Prediction of First Lactation 305-day Milk Yield Based on Bimonthly Test Day Milk Yield Records in Murrah Buffaloes

2020 ◽  
Author(s):  
Ekta Rana ◽  
Ashok Kumar Gupta ◽  
Avtar Singh ◽  
Anand Prakash Ruhil ◽  
Ravinder Malhotra ◽  
...  
Keyword(s):  
Milk Yield ◽  
Absolute Error ◽  
Ratio Method ◽  
Average Error ◽  
Interval Method ◽  
Machine Learning Methods ◽  
Murrah Buffaloes ◽  
Artificial Neural Network Ann ◽  
Error Percentage ◽  
Selection Of

The present study was conducted on 2100 first lactation bimonthly test day milk yield (BTDY) records of 350 Murrah buffaloes calved in between 1993 and 2017 at ICAR-NDRI, Karnal. A total of 6 BTDY records were taken from each animal at an interval of 60 days, from 6th day to 305th day of lactation. The prediction of First Lactation 305-Day Milk Yield (FL305DMY) was done by utilizing five conventional and machine learning methods viz., Centering Date Method (CDM), Test Interval Method (TIM), Ratio Method (RM), Multiple Linear Regression (MLR) and Artificial Neural Network (ANN). Error in prediction was estimated by absolute error, percentage absolute error, average error, percentage average error, Root Mean Square Error (RMSE) and percentage RMSE. MLR was found to be the best method with the least error in prediction (5.71% RMSE), followed by ANN (5.77% RMSE). The accuracy (R2) of MLR equation including all 6 BTDY records was 91.86%. The best MLR equation for an early prediction of FL305DMY included 3 BTDY records viz., BTDY-2 (65th day), BTDY-3 (125th day) and BTDY-4 (185th day) with 85.29% R2. The study compared the conventional and computational methods for prediction of first lactation milk yield which could be used for early selection of the animals.

Genetic Analysis of First Lactation Monthly Test Day Milk Yields, Peak Yield and 305 Days Milk Yield in Murrah Buffaloes

2020 ◽  
Author(s):  
Ekta Rana ◽  
Ashok Kumar Gupta ◽  
Avtar Singh ◽  
Atish Kumar Chakravarty ◽  
Saleem Yousuf ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Milk Yield ◽  
Genetic Parameters ◽  
Correlation Method ◽  
Phenotypic Correlation ◽  
Murrah Buffaloes ◽  
National Dairy Research Institute ◽  
Genetic And Phenotypic Correlations ◽  
Breed Improvement ◽  
Selection Of

Background: The estimates of genetic parameters are useful in determining the appropriate method of selection that could further be implemented in the breed improvement programmes. The present study was, therefore, conducted to estimate the genetic parameters (heritability, genetic and phenotypic correlations) for monthly test day (TD) milk yields, peak yield (PY) and first lactation 305 days milk yield (FL305DMY) in Murrah buffaloes.Methods: Paternal half-sib correlation method was carried out by least-squares maximum likelihood programme to estimate genetic parameters of first lactation 4,209 and 408 records of monthly test day milk yield and peak yield, respectively, of 408 Murrah buffaloes (sired by 62 bulls) calved in between 1993 and 2017 at ICAR-National Dairy Research Institute, Karnal.Result: Heritability of FL305DMY and peak yield was estimated as 0.35±0.17 and 0.33±0.16, respectively. Heritability estimates for mid-lactation monthly test day milk yields were found to be moderate. Genetic correlation of monthly test day milk yields with FL305DMY was positive and highly significant for TD-4 to TD-9 and TD-11. Peak yield showed high genetic and phenotypic association with FL305DMY. High genetic and phenotypic correlation among monthly test day milk yields, peak yield and FL305DMY suggested that TD-4 to TD-9 and TD-11 test day milk yields and peak yield could be used for the selection of elite animals.

