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Development of a Web-based Decision Support System for Broiler Management
Broiler,Automatic Weighing System,Growth Model,Environment and Nutrition Management,Decision Support System,
|Publication Year :||2016|
The production of broiler is the third largest item in the poultry industry in Taiwan, with the advantages of fast growth, good conversion rate and high survival rate, which made the value of production achieve 187 billion in 2015. Since Taiwan joined WTO and opened for imported chicken in 2005, the broiler industry has suffered from the impact of low-price imported chicken meat, which caused the profit reduced. Besides, the improved broiler breeding techniques in China and Southeast Asia in recent years has also posted a great threat. As a result, how to further reduce the feeding cost and raise the breeding efficiency is an important issue for the broiler farmers. Most of the researches about broiler have been conducted to study the influence of the environmental and nutritional conditions during the growth. However, due to the lack of reliable growth models, many broiler farmers make the management decisions mainly based on their experience. Therefore, this research aims to develop a web-based decision support system (DSS) for broiler management, and established growth models to investigate the influence of the environmental and nutritional conditions on the growth. In addition, environmental and growth information are collected from the broiler farm to provide the farm monitoring function. This research will help the broiler farmers in obtaining better breeding strategies, to improve the yield, to reduce the cost of the broiler, and to enhance the breeding efficiency.
This research was conducted in a cooperated broiler farm, and data were collected by wireless environmental sensing network, provided by the project cooperative team National Chung Hsing University, and self-developed broiler automatic weighing system. The validation result of the automatic weighing system showed that the mean average error (MRE) was within 6%, which indicated that the accuracy of the system was acceptable.
For the growth model building, related studies were collected to prepare the data and the response surface method (RSM) was applied to build the growth models. The data collected from the broiler farm was used to calibrate and validate the built models. Regarding the weight gain (WG) model, validation result showed that the coefficient of determination (R2) of calibrated WG model could achieve 0.82 and the relative standard error of validation (RSEV) is 17.78%, which demonstrated the good prediction performance of WG model. Regarding the feed conversion rate (FCR) model, R2 could be significantly improved after calibration, but R2 was still low as 0.42, and RSEV is 21.14%, which meant that further calibration is required to improve the prediction performance.
The decision making of breeding strategies was focused on the management of temperature and feed formulation. The optimal breeding temperature could be obtained base on the WG model to achieve maximum gain, and the optimal feed formulation could be calculated by least cost feed formulation (LCFF) method so as to simultaneously meet the nutritional needs of broilers and reach the lowest feed cost.
In final, the accomplished functions mentioned above were integrated to develop a website of web-based DSS for broiler management, and webpage was served as the user interface for farmers to obtain management suggestions, with a view to manage the farm more systematically and efficiently. Besides, in the form of web-based DSS, the system possesses advantages of remote use, multi-users, cross-platform, instant update and easy maintenance, which is more convenient for development and application.
|Appears in Collections:||生物機電工程學系|
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