scholarly journals Evaluation of lactation models in pasture-based dairy ewes in Bosnia and Herzegovina

2020 ◽  
Vol 70 (4) ◽  
pp. 275-283
Author(s):  
Dragica Šalamon ◽  
Alen Džidić ◽  
Neven Antunac ◽  
Stanko Ivanković ◽  
Vinko Batinić
Keyword(s):  
Root Mean Square Error ◽  
Milk Production ◽  
Bosnia And Herzegovina ◽  
Milk Fat ◽  
Milk Composition ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Dairy Ewes ◽  
Lactation Curve ◽  
Curve Model

Milk of Kupres, Privor and Stolac dairy ewe breeds is used for the production of the fine cheese varieties. To the best of our knowledge there are no information about milk production and milk composition of these pasture-based dairy ewes. The aim was to determine the best lactation curve model in autochthonous pasture-based dairy ewes in Bosnia and Herzegovina. Milk production was recorded and milk composition sampled (milk fat and protein) during early, mid and late lactation in 129 Kupres, 141 Privor and 129 Stolac pramenka ewes. Four lactation models (Wilmink, Cubic, Ali-Shaeffer and Guo-Swalve) were compared and selected based on the lowest coefficient of determination and root mean square error. The Guo-Swalve model described all of the measured variables most successfully. Kupres pramenka dairy ewe was the highest producing breed with 139 kg of milk during 175 days of lactation (0.79 kg/d; between lactation day 50 to 225) and showed the standard lactation curve. Privor pramenka produced 118 kg of milk during 175 days of lactation (0.67 kg/d) and Stolac pramenka 101 kg of milk during 175 days of lactation (0.58 kg/d). Both showed atypical constantly decreasing shape of the lactation curve common in low producing dairy ewes. The prediction of milk yield and milk composition from the Guo-Swalve model could be used by the national breeding program for the Kupres, Privor and Stolac pramenka sheep breeds. Additional research during a more stable management conditions is recommended for Privor and Stolac pramenka.

scholarly journals Selection of models of lactation curves to use in milk production simulation systems

2010 ◽  
Vol 39 (4) ◽  
pp. 891-902 ◽  
Author(s):  
Daniel de Noronha Figueiredo Vieira da Cunha ◽  
José Carlos Pereira ◽  
Fabyano Fonseca e Silva ◽  
Oriel Fajardo de Campos ◽  
José Luis Braga ◽  
...  
Keyword(s):  
Milk Production ◽  
Goodness Of Fit ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Good Convergence ◽  
Lactation Curve ◽  
Production Group ◽  
Daily Milk Production ◽  
Simulation Systems

The objective of this study was to select models of lactation curves with a better adjustment to the observed data in models of milk production simulation systems. A data base on 6,459 recordings of daily milk production was used. These data were obtained from monthly and fortnightly controls of milk between 2004 and 2007, from 472 lactations of animals from ten different milking cow herd farms. Based on rolling averages of milk production (MP-L/day) per cow, the ten herd farms were divided into low (L < 15), medium (15 <M < 20) and high (H > 20). Data were also divided according to the lactation numbers in first, second, third or greater. Eight lactation curve models commonly used in literature were compared. The models were individually adjusted for each lactation. The goodness of fit used for comparison of those models was the coefficient of determination, mean square error, mean square prediction error and the Bayesian information criterion. The values for the goodness of fit obtained in each model were compared by using 95% probability confidence interval. Wilmink (1987) model showed a better adjustment for cows of the first lactation numbers, whereas the Wood (1967) model showed a better adjustment for cows of the third or greater lactations numbers for the low milk production groups. Wood model showed a better adjustment for all the lactation numbers for the medium milk production group. Dijkstra (1997) model showed a better adjustment for all lactation numbers for the high milk production group. Despite of being more recent, the model by Pollott (2000), mechanist based and with a higher number of parameters, showed a good convergence for the used data.