MODERN CONDITION AND PROSPECTS OF DEVELOPMENT OF THE UKRAINIAN WHITEHEAD BREED

2016 ◽  
Vol 52 ◽  
pp. 194-202
Author(s):  
S. L. Voitenko ◽  
L. V. Vishnevsky
Keyword(s):  
Milk Yield ◽  
Milk Fat ◽  
Objective Assessment ◽  
Live Weight ◽  
Fat Content ◽  
Early Age ◽  
Average Value ◽  
The Future ◽  
Efficiency Of Selection ◽  
Selection Of

The article shows the state of Ukrainian Whiteheaded cattle, which includes distribution of cattle, the number of animals belonging to respective bloodlines, evaluation of young animals with live weight in the process of growing and milk production of cows during the first lactation. It reflects the historic development of the breed when it was colonism whiteheaded cattle, which turned into the original breed, undergone a significant expansion in livestock and increase of productivity, decreased in the number, was as basis for creation of Ukrainian Black-and-White dairy breed and now bred only in one breeding farm. Visual estimation of animal exterior showed good development of cows and calves and their belonging to the dairy type. In the vast majority the cows of the herd have a black suit, a white head with " glasses" around the eyes, white belly, udder, lower legs and brush of the tail. The youngsters aren’t consolidated by the exterior, and among them there are animals which are not typical for Ukrainian Whiteheaded breed. The young animals have some lag in live weight behind the breed standard [12] to 7 months’ age with exceeding of this trait in certain periods quite significantly in the future. It was established that selection of heifers on live weight will be effective at the early age (1-5 months), given the coefficient of variation of live weight – 22,63-30,21% and will not have a significant influence in the future. Milk yields of first-calf heifers vary considerably depending on the origin. The milk yield of first-calf heifers in the herd was 4238,5 kg on average, the heifers belonging to Mart 171 and Ozon 417 bloodlines had the best milk performance – 4483,1 and 4254,9 kg accordingly. The most aligned milk yield during the first lactation was in the cows belonging to Ozon 417 bloodline, the limits of the trait are 4128,5-4327,4 kg with the average value by the line 4254,9 kg. In contrast, the first-calf heifers of Ryezvyi 33 bloodline with average milk yield 4048,9 kg had limits of the trait 2199,3-4736,1 kg. Even greater range in cows’ milk yield during the first lactation R= 4939 kg (limits 1687 – 6626 kg) is characterized for the herd in general, it shows, on the one hand, the possibility of qualitative improvement of cows’ productivity due to selection on the investigated trait and lack of selection in the herd on the other hand. It was established that daughters of bull Chardash belonging to Ryezvyi 33 bloodline produced 4736,1 kg of milk for 305 days of the first lactation with fat content 3,6%, whereas Zlak’s descendants of the same line were characterized by the lowest milk yield for the first completed lactation – 2199,3 kg with fat content 3,7% and the average value by the line – 4048,9 kg of milk, fat content 3,6%. Similar variability of first-calf heifers’ milk yields, depending on the origin, is typical for other bloodlines of Ukrainian Whiteheaded breed. To increase milk productivity of Ukrainian Whiteheaded cows is recommended to repeat successful combinations of parental forms, and to preserve the breed – to carry out an objective assessment of animals by a range of traits, given the efficiency of selection of heifers on live weight at early age.

COMPARATIVE DESCRIPTION OF MILK PRODUCTIVITY OF COWS UKRAINIAN RED-AND-WHITE DAIRY CATTLE AND UKRAINIAN BLACK-AND-WHITE DAIRY CATTLE AND HOLSTEIN BREEDS IN SERF «OLEKSANDRIVSKE»

2016 ◽  
Vol 52 ◽  
pp. 6-12 ◽  
Author(s):  
M. V. Gladiy ◽  
G. S. Kovalenko ◽  
S. V. Priyma ◽  
G. A. Holyosa ◽  
A. V. Tuchyk ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Dairy Cows ◽  
Milk Yield ◽  
Fat Content ◽  
Holstein Cows ◽  
Milk Productivity ◽  
Black And White ◽  
The Third ◽  
Fat Yield ◽  
Selection Of

