Impacts of Climate Change on the Future of Biodiversity

Ecology Letters, (2012) 15: 365–377 doi: 10.1111/j.1461-0248.2011.01736.x REVIEW AND SYNTHESES Impacts of climate change on the future of biodiversity

Abstract Ce´ line Bellard,1 Cleo Many studies in recent years have investigated the effects of climate change on the future of biodiversity. Bertelsmeier,1 Paul Leadley,1 In this review, we ﬁrst examine the different possible effects of climate change that can operate at Wilfried Thuiller2 and Franck individual, population, species, community, ecosystem and biome scales, notably showing that species can Courchamp1* respond to climate change challenges by shifting their climatic niche along three non-exclusive axes: time 1Ecologie, Syste´ matique & Evolution, (e.g. phenology), space (e.g. range) and self (e.g. physiology). Then, we present the principal speciﬁcities UMR-CNRS 8079, Univ. Paris Sud, and caveats of the most common approaches used to estimate future biodiversity at global and sub- Orsay Cedex 91405, France continental scales and we synthesise their results. Finally, we highlight several challenges for future research 2Laboratoire dÕEcologie Alpine both in theoretical and applied realms. Overall, our review shows that current estimates are very variable, (LECA), UMR-CNRS 5553, Universite´ depending on the method, taxonomic group, biodiversity loss metrics, spatial scales and time periods Joseph Fourier, Grenoble 1, 38041 considered. Yet, the majority of models indicate alarming consequences for biodiversity, with the worst- Grenoble Cedex 9, France case scenarios leading to extinction rates that would qualify as the sixth mass extinction in the history of *Correspondence: E-mail: the earth. [email protected]

Equal contribution Keywords Biodiversity, climate change, species extinctions.

Ecology Letters (2012) 15: 365–377

INTRODUCTION BIODIVERSITY AND CLIMATE CHANGE: EFFECTS AND RESPONSES Predicting the response of biodiversity to climate change has become Climate change effects on biodiversity an extremely active field of research (e.g. Dillon et al. 2010; Gilman The multiple components of climate change are anticipated to affect all et al. 2010; Pereira et al. 2010; Salamin et al. 2010; Beaumont et al. the levels of biodiversity, from organism to biome levels (Fig. 1, and 2011; Dawson et al. 2011; McMahon et al. 2011). Predictions play an reviewed in detail in, e.g. Parmesan 2006). They primarily concern important role in alerting scientists and decision makers to potential various strengths and forms of fitness decrease, which are expressed at future risks, provide a means to bolster attribution of biological different levels, and have effects on individuals, populations, species, changes to climate change and can support the development of ecological networks and ecosystems. At the most basic level of proactive strategies to reduce climate change impacts on biodiversity biodiversity, climate change is able to decrease genetic diversity of (Pereira et al. 2010; Parmesan et al. 2011). Although there is relatively populations due to directional selection and rapid migration, which limited evidence of current extinctions caused by climate change, could in turn affect ecosystem functioning and resilience (Botkin et al. studies suggest that climate change could surpass habitat destruction 2007 but, see Meyers & Bull 2002). However, most studies are centred as the greatest global threat to biodiversity over the next few decades on impacts at higher organisational levels, and genetic effects of climate (Leadley et al. 2010). However, the multiplicity of approaches and the change have been explored only for a very small number of species. resulting variability in projections make it difficult to get a clear Beyond this, the various effects on populations are likely to modify picture of the future of biodiversity under different scenarios of the Ôweb of interactionsÕ at the community level (Gilman et al. 2010; global climatic change (Pereira et al. 2010). Hence, there is an urgent Walther 2010). In essence, the response of some species to climate need to review our current understanding of the effects of climate change may constitute an indirect impact on the species that depend change on biodiversity and our capacity to project future impacts on them. A study of 9650 interspecific systems, including pollinators using models. To this end, we have reviewed both the ranges of and parasites, suggested that around 6300 species could disappear possible impacts of climate change that operate at individual, following the extinction of their associated species (Koh et al. 2004). population, species, community, ecosystem and biome scales and the In addition, for many species, the primary impact of climate change different responses that could occur at individual, population or may be mediated through effects on synchrony with speciesÕ food and species levels. We then present the principal specificities and caveats habitat requirements (see below). Climate change has led to of the most common approaches used to model future biodiversity phenological shifts in flowering plants and insect pollinators, causing at global and sub-continental scales and we synthesise their results mismatches between plant and pollinator populations that lead to the focusing on how model combinations are used to project the impacts extinctions of both the plant and the pollinator with expected of climate change on species loss. Finally, we highlight several consequences on the structure of plant–pollinator networks (Kiers challenges for future research, from theoretical (e.g. emerging et al. 2010; Rafferty & Ives 2010). Other modifications of interspecific models) and applied (e.g. population conservation and exploitation) relationships (with competitors, prey ⁄ predators, host ⁄ parasites or realms.

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Genetics Natural selection Allelic diversity Mutation rates Heterozygosis richness

Physiology Fecundity Activity rates and rhythms Tsd species sex ratios Disease susceptibility Survival

Phenology Migration departure/arrival Budding/flowering Temperature Growing season length Hatchling/fledging/dispersal Means Hibernation/diapause Extremes Variability Seasonality Dynamics Recruitment Rainfall Age structure Means Sex ratio Extremes Abundance Variability Seasonality Distribution Populations Extreme events Habitat quantity and quality Ecological niche/microniche Floods Range size Droughts Storms Range localization Fires Interspecific relationships

CO2 concentr. Disynchronisation Species Organisms Disequilibrium Atmospheric Ocean pH Biodiversity component Uncoupling

Ocean Climate change components New interactions

Ocean dynamics Community productivity Sea level Biomass quantity Marine currents Energy flux

Disruptions frequency Communities Matter flux Erosion Ecosystem services Composition Function Production Ecosytems Biome integrity Catastrophes frequency Resilience Ecotypes characteristics Figure 1 Distribution shifts Biomes Summary of some of the predicted aspects of climate Desertification change and some examples of their likely effects on different levels of biodiversity. mutualists) also modify community structure and ecosystem functions altitudes and latitudes, alpine and boreal forests are expected to (Lafferty 2009; Walther 2010; Yang & Rudolf 2010). expand northwards and shift their tree lines upwards at the expense of At a higher level of biodiversity, climate can induce changes in low stature tundra and alpine communities (Alo & Wang 2008). vegetation communities that are predicted to be large enough to affect Increased temperature and decreased rainfall mean that some lakes, biome integrity. The Millenium Ecosystem Assessment forecasts shifts especially in Africa, might dry out (Campbell et al. 2009). Oceans are for 5–20% of EarthÕs terrestrial ecosystems, in particular cool conifer predicted to warm and become more acid, resulting in widespread forests, tundra, scrubland, savannahs and boreal forest (Sala et al. 2005). degradation of tropical coral reefs (Hoegh-Guldberg et al. 2007). The Of particular concern are Ôtipping pointsÕ where ecosystem thresholds implications of climate change for genetic and speciﬁc diversity have can lead to irreversible shifts in biomes (Leadley et al. 2010). potentially strong implications for ecosystem services. The most A recent analysis of potential future biome distributions in tropical extreme and irreversible form of ﬁtness decrease is obviously species South America suggests that large portions of Amazonian rainforest extinction. To avoid or mitigate these effects, biodiversity can respond could be replaced by tropical savannahs (Lapola et al. 2009). At higher in several ways, through several types of mechanisms.

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Historical set of parameters 2010). This is the case for many introduced species, for which Space Adapted set of parameters selection-driven phenotypic changes have enhanced the invasive (e.g. range) New values for two parameters potential (e.g. Phillips 2009). Recent experiments on evolutionary New values for each parameter rescue also conﬁrm that rapid evolution through mutation and selection could allow species with rapid life cycles to adapt very severe and rapid environmental changes (Bell & Gonzalez 2009).

Responses: along three axes Whatever the mechanisms involved in response to climate change, species can in theory change, and changes have already been observed, along three distinct but non-exclusive axes (Fig. 2): spatial, temporal or self. The ﬁrst two axes correspond to easily observable and well documented responses to global warming (Parmesan 2006). ÔSelfÕ Time (e.g. phenology) corresponds to less visible physiological and behavioural changes that allow species to adapt to the new climatic conditions in the same spatial and temporal frame. Spatial. First, species can track appropriate conditions in space and follow them. This is typically done through dispersion, but spatial Self changes are not limited to this: shifts to a different habitat at the local (e.g. physiology) or micro-habitat levels are also relevant. One of the best-documented responses – from both palaeontological records and recent observa- Figure 2 The three directions of responses to climate change through phenotypic tions – is a spatial shift of species tracking suitable climatic conditions plasticity or evolutionary responses : moving in space (dispersing to areas with at the regional scale. Latitudinal and altitudinal range shifts have suitable habitat or changing location on a microhabitat scale), shifting life history traits in time (adjusting life cycle events to match the new climatic conditions, already been observed in more than 1000 species – especially those including phenology and diurnal rhythms), or changing life history traits in its with high dispersal capacities like birds, insects and marine inverte- physiology to cope with new climatic conditions. Species can cope with climate brates (Parmesan 2006), leading to a reduction in range size change by shifting along one or several of these three axes. particularly in polar and mountaintop species (Forero-Medina et al. 2010). However, individuals shift their distribution to stay in quasi- equilibrium with the climatic conditions they are adapted to, but they may not be adapted to other abiotic variables such as photoperiod or Biodiversity responses to climate change novel biotic interactions (Visser 2008). In these cases, micro-evolution Because of climate changes, species may no longer be adapted to the may be needed for them to persist (Visser 2008). set of environmental conditions in a given region and could therefore Temporal. To keep up with changing abiotic factors that show cyclic fall outside its climatic niche. As other components of the ecological variation over time, such as temperature on a daily or yearly period, niche of species are not supposed to change directly, we will hereafter individuals can also respond to climate change through a shift in time refer only to climatic niches of species (i.e. the climatic components of (on a daily to seasonal basis). Phenology, i.e. the timing of life cycle the n-dimensional hypervolume sensu Hutchinson). To persist, events such as flowering, fruiting and seasonal migrations, is one of individuals, populations or species must produce adaptive responses, the most ubiquitous responses to 20th century climate warming. It has which can be of several types, and are provided by two categories of already been documented in many species (Parmesan 2006; mechanisms. Charmantier et al. 2008). In a meta-analysis of a wide range of species including animals and plants, the mean response across all species Response mechanisms: plastic vs. genetic responding to climate change was a shift in key phenological events of One of the crucial questions in the debate on ecological effects of 5.1 days earlier per decade over the last 50 years (Root et al. 2003). climate change is whether or not species will be able to adapt fast Flowering has advanced by more than 10 days per decade in some enough to keep up with the rapid pace of changing climate (Lavergne species (Parmesan 2006). These phenological changes can help species et al. 2010; Salamin et al. 2010). Whatever the type of adaptive keep synchrony with cyclical abiotic factors. Yet, they can also be responses, underlying mechanisms are either due to micro-evolution disruptive, by increasing asynchrony in predator-prey and insect-plant (i.e. species can genetically adapt to new conditions through mutations systems (Parmesan 2006), which may lead to species extinction. or selection of existing genotypes Salamin et al. 2010) or plasticity, Temporal shifts may also occur at a small temporal scale, e.g. with which provides a means of very short-term response (within behaviours or activity patterns adjusted in daily activity rhythms to individualÕs lifetimes, Charmantier et al. 2008). It may involve match the energetic costs of a different climatic condition. intraspeciﬁc variation in morphological, physiological or behavioural Self. Last, species can cope with changing climatic conditions by traits, which can occur on different time scales within the populationsÕ adapting themselves to the new conditions in their local range, rather spatial range (Botkin et al. 2007; Chevin et al. 2010). Empirical than by tracking their current optimal conditions in space or time. For evidence suggests that plastic contribution is often more important lack of a better term, we refer to these in situ changes that are not than genetic contribution, as observed in birds and marmots related to spatial or temporal changes, as changes in ÔselfÕ. Species can (Hoffman & Sgro 2011). On the other hand, there is increasing move along this third, ÔselfÕ axis by physiological alterations that allow empirical evidence that evolution can be very rapid (Lavergne et al. tolerance to warmer or drier conditions or by behavioural

2012 Blackwell Publishing Ltd/CNRS 368 C. Bellard et al. Review and Syntheses modifications of their diet, activity and energy budget, for example. environmental variables, i.e. the speciesÕÔclimate nicheÕ, defines the Although they are often less obvious than changes in time or space, suitable climatic habitat for that particular species. These Bioclimatic some physiological responses have already been reported (Johansen & Envelope Models relate current species ranges to multiple climatic Jones 2011) during the 20th century climate change, especially from variables and thereby define the climatic niche (envelope) for each many ectotherms, as their locomotion, growth, reproduction and sex species. It is then possible to project this niche for different future determination are temperature sensitive (Tewksbury et al. 2008). climate scenarios to determine the potential redistribution of the However, for many traits, plastic phenotypic responses should reach a suitable climate space of the species. The extinction risks can then be physiological limit and ÔsaturateÕ in extreme environments. For calculated in different ways using species–area relationships (Thomas example, body size or metabolic rate cannot increase or decrease et al. 2004) or IUCN status (Thuiller et al. 2005). Despite a number of indefinitely under sustained environmental change (Chevin et al. limitations (lack of biological process as well as methodological and 2010). In this case, strong selection is needed to cope with climate theoretical issues, Soberon & Nakamura 2009), species distribution change. As they remain in the same spatial and temporal frame, models still constitute the bulk of studies in this domain (Sinclair et al. thereby limiting alterations of interspecific relationships, changes in 2010). self also have different consequences for ecosystem responses than do Shifts in distinct vegetation types, often referred to as biome or changes in time and space. habitat shifts are often simulated with Dynamic Vegetation Models Failing to adapt along one or several of these three axes, population (DVM). These models forecast shifts in vegetation and associated or species will go extinct locally or globally. There is thus a multitude biogeochemical and hydrological cycles in response to climate change. of possible responses for species to cope with climate change, and in DVMs use time series of climate data (e.g. temperature, precipitation, fact relatively few taxa went extinct following climate change during humidity, sunshine days) and take into account constraints of the Quaternary period (Botkin et al. 2007). This should help temper topography and soil characteristics to simulate monthly or daily catastrophist predictions regarding the global effects of the current dynamics of ecosystem processes. Plant species are represented as climate change on biodiversity. However, the responses of many groups with similar physiological and structural properties, termed populations are likely to be inadequate to counter the speed and Plant Functional Types (PFTs), which are designed to represent all magnitude of the current climate change. In addition, unlike in past major types of plants (Sitch et al. 2008). PFT distributions can then be periods of climate change, species have now to cope with additional used to estimate changes in biome or habitat ranges. Currently, DVMs threats, some of which may act in synergy with climate change (Botkin are of limited use for projecting responses in biodiversity directly (i.e. et al. 2007). As we are already facing an irrefutable biodiversity crisis, the absence of animals and the limitation to c. 10 PFTs exclude direct the number of species that may go extinct following climate change utilisation). However, coupled with extinction models, they allow has become a major concern over the last few years. extinction risk for species to be estimated at the regional or global scale (e.g. van Vuuren et al. 2006). ASSESSING THE FUTURE OF GLOBAL BIODIVERSITY Species loss models components Our understanding of the effects of global climate change on biodiversity and its different levels of response is still insufficiently The simplest method for calculating extinction risk is to assume that well developed. Yet, it is enough to raise serious concern for the species go extinct when they no longer have any suitable habitat (Jetz future of biodiversity. The most pressing issue is to quantitatively et al. 2007). This may underestimate extinctions because species often assess the prospects for biological diversity in the face of global enter an Ôextinction vortexÕ well before they lose their entire habitat. climate change. Although several methods exist to draw inferences, Yet, it could also substantially overestimate extinctions because many starting with existing palaeontological or recent data, experiments, species have weak habitat specificity (Malcolm et al. 2006; Willis & observations and meta-analyses (e.g. Lepetz et al. 2009), ecological Bhagwat 2009). modelling is the most commonly used tool for predictive studies. The species–area relationship (SAR), an empirical relationship Progress in this field is characterised by both an extremely high pace between the number of species and the land area of a region, is often and a plurality of approaches. In particular, there are three main used to estimate extinction risk (Thomas et al. 2004). Species approaches to projecting species loss, concentrating either on future extinctions are calculated as a direct function of habitat loss or changes in species range or species extinction or changes in species climate-induced range contraction based on the observation that abundance. However, all three modelling approaches have so far extinction risk increases with decreasing range and population size. largely focused on one axis of response (change in space), largely SAR methods may over- or underestimate species extinction risk overlooking the importance of the other aspects. In addition, they depending on the capacity of species to persist in small populations or seldom account for the mechanisms of these responses (plasticity and adapt to novel environments, and they also fail to provide a time evolution). We briefly discuss herein the basic principles and the frame in which extinctions are likely to occur (Chevin et al. 2010) weakness of the models that are the most widely used at global or at because the extinction debt is not accounted for (Pereira et al. 2010). large regional scales in this context, focusing on representative The relevance of using this relationship to project biodiversity loss is examples of recent work. Table 1 summarises the specific advantages still hotly debated (He & Hubbell 2011). and limitation of each model type. The IUCN has developed criteria to assess species extinction risk for their Red List of the conservation status of plant and animal species (Baillie et al. 2004). These include 90 biological traits identified Biodiversity range models components as enhancing speciesÕ vulnerability (e.g. habitat specialisation, narrow Studies modelling speciesÕ range shifts are generally based on the environmental tolerance, dependence on specific environmental assumption that species niches are defined by a small set of triggers, dependence on interspecific interactions or poor ability to

