. Indian J Anim Sci 85:769\u0026ndash;773\u003c/li\u003e\n\u003cli\u003ePawar HR, Kumar G, Narang R (2012) Effect of parity on milk production in Murrah buffaloes. Indian J Dairy Sci 65:315\u0026ndash;319\u003c/li\u003e\n\u003cli\u003eR Core Team (2023) R: a language and environment for statistical computing (version 4.3.0). R Foundation for Statistical Computing\u003c/li\u003e\n\u003cli\u003eSachan CB, Kushwaha BP, Kundu SS (2007) Evaluation of production performance of Bhadawari buffaloes. Indian J Anim Sci 77:781\u0026ndash;783\u003c/li\u003e\n\u003cli\u003eSharma S, Dhaka SS, Patil CS (2024a) Estimation of additive and maternal covariance of production traits in Murrah buffalo. J Anim Breed Genet 141:415\u0026ndash;424\u003c/li\u003e\n\u003cli\u003eSharma S, Dhaka SS, Patil CS, Rathi P (2024b) Estimation of direct and maternal covariance along with genetic and phenotypic trends of reproduction traits in Murrah buffalo. Reprod Domest Anim 59:e14526\u003c/li\u003e\n\u003cli\u003eShashikant K, Nagda RK, Kumar V, Meena VK (2021) Effect of non-genetic factors on calving interval in Surti buffaloes. Pharma Innov J 10:984\u0026ndash;986\u003c/li\u003e\n\u003cli\u003eSigdel A, Bhatti SA, Khan MS (2015) Genetic analysis of milk production traits in Murrah buffaloes. Pak J Agric Sci 52:723\u0026ndash;728\u003c/li\u003e\n\u003cli\u003eVanRaden PM, Van Tassell CP, Wiggans GR, Sonstegard TS, Schnabel RD, Taylor JF, Schenkel FS (2009) Invited review: reliability of genomic predictions for North American Holstein bulls. J Dairy Sci 92:16\u0026ndash;24\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Surti buffalo, heritability, repeatability, genetic trend","lastPublishedDoi":"10.21203/rs.3.rs-7485713/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7485713/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study evaluated the genetic parameters and decadal trends of economic traits in Surti buffaloes using 868 lactation records maintained between 1993 and 2023 at the Livestock Research Station in Udaipur, Rajasthan, India. The analyzed traits included total milk yield, 305-day milk yield, peak yield, lactation length, calving interval, dry period, and average daily milk yield per lactation length and calving interval. Variance components were estimated using restricted maximum likelihood procedures under three repeatability animal models. Heritability estimates ranged from 0.005 (CI) to 0.093 (ADMY_LL), and repeatability estimates ranged from 0.195 to 0.331. Model 1, accounting for additive genetic and permanent environmental effects, was most suitable for practical use. Strong genetic and phenotypic correlations were observed among the milk yield traits, whereas the reproductive traits exhibited more variable associations. Genetic trends revealed substantial declines per decade in TMY (−97.71%), TMY_305 (−123.82%), PY (−231.32%), ADMY_LL (−231.90%), and DP (−104.49%). In contrast, positive genetic gains were observed for CI (166.76%), ADMY_CI (111.34%), and LL, indicating improvements in reproductive efficiency and yield persistence. These findings highlight the need for a balanced selection strategy targeting both production and reproductive traits, supported by refined management practices in Surti herd.\u003c/p\u003e","manuscriptTitle":"Direct and Maternal Genetic Effects and Temporal Trends of Economic Traits in Surti Buffaloes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-15 13:31:50","doi":"10.21203/rs.3.rs-7485713/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f69ad6c8-dc16-4506-b15d-2f8fdf5f7ec0","owner":[],"postedDate":"September 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-12T16:53:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-15 13:31:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7485713","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7485713","identity":"rs-7485713","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}