scholarly journals Evaluation of Non-linear Models to Predict Potential Milk Yield of Beef Cows According to Parity Order Under Grazing

2021 ◽  
Vol 8 ◽  
Author(s):  
Matheus Fellipe de Lana Ferreira ◽  
Luciana Navajas Rennó ◽  
Isabela Iria Rodrigues ◽  
Sebastião de Campos Valadares Filho ◽  
Luiz Fernando Costa e Silva ◽  
...  
Keyword(s):  
Milk Yield ◽  
Milk Fat ◽  
Linear Models ◽  
Nonlinear Models ◽  
Information Criterion ◽  
Beef Cows ◽  
Total Solids ◽  
Lactation Curve ◽  
Non Linear ◽  
Milking Machine

This study aimed to evaluate the effect of parity order on milk yield (MY) and composition over time of grazing beef cows and to evaluate non-linear models to describe the lactation curve. Thirty-six pregnant Nellore cows (12 nulliparous, 2 years; 12 primiparous, 3 years; and 12 multiparous, 4–6 years) were included in the study. With calving day assigned as day 0, milking was performed using a milking machine to estimate MY on days 7, 14, 21, 42, 63, 91, 119, 154, and 203. Dummy variable analyses were applied to estimate its effects on MY, composition (kg and percentage), afternoon/morning, and afternoon/total proportions. Since multiparous cows had higher MY than nulliparous and primiparous cows, two different groups were used for lactation curve analysis: Mult (multiparous) and Null/Prim (nulliparous and primiparous). The MY estimated by the last edition of BR-Corte (Nutrient Requirements of Zebu and Crossbred Cattle) equation was compared with the observed values from this study. Five nonlinear models proposed by Wood (WD), Jenkins & Ferrell (JF), Wilmink (WK), Henriques (HR) and Cobby & Le Du (CL) were evaluated. Models were validated using an independent dataset of multiparous and primiparous cows. The estimates for parameters a, b, and c of the CL equation were compared between groups, and the BR-Corte equation used the model identity methodology. Nulliparous and primiparous cows displayed similar MY (P > 0.05); however, multiparous cows had an average MY that is 0.70 kg/day greater than that of nulliparous and primiparous cows (P < 0.05). Milk protein and total solids were higher for multiparous cows (P < 0.05). Effect of days in milking was found for milk fat, protein, and total solids (P < 0.05). The yield of all milk components was higher for multiparous cows than for nulliparous and primiparous cows. The afternoon/morning and afternoon/total proportions of milk production were not affected by parities and days in milking (P > 0.05), with an average of 0.76 and 0.42, respectively. The BR-Corte equation did not correctly estimate the MY (P < 0.05). The equations of WD, WK, and CL had the best estimate of MY for both Mult and Null/Prim datasets. The equations had a very similar Akaike's information criterion with correction and mean square error of prediction.

scholarly journals Comparison of the parameters of the lactation curve between normal and difficult calvings in Iranian Holstein cows

2019 ◽  
Vol 17 (1) ◽  
pp. e0401
Author(s):  
Navid Ghavi Hossein-Zadeh
Keyword(s):  
Dairy Cows ◽  
Milk Yield ◽  
Goodness Of Fit ◽  
Linear Models ◽  
Information Criterion ◽  
Holstein Cows ◽  
Coefficient Of Determination ◽  
Lactation Curve ◽  
Non Linear ◽  
Best Fit

