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Modeling Nutrient Requirements and M...

Kenney-Rambo, Nicole M.

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Modeling Nutrient Requirements and Measuring Nutrient Outputs of Feedlot Cattle.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modeling Nutrient Requirements and Measuring Nutrient Outputs of Feedlot Cattle./
Author:	Kenney-Rambo, Nicole M.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	115 p.
Notes:	Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
Contained By:	Dissertation Abstracts International79-08B(E).
Subject:	Animal sciences. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10641532
ISBN:	9780355769395

Modeling Nutrient Requirements and Measuring Nutrient Outputs of Feedlot Cattle.
Kenney-Rambo, Nicole M.

Modeling Nutrient Requirements and Measuring Nutrient Outputs of Feedlot Cattle. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 115 p.

Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.

Thesis (Ph.D.)--University of Minnesota, 2017.

Improvements in genetics, nutritional strategies, and management, combined with the use of growth promoting agents have resulted in heavier carcass weights. Animal nutrient requirements are dynamic and the ability to accurately predict the requirements of modern, heavy cattle based on historic data has not been explored. A dataset derived from 19 studies containing 289 means for treatments testing ractopamine during the final 14 to 42 days of finishing was subjected to a meta-analysis to develop a regression equation (Heavy Model; HM) describing energy requirements of heavy weight steers at the end of finishing. A validation dataset derived from 13 studies containing 41 means for treatments testing zilpaterol during the final 21 to 35 days of finishing was used to validate the regression equation generated by the HM and the Nutrient Requirements of Beef Cattle (NRC; 2016) energy equation in a group of heavy weight steers. The equation developed from the test dataset for the HM is RE = (68.82*EBG)-(3.68*EBG.

ISBN: 9780355769395Subjects--Topical Terms:

3174829
Animal sciences.

Modeling Nutrient Requirements and Measuring Nutrient Outputs of Feedlot Cattle.
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245 1 0 $a Modeling Nutrient Requirements and Measuring Nutrient Outputs of Feedlot Cattle.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 115 p.
500 $a Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
500 $a Adviser: Alfredo DiCostanzo.
502 $a Thesis (Ph.D.)--University of Minnesota, 2017.
520 $a Improvements in genetics, nutritional strategies, and management, combined with the use of growth promoting agents have resulted in heavier carcass weights. Animal nutrient requirements are dynamic and the ability to accurately predict the requirements of modern, heavy cattle based on historic data has not been explored. A dataset derived from 19 studies containing 289 means for treatments testing ractopamine during the final 14 to 42 days of finishing was subjected to a meta-analysis to develop a regression equation (Heavy Model; HM) describing energy requirements of heavy weight steers at the end of finishing. A validation dataset derived from 13 studies containing 41 means for treatments testing zilpaterol during the final 21 to 35 days of finishing was used to validate the regression equation generated by the HM and the Nutrient Requirements of Beef Cattle (NRC; 2016) energy equation in a group of heavy weight steers. The equation developed from the test dataset for the HM is RE = (68.82*EBG)-(3.68*EBG.
520 $a 2)*EQEBW0.75/1000.Predicted RE of cattle in the validation dataset did not differ from observed RE for the NRC (P = 0.65) or HM (P = 0.49). The NRC model explained 78% of the variation in RE, with a 1.11% over-prediction bias and the HM explained 88% of the variation in RE, with a 0.27% under-prediction bias. Compared with the NRC model, utilization of the HM to predict energy requirements in heavy weight steers at the end of finishing explains a greater portion of the variation in observed versus predicted RE with a lower bias. Volatility in fertilizer prices have generated interest in capturing value through manure in integrated crop and cattle feeding systems. The impact of facility design (Open lot, Pack, Stockpile, Pit, Lagoon), cattle type (Beef or Holstein), and dietary energy value (Grower or Finisher) on manure nutrient concentration was measured. Manure nutrients were expressed as kg nitrogen (N), phosphate (P2O5), or potash (K2O)/tonne of as-is solid material or as kg/3,785 L liquid material. Projected annual manure production values of 2.7 and 4.5 tonne/head and 9,463 L/head, respectively, for Open lot, Pack and Pit, were utilized to project effects of facility on yearly manure nutrient contributions per head space. Nutrient concentrations of liquid manure samples from Pit was greater (P < 0.05) than that from Lagoon across all nutrients. Nitrogen concentrations of liquid samples was greater (P < 0.05) for Holstein, and P2O5 tended to be greater (P 0.10); however, P2O5 concentrations were lower (P < 0.05) in Pack compared to Open lot and Stockpile. Concentrations of nutrients in solid manure samples were greater (P 0.10) nutrient concentrations of solid manure samples. Projected annual manure N yield per animal headspace was greatest for Pit, intermediate for Pack, and least for Open lot (P < 0.05). Projected manure P2O5 yield was greater (P < 0.05) for Pack and Pit facilities as compared to Open lot. These results confirm that greater manure nutrients are captured by indoor confinement feedlot facilities.
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710 2 $a University of Minnesota. $b Animal Sciences. $3 2093074
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793 $a English
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