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Table 1 Advantages and disadvantages of the two components of major modelling approaches used to estimate loss of biodiversity due to climate change. See Fig. 3 for illustrations of how the two components can be combined to estimate biodiversity loss

Advantages Disadvantages Key references

Biodiversity range model components Bioclimatic envelope Can be applied to a large number of Do not explicitly account for mechanisms that Alo & Wang (2008), Arau`jo et al. models (BEM) species and a variety of taxonomic groups mediate species range (2011), Baillie et al. (2004), Beaumont Implicitly capture many ecological May handle novel climates poorly et al. (2008, 2011), Botkin et al. processes in the relationship between Lack temporal dynamics (2007), Bradley et al. (2010), occurrence data and spatial information Assume that the current distribution of a species Brook et al. (2008) Require few data is a good indicator of suitable climate Dynamic vegetation Include the dynamics of plant growth, Require detailed physiological data Akcçakaya et al. (2006), Anderson et al. models (DVMs) competition and, in a few cases, migration Do not include plant interactions with other (2009), Bakkenes et al. (2002), Brook Allow the identification of future trends in taxonomic groups et al. (2008, 2009), Campbell et al. (2009) ecosystem function and structure Limit biodiversity to a very small number of plant Can be used to explore feedbacks between functional types biosphere and atmospheric processes Do not take into account fine scale spatial heterogeneity Are not adapted for predicting species extinctions at local scales Species loss model component Species–area Are easy to couple with distribution Use values of key parameters that are not well Alo & Wang (2008), Campbell et al. relationships (SAR) models because they are based on range constrained (2009), Carnaval et al. (2009), or habitat loss Lack empirical evidence concerning applicability of Charmantier et al. (2008) Can be applied to a variety of taxonomic groups SAR for climate change or at species range level Require few data Lack temporal dynamics DonÕt account for processes influencing extinction rates (e.g. population dynamics, adaptive responses)

IUCN status methods Use a widely accepted measure of threat Depend on thresholds that are somewhat arbitrary Alo & Wang (2008), Arau`jo et al. Are simple to couple with distribution Rely on sole criteria of declining range size in (2011), Chevin et al. (2010) models because partly based on criteria most studies of range or habitat loss Often donÕt respect time frame for declines (i.e. 10 years or three generations in most cases) Dose–response Are anchored in measured responses of ÔUndisturbedÕ ecosystems used as baseline are Bakkenes et al. (2002), Barnosky et al. relationships biodiversity to global change drivers difficult to define (2011) Can assess the impact of a wide range of Inadequately account for interactions between global change factors alone or their global change drivers cumulative effects Lack validation at large regional or global scales Can include time lags Use metrics that are difficult to relate to common biodiversity indices disperse to or colonise a new range, Foden et al. 2008). Recent two land use or land cover categories, derived from empirical attempts at projecting climate change impacts on biodiversity have studies (Leadley et al. 2010). These types of models use species used the IUCN status metric to obtain estimates of extinction rates abundances in pristine ecosystems as a baseline, and thereby based on projected range shifts (Thuiller et al. 2005), although the use provide a measure of the distance from ÔnaturalnessÕ of plant and of IUCN status for this purpose has also been questioned (Akcçakaya animal communities following human disturbance. Species abun- et al. 2006). dance models based on plant traits and abiotic characteristics can In dose–response relationship (DRR) models, observational data also provide evidence of changes in ecosystem services (Lavorel and experiments can be employed to generate empirical relationships et al. 2011). between the relative importance of global drivers (dose) and changes There are, however, several major limitations to these models, in species loss (response). especially concerning the indices of biodiversity derived from DRR Most models focus on species extinctions, which are only the last applied to species abundance. They depend largely on the quality of step of a decline in abundance and are a less immediate (although the input data (of which much more is needed than in most other dramatic) impact of climate change. This fact has led to the models), uncertainty cannot be taken into account, and they are development of new models that attempt to quantify the impact of difficult to relate to commonly used biodiversity indices (Alkemade human activities on species abundance (i.e. an equivalent of DRR et al. 2009). for species abundance). Impacts on biodiversity are estimated using biodiversity indicators including mean species abundance (MSA, Model combinations Alkemade et al. 2009) and Biodiversity Intactness Index (Biggs et al. 2008). For example, the GLOBIO model uses a matrix of changes Current modelling techniques usually incorporate a succession of one in mean local species abundances following conversion between (or several) future socio-economic scenario(s), one (or several)

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Socio-economic scenarios IPCC SRES scenarios, MA scenarios, GBO 2 scenarios

Drivers Climate change, land use change, N-deposition, infrastructure, fragmentation

Biodiversity range DVM BEM component Dose Response Relationships

Species loss Current range Species Area Relationships IUCN status component

Biodiversity measure Commited to extinction Evaluation of extinction risk Abundance changes (1)* (2) (3) (4) (5) (6)** (7) (8)

Figure 3 Examples on successive combinations of socioeconomic scenarios, projections of extinction drivers, biodiversity range or species loss models and biodiversity metrics, leading to projections of biodiversity losses following climate change. Numbers correspond to references (see Appendix S2 for details and reference list).

Biodiversity loss Climate change (14) (14) % Other drivers 100 -

90 - (5) (11)

80 - (13)

70 - (14) 60 - (3) 50 - (4) 40 - (9) (6) 30 - (8) (10) (2) (12) (7) 20 - (2)

10 - (1)

2050 2100 2050 2080 2100 2050 2070 2080 2100 MSA Committed to extinction Local loss Time scale

Figure 4 Projections of loss of biodiversity due to climate change, for different taxonomic, temporal and spatial scales. The width of the box illustrates three levels of generality: global scale and several taxonomic groups ( ), global scale and only one taxonomic group, continental scale and only one taxonomic group. The box is delimited by the upper and lower boundaries of the intermediate scenario, while maximum and minimum values of the whiskers indicate the highest and lowest biodiversity losses across all projections. This figure illustrates that the different studies (i) generally predict significant biodiversity loss and (ii) use a combination of different biodiversity metrics, taxonomic groups and spatial scale and time horizon, making generalisations difficult. Numbers correspond to references (see Appendix S2 for details and references). extinction driver(s), one biodiversity range or species loss models have synthesised in Fig. 4 the results of all such projections made at a (Fig. 3). They then express their projections in terms of various global scale either geographically or taxonomically. Despite important extinction metrics. This has generated a variety of Ômodel combina- differences and some bias (e.g. marine biodiversity is still poorly tionsÕ leading to a wide range of projections that are not straightfor- represented), these models generally indicate that many species can be ward to compare, as the underlying assumptions differ greatly. We expected to decline rapidly at a global scale.

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Issues Challenges Quantitative Climate scenarios ?

Scale choice Multiply studies

Positive effects

Biodiversity Gather more data Biodiversity studied (Number of species, taxonomy ? loss bias , type of biodiversity) Figure 5 Issues and challenges for climate change driven biodi- ? Extend study focus versity loss. Examples of each are presented and discussed in the Species responses main text. The issues are in the ﬁrst box, which represents the (dispersal abilities, adaptative main factors of uncertainty and the direction of the likely current capacity, extinction debt, bias (green factors and arrow down is a likely overestimation, species vulnerability) while red factors and arrow up is a likely underestimation; both Resource/biotic factors Develop new tools can occur simultaneously, according to the approach taken in (Resources, Co-extinctions) different studies; black factors and question marks are for unknown direction of misestimation; double arrow means very Ecosystems issues large expected effect). Challenge types range from quantitative (Tipping points) Handling complexity (such as increasing the number of studies and the quantity of Other drivers available data, or extend the focus of studies to include more (Synergies) factors) to more qualitative ones (developing new tools and Qualitative progressing in resolving complexity).

Studies based on changes in species abundance generally predict an Projections of species loss erosion of biodiversity in the same order of magnitude as species The field of climate change biology has thus followed several distinct extinction models do, i.e. losses of 11–17% of MSA by the end of the and independent lines of model development (Fig. 3). This is century (Alkemade et al. 2009). Using data from 9856 birds, 6222 potentially advantageous, as the convergent predictions can be amphibians and 799 coral reefs, the IUCN forecasted that c. 35% of regarded as an indication of robustness. In essence, the results show the worldÕs birds, 52% of amphibians and 71% of warm water reef- that local species extinctions cover an extremely large range, with building corals are particularly susceptible to climate change (Foden some areas experiencing virtually no losses and others facing nearly et al. 2008). Variability in local extinctions is significantly higher than complete loss of current species. High local losses can concern in global extinctions projections. Indeed, in some regions the species relatively large areas; for example, Bakkenes et al. (Bakkenes et al. composition will not change at all and in others almost all species will 2002) estimated that more than 16% of European landmass would be lost following climate change. Estimated losses of riverine fish have local species losses exceeding 50% by 2050. This must be richness at a local scale vary considerably between 0 and 75% distinguished from projections of species extinctions at the global according to the river considered (Xenopoulos et al. 2005). Similarly, level, which are unsurprisingly lower than at the local level, since local estimated local losses of plant diversity in Europe range from 2 to extinctions do not necessarily lead to global extinctions. Nevertheless, 84% of species lost per pixel (Thuiller et al. 2005). even these global estimates suggest major losses of biodiversity due to Overall, the diversity of taxonomic groups studied, of biodiversity global climate change that are generally higher than current rates of loss metrics, of spatial scales and time periods render complex and loss and far higher than rates of species extinctions documented in the uncertain comparisons between these studies. In addition, the large fossil record (Pereira et al. 2010; Barnosky et al. 2011). For example, array of modelling techniques further complicates the comparisons. one of the earliest global studies estimated that by 2050 15–37% of Moreover, several studies assess the combined effects on future species are committed to extinction under intermediate climate biodiversity of both climate change and land use, making it difficult to warming (Thomas et al. 2004). Birds are projected to be particularly distinguish the effect of each. Finally, there is growing concern that sensitive to climate change. Two studies have calculated losses by the underlying assumptions of all of these models could lead to large 2100 due to climate change: these range from less than 0.3% of the over- or underestimations of potential species losses (Pereira et al. worldÕs 8750 species of land birds would be committed to extinction 2010; Dawson et al. 2011). These are summarised in Fig. 5. As an (Jetz et al. 2007) and up to 30% of the 8400 land bird species in the example, a recent study suggests that the realised effects of climate Western Hemisphere could go extinct (Sekercioglu et al. 2008). change might far exceed the current predictions (Maclean & Wilson Regarding the vulnerability of 25 major biodiversity hotspots, 2011). Based on a global multi-taxa meta-analysis of recorded Malcolm and colleagues suggested that the extinctions of endemic ecological responses, they estimated that the mean observed extinc- species could reach 39–43% under in worst-case scenarios, tion risk by 2100 is systematically higher than the mean predicted representing the potential loss of 56 000 endemic plant species and extinction risk across the theoretical studies considered in the 3700 endemic vertebrate species (Malcolm et al. 2006). meta-analysis: 12.6% in plants (vs. 4% predicted), 9.4% in inverte-

