Monitoring Daily Milk Yields with a Recursive Test Day Repeatability Model (Kalman Filter)

The present investigation was carried out using data of first lactation bimonthly (BTDY) test day milk yields and 305-day milk yield records of 1345 Karan Fries cows sired by 84 bulls from 1984-2006 maintained at NDRI, Karnal. The test day milk yields recorded from 6th to 305th day at bimonthly intervals were used to predict 305 day milk. Based on simple linear regression analysis with only one independent variable, the regression coefficients showed a consistently increasing trend up to BTDY-3 and thereafter a consistently declining trend up to BTDY-7 test day milk yield was noticed. The accuracy of prediction (R2) of 305-day milk yield based on individual bimonthly test day milk yields varied between 19% (BTDY-1) and 59% (BTDY-4). However, slightly higher estimates of accuracy of prediction in the middle portion of lactation in bimonthly test day milk yields were observed. The absolute error, percent error and error variance showed continuously declining trend from BTDY-1 to BTDY-3 but then increasing trend from BTDY-3 to BTDY-6. Based on fitting of stepwise multiple regressions, the regression equation with 3 variables viz. BTDY-2, BTDY-3 and BTDY-5 was considered more appropriate for prediction of first lactation 305-day milk yield with 83 % accuracy and 7.54 per cent error of prediction in 305-day milk yield.

Estimation of daily and total lactation milk yield of Chios ewes from single morning or evening records

Fit of standard models of the lactation curve to weekly records of milk production of cows in a single herd

The objective of the study was twofold: (i) to quantify the differences in daily milk yield (DMY) and total milk yield (TMY) between lame and non-lame dairy ewes and (ii) to determine the shape of lactation curves around the lameness incident. The overall study was a prospective study of lameness for the surveyed sheep population, with a nested study including the selection of matching controls for each lame ewe separately. Two intensively reared flocks of purebred Chios ewes and a total of 283 ewes were used. Data, including gait assessment and DMY records, were collected on a weekly basis during on-farm visits across the milking period. A general linear model was developed for the calculation of lactation curves of lame and non-lame ewes, whereas one-way ANOVA was used for the comparisons between lame ewes and their controls. Lameness incidence was 12.4 and 16.8% on Farms A and B, respectively. Average DMY in lame ewes was significantly lower (213.8 g, P < 0.001) compared with the rest of the flock, where DMY averaged 1.340 g. The highest DMY reduction in lame ewes was observed during the week 16 of the milking period (P < 0.001), whereas the reduction of DMY, for lame ewes, remained significant at P < 0.001 level from week 8 to week 28 of milking. Comparisons between lame and controls revealed that at the week of lameness diagnosis a significant DMY reduction (P ≤ 0.001) was observed in lame ewes (about 32.5%), which was maximised 1 week later (35.8%, P ≤ 0.001) and continued for several weeks after recovery, resulting in 19.3% lower TMY for lame ewes for the first 210 d of the milking period (P < 0.01). Moreover, at flock level, TMY for non-lame and lame ewes, as calculated by the general linear model, was 318.9 and 268.0 kg, respectively. The results of this study demonstrate evidence of significant financial losses in dairy sheep due to lameness which, however, need to be accurately estimated in further, more detailed, analyses.

On-farm methane measurements during milking correlate with total methane production by individual dairy cows

Os registros de produção de leite de 11.023 primeiras lactações de vacas da raça Holandesa pertencentes a 251 rebanhos distribuídos no estado de Minas Gerais foram usados para comparar os polinômios de Legendre e a função Wilmink em modelos de regressão aleatória (MRA) quanto aos seus efeitos na estimação de parâmetros genéticos e predição de valores genéticos para nove medidas de persistência na lactação e produção de leite até 305 dias. Os modelos de regressão aleatória ajustados aos controles leiteiros entre o sexto e o 305(0) dia de lactação incluíram o efeito de rebanho-ano-mês do controle, os parâmetros dos polinômios de Legendre de ordens 3 a 5 ou da função de Wilmink, para modelar as curvas fixas da regressão dentro das subclasses de idade-estação de parto da vaca e os parâmetros dos polinômios de Legendre de ordens 3 a 5, para modelar os efeitos aleatórios genético-aditivo e permanente de ambiente. Os testes do critério de informação de Akaike e Bayesiano indicaram o modelo com maior número de parâmetros como o que melhor se ajustou aos dados de produção de leite. Observaram-se grandes variações nas estimativas de herdabilidade para a maioria das medidas de persistência na lactação, com uso dos modelos que envolveram ajustes dos polinômios de Legendre. As estimativas de herdabilidade variaram de 0,11 a 0,33 para produção de leite ao longo da lactação, de 0,33 a 0,36 para a produção de leite até 305 dias e de 0,00 a 0,32 para persistência na lactação. As correlações genéticas entre persistência e produção de leite até 305 dias diferiram com o modelo e a medida de persistência. A utilização da função de Wilmink, comparada aos polinômios de Legendre, proporcionou mudanças expressivas na ordem dos animais quando classificados para persistência na lactação.

