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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 葉仲基(Chung-Kee Yeh) | |
dc.contributor.author | Cheng-Hao Wang | en |
dc.contributor.author | 王政皓 | zh_TW |
dc.date.accessioned | 2021-07-11T14:40:10Z | - |
dc.date.available | 2022-02-21 | |
dc.date.copyright | 2017-02-21 | |
dc.date.issued | 2016 | |
dc.date.submitted | 2017-01-20 | |
dc.identifier.citation | 中央畜產會。2016。台灣地區進口飼料原料行情參考表。財團法人中央畜產會。網址:http://www.naif.org.tw/infoFeed.aspx?frontTitleMenuID=37。上網日期:2016-12-5。
王奕鈞。2005。神經網路應用於地籍坐標轉換之研究。碩士論文。台北: 國立政治大學地政研究所。 王斌永。2012。雞場管理決策支援系統。博士論文。台中: 國立中興大學動物科學研究所。 王斌永、阮喜文。2009。台灣蛋雞場經營管理知識庫之建立. 畜產研究 42(1):13-18。 王漢雯。2010。肉豬生長線上模擬模式。碩士論文。台中: 國立中興大學動物科學研究所。 台灣趨勢研究。2015。雞肉產業發展趨勢。TTR台灣趨勢研究報告。 安偉捷肉雞養殖公司。2014a。ROSS 308商品代肉雞生產性能指標。安偉捷肉雞養殖公司。 安偉捷肉雞養殖公司。2014b. ROSS 308 商品代肉雞飼養管理手冊。安偉捷肉雞養殖公司。 安偉捷肉雞養殖公司。2014c。ROSS 308 商品代肉雞營養標準。安偉捷肉雞養殖公司。 行政院農業委員會。2001。加入WTO家禽產業因應對策。農業政策。網址:http://www.coa.gov.tw/ws.php?id=952。上網日期:2015-10-4。 行政院農業委員會。2014。農產貿易統計要覽。行政院農業委員會統計室。 行政院農業委員會。2015a。農業統計年報(104年)。農業統計資料庫。 行政院農業委員會。2015b。農業指標。農業統計資料庫。網址:http://agrstat.coa.gov.tw/sdweb/public/indicator/Indicator.aspx。上網日期:2016-12-5。 行政院農業委員會。2015c。民國104年台灣主要畜禽產品生產成本與收益分析。農業統計資料庫。 行政院農業委員會。2015d。104農產貿易統益要覽。農業統計資料庫。 行政院農業委員會。2016。畜禽產品物價統計月報。行政院農業委員會統計室。 李志勇。2010。肉種雞經營管理專家系統的開發。碩士論文。台北: 國立臺灣大學生物產業機電工程學研究所。 阮喜文、胡見龍。1996。飼糧能量對白肉雞屠體性狀之影響。農林學報 45(4):49-62。 阮喜文、范文彬。1998。母豬更新決策支援系統。中畜會誌 27(2):199-215。 沈天富、王金和、李淵百、吳和光、吳憲郎、范揚廣、胡怡浩、許振忠、郭猛德、陳保基。2001。中國畜牧學會畜牧要覽家禽篇 (增修版)。中國畜牧學會。台北。 沈明來。2002。試驗設計學。九州圖書文物有限公司。 林興誠。2009。正確認知雞隻飼養期。中央畜產協會畜產報導,02。網址:http://www.naif.org.tw/skill/skillfulContent.aspx?param=pn%3D5&eventsID=4。上網日期:2015-10-4。 馬綱宏。2011。結合專家系統之肉種雞環控平台的開發。碩士論文。台北: 國立臺灣大學生物產業機電工程學研究所。 張岳錡。2015。以反應曲面法探討杏鮑菇之光環境生長模式。碩士論文。台北: 國立臺灣大學生物產業機電工程學研究所。 茂群峪畜牧網。2009。我國加入WTO肉雞產業白皮書。茂群峪有限公司。網址:http://www.miobuffer.com.tw/landpoultry/199807/01.htm。上網日期:2015-10-4。 梁定澎。2006。決策支援系統與企業智慧。智勝文化事業有限公司。 陳仙琪。2004。台灣肉雞產業價格決定過程之研究--以白色肉雞為例。碩士論文。 台北: 臺灣大學農業經濟學研究所. 陳保基。2001。肉雞飼養與管理。P251-280 IN:沈天富、王金和、李淵百、吳和光、吳憲郎、范揚廣、胡怡浩、許振忠、郭猛德、陳保基編輯之畜牧要覽家禽篇 (增修版)。中國畜牧學會。台北。 陳志峰。2014。2013台灣家禽統計手冊。財團法人獸醫畜產發展基金會. 陳育菘。2008。以 LED 可調控系統探討星辰花與龍膽組培苗之光環境。碩士論文。台北: 臺灣大學生物產業機電工程學研究所。 陳威廷。2014。母豬繁殖性能線上諮詢專家系統。碩士論文。台中: 國立中興大學動物科學研究所。 陳毓良、陳世銘、蔡錦銘、蔡兆胤、方信雄、劉森源、孫睿鴻。2011。應用反應曲面法探討柴油引擎添用生質柴油最適摻混比例之研究。農業機械學刊 20(2):45-58。 黃國盛。2016。家禽產業現況分析與新知。行政院農業委員會,農業統計資料查詢系統。 廖柏權。2012。豬場肉豬最適上市體重之線上模擬模式。碩士論文。台中: 國立中興大學動物科學研究所。 鄭宇帆、陳世銘、陳育崧、王慶茵、陳金男、陳俊吉。2010。應用反應曲面法於 龍膽組培苗光環境生長條件之探討。出自”2010年農機與生機論文發表會論文集”,984-988。屏東:中華農業機械學會。 謝廣文。2001。甘藍種苗栽培環境對成長品質之影響與生長模式。博士論文。台北: 國立臺灣大學生物產業機電工程學研究所。 蕭庭訓、阮喜文。1996。飼料能量與蛋白質含量對肉雞生長性能之影響。畜產系刊 25:70-78。 