2012 Blackwell Publishing Ltd/CNRS 372 C. Bellard et al. Review and Syntheses brates (vs. 7.2% predicted) and 17.7% in vertebrates (vs. 12.4% Positive effects predicted). Overall, these estimates of biodiversity loss are obviously a major concern. Climatic changes could also have positive effects on biodiversity. For instance, more clement temperatures and increased CO2 are likely to be beneficial to many plants, resulting in an acceleration of biomass WHAT FURTHER IMPROVEMENTS ARE NEEDED? production. Milder winters might increase survival of many currently Climate change ecology is still in its infancy, and tremendous threatened species in temperate regions. Increased precipitation may improvements are made rapidly in virtually all aspects of this emerging also benefit some plant communities and species depending on them. field. Critical requirements to be able to predict future trends include Moreover, several studies reported detrimental effects of climate the need to study a much larger part of biodiversity, to overcome change on biological invasions (e.g. Peterson et al. 2008). Although several major model limitations, to account for co-extinctions and few studies report beneficial effects of global changes on biodiversity, other major drivers of biodiversity loss and to validate models by they certainly exist and add to the difficulty of getting a clear overview comparing projections with observations. These limitations are of the effects of climate changes on the biodiversity of our planet. detailed below. Figure 5 provides a summary of the directions in which each of these weaknesses is likely to affect the current Biodiversity measures projections on biodiversity loss, as well as the major types of challenges that have to be faced to overcome these uncertainties. Even in the most ambitious studies, the range of species studied always represents a small percentage of known biodiversity. All studies are taxonomically biased, as they generally concentrate on a few Climate scenarios conspicuous taxonomic groups such as plants, mammals and birds Climate scenarios depend on a wide range of socio-economic (Thuiller et al. 2011), with a particularly strong bias towards terrestrial storylines for greenhouse gas emissions in the future, including the vs. marine biodiversity. However, it is generally recognised that the Special Report on Emissions Scenarios Millenium Assessment and vast majority of biodiversity in terms of species richness, evolutionary Global Biodiversity Outlook scenarios (Pereira et al. 2010), and on a divergence, biomass and even ecosystem functioning is represented by broad suite of General Circulation Models used to calculate climate Ôcryptic biodiversityÕ, especially micro-organisms and insects (Esteban change for given trajectories of greenhouse gas emissions. This means & Finlay 2010). Similarly, there are important biases in data collection that the projections of species loss can yield highly contrasting results both across regions and ecosystems (McMahon et al. 2011). Further- depending on the choice of combinations of emissions scenarios and more, most studies focus on species richness, because it is thought to climate models, independently of the model of biodiversity response influence the resilience and resistance of ecosystems to environmental that is used (Beaumont et al. 2008). In addition, internal climate model change. However, a few studies have explored the impact of climate variability could result in greater differences in projected speciesÕ change on functional (Thuiller et al. 2006) and phylogenetic diversity distributions than variability between climate models (Beaumont et al. (Thuiller et al. 2011), and the effects on genetic diversity are only 2007). In addition, 4–39% of the worldÕs landmass will experience beginning to be explored. Moreover, it is likely that different levels of combinations of climate variables that do not currently have diversity are affected differently by climate change, so these should be equivalent values anywhere on the globe (so called no-analogue evaluated in parallel to provide a broad picture of biodiversity climates, Williams et al. 2007). One key challenge is to provide robust response to climate change (Devictor et al. 2010). and credible uncertainty intervals for all model outcomes and, if In addition, there are different indicators of biodiversity change, possible, to reduce them. such as the number of species ÔcommittedÕ to extinction (Thomas et al. 2004; Pereira et al. 2010), extinction risk (Thuiller et al. 2005), or change in abundances (Alkemade et al. 2009; Leadley et al. 2010). The Scale choice number of species ÔcommittedÕ to extinction is probably not the most The choice of the spatial resolution scale is probably one of the most appropriate metric to forecast the future of biodiversity because the important factors generating variability. For example, a coarse, extinction debt could vary from decades to centuries (Kuussaari et al. European-scale model (with 10¢·10¢ grid cells) predicted a loss of 2009). A complementary metric of biodiversity can be changes in all suitable habitats during the 21st century, whereas a model using MSA, which is an index defined as the mean abundance of original local-scale data (25 · 25 m grid cells) predicted persistence of suitable species relative to their abundance in undisturbed ecosystems. habitats in up to 100% of plant species (Randin et al. 2009). These As extinction is only the last step in the process of species decline, differences are probably explained by the failure of coarser spatial the MSA can provide a metric to evaluate ecosystem degradation. scale models to capture both local topographic diversity and habitat In addition, we need new approaches to predict the impacts of climate heterogeneity (Luoto & Heikkinen 2008; Randin et al. 2009). On one change at the community level (i.e. Mokany & Ferrier 2011). hand, global models can be used for a large number of species but Currently, neither the estimates of species losses nor of the qualitative focus on one type of speciesÕ response and therefore lack biological impacts on ecosystem functioning and services are satisfactory to give realism. On the other hand, population or species models provide a global overview of the impacts of climate change (Dale et al. 2010). insight into a very limited range of species, typically at regional scales (i.e. adaptation phenology, dynamic population) but cannot provide Limitations of predictive tools global scale trends. This is a classical trade-off between precise small- scale models and coarse large-scale models that lack biological realism Each of the modelling approaches reviewed has methodological, (Thuiller 2003). spatial and temporal limitations that constrain their predictive power

2012 Blackwell Publishing Ltd/CNRS Review and Syntheses Biodiversity and climate change 373

(e.g. McMahon et al. 2011). In general, models do not take into determine species distributions, population structure and extinction account the multiple responses of species to climate change, but rather risk at a local scale. Models are often based on habitat reduction and focus mainly on one axis, spatial shifts of potential habitat (Fig. 2). do not offer a mechanism by which the speciesÕ fitness is reduced and The temporal and the physiological responses are generally over- ultimately led to extinction. Recently, new mechanistic niche models looked, as are the genetic and plastic capacity of species (Lavergne have endeavoured to estimate key fitness components such as et al. 2010; Salamin et al. 2010). In addition, species are commonly survival, growth, development and reproduction as a function of considered as static and independent entities, although their dynamics species attributes (i.e. physiology, phenology, behaviour) that vary and their role in ecological networks are both known to be essential. with climatic conditions (Kearney et al. 2010). However, a new generation of models is emerging that focuses on Currently the probability of extinction is based on a projection of more realistic biological hypotheses and meets some of the challenges suitable habitats entirely lost (species will be committed to extinction) posed by each limitation (e.g. Thuiller et al. 2008; Brook et al. 2009; or partially lost (species will increase their extinction risk), although it Gallien et al. 2010). is possible to combine niche models and abundance data (Iverson & Prasad 1998). In fact, on one side some species are more likely to go extinct because they consist of small and disconnected populations Species responses and on the other side, some species have shown compensatory Current global extinction models make very coarse assumptions about changes in demographic rates following climate change (Doak & species responses. For example, the dispersal capability is a major Morris 2010). The approach of population viability analysis (PVA) in issue for projections of future biodiversity. Until recently, models this context could provide a good alternative. It is used to estimate the often addressed dispersal issues by using two extreme assumptions of likelihood of a populationÕs extinction within a given number of years either unlimited or no species dispersal (Thomas et al. 2004). This is through the speciesÕ demographic characteristics and environmental clearly convenient for practical purposes, but most species are variability, instead of species range shifts. Only a few studies have used between these extremes. In addition, exceptional occurrences of long- PVA to predict the effects of climate change on extinction risks (Keith distance dispersal are thought to have helped past species surmount et al. 2008; Anderson et al. 2009), in part because this approach prehistoric climate changes (Dawson et al. 2011 and references requires data on population dynamics that are not available for most therein). Although rare, these events are of crucial significance in species. the current context, as they could in many cases make the difference In addition, physio-demo genetic models could provide the most between species survival and extinction, especially as human-mediated realistic method to forecast the future of key species, as they consider long-distance dispersion is now common for many organisms. simultaneously demographic (e.g. growth, rate of survival and Inherent differences in vulnerability are not always taken into reproduction, migration processes, environmental stochasticity), physio- account when making large-scale correlative predictions of the future logical (e.g. metabolic rate) and genetics parameters (heritability and of biodiversity. Indeed, most models work under the very strong norm of reaction). However, studies using physio-demo genetic hypotheses that species currently live in an optimal climatic niche and models to evaluate the impacts of climate changes remain scarce that they cannot survive if there is a change in the climatic conditions (Kramer et al. 2010). that contribute to define this niche. Such a hypothesis amounts to overlook the potential of adaptability of species. Numerous studies Co-extinctions have shown that species are capable of fast responses through phenotypic plasticity or microevolution (Lavergne et al. 2010). Before Whether centred on a single species or taking into account large being able to forecast species trajectories with some credibility, we taxonomic groups, most studies and all models have disregarded therefore need to assess properly the vulnerability of species interspecific relationships such as competition, facilitation or mutual- (including exposure, sensitivity, adaptive capacity and migration ism. Beyond single-species extinctions, both direct and indirect potential), habitats and regions to different components of climate processes can lead to cascading and catastrophic co-extinctions, also change (Dawson et al. 2011; McMahon et al. 2011). Despite growing called Ôchains of extinctionÕ (Brook et al. 2008). Despite the importance evidence for rapid adaptive evolution in response to climate change, of interspecific interactions, these relationships are exceptionally the consequences of such evolution on species persistence remain to difficult to model; this is especially cogent in the context of lack of be explored (Lavergne et al. 2010). Currently, more flexible models of data on population dynamics and trophic webs (McMahon et al. 2011). micro-evolutionary processes combine population-based information As each species comes with its cortege of specific parasites and with phylogenetic comparative methods to provide an estimation of symbionts, as well as many trophic relationships, the consequences of the evolutionary potential (Salamin et al. 2010). Moreover, populations global change on biodiversity might be substantially underestimated can be locally adapted to specific climatic conditions and therefore, when focusing on species-specific extinction rates (Koh et al. 2004; models treating a species as a single homogeneous unit might be Yang & Rudolf 2010). There is an urgent need not only to go beyond flawed. Consequently, studying under which circumstances losses in the single-species approach, but also to get past the species richness genetic and species diversity at local to regional scales occurred in the approach and to consider interspecific interactions, trophic webs and past could improve models outputs (Dawson et al. 2011; McMahon ecological networks (Bascompte 2009). et al. 2011). Synergies between extrinsic drivers of extinction Population and metapopulation dynamics Research on future species extinctions has so far mostly considered a An inherent property of current global extinction models is that they limited set of the predicted changes. For example, sea level rise has do not take into account population or metapopulation dynamics that seldom been considered, although the most recent scenarios

2012 Blackwell Publishing Ltd/CNRS 374 C. Bellard et al. Review and Syntheses projecting up to 2 m of rise by 2100 are raising new concerns for Finally, model predictions of where, when and how future risks may coastal and insular biodiversity (Grinsted et al. 2009). In addition, affect species, biomes or ecosystems could assist in identifying the studies have mainly focused on the sole impact of climate change, most appropriate conservation measures. For instance, species or sometimes considering a single additional driver (e.g. land-use ecosystem projected to be primarily affected by climate change may change). These limitations may often lead to overly optimistic require adapted measures compared to species negatively affected by estimates. Indeed, other components of global change, such as land-use change that could persist through protection of their habitat fragmentation, pollution, overexploitation and biological remaining natural habitat (Hannah et al. 2007). In many cases there invasions have all been documented as major, additional threats for may no longer be any overlap between a speciesÕ current range and its the future of biodiversity, with possible synergies, or reinforcing possible future range. As this situation will lead to extinction, climate feedbacks between them (Sala et al. 2000). For instance climate- change has been a major argument for the proponents of human- induced facilitation of invasions can occur at all stages of the invasion assisted colonisation (Loss et al. 2011). In this increasingly hot debate, process (Walther et al. 2009; Bradley et al. 2010). In an experimental case-by-case decisions have been advocated, based on the balance context, habitat fragmentation and overfishing combined with global between threatened status of a species and threat of that species for warming have led to a decline in rotifer populations of up to 50 times the recipient ecosystem, as well as the socioeconomic context in which faster than when either threat acts alone (Mora et al. 2007). In this conservation is taking place (Richardson et al. 2009; Dawson et al. case, dose-response relationship models could offer a powerful 2011). A widespread view is that an important strategy is to enhance approach because they could simulate the impacts of many drivers of landscape connectivity to enable species to move through a matrix of species extinctions. interconnected habitats to favour escapes from unsuitable climatic Overall, despite important uncertainties and much conflicting conditions (Hannah et al. 2007). imprecision, both under- and overestimating species loss, the very It is also essential to shift from a species centred focus to a holistic large underestimations due to co-extinctions, synergies and tipping view encompassing species interaction networks, and other aspects of points are extremely worrisome for the future of biodiversity (Fig. 5). biodiversity such as functional and phylogenetic diversities (Devictor Given the attractiveness of quantitative projections from mathematical et al. 2010). Beyond these various strategies, there is a growing call to models, it is tempting to underestimate their limitations. To minimise go past the predictive focus and start aiming for an integrated and this risk, it is crucial to use a variety of complementary approaches, unified framework to identify species vulnerability and adapt ranging from observational studies (palaeontological data, phenological biodiversity management interventions (Dawson et al. 2011). In this responses, adaptive capacity) to field and laboratory experiments (on regard, preventive actions are of foremost importance. For example, it genetic, species and ecosystem levels) (Dawson et al. 2011). should be remembered that the proportion of species extinction is a power function of the expected global warming (Hansen et al. 2010). Minimising global warming could therefore have nonlinear effects in BIODIVERSITY MANAGEMENT the preservation of species from extinction, with each tenth of degree The large variation of responses of different species necessitates avoided saving an increasing number of species (Hansen et al. 2010). the use and integration of multiple approaches to further our Acting to reduce global warming itself, and not only its effect on understanding of the impacts climate change can have on biodiversity biodiversity, should remain a priority in conservation sciences. In (Dawson et al. 2011). Similarly, our responses in terms of biodiversity addition, reducing other global change drivers could increase overall management ought to transcend disciplines. Beyond this, global resilience of biodiversity in the face of climate change (Hughes et al. climate change prompts several methodological issues and the 2003). implications for conservation and management of biodiversity and ecosystem services. Ecosystem services Other aspects of biodiversity management will be affected by global Conservation of species and ecosystems change and will need adapting, including wildlife exploitation [e.g. The large projected impacts of climate change on biodiversity at all forestry (Dale et al. 2010) or fisheries (Stram & Evans 2009)], levels mean that ecologists must quickly rise to the challenge of agronomy (Howden et al. 2007), pest and invasive species control providing scientific guidance for the development of conservation (Ziska et al. 2011) or human and wildlife disease management (Harvell strategies (Pressey et al. 2007; Arau`jo et al. 2011; Dawson et al. 2011). et al. 2002). For example, major challenges in agronomy include the A major role of conservation planning is to design reserve networks need to shift to species or varieties better adapted to particular that protect biodiversity in situ. Currently few studies have attempted components of climate change or to rethink strategies to control to use modelling for conservation purposes (Arau`jo et al. 2011). It is invasive and pest outbreaks, finding solutions in the increasing increasingly important to protect the heterogeneity of habitats as well competition for water between the natural and the agricultural as genetic diversity within a species to sustain the capacity of a species ecosystems, improving infrastructures and adapting cropping systems to adapt. Also, the characteristics of protected areas, where planning to meet future demands of a growing population living on poorer has to be done decades in advance (Hansen et al. 2010), need to be biodiversity resources (Howden et al. 2007). reviewed under climate change. Climate modelling can help to re- Given the tremendous ecological impact of alien invasive species, evaluate the current set of protected areas, their places, size, layout and and the expected exacerbation of invasion due to climate change design (Arau`jo et al. 2011). In particular, protection should be (Walther et al. 2009), it is urgent to increase predictive power in this prioritised for places that minimise the effects of climate change – field. It is also crucial to move beyond predictions (Dawson et al. like forests, which contribute strongly to local climatic conditions – as 2011) and to strengthen risk assessment, protocols of screening and well as for climate refuges for biodiversity (Carnaval et al. 2009). of early detection, vector control and integrated management in area

2012 Blackwell Publishing Ltd/CNRS Review and Syntheses Biodiversity and climate change 375 and ⁄ or of invasive species that will become at higher risk following DIVERSITALP (ANR-07-BDIV-014) and from the European climate change. Similar efforts are imperative for other drivers of CommissionÕs FP6 ECOCHANGE project (GOCE-CT-2007- biodiversity loss, such as overexploitation or habitat destruction. 036866) and FC from ANR with the project RARE (2009 PEXT 010 01).

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2012 Blackwell Publishing Ltd/CNRS

Working Paper

Climate Change Mitigation and the Irish Agriculture

and Land Use Sector

Jeremy P. Emmet-Booth, Sabrina Dekker, Phillip O’Brien

July 2019

Disclaimer

Although every effort has been made to ensure the accuracy of the material contained in this publication, complete accuracy cannot be guaranteed. Neither the Climate Change Advisory Council nor the authors accept any responsibility whatsoever for loss or damage occasioned or claimed to have been occasioned, in part or in full, as a consequence of any person acting, or refraining from acting, as a result of a matter contained in this publication. The advice of relevant experts has been sought where possible during the preparation of this publication, however the views contained within this publication, are those of the authors. All or part of this publication may be reproduced without further permission, provided the source is acknowledged.