To evaluate effect of dystocia on the lactation curve characteristics for milk yield and composition in Holstein cows, six non-linear models (Brody, Wood, Sikka, Nelder, Dijkstra and Rook) were fitted on 5,917,677 test day records for milk yield (MY), fat (FP) and protein (PP) percentages, fat to protein ratio (FPR) and somatic cell score (SCS) of 643,625 first lactation Holstein cows with normal calving or dystocia from 3146 herds which were collected by the Animal Breeding Center of Iran. The models were tested for goodness of fit using adjusted coefficient of determination, root means square error, Akaike’s information criterion and Bayesian information criterion. Rook model provided the best fit of the lactation curve for MY and SCS in normal and difficult calvers and dairy cows with dystocia for FP. Dijkstra model provided the best fit of the lactation curve for PP and FPR in normal and difficult calvers and dairy cows with normal calving for FP. Dairy cows with dystocia had generally lower 100-d, 200-d and 305-d cumulative milk yield compared with normal calvers. Time to the peak milk yield was observed later for difficult calvers (89 days in milk vs. 79 days in milk) with lower peak milk yield (31.45 kg vs. 31.88 kg) compared with normal calvers. Evaluation of the different non-linear models indicated that dystocia had important negative effects on milk yield and lactation curve characteristics in dairy cows and it should be reduced as much as possible in dairy herds.

Introducing a sinusoidal equation to describe lactation curves for cumulative milk yield and composition in Holstein cows

2020 ◽  
Vol 87 (2) ◽  
pp. 220-225
Author(s):  
Navid Ghavi Hossein-Zadeh ◽  
Hassan Darmani Kuhi ◽  
James France ◽  
Secundino López
Keyword(s):  
Linear Model ◽  
Milk Yield ◽  
Milk Fat ◽  
Goodness Of Fit ◽  
Growth Models ◽  
Information Criterion ◽  
Holstein Cows ◽  
Coefficient Of Determination ◽  
Production Traits ◽  
Lactation Curve

AbstractThe aim of the work reported here was to investigate the appropriateness of a sinusoidal function by applying it to model the cumulative lactation curves for milk yield and composition in primiparous Holstein cows, and to compare it with three conventional growth models (linear, Richards and Morgan). Data used in this study were 911 144 test-day records for milk, fat and protein yields, which were recorded on 834 dairy herds from 2000 to 2011 by the Animal Breeding Centre and Promotion of Animal Products of Iran. Each function was fitted to the test-day production records using appropriate procedures in SAS (PROC REG for the linear model and PROC NLIN for the Richards, Morgan and sinusoidal equations) and the parameters were estimated. The models were tested for goodness of fit using adjusted coefficient of determination $\lpar {R_{{\rm adj}}^2 } \rpar $, root mean square error (RMSE), Akaike's information criterion (AIC) and the Bayesian information criterion (BIC). $R_{{\rm adj}}^2 $ values were generally high (>0.999), implying suitable fits to the data, and showed little differences among the models for cumulative yields. The sinusoidal equation provided the lowest values of RMSE, AIC and BIC, and therefore the best fit to the lactation curve for cumulative milk, fat and protein yields. The linear model gave the poorest fit to the cumulative lactation curve for all production traits. The current results show that classical growth functions can be fitted accurately to cumulative lactation curves for production traits, but the new sinusoidal equation introduced herein, by providing best goodness of fit, can be considered a useful alternative to conventional models in dairy research.

Modelling lactation curve for milk fat to protein ratio in Iranian buffaloes (Bubalus bubalis) using non-linear mixed models

2016 ◽  
Vol 83 (3) ◽  
pp. 334-340 ◽  
Author(s):  
Navid Ghavi Hossein-Zadeh
Keyword(s):  
Mixed Models ◽  
Milk Fat ◽  
Goodness Of Fit ◽  
Linear Mixed Models ◽  
Information Criterion ◽  
Protein Ratio ◽  
The Third ◽  
Lactation Curve ◽  
Non Linear ◽  
Best Fit

