Comparison of nine growth curve models to describe growth of partridges(<i>Alectoris chukar</i>)

Wen, Yaya; Liu, Ke; Liu, Honghua; Cao, Haiyue; Mao, Haoyu; Dong, Xin; Yin, Zhan

doi:10.1080/09712119.2019.1599900

Cited by 15 publications

(20 citation statements)

References 13 publications

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“…Body weight is one of the most significant traits in poultry production because it is associated with growth rate, feed conversion efficiency and occurrence of diseases in a flock (Amraei et al 2017). Poultry growth patterns have been predicted using different mathematical models (Darmani et al 2003;Sengul and Kiraz 2005;Roush and Branton 2006;Yaya et al 2019). Several factors both nongenetic (health, age, welfare, location and feed) and genetic (breed or strain) have been implicated in the huge variations in growth curves fitted to the mathematical models (Darmani et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Comparison of five mathematical models that describe growth in tropically adapted dual-purpose breeds of chicken

Akinsola

Sonaiya

Bamidele

et al. 2021

Journal of Applied Animal Research

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Mathematical models provide valuable information for livestock improvement programmes. In this study, we evaluated the ability of five mathematical models (3P and 4P Gompertz, 3P and 4P logistic and neural network) to predict the growth of six tropically adapted dual purpose (TADP) chicken breeds (Fulani, FUNAAB Alpha, Kuroiler, Noiler, Sasso and Shika-Brown) under on-station and on-farm in Nigeria. Data for body weight were collected every 14 days from 1939 birds reared on-station, and every 28 days from 58,639 birds reared on-farm. Parameters used to evaluate the growth models were the adjusted coefficient of determination (AdjR 2 ), Akaike's information criterion (AIC), Bayesian information criterion (BIC) and root mean square error (RMSE). The AdjR 2 for Gompertz 3P was higher than or equal to the AdjR 2 for logistics 3P, Gompertz 4P and logistics 4P but was equal to or lower than the AdjR 2 for the neural network (NN) for all TADP chickens raised on-station. Based on the goodness-of-fit criteria, Gompertz 3P had the best predictive values (AdjR 2 = 0.989-0.998) for TADP chickens raised on-station, while logistic 3P was the best-fit model for TADP chickens raised on-farm. In conclusion, non-linear models and NN models yielded a good fit with the age-weight data of TADP chickens on-station and on-farm.

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Section: Introductionmentioning

confidence: 99%

Comparison of five mathematical models that describe growth in tropically adapted dual-purpose breeds of chicken

Akinsola

Sonaiya

Bamidele

et al. 2021

Journal of Applied Animal Research

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“…Riazoshams et al (2019) discussed different robust non-linear regression models, and Satoh (2019) worked on the model selection procedures for various growth models having an equal number of parameters. Wen et al (2019) compared nine different growth models for describing the growth of partridges. Dharmaraja et al (2020) investigated an empirical analysis for crop yield production forecasting in India, while Lavanya et al (2020) explored a multiple linear regressions model for crop production prediction using the Adam optimizer and the Neural Network Mlraonn.…”

Section: Introductionmentioning

confidence: 99%

“…,Kaplan and Gurcan (2018),Nimase et al (2018),Riazoshams et al (2019),Satoh (2019),Wen et al (2019),Murugan et al (2020) andGuo et al (2021) worked on different growth models and showed the best model through the measures of model adequacy for different crops and for different areas of applications. Different growth models suggested by above authors are given below.…”

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confidence: 99%

Naive Regression Growth Models for Prediction of Peppermint Yield Production

Yadav

Sharma

Alade

et al. 2021

JRSS

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In this study, three novel regression models are introduced for estimating and forecasting peppermint yield production. Several indices of the goodness of fit are used to assess the quality of the suggested models. The proposed models for yield production are compared to current regression models that are well-known. Primary data from the Banki block of the Barabanki District of Uttar Pradesh State in India was used to validate the efficiency conditions for the suggested models to outperform the competition models. The empirical results suggest that the proposed models for estimating and predicting peppermint yield production are more efficient than competing estimators.

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“…The growth curve parameters determination, effectively, describe issues such as growth, livestock performance and optimum slaughter age, as well as preparing an appropriate feeding process and selection (Sariyel et al, 2017). As for partridges, these parameters facilitate the judgment and analysis the standards of feeding, management and epidemic prevention, and to compare and test the genetic quality of different genders (Wen et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

The Growth Curve of Body Weight in Mixed-sex Rock Partridges (Alectoris graeca) Kept in captivity

Putra

2020

Tavukçuluk Araştırma Dergisi

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Rock partridge (Alectoris graeca) can be domesticated for meat and egg production poultry. This study was carried out to obtain the growth curve model of body weight (BW) in mixed-sex Rockpartridges using non-linear regression of Gompertz (G), Logistic (L)and Richards (R) models. The computer program of CurceExpert 1.4. was used in this study to calculate the growth parameters of asymptotic weight (A), maximum growth rate (MGR), inflection of weight (IW), inflection of age (IA), coefficient of determination (R 2 ) and mean squared error (MSE). Study showed that all models have similar R 2 value but the lower MSE value was showed in R model. Hence, the R model can be used to predict the BW in mixed-sex rockpartridges from hatching to adult age accurately. In conclusion, the A, IW and IA values in rockpartridges with all models were 442.57 -513.25 g; 188.69-221.29 g and 5.48-6.69 weeks respectively.

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Comparison of nine growth curve models to describe growth of partridges(Alectoris chukar)

Cited by 15 publications

References 13 publications

Comparison of five mathematical models that describe growth in tropically adapted dual-purpose breeds of chicken

Comparison of five mathematical models that describe growth in tropically adapted dual-purpose breeds of chicken

Naive Regression Growth Models for Prediction of Peppermint Yield Production

The Growth Curve of Body Weight in Mixed-sex Rock Partridges (Alectoris graeca) Kept in captivity

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