The authors acknowledge the advice gratefully received during the preparation of this working paper from the following;

K. Black1,2, J. Breen3, E. Curran4, S. Green5, M. Gorman3, K. Hanrahan5, T. Hennessy6, T. Hochstrasser3, N.M. Holden3, M. Jones7, J. Kinsella3, J. McAdam8,9, P.N.C. Murphy3, S. Murphy7, J. Nolan4, A. O’Sullivan3, F. Renou-Wilson3, K.G. Richards5, M. Saunders7, B. Tobin3, H. Sheridan3 & J.C. Stout7

1. COFORD 2. FERS Ltd. 3. University College Dublin

4. Department of Agriculture Food and the Marine 5. Teagasc

6. University College Cork 7. Trinity College Dublin

8. Agri-food and Biosciences Institute 9. Queens University Belfast

Executive Summary

In its Annual Review 2018, the Climate Change Advisory Council (CCAC) stated that Ireland is not on a trajectory to meet greenhouse emission reduction targets. The Agriculture sector, the predominant land use in Ireland, is a major and increasing source of greenhouse gasses, currently accounting for 33% of Ireland’s national greenhouse emissions. Land use also currently represents a net source of emissions. The potential for greenhouse gas mitigation within agriculture, forestry and other land use (AFOLU) is well recognised and achievable through the collective implementation of multiple mitigation measures.

This expert working paper was commissioned by the Climate Change Advisory Council secretariat in response to the council’s request for further information following a seminar on agriculture and forestry, hosted by the Climate Change Advisory Council in September 2018. The purpose of this document is to provide the council with science-based discussion on options regarding greenhouse gas mitigation within the AFOLU sector. More specifically, this paper aims to inform the council on (i) the Irish AFOLU sector (ii) the associated greenhouse gas emissions profile (iii) potential greenhouse gas mitigation options and (iv) related issues that may require policy development to enable transition within the sector.

The paper consists of a detailed literature review and analysis of a number of indicative scenarios for the sector. In addition, the authors conducted a series of semi-structured interviews with selected experts, between November 2018 and April 2019. Public and stakeholder acceptability of the mitigation options presented in the working paper have not been considered in detail. It is emphasised that mitigation options outlined would require consultation and engagement with relevant stakeholders prior to potential implementation.

The paper finds that (i) there is an urgent need for changes in management within the AFOLU sector while (ii) the necessity for change also presents opportunity, to tackle climate change, but also to provide multiple co-benefits to society.

In addition to greenhouse gas emissions, agricultural activity appears in certain cases, to be causing environmental degradation, while also generating insufficient economic returns. In its current structure, the sustainability of the Irish agricultural sector is at risk. Farmers, as key land managers, have successfully responded to policy, market and institutional signals in the past, and will form a necessary part of the solution. Their knowledge and expertise should be recognised as crucial in addressing challenges.

Agriculture is at the core of rural communities and identity across Ireland and has societal value beyond just the production of food. Change will be challenging, but the benefits to those involved and wider society, will be substantial if appropriately implemented. Regarding other land use, current low afforestation rates are considerably below target, which may limit the contribution that forestry can make to future greenhouse gas mitigation, while the continued drainage of peatlands and organic soils is a significant source of carbon dioxide emissions.

The following mitigation options to should be considered:

1. A gradual reduction in national bovine numbers may be necessary to achieve greenhouse gas emission reduction. This may also help address localised environmental degradation if implemented appropriately. Further expansion of the dairy herd may increase the risk of additional adverse environmental impacts. Continuation of the observed decline in suckler cow numbers, in conjunction with stabilisation of dairy cow numbers, would represent an important contribution to national efforts to reach Effort Sharing Regulation targets. Any reductions in animal numbers should be facilitated by long-term and consistent supports for stable incomes to provide favourable environmental outcomes through land management. 2. Management options for wetlands, especially peatland, require urgent assessment and implementation. Time is of the essence, as it will take a number of years for peatland ecosystems to re-establish and built resilience to projected changes in climate. The drainage of peat for multiple land uses, including peat extraction, must cease. Areas for rewetting should be identified and associated land management programmes started. Identification of agricultural land on which drainage has already ceased is required for inventory

accounting. Bord na Móna’s plans for peatlands under its management are an important opportunity for leadership, learning and public engagement. 3. Low afforestation rates need to be addressed with recognition and consideration of behavioural barriers. The type of afforestation, in terms species and environmental impacts, needs to be considered. Agroforestry appears to be a resilient system that permits agricultural production with limited afforestation, bringing multiple co-benefits and with further research, should be encouraged. 4. Expanding on approaches in the National Planning Framework, there is merit in the development of a national land use strategy. This should not be prescriptive but would enable design of policy to promote the sustainable delivery of multiple and competing land functions, while ensuring long-term environmental sustainability. 5. Cost-effective mitigation measures, identified in the Teagasc Marginal Abatement Cost Curve analysis, should be implemented as appropriate. These

mitigation options that would deliver reductions of 2.9 Mt CO2-eq per year, by 2030. The Common Agricultural Policy provides the mechanism for aiding this as it moves to greater national control. 6. There is a need for specific research into mitigation options that are detailed in this paper. Research requirements concern existing mitigation measures, the development of new measures, their technical implementation, impacts or trade-offs and associated development and refinement of inventory accounting methodologies. 7. Adoption and successful implementation of climate change mitigation policy and measures depends on farmers’ acceptance based on their lived experience, knowledge and understanding. Additional research and resources to enable effective knowledge exchange are required. 8. Noting the success of participatory approaches to engagement, for example the Citizens’ Assembly, a process of co-design could be implemented to facilitate engagement and ultimately strong stakeholder ownership of mitigation policies, which would help to achieve a just transition. 9. Identification and review of existing incentives and schemes that may be in conflict with greenhouse gas mitigation objectives is required, as coherence in policy is vital.

10. Ireland needs to engage with national and international experts to demonstrate and validate its environmental sustainability or ‘green’ credentials regarding food production. 11. Ireland should continue to support research into balance and neutrality concepts while promoting international research and policy development on this topic. Specifically, regarding the development of metrics that appropriately account for the lifetimes of short-lived greenhouse gases, such as methane.

Finally, it is emphasised many of the mitigation measures within the AFOLU sector are likely to bring multiple co-benefits, contribute to economic, social and environmental sustainability and are associated with basic, good land stewardship.

Abbreviations

AD Anaerobic Digestion AFOLU Agriculture, Forestry and Other Land Use C Carbon CAN Calcium Ammonia Nitrate CAP Common Agriculture Policy CCAC Climate Change Advisory Council CCC The Committee on Climate Change

CH4 Methane CICERO Centre for International Climate Research, Oslo CLT Cross Laminated Timber

CO2 Carbon Dioxide

CO2-eq Carbon Dioxide Equivalent COP Conference of Parties (to the UNFCC) CSO Central Statistics Office DAFF Department of Agriculture, Fisheries and Food (former) DAFM Department of Agriculture, Food and Marine DAHG Department of Arts, Heritage and the Gaeltacht DCCAE Department of Communications, Climate Action and Environment EASAC European Academics’ Science Advisory Council EBI Economic Breeding Index EC European Commission EPA Environment(al) Protection Agency ESD Effort Sharing Decision ESR Effort Sharing Regulation ETS Emissions Trading System EU European Union FAO Food and Agriculture Organisation FPCM Fat and Protein Corrected Milk GHG Greenhouse Gas GLAS Green Low-Carbon Agri-Environmental Scheme

Gt Gigatonnes (= 1013 t) GWP Global Warming Potential GWP* Global Warming Potential Star HWP Harvested Wood Products IFA Irish Farmers Association IPCC Intergovernmental Panel on Climate Change kt Kilotonnes (= 103 t) LULUCF Land Use, Land Use Change and Forestry MACC Marginal Abatement Cost Curve Mt Megatonnes (= 106 t) N Nitrogen

N2O Nitrous Oxide NBPT urease inhibitor - NBPT (N-(n-butyl) thiophosphoric triamide) NESC National Economic and Social Council NFS National Farm Survey

NH3 Ammonia OM Organic Matter SEAI Sustainable Energy Authority Ireland SOC Soil Organic Carbon Tg Teragrammes (= 1012 g) UK United Kingdom UNFCCC United Nations Framework Convention on Climate Change USA United States of America WHO World Health Organisation

Table of Contents

1 Introduction ...... 1

2 Setting the Scene ...... 3

2.1 Agricultural and land use greenhouse gas emissions and removals ...... 3

2.2 Irish agriculture and land use sector emissions profile ...... 5

2.3 Drivers within the Agriculture and Land Use Sector ...... 13

2.3.1 The Common Agricultural Policy ...... 13

2.3.2 Drivers of greenhouse gas emissions...... 14

2.3.3 Drivers of greenhouse gas mitigation ...... 24

2.4 Common metrics for emissions accounting ...... 29

3 Agriculture and Land Use Mitigation Options ...... 34

3.1 Reduction of agricultural greenhouse gas emissions ...... 35

3.1.1 Greenhouse gas emissions from livestock ...... 35

3.1.2 Greenhouse gas emissions from soils ...... 41

3.2 Carbon stocks and sequestration potential in soils and biomass ...... 46

3.2.1 Carbon stocks and sequestration potential in grasslands ...... 46

3.2.2 Forests and Woodland ...... 47

3.2.3 Carbon sequestration by farm hedgerows ...... 50

3.2.4 Carbon stocks in organic soils ...... 51

3.3 Avoiding carbon dioxide emissions through reduced fossil fuel use ...... 53

3.3.1 Reduction in on-farm energy consumption ...... 53

3.3.2 Energy from biomass ...... 53

3.3.3 The bioeconomy and circular bioeconomy ...... 54

3.4 Enabling greenhouse gas mitigation ...... 57

3.4.1 Changes to the Common Agricultural Policy ...... 57

3.4.2 National Land Use Strategy ...... 58

3.4.3 Improved knowledge exchange ...... 64

3.4.4 Agriculture and just transition ...... 66

4 Discussion and Conclusions ...... 82

References ...... 85

Appendix 1...... 123

A.1.1 Wetlands ...... 123

A.1.2. Grasslands ...... 124

A.1.3. Croplands ...... 125

A.1.4. Forest land ...... 126

Appendix 2...... 128

A.2.1. Reduction of agricultural greenhouse gas emissions ...... 128

A.2.2. Carbon stocks and sequestration potential in soils and biomass ...... 145

A.2.3. Avoiding carbon dioxide emissions through reduced fossil fuel use ...... 159

1 INTRODUCTION

In its Annual Review 2018, the Climate Change Advisory Council (CCAC) noted that Ireland was “completely off-course in terms of its commitments to addressing the challenges of Climate Change”. It noted that Ireland’s emissions were rising rather than falling and that under existing and proposed additional measures, Ireland is not projected to meet 2020 or 2030 emissions reduction targets.

Activities within the Agriculture sector are responsible for the largest share of greenhouse gas emissions in Ireland’s emission profile. Methane and nitrous oxide constitute the majority of these emissions. In recent years, agricultural emissions have increased and are projected to continue increasing. This has been due to the expansion of agriculture production, envisaged under to the Food Wise 2025 (DAFM, 2015a) development strategy, the sectoral response to the removal of milk quotas and growing international demand for animal-sourced foods.

The Land Use sector is currently estimated to be a large source of emissions, despite the significant removal of carbon dioxide from the atmosphere to forests and soils. The drainage of organic soils is the main cause of carbon dioxide emissions and occurs across all land uses. Ireland has, over the last century, increased forest cover from about 1% to nearly 11%. At least half of this afforestation occurred since 1980. However, the rate of afforestation has declined in the last decade, and at current rates, Ireland is unlikely to achieve the objective of 18% forest cover by 2046 (DAFM, 2014).

The National Policy Position (Government of Ireland, 2015) takes an integrated approach to Agriculture and Land Use, recognising the relationship between the two sectors in an Irish context and the impact of agricultural and other land management practices on the emissions profile of both sectors.

This working paper follows on from a seminar hosted by the CLIMATE CHANGE ADIVSORY COUNCIL in September 2018, and explores in greater detail the emerging science, mitigation opportunities, potential issues and policy options for the sector. Specifically, this paper aims to inform the council on (i) the Irish AFOLU sector (ii) the associated greenhouse gas emissions profile (iii) possible greenhouse gas mitigation

options and (iv) related issues that may require policy development to enable transition within the sector.

Except where otherwise stated, this working paper has adopted Global Warming

Potential evaluated over 100 years, GWP100, as the common metric by which the climate impact of emissions of different greenhouse gases are compared and expressed as carbon dioxide equivalent, CO2-eq. Although the Intergovernmental Panel on Climate

Change (IPCC) provided updated GWP100 values for methane and nitrous oxide of 28 and 265 (IPCC, 2014a), this report has adopted the values of 25 and 298 respectively are used here in accordance with national and international reporting requirements (Duffy et al., 2019). This is consistent with current reporting and accounting rules under the United Nations Framework Convention on Climate Change (UNFCCC) and European Union (EU) Energy and Climate Package.

2 SETTING THE SCENE

2.1 AGRICULTURAL AND LAND USE GREENHOUSE GAS EMISSIONS AND

REMOVALS

Ireland is food secure, but not food independent (Global Food Security Index, 2018). It has developed significant, specialised capacity in grass-based dairy, beef and sheep production. Exports of meat far exceed domestic consumption, while in contrast, Ireland imports a high proportion of the cereals, vegetables and fruit consumed nationally (CSO, 2018c).

Globally, agriculture, forestry and other land use (AFOLU) accounts for roughly 21% of greenhouse gas emissions (FAO, 2016). Three greenhouse gasses are associated with

the AFOLU Sector, carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) (Smith et al. 2008), accounting for 49%, 30% and 21% of emissions respectively (FAO, 2016).

Deforestation is the principal source of CO2 emissions. With respect to agricultural activities, carbon dioxide emissions are associated with the oxidation of organic matter by microbes in soils or biomass and fossil fuel combustion. Methane emissions are due to enteric fermentation, manure storage, and water table management in rice paddy fields or peatlands. Nitrous oxide emissions arise from management and use of fertilisers, manures and soils (Smith et al., 2008; Schaufler et al., 2010; Renou-Wilson & Wilson, 2018).

Agriculture accounts for approximately 10% of European greenhouse emissions, while European agriculture is responsible for 12% of global agricultural emissions (EC, 2018a). It is estimated that by 2030, livestock production will generate 72% of Europe’s

agricultural non-carbon dioxide (methane and nitrous oxide) emissions (EC, 2017).

The potential for measures within the Agriculture, Forestry and Other Land Use, AFOLU, sector to mitigate climate change is recognised internationally (Lal 2004; Aertsens et al., 2013; Bustamente et al., 2014, EC, 2018a) and are cost effective. Smith et al. (2008) estimated global mitigation potential of AFOLU by 2030 of between 2,500 and 2,700 Mt

-1 -1 CO2-eq yr when abatement costs were capped at US$ 50 t CO2-eq . This analysis did not include the displacement of fossil fuels by agriculture, which was estimated to offset

-1 -1 a further 2,240 Mt CO2-eq yr (at US$ 50 t CO2-eq ).

Table 1 Proportion of Total Emissions derived from Agriculture and Land Use (Source, FAO, 2016; Duffy et al., 2019)

Gas Percentage Percentage Total Emissions AFOLU Emissions

Total AFOLU All 21% 100% Carbon Dioxide CO2 14% 49% Global Methane CH4 42% 30% AFOLU Nitrous Oxide N2O 75% 21%

Total AFOLU All 38% a 100% Agriculture All 29% a 77%

LULUCF* All 9% a 23% Irish b Agriculture Carbon Dioxide CO2 1% 2% b Methane CH4 92% 51% b Nitrous Oxide N2O 93% 24%

* LULUCF = Land Use, Land Use Change and Forestry a Total national emissions including LULUCF b Total national emissions excluding LULUCF

Carbon sequestration by forestry and grasslands combined were estimated to balance the methane and nitrous oxide emissions across Europe between 2000 and 2005 (Schulze et al., 2009) while Aertsens et al. (2013) estimated potential sequestration of

-1 1,566 Mt CO2-eq yr from changes in agricultural practices within the 27 EU member state, equating to 37% of the total CO2-eq emissions in 2007. However, a number of studies of the impact of the 2003 European heat-wave observed significant emissions of carbon from ecosystems, with Reichstein et al. (2013) noting losses equivalent to the carbon sequestration observed in the previous 3-5 years. This sensitivity to extreme events needs to be factored into mitigation policies which rely on significant contributions from carbon uptake due to land use management.

