scholarly journals Laktasyon Eğrisinin Tahmininde Kullanılan Üç Farklı Modelin Karşılaştırılması

2019 ◽  
Vol 7 (3) ◽  
pp. 487
Author(s):  
Melis Çelik Güney ◽  
Gökhan Tamer Kayaalp ◽  
Gökhan Gökçe ◽  
Serap Göncü
Keyword(s):  
Dairy Cattle ◽  
Milk Yield ◽  
Exponential Function ◽  
Gamma Function ◽  
Mean Squared Error ◽  
Significance Test ◽  
Microsoft Excel ◽  
Function Model ◽  
Squared Error ◽  
Lactation Curve

In this study, the lactation curve of the milk yield datas of 45 Holstein which were taken from Cukurova University, Faculty of Agriculture, Research and Application Farm, Dairy Cattle Unit were estimated. Three different models, gamma function, exponential function and parabolic exponential function, were used in the estimation of the lactation curve. When compared models, R-squared and mean squared error (MSE) were used as criteria. The analyses were made with Minitab 13.0 V. The graph was drawn with Microsoft Excel 2007. As a result of the study, the model giving the lowest mean squared error and the highest R-squared value was determined as Gama function model. This model is the best among the models used. When the significance test of the parameters, all the parameters were found statistically significant.

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.

Influence of environment and heredity on the shape of lactation curves in Sahiwal cows

1979 ◽  
Vol 92 (2) ◽  
pp. 393-401 ◽  
Author(s):  
M. K. Rao ◽  
D. Sundaresan
Keyword(s):  
Milk Yield ◽  
Gamma Function ◽  
Yield Traits ◽  
Gangetic Plains ◽  
Northern India ◽  
Lactation Length ◽  
Service Period ◽  
Lactation Curve ◽  
Sahiwal Cows ◽  
Genetic And Phenotypic Correlations

SummaryThe shape of the lactation curve of Sahiwal cows was estimated by fitting a gamma function to 2034 lactations made by 681 cows at two farms in Indo-Gangetic plains of Northern India. The persistency of lactation yield was estimated by three methods: P1, the coefficient of variation among weekly yields; P2, ratio of lactation yield to peak yield, and P3, from the gamma function.The gamma function fitted to the weekly yields explained 75·9% of the variation. A least-squares analysis of different traits associated with lactation curve shape indicated significant influence of parity, period and season of calving on the lactation curve. The lactation yield, peak yield and daily yield up to the peak were highest for winter calvers, while persistency was highest for monsoon calvers. The milk yield traits showed an increase up to the second or third lactation, while the persistency decreased from the first to eighth lactation with increase in parity order. The lactation curve was also more flat in the first lactation than later. The lactation yield and persistency increased with increase in age at calving independent of parity order. The lactation yield, peak yield, persistency and daily yield up to the peak were positively correlated with service period, lactation length and calving interval.The heritability and repeatability estimates of different traits, genetic and phenotypic correlations of lactation milk yield with different persistency measures indicated that P2 is a better measure of persistency. The peak yield could be used as a criterion of selection in early lactation to bring about improvement in lactation yield and persistency.

scholarly journals The change in estimate method for selecting confounders: A simulation study

2021 ◽  
pp. 096228022110342
Author(s):  
Denis Talbot ◽  
Awa Diop ◽  
Mathilde Lavigne-Robichaud ◽  
Chantal Brisson
Keyword(s):  
Confidence Intervals ◽  
Simulation Study ◽  
Mean Squared Error ◽  
Real Data ◽  
Significance Test ◽  
Simulation Studies ◽  
Squared Error ◽  
Estimate Method ◽  
The Mean ◽  
Work And Health

Background The change in estimate is a popular approach for selecting confounders in epidemiology. It is recommended in epidemiologic textbooks and articles over significance test of coefficients, but concerns have been raised concerning its validity. Few simulation studies have been conducted to investigate its performance. Methods An extensive simulation study was realized to compare different implementations of the change in estimate method. The implementations were also compared when estimating the association of body mass index with diastolic blood pressure in the PROspective Québec Study on Work and Health. Results All methods were susceptible to introduce important bias and to produce confidence intervals that included the true effect much less often than expected in at least some scenarios. Overall mixed results were obtained regarding the accuracy of estimators, as measured by the mean squared error. No implementation adequately differentiated confounders from non-confounders. In the real data analysis, none of the implementation decreased the estimated standard error. Conclusion Based on these results, it is questionable whether change in estimate methods are beneficial in general, considering their low ability to improve the precision of estimates without introducing bias and inability to yield valid confidence intervals or to identify true confounders.

scholarly journals Extension model of lactation curves to evaluate the effect of the recombinant bovine somatotropin on milk yield in Holstein cows