The main goal of dairy breeds selection should be improving breeding and productive qualities of animals under modern conditions. The majority of farms, using native breeds to produce milk, has created optimal conditions for keeping and feeding, selection and matching, growing of replacements etc. Further improvement of created native dairy breeds for economically useful traits occurs at total use of purebred Holstein bulls (semen) of foreign selection. In order to realistically assess milk productivity (milk yield, fat content in milk and fat yield) of Ukrainian Black-and-White and Red-and-White Dairy cows should be conducted a comparative analysis of Holstein cows under the same conditions of feeding and keeping. It was established that Ukrainian Red-and-White Dairy cows were characterized by the highest milk yields for 305 days of all lactations, taken into account, the among three investigated breeds. Their milk yield during the first lactation was 5933 kg of milk, during the second – 6393 kg, the third – 6391 kg and during higher lactation – 6650 kg. Ukrainian Black-and-White Dairy cows were second by milk yield (except for the second lactation), during the first lactation – 5932 kg of milk, the third – 6462 kg and higher – 6541 kg, and Holstein cows were third, during the first lactation – 5794 kg of milk, the second – 6381 kg, the third – 6335 kg and higher – 6469 kg. The fat content was almost the same and varied within 3.49-3.58% in milk of Ukrainian Red-and-White Dairy cattle, 3.50-3.60% in milk of Ukrainian Black-and-White Dairy cattle and 3.50-3.56% in Holsteins’ milk. The difference between the breeds was within 0.01-0.04%. All the investigated breeds had predominance in fat yield for three lactations over standards of these breeds: Ukrainian Red-and-White Dairy cows from 75.1 to 93.4 kg, Ukrainian Black-and-White Dairy cows – 75.1-89.0 kg respectively and Holstein cows – 41.9-60.2 kg. It was found different level of positive correlation between milk yield and fat yield in all the cases and high correlation (r = 0.604-0.921, P < 0.001) in five cases (41.7%) Negative correlation coefficients indicate that selection of animals to higher milk yield in the herd will decrease the second trait – fat content in milk. Positive and highly significant correlation between milk yield and fat yield indicates that selection of cows in the herd to higher milk yields will increase fat yield. It was revealed that bulls were among the factors impacted the milk productivity (milk yield, fat content, fat yield) of three investigated breeds. So, the force (η²x) of father’s impact on milk yield was15.4-47.9%, fat content – 22.0-43.4% and fat yield – 14.9-47.7% taking into account a lactation and a breed. The force of lines impact (η²x) was second; it was on milk yield 6.1-24.5%, fat content – 4.1-17.1 and fat yield – 5.8-23.5%. The force of breeds impact (η²x) was last; it was on milk yield 0.3-2.9%, fat content – 0.2-0.3% and fat yield – 0.6-2.7%. So, the comparative studies of milk productivity of Ukrainian Red-and-White and Black-and-White Dairy cattle with Holsteins indicate that under similar conditions of feeding and keeping, these native breeds can compete with Holstein cattle. The milk yield for 305 days of higher lactation was 6650 kg of milk in Ukrainian Red-and-White Dairy cows, 6541 kg in Ukrainian Black-and-White Dairy cows and 6469 kg in Holsteins. It was found the inverse correlation r = -0.025-0.316 between milk yield and fat content in milk in most cases. Selection and matching of animals in the herd should be carried out simultaneously on these traits. It was found positive repeatability of milk yields between the first and second, the third and higher lactations (rs = 0.036-0.741), indicating the reliability of forecasting increase in milk productivity during the next lactations in all herd. Bulls have the greatest impact (η²x) on milk productivity among the factors taken into account: milk yield – 15.4-47.9%, fat content in milk – 22.0-43.4% and fat yield – 14.9-47.7%.