In this paper, the discussion of agricultural emissions is in the context of targets under the EU Energy and Climate Policy. The EU policy treats the Land Use sector separately from both the Emissions Trading System (ETS) sector (which includes energy production and industrial processing) and the non-ETS sector. The non-ETS sector includes activities in agriculture, transport, smaller industries and commercial enterprises, public and residential buildings and waste. Non-ETS sector targets are defined under the Effort Sharing Decision ESD for the period 2013-2020, and Effort Sharing Regulation (ESR) for the period 2021-2030.

Agriculture in Ireland accounted for 32% of national greenhouse gas emissions in 2017 (Duffy et al., 2019) and approximately 44% of non-ETS sector emissions (Lynch et al., 2016b), demonstrating the significance of the sector (DCCAE, 2017). Agriculture is the single largest sectoral emitter (EPA, 2018a). Excluding emission from fossil fuel use, agriculture emitted 19.6 Mt CO2-eq in 2017 (Duffy et al., 2019). This is compared to the United Kingdom, where agriculture represents approximately 10% of total greenhouse gas emissions (CCC, 2019) and 16% of the non-ETS sector emissions (Lynch et al., 2016b). Denmark has the second highest share of emissions from agriculture at 20.5% in 2017 (UNFCCC, 2019). Irish agricultural emissions in 2017 were 0.24% higher than in 1990 (Duffy et al., 2019), with a negative trend noted from 1998 to 2011. However, emissions have gradually increased since 2011 and equated to a 14% increase by 2017. Emissions increased by 2.8% between 2016 and 2017. This recent trend has been principally attributed to expansion in dairy cow numbers and milk production (EPA, 2018a) with a 2.7% increase in the national dairy cow herd observed between 2017 and 2018 (CSO, 2019).

In the absence of additional mitigation measures, the upward trend in agricultural emissions is projected to continue as a response to market drivers. The EPA have projected a 7% increase from 2017 to 21.1 Mt CO2-eq in 2030 (without adopting additional mitigation measures), with increases of 22% in the dairy cow herd, 2% in other cattle and 21% in nitrogen (N) fertiliser use by 2030 (EPA, 2018c). Teagasc outlined a baseline scenario where in the absence of new mitigation measures, there would be a 9% increase in greenhouse gas emissions relative to 2005 levels by 2030 (Lanigan et al., 2018; Donnellan et al., 2018).

2.2 IRISH AGRICULTURE AND LAND USE SECTOR EMISSIONS PROFILE

Ireland, which has a temperate oceanic climate (Keane & Sheridan, 2004), is comprised of 6.9 million hectares of which 4.4 million are under agriculture, principally grassland. Managed grasslands and land under rough grazing, accounts for approximately 4.1 million hectares or 93% of the agricultural area (EPA, 2016). The majority of this consists of improved grassland with a smaller area under more extensive management (Sheridan et al., 2017). Arable production accounts for 0.36 million hectares or 8.2% of the agricultural area (EPA, 2016). As of 2018, the national herd stood at 6.9 million animals (CSO, 2019). Historic cattle numbers (June survey numbers) from 1900 are outlined in

Table 2. Since 2011, dairy cow numbers have been gradually increasing, with a 32% increase observed between 2011 and 2018, and currently stand at 1.4 million animals (CSO, 2019). Suckler (non-dairy) cow numbers have gradually declined since 2008 and currently stand at approximately 1.0 million animals, representing a 15% reduction from 2008 levels. However, the recent increase in dairy cows has driven an overall increase in breeding animals, with a corresponding increase in total herd size (Figure 1).

Table 2 Historic cattle June numbers (millions) since 1900 (Source: CSO, 1997; CSO, 2019)

Year

1900 1950 1960 1970 1980 1990 2000 2005 2010 2017 2018

Total 3.8 4.3 4.7 6.0 6.9 6.8 7.0 7.0 6.6 7.4 7.3 Cows a 1.2 1.2 1.3 1.8 2.0 2.1 2.4 2.3 2.2 2.5 2.5 Other Cattle b 2.6 3.1 3.5 4.2 4.9 4.7 4.8 4.6 4.4 4.8 4.8

a Includes dairy and suckler cows b Includes all replacement heifers, beef cattle and bulls

Breeding Bovine numbers 1991-2018 3000

2500

2000

1500

1000 Numner Numner of Animals(000s)

500

0 1990 1995 2000 2005 2010 2015 2020

All breeding Cows Dairy cows Other cows

Note: Difference between 2004 and 2005 was due to a change in statistical methodology

Figure 1 Time series of total, dairy and non-dairy cow numbers (Extracted from CSO June Tables).

There were 4.4 million sheep and 1.6 million pigs in 2018, with a decrease in sheep numbers (-3.5%) and slight increase in pigs (0.7 %) nationally since 2017 (CSO, 2019).

Livestock production systems in Ireland rely on grassland, with grazed or conserved grass representing 80 to 90% of the diet of dairy and beef cattle (EPA, 2016).

Historic net greenhouse gas emissions associated with agriculture and land use are illustrated in Figure 2. Agriculture emissions are projected to continue to increase (EPA,

2018c, Lanigan et al., 2018; Donnellan et al., 2018). Methane dominates the Irish agricultural emissions profile, accounting for 66% with nitrous oxide and carbon dioxide accounting for 32% and 2% respectively (Duffy et al., 2019). The EPA identified enteric fermentation (59%), agricultural soils (29%) and manure management (10%) as key emission sources, with minor sources of emissions from lime (1.7%) and urea applications (0.2%) (Duffy et al., 2019). It is worth noting that ruminant enteric fermentation from cattle and sheep alone, was estimated to represent 78% of national methane and 19% of total national greenhouse gas emissions in 2017, while agriculture was responsible for 93% of national nitrous oxide emissions (Duffy et al., 2019).

Ammonia (NH3) emissions arising from the same agricultural practices are also of serious concern both as an atmospheric pollutant (Donnellan et al., 2018) and major source of indirect nitrous oxide emissions. Atmospheric nitrogen pollution from ammonia is major threat to biodiversity (Kelleghan et al., 2019). Ammonia emissions exceeded targets under the National Emission Ceiling Directive (2001/81/EC) in 2016, directly attributed to increasing livestock numbers and fertiliser use (EPA, 2018d). Agriculture accounts for approximately 99% of national ammonia emissions, of which 90% are associated with the management of livestock manure (EPA, 2018d; Kelleghan et al., 2019; EPA, 2019).

In addition to greenhouse gas and ammonia emissions, agricultural activities have caused considerable localised environmental degradation. Agriculture was identified as responsible for 53% of pollution cases in rivers from 2010 to 2012 (EPA. 2016), primarily responsible for fish kills between 2000 and 2017 (EPA, 2018e), a principle cause of water eutrophication and ground water contamination (EPA, 2018e), while also being the

main indirect source of particulate matter in ambient air (EPA. 2016). Agriculture is also identified as both a threat and pressure to biodiversity (DCHG, 2017a) with over 35% of protected habitats highly threatened by agricultural activities (NPWS, 2013).

On the other hand, low-input or extensive agricultural systems are important in maintaining certain habitats and biodiversity (NPWS, 2013; Sheridan et al., 2017) as demonstrated by areas classified as High Nature Value (HNV) farmland (Paracchini et al., 2008; Lomba et al., 2014; Stohback et al., 2015). Martin et al. (2016) estimated the likely prevalence of HNV farmland in Ireland, with notably high distribution in Western regions, associated with low-input systems (EPA, 2016).

It should be noted that if negative externalities from agriculture, including greenhouse gas emissions and localised environmental degradation, were quantified in monetary terms and payable by producers, the net economic returns to livestock enterprises would be lower. The imposition of these costs would create the incentive for enterprises to address environmental damage, in order to avoid the cost, and would be consistent with environmental sustainability objectives of the sector.

Total Greenhouse gas emissions from Agriculture and Land Use 1990-2017 25,000

19,581

20,000 19,534

eq)

- 2 15,000

10,000

5,997

GHG Emissions CO (kt Emissions GHG 4,768 5,000

0 1990 1994 1999 2004 2009 2014

Agriculture Land use, land-use change and forestry

Figure 2 Time series of Net Greenhouse Gas emissions and removals for Ireland

When assessing emissions for land use, the flow diagram shown in Figure 3 illustrates the approach to land use classification for reporting under IPCC guidelines in a hierarchal manner. This ensures consistency of reporting the impact of human management on greenhouse gas emission and removals across all land use categories. The approach aims to ensure that all land types in the country are considered, whilst avoiding double counting.

Figure 3 Hierarchy approach to Land Use classification adopted for reporting

Estimates of Land Use, Land Use Change and Forestry (LULUCF) sector emissions and removals are illustrated in Figure 4. In the long-term, the carbon storage capacity of any landscape is finite. However, in many instances the current carbon stocks are not saturated and there may be options to increase sequestration of carbon. In principle, it is also possible to integrate biomass sequestration with carbon capture and storage technologies to enhance the carbon removal potential. However, as yet, these so-called Biomass Energy and Carbon Capture and Storage (BECCS) technologies have not been demonstrated at large-scale and uncertainty remains as to their potential contribution to mitigation. Detailed discussion of key land use types is contained in the Appendix 1. A summary is presented here.

Figure 4 Time series of net emissions and removals associated with Land Use in Ireland

Ireland has one of the lowest proportions of forest cover in Europe with almost complete deforestation of Ireland observed at the beginning of the 20th century. National policy initiatives have increased forest cover from ≈ 1% in early 1900s to ≈ 7% in 1990, to ≈ 11% in 2016 (Annual Forest Statistics, 2018, DAFM). This represents a considerable

investment by the State in development of the national forestry sector. The national policy is to increase coverage to 18% by mid-century. To achieve this target, afforestation rates will need to increase significantly, including on agricultural land. Conifers account for 71% of the national stand and deciduous 29%. Ireland’s forests are considered relatively young with 45% < 20 years old (DAFM 2018a). Accounting for impact of afforestation or deforestation in National greenhouse gas inventories, only permits changes that have occurred since 1990 to be included.

An average afforestation rate of 7,700 ha yr-1 was observed between 2012 and 2017 (DAFM, 2018a), falling to an estimated 4,000 hectares in 2018. Table 3 shows the estimated changes in carbon stock in forest lands between 2006 and 2012. It is worth noting that although the large majority of carbon stock is maintained in the soil, most would not be accountable in the national inventory as much of this carbon was already present in the soil before afforestation. Biomass (including litter and dead wood) accounted for 11.9 and 14.9% of total carbon stocks in 2006 and 2012 respectively. Considering forest area in both years, the carbon stock per hectare in non-soil pools, excluding harvested wood products, increased by 34% between 2006 (58.5 t C ha-1) and 2012 (78.3 t C ha-1). This reflects the maturing of the national forest.

Table 3 Extract from Annual Forest Statistics, 2018, DAFM. To convert between mass of carbon to mass of carbon dioxide multiple by 3.667 (44/12)

2012 2016

Carbon Stock Million % Million % Total Tonnes Total Tonnes

Above-ground Biomassa 30.6 8.9 39.7 10.4 Below-ground Biomassb 6.7 1.9 8.8 2.3 Deadwoodc 1.2 0.4 2.5 0.6 Litter 2.3 0.7 6.3 1.6 Soil 304.9 88.1 323.7 85.1 Total 348.4 100.0 381.0 100.0

a Above-ground biomass includes all living stems, branches and needles / leaves based at stump height at 1% of total tree height.

b Below-ground biomass includes all roots to a minimum diameter of 5 mm

c Deadwood includes all logs, stumps and branches with a minimum diameter of 7 mm

Grassland management is reported as a significant source of carbon dioxide emissions, dominated by emissions of carbon associated with drainage of organic soils (Duffy et al.,

2019). Duffy et al. (2019) reported emissions from grasslands to be 6.8 Mt CO2-eq in 2017. Research suggests medium intensity management of grazing lands can enhance soil carbon of mineral soils (e.g. Soussana et al., 2004; Moxley et al., 2014; Hewins et al. 2018). However, there is insufficient activity data to provide robust inventory evaluation of this. There is evidence that high intensity management can lead to carbon losses (Soussana et al., 2004; Ward, 2016). Systems such as adaptive multi-paddock (AMP) grazing, which involve high stocking rates over short grazing periods, may increase carbon sequestration (Stanley et al., 2018). There is likely an optimum range of grass- based livestock management intensity to achieve sustainable production, maintain soil function and enhance carbon stocks. The optimum management will depend on farming type, soil type, topography and regional climate (McSherry & Ritchie, 2013). Grasslands occurring on organic soils require different management (Soussana et al., 2004) as discussed in Section 3.2.4.

Croplands account for less than 10% of utilised agriculture area in Ireland. Greenhouse gas emissions are principally associated with soil cultivation and nitrogen fertiliser application. Mitigation measures for croplands include improved nutrient management, the incorporation of straw and the inclusion of cover or catch crops within crop rotations (Eory et al., 2015; RICARDO-AEA, 2016; Lanigan et al. 2018). Cover or catch crops are established between principal crops to protect potentially exposed soil over winter months and capture any available soil nutrients, therefore reducing leaching and run-off. Further details are provided in Appendix 1. Due to the comparatively small area under arable production in Ireland, the impact of associated mitigation measures on overall agricultural emissions would be small, but none the less, are an important contribution.

Wetlands in Ireland are a significant source of carbon emissions, due to drainage of peatlands for agriculture, forestry or extraction for power generation, residential heat and horticulture. Peatlands contain up to 75% of Ireland’s soil carbon, with carbon dioxide emissions associated with changes in soil temperature, vegetative cover and water table level (Renou-Wilson & Wilson, 2018). It is important to remember that emissions and removals are reported based on current land use, therefore much of the loss of carbon

from drained peatlands are reported under grasslands and to a lesser extent forest land. Other studies may classify peatlands on the basis of ecosystem function or ecological status, where drained peatlands may be classified as degraded, regardless of current land use. Based on the Irish soil classification system (Simo et al., 2014) approximately 21% of Ireland or 1.47 million hectares are peatlands (Connolly & Holden, 2009) classified as either ombrotrophic (fed by precipitation and described as bogs) or minerotrophic (fed by springs and described as fens). Raised and blanket bogs account for 311,000 and 774,000 hectares respectively. However, only 10% of raised bogs and 28% of blanket bogs remain intact (DAHG, 2015).

2.3 DRIVERS WITHIN THE AGRICULTURE AND LAND USE SECTOR

Emissions of greenhouse gases are an inevitable consequence of food production. Demand for food type, quality and volume is influenced by factors including price, income, culture, and other consumer preferences. Production of feed and fodder for animals can also cause significant emissions of greenhouse gases.

Improvements to production efficiency can reduce emissions per unit of product, however there are no mitigation options which can eliminate greenhouse gas emissions completely short of cessation of production.

2.3.1 The Common Agricultural Policy

The Common Agricultural Policy (CAP) in its current format consists of two elements: Pillar I provides direct payments to farmers, known as Basic Payment Scheme; while Pillar II provides further financial incentives for rural development and environmentally sustainable practices.

