scholarly journals Comparison of models for describing the lactation curve of Awassi, Morkaraman and Tushin sheep

2010 ◽  
Vol 53 (4) ◽  
pp. 447-456
Author(s):  
O. C. Bilgin ◽  
N. Esenbuga ◽  
M. E. Davis
Keyword(s):  
Prediction Error ◽  
Empirical Models ◽  
Coefficient Of Determination ◽  
Peak Time ◽  
Mean Square ◽  
Lactation Curve ◽  
Breed Differences ◽  
Watson Statistic ◽  
Wood Model ◽  
Awassi Breed

Abstract. The aim of this study was to identify a suitable mathematical model for describing the lactation curve of Awassi, Morkaraman and Tushin sheep breeds and to determine breed differences. Data on milk yield of 182 Awassi, 47 Morkaraman and 74 Tushin ewes were used. Eight empirical models from the literature were used to fit the standard lactation curves. Among them the Wood model (WD) appeared the most appropriate according to mean square prediction error (MSPE), coefficient of determination (R2), Durbin-Watson statistic (DW), and its applicability to the data for all three breeds. There were statistically significant (P<0.05) differences among Awassi, Morkaraman and Tushin breeds in accordance with a, b and c parameters and peak yield. The Awassi breed had the highest peak yield and the Morkaraman and Tushin breeds had statistically similar lower peak yields. There were no significant differences among the parameters of the WD model except for peak yield and peak time in accordance with parities. Breed and parity interaction was significant (P<0.05) only for peak yield.

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.

Thin-Layer Drying Characteristics and Modeling of Paper Sludge

2012 ◽  
Vol 190-191 ◽  
pp. 575-580
Author(s):  
Han Min Xiao
Keyword(s):  
Goodness Of Fit ◽  
Effective Diffusivity ◽  
Empirical Models ◽  
Coefficient Of Determination ◽  
Paper Sludge ◽  
Mean Square ◽  
Statistical Parameters ◽  
Drying Characteristics ◽  
Thin Layer Drying ◽  
Drying Conditions

In this paper, the drying experiments of paper sludge were performed at different drying conditions. The drying kinetics and phenomena of paper sludge were investigated. The effective diffusivity and the activation energy of the paper sludge during drying had been evaluated. At the same time, seven empirical models were used to model the experimental data, such as Newton, Page, Modified Page, Henderson and Pabis, Logarithmic, Two term, Two Term exponential et al. Three statistical parameters (The coefficient of determination (R2), root mean square error (RMSE) and the residual sum of square (RSS) ) were used to evaluate goodness of fit of the tested models.

scholarly journals Comparison of the fit of automatic milking system and test-day records with the use of lactation curves

2020 ◽  
Vol 33 (3) ◽  
pp. 408-415
Author(s):  
B. Sitkowska ◽  
M. Kolenda ◽  
D. Piwczyński
Keyword(s):  
Bayesian Information Criterion ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Dairy Herds ◽  
Lactation Curve ◽  
Milk Performance ◽  
Automatic Milking System ◽  
Automatic Milking ◽  
Automatic Milking Systems ◽  
Wood Model

Objective: The aim of the paper was to compare the fit of data derived from daily automatic milking systems (AMS) and monthly test-day records with the use of lactation curves; data was analysed separately for primiparas and multiparas.Methods: The study was carried out on three Polish Holstein-Friesians (PHF) dairy herds. The farms were equipped with an automatic milking system which provided information on milking performance throughout lactation. Once a month cows were also subjected to test-day milkings (method A4). Most studies described in the literature are based on test-day data; therefore, we aimed to compare models based on both test-day and AMS data to determine which mathematical model (Wood or Wilmink) would be the better fit.Results: Results show that lactation curves constructed from data derived from the AMS were better adjusted to the actual milk yield (MY) data regardless of the lactation number and model. Also, we found that the Wilmink model may be a better fit for modelling the lactation curve of PHF cows milked by an AMS as it had the lowest values of Akaike information criterion, Bayesian information criterion, mean square error, the highest coefficient of determination values, and was more accurate in estimating MY than the Wood model. Although both models underestimated peak MY, mean, and total MY, the Wilmink model was closer to the real values.Conclusion: Models of lactation curves may have an economic impact and may be helpful in terms of herd management and decision-making as they assist in forecasting MY at any moment of lactation. Also, data obtained from modelling can help with monitoring milk performance of each cow, diet planning, as well as monitoring the health of the cow.