СОВЕРШЕНСТВОВАНИЕ СТАД СКОТА СИММЕНТАЛЬСКОЙ ПОРОДЫ ПО МОЛОЧНОЙ И МЯСНОЙ ПРОДУКТИВНОСТИ

2020 ◽  
Author(s):  
N.V. SIVKIN ◽  
N.V. STREKOZOV ◽  
V.I. CHINAROV
Keyword(s):  
Milk Yield ◽  
Live Weight ◽  
Breeding Value ◽  
Early Age ◽  
Group I ◽  
Breeding Herd ◽  
Simmental Cattle ◽  
Meat Productivity ◽  
Selection Of ◽  
Meat Type

В симментальской породе предусматривается разведение скота, сбалансировано сочетающего молочную и мясную продуктивность. Однако в практике совершенствования племенных стад в подборах быков доминируют улучшатели удоя, что во многом предопределяет результаты селекции и продуктивный тип животных. Объектом нашего исследования стало стадо чистопородного симментальского скота в условиях стойловой системы беспривязного и привязного содержания коров. Для изучения эффективности использования быков-производителей разного племенного достоинства сформировали 2 опытные группы: I состояла из бычков, полученных от отцов с племенной ценностью (ПЦ) по удою 100 кг и более, а во II с ПЦ от 0 до 100 кг молока. Симментальские бычки, отобранные для контрольного убоя, достигали весовых кондиций 500 кг и более в 17,5 мес при среднесуточном приросте 911 г. При использовании на маточном поголовье быков-производителей с улучшающим эффектом по удою 100 кг и более, их сыновья (I группа), на фоне более высоких суточных приростов (на 30 г) и раннем возрасте достижения живой массы 500 кг (на 18 дней) имели массу и выход туши на 21,4 кг и 2,7 ниже, чем у бычков II группы. При формировании молочно-мясного типа быки-производители с умеренной племенной ценностью по удою обеспечивали получение потомства, сочетающего молочную и мясную продуктивность в экономически значимых пропорциях.The Simmental breed provides for the breeding of cattle that combines milk and meat productivity in a balanced proportion. However, in the practice of improving breeding herds, the selection of bulls is dominated by milk yield improvers, which largely determines the results of selection and the productive type of animals. The object of our research was a breeding herd of purebred Simmental cattle in variety feeding and housing practices. To study the effectiveness of using bulls-producers of different breeding values, 2 experimental groups were formed: I consisted of bulls received from fathers with a breeding value (BV) of milk yield 100 kg or more, and II with a BV from 0 to 100 kg of milk. Simmental bulls selected for control slaughter reached weight standards of 500 kg or more in 17.5 months with an average daily increase of 911 g. When used on breeding of bulls with an improving effect on the yield of 100 kg or more, their sons (group I), against the background of higher daily gains (30 g) and an early age of reaching a live weight of 500 kg (18 days), had a mass and carcass yield of 21.4 kg and 2.7 lower than that of group II bulls. When forming a dairy-meat type, producing bulls with a moderate breeding value for milk yield provided for the production of offspring that combined dairy and meat productivity in economically significant proportions.

Application of the turbidity ratio method in the spherical particle size determination in the range m ∈ and α ∈

1979 ◽  
Vol 44 (7) ◽  
pp. 2064-2078 ◽  
Author(s):  
Blahoslav Sedláček ◽  
Břetislav Verner ◽  
Miroslav Bárta ◽  
Karel Zimmermann
Keyword(s):  
Refractive Index ◽  
Spherical Particle ◽  
Ratio Method ◽  
Refractive Indices ◽  
Relative Refractive Index ◽  
Particle Size Determination ◽  
The Individual ◽  
The Given ◽  
Turbidity Ratio ◽  
Selection Of

Basic scattering functions were used in a novel calculation of the turbidity ratios for particles having the relative refractive index m = 1.001, 1.005 (0.005) 1.315 and the size α = 0.05 (0.05) 6.00 (0.10) 15.00 (0.50) 70.00 (1.00) 100, where α = πL/λ, L is the diameter of the spherical particle, λ = Λ/μ1 is the wavelength of light in a medium with the refractive index μ1 and Λ is the wavelength of light in vacuo. The data are tabulated for the wavelength λ = 546.1/μw = 409.357 nm, where μw is the refractive index of water. A procedure has been suggested how to extend the applicability of Tables to various refractive indices of the medium and to various turbidity ratios τa/τb obtained with the individual pairs of wavelengths λa and λb. The selection of these pairs is bound to the sequence condition λa = λ0χa and λb = λ0χb, in which b-a = δ = 1, 2, 3; a = -2, -1, 0, 1, 2, ..., b = a + δ = -1, 0, 1, 2, ...; λ0 = λa=0 = 326.675 nm; χ = 546.1 : 435.8 = 1.2531 is the quotient of the given sequence.