The Effect of Concentrate Type and Supplementary Lactic Acid or Soya-Oil on Milk Production in Dairy Cows Offered Restricted or Extensively Fermented Grass Silages

1994 ◽  
Vol 1994 ◽  
pp. 38-38
Author(s):  
J G Doherty ◽  
C S Mayne
Keyword(s):  
Dairy Cows ◽  
Milk Production ◽  
Milk Fat ◽  
Milk Composition ◽  
Fresh Weight ◽  
Single Strain ◽  
High Fibre ◽  
Soyabean Meal ◽  
High Starch ◽  
Aliphatic Carboxylic Acids

Several studies have shown increased silage dry-matter intake (SDMI) and improved milk fat concentrations in dairy cows offered restricted fermented grass silages compared to more extensively fermented silages. A recent study suggested that differences in silage intakeper serather than an alteration in rumen fermentation may be responsible for the changes in milk composition observed in the previous studies (Doherty and Mayne, 1993). The aim of the present study was to examine the effect of changes in concentrate composition on milk production parameters in dairy cows offered grass silages of contrasting fermentation type.Two direct cut grass silages were prepared using either an inoculant, containing a single strain ofLactobacillus plantarum, (Ecosyl, 3 1/t fresh weight, Zeneca Products Ltd) or a mixture of aliphatic carboxylic acids (Maxgrass, 6 1/t fresh weight, BP Chemicals Ltd). Two concentrates (high starch or high fibre) were formulated containing either: barley, 300; wheat, 355; and soyabean-meal, 270 g/kg (high starch) or unmolassed sugar-beet pulp, 555; citrus pulp, 100; and soyabean-meal, 270 g/kg (high fibre).

scholarly journals The effects of age and body weight on powerlifters: An analysis model of powerlifting performance based on machine learning

2019 ◽  
Vol 18 (3) ◽  
pp. 89-99
Author(s):  
Vinh Huy Chau ◽  
Anh Thu Vo ◽  
Ba Tuan Le
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Body Weight ◽  
Root Mean Square Error ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Analysis Model ◽  
Factors Affecting ◽  
Artificial Intelligence Algorithm ◽  
The Relationship

Abstract As a up and coming sport, powerlifting is gathering more and more attetion. Powerlifters vary in their strength levels and performances at different ages as well as differing in height and weight. Hence the questions arises on how to establish the relationship between age and weight. It is difficult to judge the performance of athletes by artificial expertise, as subjective factors affecting the performance of powerlifters often fail to achieve the desired results. In recent years, artificial intelligence has made groundbreaking strides. Therefore, using artificial intelligence to predict the performance of athletes is among one of many interesting topics in sports competitions. Based on the artificial intelligence algorithm, this research proposes an analysis model of powerlifters’ performance. The results show that the method proposed in this paper can predict the best performance of powerlifters. Coefficient of determination-R2=0.86 and root-mean-square error of prediction-RMSEP=20.98 demonstrate the effectiveness of our method.

scholarly journals Authentication of Patin Fish Oil (Pangasius micronemus) using FTIR Spectroscopy Combined with Chemometrics

2020 ◽  
pp. 23
Author(s):  
Anggita Rosiana Putri ◽  
Abdul Rohman ◽  
Sugeng Riyanto ◽  
Widiastuti Setyaningsih
Keyword(s):  
Root Mean Square Error ◽  
Fish Oil ◽  
Ftir Spectroscopy ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square

Authentication of Patin fish oil (MIP) is essential to prevent adulteration practice, to ensure quality, nutritional value, and product safety. The purpose of this study is to apply the FTIR spectroscopy combined with chemometrics for MIP authentication. The chemometrics method consists of principal component regression (PCR) and partial least square regression (PLSR). PCR and PLSR were used for multivariate calibration, while for grouping the samples using discriminant analysis (DA) method. In this study, corn oil (MJ) was used as an adulterate. Twenty-one mixed samples of MIP and MJ were prepared with the adulterate concentration range of 0-50%. The best authentication model was obtained using the PLSR technique using the first derivative of FTIR spectra at a wavelength of 650-3432 cm-1. The coefficient of determination (R2) for calibration and validation was obtained 0.9995 and 1.0000, respectively. The value of root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were 0.397 and 0.189. This study found that the DA method can group the samples with an accuracy of 99.92%.

Estimating Center of Mass Kinematics During Perturbed Human Standing Using Accelerometers

2021 ◽  
pp. 1-10
Author(s):  
Sandra K. Hnat ◽  
Musa L. Audu ◽  
Ronald J. Triolo ◽  
Roger D. Quinn
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Squared Error ◽  
Balance Control ◽  
Center Of Mass ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Cord Injury ◽  
Movement Type

