scholarly journals Nonlinear models for morphometric analysis in Bullfrog Tadpoles

2016 ◽  
Vol 17 (2) ◽  
pp. 280-290 ◽  
Author(s):  
Cleber Fernando Menegasso MANSANO ◽  
Marcelo Maia PEREIRA ◽  
Nelson José PERUZZI ◽  
Beatrice Ingrid MACENTE ◽  
Marta Verardino DE STÉFANI

SUMMARY Biometric relationships are important to illustrate the growth of animals. When adjusted using nonlinear models, these relationships can provide important information that contributes to the improvement of breeding techniques. In this study, morphometric data as a function of weight obtained in four experiments involving bullfrog tadpoles were adjusted using Gompertz, Logistic, Von Bertalanffy and Brody nonlinear models and the best-fit model was determined. After fitting the parameters to the different models in each experiment, the models were compared based on confidence intervals (α = 0.05). The following criteria were used for selection of the best model: biological interpretation, residual mean square, coefficient of determination, graphic analysis, and number of iterations. Standard and total length data as a function of tadpole weight converged in the four models. The Logistic and Gompertz models had no biological interpretation for some datasets. The Brody model provided the lowest residual mean square and number of iterations for the variables studied in all experiments. The Brody relative growth rate (K) was lower for total length when compared to standard length, indicating a greater initial growth in standard length. The Brody model was the best to describe the growth in standard and total length of bullfrog tadpoles as a function of weight.

2018 ◽  
Vol 44 (1) ◽  
pp. 6
Author(s):  
Michelli De Fátima Sieklicki ◽  
Victor Breno Pedrosa ◽  
Caroline Gomes Rocha ◽  
Raphael Patrick Moreira ◽  
Paula Roberta Falcão ◽  
...  

Background: The consumption of lamb meat is growing due to improved farming methods. However, to be economically feasible, the animal should stand out for its precocity, fast finishing and muscular force, such as seen in Texel breed. Besides, knowledge about weight gain and development can facilitate the selection of the best animals, and allow a better fitting to farming systems. Growth curves are an effective method that describes animal development, modeling the relationship between weight and age and help to predict the growth rate. Thus, this study aimed to analyze which nonlinear model, including Brody, Gompertz, Von Bertalanffy and Logistic best describe the growth curve of Texel sheep.Materials, Methods & Results: In this experiment, the lambs were kept in confined system while the ewes, in a semi-extensive system. This study followed 42 Texel male lambs, which were confined from birth to slaughter, and fed concentrated feed (3% of body weight) and corn silage (average 1.5 kg/animal/day), 4 times a day. The lambs were weighed fortnightly, in different classes considered as follows, weight at birth (BW), 15 days (P15), 30 days (P30), 45 days (P45), 60 days (P60), 75 days (P75), 90 days (P90), 105 days (P105), and 120 days (P120), which was defined as the slaughtering weight. The growth curves were determined using the nonlinear models of Brody, Von Bertalanffy, Gompertz and Logistic. The following parameters were used in the curves, Y, slaughtering weight; A, asymptotic weight; k, growth rate, t, animal age; B, constant related to the initial weight; and, m, constant of the curve shape. The criteria used for selecting the model that best described the curve were the mean square error (MSE), which was calculated by dividing the sum of squared error by the number of observations, and also the coefficient of determination (R²), calculated as the square of the correlation between the observed and estimated weights. The average weights observed were as follows, 4.02 kg at birth, 21.68 kg at weaning (P60) and 32.55 kg at slaughtering (P120). The solution of the nonlinear models allows, thru the parameters, establish specific feeding programs and define the optimal slaughtering age. Furthermore, the coefficients of determination, with values close to 97.3%, showed good fits for all models. Still, considering the mean square error, where the lower value indicates the best fit to the data evaluated, the results were 13.1564 (Brody), 13.3421 (Von Bertalanffy), 13.4876 (Gompertz) and 13.6717 (Logistic). The results showed that Brody could be considered the model that best describes the growth rate up to 120 days old of Texel lambs.Discussion: Compared to other studies, the average weights obtained in the experiment varied widely. This large variation can be explained by the used rearing system that might favor or not the performance of lambs. However, the average weaning weight obtained was similar to several studies in the literature, confirming the potential of Texel breed. This breed demonstrated to be capable to provide a precocious animal, with good growth results from the early developmental stage until the slaughtering age. Regarding the growth curves, the Brody model was the best fit for the estimated and observed weights. Moreover, the coefficient of determination indicated good fits for all models. However, an important aspect is the negative correlation between the A and k parameters, demonstrating that the higher the animal growth rate, the lower its asymptotic size.


2016 ◽  
Vol 37 (4Supl1) ◽  
pp. 2749 ◽  
Author(s):  
Raphael Patrick Moreira ◽  
Maria Eugênia Zerlotti Mercadante ◽  
Victor Breno Pedrosa ◽  
Joslaine Noely dos Santos Gonçalves Cyrillo ◽  
Wignez Henrique

The objective of the study was to analyze nonlinear models that best fit the growth of Caracu cows. The experiment was conducted at the Instituto de Zootecnia, Centro APTA Bovinos de Corte, Sertãozinho, SP. Data of weight at birth to 63 months of age, from 500 females of the Caracu breed were used. The mean weight at birth (BW), weaning weight (W7), weight at 26 months (W26) and weight at 63 months (W63) were, respectively, 32 kg, 198 kg, 354 kg and 488 kg, providing an average daily gain (ADG) of 0.241 kg/day. The nonlinear models used were: Brody, Von Bertalanffy, Logistic and Gompertz. All of the models tended to describe accordingly the growth curve of these animals, but, according to the mean square residual and coefficient of determination adopted to select the most appropriate model, Brody showed the best fit. All models presented a high and negative correlation between the A and k parameters, indicating that the most precocious animals are less likely to reach elevated weights at 63 months of age. The effect of year of birth significantly affected (P < 0.01) the parameters A and k, concluding that the animal selection based on growth traits favored the increase of mature weight and growth precocity over the generations.