2010 ◽  
Vol 62 (1) ◽  
pp. 124-129
Author(s):  
A. Palacios-Espinosa ◽  
J.L. Espinoza-Villavicencio ◽  
R. de Luna ◽  
A. Guillén ◽  
N.Y. Avila
Keyword(s):  
Growth Hormone ◽  
Dairy Cattle ◽  
Milk Yield ◽  
Bovine Somatotropin ◽  
Holstein Cows ◽  
Subcutaneous Injections ◽  
Recombinant Bovine Somatotropin ◽  
Extension Model ◽  
Lactation Curve ◽  
Wood Model

An extension model of lactation curves was used to determine the effect of recombinant bovine somatotropin (bST-r) on milk yield in Holstein dairy cattle. This model use the fitted values obtained by the Wood model, and was tested on the records of 66 cows. The milk yield predicted with the extension model and the observed yield were compared and no significant differences were observed (P>0.05). Once the extension model was validated, the milk yield tests of 199 cows were used. The cows received bST-r 500mg by subcutaneous injections. The injections were applied after 100 days in milk at 14-day intervals (seven injections). The observed milk yield was compared with the yield expected by the extension model. An increase of 5.3% was observed in milk yield in response to the bST-r. This increase is lower than that reported in the literature in response to the growth hormone in dairy cattle. It is concluded that extension model used in the present work is reliable for extending the lactation curve in Holstein cows, and the increase in milk yield in response to the application of bST-r, determined in the same animal using the extension model, was lower than that reported by other authors.

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 Predicting 305-day Milk Yield by the Use of Lactation Curve Formula in Dairy Cattle

1982 ◽  
Vol 53 (1) ◽  
pp. 23-28
Author(s):  
Kazuo KISHI ◽  
Hiroshi IINO ◽  
Yukio TACHIKAWA ◽  
Takaharu TAGUSARI ◽  
Naoki KAWANISHI ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Milk Yield ◽  
Lactation Curve

scholarly journals Selecting for Lactation Curve and Milk Yield in Dairy Cattle

1985 ◽  
Vol 68 (6) ◽  
pp. 1438-1448 ◽  
Author(s):  
T.A. Ferris ◽  
I.L. Mao ◽  
C.R. Anderson
Keyword(s):  
Dairy Cattle ◽  
Milk Yield ◽  
Lactation Curve

Use of reflectance spectroscopy to estimate the organic carbon and CaCO3 contents of soils

2012 ◽  
Vol 61 (2) ◽  
pp. 277-290 ◽  
Author(s):  
Ádám Csorba ◽  
Vince Láng ◽  
László Fenyvesi ◽  
Erika Michéli
Keyword(s):  
Organic Carbon ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Mean Squared Error ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Root Mean Squared Error ◽  
Squared Error

Napjainkban egyre nagyobb igény mutatkozik olyan technológiák és módszerek kidolgozására és alkalmazására, melyek lehetővé teszik a gyors, költséghatékony és környezetbarát talajadat-felvételezést és kiértékelést. Ezeknek az igényeknek felel meg a reflektancia spektroszkópia, mely az elektromágneses spektrum látható (VIS) és közeli infravörös (NIR) tartományában (350–2500 nm) végzett reflektancia-mérésekre épül. Figyelembe véve, hogy a talajokról felvett reflektancia spektrum információban nagyon gazdag, és a vizsgált tartományban számos talajalkotó rendelkezik karakterisztikus spektrális „ujjlenyomattal”, egyetlen görbéből lehetővé válik nagyszámú, kulcsfontosságú talajparaméter egyidejű meghatározása. Dolgozatunkban, a reflektancia spektroszkópia alapjaira helyezett, a talajok ösz-szetételének meghatározását célzó módszertani fejlesztés első lépéseit mutatjuk be. Munkánk során talajok szervesszén- és CaCO3-tartalmának megbecslését lehetővé tévő többváltozós matematikai-statisztikai módszerekre (részleges legkisebb négyzetek módszere, partial least squares regression – PLSR) épülő prediktív modellek létrehozását és tesztelését végeztük el. A létrehozott modellek tesztelése során megállapítottuk, hogy az eljárás mindkét talajparaméter esetében magas R2értéket [R2(szerves szén) = 0,815; R2(CaCO3) = 0,907] adott. A becslés pontosságát jelző közepes négyzetes eltérés (root mean squared error – RMSE) érték mindkét paraméter esetében közepesnek mondható [RMSE (szerves szén) = 0,467; RMSE (CaCO3) = 3,508], mely a reflektancia mérési előírások standardizálásával jelentősen javítható. Vizsgálataink alapján arra a következtetésre jutottunk, hogy a reflektancia spektroszkópia és a többváltozós kemometriai eljárások együttes alkalmazásával, gyors és költséghatékony adatfelvételezési és -értékelési módszerhez juthatunk.

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