Estimating center of mass (COM) through sensor measurements is done to maintain walking and standing stability with exoskeletons. The authors present a method for estimating COM kinematics through an artificial neural network, which was trained by minimizing the mean squared error between COM displacements measured by a gold-standard motion capture system and recorded acceleration signals from body-mounted accelerometers. A total of 5 able-bodied participants were destabilized during standing through: (1) unexpected perturbations caused by 4 linear actuators pulling on the waist and (2) volitionally moving weighted jars on a shelf. Each movement type was averaged across all participants. The algorithm’s performance was quantified by the root mean square error and coefficient of determination (R2) calculated from both the entire trial and during each perturbation type. Throughout the trials and movement types, the average coefficient of determination was 0.83, with 89% of the movements with R2 > .70, while the average root mean square error ranged between 7.3% and 22.0%, corresponding to 0.5- and 0.94-cm error in both the coronal and sagittal planes. COM can be estimated in real time for balance control of exoskeletons for individuals with a spinal cord injury, and the procedure can be generalized for other gait studies.

scholarly journals Effect of concentrate supplementation during pre-kidding on the productive and reproductive performance of goats raised on Guinea grass (Panicum maximum cv. Tobiatã) pasture

2016 ◽  
Vol 37 (3) ◽  
pp. 1489
Author(s):  
Raquel Ornelas Marques ◽  
Heraldo Cesar Gonçalves ◽  
Paulo Roberto De Lima Meirelles ◽  
Gil Ignacio Lara Cañizares ◽  
Giuliana Micai de Oliveira ◽  
...  
Keyword(s):  
Birth Weight ◽  
Milk Production ◽  
Milk Fat ◽  
Panicum Maximum ◽  
Dry Extract ◽  
Concentrate Supplementation ◽  
Stage Of Lactation ◽  
Lactation Curve ◽  
Guinea Grass ◽  
Boer Goats

Sixty goats (20 Alpine, 18 Anglo-Nubian, and 22 crossbred Boer), with average body weight 49.33 ± 1.41 kg, were raised on Panicum maximum cv. Tobiatã pasture with two different levels of concentrate supplementation, 300 (SL30) and 600 g kg-1 (SL60) of the daily requirements, and evaluated from the pre-mating season until an average of 110 days of lactation. Milk controls were performed every 14 days. The following milk production curve parameters were estimated: time to reach peak milk production (TP), peak milk production (PP) and milk production during the first 110 days of lactation (MP). The following milk components were determined: fat, protein, lactose, total solids (TS), defatted dry extract (DDE), urea nitrogen (UN) concentrations, and somatic cell count (SCC). Goat prolificacy and birth weight of the kids were also determined. Breed affected the lactation curve, with Alpine and Anglo- Nubian goats presenting higher TP, PP, and MP. Protein, TS, and DDE concentrations were also affected by breed, being higher for crossbred Boer goats. Milk fat, lactose concentrations, and the log of SCC were affected by the concentrate supplementation level, being higher for SL30, as well as by the breed, with crossbred Boer goats presenting higher fat concentrations and log of SCC, and crossbred Boer and Alpine goats presenting higher lactose concentrations. UN was affected by the stage of lactation. Prolificacy and birth weight were affected by breed and concentrate supplementation level, being higher for Anglo-Nubian and crossbred Boer goats with SL60. Kids from single births presented higher birth weights. The Anglo-Nubian breed presented good milk production and the best body condition, which might indicate the effectiveness of this production system, SL60 supplementation resulted in higher birth weight and prolificacy.

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.

scholarly journals Estimating Maximum Daily Precipitation in the Upper Vistula Basin, Poland

Atmosphere ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 43 ◽  
Author(s):  
Dariusz Młyński ◽  
Andrzej Wałęga ◽  
Andrea Petroselli ◽  
Flavia Tauro ◽  
Marta Cebulska
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Goodness Of Fit ◽  
Daily Precipitation ◽  
Probability Distributions ◽  
Random Variables ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Probability Of Exceedance

The aim of this study was to determine the best probability distributions for calculating the maximum annual daily precipitation with the specific probability of exceedance (Pmaxp%). The novelty of this study lies in using the peak-weighted root mean square error (PWRMSE), the root mean square error (RMSE), and the coefficient of determination (R2) for assessing the fit of empirical and theoretical distributions. The input data included maximum daily precipitation records collected in the years 1971–2014 at 51 rainfall stations from the Upper Vistula Basin, Southern Poland. The value of Pmaxp% was determined based on the following probability distributions of random variables: Pearson’s type III (PIII), Weibull’s (W), log-normal, generalized extreme value (GEV), and Gumbel’s (G). Our outcomes showed a lack of significant trends in the observation series of the investigated random variables for a majority of the rainfall stations in the Upper Vistula Basin. We found that the peak-weighted root mean square error (PWRMSE) method, a commonly used metric for quality assessment of rainfall-runoff models, is useful for identifying the statistical distributions of the best fit. In fact, our findings demonstrated the consistency of this approach with the RMSE goodness-of-fit metrics. We also identified the GEV distribution as recommended for calculating the maximum daily precipitation with the specific probability of exceedance in the catchments of the Upper Vistula Basin.