Aggrey, S. E.. 2002. Comparison of three nonlinear and spline regression models for describing chicken growth curves. Poultry Science 81(12):1782-1788. Ahmadi, H., 2009. Poultry growth modeling using neural networks and simulated data. The Journal of Applied Poultry Research 18(3):440-446. Ahmadi, H., and A. Golian. 2010. Growth analysis of chickens fed diets varying in the percentage of metabolizable energy provided by protein, fat, and carbohydrate through artificial neural network. Poultry Science 89(1):173-179. Akşit, M., S. Yalcin, S. Özkan, K. Metin, and D. Özdemir. 2006. Effects of temperature during rearing and crating on stress parameters and meat quality of broilers. Poultry Science 85(11):1867-1874. Behzadi, M. R. B., and A. A. Aslaminejad. 2010. A comparison of neural network and nonlinear regression predictions of sheep growth. Journal of animal and veterinary advances 9(16):2128-2131. Blahová, J., R. Dobšíková, E. Straková, and P. Suchý. 2007. Effect of low environmental temperature on performance and blood system in broiler chickens (Gallus domesticus). Acta Veterinaria Brno 76(8):17-23. Bracke, M., B. Spruijt, J. Metz, and W. Schouten. 2002. Decision support system for overall welfare assessment in pregnant sows A: Model structure and weighting procedure. Journal of Animal Science 80(7):1819-1834. Cerrate, S., and P. Waldroup. 2009. Maximum profit feed formulation of broilers: 1. Development of a feeding program model to predict profitability using non linear programming1. International Journal of Poultry Science 8(3):205-215. Cheng, T. K., M. L. Hamre, and C. N. Coon. 1997. Effect of environmental temperature, dietary protein, and energy levels on broiler performance. The Journal of Applied Poultry Research 6(1):1-17. Dobos, R. C., and W. J. Fulkerson. 2004. A database program to assist in the allocation of pasture and supplements to grazing dairy cows. Environmental Modelling & Software 19(6):581-589. Faridi, A., A. Golian, J. France, and A. Heravi Mousavi. 2013. Study of broiler chicken responses to dietary protein and lysine using neural network and response surface models. British Poultry Science 54(4):524-530. France, J., J. Dijkstra, and M. S. Dhanoa. 1996. Growth functions and their application in animal science. Annales de zootechnie 45:165-174. ini Behzadi, M. R. B., and A. A. Aslaminejad. 2010. A comparison of neural network and nonlinear regression predictions of sheep growth. Journal of animal and veterinary advances 9(16):2128-2131. Jackson, S., J. D. Summers, and S. Leeson. 