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Applying a mechanistic fermentation and digestion model for dairy cows with emission and nutrient cycling inventory and accounting methodology

Published online by Cambridge University Press:  30 June 2020

A. Bannink Open the ORCID record for A. Bannink [Opens in a new window] ,
R. L. G. Zom ,
K. C. Groenestein ,
J. Dijkstra  and
L. B. J. Sebek
A. Bannink*
Affiliation:
Wageningen Livestock Research, Wageningen University & Research, Wageningen6700 AH, The Netherlands
R. L. G. Zom
Affiliation:
Wageningen Livestock Research, Wageningen University & Research, Wageningen6700 AH, The Netherlands
K. C. Groenestein
Affiliation:
Wageningen Livestock Research, Wageningen University & Research, Wageningen6700 AH, The Netherlands
J. Dijkstra
Affiliation:
Animal Nutrition Group, Wageningen University & Research, Wageningen6700 AH, The Netherlands
L. B. J. Sebek
Affiliation:
Wageningen Livestock Research, Wageningen University & Research, Wageningen6700 AH, The Netherlands
*
†E-mail: [email protected]
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Abstract

In mitigating greenhouse gas (GHG) emissions and reducing the carbon footprint of dairy milk, the use of generic estimates in inventory and accounting methodology at farm level largely ignores variation of on-farm GHG emissions. The present study aimed to implement results of an extant dynamic, mechanistic Tier 3 model for enteric methane (CH4) (applied in Dutch national GHG inventory) in order to capture variation in enteric CH4 emission, and in faecal N and organic matter (OM) digestibility, ultimately required to predict manure CH4 and ammonia emission. Tier 3 model predictions were translated into calculation rules that could easily be implemented in an annual nutrient cycling assessment tool including GHG emissions, which is currently used by Dutch dairy farmers. Calculations focussed on (1) enteric CH4 emission, (2) apparent faecal OM digestibility and (3) apparent faecal N digestibility. Enteric CH4 was expressed in CH4 yield indicated with the term emission factor (EF; g CH4/kg DM) for individual dietary components and feedstuffs. Factors investigated to cover predicted variation in EF value included the level of feed intake, the type of roughage fed (proportions of grass silage and maize silage) and the quality of roughage fed. A minimum number of three classes of roughage type (i.e. 0. 40% and 80% maize silage in roughage DM) appeared necessary to obtain correspondence between interpolated EF values from EF lists and Tier 3 model predictions. A linear decline in EF value with 1% per kg increase in DM intake is adopted based on model simulations. The quality of roughage was represented by the effect of maturity of harvested grass or of the whole plant maize at cutting, based on a survey of modelling as well as experimental work. Also, predictions were assembled for apparent faecal OM digestibility which could be used in national inventory and in farm accounting. Apparent faecal N digestibility (as a major determinant of predicted urinary N excretion) was predicted, to support current Dutch national ammonia emission inventory and to correct the level of N digestibility in farm accounting. Compared to generic values or values retrieved from the Dutch feeding tables, predicted OM and N digestibility and enteric CH4 are better rooted in physiological principles and better reflect observed variation under experimental conditions. The present results apply for conditions with fairly intensive grassland management in temperate regions.

Keywords

enteric methanenitrogendigestibilityTier 3farm accounting
Type
Research Article
Information
animal , Volume 14 , Issue S2: 9th Workshop on Modelling Nutrient Digestion and Utilization in Farm Animals (MODNUT) , August 2020 , pp. s406 - s416
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of The Animal Consortium

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