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Development of a dynamic energy-partitioning model for enteric methane emissions and milk production in goats using energy balance data from indirect calorimetry studies

Published online by Cambridge University Press:  24 June 2020

C. Fernández Open the ORCID record for C. Fernández [Opens in a new window] ,
I. Hernando ,
E. Moreno-Latorre  and
J. J. Loor Open the ORCID record for J. J. Loor [Opens in a new window]
C. Fernández*
Affiliation:
Departamento de Ciencia Animal, Edificio 7G, Camino de Vera s/n, Instituto de Ciencia y Tecnología Animal, Universitat Politecnica de Valencia, 46022Valencia, Spain
I. Hernando
Affiliation:
Campus Edetania, Facultad de Magisterio y Ciencias de la Educación, Sagrado Corazón, 5, Universidad Católica de Valencia, 46110 Godella, Valencia, Spain
E. Moreno-Latorre
Affiliation:
Campus Edetania, Facultad de Magisterio y Ciencias de la Educación, Sagrado Corazón, 5, Universidad Católica de Valencia, 46110 Godella, Valencia, Spain
J. J. Loor
Affiliation:
Department of Animal Sciences, Division of Nutritional Sciences, University of Illinois, 1207 West Gregory Drive, Urbana, IL61801, USA
*
†E-mail: [email protected]
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Abstract

The main objective of this study was to develop a dynamic energy balance model for dairy goats to describe and quantify energy partitioning between energy used for work (milk) and that lost to the environment. Increasing worldwide concerns regarding livestock contribution to global warming underscore the importance of improving energy efficiency utilization in dairy goats by reducing energy losses in feces, urine and methane (CH4). A dynamic model of CH4 emissions from experimental energy balance data in goats is proposed and parameterized (n = 48 individual animal observations). The model includes DM intake, NDF and lipid content of the diet as explanatory variables for CH4 emissions. An additional data set (n = 122 individual animals) from eight energy balance experiments was used to evaluate the model. The model adequately (root MS prediction error, RMSPE) represented energy in milk (E-milk; RMSPE = 5.6%), heat production (HP; RMSPE = 4.3%) and CH4 emissions (E-CH4; RMSPE = 11.9%). Residual analysis indicated that most of the prediction errors were due to unexplained variations with small mean and slope bias. Some mean bias was detected for HP (1.12%) and E-CH4 (1.27%) but was around zero for E-milk (0.14%). The slope bias was zero for HP (0.01%) and close to zero for E-milk (0.10%) and E-CH4 (0.22%). Random bias was >98% for E-CH4, HP and E-milk, indicating non-systematic errors and that mechanisms in the model are properly represented. As predicted energy increased, the model tended to underpredict E-CH4 and E-milk. The model is a first step toward a mechanistic description of nutrient use by goats and is useful as a research tool for investigating energy partitioning during lactation. The model described in this study could be used as a tool for making enteric CH4 emission inventories for goats.

Keywords

energy transferenvironmentmixed dietslactationgoats
Type
Research Article
Information
animal , Volume 14 , Issue S2: 9th Workshop on Modelling Nutrient Digestion and Utilization in Farm Animals (MODNUT) , August 2020 , pp. s382 - s395
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of The Animal Consortium

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