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.

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 Global Analysis of Atmospheric Transmissivity Using Cloud Cover, Aridity and Flux Network Datasets

2021 ◽  
Vol 13 (9) ◽  
pp. 1716
Author(s):  
Ankur Srivastava ◽  
Jose F. Rodriguez ◽  
Patricia M. Saco ◽  
Nikul Kumari ◽  
Omer Yetemen
Keyword(s):  
Cloud Cover ◽  
Climatic Factors ◽  
Global Analysis ◽  
Empirical Relationship ◽  
Aridity Index ◽  
Global Scale ◽  
Empirical Models ◽  
Coefficient Of Determination ◽  
Crop Modeling ◽  
Atmospheric Transmissivity

Atmospheric transmissivity (τ) is a critical factor in climatology, which affects surface energy balance, measured at a limited number of meteorological stations worldwide. With the limited availability of meteorological datasets in remote areas across different climatic regions, estimation of τ is becoming a challenging task for adequate hydrological, climatic, and crop modeling studies. The availability of solar radiation data is comparatively less accessible on a global scale than the temperature and precipitation datasets, which makes it necessary to develop methods to estimate τ. Most of the previous studies provided region specific datasets of τ, which usually provide local assessments. Hence, there is a necessity to give the empirical models for τ estimation on a global scale that can be easily assessed. This study presents the analysis of the τ relationship with varying geographic features and climatic factors like latitude, aridity index, cloud cover, precipitation, temperature, diurnal temperature range, and elevation. In addition to these factors, the applicability of these relationships was evaluated for different climate types. Thus, empirical models have been proposed for each climate type to estimate τ by using the most effective factors such as cloud cover and aridity index. The cloud cover is an important yet often overlooked factor that can be used to determine the global atmospheric transmissivity. The empirical relationship and statistical indicator provided the best performance in equatorial climates as the coefficient of determination (r2) was 0.88 relatively higher than the warm temperate (r2 = 0.74) and arid regions (r2 = 0.46). According to the results, it is believed that the analysis presented in this work is applicable for estimating the τ in different ecosystems across the globe.

scholarly journals Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 885
Author(s):  
Sergio Ghidini ◽  
Luca Maria Chiesa ◽  
Sara Panseri ◽  
Maria Olga Varrà ◽  
Adriana Ianieri ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Tuna Fish ◽  
Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

Modelling response of infiltration loss toward water table depth using RBFN, RNN, ANFIS techniques

2021 ◽  
Vol 25 (2) ◽  
pp. 227-234
Author(s):  
Sandeep Samantaray ◽  
Abinash Sahoo
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Water Table ◽  
Environmental Sustainability ◽  
Model Development ◽  
Water Table Depth ◽  
Water Levels ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Inference System

Accurate prediction of water table depth over long-term in arid agricultural areas are very much important for maintaining environmental sustainability. Because of intricate and diverse hydrogeological features, boundary conditions, and human activities researchers face enormous difficulties for predicting water table depth. A virtual study on forecast of water table depth using various neural networks is employed in this paper. Hybrid neural network approach like Adaptive Neuro Fuzzy Inference System (ANFIS), Recurrent Neural Network (RNN), Radial Basis Function Neural Network (RBFN) is employed here to appraisal water levels as a function of average temperature, precipitation, humidity, evapotranspiration and infiltration loss data. Coefficient of determination (R2), Root mean square error (RMSE), and Mean square error (MSE) are used to evaluate performance of model development. While ANFIS algorithm is used, Gbell function gives best value of performance for model development. Whole outcomes establish that, ANFIS accomplishes finest as related to RNN and RBFN for predicting water table depth in watershed.

