Modeling the lactation curve for test-day milk yield in Murciano-Granadina goats

2002 ◽  
Vol 46 (1) ◽  
pp. 29-41 ◽  
Author(s):  
C Fernández ◽  
A Sánchez ◽  
C Garcés
Keyword(s):  
Milk Yield ◽  
Lactation Curve

The control of appetite in lactating cows

1972 ◽  
Vol 14 (3) ◽  
pp. 263-281 ◽  
Author(s):  
L. S. Monteiro
Keyword(s):  
Body Weight ◽  
Food Intake ◽  
Milk Yield ◽  
Closed Loop ◽  
Loop System ◽  
Closed Loop System ◽  
Lactating Cows ◽  
Body Reserves ◽  
Lactation Curve ◽  
Rate Of Increase

SUMMARYA closed-loop system is proposed for the control of voluntary food intake in lactating cows, and an expression is deduced relating the response of food intake to changes in milk yield and body-weight gain.A closed-loop system necessarily involves a delay in the response to changes in production. The rate of increase of food intake is there- fore slower than the rate of increase in milk yield. The consequent deficit in energy during the rising part of the lactation curve is met by the mobilization of body reserves, which are partly accounted for by losses in body weight. During the declining part of the lactation the delay effect leads to an excess of energy intake and to the replacement of body reserves and, consequently, of body weight.The expression deduced from the model was fitted to four different types of lactation curve corresponding to long and short lactations of Friesians and Jerseys fed ad libitum on a complete diet. The expected food intake based on the control model was contrasted with a linear regression model. The former gave a better account of the variation in food intake in all four types of lactation.The total change in body weight during lactation was partitioned between changes in weight due to the mobilization and replacement of reserves and gain directly attributable to food intake. There was, in general, good agreement between the observed losses in weight occurring at the beginning of lactation and those predicted from the mobilization of reserves for milk production.The physiological implications of the model and the values estimated for the parameters are discussed.

scholarly journals The use of dijkstra mechanistic model for genetic analysis of the lactation curve characteristics and their relationships with age at first calving and somatic cell score of Iranian dairy cows

2020 ◽  
Vol 42 ◽  
pp. e50181
Author(s):  
Mahdi Elahi Torshizi ◽  
Homayoun Farhangfar
Keyword(s):  
Somatic Cell ◽  
Genetic Correlation ◽  
Milk Yield ◽  
Mechanistic Model ◽  
Genetic Correlations ◽  
Somatic Cell Score ◽  
Specific Rate ◽  
Lactation Period ◽  
Age At First Calving ◽  
Lactation Curve

The objective of this study was to estimate lactation curve parameters with Dijkstra mechanistic model and to evaluate genetic and phenotypic relationships between the parameters and the average somatic cell count in primiparous cows. The finding indicated that heritability estimates for partial milk yield (PMY1, PMY2 and PMY3), total 305-day milk yield (TMY305), decay parameter (λ2), age at first calving (AFC) and peak yield (PY) were moderate while the heritability of persistency (PS%), average somatic cell score (AVGSCS), time to peak yield (TP), initial milk production (λ0), specific rate of cell proliferation at parturition (λ1), and specific rate of cell death (λ3) were quite low. Genetic correlations between both AFC and PS% traits with average somatic cell scores was negative (-0.047 and -0.060) but low positive genetic correlation were between partial milk yields (PMY1 and PMY3) while negative genetic correlation (-0.06) was obtained between TMY305 and AVGSCS. Differences between TMY305 of cows with less than 100000 cells mL-1 and cows with >1,500,000 cells mL-1 was approximately 708 Kg and is equivalent to 8% loss of milk yield/cow during lactation period and also loss of persistency (11.1 %( was shown for the extreme classes of SCC in this study.

scholarly journals Comparison of different lactation curve models in Holstein cows raised on a farm in the south-eastern Anatolia region