Artificial Neural Networks Used As a Rule Selector for Fuzzy Logic Controllers

1993 ◽  
Author(s):  
Shou-Heng Huang ◽  
Ron M. Nelson
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Absolute Error ◽  
Operating Conditions ◽  
Delta Rule ◽  
Rule Sets ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Set Up ◽  
And Control

Abstract A feedforward, three-layer, partially-connected artificial neural network (ANN) is proposed to be used as a rule selector for a rule-based fuzzy logic controller. This will allow the controller to adapt to various control modes and operating conditions for different plants. A principal advantage of an ANN over a look up table is that the ANN can make good estimates to fill in for missing data. The control modes, operating conditions, and control rule sets are encoded into binary numbers as the inputs and outputs for the ANN. The General Delta Rule is used in the backpropagation learning process to update the ANN weights. The proposed ANN has a simple topological structure and results in a simple analysis and relatively easy implementation. The average square error and the maximal absolute error are used to judge if the correct connections between neurons are set up. Computer simulations are used to demonstrate the effectiveness of this ANN as a rule selector.

Determination of heat stress zone for daily milk yield using carryover heat effect model in Murrah buffaloes

2021 ◽  
Vol 53 (5) ◽  
Author(s):  
Ramandeep Kaur Dhaliwal ◽  
Puneet Malhotra ◽  
Neeraj Kashyap ◽  
Shakti Kant Dash ◽  
Lakhvir Kaur Dhaliwal ◽  
...  
Keyword(s):  
Heat Stress ◽  
Milk Yield ◽  
Heat Effect ◽  
Murrah Buffaloes ◽  
Effect Model ◽  
Stress Zone ◽  
Daily Milk Yield ◽  
Daily Milk

Utilizing Data-Driven Models to Predict Brittleness in Tuscaloosa Marine Shale: A Machine Learning Approach

2021 ◽  
Author(s):  
Jamal Ahmadov
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Brittleness Index ◽  
Estimation Methods ◽  
Gradient Boosting ◽  
Average Error ◽  
Support Vector ◽  
Marine Shale ◽  
Effective Manner ◽  
Selection Of

Abstract The Tuscaloosa Marine Shale (TMS) formation is a clay- and liquid-rich emerging shale play across central Louisiana and southwest Mississippi with recoverable resources of 1.5 billion barrels of oil and 4.6 trillion cubic feet of gas. The formation poses numerous challenges due to its high average clay content (50 wt%) and rapidly changing mineralogy, making the selection of fracturing candidates a difficult task. While brittleness plays an important role in screening potential intervals for hydraulic fracturing, typical brittleness estimation methods require the use of geomechanical and mineralogical properties from costly laboratory tests. Machine Learning (ML) can be employed to generate synthetic brittleness logs and therefore, may serve as an inexpensive and fast alternative to the current techniques. In this paper, we propose the use of machine learning to predict the brittleness index of Tuscaloosa Marine Shale from conventional well logs. We trained ML models on a dataset containing conventional and brittleness index logs from 8 wells. The latter were estimated either from geomechanical logs or log-derived mineralogy. Moreover, to ensure mechanical data reliability, dynamic-to-static conversion ratios were applied to Young's modulus and Poisson's ratio. The predictor features included neutron porosity, density and compressional slowness logs to account for the petrophysical and mineralogical character of TMS. The brittleness index was predicted using algorithms such as Linear, Ridge and Lasso Regression, K-Nearest Neighbors, Support Vector Machine (SVM), Decision Tree, Random Forest, AdaBoost and Gradient Boosting. Models were shortlisted based on the Root Mean Square Error (RMSE) value and fine-tuned using the Grid Search method with a specific set of hyperparameters for each model. Overall, Gradient Boosting and Random Forest outperformed other algorithms and showed an average error reduction of 5 %, a normalized RMSE of 0.06 and a R-squared value of 0.89. The Gradient Boosting was chosen to evaluate the test set and successfully predicted the brittleness index with a normalized RMSE of 0.07 and R-squared value of 0.83. This paper presents the practical use of machine learning to evaluate brittleness in a cost and time effective manner and can further provide valuable insights into the optimization of completion in TMS. The proposed ML model can be used as a tool for initial screening of fracturing candidates and selection of fracturing intervals in other clay-rich and heterogeneous shale formations.

