Analysis of laying performance and influencing factors of laying hens based on VAR model

doi:10.3969/j.issn.1004-1524.2022.04.19

Acta Agriculturae Zhejiangensis ›› 2022, Vol. 34 ›› Issue (4): 824-830.DOI: 10.3969/j.issn.1004-1524.2022.04.19

• Biosystems Engineening • Previous Articles Next Articles

Analysis of laying performance and influencing factors of laying hens based on VAR model

JI Xunsheng¹(), HUANG Qilin¹^,^*(), XIA Shengkui²

1. College of Internet of Things Engineering, Jiangnan University, Wuxi 214122, Jiangsu, China
2. Nantong Tiancheng Modern Agricultural Technology Co., Ltd., Nantong 226000, Jiangsu, China

Received:2020-11-02 Online:2022-04-25 Published:2022-04-28
Contact: HUANG Qilin

Abstract

Abstract:

The laying performance indicators of laying hens include laying rate and average egg weight of qualified eggs. The laying performance of laying hens is affected by many factors such as the individual laying hens, feeding management, breeding environment, disease and other factors. It is very important to study the influence of various factors on laying performance and its time delay effect for laying hen breeding. A vector auto-regressive (VAR) model was established with laying hens’ feeding, drinking water, environmental temperature and humidity as the main influencing factors to realize the medium and short-term prediction of egg production rate. The prediction accuracy rate was 98.6%. Based on this model, the analysis showed that the environmental temperature had the greatest impact on the laying rate of laying hens and the impact lasted for 3-4 days and reached the peak on the first day. The food intake had the greatest impact on the average egg weight and the impact lasted for 4-5 days, also peaking on the first day.

Key words: laying performance, VAR model, impulse response, variance decomposition

CLC Number:

S831

JI Xunsheng, HUANG Qilin, XIA Shengkui. Analysis of laying performance and influencing factors of laying hens based on VAR model[J]. Acta Agriculturae Zhejiangensis, 2022, 34(4): 824-830.

Figures/Tables 8

Fig.1 Laying rate, average daily feed intake and average daily water intake for the complete laying cycle a, Laying rate; b, Average daily feed intake; c, Average daily water intake.

Fig.2 Indoor maximum temperature and humidity changes from 2019-07-16 to 2019-12-16 a, Indoor maximum temperature; b, Indoor maximum humidity.

Fig.3 Flow chart of laying rate prediction

Table 1 The prediction results of the VAR model with different lag orders

Lag order	AIC	FPEC	HQC	MAPE/%	MSE
0	-19.57	3.174×10^-9	-19.51	1.749	2.417
1	-20.14	1.797×10^-9	-19.80	1.680	2.322
2	-20.43^*	1.345×10^-9*	-19.81^*	1.376^*	1.825^*
3	-20.41	1.404×10^-9	-19.49	1.990	2.460
4	-20.39	1.469×10^-9	-19.19	1.696	2.294

Fig.4 Prediction of laying rate of laying hens based on VAR model

Table 2 Forecast results of different models

模型Models	精度Precision/%	MAPE/%	MSE
ARIMA	97.8	2.17	0.060
BP Neural Networks	98.5	1.46	0.022
SVM	97.9	2.06	0.058
LSTM	98.6	1.32	0.016
VAR	98.6	1.37	0.018

Fig.5 Impulse response results a, Impulse response results of laying rate; b, Impulse response results of egg weight.

Fig.6 Variance decomposition results a, Variance decomposition results of laying rate; b, Variance decomposition results of egg weight.

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Analysis of laying performance and influencing factors of laying hens based on VAR model

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