2008 ◽  
Vol 51 (4) ◽  
pp. 329-337
Author(s):  
Ö. Koçak ◽  
B. Ekiz
Keyword(s):  
Milk Yield ◽  
Goodness Of Fit ◽  
Mean Squared Error ◽  
Exponential Model ◽  
Eastern Anatolia ◽  
Mean Squared Errors ◽  
Squared Error ◽  
Lactation Curve ◽  
Daily Milk Yield ◽  
Daily Milk

Abstract. The objective of this study was to compare the goodness of fit of seven mathematical models (including the gamma function, the exponential model, the mixed log model, the inverse quadratic polynomial model and their various modifications) on daily milk yield records. The criteria used to compare models were mean R2, root mean squared errors (RMSE) and difference between actual and predicted lactation milk yields. The effect of lactation number on curve parameters was significant for models with three parameters. Third lactation cows had the highest intercept post-calving, greatest incline between calving and peak milk yield and greatest decline between peak milk yield and end of lactation. Latest peak production occurred in first lactation for all models, while third lactation cows had the earliest day of peak production. The R2 values ranged between 0.590 and 0.650 for first lactation, between 0.703 and 0.773 for second lactation and between 0.686 and 0.824 for third lactation, depending on the model fitted. The root mean squared error values of different models varied between 1.748 kg and 2.556 kg for first parity cows, between 2.133 kg and 3.284 kg for second parity cows and between 2.342 kg and 7.898 kg for third parity cows. Lactation milk yield deviations of Ali and Schaeffer, Wilmink and Guo and Swalve Models were close to zero for all lactations. Ali and Schaeffer Model had the highest R2 for all lactations and also yielded smallest RMSE and actual and predicted lactation milk yield differences. Wilmink and Guo and Swalve Models gave better fit than other three parameter models.

scholarly journals On designing the average lactation curve at the ascending phase

1962 ◽  
Vol 34 (1) ◽  
pp. 162-168
Author(s):  
Aarne Mäkelä
Keyword(s):  
Dairy Cows ◽  
Milk Yield ◽  
Lactation Period ◽  
Lactation Curve ◽  
The Mean ◽  
The Individual ◽  
Daily Milk Yield ◽  
Daily Milk

Comparisons are made between different methods to find the peak production (maximum daily milk yield) and methods to design the average lactation curve at the ascending phase in dairy cows. It was noted that in order to determine the height and location of the maximal producing capacity of a cow in a known lactation period, it is preferable to choose the peak production as a mean of three subsequent best days. It was also noted that the usual methods for drawing the average lactation curves do not give a true picture of the height and location of the peak. The author suggests a method for determining the average lactation curve at the ascending phase by using the averages of both milk productions and times involved in reaching the peak and known fractions (e.g. 1/8, 1/4, 1/2, 3/4, and 5/4) of it. In this lactation curve the peak production is the mean of the peaks of individual cows, and the time involved in reaching it is the mean of the durations of the ascending phases of the individual cows.

Factors affecting milk yield and lactation curve fitting in the creole sheep of Chiapas-Mexico

2005 ◽  
Vol 58 (3) ◽  
pp. 265-273 ◽  
Author(s):  
Marisela Peralta-Lailson ◽  
Arturo Ángel Trejo-González ◽  
Pastor Pedraza-Villagómez ◽  
José M. Berruecos-Villalobos ◽  
Carlos G. Vasquez
Keyword(s):  
Milk Yield ◽  
Curve Fitting ◽  
Factors Affecting ◽  
Lactation Curve ◽  
Fitting In

Variation of freezing point of cow's milk free from extraneous water during lactation

2000 ◽  
Vol 25 ◽  
pp. 165-168
Author(s):  
B.A. Slaghuis ◽  
G.H. Klungel
Keyword(s):  
Milk Yield ◽  
Freezing Point ◽  
Bovine Milk ◽  
Cow’S Milk ◽  
Cow's Milk ◽  
Freezing Points ◽  
Stage Of Lactation ◽  
Lactation Curve ◽  
One Year ◽  
Morning Milk

AbstractThe freezing point of bovine milk is known to vary between narrow limits. However, some variation is possible, because of the osmotic relationship between blood and milk. The extent of variation in freezing points of cow's milk was studied. For one year, freezing points were determined in individual milk samples from a high producing herd. Differences (P<0.05) were found between evening and morning milk yield and freezing points. A ‘lactation curve’ for freezing points was fitted and showed some similarity with milk yield curves. Stage of lactation explained part of the variation of freezing points of cow's milk.