scholarly journals IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1631
Author(s):  
Bruno Guilherme Martini ◽  
Gilson Augusto Helfer ◽  
Jorge Luis Victória Barbosa ◽  
Regina Célia Espinosa Modolo ◽  
Marcio Rosa da Silva ◽  
...  
Keyword(s):  
Technology Acceptance ◽  
Mean Square Error ◽  
Ease Of Use ◽  
Coefficient Of Determination ◽  
Square Root ◽  
Mean Square ◽  
Use Context ◽  
The Mean ◽  
Acceptance Model ◽  
The Internet Of Things

The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse. Recently, a series of studies have proposed the use of sensors to monitor production and/or the use of cameras to obtain information about cultivation, providing data, reminders, and alerts to farmers. This article proposes a computational model for indoor agriculture called IndoorPlant. The model uses the analysis of context histories to provide intelligent generic services, such as predicting productivity, indicating problems that cultivation may suffer, and giving suggestions for improvements in greenhouse parameters. IndoorPlant was tested in three scenarios of the daily life of farmers with hydroponic production data that were obtained during seven months of cultivation of radicchio, lettuce, and arugula. Finally, the article presents the results obtained through intelligent services that use context histories. The scenarios used services to recommend improvements in cultivation, profiles and, finally, prediction of the cultivation time of radicchio, lettuce, and arugula using the partial least squares (PLS) regression technique. The prediction results were relevant since the following values were obtained: 0.96 (R2, coefficient of determination), 1.06 (RMSEC, square root of the mean square error of calibration), and 1.94 (RMSECV, square root of the mean square error of cross validation) for radicchio; 0.95 (R2), 1.37 (RMSEC), and 3.31 (RMSECV) for lettuce; 0.93 (R2), 1.10 (RMSEC), and 1.89 (RMSECV) for arugula. Eight farmers with different functions on the farm filled out a survey based on the technology acceptance model (TAM). The results showed 92% acceptance regarding utility and 98% acceptance for ease of use.

scholarly journals Multi-State Load Demand Forecasting Using Hybridized Support Vector Regression Integrated with Optimal Design of Off-Grid Energy Systems—A Metaheuristic Approach

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1166
Author(s):  
Bashir Musa ◽  
Nasser Yimen ◽  
Sani Isah Abba ◽  
Humphrey Hugh Adun ◽  
Mustafa Dagbasi
Keyword(s):  
Support Vector Regression ◽  
Mean Square Error ◽  
Prediction Accuracy ◽  
Demand Forecasting ◽  
Load Forecasting ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Hybrid Renewable Energy System

The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this problem, this paper combines SVR with the emerging Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithms to form two hybrid SVR algorithms, SVR-HHO and SVR-PSO. Both the two proposed algorithms and traditional SVR were applied to load forecasting in four different states of Nigeria. The correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used as indicators to evaluate the prediction accuracy of the algorithms. The results reveal that there is an increase in performance for both SVR-HHO and SVR-PSO over traditional SVR. SVR-HHO has the highest R2 values of 0.9951, 0.8963, 0.9951, and 0.9313, the lowest MSE values of 0.0002, 0.0070, 0.0002, and 0.0080, and the lowest MAPE values of 0.1311, 0.1452, 0.0599, and 0.1817, respectively, for Kano, Abuja, Niger, and Lagos State. The results of SVR-HHO also prove more advantageous over SVR-PSO in all the states concerning load forecasting skills. This paper also designed a hybrid renewable energy system (HRES) that consists of solar photovoltaic (PV) panels, wind turbines, and batteries. As inputs, the system used solar radiation, temperature, wind speed, and the predicted load demands by SVR-HHO in all the states. The system was optimized by using the PSO algorithm to obtain the optimal configuration of the HRES that will satisfy all constraints at the minimum cost.

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