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Feedlot Mass Balance and Greenhouse Gas Emissions - A Literature Review

Enhanced scrutiny of greenhouse gas (GHG) emissions from animal agriculture within Australia is increasing the pressure on livestock industries to validate estimates of GHG emissions. As a component of the whole red meat industry, the feedlot sector is affected by the emission factors detailed for livestock and manure management arising from lot-feeding beef cattle. The greenhouse gases methane (CH4) and nitrous oxide (N2O) are produced directly and indirectly from animal production and manure management, and are reported to have 25 and 298 times the greenhouse potential of carbon dioxide (IPCC 2006). Carbon dioxide (CO2) production is not estimated from animal production, since net CO2 emissions are assumed to be zero.

The Intergovernmental Panel on Climate Change (IPCC) outline a 3 tiered system for estimating GHG emissions from animal agriculture (Dong et al. 2006). The IPCC three-tiered system for GHG estimation are summarised below. Tier 1 Involves the simplistic use of IPCC default emission factors. Tier 2 Follows the same calculation equation, but uses country-specific data for some or all of the variables. For example, the use of emission factors developed for Australian feedlots. Tier 3 Is relevant where emissions are particularly important, and goes beyond industry defaults. This method of estimation requires a clearly described country-specific methodology, for example, a process-based mass balance approach (Dong et al. 2006).

In broad terms, the Department of Climate Change and Energy Efficiency (DCCEE) utilise the same methodology for the Tier 2 estimation as prescribed by the IPCC (Dong et al. 2006). The alternative for use of the prescribed DCCEE emission factors and estimates of N2O are the implementation of a Tier 3 style system for emission estimation (Dong et al. 2006). Methodology for construction of a Tier 3 system for emission estimation is not prescribed by IPCC, however guidelines for such a methodology are described (Dong et al. 2006).

Currently, the mass balance approach is the recommended method for development of country-specific (Tier 3) estimation procedures by the IPCC, particularly for N2O emissions (Dong et al. 2006). Prediction of manure production and nutrient composition is a critical component to estimating GHG production from livestock manure. Manure is composed of total solids (TS), which contain macro and micro nutrients, and water. Total solids fraction is composed of organic matter, measured as volatile solids (VS) and ash or fixed solids (FS).

Estimation of VS is of two fold importance for GHG estimation sourced from manure;

(i) the vast majority of N is within the VS fraction of manure, and

(ii) estimated methane emissions is the product of VS x the ultimate methane potential (Bo) x the methane conversion factor (MCF).

The Digestibility Approximation of Manure Production (DAMP) technique, was proposed by Barth (1985a) to predict the organic content of excreted manure using animal performance data. McGahan and Casey (1998) proposed a modified version of the DAMP model called the Dry Matter Digestibility Approximation of Manure Production (DMDAMP) to predict the amount of TS, VS and FS excreted by pigs. This method uses dry matter digestibility (DMD) instead of TDN values of individual ingredients to predict TS output. BEEFBAL is a Microsoft Excel? model that can be used to determine the waste characteristics from a feedlot (QPIF 2004). It estimates the TS, VS, FS, N, P, K and salt in the manure from a feedlot, where the cattle are fed a ration of known composition and intake.

The DMDAMP model (van Sliedregt et al. 2000), within BEEFBAL is used to calculate TS excreted and mass balance principles (Watts et al. 1994a), are used to determine the N, P, K, total salt and FS excreted. BEEFBAL was not developed as a total feedlot mass balance tool, rather for the prediction of (i) quantity and nutrient composition of manure produced, and (ii) land area required for application of manure produced. As such, gaps exist in BEEFBAL as a mass balance tool of component (solids and nutrients) flows within a feedlot. To achieve realistic values of manure composition from the current version, (BEEFBAL_v9.1_TI), professional judgement, and knowledge of the composition of manure and effluent is required. A theoretical mass balance has been developed using the FSA Consulting Feedlot Simulation Model (FSA2) to estimate nitrogen flows (NH3 and N2O) and CH4 emissions from feedlot manure management sources (i.e. feedpad, stockpiled/composted manure, and liquid storage systems). The BEEFBAL model and FSA2 enables the estimation of excreted VS and N. Nitrogen is then traced through the feedlot system with a series of back-calculated partitioning and emission estimates.

Volatile solids lost at each manure management stage are also estimated through back-calculation by inputting the VS to TS ratio of the manure at these manure stages. Availability of usable scientific data within the literature, with which to validate VS and N losses from the various manure management sources is limited. For some manure management types, there is an absence of data to validate N2O and CH4 emissions from Australian feedlot manure. For these circumstances, DCCEE estimated emission factors, and/or best judgement values were used.

The validation of a Tier 3 method requires country-specific emission factors to be validated by peer reviewed publications (Dong et al. 2006). It is therefore a recommendation of this review that further Australian studies are supported to measure GHG emissions (N2O and CH4) from feedlot manure (feedpad, stockpiled/composed manure, and liquid storage systems), to validate emission factors for use by Australian feedlots. Future studies should provide understanding into the relative influence of climatic, seasonal and management conditions; to inform the necessity for regional based emission factors.


The following estimates are made from the theoretical mass balance using FSA2:

Approximately 86% of N fed to feedlot cattle is excreted and 14% is retained in liveweight gain and lost to mortalities. About 0.5% of intake N onto the pen surface is lost to the pond (approximately 0.4 kg/hd/yr). Approximately 62% of intake N is volatilised from the feedpad to the atmosphere as ammonia, N2O and other N compounds. Total ammonia-N loss represents approximately 70% of intake N from the combined volatilisation of the feedpad, manure stockpile/compost, effluent and from application losses. Using an emission factor of 1% for N2O from DCCEE for indirect N2O from volatilised ammonia, this represents approximately 0.70% of intake N. Approximately 21 kg/hd/yr (23.6% of excreted N) is harvested from the pens in manure.

​Estimated emissions from each manure management source are represented as a percentage of estimated GHG in CO2 equivalents. Estimates provided indicate that N20 emissions from the feedpad, (both direct and indirect) and direct N20 emissions from the stockpile account for the three largest sources of GHG (in CO2 equivalents) from feedlot manure management. Greenhouse gas produced from the feedpad is the largest source of GHG (in CO2 equivalents) from feedlot manure, representing approximately 73% of total manure GHG. Conversely, GHG emissions from the pond are a small proportion of total GHG produced from feedlot manure, representing an estimated 1% of total manure GHG production (in CO2 equivalents). Greenhouse gasses sourced from solid manure storage are intermediate, with approximately 19% of total GHG (in CO2 equivalents) and manure/effluent application representing the remaining 7%. These estimates are derived from a theoretical mass balance, which limits their application. However, these estimates of GHG produced from feedlot manure are useful to assist in prioritising research efforts in the area of GHG from feedlot manure sources.

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Title Size Date published
3.2MB 09/11/2011

Contracts

Contract No. Title Start date End date Funding type
B.FLT.0361
Mass balance literature review.
07/06/2010 20/09/2010
Industry

This page was last updated on 05/07/2018

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