Using Wood’s function to estimate phenotypic correlations among lactation curve parameters in Iranian first lactation buffaloes

2007 ◽  
Vol 2007 ◽  
pp. 154-154
Author(s):  
H. Farhangfar ◽  
P. Rowlinson ◽  
J. Rahmaninia

In dairy farm animals, production of milk and its components varies in a curvilinear pattern over the course of the lactation. Knowledge of the lactation curve may provide a worthwhile information source about the pattern of milk production which in turn could be used for herd management decisions. Moreover, inter-relationships among lactation curve parameters could be utilised in applied animal breeding programmes to change more effectively the shape of the lactation. Many studies have been undertaken to apply different mathematical models to obtain more accurate prediction of the shape of the lactation curve among which the incomplete gamma function first proposed by Wood (1967) has been broadly used by previous research workers (Rekaya et al., 2000; Tekerli et al., 2000). The main objective of the present research was to apply Wood’s function for estimating phenotypic correlations among lactation curve parameters in Iranian first lactation buffaloes.

Author(s):  
Fariz Mikailsoy

An overview of mathematical models on cattle lactation is herein provided. A technique for constructing lactation curves using logical assumption and Verhulst’s logistic function for predicting the cows’ milk production is described. A new modeling lactation method that is based on solving the equation that describes the rate of change of milk production, depending on time elapsed since calving is presented. The statistical parameters of the adequacy of the models recommended in the literature for describing the lactation curve of cows and our proposed models were calculated Approximation accuracy allows for the identification of models that most reliably describe the lactation curve in the example of cattle.


Author(s):  
G Simm ◽  
N R Wray

Two of the major steps in animal breeding programmes are (i) estimation of breeding values for a defined selection objective (such as milk production or carcass lean content), and (ii) design of optimum breeding programmes, including proportion of animals selected as parents, population size etc. Advances in electronics, and particularly in computer technology, have had a major Impact on these procedures in a number of ways. In this paper we aim to highlight four of these.The preferred method of estimating breeding values is universally recognised to be BLUP (Best Linear Unbiased Prediction). BLUP is superior to classical procedures, such as contemporary comparison, for several reasons. The most important is that it is more accurate in separating differences between animals which are attributable to genetic rather than environmental factors. BLUP was first proposed by Henderson in 1949 but the first BLUP evaluation was not implemented until 1970 (Henderson, 1987). This delay is almost entirely attributable to inadequate computing facilities and technology at that time, since a BLUP evaluation system requires a large number of equations to be stored and solved.


2019 ◽  
Author(s):  
Ben Abdelkrim Ahmed ◽  
Puillet Laurence ◽  
Gomes Pierre ◽  
Martin Olivier

AbstractBackgroundUnderstanding the effects of environment on livestock provides valuable information on how farm animals express their production potential, and on their welfare. Ruminants often face perturbations that affect their performance. Evaluating the effect of these perturbations on animal performance could provide metrics to quantify how animals cope with their environment and therefore, better manage them. In dairy systems, milk production records can be used to evaluate perturbations because (1) they are easily accessible, (2) the overall dynamics throughout the lactation process have been widely described, and (3) perturbations often occur and cause milk loss. In this study, a lactation curve model with explicit representation of perturbations was developed.MethodsThe perturbed lactation model is made of two components. The first one describes a theoretical unperturbed lactation curve (unperturbed lactation model), and the second describes deviations from the unperturbed lactation model. The model was fitted on 319 complete lactation data from 181 individual dairy goats allowing for the characterization of individual perturbations in terms of their starting date, intensity, and shape.ResultsThe fitting procedure detected a total of 2,354 perturbations with an average of 7.40 perturbations per lactation. Loss of production due to perturbations varied between 2% and 19%. Results show that the number of perturbations is not the major factor explaining the loss in milk yield over the lactation, suggesting that there are different types of animal response to challenging factors.ConclusionsBy incorporating explicit representation of perturbations, the model allowed the characterization of potential milk production, deviations induced by perturbations (loss of milk), and thereby comparison between animals. These indicators are likely to be useful to move from raw data to decision support tools in dairy production.