The aim of this study was to compare seven non-linear mathematical models (Brody, Wood, Dhanoa, Sikka, Nelder, Rook and Dijkstra) to examine their efficiency in describing the lactation curves for milk fat to protein ratio (FPR) in Iranian buffaloes. Data were 43 818 test-day records for FPR from the first three lactations of Iranian buffaloes which were collected on 523 dairy herds in the period from 1996 to 2012 by the Animal Breeding Center of Iran. Each model was fitted to monthly FPR records of buffaloes using the non-linear mixed model procedure (PROC NLMIXED) in SAS and the parameters were estimated. The models were tested for goodness of fit using Akaike's information criterion (AIC), Bayesian information criterion (BIC) and log maximum likelihood (−2 Log L). The Nelder and Sikka mixed models provided the best fit of lactation curve for FPR in the first and second lactations of Iranian buffaloes, respectively. However, Wood, Dhanoa and Sikka mixed models provided the best fit of lactation curve for FPR in the third parity buffaloes. Evaluation of first, second and third lactation features showed that all models, except for Dijkstra model in the third lactation, under-predicted test time at which daily FPR was minimum. On the other hand, minimum FPR was over-predicted by all equations. Evaluation of the different models used in this study indicated that non-linear mixed models were sufficient for fitting test-day FPR records of Iranian buffaloes.

scholarly journals Linear and Non-Linear Modelling of Nigerian Crude Oil Prices

2021 ◽  
pp. 52-63
Author(s):  
Wiri Leneenadogo ◽  
Sibeate Pius U
Keyword(s):  
Crude Oil ◽  
Linear Model ◽  
Regime Switching ◽  
Linear Models ◽  
Nonlinear Models ◽  
Oil Prices ◽  
Information Criterion ◽  
Ar Model ◽  
Crude Oil Prices ◽  
Non Linear

To model Nigeria crude oil prices, this analysis compared univariate linear models to univariate nonlinear models. The data for this analysis was gathered from the Central Bank of Nigeria (CBN) Monthly Statistical Bulletin. The upward and downward movement in the series revealed by the time plot suggests that the series exhibit a regime-switching pattern: the cycle of expansion and contraction. At lag one, the Augmented Dickey-Fuller test was used to test for stationarity. For univariate linear ARIMA (p, d, q)) and univariate non-linear MS-AR, seven models were estimated for the linear model and two for the non-linear model. The best model was chosen based on the criterion of least information criterion,  AIC (2.006612), SC (2.156581), and the maximum log-likelihood of   (-150.5480) for the crude oil prices were used to pick MS-AR (1) for the series. In analysing crude oil prices data, the MS-AR model proposed by Hamilton outperforms the linear autoregressive models proposed by Box- Jenkins. The model was used to predict the series' values over a one-year cycle (12 months).

Characterization of Lactation Curve Patterns using Non-linear Models in Crossbred Dairy Cattle

2021 ◽  
Author(s):  
Tassew Mohammed Ali ◽  
Raman Narang ◽  
P.P. Dubey ◽  
Simarjeet Kaur
Keyword(s):  
Dairy Cattle ◽  
Milk Yield ◽  
Linear Models ◽  
Biological Efficiency ◽  
Lactation Curve ◽  
Non Linear ◽  
Crossbred Dairy Cattle ◽  
Daily Milk Yield ◽  
Curve Patterns ◽  
Daily Milk

Background: Lactation curve patterns are currently integrated in dairy cow’s management software. Lactation curve modeling is useful for monitoring individual yields for diet planning, determining optimum strategies for insemination and genetic evaluation. It also helps for predicting expected missing values on field records and gives concise summary of biological efficiency and persistency of dairy cows.Methods: The study was aimed to characterize the lactation curve pattern for crossbred dairy cattle using different non-linear models. During the period 1991 to 2018, daily milk yield (DMY) consisted of 281698 records of 750 crossbred dairy cows maintained at Livestock Farms. GADVASU, Ludhiana, were collected for the study. Different non-linear models viz. exponential decline function (EDF), parabolic exponential model (PEM), inverse polynomial model (IPM), gamma-type function (GTF), mixed log function (MLF) and Ali and Schaeffer model (ASF) were used for the analysis. The model(s) that best fit and describe the curve characteristics was selected on the basis of coefficient of determination (R2), coefficient of variation (CV), Akaike information criterion (AIC) and mean square error (MSE).Result: The study clearly revealed that the PRM gave highest fit to DMY data with R2, MSE, AIC and CV values of 98.10%, 0.087, -743.31 and 2.37%, respectively. The IPM had also best fitted the observed DMY data with highest R2 (98.05%), lower MSE (0.089), low AIC (-735.8972) and lower CV (2.40%) values. The fitting of observed DMY data with predicted DMY were also found to be higher in the MLF (R2= 96.46%, MSE= 0.159, AIC= -558.16 and CV= 3.21%) and GTF (R2= 95.85%, MSE= 0.190, AIC= -505.24 and CV= 3.50%), whilst the EDF and PEM Models depicted relatively low fit to the DMY data when compared with the other non-linear models. However, IPM and GTF models can be used for accurate prediction of daily milk yield in the crossbred cattle population because they were typical standard lactation curves.