1982. Effect of dietary protein and energy on broiler performance and production costs. Poultry Science 61(11):2232-2240. Jensen, A. L., P. S. Boll, I. Thysen, and B. K. Pathak. 2000. Pl@ nteInfo®—a web-based system for personalised decision support in crop management. Computers and Electronics in Agriculture 25(3):271-293. Kuhi, H. D., E. Kebreab, S. Lopez, and J. France. 2003. An evaluation of different growth functions for describing the profile of live weight with time (age) in meat and egg strains of chicken. Poultry Science 82(10):1536-1543. Lokhorst, C. 1996. Automatic weighing of individual laying hens in aviary housing systems. British Poultry Science 37(3):485-499. Lott, B., F. Reece, and J. McNaughton. 1982. An Automated Weighing Systemdc for Use in Poultry Research. Poultry Science 61(2):236-238. May, J. D., and B. D. Lott. 2001. Relating weight gain and feed: gain of male and female broilers to rearing temperature. Poultry Science 80(5):581-584. Méda, B., M. Quentin, P. Lescoat, M. Picard, I. Bouvarel, N. Sakomura, R. Gous, I. Kyriazakis, and L. Hauschild. 2014. 9 INAVI: A Practical Tool to Study the Influence of Nutritional and Environmental Factors on Broiler Performance. Nutritional Modelling for Pigs and Poultry:np. Morris, T. R., and D. M. Njuru. 1990. Protein requirement of fast‐and slow‐growing chicks. British Poultry Science 31(4):803-809. NRC. 1994. Nutrient requirements of poultry. National Research Council. National Academy Press Washington^ eUSA USA. Olanrewaju, H. A., J. L. Purswell, S. D. Collier, and S. L. Branton. 2010. Effect of ambient temperature and light intensity on physiological reactions of heavy broiler chickens. Poultry Science 89(12):2668-2677. Olanrewaju, H. A., J. P. Thaxton, W. A. Dozier III, J. Purswell, W. B. Roush, and S. L. Branton. 2006. A review of lighting programs for broiler production. International Journal of Poultry Science 5(4):301-308. Pla, L., C. Pomar, and J. Pomar. 2004. A sow herd decision support system based on an embedded Markov model. Computers and Electronics in Agriculture 45(1):51-69. Power, D. J., and S. Kaparthi. 2002. Building Web-based decision support systems. Studies in Informatics and Control 11(4):291-302. Power, D.J. 2007. A Brief History of Decision Support Systems. DSSResources.COM, World Wide Web, http://DSSResources.COM/history/dsshistory.html, version 4.0, March 10, 2007. Roush, W. B., W. A. Dozier III, and S. L. Branton. 2006. Comparison of Gompertz and neural network models of broiler growth. Poultry Science 85(4):794-797. Rozenboim, I., I. Biran, Y. Chaiseha, S. Yahav, A. Rosenstrauch, D. Sklan, and O. Halevy. 