Factors affecting the lactation curves of intensively managed sheep based on a clustering approach

2013 ◽  
Vol 80 (4) ◽  
pp. 439-447 ◽  
Author(s):  
Laura Elvira ◽  
Fernando Hernandez ◽  
Pedro Cuesta ◽  
Santiago Cano ◽  
Juan-Vicente Gonzalez-Martin ◽  
...  
Keyword(s):  
Milk Yield ◽  
Clustering Analysis ◽  
Dairy Sheep ◽  
Dry Period ◽  
Factors Affecting ◽  
Lactation Curve ◽  
Total Milk ◽  
Clustering Approach ◽  
Intensively Managed ◽  
Selection Of

This study investigated factors affecting milk production and lactation curves from complete lactations of Lacaune dairy sheep. Animals were part of a single flock under intensive management and were milked twice daily starting at lambing. The results of the analyses of 7788 complete lactations showed an average total milk yield of 434±183 l from lactations 234±63 d long, with an average lambing interval of 302±65 d. A Pollott additive mathematical model was used to estimate complete lactation curves. Clustering analysis identified four lactation types among Lacaune dairy sheep differing mainly in productivity i.e. milk yield per lactation (MY) and length of lactation (DIM). The so-called SL type involved short, less productive lactations (n=2137; 27·4%; MY=222±75·5 l and DIM=182±52·9 d). The SN type involved short lactations of normal productivity (n=2039; 26·2%; MY=396±73·7 l and DIM=205±33·1 d). The LP type involved long and productive lactations (n=2169; 27·9%; MY=487±70·5 l and DIM=265±40·7 d), while the LVP type included long and extremely productive lactations (n=1443; 18·5%; MY=694±114·0 l and DIM=295±54·7 d). Sheep showing the best lactation curves were usually younger than other sheep, and they had higher yield during the previous lactation, a shorter previous dry period (55±50·4 for LP and 61±55·0 d for LVP types) and longer lambing intervals. In addition, they tended to be born in September and to lamb in March, October and December. Sheep were remarkably stable in their lactation curve behaviour: the curve type observed for the first lactation was highly likely to persist in subsequent lactations (P<0·0001). These results suggest that farmers can use the shape of the first lactation curve to guide their selection of ewes for breeding and retention on the farm, thereby improving flock productivity.

Application of the Wood model to analyse lactation curves of organic dairy sheep farming

2014 ◽  
Vol 54 (10) ◽  
pp. 1609 ◽  
Author(s):  
Juan Carlos Ángeles Hernández ◽  
Octavio Castelán Ortega ◽  
Benito Albarrán Portillo ◽  
Hugo H. Montaldo ◽  
Manuel González Ronquillo
Keyword(s):  
Milk Yield ◽  
Goodness Of Fit ◽  
Evaluation Criteria ◽  
Coefficient Of Determination ◽  
Dairy Sheep ◽  
Organic System ◽  
Dairy Ewes ◽  
Lactation Curve ◽  
Total Milk ◽  
Wood Model

The aim of the present study was to evaluate the performance of the Wood model to describe the characteristics of lactation curves of dairy ewes under organic management in Mexico. In total, 4861 weekly test-day milk yield records from 194 lactations of crossbred dairy ewes were analysed to assess the performance of an empirical model to fit their lactation curve. We used the mathematical model proposed by Wood. The evaluation criteria were the correlation coefficient (r) between the values of total milk yield observed and estimated, the coefficient of determination (R2), and the mean square prediction error (MSPE). In addition, the peak yield (PYest) and time at peak yield (PTest) were calculated. The Wood model showed adequate goodness of fit (r = 0.95, R2 = 0.92 and MSPE = 0.024). The Wood model detected that 52.06% of lactation curves had a continuously decreasing shape (atypical curve), probably as a consequence of the characteristic management of the organic system, mainly due to the genotype used and the nutritional management. Residuals were greater for atypical curves than for typical ones, indicating differences in the ability of the Wood model to fit the two types of shapes. In typical curves, the Wood model showed adequate estimates of total milk yield and time at peak yield. The peak yield was underestimated both in typical and atypical curves. The Wood model in atypical curves underestimated the time at peak yield and milk yields in late lactation. The Wood model showed a reasonable fit of lactation curve in dairy sheep in organic systems but presented deficiencies of fit in atypical curves; therefore, estimates should be interpreted carefully.