Author(s):  
Hugo Andrés Rodríguez-Álvarez ◽  
José Alfonso Hinojosa-Cuéllar ◽  
Roberto González-Garduño ◽  
Jaime Gallegos-Sánchez ◽  
Moisés Rubio-Rubio ◽  
...  

Objective: To estimate the lactation curve and milk production of Pelibuey ewes andthe relationship with preweaning growth rate of the lambs.Design/methodology/approach: Forty five Pelibuey ewes were milked during 70days in Montecillo, México, in 2018, to estimate daily and total milk production. Thelactation curve was fitted with the incomplete gamma function. In addition, the effectsof type of birth and ewe weight at milking on milk production were analyzed, andcorrelations were calculated between ewe milk production and growth rate of thelambs, per week and for the entire lactation Results: A “typical” lactation curve was found, average ewe milk production for theentire lactation, weighted for the number of lambs suckling, was 131±8 L, with444±24 g d -1 . Ewe weight at milking had an effect (p<0.01) on milk production.Positive correlations were found (p<0.05) between ewe milk production andpreweaning growth rate of the lambs.Limitations on study/implications: There is a strong dependency of the lambs forthe milk production of the Pelibuey ewe, a factor of great relevance so that lambs cangain body weight and survive during lactation.Findings/conclusions: Pelibuey ewes produce less milk than dairy ewes. Therefore,lambs should be weaned at a maximum of 10 weeks of lactation.


Author(s):  
Juan Carlos Angeles-Hernandez

Abstract Lactation is defined as the combined processes of synthesis, secretion, and excretion of milk, starting at calving and continuing until natural or induced drying off in the lactating female. Milk production follows a common pattern in several mammalian species known as a lactation curve. A typical lactation curve starts at day four after calving, reaching peak yield in early lactation, followed by a daily decrease in milk yield (persistency) until the lactating female is artificially dried off, or the lactation comes to a natural end. Mathematical models have been applied to lactation curves to provide information in relation to lactation curve parameters, providing valuable information for herd management and breeding decisions. Therefore, the aim of this study was to review the most used mathematical models for the prediction of dairy production curves. The importance of modelling lactation curves is to predict the yield for each day with the minimum possible error, to be able to elucidate the underlying pattern of milk production in the presence of local variation associated with the effect of the environment. The usefulness of any model of adjustment to the lactation curve depends on its capacity to mimic the biological process of developing milk production and to adjust the factors that affect it. Furthermore, these models can only be used to adjust productive records adequately, or to provide a biological understanding of the lactation process.


Author(s):  
C. Van der Geest

I am a 30-year-old sharemilker on my parent's 600 cow developing farm near Blackball on the western side of the Grey Valley. Earlier this year I competed in the National Young Farmer of the Year competition and finished a close third. So what is information? There are two types of information that I use. There is data gathered from my farm to help fine tune the running of the day to day operations on the farm And directional information This is the information that arrives in papers and directs the long-term direction and plans of the farm and farming businesses. Due to the variability in weather on the Coast there is a greater need to monitor and adjust the farming system compared to an area like Canterbury. This was shown last year (2001/02) when the farm was undergoing a rapid period of development and I was under time restraints from increasing the herd size, building a new shed as well as developing the farm. The results of the time pressure was that day to day information gathering was lower resulting in per cow production falling by 11% or around $182 per cow. So what information was lacking that caused this large drop in profit. • Pasture growth rates • Cow condition • Nitrogen requirements • Paddock performance • Milk production • Pre-mating heat detection As scientists and advisers I hear you say that it is the farmer's responsibility to gather and analyse this information. You have the bigger topics to research and discover, gene marking, improving pasture species, sexing of sperm and ideas that I have not even contemplated yet. This is indeed very valuable research. Where would farming be without the invention of electric fences, artificial breeding and nitrogen research? But my problem is to take a farm with below average production to the top 10% in production with the existing technology and farming principles. I have all the technical information I need at the end of a phone. I can and do ring my consultant, fertiliser rep, vet, neighbour and due to the size and openness of New Zealand science, at present if they do not know I can ring an expert in agronomy, nutrition, soils and receive the answer that I require. I hope that this openness remains as in a time of privatisation and cost cutting it is a true advantage. I feel that for myself the next leap in information is not in the growing of grass or production of milk but in the tools to collect, store and utilise that information. This being tied to a financial benefit to the farming business is the real reason that I farm. Think of the benefits of being able to read pasture cover on a motorbike instantly downloaded, overlaying cow intake with milk production, changes in cow weight, daily soil temperature and predicted nitrogen response. Telling me low producing cows and poor producing paddocks, any potential feed deficits or surpluses. This would be a powerful information tool to use. The majority of this information is already available but until the restraints of time and cost are removed from data gathering and storage, this will not happen.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Valentina Tsartsianidou ◽  
Vanessa Varvara Kapsona ◽  
Enrique Sánchez-Molano ◽  
Zoitsa Basdagianni ◽  
Maria Jesús Carabaño ◽  
...  