2004. The effect of a green and blue monochromatic light combination on broiler growth and development. Poultry Science 83(5):842-845. Sakomura, N., F. Longo, E. Oviedo-Rondon, C. Boa-Viagem, and A. Ferraudo. 2005. Modeling energy utilization and growth parameter description for broiler chickens. Poultry Science 84(9):1363-1369. Silva, E. P., , N. K. Sakomura, , S. M. Marcato, and R. Neme. 2015. Description of the Growth of Body Components of Broilers and Laying Pullets. Nutritional Modelling for Pigs and Poultry:250-258. Talpaz, H., J. De La Torre, P. Sharpe, and S. Hurwitz. 1986. Dynamic optimization model for feeding of broilers. Agricultural Systems 20(2):121-132. Tao, X., and H. Xin. 2003. Temperature-humidity-velocity index for market-size broilers. In 2003 ASAE Annual Meeting. American Society of Agricultural and Biological Engineers. van Milgen, J., A. Valancogne, S. Dubois, J.-Y. Dourmad, B. Sève, and J. Noblet. 2008. InraPorc: A model and decision support tool for the nutrition of growing pigs. Animal Feed Science and Technology 143(1):387-405. Wang, S. C. 2003. Artificial neural network. In Interdisciplinary Computing in Java Programming, 81-100. Springer. Wang, Z., and M. Zuidhof. 2004. Estimation of growth parameters using a nonlinear mixed Gompertz model. Poultry Science 83(6):847-852. Yahav, S., S. Goldfeld, I. Plavnik, and S. Hurwitz. 1995. Physiological responses of chickens and turkeys to relative humidity during exposure to high ambient temperature. Journal of Thermal Biology 20(3):245-253. Yahav, S., A. Straschnow, D. Luger, D. Shinder, J. Tanny, and S. Cohen. 2004. Ventilation, sensible heat loss, broiler energy, and water balance under harsh environmental conditions. Poultry Science 83(2):253-258. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/78031 | - |
dc.description.abstract | 白肉雞具有生長快速、飼料轉換率以及育成率佳的優勢,且其價格僅為有色肉雞之一半,於2015年產值達到187億元,為家禽產業中第三大之項目。隨著台灣加入WTO,並於2005年開放國外雞肉進口後,台灣的白肉雞產業受到進口低價雞肉的衝擊,導致整體利潤降低,而近年來大陸與東南亞地區的飼養技術逐漸提升更是一大威脅,如何在面對進口雞肉競爭時,降低飼養成本並提升飼養效率,是養雞業者應努力之方向。目前針對白肉雞所作的研究,多是針對其環境與營養條件之影響進行探討,然而許多養雞業者仍是依循經驗法則飼養,缺乏較明確的飼養管理模式。因此,本研究嘗試建立一套以網站為基礎的決策支援管理系統,其中包含生長模式以探討環境和營養對肉雞生長之影響,以方便養雞業者能夠取得較佳的飼養管理策略,使肉雞飼養的體重提升且成本降低,並於場內蒐集環境與生長資料,提供雞場監控之功能,以提升管理之效益。
本研究於合作之養雞場進行實驗,以計畫合作單位中興大學架設之無線環境感測網路以及本研究自行開發之肉雞自動秤重系統進行資料蒐集。秤重系統之驗證結果顯示,平均相對誤差MRE能夠維持在6% 以內,表示系統量測重量之準確度在可接受之範圍。另外,本研究透過蒐集肉雞生長模式之相關文獻資料,以反應曲面法建立生長模式,接著以實驗雞場蒐集之資料對模式進行修正與驗證。驗證結果顯示,修正後之雞隻增重模式之判定係數R2達到0.82,而相對標準驗證誤差RSEV為17.89%,顯示其具有不錯之預測能力。修正後之飼料轉換率模式的判定係數R2有顯著提升,但仍偏低為0.42,而相對標準驗證誤差RSEV則為21.14%,有待日後持續修正以進一步改善其預測能力。決策建議部分,針對溫度與飼料配方進行建議,由增重模式取得最佳飼養溫度以期達到最大增重,並由最低成本飼料配方運算方式取得最佳飼料配方,以期在滿足肉雞營養需求的情況下達到最低飼料成本。本研究之決策建議結果以飼養手冊之建議值驗證其合理性,在趨勢上有一致性。 最後,本研究整合上述功能,建置成系統網站以及網頁程式作為使用者及飼養經營者介面,使用者透過網路便能使用此系統,並取得溫度與飼料配方之管理建議,以更有系統的方法進行雞場管理。而網站平台之決策支援管理系統,具有可遠端使用、多使用者、跨平台、即時更新與方便維護之優點,對於未來持續開發或是應用上皆較為方便。 | zh_TW |