A simulation study of the effects of the calving interval on milk yields of dairy cows in fixed time periods

1992 ◽  
Vol 55 (3) ◽  
pp. 309-314 ◽  
Author(s):  
A. Genizi ◽  
H. Schindler ◽  
S. Amir ◽  
S. Eger ◽  
M. Zarchi ◽  
...  
Keyword(s):  
Dairy Cows ◽  
Milk Yield ◽  
Simulation Study ◽  
Fixed Time ◽  
Distinct Advantage ◽  
Calving Interval ◽  
Lactation Curve ◽  
Time Periods ◽  
Calving Intervals ◽  
Linear Decline

AbstractMultiparous cows were assigned before calving to three calving to first insemination intervals. Records of cows conceiving at first or second insemination, were used to construct a model of the lactation curve which incorporated peak production and the effect of progressing pregnancy. The model was used to simulate milk yield during a 4-year period for three production levels and five calving intervals. The model separated the descending part of the lactation curve into a linearly and an exponentially declining component, with the latter becoming distinct at about 20 weeks after conception. Peak yield was negatively correlated with the slope of the linear decline. Within a simulated 4-year period, cumulative milk yields at fixed time periods after calving depended upon the period chosen and the calving-to-conception interval of the cow. Late conceptions resulted in higher cumulative yields at the end of the 1st year, and in lower yields at the 2nd year end, with respect to early conceptions. Smaller differences were found between the intermediate calving intervals. During the 3rd and 4th years the early conceptions had a distinct advantage. Different rates of the linear decline, obtained for the different production levels, changed the magnitude of the yield differences between the calving intervals but not their relative ranking. The model presented offers a means for the suitable choice of the calving cycle according to the length of the period for which a cow is expected to remain in the herd.

scholarly journals Genetic parameters for milk yield and lactation persistency in the three first parities of Iranian Holstein cows

2019 ◽  
Vol 32 (2) ◽  
pp. 100-106 ◽  
Author(s):  
Farzane Shokri-Sangari ◽  
Hadi Atashi ◽  
Mohammad Dadpasand ◽  
Fateme Saghanejad
Keyword(s):  
Milk Yield ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Holstein Cows ◽  
Lactation Curve ◽  
Breeding Center ◽  
Selection For ◽  
The Difference ◽  
Feed Costs ◽  
Cow Health

