Exploring systems responses to mitigation of GHG in UK dairy farms

Abstract

A variety of options exist for mitigation of greenhouse gas (GHG) emissions from agriculture.

This paper discusses the use of the SIMS_DAIRY modelling framework to analyse the whole-systems response to different methods, acting singly or in combination, to mitigate GHG emissions from a typical dairy farm in the UK. These methods comprise farm management changes, new technologies and/or plant and/or animal new functional traits focused on mitigating GHG. Different functional units were used in order to ensure that the different aspects of the farm performance were analyzed in the most relevant way (e.g. concentration of NO₃⁻ in leachate or kg GHG emitted per unit of milk).

Examples of the methods that were tested and discussed are some relating fertilization, diet, systematic and genetic changes in the system.

We conclude that the effectiveness of the combined GHG mitigation methods cannot be assessed simply by adding the effectiveness of each method applied singly. Effective GHG mitigation methods may in some cases increase emissions of other forms of pollution and have very different impact on farm profitability. This study also evidences the importance of selecting the right unit of reference to measure efficacy to reduce GHG emissions and highlights the importance to integrate this type of study with socio-economic studies in order to assess the full potential of these mitigation methods.

Introduction

Climate change is a major concern for citizens and policy-makers across Europe, and agriculture can both contribute to global warming and climate change through emissions of greenhouse gases (GHG), a well as offer the opportunity to ‘lock-up’ carbon (C) via C storage in soils and biomass. Agriculture worldwide was responsible for ca. 6.1 Gt carbon dioxide (CO₂)-eq/year in 2005 (10–12% of total global anthropogenic emissions of GHG), of which 2.8 Gt CO₂-eq/year was nitrous oxide (N₂O) and 3.3 Gt CO₂-eq/year was methane (CH₄) (Smith et al., 2007). Agriculture has emitted about 60% of N₂O and about 50% of CH₄ (Smith et al., 2007). The principle direct sources of N₂O emissions are N fertilizer and manure applications to soil, dung and urine voided by grazing livestock and crop residues. Indirect sources are via nitrate leaching and N deposition. The main source of CH₄ emissions, in a European context, is dominated by enteric fermentation by dairy cows, beef cattle and sheep.

Within the European agriculture sector, dairy farming is a major source of global warming potential through emissions of N₂O from fertilizer (mineral and manure) applications to soil and CH₄ from ruminant livestock. Many countries consider agriculture to be CO₂-neutral in respect of global warming potential, but grassland-based farming has a substantial capacity for C storage, where soil cultivation is limited (Scholefield et al., 2005). A planned reduction in global warming potential is sought in the UK and throughout the world, through the mitigation of GHG emissions from all industry sectors, including agriculture (IPCC, 2007). Consequently, it is important to identify suitable effective methods for mitigation of GHG emissions from dairy farms, which do not have undue negative consequences for other ecosystem services, such as emissions of other environmental pollutants (to air or water), biodiversity or the economic viability of the farm.

Nitrous oxide emissions from soils can be reduced by implementing practices aimed at enhancing the ability of the crop to compete with processes that lead to the escape of N from the soil-plant system (Freney, 1997). This can be done by using slow release/controlled fertilizers (e.g. Eichner, 1990), nitrification inhibitors (NIs) (e.g. Dittert et al., 2001, Merino et al., 2002, Di et al., 2007, Zaman et al., 2009), precision management techniques targeting both mineral and organic fertilizers timing and rate (e.g. Dosch and Gutser, 1996, Brown et al., 2005), breeding for more efficient plants (e.g. Wilkins et al., 2000, Abberton et al., 2008) or animals (e.g. Alford et al., 2006) and dietary-based methods, e.g. the manipulation of the urine hippuric acid content (Kool et al., 2006). Other management strategies for reducing N₂O emissions include reducing the grazing time during wet periods, manipulation of drainage and irrigation systems, use of dietary amendments for animals (e.g. salt supplementation), animal delivery of NIs (e.g. Di and Cameron, 2003, Ledgard et al., 2008), and using different manure application techniques (De Klein and Eckard, 2008).

Methane emissions can be reduced by: (i) adopting dietary methods, including; e.g. increasing the level of starch or rapidly fermentable carbohydrates, diet alteration resulting in improved animal productivity (Johnson and Johnson, 1995), addition of fat in the diet (e.g. Giger-Reverdin et al., 2003, Martin et al., 2008) or stimulation of acetogenic bacteria and reduction of methanogens or removal of protozoa through additives or probiotics (Hart et al., 2008), (ii) housing and manure storage methods, including; deep cooling of manure or reduction of manure pH, removal of the gas source, generation of biogas from waste and careful management of the bedding and manure heaps (Monteny et al., 2006).

