Prediction of the voluntary intake of grass silages by beef cattle 3. Precision of alternative prediction models

1990 ◽  
Vol 50 (3) ◽  
pp. 455-466 ◽  
Author(s):  
A. J. Rook ◽  
M. S. Dhanoa ◽  
M. Gill
Keyword(s):  
Beef Cattle ◽  
Least Squares ◽  
Test Data ◽  
Ridge Regression ◽  
Prediction Models ◽  
Agricultural Research ◽  
Poor Performance ◽  
Least Squares Regression ◽  
Agricultural Research Council ◽  
New Models

ABSTRACTThe precision of a number of new models for predicting silage intake by beef cattle was investigated with independent data using the mean-square prediction error and compared with two previously published models (Agricultural Research Council, 1980; Lewis, 1981). The new models generally performed well relative to the previous models.The new models included a number constructed using the technique of ridge regression which were shown to be consistently better predictors than the models obtained from the same estimation data by stepwise least-squares regression. Better prediction was also obtained by reducing the number of variables in the least-squares models below that required to maximize R2 in the estimation data. The poor performance of the least-squares models with the best R2 may be attributed to collinearity between the independent variates in the estimation data.Most of the models considered overpredicted relative to observed intakes. This may have been the result of differences in breed type and management of the animals between the test data and the estimation data used to construct the models, that is the use of the models with the test data involved a degree of extrapolation.It is concluded that ridge regression and deletion of variables offer a positive step forward in intake prediction compared with models based on maximizing R2 in the estimation data. However, further work is needed to clarify the effect of factors such as breed and rearing system on intake and to clarify the usefulness of various fibre measures in intake prediction. A number of new models are proposed which utilize a range of input variables thus allowing flexibility in their use in practical situations.

scholarly journals Prediction of the Leaf Primordia of Potato Tubers Using Sensor Fusion and Wavelength Selection

2019 ◽  
Vol 5 (1) ◽  
pp. 10 ◽  
Author(s):  
Ahmed Rady ◽  
Daniel Guyer ◽  
William Kirk ◽  
Irwin R Donis-González
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Prediction Models ◽  
Calibration Model ◽  
Data Sets ◽  
Least Squares Regression ◽  
Potato Tubers ◽  
Russet Norkotah ◽  
Systems Models

The sprouting of potato tubers during storage is a significant problem that suppresses obtaining high quality seeds or fried products. In this study, the potential of fusing data obtained from visible (VIS)/near-infrared (NIR) spectroscopic and hyperspectral imaging systems was investigated, to improve the prediction of primordial leaf count as a significant sign for tubers sprouting. Electronic and lab measurements were conducted on whole tubers of Frito Lay 1879 (FL1879) and Russet Norkotah (R.Norkotah) potato cultivars. The interval partial least squares (IPLS) technique was adopted to extract the most effective wavelengths for both systems. Linear regression was utilized using partial least squares regression (PLSR), and the best calibration model was chosen using four-fold cross-validation. Then the prediction models were obtained using separate test data sets. Prediction results were enhanced compared with those obtained from individual systems’ models. The values of the correlation coefficient (the ratio between performance to deviation, or r(RPD)) were 0.95(3.01) and 0.9s6(3.55) for FL1879 and R.Norkotah, respectively, which represented a feasible improvement by 6.7%(35.6%) and 24.7%(136.7%) for FL1879 and R.Norkotah, respectively. The proposed study shows the possibility of building a rapid, noninvasive, and accurate system or device that requires minimal or no sample preparation to track the sprouting activity of stored potato tubers.

Prediction of voluntary Intake of grass silage by growing cattle using ridge regression

1989 ◽  
Vol 1989 ◽  
pp. 68-68 ◽  
Author(s):  
A. J. Rook ◽  
M. Gill ◽  
M. S. Dhanoa
Keyword(s):  
Least Squares ◽  
Ridge Regression ◽  
Prediction Models ◽  
Ordinary Least Squares ◽  
Independent Data ◽  
College Of Agriculture ◽  
Grass Silage ◽  
Backward Elimination ◽  
Growing Cattle ◽  
Controlled Experimentation

Due to collinearity among the independent varlates, intake prediction models based on least squares multiple regression are likely to predict poorly with independent data. In addition, the regression coefficients are sensitive to small changes in the estimation data and tend not to reflect causal relationships expected from the results of controlled experimentation. Ridge regression (Hoerl and Kennard, 1970) allows the estimation of new coefficients for the independent variables which overcome these effects of collinearity. In order to assess the usefulness of the method for Intake prediction, ordinary least squares (OLS) models, obtained using backward elimination of variables, and ridge regression models were constructed from the same data and then tested with independent data.Estimation data consisted of results of experiments of IGAP, Hurley and Greenmount College of Agriculture in which growing cattle were individually fed grass silage ad-libitum with or without supplementary feeds. Two subsets of the estimation data were used. Subset A included 395 animals and 36 silages; subset B included 192 animals and 16 silages and was for Hurley data only.