Factors affecting milk production in Improved Awassi dairy ewes

2000 ◽  
Vol 71 (3) ◽  
pp. 607-615 ◽  
Author(s):  
E. Gootwine ◽  
G. E. Pollott
Keyword(s):  
Milk Production ◽  
Litter Size ◽  
Milk Yield ◽  
Intensive Management ◽  
Short Term ◽  
Artificial Rearing ◽  
Factors Affecting ◽  
Lactation Curve ◽  
Total Milk ◽  
Term Variation

AbstractThis study investigated the factors affecting milk production and lactation curve parameters from the complete lactations of Awassi dairy sheep. The animals were kept in a single flock under intensive management and milked twice daily starting at lambing. Lambs were removed from the ewes at birth into an artificial rearing unit. The results of the analyses of 3740 complete lactations showed a mean litter size of 1·28 lambs born per ewe lambing and average total milk yield of 506 l from lactations 214 days in length and with an average lambing interval of 330 days. Mean lactation number was 3·71 and ewes conceived on average in the 6th month of the lactation. The lactations peaked on day 45 at a yield of 3·44 l. The maximum milk secretion potential of the ewes was 3·9 l/day, with milk yield increasing at 62 g/day mid way between lambing and peak yield and declining at 16·5 g/day mid way between peak and the end of lactation. Age at first lambing, lactation number, litter size, month of lambing and month of conception during the lactation had significant effects (P < 0·05) on some or all of the lactation parameters investigated. Relatively high milk yield was obtained in lactations starting in the January to March period. The monthly effect on milk production was explained by significant (P < 0·05) heat load and photoperiod effects. High milk production was found to have a significant (P < 0·05) adverse effect on reproductive performance. Conception followed a significant (P < 0·05) short term variation in milk production.

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

scholarly journals Genetic parameters of total milk yield and factors describing the shape of lactation curve in dairy buffaloes

2011 ◽  
Vol 79 (1) ◽  
pp. 60-65 ◽  
Author(s):  
Rúsbel R Aspilcueta-Borquis ◽  
Fernando Baldi ◽  
Francisco R Araujo Neto ◽  
Lucia G Albuquerque ◽  
Milthon Muñoz-Berrocal ◽  
...  
Keyword(s):  
Animal Model ◽  
Factor Analysis ◽  
Milk Yield ◽  
Genetic Parameters ◽  
Correlated Response ◽  
Early Lactation ◽  
Latent Factors ◽  
Latent Factor ◽  
Lactation Curve ◽  
Total Milk

The objective of this study was to apply factor analysis to describe lactation curves in dairy buffaloes in order to estimate the phenotypic and genetic association between common latent factors and cumulative milk yield. A total of 31 257 monthly test-day milk yield records from buffaloes belonging to herds located in the state of São Paulo were used to estimate two common latent factors, which were then analysed in a multi-trait animal model for estimating genetic parameters. Estimates of (co)variance components for the two common latent factors and cumulated 270-d milk yield were obtained by Bayesian inference using a multiple trait animal model. Contemporary group, number of milkings per day (two levels) and age of buffalo cow at calving (linear and quadratic) as covariate were included in the model as fixed effects. The additive genetic, permanent environmental and residual effects were included as random effects. The first common latent factor (F1) was associated with persistency of lactation and the second common latent factor (F2) with the level of production in early lactation. Heritability estimates for F1 and F2 were 0·12 and 0·07, respectively. Genetic correlation estimates between F1 and F2 with cumulative milk yield were positive and moderate (0·63 and 0·52). Multivariate statistics employing factor analysis allowed the extraction of two variables (latent factors) that described the shape of the lactation curve. It is expected that the response to selection to increase lactation persistency is higher than the response obtained from selecting animals to increase lactation peak. Selection for higher total milk yield would result in a favourable correlated response to increase the level of production in early lactation and the lactation persistency.

A Bayesian estimation of the lactation curve of a dairy cow

1985 ◽  
Vol 40 (2) ◽  
pp. 189-193 ◽  
Author(s):  
E. A. Goodall ◽  
D. Sprevak
Keyword(s):  
Kalman Filter ◽  
Bayesian Estimation ◽  
Milk Yield ◽  
Dairy Cow ◽  
Prior Information ◽  
Recursive Procedure ◽  
Early Stages ◽  
Lactation Curve ◽  
Total Milk

ABSTRACTA recursive procedure for the estimation of the lactation curve of a dairy cow, which allows the inclusion of prior information on the curve and which takes account of the correlation between successive observations, is described. The method is based on the Kalman filter. It was found to give accurate estimates of the total milk yield at early stages of lactation.