Other, yet more speculative mitigation practices (Moorby et al., 2007), include the potential use of cloned animals with low residual feed intake and/or low rumen propensity for CH₄ emissions (Chagunda et al., 2009, Hegarty et al., 2007).

Soil C storage may be achieved by reducing the frequency of cropping through reducing the duration of grass leys and thereby, reducing organic matter decomposition (Freibauer et al., 2004). However, it should be noted that there are large uncertainties in estimating C-gain/losses following changes in grassland management due to a multitude of possible scenarios (Soussana et al., 2004).

Some of the methods that increase soil C storage, e.g. addition of manure, may also exacerbate N₂O losses. Conversely, some of these methods may cause net loss of C from the system through greater soil respiration (Scholefield et al., 2005) The interrelationships between manure storage and feeding strategies must also be studied in order to guarantee that methods taken to alter N excretion through diet manipulation (e.g. Misselbrook et al., 2005) are not counterbalanced by increased production CH₄ in the rumen.

Furthermore, some of these mitigation methods may have undesirable effects on other types of pollutants (e.g. NH₃, volatilization, NO₃ leaching), biodiversity, and/or productivity (Hopkins and Del Prado, 2007). To date, most studies (e.g. Weiske et al., 2006) that have evaluated these mitigation options in the agricultural context, have explored them acting as a single method to mitigate a specific pollutant, including specific GHGs. There are also some studies that have evaluated combinations of multiple mitigation methods on pollutant losses that have looked at assessing their combined effect (IGER, 2001, Chadwick et al., 2008, Haygarth et al., 2009) through a simple additive approach ensuring where possible that the any combination of mitigation methods does not act on the same pool of pollutant. Even so, it is not always clear if there has been some potential double counting, as some mitigation methods may actually act on the same source of pollutant.

Experimental evidence suggests, however, that non-linearity should be expected (Leip and Mulligan, 2004). There is also lack of evidence of how combinations of mitigation methods targeting specific GHG may have synergies with other GHG (Smith et al., 2001b), and secondary impacts on non-GHG pollutants and other agricultural services and goods (Hopkins and Del Prado, 2007).

Mitigation of emissions of GHG from farming systems must be studied at farm scale and with system approaches (e.g. Schils et al., 2007), as sensitivity analysis shows that most variability within the lifecycle of agricultural products may actually occur within the farming system, i.e. pre-farm gate, and not during the rest of the life cycle of milk production (Oenema et al., 2003). There are few models or modelling systems capable of fully exploring the complex interactions between farm inputs, response of system components and inherent site factors that give rise to emissions of GHGs and other pollutants of air and water and effects on farm economics.

The SIMS_DAIRY modelling framework is one of these and was used in this study. SIMS_DAIRY has been described in detail elsewhere (Del Prado et al., 2009) and so only essential information is given in the present paper below. Other modelling approaches at the farm level have been successfully used: e.g. FarmGHG (Olesen et al., 2006) has been used to compare European conventional and organic dairy farms; DairyWise (Schils et al., 2005, Schils et al., 2006) was used to define successful mitigation strategies for GHG emissions; and OVERSEER (Wheeler et al., 2008) was used to estimate on-farm GHG emissions in New Zealand.

A whole farm system-based analysis, such as that provided by the SIMS_DAIRY integrated approach, may help to elucidate which method or combination of methods for reducing GHG emissions are more successful and potentially promising for implementation. For example, some mitigation methods may not be as effective as expected when considered at a farm-systems scale and considering all the main GHG collectively (as CO₂ equivalents). Some promising methods may promote undesirable secondary effects (‘pollution swapping’) and/or affect other important pillars of farm sustainability.

The main objective of the study was to assess the whole-system response of a typical UK dairy farm after implementing a selection of GHG mitigation methods acting singly or in combination. Such response was evaluated as: the effectiveness of the method/s to mitigate GHG (CO₂-equivalents from N₂O, CH₄, CO₂ emissions and potential C storage). Importantly, the secondary effects on other forms of pollutants (e.g. NH₃, NO_x, NO₃ leaching, P losses) were quantified, as were the secondary effects on other parameters such as farm income, soil quality, milk quality, animal welfare and biodiversity.

We used comparisons of the whole-system responses to challenge the following 3 hypotheses, namely:

• The effectiveness of the combined GHG mitigation methods cannot be assessed simply by adding the effectiveness of each method applied singly;

• Effective GHG mitigation increases emissions of other forms of pollution;

• Effective GHG mitigation can jeopardize farm profitability.