scholarly journals Effect of season, genotype and lactation on milk yield and composition of local and crossbred dairy cows reared under different feed base region

2016 ◽  
Vol 19 (1-2) ◽  
pp. 50-65
Author(s):  
MA Baset ◽  
KS Huque ◽  
NR Sarker ◽  
MM Hossain ◽  
MN Islam
Keyword(s):  
Milk Yield ◽  
Milk Fat ◽  
Milk Protein ◽  
Block Design ◽  
Milk Composition ◽  
Base Region ◽  
Total Solids ◽  
Randomized Block Design ◽  
Daily Milk Yield ◽  
Daily Milk

A total of 160 cows, 10 cows in each of native (local cow) and crossbred (local × Holstein Friesian) origins differing in lactation were used in 2×2×2×2 factorial experiment using Randomized Block Design (RBD) to evaluate milk yield and composition of cows considering regions (good & poor feed base region), seasons (dry: Nov.–Feb. 2009 & wet: Jun.–Oct. 2009), genotypes and lactation. A “good and/or poor feed base” region was classified based on the availability of quantity and quality roughages throughout the year. The study revealed that the daily milk yield and 4% FCM of cows under good feed base condition were 6.76 and 6.49 kg, respectively and under poor feed base condition were 3.67 and 3.31 kg, respectively. Feed base region did not affect on milk fat and it was observed that the milk protein, lactose, solids-not-fat (SNF), minerals and total solids under good feed base condition were 37.9, 54.9, 100.9, 6.3 and 140.6 g/kg, respectively, whereas, under poor feed base condition the values were 36.3, 52.9, 98.0, 6.1 and 135.2 g/kg, respectively. Season did not affect milk yield and composition except minerals (6.5 g/kg vs. 5.9 g/kg). Genotypes significantly (p?0.01) influenced daily milk yield, the milk protein and minerals. Lactation did not affect milk yield and the milk protein, but influenced the fat, lactose, SNF, minerals and total solids. The interaction of feed base regions and seasons significantly (p?0.01) influenced milk yield and the milk fat and SNF. The milk protein and lactose was influenced by the interaction of feed bases region, seasons and lactation. Milk yield negatively correlated with fat per cent. The percentage of fat significantly (p?0.01) correlated with protein, lactose, SNF, and minerals %. The percentage protein correlated with lactose, SNF and minerals. Lactose % significantly (p?0.01) correlated with SNF%. It may be concluded that milk yield and composition depends on feed base region, genotype and lactation of cows. Season did not influence milk yield and the composition. Milk yield negatively correlated with the percentage of fat, protein, lactose, SNF and milk composition strongly correlated with each other.Bangladesh J. of Livestock Res. 19(1-2): 50-65, Jan-Dec 2012

scholarly journals The chemical composition and technological properties of milk obtained from the morning and evening milking

2008 ◽  
Vol 56 (5) ◽  
pp. 187-198 ◽  
Author(s):  
Martin Skýpala ◽  
Gustav Chládek
Keyword(s):  
Protein Content ◽  
Milk Yield ◽  
Milk Fat ◽  
Protein Production ◽  
Milk Protein ◽  
Fat Content ◽  
Total Solids ◽  
Milk Protein Content ◽  
Solids Content ◽  
Morning Milk