Background: Lactation persistency influences cow health and reproduction and has an impact on the feed costs of dairy farms. Objective: To estimate (co)variance components and genetic parameters of 100- and 305-d milk yield, and lactation persistency in Holstein cows in Iran. Methods: Records collected from January 2000 to December 2012 by the Animal Breeding Center of Iran (Karaj, Iran) were used. The following four measures of lactation persistency were used: P21: Ratio of milk yield in the second 100-d in milk (DIM) divided by that of the first 100-d. P31: Ratios of milk yield in the third100-d divided by that of the first 100-d. PW: The persistency measure derived from the incomplete gamma function. PJ: The difference between milk yield in day 60th and 280th of lactation. Results: The estimated heritability of lactation persistency for the three first parities (first, second, and third lactation) ranged from 0.01 to 0.06, 0.02 to 0.10, and 0.01 to 0.12, respectively. Genetic correlations among lactation persistency measures for the three first parities ranged from 0.77 to 0.98, 0.65 to 0.98, and 0.58 to 0.98, respectively; while corresponding values for genetic correlations among lactation persistency with 305-d milk production ranged from 0.18 to 0.63, 0.32 to 0.75, and 0.41 to 0.71, respectively. The estimated repeatability for lactation persistency measures ranged from 0.06 to 0.20. Conclusion: The moderate positive genetic correlation between lactation persistency and 305-d milk yield indicates that selection for increasing milk yield can slightly improve lactation persistency.Key words: dairy cattle, heritability, lactation curve, milk yield, persistency, repeatability. ResumenAntecedentes: La persistencia de la lactancia tiene una gran influencia en la salud, la reproducción y los costos de alimentación de las granjas lecheras. Objetivo: Estimar los componentes de (co)varianza y los parámetros genéticos de la producción de leche a 100 y 305 d, asi como la persistencia de la lactancia en vacas Holstein en Irán. Métodos: Se utilizaron registros recopilados entre enero de 2000 y diciembre de 2012 por el Centro de cría de animales de Irán (Karaj, Irán). Se utilizaron las siguientes cuatro medidas de persistencia de la lactancia: P21: Proporción de producción de leche en los segundos 100-d en leche (DIM) dividida por la de los primeros 100-d. P31: Proporcion de producción de leche en los terceros 100-d dividido por el de los primeros 100-d. PW: medida de persistencia derivada de la función gamma incompleta. PJ: diferencia entre el rendimiento de leche en el 60 y el 280 día de lactancia. Resultados: La heredabilidad estimada de la persistencia de la lactancia para los tres primeros partos (primera, segunda y tercera lactancia) varió de 0,01 a 0,06; 0,02 a 0,10; y 0,01 a 0,12, respectivamente. Las correlaciones genéticas entre las medidas de persistencia de lactancia para los tres primeros partos variaron de 0,77 a 0,98; 0,65 a 0,98; y 0,58 a 0,98, respectivamente; mientras que los valores correspondientes para las correlaciones genéticas entre la persistencia de la lactancia con la producción de leche a 305 d variaron de 0,18 a 0,63; 0,32 a 0,75; y 0,41 a 0,71, respectivamente. La repetibilidad estimada para las medidas de persistencia de la lactancia varió de 0,06 a 0,20. Conclusión: La correlación genética positiva moderada entre la persistencia de la lactancia y la producción de leche a 305-d indica que la selección para aumentar la producción de leche puede mejorar ligeramente la persistencia de la lactancia.Palabras clave: curva de lactancia, ganado lechero, heredabilidad, persistencia, producción de leche, repetibilidad. ResumoAntecedentes: A persistência da lactação tem grande influência nos custos de saúde, reprodução e alimentação em fazendas leiteiras. Objetivo: Estimar os componentes da variância (co)variância e os parâmetros genéticos da produção de leite de 100 e 305 d e a persistência da lactação em vacas Holandesas no Irã. Métodos: Os dados utilizados foram registros coletados de janeiro de 2000 a dezembro de 2012 pelo Centro de Criação de Animais do Irã (Karaj, Irã). As seguintes quatro medidas de persistência de lactação foram utilizadas: P21: Razão da produção de leite no segundo 100-d em leite (DIM) dividido pelo primeiro 100-d. P31: Razões da produção de leite na terceira 100d dividida pela da primeira 100-d. PW: A medida de persistência derivada da função gama incompleta. PJ: A diferença entre a produção de leite no 60º e 280º dia de lactação. Resultados: A hereditariedade estimada da persistência da lactação para as três primeiras paridades (primeira, segunda e terceira lactação) variou de 0,01 a 0,06; 0,02 a 0,10; e 0,01 a 0,12, respectivamente. As correlações genéticas entre as medidas de persistência da lactação para as três primeiras paridades variaram de 0,77 a 0,98; 0,65 a 0,98; e 0,58 a 0,98, respectivamente; enquanto os valores correspondentes para correlações genéticas entre a persistência da lactação com produção de leite de 305d variaram de 0,18 a 0,63; 0,32 a 0,75; e 0,41 a 0,71, respectivamente. A repetibilidade estimada para medidas de persistência de lactação variou de 0,06 a 0,20. Conclusão: A correlação genética positiva moderada entre a persistência da lactação e a produção de leite de 305d indicou que a seleção para aumentar a produção de leite melhoraria ligeiramente a persistência da lactação.Palavras-chave: curva de lactação, gado de leite, hereditariedade, persistência, produção de leite, repetibilidade.

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.

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