Influence of dry period length on reproductive performance and productivity of Lacaune dairy sheep under an intensive management system

2012 ◽  
Vol 79 (3) ◽  
pp. 352-360 ◽  
Author(s):  
Fernando Hernandez ◽  
Laura Elvira ◽  
Juan-Vicente Gonzalez-Martin ◽  
Susana Astiz
Keyword(s):  
Milk Yield ◽  
Global Analysis ◽  
Dairy Farm ◽  
Period Length ◽  
Intensive Management ◽  
Dairy Sheep ◽  
Dry Period ◽  
Key Factor ◽  
Daily Milk Yield ◽  
Daily Milk

Intensive management is almost the only way to ensure dairy farm profitability. The dry period length (DPL) is a key factor in the productivity and health of dairy cows, but whether the same is true of dairy sheep is unclear. This study investigated the effects of DPL on the performance of Lacaune sheep under intensive management. We recorded 8136 lactations from 4220 ewes on one farm for the period 2005–2010, and data from a total of 6762 complete lactations 1–4 were included in the study. The length of the dry period following the current lactation was studied. The larger the total milk yield (MY) and daily milk yield (DMY), the shorter was the DPL before the next lactation. DPL correlated with MY (r=−0·384), DMY (r=−0·277) and the lambing-to-conception interval (LC; r=0·201, P<0·0001) in the global analysis of all lactations (lactations 1–4). The influence of previous-DPL (P-DPL), or the length of the period prior to the start of the next lactation, was studied for 4318 lactations. P-DPL was classified into five intervals: very short (P-DPL-XS), 1–30 d; short (P-DPL-S), 31–60 d; medium (P-DPL-M), 61–90 d; long (P-DPL-L), 91–120 d; and very long (P-DPL-XL), >120 d. P-DPL positively correlated with lambing-to-next conception interval (LNC; r=0·095, P<0·0001) for lactations 1–4. LNC was significantly shorter for P-DPLs that were very short, short, or long (P-PDL-XS, 144·2±67·8 d; P-PDL-S, 149·1±57·2 d; P-PDL-L, 152·0±53·7 d) than for groups with very long or medium P-PDLs (P-DPL-XL, 161·5±62·9 d; P-DPL-M, 169·0±74·8 d; P<0·0001). Moreover, P-DPLs that were very short, long, or very long were associated with the lowest milk yields (P-PDL-XS, 377±215 l; P-PDL-l, 370±168 l; P-PDL-XL, 396±196 l). These yields were significantly lower than the yields for short and medium P-DPLs (P-DPL-S, 432±187 l; P-DPL-M, 436±191 l; P<0·0001) when averages of lactations 1–4 were analysed. These results indicate that lactations with larger MY are followed by a shorter dry period, and that a dry period of 30–90 d leads to larger yields in the next lactation. The best LNC was associated with the shortest Previous-DPL. Hence, 30–60 d should be the optimal dry period length for Lacaune sheep under intensive conditions.

Some factors affecting milk yield

1923 ◽  
Vol 13 (1) ◽  
pp. 74-119 ◽  
Author(s):  
J. Hammond ◽  
H. G. Sanders
Keyword(s):  
Milk Yield ◽  
Maximum Yield ◽  
Dry Period ◽  
Factors Affecting ◽  
Live Stock ◽  
The Subject ◽  
The Mean

(1) The variation of the total yields according to the month of calving is given, and causes for this variation are suggested from the mean lactation curves of cows calving in each month.(2) A curve showing the effect of pregnancy on the lactation yield has been prepared and corrections are given for different lengths of service periods after calving—“100 days” being chosen as a standard, as that was found to be the average of all service periods obtained, and as it gives a calf at about the same time in the following year.(3) The variation in yield as the cow gets older has been studied and corrections are given. The maximum yield appears to be reached at the fifth calf; it was found that service periods varied in a definite way with age.(4) The effect of the dry period before calving on the subsequent yield is shown; “85 days” has been taken as a standard and corrections given accordingly; the standard was the mean of all dry periods obtained, but is probably above the average throughout the country.(5) By applying these corrections, the variation of individual totals is reduced by approximately 20 per cent, and the number of lactations showing less than 5 per cent, variation is increased by 27·3 per cent.(6) A brief review of other literature on the subject is given, and the results obtained in this paper are compared with those of other investigators.The thanks of the authors are due to the members of the Penrith Milk Recording Society for the loan of their registers, which made this investigation possible, and to Mr Garnet, the Live Stock Officer of the district, and the Live Stock Branch, of the Ministry of Agriculture and Fisheries for the collection of the data; the authors are also greatly indebted to Mr G. Udny Yule, M.A., F.R.S., for much assistance in the purely statistical part of the work, and to Dr F. H. A. Marshall, F.R.S., for his advice during the course of the investigation.