Farmers are eligible for Basic Payments provided they satisfy of a number of cross compliance standards that include thirteen statutory management requirements and Good Agricultural and Environmental Condition (GAEC) regulations. In addition, a proportion of the Basic Payment represents “Greening” payments which are received following the implementation of specific on-farm measures. Pillar II incorporates measures such as the Rural Development Program which funds the Green, Low-Carbon, Agri-Environment Scheme (GLAS), the Organic Farming Scheme, disadvantaged areas supports and a number of other schemes.

In 2018, the European Commission (EC) published proposals for CAP reform post 2020, covering the period 2021 to 2027 (COM 2018, 392). In this, the EC recognised shifting requirements and challenges within the agricultural sector such as the importance of climate change mitigation in the context of the ambitions set out in the Paris Agreement. The CAP reform proposes a shift in responsibility and design of CAP implementation to Member States, thereby providing potentially greater flexibility and focus in the design of supports. Further discussion on planned changes within the CAP and how these may enable greenhouse gas mitigation within the Irish Agricultural Sector is outlined in Section 3.4.1.

2.3.2 Drivers of greenhouse gas emissions

FoodWise 2025

Foodwise 2025 (FW2025) is an industry led strategy for the development of the agri-food sector in Ireland. FW2025 was published in 2015 and superseded Food Harvest 2020 (DAFF, 2010). FW2025 targets an increase in the agri-food export value by 85% along with a 65% increase in the value of primary production (DAFM, 2015a). The objective of FW2025 is to develop the sector to be achieve economic, social and environmental sustainability. Its successful implementation has been enabled by the removal of the Milk Quota and increased international demand for animal-sourced foods. The value-added focus of FW2025 means, although it was not a direct driver of emissions, it may have resulted in a rebound effect which saw the re-investment of income into production expansion. Environmental sustainability is identified as a core aspect of agri-food sector development, with the ambition that Ireland will become a global leader in sustainable food production (DAFM, 2018b). However, the EPA (2016) has expressed concerns over the delivery of FW2025 targets regarding environmental sustainability, notably around increased greenhouse gas and ammonia emissions.

FW2025 has largely achieved its economic objectives, with significant increase in the value of the agriculture sector (DAFM, 2018b). Much of these gains were achieved through expansion of production, especially in the dairy sector. Despite increased efficiency per unit of product, expansion of dairy production is identified as a principal cause of increases in agricultural greenhouse gas emissions (EPA, 2018a). There is

concern over compliance with other environmental targets such as ammonia emissions (EPA, 2016, EPA, 2018d). The Director of Teagasc, speaking before the Committee of Public Accounts, expressed concern about FW2025 regarding increased bovine numbers and meeting 2030 emission reduction targets, suggesting that greenhouse gas mitigation measures “will not be sufficient to off-set” the increase in emissions due to the changes in herd numbers and structure (Houses of the Oireachtas, 2018). Concerns have also been raised over the potential impact of FW2025 on farmland biodiversity (BirdWatch Ireland, 2015), water and soil quality (EPA, 2016).

Indirect Production Support Incentives or Schemes

Although decoupling of CAP direct payments from production was implemented in 2003, a number of support schemes in the beef sector, continued to provide payments in proportion to the number of bovines held on farms (Hanrahan, 2016). Suckler schemes supported expansion of the beef sector following the introduction of the Milk Quota in 1984 (Hennessy & Kinsella, 2013). More recent suckler support schemes include the Suckler Welfare Scheme, Animal Welfare, Recoding and Breeding Scheme (AWRBS) and the Beef Environmental Efficiency Pilot Scheme (BEEPS). The primary aim of more recent schemes has been to enhance the efficiency of production with potential co- benefit for mitigation of greenhouse gas emissions. However, the schemes may have indirectly incentivised the maintenance of livestock numbers.

The Targeted Agricultural Modernisation Scheme (TAMS), which aims to improve the performance and efficiency of on-farm machinery, may also incentivise expansion within dairy enterprises by enabling greater on-farm processing and storage. For example, support is available for milking, milk storage and cooling equipment, as well as in-parlour feeding systems. Other TAMS supports are explicitly focused on achieving improved environmental outcomes, for example supports for low emission slurry spreading equipment, which enable emissions reductions.

The review, monitoring and appropriate modification of agricultural incentives and schemes to avoid conflict and ensure coherence with climate policy is vital. Policies and associated measures should support reductions in absolute emissions in order to contribute to the National Policy objective.

The EU Nitrates Directive permits a maximum application rate for organic nitrogen of 170 kg N ha yr-1 (EC, 1991; Statutory Instruments, 2017). This limits the quantity of manure that can be applied, including that deposited to land by livestock itself. It also constrains the number of livestock that can be carried on a particular holding. Authorised derogation from the Nitrates Directive (Statutory Instruments, 2017) allows higher stocking rates provided on-farm measures are taken to avoid pollution but may have enabled dairy production expansion. Derogations are currently under review and could play an important role in ensuring that production is maintained within certain environmental limitations (Government of Ireland, 2019).

Carbon Footprint and Carbon Leakage

Ireland’s contribution to global food production is relatively minor, although Ireland is a high net exporter of food. For example, 91% of the beef and veal produced was exported in 2017 (CSO, 2018b). The increase in emissions associated with the expansion of Irish food production to supply export markets (EPA, 2018a; CSO, 2019), has been rationalised on the basis of the low-carbon footprint of Irish production systems and potential for carbon leakage (Lanigan et al., 2018; DAFM, 2018b).

Do Irish dairy and beef production systems have a low carbon footprint? Life Cycle Assessment (LCA) analysis can be used to estimate the environmental impacts, for example the carbon footprint, of production systems. LCA considering both the inputs and outputs associated with defined stages of a product’s ‘life’ (Finnveden et al., 2009). LCA differs from national inventory accounting (Duffy et al., 2018), which only considers absolute activity emissions and not for specific goods, and those generated within Ireland. Therefore, emissions from processing, manufacturing and distributing potential inputs, for example fertilisers associated with a good, may or may not be included in an LCA study, at the discretion of the researcher.

LCA analysis indicates that temperate grass-based milk production may have a lower carbon footprint compared to other systems in other regions (FAO, 2010; Leip et al., 2010). Some internationally reported values are outlined in Table 4, while Crosson et al. (2011) provide a more extensive meta-analysis. It must be remembered that direct comparison between studies is difficult and must be viewed with caution due to what has been included within the methodology that is variations in systems boundaries (Yan et al., 2011).

Of the studies that have compared data from regions or countries using the same methodology., Leip et al. (2010) found Ireland had one of the lowest emissions per unit of milk across the EU-27 using LCA analysis (1 kg CO2-eq kg milk, compared to an average of 1.4 kg CO2-eq kg milk). In contrast, Lesschen et al. (2011), using a different model to LCA, found Ireland had the fourth highest emissions per kg milk across the EU-

27 (~ 1.6 kg CO2-eq kg milk compared to an average of 1.3 kg CO2-eq kg milk). It is important to note that the studies conducted Leip et al., (2010) and Lesschen et al. (2011) used 2004 and 2003 to 2005 data respectively, and therefore may be out of date.

O’Brien et al. (2014b) compared high-performance Irish grass-based dairy systems to confinement systems in the UK and the USA using LCA analysis. The confinement systems involved total mixed ration diets and higher concentrate reliance. Irish systems were found to have a 5% and 7% lower carbon footprint than the UK and USA systems respectively. Interestingly, when additional carbon sequestration to grassland soils within the Irish system was excluded, all systems were found to have a similar footprint.

The Origin Green Sustainability Report estimates participating dairy enterprises have an average carbon footprint of 1.1 kg CO2-eq per kg milk product, but noted large variation in performance between enterprises, from 0.8 to 1.7 kg CO2-eq per kg (Bord Bía, 2016).

Teagasc has estimated average emissions of 0.73 kg CO2-eq kg milk but varied from ~

0.68 to 0.80 kg CO2-eq kg milk for the best and poorest economically performing enterprises respectively. It is important to note that national inventory accounting methodology was used rather than LCA modelling in this study (Buckley et al., 2019), the difference between which has been discussed.

Overall research suggests that Ireland has a relatively low carbon footprint for dairy production. There is clearly room for improvement across the sector, in terms of closing the gap between the best and worst performing producers. However, not all farms will be able to achieve a high level of efficiency, due to local environmental and geographic limitations.

Ireland is the fifth largest beef exporter in the word, exporting principally to Europe (DAFM, 2018b), and estimated to account for 9% of EU beef production (Eurostat, 2018). Beef production systems take various forms globally, including feedlot grain- based or grass-based systems, while stock may come from dairy or suckler herds. The majority of Irish beef is produced by grass-based systems. Leip et al. (2010) found

Ireland had the fifth lowest carbon footprint within the EU 27 (19 (as outlined by DAFM,

2018b) kg CO2-eq kg beef, compared to an average of 22 kg CO2-eq kg beef) using an LCA approach and based on 2004 data. Using a different approach to LCA, Lesschen et al. (2011) found Ireland had the ninth highest carbon footprint within the EU-27 (~ 28 kg

CO2-eq kg beef compared to an average of 22.6 kg CO2-eq kg beef) using data from 2003 to 2005.

A review conducted by Desjardins et al. (2012) indicated that the carbon footprint for some of the main beef production regions (Canada, USA, EU, Australia, Brazil) ranged from 8 to 22 kg CO2-eq kg of Live Weight (LW). Casey & Holden (2006a) reported 11.26

-1 kg CO2-eq kg LW yr for Ireland and suggested this was similar to beef produced in other EU countries when comparing their results to a limited number of LCA studies available at that time. An overview of recent studies is outlined (Table 5). Crosson et al. (2011) provides a more in-depth meta-analysis. It is important to note that values may or may not include impacts from changes in land use or land management as a result of beef production, an important component when estimating carbon footprints (Cederberg et al. 2011; Stanley et al., 2018). This highlights the need for caution when comparing studies (Desjardins et al., 2012). For example, an average value of ~ 28 kg CO2-eq kg carcass weight is described for Brazilian systems, not accounting for land use change.

When land use change (deforestation) was included, a value of 726 (±252) kg CO2-eq kg carcass weight was estimated. However, such land use change concerned 6% of production in 2006, giving an average carbon footprint of 44 kg CO2-eq kg carcass weight for total Brazilian beef production that year (Cederberg et al. 2011).

Table 4 Summary of selected international Life Cycle Analysis outputs reported for the carbon footprint values of milk production systems

Region / Carbon Footprint Measurement Source Country (CO2-eq) Unit

Global 1.5 kg kg of milk Hagemann et Average al. (2012) EU 27 1.4 kg kg of milk Leip et al. Average (2010) Ireland 1.0 kg kg of milk Leip et al. (2010) Ireland 1.11 kg kg of fat and protein O’Brien et al. corrected milk (FPCM) (2014a) Ireland 0.837 kg (high performance kg of energy corrected milk O’Brien et al. systems) (ECM) (2014b)

Ireland Between 0.92 and 1.51 kg kg of energy corrected milk Casey & (ECM) Holden (2005) Ireland 1.06 kg (well drained soils) kg of energy corrected milk Sharma et al. 1.18 kg (poorly drained (ECM) (2018) soils) Netherlands 1.4 kg kg of fat and protein Thomassen et corrected milk (FPCM) al. (2008) New Zealand 1.0 kg kg of energy corrected milk Flysjö et al. (ECM) (2011) Sweden 1.16 kg kg of energy corrected milk Flysjö et al. (ECM) (2011) UK 0.884 kg (high performance kg of energy corrected milk O’Brien et al. systems) (ECM) (2014b) USA 0.898 kg (high performance kg of energy corrected milk O’Brien et al. systems) (ECM) (2014b)

The Origin Green Sustainability Report estimates that participating beef enterprises have an average carbon footprint of 11.6 kg CO2-eq per kg beef liveweight (Bord Bía, 2016).

The report also notes the large variation in performance from 5 to 18 kg CO2-eq per kg. Using national inventory accounting methodology, Teagasc estimated average emissions of 11.9 kg CO2-eq kg live weight beef. However, values ranged from 9.6 to

14.9 kg CO2-eq kg live weight beef, for the best and poorest economically preforming enterprises respectively (Buckley et al., 2019).

Table 5 Some internationally reported carbon footprint values of beef production

Region / Carbon Footprint Measurement Source Country (CO2-eq) Unit

EU 27 22 kg of beef Leip et al. Average (2010) EU 27 Between 10.4 and 15.6 kg LW Desjardins et Average (Suckler) al. (2012)*

Ireland 19 kg of beef Leip et al. (2010) Ireland 11.26 kg of LW yr-1 Casey & Holden (2006a) Ireland 13.0 kg of LW yr-1 Casey & (Conventional) Holden (2006b) Ireland 12.2 kg LW yr-1 Casey & (Extensive) Holden (2006b) Ireland 11.1 kg LW yr-1 Casey & (Organic) Holden (2006b)

Ireland 13 kg LW Desjardins et (Suckler) al. (2012)* Brazil 14.3 kg of LW Desjardins et (Conventional) al. (2012)* Brazil 22.4 kg LW Desjardins et (National) al. (2012)* Canada (East) 15.3 kg LW Desjardins et (Conventional) al. (2012)* Canada 8.4 kg LW Desjardins et (West) (Conventional) al. (2012)*

Australia 8 kg LW Desjardins et (Conventional) al. (2012)* Sweden 11.6 kg LW Desjardins et (Organic) al. (2012)* UK 25.3 kg of beef carcass Williams et al. (Suckler) (2006) UK 8.7 kg LW Desjardins et (Conventional) al. (2012)* USA 14.8 kg LW Desjardins et (Midwest) (Feedlot finished) al. (2012)*

USA 19.2 kg LW Desjardins et (Midwest) (Pasture finished) al. (2012)*

* As outlined in a meta-analysis complied by Desjardins et al. (2012). Please refer to Desjardins et al. (2012) for original sources

Therefore, Irish beef production systems appear to have an average to low carbon footprint, though as with studies examining milk, differences in methodology and results make definitive conclusions difficult. However, when compared to non-EU systems while accounting for impacts of land use change, Irish systems have a lower footprint. Similarly to dairy production, there is room for improvement within Ireland and closing the gap between the best performers within the beef sector. As with dairy farms, not all beef farms will be able to achieve a high level of efficiency due to local environmental and geographic limitations at farm scale.

Will a reduction in Irish dairy and beef production lead to a net global increase in emissions? It is suggested that reductions in beef and dairy production to reduce greenhouse gas emissions in Ireland will result in carbon leakage (Hennessy et al., 2018; Lanigan et al., 2018). Carbon leakage describes the response where a reduction in milk or beef production in Ireland would be compensated by increased production in other countries, where, in addition, production systems may be less greenhouse gas efficient.

Scenario modelling allows the potential extent of leakage to be quantified. Fellmann et al. (2018) projected that reductions in EU livestock production would lead to increased production in non-EU countries, limiting the global net benefits of emission reductions within the EU (Fellmann et al., 2018). Considering all agricultural production, leakage was projected to offset 91% of potential emissions reductions achieved within the EU. However, 90% of the resulting emissions generated outside the EU were associated with livestock and animal products suggesting livestock production may be associated with a high leakage effect. Styles et al. (2017) indicated that global greenhouse gas emissions may increase if the intensification of UK dairy production led to reduced beef production and an associated international displacement satisfied by low-intensive systems in Brazil. The reduction in beef output from dairy intensification in the UK was assumed due to the same level of milk being produced by fewer cows, resulting in less dairy beef calves.