Milk yield varies during lactation, following what is termed a lactation curve. ŽIŽLAVSKÝ and MIKŠÍK (1988) recorded changes in milk yield within a day, too. TEPLÝ et al. (1979) a KOUŘIMSKÁ et al. (2007) published variation within a day ± 1.10 kg in milk yield, ± 0.75 % in milk fat content and ± 0.20 % in milk protein content. Milk yield of cows can be expressed in many different ways, for instance, in kilograms per lactation or in kilograms per day. A practical parameter describing milk production is milk yield (kg) per milking.The object of experiment were 12 cows of Holstein cattle on the first lactation from the 100-day of lactation to 200-day of lactation. The samples of milk were collected from January to May 2007, once a month from the morning and evening milking (milking interval 12 h ± 15 min.). The following parameters were monitored: milk production – milk yield (kg), milk protein production (kg), milk fat production (kg); milk composition – milk protein content (%), milk fat content (%), lactose content (%), milk solids-not-fat content (%), milk total solids content (%); technological properties of milk – ti­tra­tab­le acidity (SH), active acidity (pH), rennet coagulation time (s), quality of curd (class) and somatic cell count as a parameter of udder health.Highly significant differences were found (P < 0.01) between morning milk yield (15.7 kg) and evening milk yield (13.8 kg), between morning milk protein production (0.51 kg) and evening milk protein production (0.45 kg) and between evening milk fat content (4.41 %) and morning milk fat content (3.95 %). A significant difference (P < 0.05) was found between morning milk total solids content (12.62 %) and evening milk total solids content (12.07 %). No significant differences were found between morning (M) and evening (E) values of the remaining parameters: milk fat production (M 0.62 kg; E 0.60 kg), milk protein content (M 3.24 %; E 3.27 %), milk lactose content (M 4.78 %; E 4.86 %), milk solids-not-fat content (M 7.69 %; E 7.71 %), somatic cell count (M 80 000/1 mL; E 101 000/1 mL), titratable aci­di­ty (M 7.75 SH; E 7.64 SH), active acidity (M pH 6.58; E pH 6.61), rennet coagulation time (M 189 s.; E 191 s.), quality of curd (M 1.60 class; E 1.57 class).

A comparison among equations to characterize lactation curves in beef cows

1992 ◽  
Vol 55 (1) ◽  
pp. 23-28 ◽  
Author(s):  
W. D. Hohenboken ◽  
A. Dudley ◽  
D. E. Moody
Keyword(s):  
Milk Production ◽  
Milk Yield ◽  
Analysis Of Variance ◽  
Beef Cows ◽  
The Other ◽  
Crossbred Cows ◽  
Lactation Curve

AbstractMonthly and fortnightly milk production records were analysed from 59 autumn-calving Angus and Angus × Holstein crossbred cows. Half the cows had been administered 10 mg thyroxine per day from day 60 to 120 of lactation and half were controls. Four published equations to characterize individual lactation curves were compared. These were: (1) log Y(n) = log –a1 + b1log n – c1n (Wood); (2) equation 1 with each log Y(n)2 weighted by Yin)2 (Wood weighted); (3) log [Y(n)/n7 = log l/a3 – k3n(Jenkins); and (4) log Y(n) = a4 – b4n‘(l + 25·5 n’) + c4n2 = d 4/ n (Morant), where Y(n) is milk yield on day n of lactation, n' is n–110 (the mid point of lactation), and the a, b, c, k and d parameters are estimated from solution of the equations. The lactation curve from the Jenkins equation projected peak milk yield to occur some 30 days later than estimates from the other equations. It underestimated production early and late in lactation and overestimated it during mid lactation. For several cows, the Morant equation projected that peak production occurred at the end of lactation. Also, analysis of variance of milk production variables was less sensitive when the traits were estimated by the Morant equation than when they were estimated by one of the others. The Wood weighted equation resulted in estimates of peak day of lactation and peak yield that were less variable and more realistic than estimates from the Wood equation. Collectively, therefore, the Wood weighted equation was deemed most suitable to characterize variability among and within these beef cows in milk production. All four equations, however, ranked the 59 cows similarly for estimated 220-day yield.