dc.description.abstract | 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. | en |
dc.description.provenance | Made available in DSpace on 2021-07-11T14:40:10Z (GMT). No. of bitstreams: 1 ntu-105-R03631013-1.pdf: 6066708 bytes, checksum: c80fe6302198f8df16ea839479fb15f7 (MD5) Previous issue date: 2016 | en |
dc.description.tableofcontents | 誌 謝 i
摘 要 ii Abstract iv 目 錄 vii 圖目錄 xi 表目錄 xv 第一章 前 言 1 1.1 前言 1 1.2 研究目的 2 第二章 文獻探討 3 2.1 肉雞產業發展現況 3 2.2 肉雞飼養管理方式 5 2.3 環境與飼料營養對肉雞生長之影響 7 2.3.1 環境溫度 7 2.3.2 環境濕度 7 2.3.3 風速 8 2.3.4 光照 9 2.3.5 代謝能 10 2.3.6 粗蛋白質 11 2.4 生長模式 12 2.4.1 經驗模式 12 2.4.2 類神經網路 13 2.4.3 反應曲面法 15 2.5 決策支援系統 17 2.5.1 決策支援系統的定義 17 2.5.2 決策支援系統之架構 17 2.5.3 網站形式之決策支援系統 18 2.5.4 決策支援系統在農牧業上之應用 20 第三章 材料與方法 23 3.1 無線環境感測網路 23 3.1.1 實驗環境 23 3.1.2 環境感測器與無線傳輸設備之架設 23 3.2 肉雞自動秤重系統 27 3.2.1 秤重系統平台設計 27 3.2.2 秤重系統架構 31 3.2.3 體重分析演算法 37 3.3 生長模式之建立 40 3.3.1 分析國內外肉雞生長模式 40 3.3.2 反應曲面法設計 41 3.3.3 生長模式修正方法 44 3.4 決策支援機制建立 46 3.4.1 飼養溫度調控方式 46 3.4.2 飼料配方建議 46 3.5 養雞決策支援資訊管理系統之整合 52 3.5.1 軟體與程式語言 52 3.5.2 系統架構 52 第四章 結果與討論 54 4.1 肉雞自動秤重系統 54 4.1.1 系統硬體建立與測試情形 54 4.1.2 重量校正實驗 57 4.1.3 秤重系統驗證實驗 62 4.2 生長模式 71 4.2.1 模式回歸分析 71 4.2.2 生長模式驗證與修正實驗 77 4.3 決策支援建議機制 83 4.3.1 最佳飼養溫度 83 4.3.2 飼料配方運算結果 85 4.4 養雞決策支援資訊管理系統 93 第五章 結論與建議 101 5.1 結論 101 5.2 建議事項 103 5.2.1 環境感測工作站改良 103 5.2.2 肉雞自動秤重系統改良 103 5.2.3 生長模式持續修正 104 參考文獻 105 附錄 112 附錄一 參考之生長模式 112 附錄二 模式資料之準備方式 115 附錄三 重量校正模式之驗證 117 附錄四 不同參數之體重分析結果 118 附錄五 實驗雞場資料蒐集方式 120 附錄六 肉雞屠體組成比例 122 附錄七 飼料原料之營養成份及單價 124 附錄八 最佳飼料配方之代謝能與粗蛋白質 126 | |
dc.language.iso | zh-TW | |
dc.title | 建立肉雞飼養管理之決策支援資訊系統 | zh_TW |
dc.title | Development of a Web-based Decision Support System for Broiler Management | en |
dc.type | Thesis | |
dc.date.schoolyear | 105-1 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 陳世銘(Suming Chen) | |
dc.contributor.oralexamcommittee | 連振昌,林美峰,謝廣文 | |
dc.subject.keyword | 白肉雞,自動秤重系統,生長模式,環境與營養管理,決策支援系統, | zh_TW |
dc.subject.keyword | Broiler,Automatic Weighing System,Growth Model,Environment and Nutrition Management,Decision Support System, | en |
dc.relation.page | 126 | |
dc.identifier.doi | 10.6342/NTU201700151 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2017-01-20 | |
dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
dc.contributor.author-dept | 生物產業機電工程學研究所 | zh_TW |
顯示於系所單位: | 生物機電工程學系 |
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