The leakage rate, which refers to the proportion of an emission reduction achieved in one country that results in increase in emissions in other countries, was examined for Denmark (De ØKonomiske Råd, 2019). Denmark’s overall, cross-economy leakage rate was estimated to be between 45% and 53%. However, the leakage rate for agriculture was estimated to be higher, at approximately 75%. This was due partly to food consumption being relatively inelastic to variations in price and income, whereby reduced food production in Denmark would lead to increase food imports. However, this would still represent a net decrease in global emissions, albeit less than the emission reductions reported within Denmark. De ØKonomiske Råd (2019) also noted difficulty in estimating leakage rates for the agricultural sector. This may differ in Ireland as a large proportion of the food produced is exported, therefore, domestic demand for food is not the key determinant of production levels. For this evidence to remain valid in the Irish context, the response of the international markets that Ireland supplies must be considered.

Potential leakage within Europe is constrained by the other EU member states being party to non-ETS emissions reduction targets under the ESR. This may generate demand for imports from non-EU countries. The potential higher carbon footprint per unit of product which non-EU countries have, would lead to increased leakage. Both EU trade policy and non-EU climate policy, may therefore have a considerable impact on leakage potential. EU policy governs imports into the EU, thereby determining the

opportunity for a commodity with a potentially higher carbon footprint, to displace those produced in the EU. In terms of non-EU climate policy, individual Nationally Determined Contributions (NDC’s) of Paris Agreement signatories, could determine non-EU counties’ ambitions towards targets within the sector under their jurisdiction, thereby potentially reducing incentives to increased production to supply EU markets.

The Danish study assumed emission reduction would be largely achieved by a decline in agricultural production. Leakage only occurs from a reduction in activity in one country and displacement in another country. Considering EU agriculture, a study by the Joint Research Council (JRC) indicated that providing subsidies for mitigation measures in agriculture could lesson leakage potential (Van Doorslaer et al., 2015). Subsidised mitigation facilitates emission reductions while ensuring a continued level of competitive agricultural production. However, in this study, more ambitious targets required a reduction in production, increasing the projected leakage.

As far as the authors are aware, no Ireland specific research on emissions leakage has been conducted, except for studies that present leakage as a plausible scenario, when discussing their main findings with respect to production efficiency (Crosson et al., 2011; O’Brien et al., 2014c).

In summary, leakage is likely to occur but there is insufficient evidence to provide a definitive answer to whether a reduction in agricultural production in Ireland will lead to a net increase in global greenhouse gas emissions. The balance of probability suggests that mitigation measures implemented with the support of subsidies, together with an extended range of mitigation options, would not increase global emissions.

Is the rationalisation of current or increased production levels based on production efficiency, avoiding leakage and a net increase in global emissions valid? Maintenance of current production within the dairy and beef sectors, in the absence of improved efficiency and mitigation, will sustain agricultural greenhouse emissions and potential localised environmental degradation at current levels. Increased production will lead to an increase in agricultural, and non-ETS emissions unless balanced by emission reductions elsewhere and may generate further environmental degradation.

With respect to our national commitments, it must be remembered that EU greenhouse gas emissions reduction targets are based on absolute emissions. The National Policy

Position also aims for carbon neutrality in terms of absolute agricultural emissions. There are no criteria within EU or National policy regarding the efficiency of activities leading to global greenhouse gas emissions, within the agricultural sector.1 Production efficiency is only relevant in as far as it can lead to reductions in absolute emissions. Leakage is not catered for within the current non-ETS framework. Leakage concerns are addressed in the ETS by issuing allowances to free to exposed sectors.

The leakage argument is only valid when an increase in Irish production displaces less efficient production in other countries. In addition to greenhouse gas emissions, increasing production intensity may negatively impact biodiversity, air and water quality in Ireland (Section 2.2). The drivers for expanded production are international markets and the growing global demand for high carbon intensity products. Supplying growing export markets at the expense of national environmental integrity and reputation, is unwise, regardless of a potential efficiency or leakage. In all cases, agricultural production should occur within local environmental constraints.

Indeed, the Danish Economic Council concluded that despite the effects of reducing agricultural emissions being tempered when accounting for leakage, reductions in agricultural emissions should still be pursued in part due to socio-environmental co- benefits regarding improved water and air quality (De ØKonomiske Råd, 2019)

In conclusion, rationalisation for the maintenance or expansion of the dairy and beef sectors in Ireland based on carbon efficiency, potential leakage and impact on global emissions is not well supported. This is due to; (i) a lack of recent or specific research and differences in findings from studies within the peer review literature, making the potential extent and impact of leakage unclear (ii) current national and EU policy framework not including criteria based on leakage or emissions efficiency in setting

1 It is worth noting that the EU has established efficiency targets with respect to greenhouse gas emissions from other non-ETS activities such as commercial and passenger vehicles as well as household appliances.

emissions objectives for the sector and (iii) the risk of localised environmental degradation not being addressed under these criteria.

If national and EU policy objectives shift to reduce global rather national greenhouse gas emissions, there may be reason to provide flexibilities and additional supports on sectors with high leakage potential. This approach has been implemented within the ETS sector and may be valid for some non-ETS activities. However, agriculture cannot be exempt from addressing localised environmental degradation or contributing to emission reductions.

2.3.3 Drivers of greenhouse gas mitigation

Efforts to reduce Irish AFOLU sector greenhouse gas emissions are currently guided by (1) a pursuit of carbon neutrality by 2050 and (2) a required 30% reduction (relative to 2005 levels) in non-ETS sector emissions by 2030. The first is the National Policy Position which is consistent with the EU 2050 greenhouse gas low carbon economy roadmap (EC, 2011), while the second is Ireland’s agreed contribution under the Effort Sharing Regulation (EC, 2018b) as part of the EU Energy Union and Paris Agreement strategies (EC, 2018c). The National Policy Position on climate action and low-carbon development was published on 23rd April 2014 and the Climate Action and Low-Carbon Development Bill 2015 was published on 19th January 2015 (DAHG, 2015).

National Planning Framework and National Development Plan

The National Planning Framework (Government of Ireland, 2018a) identifies the role of planning and development in providing a mechanism for maintaining and enhancing carbon stocks, especially in the context of forest and peatland protection. This is highlighted again in the Annual Transition Statement (DCCAE, 2018). With regard to agriculture and land use, the National Development Plan is focused on implementation of adaptation measures to manage flooding in vulnerable areas, including nature-based solutions, which would also manage carbon stocks.

National Policy Position and Carbon Neutrality

The Irish Government, following an initial National Economic and Social Council report (NESC, 2012), proposed an approach towards carbon neutrality by 2050 as a policy objective (DCCAE, 2014). Schulte et al. (2013) explored the concept of Carbon Neutrality in the context of current reporting and accounting rules. A key finding from this study was that Ireland should consider an “approach towards neutrality” rather than adopt neutrality as an endpoint. Additional research into carbon neutrality commenced in April 2019 with funding from the EPA and DAFM as an action item under the National Mitigation Plan (DCCAE, 2017). Carbon neutrality highlights the potential for LULUCF to mitigate agricultural emissions.

Emissions Targets and Agriculture, Land Use and Forestry

Within the Climate and Energy Package to 2030, land use and forestry are treated separately from the other sectors. Agriculture continues to be considered in the context of agreed national targets for emissions reduction under the Effort Sharing Regulation. Ireland has agreed to a target of 30% emissions reduction (for the non-ETS sector) by 2030 relative to 2005 and has also negotiated access to two flexible mechanisms to enable compliance with this. The first flexible mechanism is essentially a limited transfer of allowances from the Emissions Trading System. The second is limited transfer of credits for removals from accountable LULUCF activities. Access to LULUCF credits are restricted to a maximum of 26.8 Mt CO2-eq (EC, 2018b) or 5.6% of the non-ETS emissions in 2005. The total removal capacity of LULUCF in the period from 2021 to 2030 is likely to be larger than this, but it assumes the projections of the forest removals are realised and a contribution from the improved management of drained organic soils (Lanigan et al., 2018).

Access to the LULUCF mechanism is contingent on no net loss of carbon from the accountable activities. Where there are source activities within accountable LULUCF activities, then these must be balanced by equivalent removals by other LULUCF activities in the first instance. The available LULUCF removals credits will be the net removal across all accountable LULUCF activities. Even where Ireland out performs in accountable LULUCF activities out to 2030, improvement in land management will contribute towards the National Policy position.

The accounting rules for forestry are complex, but there is confidence that forest land and afforestation will provide significant removals in the period to 2030. Grassland and croplands have less complex accounting rules, with emissions and removals accounted relative to the average during the reference period 2005 to 2009 in other words, reflecting the impact of changes in management relative to the reference period. There is significant uncertainty as to whether changes in land management since 2005 can be documented and whether these will result in a net sources or removals over the period between 2021 to 2030.

Ireland will also need to consider whether to elect to account for changes in wetland management for the period 2021-2030. The potential net removals likely from cessation of peat extraction from Bord na Móna lands are an obvious incentive to elect wetlands. However, the task of providing credible reporting of activities on all national wetlands would be challenging. The additional resources required to achieve this need to be considered in this decision-making process.

The Origin Green Programme

Bord Bía’s Origin Green programme is a high-profile initiative that provides supports to farm, manufacturing, retail and food service levels to adopt best practice to enable environmentally sustainable food production. Knowledge transfer, through the Carbon Navigator and other emissions assessment tools, is a core feature of the programme, with voluntary actions and targets for participants. Origin Green also conducts regular audits and data collection on participating farms. The programme has achieved high participation rates with 50,000 beef farms which produce 90% of the beef exported, along with 70% of dairy farms signed up to related schemes. The most recent Origin Green Sustainability Report 2016 (Bord Bía, 2016) outlines significant potential for emissions reduction within beef (7%) and dairy (14%) production based on successful achievement of the individual improvement targets by the cohort of participating farmers. Demonstrating progress in achieving this potential emissions reduction will be important as Origin Green develops.

The IFA/EPA-led Smart Farming initiative complements Origin Green but has a broader environmental scope. The EPA has collaborated with key agriculture stakeholders, including the Irish Farming Association, the Department of Agriculture, Food and Marine, Teagasc, Sustainable Energy Association Ireland (SEAI) and third-level institutions. The

aim of the initiative is to enable farmers to adopt sustainable farming practices, including greenhouse gas mitigation measures, through knowledge transfer, while also achieving significant cost co-benefits and improving farm environmental and economic sustainability. In its 2017 report, the Smart Farming programme estimated a 10% average potential greenhouse gas emissions reduction on participating farms while also increasing profitability.

Realisation and verification of the emissions reductions potential under these voluntary programmes requires robust data analysis and monitoring, including commitment to allow access to data and regular, independent assessment and reporting on progress. Data on the uptake of these practices and real world impact on carbon stocks are needed to ensure they are reflected in national reporting of emissions and removals. It must be noted that any failure to meet emission reduction targets or reverse environmental degradation caused by agriculture may have considerable consequences regarding Ireland’s international image (Donnellan et al., 2018), greatly undercutting programmes such as Origin Green. The adverse market response and economic impact of reputational damage is difficult to assess but may be greater than the direct cost of non-compliance with greenhouse gas emissions targets, or the marginal costs of implementation of mitigation measures.

Market Trends in Food and Consumer Choice

Ireland exports the majority of the food it produces (DAFM, 2018b) and is dependent on international markets and consumer trends. Diets are changing globally with greater animal-sourced food consumption in developing regions and the encouragement of less animal-sourced food consumption in developed regions.

Fellmann et al. (2018) identified changes in consumption of meat as a plausible mitigation strategy within the EU. There is growing recognition internationally that current food systems are exacerbating two growing consumption concerns regarding the delivery of balanced nutritional requirements: under-nutrition contributing to the prevalence of diseases and over-consumption of high-calorie foods contributing to obesity (Friel & Ford, 2015; EASAC, 2017). In addition, food production systems are pushing resource use beyond planetary limits – generating significant global environmental degradation and greenhouse gas emissions. Changes in production and consumption patterns may be required in response to these challenges (Sage, 2012;

Smith & Gregory, 2013; Tilman & Clarke, 2014; EASAC, 2017; FAO, 2018; Willett et al., 2019). The European Academics Science Advisory Council (EASAC) have suggested that reductions in animal-sourced food consumption may improve public health and reduce greenhouse gas emissions as a co-benefit (EASAC, 2017). Friel et al. (2009) estimated that a reduction in animal-sourced fat consumption by 30% in the UK could lead to a 15% and 17% reduction in heart disease and premature deaths respectively.

The EAT-Lancet Commission (Lucas & Horton, 2019; Willett et al., 2019; EAT, 2019) recently recommended reduced animal-sourced food consumption in an attempt to define sustainable diets in the context of the UN Sustainable Development Goals and the Paris Agreement. A 50% reduction in the consumption of foods deemed less healthy, including red meat was advocated in conjunction with a 100% increase in consumption of fruit, nuts and vegetables. Similar advice on dietary guidelines was issued by the Canadian Government which recommend higher levels of consumption of plant-based proteins over meat (Health Canada, 2019). In both cases, a reduction but not the elimination of animal-sourced food consumption was recommended. However, the Eat- Lancet Commission also noted difficulty in some regions to satisfy nutritional requirements on plant-based diets, while recognising the dependence of some populations on pastoral based and associated livestock production systems for livelihoods (EAT, 2019). The Global Panel on Agriculture and Food Systems (2016) also highlighted the necessity for animal-based foods, particularly in low income contexts.

Consideration must be given to nutrient requirements of different social groups (EASAC, 2017). There may be cause for a reduction in animal-sourced food consumption at a population scale. However, for certain societal groups including infants, children and women during pregnancy, the consumption of animal-sourced food is preferable in fulfilling critical nutritional requirements, due to its high nutrient density. In low-income situations, nutritional requirements for these societal groups could be difficult to meet without animal-sourced foods (Global Panel on Agriculture and Food Systems for Nutrition, 2016). The satisfaction of critical nutrient requirements from alternative sources such as cereals, may lead to excess energy consumption, promoting obesity. White & Hall (2017) highlighted the contribution of animal-sourced foods in the USA, providing 24%, 48%, 23 to 100% and 34 to 67% of total energy, protein, essential fatty and amino acids respectively. Plant-only diets were projected to cause more nutrition deficiencies, a need for greater intake of food solids and greater excess of energy. Animal-sourced food

may provide the most efficient delivery of critical nutrients compared to plant-based foods.

Additionally, from a global food supply perspective, grass-based livestock systems are an efficient use of resources that may not support arable or horticultural production. It is recognised that a “no meat consumption” argument is an oversimplification (Godfray et al., 2010). The FAO projected increased demand for non-staple foods including meat and dairy products in developing regions (OECD & FAO, 2016; FAO, 2017). Godfray et al. (2010) also highlighted changes in affluence as a key driver of increased consumption of meat and dairy products, notably in countries such as India and China. Therefore, it is suggested that the impact of trends of lower animal-sourced food consumption in developed regions, may be less than the impact of increased demand for these products in developing regions.

2.4 COMMON METRICS FOR EMISSIONS ACCOUNTING

The issues discussed in this section are important for the long-term transition objective, and an approach towards neutrality. It is important to note that Ireland’s targets for emissions reduction by 2020 and 2030 are accountable in terms of Global Warming

Potential evaluated over 100 years, GWP100. Any contributions of carbon emissions and removals within the land use sector to achieving targets will be assessed in the context of existing accounting rules. However, it is open to the Irish Government to use different metrics, including split gas approach, to set domestic targets, distribute the non-ETS burden across sectors, provided that they meet the national target in aggregate in

GWP100 terms.