scholarly journals Evaluation of Mekong River commission operational flood forecasts, 2000–2012

2014 ◽  
Vol 18 (7) ◽  
pp. 2645-2656 ◽  
Author(s):  
T. C. Pagano
Keyword(s):  
Protective Action ◽  
Absolute Error ◽  
Probability Of Detection ◽  
Mekong River ◽  
Average Error ◽  
Wet Season ◽  
Phnom Penh ◽  
Mekong River Commission ◽  
Flood Level ◽  
Skill Scores

Abstract. This study created a 13-year historical archive of operational flood forecasts issued by the Regional Flood Management and Mitigation Center (RFMMC) of the Mekong River Commission. The RFMMC issues 1- to 5-day daily deterministic river height forecasts for 22 locations throughout the wet season (June–October). When these forecasts reach near flood level, government agencies and the public are encouraged to take protective action against damages. When measured by standard skill scores, the forecasts perform exceptionally well (e.g., 1 day-ahead Nash–Sutcliffe > 0.99) although much of this apparent skill is due to the strong seasonal cycle and the narrow natural range of variability at certain locations. Five-day forecasts upstream of Phnom Penh typically have 0.8 m error standard deviation, whereas below Phnom Penh the error is typically 0.3 m. The coefficients of persistence for 1-day forecasts are typically 0.4–0.8 and 5-day forecasts are typically 0.1–0.7. RFMMC uses a series of benchmarks to define a metric of percentage satisfactory forecasts. As the benchmarks were derived based on the average error, certain locations and lead times consistently appear less satisfactory than others. Instead, different benchmarks were proposed and derived based on the 70th percentile of absolute error over the 13-year period. There are no obvious trends in the percentage of satisfactory forecasts from 2002 to 2012, regardless of the benchmark chosen. Finally, when evaluated from a categorical "crossing above/not-crossing above flood level" perspective, the forecasts have a moderate probability of detection (48% at 1 day ahead, 31% at 5 days ahead) and false alarm rate (13% at 1 day ahead, 74% at 5 days ahead).

scholarly journals Design of Monitoring Tool Heartbeat Rate and Human Body Temperature Based on WEB

2018 ◽  
Vol 164 ◽  
pp. 01017 ◽  
Author(s):  
Jalinas ◽  
Wahyu Kusuma Raharja ◽  
Bobby Putra Emas Wijaya
Keyword(s):  
Heart Rate ◽  
Body Temperature ◽  
Human Body ◽  
Average Error ◽  
Web Pages ◽  
Test Results ◽  
Pulse Sensor ◽  
Measurement Results ◽  
Human Body Temperature ◽  
Error Percentage

The heart is one of the most important organs in the human body. One way to know heart health is to measure the number of heart beats per minute and body temperature also shows health, many heart rate and body temperature devices but can only be accessed offline. This research aims to design a heart rate detector and human body temperature that the measurement results can be accessed via web pages anywhere and anytime. This device can be used by many users by entering different ID numbers. The design consists of input blocks: pulse sensor, DS18B20 sensor and 3x4 keypad button. Process blocks: Arduino Mega 2560 Microcontroller, Ethernet Shield, router and USB modem. And output block: 16x2 LCD and mobile phone or PC to access web page. Based on the test results, this tool successfully measures the heart rate with an average error percentage of 2.702 % when compared with the oxymeter tool. On the measurement of body temperature get the result of the average error percentage of 2.18 %.

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