Author(s):  
Marcos Speroni Ceron ◽  
Vladimir de Oliveira ◽  
Natielei Alexandre Nunes Novais Pieve ◽  
Nhayandra Christina Dias e Silva ◽  
Carlos Augusto Rigon Rossi ◽  
...  

Abstract: The objective of this work was to compare the performance of the nonlinear Gompertz, logistic, and von Bertalanffy equations, to describe the growth curve of immunocastrated male pigs from birth until reaching 140 kg body weight. Standard error, number of iterations, mean square of the error, and coefficient of determination were compared between the models. The logistic and von Bertalanffy equations do not accurately estimate the initial and slaughter weights. The Gompertz equation shows the highest reliability and is, therefore, the most suitable one to describe the growth curve of immunocastrated pigs from birth until 140 kg body weight is reached.


2022 ◽  
Vol 8 (3) ◽  
pp. 339-347
Author(s):  
Nelufa Aktar ◽  
Zoarder Faruque Ahmed ◽  
Mst Kaniz Fatema

The generalized length-length relationships of Chanda nama ((Hamilton, 1822) belongs to Ambassidae were studied separately for a period of a calendar year collected from the Old Brahmaputra River, Mymensingh, Bangladesh for male, female and combined populations. A total of 1170 specimens were examined where 599 were male and 571 were female. The standard length, fork length and total length of male ranged from 19 to 79 mm, from 23 to 90 mm, and 28 to 100 mm respectively. The standard length, fork length and total length of female were found from 15 to 81 mm, 24 to 89 mm, and from 29 to 100 mm respectively. The generalized relationships of standard length and fork length, fork length and total length, and standard length and total length of male, female and combined populations were FL = 1.08 SL+1.41, TL = 1.11 FL + 1.54 and TL = 1.20 SL+2.81; FL = 1.09 SL+1.23, TL = 1.12 FL+1.04 and TL = 1.22 SL+2.22; and FL = 1.09 SL+1.20, TL = 1.11 FL+1.27 and TL = 1.22 SL+2.38 respectively. The coefficient of determination (R2) revealed high values in all regression analyses. In length–length relationships, the coefficient of determination (R2) ranged from 0.973-0.990. The present findings of this study will be helpful for a well-organized and significant exploitation and regulation of the Chanda nama fishery in the Old Brahmaputra River and surrounding ecosystems. Res. Agric., Livest. Fish.8(3): 339-347, December 2021


Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 885
Author(s):  
Sergio Ghidini ◽  
Luca Maria Chiesa ◽  
Sara Panseri ◽  
Maria Olga Varrà ◽  
Adriana Ianieri ◽  
...  

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.


Author(s):  
Sandeep Samantaray ◽  
Abinash Sahoo

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.


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 ◽  
...  

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.


Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1166
Author(s):  
Bashir Musa ◽  
Nasser Yimen ◽  
Sani Isah Abba ◽  
Humphrey Hugh Adun ◽  
Mustafa Dagbasi

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.


1970 ◽  
Vol 6 (2) ◽  
pp. 349-353 ◽  
Author(s):  
M Begum ◽  
Abdullah Al-Mamun ◽  
ML Islam ◽  
MJ Alam

The morphometric observations were made on total length, standard length, pre-caudal length, head length, eye diameter and depth of body at pectoral fin base of estuarine catfish M. gulio. Males and females showed homogeneity in characters. A linear relationship was found between total length and morphometric characters. Regression of length and weight did not deviate significantly from cube law indicating isometric growth. The fish exhibited sexual dimorphism. Keywords: Morphometric characters; Estuarine catfish; Mystus gulio DOI: 10.3329/jbau.v6i2.4833 J. Bangladesh Agril. Univ. 6(2): 349-353, 2008


2017 ◽  
Vol 48 (1) ◽  
Author(s):  
Thais Destefani Ribeiro ◽  
Taciana Villela Savian ◽  
Tales Jesus Fernandes ◽  
Joel Augusto Muniz

ABSTRACT: The goal of this study was to elucidate the growth and development of the Asian pear fruit, on the grounds of length, diameter and fresh weight determined over time, using the non-linear Gompertz and Logistic models. The specifications of the models were assessed utilizing the R statistical software, via the least squares method and iterative Gauss-Newton process (DRAPER & SMITH, 2014). The residual standard deviation, adjusted coefficient of determination and the Akaike information criterion were used to compare the models. The residual correlations, observed in the data for length and diameter, were modeled using the second-order regression process to render the residuals independent. The logistic model was highly suitable in demonstrating the data, revealing the Asian pear fruit growth to be sigmoid in shape, showing remarkable development for three variables. It showed an average of up to 125 days for length and diameter and 140 days for fresh fruit weight, with values of 72mm length, 80mm diameter and 224g heavy fat.


Sign in / Sign up

Export Citation Format

Share Document