scholarly journals Describing the growth curve of local turkey using non-linear models

2018 ◽  
pp. 7104-7107
Author(s):  
Aureliano Juárez-Caratachea ◽  
Iván Delgado-Hurtado ◽  
Ernestina Gutiérrez-Vázquez ◽  
Guillermo Salas-Razo ◽  
Ruy Ortiz-Rodríguez ◽  
...  
Keyword(s):  
Growth Curve ◽  
Visual Analysis ◽  
Linear Models ◽  
Logistic Models ◽  
Information Criterion ◽  
Von Bertalanffy ◽  
Commercial Feed ◽  
Non Linear ◽  
Best Fitting ◽  
Multiple Determination

Objective. Determine the best non-linear model to fit the growth curve of local turkeys managed under confinement in Michoacan, Mexico. Material and methods. Twenty-four and 43 female and male turkeys, reared under commercial conditions were given commercial feed. Birds were weighed weekly from hatch to 29 weeks of age. The Gompertz, Brody, Richards, von Bertalanffy and Logistic models were chosen to describe the age-weight relationship. Results. The best fitting model was selected based on the multiple determination coefficient (R2), the Akaike information criterion (AIC) and visual analysis of the observed and predicted curves. In both female and male, von Bertalanffy was the best model. The highest estimates of parameter A (mature weight) for both females and males were obtained with the von Bertalanffy model followed by the Gompertz and Logistic. The estimates of A were higher for males than for females. The highest estimates of parameter k (rate of maturity) for both females and males were, in decreasing order, for the Logistic, Gompertz, and von Bertalanffy models. k values for female turkeys was higher than for males. The age at the point of inflection (TI) and body weight at the age of point of inflection (WI) varied with the model used. The largest values of TI and WI corresponded to the Logistic model. Between sexes, the largest TI and WI values corresponded to males. Conclusions. The best models to describe turkey growth was the von Bertalanffy because it present the highest R2 and lowest AIC values.

scholarly journals The influence of THI values at different periods of lactation on milk quality and characteristics of lactation curve

2010 ◽  
Vol 55 (3) ◽  
pp. 235-241 ◽  
Author(s):  
Marko Cincovic ◽  
Branislava Belic ◽  
Bojan Toholj ◽  
Ivan Radovic ◽  
Bojana Vidovic
Keyword(s):  
Heat Stress ◽  
Milk Yield ◽  
Milk Fat ◽  
Significant Negative Correlation ◽  
Moderate Intensity ◽  
Lactation Period ◽  
Yield And Quality ◽  
Lactation Curve ◽  
Quality Of Milk

The experiment included 90 cows. Cows were chosen according to the time of calving, so that the first third of lactation occurred during the summer in 30 cows (G1), the second third of lactation occurred during the summer in 30 cows (G2), and in the last 30 cows the last third of lactation was in summer period (G3). The value of THI was between 72 and 82, which indicates the existence of the moderate intensity of heat stress. Heat stress does not damage the milk yield, milk fat and protein percentage on the level of the whole lactation, regardless of the lactation period in which the cows were exposed to stress. There was no correlation between THI and milk yield and quality at the level of the whole lactation. Heat stress did not show a significant effect on the parameters of lactation curve, except the peak of yield, which occurred later in cows exposed to heat stress. Increased value of THI showed nonsignificant effect on yield and quality of milk in the first third of lactation. In the middle and at the end of lactation THI was in a significant negative correlation with the yield and quality of milk. Our study showed a significantly lower heat-induced milk yield, milk fat and protein percent in the middle and at the end of lactation.

Sign in / Sign up

Export Citation Format

Share Document