Lactation response of cows to different levels of ruminally inert conjugated linoleic acids under commercial conditions

2005 ◽  
Vol 85 (2) ◽  
pp. 231-242 ◽  
Author(s):  
Rachel Gervais ◽  
Richard Spratt ◽  
Martin Léonard ◽  
P. Yvan Chouinard
Keyword(s):  
Linoleic Acid ◽  
Dairy Cows ◽  
Conjugated Linoleic Acid ◽  
Milk Production ◽  
Milk Fat ◽  
Metabolic Profile ◽  
Milk Composition ◽  
Dietary Lipids ◽  
Calcium Salts ◽  
Lactating Cows

Dietary conjugated linoleic acid (CLA) supplements have been shown to reduce milk fat synthesis in dairy cows. A rumen-inert source of CLA is required for commercial feed applications. The conversion of dietary lipids to a calcium salt is considered as a method to counter the extensive hydrogenation of dietary lipids that occurs in the rumen. Our objective was to determine whether feeding calcium salts of CLA under commercial conditions would affect milk production, milk composition and blood metabolic profile. A total of 240 dairy cows from eight farms were blocked according to the calving date, and randomly assigned to four treatments providing CLA at 0, 8, 16 and 32 g d-1. Milk production was recorded and milk was sampled on day 0, 7, 14, 28 and 42 of the feeding period. Blood samples were taken on day 42 from early-lactating cows (< 157 d in milk) to determine the metabolic profile. Milk fat yield was decreased 11, 20 and 28%, and milk fat concentration was reduced 13, 22 and 28% (linear; P < 0.001) when cows received 8, 16 and 32 g d-1 of CLA, respectively. Milk yield, milk protein and blood metabolic parameters were not affected by experimental treatments. Calcium salts of CLA can be used as an effective tool to manage milk fat content on commercial dairy farms. Key words: Conjugated linoleic acid, milk fat, ruminally inert fat

scholarly journals Using Multi-Temporal MODIS NDVI Data to Monitor Tea Status and Forecast Yield: A Case Study at Tanuyen, Laichau, Vietnam

2020 ◽  
Vol 12 (11) ◽  
pp. 1814
Author(s):  
Phamchimai Phan ◽  
Nengcheng Chen ◽  
Lei Xu ◽  
Zeqiang Chen
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Vegetation Index ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Multi Temporal

Tea is a cash crop that improves the quality of life for people in the Tanuyen District of Laichau Province, Vietnam. Tea yield, however, has stagnated in recent years, due to changes in temperature, precipitation, the age of the tea bushes, and diseases. Developing an approach for monitoring tea bushes by remote sensing and Geographic Information Systems (GIS) might be a way to alleviate this problem. Using multi-temporal remote sensing data, the paper details an investigation of the changes in tea health and yield forecasting through the normalized difference vegetation index (NDVI). In this study, we used NDVI as a support tool to demonstrate the temporal and spatial changes in NDVI through the extract tea NDVI value and calculate the mean NDVI value. The results of the study showed that the minimum NDVI value was 0.42 during January 2013 and February 2015 and 2016. The maximum NDVI value was in August 2015 and June 2017. We indicate that the linear relationship between NDVI value and mean temperature was strong with R 2 = 0.79 Our results confirm that the combination of meteorological data and NDVI data can achieve a high performance of yield prediction. Three models to predict tea yield were conducted: support vector machine (SVM), random forest (RF), and the traditional linear regression model (TLRM). For period 2009 to 2018, the prediction tea yield by the RF model was the best with a R 2 = 0.73 , by SVM it was 0.66, and 0.57 with the TLRM. Three evaluation indicators were used to consider accuracy: the coefficient of determination ( R 2 ), root-mean-square error (RMSE), and percentage error of tea yield (PETY). The highest accuracy for the three models was in 2015 with a R 2 ≥ 0.87, RMSE < 50 kg/ha, and PETY less 3% error. In the other years, the prediction accuracy was higher in the SVM and RF models. Meanwhile, the RF algorithm was better than PETY (≤10%) and the root mean square error for this algorithm was significantly less (≤80 kg/ha). RMSE and PETY showed relatively good values in the TLRM model with a RMSE from 80 to 100 kg/ha and a PETY from 8 to 15%.

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