AbstractAs future climate challenges become increasingly evident, enhancing performance resilience of farm animals may contribute to mitigation against adverse weather and seasonal variation, and underpin livestock farming sustainability. In the present study, we develop novel seasonal resilience phenotypes reflecting milk production changes to fluctuating weather. We evaluate the impact of calendar season (autumn, winter and spring) on animal performance resilience by analysing 420,534 milk records of 36,908 milking ewes of the Chios breed together with relevant meteorological data from eastern Mediterranean. We reveal substantial seasonal effects on resilience and significant heritable trait variation (h2 = 0.03–0.17). Resilience to cold weather (10 °C) of animals that start producing milk in spring was under different genetic control compared to autumn and winter as exemplified by negative genetic correlations (− 0.09 to − 0.27). Animal resilience to hot weather (25 °C) was partially under the same genetic control with genetic correlations between seasons ranging from 0.43 to 0.86. We report both favourable and antagonistic associations between animal resilience and lifetime milk production, depending on calendar season and the desirable direction of genetic selection. Concluding, we emphasise on seasonal adaptation of animals to climate and the need to incorporate the novel seasonal traits in future selective breeding programmes.


2021 ◽  
Vol 3 (90) ◽  
pp. 101-106
Author(s):  
А.I. Shilov ◽  
◽  
R.N. Lyashuk ◽  
Keyword(s):  

2002 ◽  
Vol 2002 ◽  
pp. 66-66
Author(s):  
N. Ball ◽  
M.J. Haskell ◽  
J.L. Williams ◽  
J.M. Deag

Farm animals show individual variation in their behavioural responses to handling and management systems on farms. These behavioural responses are presumed to reflect underlying temperament traits such as fear or aggression. Information about the location of genes that influence temperament traits could be used in selective breeding programmes to improve animal welfare, as selection for desirable behavioural responses would increase the ability of animals to cope with stressors encountered on farms. The aims of this study were to obtain reliable temperament measurements in cattle using behavioural tests, and to use this data to localise the genes (quantitative trait loci) that are involved in such traits.Behavioural data obtained in temperament tests must be shown to reflect underlying traits by demonstrating intra-animal repeatability, inter-animal variability and validity. The objectives of this experiment were i) to carry out four behaviour tests on a group of heifers, and examine the repeatability, variability and validity of the results obtained; ii) to correlate the behavioural data with genotyping data from a large number of heifers to look for associations between behavioural phenotypes and genetic markers. Associations localise quantitative trait loci (QTLs), or regions of the genome, that are involved in these traits.


1997 ◽  
Vol 45 (3) ◽  
pp. 361-379
Author(s):  
P.B.M. Berentsen ◽  
G.W.J. Giesen ◽  
J.A. Renkema

A linear programming model of a dairy farm was used to explore the future for different types of Dutch dairy farms under different scenarios. The scenarios are consistent sets of changing factors that are considered external at farm level. The factors included are technical, such as efficiency of milk production and feed production, or institutional, such as national environmental legislation and EU market and price policy. Income and nutrient losses for farms differing in intensity and size are generated for the base year 1992 and for the year 2005. The results show that technical change up to the year 2005 has a positive influence on labour income as well as on nutrient losses. The increase of labour income is higher for farms with a higher total milk production in the basis situation. The influence of environmental policy on labour income and environmental results is bigger for farms with a higher intensity, as these farms have to take more measures to comply with governmental policy. Replacement of the price support policy for milk by a 2-price system with a high price for a restricted amount of milk and a low price for an unrestricted amount of milk has negative consequences for labour income, especially for intensive farms.


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