scholarly journals Modelling of the lactation curve of grazing Mongolian mare

2020 ◽  
Vol 29 (1) ◽  
pp. 64-71
Author(s):  
Sandagdorj Badrakh ◽  
Baigalmaa Tserenpil ◽  
Burenjargal Sedkhuu ◽  
Nyam-Osor Purevdorj
Keyword(s):  
Mathematical Models ◽  
Milk Yield ◽  
Lactation Period ◽  
Linear Quadratic ◽  
Quartic Curve ◽  
Lactation Curve ◽  
Total Milk ◽  
Curve Model ◽  
Composition Curve ◽  
Best Fit

Current research was performed to determine the yield and composition of milk during entire lactation period, which lasts June to February of following year and describe fit of lactation curve using different mathematical models. Total 8 mares, aged from 5 to 12 years were used in the study. The total milk yield, calculated by Fleischmann's method, as well as the models were various and shape of curves for Linear, Quadratic, Cubic, and Quartic equations, and Wood's curve were different. The determination index (R2) of the milk yield and composition curve models ranged from 0.474-0.987, and R2 of Quartic curve model for milk yield (0.987) was the highest. Study result showed that the above models were able to describe the lactation curve of the milk yield and composition, and the Quartic model best fit to data collected from Mongolian mare and allowed a suitable description of the shape and dynamics of curve. Бэлчээрийн маллагаатай Монгол гүүний лактацын муруйн загварчлал Монгол гүүний саалийн нийт хугацаанд буюу VI сараас дараа жилийн III сар хүртэл саамны гарц, найрлагын хэмжээг үндэслэн математик загваруудаар лактацын муруй байгуулах, монгол гүүний лактацын муруйд хамгийн нийцэл сайтай загварыг сонгон тодорхойлох зорилгоор энэхүү ажлыг хийж гүйцэтгэлээ. Судалгаанд 5-12 насны 8 гүүг ашиглав. Сүүний нийт гарц, найрлагын хэмжээг Вүүд (Wood’s)-ын загвар болон шугаман, шугаман бус регрессын тэгшитгэлээр загварчлахад лактацын муруйн загварууд харилцан адилгүй байв. Саамны гарц, найрлагын муруйн загваруудын детерминацийн индекс (R2) нь 0.474-0.987-ийн хооронд хэлбэлзэж, биквадрат загварын хувьд хамгийн нийцэл өндөр буюу 0.987 байв. Судалгааны үр дүнд дээрх аргуудаар саамны гарц, найрлагын лактацын муруйг загварчлах боломжтой нь харагдаж байсан ба эдгээрээс биквадрат тэгшитгэлийн загвар нь  бэлчээрийн маллагаатай монгол гүүний саамны өгөгдөлтэй хамгийн сайн нийцэж, лактацын муруйн хэлбэр, хөдлөлзүйн хувьд тохирч байв.   Түлхүүр үг: саамны гарц, саамны найрлага, лактацын муруй  

scholarly journals Determining the Factors Affecting 305-Day Milk Yield of Dairy Cows with Regression Tree

2021 ◽  
Vol 9 (6) ◽  
pp. 1154-1158
Author(s):  
Serdar Genç ◽  
Mehmet Mendes
Keyword(s):  
Dairy Cows ◽  
Milk Yield ◽  
Regression Tree ◽  
Dry Period ◽  
Data Set ◽  
Tree Analysis ◽  
Factors Affecting ◽  
Lactation Length ◽  
Regression Tree Analysis ◽  
Service Period

The purpose of this study was to determine the factors affecting the 305-day milk yield of dairy cattle by using Regression Tree Analysis (RTA). The data set of this study consisted of 8 different cattle breeds grown in Turkey. Breed (B), Province (P), Lactation Length (LL), Service Period (SP), Dry Period (DP), Parity (PR), Calving Year (CY), Calving Age (CA) and Calving Month (CM) were used to predict the 305-day milk yield. Results of RTM showed that the usage of this method might be appropriate for determining the important factors that would be able to affect the 305-day milk yield (R2=71.3%). It was seen that the most important factors affecting the 305-day milk yield were the Breed, Lactation Length, Province, and Parity. Therefore, those selected factors were more efficient than the others in predicting the 305-day milk yield. RTA results also indicated that the lowest milk yield was estimated for Jersey, Jersey Crossbred, and Yerli Kara. Among the highest 305-day milk yield cows, the milk yield estimates of the cows in the second, third, fourth, fifth, and the sixth parities were found significantly higher than that of the cows in the first and seventh parities.

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