An evaluation of prediction equations incorporated in a computer program to ration beef cattle

1988 ◽  
Vol 46 (2) ◽  
pp. 169-179 ◽  
Author(s):  
H. D. St C. Neal ◽  
M. Gill ◽  
J. France ◽  
A. Spedding ◽  
S. Marsden
Keyword(s):  
Beef Cattle ◽  
Computer Program ◽  
Agricultural Research ◽  
Live Weight ◽  
Metabolizable Energy ◽  
Mathematical Basis ◽  
Agricultural Research Council ◽  
The Mean ◽  
Protein Supply ◽  
On Farm

AbstractEquations for the prediction of forage dry-matter intake, metabolizable energy (ME), rumen degradable protein and undegraded protein, based on those in the current Agricultural Research Council system, were incorporated into a computer program designed to be used by livestock advisors for on-farm rationing of beef cattle. The predictions of silage intake and live-weight gain are compared with experimental data.Voluntary intake of grass silage was generally over-estimated by the program by proportionately at least 0·06, with a root mean square error of ±0·18 of the mean observed silage intake for the all-silage rations. The prediction of ME requirement for observed production had an error of +0·15 of average ME intake but the calculations of ME intake were themselves dependent on the predictions of the ME concentrations of the silages and supplements. Similarly the comparison of protein supply with requirement was highly dependent on the value assigned to N-degradability. However, the program can be used to assess how changes in the input values would affect ration formulation.The mathematical basis of the program is described in the Appendix.

scholarly journals Análisis de métodos de validación cruzada para la obtención robusta de parámetros biofísicos

2015 ◽  
pp. 55 ◽  
Author(s):  
Ll. Pérez-Planells ◽  
J. Delegido ◽  
J. P. Rivera-Caicedo ◽  
J. Verrelst
Keyword(s):  
Linear Regression ◽  
Gaussian Process ◽  
Least Squares ◽  
Partial Least Squares ◽  
Ridge Regression ◽  
Partial Least Squares Regression ◽  
Gaussian Process Regression ◽  
Least Squares Regression ◽  
Kernel Ridge Regression

<p class="Bodytext">Los métodos de regresión no paramétricos son una gran herramienta estadística para obtener parámetros biofísicos a partir de medidas realizadas mediante teledetección. Pero los resultados obtenidos se pueden ver afectados por los datos utilizados en la fase de entrenamiento del modelo. Para asegurarse de que los modelos son robustos, se hace uso de varias técnicas de validación cruzada. Estas técnicas permiten evaluar el modelo con subconjuntos de la base de datos de campo. Aquí, se evalúan dos tipos de validación cruzada en el desarrollo de modelos de regresión no paramétricos: hold-out y k-fold. Los métodos de regresión lineal seleccionados fueron: Linear Regression (LR) y Partial Least Squares Regression (PLSR). Y los métodos no lineales: Kernel Ridge Regression (KRR) y Gaussian Process Regression (GPR). Los resultados de la validación cruzada mostraron que LR ofrece los resultados más inestables, mientras KRR y GPR llevan a resultados más robustos. Este trabajo recomienda utilizar algoritmos de regresión no lineales (como KRR o GPR) combinando con la validación cruzada k-fold con un valor de k igual a 10 para hacer la estimación de una manera robusta.</p>

scholarly journals NIR-chemometric approaches for evaluating carbonization characteristics of hydrothermally carbonized lignin

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sung-Wook Hwang ◽  
Un Taek Hwang ◽  
Kyeyoung Jo ◽  
Taekyeong Lee ◽  
Jinseok Park ◽  
...  
Keyword(s):  
Least Squares ◽  
Infrared Spectra ◽  
Regression Models ◽  
Near Infrared ◽  
Prediction Models ◽  
Hydrothermal Carbonization ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
Near Infrared Spectra ◽  
Mean Square Errors

AbstractThe aim of this study is to establish prediction models for the non-destructive evaluation of the carbonization characteristics of lignin-derived hydrochars as a carbon material in real time. Hydrochars are produced via the hydrothermal carbonization of kraft lignins for 1–5 h in the temperature range of 175–250 °C, and as the reaction severity of hydrothermal carbonization increases, the hydrochar is converted to a more carbon-intensive structure. Principal component analysis using near-infrared spectra suggests that the spectral regions at 2132 and 2267 nm assigned to lignins and 1449 nm assigned to phenolic groups of lignins are informative bands that indicate the carbonization degree. Partial least squares regression models trained with near-infrared spectra accurately predicts the carbon content, oxygen/carbon, and hydrogen/carbon ratios with high coefficients of determination and low root mean square errors. The established models demonstrate better prediction than ordinary least squares regression models.

Application of Natural Frequencies for Prediction of Apple Texture Based on Partial Least Squares Regression

2017 ◽  
Vol 13 (10) ◽  
Author(s):  
Jumin Hou ◽  
Yonghai Sun ◽  
Fangyuan Chen ◽  
Lu Wang ◽  
Xue Bai ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Natural Frequencies ◽  
Prediction Models ◽  
Good Prediction ◽  
Least Squares Regression ◽  
Prediction Ability ◽  
Initial Modulus ◽  
Calibration Models

AbstractExperimental modal analysis was performed to identify natural frequencies to predict the texture of inhomogeneous tissues of apple (Malus domestinacv. ‘Golden Delicious’). Partial least squares calibration models based on natural frequencies with or without weight and density were created for predicting apple texture representing by yield gradient and initial modulus. The prediction models shown good prediction ability for texture of skin but impossible for flesh (all determination coefficients for skin models were more than 0.5 while for flesh models less than 0.5). A nondestructive and rapid method was provided to evaluate the fruit texture.

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