Abstract. Aiming at the determination of the metabolic disorder prevalence as well as the effect of subclinical disorders on daily milk traits of Holsteins, over 1 million (1 962 831) test day records were analysed. The metabolic disorder (ketosis or acidosis) prevalence was indicated by the fat-to-protein (F ∕ P) ratio, while subclinical disorder was indicated by the F ∕ P ratio and cows' daily production. The effect of subclinical disorders on daily milk traits was tested by Scheffe's method (SAS/STAT). The highest ketosis prevalence occurred in early lactation. In first-parity cows ketosis prevalence was highest in the first 15 days of lactation, while in cows in higher lactations, the prevalence peak occurred on the 25th day. A higher ketosis prevalence during the entire lactation was observed in multiparous cows compared to first-parity cows. Regarding the acidosis prevalence, high values were determined at the beginning of the lactation with a decreasing trend until mid-lactation, when the prevalence increased up to 22 %. The highest decrease in daily milk yield as a consequence of subclinical ketosis was determined as 4.21 and 3.72 kg day−1 in first-parity cows and those with more than four lactations, respectively. A significant negative effect of subclinical acidosis on daily milk yield (2.79 kg day−1) was highest in cows in the third lactation. A production decline in subsequent milk controls due to subclinical ketosis or acidosis in all cows was also determined. Subclinical disorders also significantly alter daily milk quality. This indicates that the test day records could be used as a cost-effective and non-invasive method for monitoring herd health.

Phenotypic factors affecting coagulation properties of milk from Sarda ewes

Sub-clinical ketosis (SCK) significantly affects post-partum dairy cow performance and welfare. A total of 11,327,959 test-day (TD) records over two years on 1.76 million Holstein cow lactations and 2840 farms were processed to ascertain thresholds for milk acetone (mACE) and β-hydroxybutyrate (mBHB) as indicators of SCK on the basis of a significant milk yield loss at the TD. The set thresholds for mACE and mBHB were 0.10 mmol/L and 0.14 mmol/L, respectively. The prevalence of SCK in the population during the first 60 days in milk (DIM) was estimated based on herd size and milk yields, utilizing one or both of these metabolites surpassing their respective thresholds. Analyzing both mACE and mBHB together revealed a higher occurrence of SCK in small herds (fewer than 100 cows) and a lower occurrence in the two most productive milk categories. The prevalence had an inverse relationship with the daily milk yield at 60 DIM, indicating a surprisingly high frequency of low-productivity herds in the risk classes exceeding 30%. These results suggest that assessing SCK prevalence through the combined evaluation of mACE and mBHB is a more effective approach than using the milk fat to protein ratio, especially when considering different herd sizes and daily milk yield at 60 DIM.

Variables Affecting Ratio Factors for Estimating 305-Day Production from Part Lactations

Using the Kalman filter and dynamic models to assess the changing HIV/AIDS epidemic.

This study develops and illustrates a hybrid k-medoids, random forest, and support vector regression (K-R-S) approach for predicting the lactation curves of individual primiparous cows within a targeted environment using monthly milk production data from their dams and paternal siblings. The model simulation and evaluation were based on historical test-day (TD) milk production data from 2010 to 2016 for 260 Wisconsin dairy farms. Data from older paternal siblings and dams were used to create family units (n = 6,400) of individual calves, from which their future performance was predicted. Test-day milk yield (MY) records from 2010 to 2014 were used for model training, whereas monthly milk production records of Holstein calves born in 2014 were used for model evaluation. The K-R-S hybrid approach was used to generate MY predictions for 5 randomly selected batches of 320 primiparous cows, which were used to evaluate model performance at the individual cow level by cross-validation. Across all 5 batches, the mean absolute error and the root mean square error of the K-R-S predictions were lower (by 24.2 and 23.4%, respectively) than that of the mean daily MY of paternal siblings. The K-R-S predictions of TD MY were closer to actual values 74.2 ± 2.0% of the time, as compared with means of paternal siblings'. The correlation between actual TD MY and K-R-S predictions was greater (0.34 ± 0.01) than the correlation between the actual yield and the mean of paternal siblings (0.08 ± 0.01). The results of this study demonstrate the effectiveness of the K-R-S hybrid approach for predicting future first-lactation MY of dairy calves in management applications, such as milk production forecasting or decision-support simulation, using only monthly TD yields of within-herd relatives and in the absence of detailed genomic data.

This paper treats the problem of source dynamic motion evaluation in underwater applications using recursive prediction error estimation techniques. The issue of compensating for underwater motion effects arises in a number of areas of current interest such as control and operations of autonomous remotely operated vehicles, underwater seismic exploration, and buoy wave data analysis. Earlier treatments of the problem relied on frequency response methods and Kalman filtering. The present paper discusses the compensation problem using an alternative discrete model of the process and proposes use of the recursive prediction error algorithm for its solution. The algorithm is simpler than Kalman filtering in terms of the required knowledge of noise statistics and provides an attractive alternative to Kalman Filtering.© (1990) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Background: Various mathematical models have been tried by different researchers to fit the lactation curves in indigenous, exotic and crossbred cows maintained in organised farms only. Hence, this study was aimed to fit various lactation models and to identify the best fit model (s) for prediction of milk production in crossbred dairy cows in field conditions. Methods: Multistage random sampling procedure was used for selection of 100 dairy cows for the purpose of data collection. The monthly milk yield data (up to 10 months of lactation) of the selected crossbred cows reared in field conditions of Tamil Nadu State, relating to the period from 2017-20, were collected for lactation curve modelling. The milk data so collected were used to fit 14 lactation curve models in the study. The highest Coefficient of Determination (R²) value and the least Root Mean Square Error (RMSE) value were used as the criteria to select the best fit model(s). Result: The overall average total milk production was estimated to be 2223.19 kg per lactation in 10 months of lactation in the selected crossbred cows. Average daily milk yield at the first month of lactation was 9.16 kg and the highest daily milk yield of 9.56 kg was observed at the second month of lactation. At the end of the lactation, the lowest yield was 5.01 kg per day and hence the overall average daily milk yield was found to be 7.41 kg. Based on the selection criteria, the Mitscherlich X Exponential model was identified as the best fit model, followed by Ali and Schaeffer and Morant and Gnanasakthy model for prediction of milk production in crossbred dairy cows reared in field conditions and the estimated lactation curve model parameters a, b, c and d values for the best fit model were 331.71, 1519.82, 9.56 and 0.07 kg, respectively.

The objective of this study was to determine the effect of various milk recording methods (A4, AT4, A6, AT6) on prediction accuracy of 100, 200 and 305-day milk yield. The data used in this study were 11,430 individual test-day milk yield records collected from November 2004 to November 2006 on 813 cows reared on 15 family farms in Croatia. Milk recording was performed according to A4 and A6 milk recording method by the field officer of the Croatian Livestock Centre. From the corrected database with test-day records, two different datasets were created. The first dataset (A4; n = 7,500) included test day records collected every four weeks, while the second dataset (A6; n = 3,830) included test day records collected every six weeks. When lactation milk yields were predicted from alternative milk recording methods (AT4, AT6), daily (24 h) milk yield was estimated from single evening or morning milk yield using linear model that taken into account effect of interval between successive milkings. Lactation milk yield was calculated for three different days in milk (100, 200 and 305 days) using the Test Interval Method (TIM) that is reference method by ICAR (ICAR, 2003). Results show that the alternate milk recording method at 4-week intervals provides low bias and high accuracy of prediction of 100, 200 and 305-milk yields, while milk recording methods at 6-week intervals gives prediction of 305-milk yield with higher bias and lower accuracy.