Concerns about the use of GWP have been raised both by the scientific community and by some parties to the United Nations Framework Convention on Climate Change (UNFCCC). These concerns have given rise to a renewed focus on the use of GWP within the UNFCCC and among the scientific community. This has resulted in alternative metrics being considered in the scientific literature as reported by the IPCC in its AR5 (IPCC, 2014a). GWP* has emerged recently and seeks to reconcile the impact of emissions of long-lived species, such as carbon dioxide and nitrous oxide, with the on- going emission of short-lived species such as methane. GWP* has the advantage of being a logical extension of previously agreed metrics, but providing the link to policy

objectives, namely the stabilisation of global temperature. Allen et al. (2016), highlight the equivalence in global temperature response to a once-off, pulse emission of carbon dioxide, and the sustained emission of short-lived gases, including methane. Figure 5 illustrates the long-term impact in a comparison between the pulse emission of 38 Gt

CO2, equal to total global emissions in 2011, and sustained emissions of methane, black carbon and Hydroflourocabons (HCFs). Essentially, the climate impact of long-lived and short-lived species have stabilised by the end of the century. This equivalence can be reflected in a revised interpretation of GWP. The conventional usage of GWP100 dictates that the equivalent carbon dioxide of a methane emission is given by:

CO2-eq [tonnes] =GWP100 x CH4 Emission [tonnes]

Using GWP*, the CO2 equivalence is based on the change in the rate of methane emissions:

CO2-eq* [tonnes] = H x GWPH x change in CH4 emissions [tonnes per year] where H is the time period and GWPH is Global Warming Potential for a given time period.

The equivalence is determined in terms of temperature change rather than the more abstract integrated radiative forcing. The authors consider this to be more aligned with the objective of the Paris Agreement. They consider a period of H=100 to be consistent with policy relevant timeframes. From this, where GWP100 from the IPCC AR5 for methane = 28 (IPCC, 2014a), GWP* would be 2,800, however, the metric only applies to changes in the rate of emission of methane. A sustained increase in the rate of methane emissions is equivalent to a one-off pulse of 2,800 tonnes of carbon dioxide. The opposite is also true, a sustained decrease in methane emissions is equivalent to the removal of carbon dioxide from the atmosphere. In this way, it is evident that the management of sustained emissions of short-lived climate forcers, including methane emissions, is a very important tool for the mitigation of climate change.

Figure 5 Equivalence between pulse carbon dioxide and sustained change in rate of emission of a short- lived greenhouse gas

The Climate Change Advisory Council requested CICERO (Aamass, 2017) to replicate a study produced for the Norwegian government that estimated the long-term global climate (warming) impact of historic national emissions of the major greenhouse gases.

The study also explored a number of simple scenarios for emissions of carbon dioxide, methane and nitrous oxide and the additional impact of increased or reduced rates of emission on global warming. Figure 6 shows scenarios in which emissions remain constant from 2015 to 2100, and an 80% carbon dioxide emissions reduction by 2050 scenario. The impact of constant methane emissions on global temperature stabilises at approximately 1moC, on a par with the impact of cumulative carbon dioxide emissions scenario consistent with the national policy position. Figure 7 shows the impact of increased or decreased rates of methane and nitrous oxide emissions on global temperature. It demonstrates that even modest changes in the rate of emission of methane have a notable impact on Ireland’s contribution to global change, most remarkable being that a reduction in the rate of emission of methane will reduce Ireland’s overall impact on climate.

Figure 6 Simple scenario Constant emissions from 2015 to 2100 for carbon dioxide, methane and nitrous oxide and Scenario where carbon dioxide emissions reduce to 80% by 2050

Figure 7 Impact of different rates of emission of methane and nitrous oxide on global temperature

Table 6 Requirement for cumulative removal of carbon dioxide (Mt CO2-eq) in the period to 2050 and 2100 to balance the temperature response to constant emission of methane and nitrous oxide

Emission scenario for CH4 and N2O 2050 2100 Cumulative Emissions in Mt CO2-eq CH4 N2O CH4 N2O

Constant CH4 and N2O emissions (2016-2100) -1,300 -220 -1,600 -550 As above, but added 5% from agricultural (Case 7) -1,400 -230 -1,700 -570 As above, but added 10% from agricultural (Case 7) -1,400 -240 -1,800 -600 As above but added 20% from agriculture (Case7) -1,600 -260 -1,900 -650 Historic (1990-2015) + constant CH4 and N2O emissions (2016-2100) -1,600 -430 -1,700 -700

The analysis in Table 6 provides an estimate for equivalent removal of carbon dioxide from the atmosphere required to compensate for constant rates of emission of methane and nitrous oxide at 2015 levels. The table indicates the total cumulative removal of carbon required based on simple scenarios. The area of afforestation required to sequester this mass of carbon in biomass is greater than the total area of Ireland.

An estimate of potential biomass carbon stocks in 1.2 million hectares of forestry land is

260 Mt CO2-eq, this biomass contained in the entire national forest would balance approximately 16% of warming due to methane emissions from the national herd. This type of long-term analysis highlights the limited capacity of the land sector through sequestration, to balance emissions of long-lived greenhouse gases or the sustained emission of additional short-lived species.

It is worth noting that a reduction in the rate of emission of short-lived species can reverse warming in the medium to long term.

Options on Metrics

In the medium to long-term, it is important that common metrics reflect and support the policy objective in as clear and transparent a manner as possible. The reporting and accounting systems decided at EU and international level are important signals to policy priorities. GWP* is better for demonstrating policy on climate and therefore, is more policy relevant that GWP, due to accounting for short-lived gasses more appropriately. However, it is important to note that the rules and metrics to be used to assess progress towards 2020 and 2030 targets are already agreed at EU level, and during the UNFCCC negotiations at COP24, in Katowice, Poland.

Ireland can continue to support research into balance and neutrality concepts and promote international research and policy development on this topic.

3 AGRICULTURE AND LAND USE MITIGATION OPTIONS

There are no quick fix or single high impact mitigation measures that can achieve profound reduction in greenhouse gas emissions within the Agriculture and Land Use sector. Instead, multiple measures can provide significant cumulative emissions reductions. Moran et al. (2011) highlighted the value of establishing a Marginal Abatement Cost Curve (MACC) for policy development in the United Kingdom. Lanigan et al. (2018) provide an update to the MACC for Ireland, which detailed 19 potentially cost-effective mitigation measures (< € 50 t CO2-eq mitigated). The description provided here is not intended to be an exhaustive review, but a brief outline of the most suitable measures currently available for deployment in Ireland following consultation with experts and with focus on those identified as cost-neutral or negative, described as “win- win”.

In addition to reducing greenhouse gas emissions, mitigation measures can deliver multiple co-benefits to society, help address negative externalities of agriculture and are therefore linked with environmental, social and economic sustainability. The International Monetary Fund, IMF, has indicated that a shift in the French agri-food system, including the adoption of more environmentally sustainable practices within agriculture, will ultimately bring macro-economic benefits (Batini, 2019).

Mitigation measures are often implemented at farm scale. However, consideration must also be given to impacts of measures at a landscape scale. Farming systems interact with each other and with wider landscape processes, notably water catchments, and therefore the implementation of certain measures should be sustainable at multiple levels. Additionally, farm scale measures are subject to the resources and capacity available to the individual farmer.

Based on technical costs, the Teagasc MACC analysis does not consider the full range of opportunity costs of implementation of mitigation measures. Some mitigation measures, though cost-neutral or negative in direct terms, may be time consuming or require adjusted management and organisation. Such additional resources are difficult to cost appropriately but may dis-incentivise implementation and restrict uptake. Lanigan et al. (2018) noted that the indirect cost of measure implementation was not included, for example the establishment of genetic breeding schemes.

AFOLU mitigation strategies are typically divided into three categories; (i) reduction of emissions, (ii) enhanced removal of carbon from the atmosphere and (iii) avoidance of the use of fossil resources (Smith et al., 2008; Moran et al., 2011; Lanigan et al., 2018). Further technical information on individual mitigation measures is provided in Appendix 2. A summary of the mitigation measures and classification of ease of deployment is provided in Tables 9, 10 and 11. Potential mitigation and associated costs are outlined as mean annual values estimated for the period 2021 to 2030 assuming linear adoption, calculated by Lanigan et al. (2018) unless specified.

3.1 REDUCTION OF AGRICULTURAL GREENHOUSE GAS EMISSIONS

The majority of the agricultural area of Ireland is under grassland (EPA, 2016; Sheridan et al., 2017) and supporting associated enterprises, notably bovine production (CSO, 2018a; Dillon et al., 2018). Therefore, this section concentrates on mitigation strategies related to bovine systems and grassland management, with limited discussion of options within arable or other livestock production.

3.1.1 Greenhouse gas emissions from livestock

The generation of methane from enteric fermentation is an important agricultural emissions source (Moran et al., 2011). Bovines were estimated to directly produce 10.7

Mt CO2-eq from methane emissions in Ireland in 2017 or 54.7% of total agricultural emissions (Duffy et al., 2019). When methane and nitrous oxide from associated manure (including both pasture deposition and management) are included, bovines were responsible for 61.3% of total agricultural emissions. Sheep and pigs accounted for 4.1 and 1.7% respectively, including associated manure (Duffy et al., 2019). A number of measures to reduce bovine emissions have been proposed, some of which Teagasc estimated as cost-negative (Lanigan et al., 2018). In all cases, supporting information is provided in Appendix 2.

A Gradual Reduction in Bovine Numbers

As with most mitigation options, a reduction in bovine numbers will have environmental, social and economic impacts and associated co-benefits and trade-offs. Consideration of, and appropriate balancing of these different aspects is crucial.

Dairy production currently is economically sustainable (Dillon et al., 2018) and therefore, there are economic incentives for its maintenance at current levels of activity, or for expansion. Thus, incentives to reduce dairy cow numbers as a mitigation measure, would be costly, entailing high opportunity costs. However, adverse environmental impacts have been observed in intensive dairy regions regarding habitat diversity (Sheridan et al., 2011; Sheridan et al., 2017). On average, dairy production is estimated to be almost two times more greenhouse gas emission intensive per hectare, than beef production (Buckley et al., 2019). Dairy production systems also generate higher nitrogen and phosphorous surpluses per hectare, therefore, all things being equal, generate potentially greater risk of nutrient losses to water courses (Buckley et al., 2019).

In all cases, production should only be within environmental limitations. Further expansion of the dairy herd may increase the risk additional adverse impacts.

The large proportion of beef production enterprises are considered economically unviable (Lynch et al., 2016a; Buckley et al., 2019), with cattle rearing enterprises on average making losses per unit of product at market price (Dillon et al., 2018). Therefore, from an economic perspective, incentives to reduce the national beef herd, through a reduction in the suckler herd, would be more cost effective than to do so in the dairy herd. At farm scale, a reduction in livestock may increase farm income as currently, direct payments appear to support production in suckler farming enterprises (Dillon et al., 2018).

However, from a social perspective and considering the wider rural economy, suckler farming provides socio-economic (Hennessy et al., 2018) and cultural benefits. A rescaling of activity may have significant social consequences and any reduction in numbers should take full cognisance of a just transition (Section 3.4.4). In addition, potential impacts of reduced numbers on the rural landscape must be considered. Extensive suckler farming systems support important habitats (Sheridan et al., 2017) and maintenance of current production may be desirable. A decline or cessation of agricultural activity may negatively impact farmland biodiversity (NPWS, 2013; Strohback et al., 2015). Therefore, caution is required regarding geographic implementation of suckler cow numbers reductions.

From a greenhouse gas emissions perspective, the impact of a gradual reduction in national suckler cow numbers and stabilisation of the dairy herd, in the absence of other agricultural mitigation measures, can be explored through the following simple scenarios for the period out to 2030. EPA national inventory methodology and Irish country specific emission factors were used.

Scenario A

• The dairy herd is maintained at 2018 levels • The suckler herd declines by 15% relative to 2018 levels

Over the last decade (2008-2018), the suckler herd has been steadily declining at an average rate of approximately 1.4% per annum. If this trend continues, suckler cow numbers would fall by approximately 15% by 2030, relative to 2018. In this scenario, total agricultural greenhouse gas emissions would be 19.2 Mt CO2-eq in 2030, or 1.7% less than 2017 and 2.9% above 2005 levels (Table 7). This estimate considers the associated reduction in replacement heifers (followers) and cattle in different age classes, along with altered nitrous oxide emissions from manure management and reduced livestock manure deposition. Fertiliser use was assumed to stabilise at projected 2018 levels, due to a lack of demand from the dairy sector. Sheep numbers in 2030 were assumed to reduce by 45% and total pig numbers by 17% relative to 2005, in accordance to Teagasc baseline projections (Lanigan et al., 2018; Donnellan et al., 2018).

Scenario B

• The dairy herd is maintained at 2018 levels • The suckler herd declines by 30% relative to 2018

In this scenario, total agricultural greenhouse gas emissions would be 18.5 Mt CO2-eq in 2030, or 5.4% less than 2017 and 0.9% less than 2005 levels (Table 7). It is worth noting that this level of reduction within the suckler herd is approximately the level of reduction suggested in the Teagasc baseline (S1) emissions projection for 2030.

Again, this projection accounts for reductions in replacement heifers (followers), cattle in different age classes and nitrous oxide emissions from manure. The national sheep flock was assumed to reduce by 45% and the pig herd by 17% compared to 2005 levels in

2030 (Lanigan et al. 2018; Donnellan et al., 2018). Fertiliser use was assumed to stabilise at projected 2018 levels.

Scenario C

• The dairy herd is maintained at 2018 levels • The suckler herd declines to pre-Milk Quota (1984) levels

This scenario explores the reduction required to reduce the suckler herd to 1984 levels (~ 479,000 cows), the year the Milk Quota was introduced. To reach this level by 2030, it is estimated that approximately a 53% reduction in suckler cows, would be required, relative to 2018. With this reduction and in conjunction with the stabilisation of the dairy

herd at 2018 levels, total agricultural emissions in 2030 are projected to be 17.4 Mt CO2- eq in 2030 or 6.7% less than 2005 levels and 10.9% less than 2017 levels (Table 7).

As with Scenarios A and B, reductions in replacement heifers, cattle of different age classes and manure nitrous oxide emissions are accounted for. Sheep and pig baseline projections outlined by Lanigan et al., (2018) and Donnelan et al. (2018) were included.

Table 7 Summary of some projected impacts from gradual reductions in suckler cow numbers

2005 2017 2018a 2030 2030 2030

Scenario A Scenario B Scenario C Dairy cows (000s) 1,025 1,388 1,425 1,425 1,425 1,425

Suckler Cows (000’s) 1,121 1,050 1,015 863 711 479

b Total Cattle (000’s) 6,951 7,306 7,402 7,002 6,594 5,973

Bovine enteric fermentation 9,840 10,720 10,855 10,537 10,005 9,193 CH4 emissions (kt CO2-eq)

Bovine Manure CH4 925 999 1,010 960 910 835 emissions (kt CO2-eq)

Total agricultural emissions 18,699 19,581 19,919 19,244 18,531 17,445 (kt CO2-eq)

Data Source: CSO, 2019; Duffy et al., 2019 and EPA inventory methodologies. All projected figures are outlined in blue. a 2018 emission values were not available at the time of publishing and therefore projections are outlined. b Total cattle numbers differ from CSO figures due to differences in accounting methodology used within EPA emissions inventories. As these scenarios explore impacts on emissions, EPA inventory methodology was employed.

Gradual number reductions could, as and where appropriate, result from re-structuring, re-scaling, extensification of intensive systems or diversification within existing enterprises. For example, re-structuring could include greater integration between dairy and beef systems through contract rearing or enhanced dairy-beef production, therefore potentially releasing land from beef production or reducing the requirement for suckler cows. The newly developed Dairy Beef Index may enable restricting of the sector (ICFA, 2018). The carbon footprint of dairy-beef production was found to be considerably lower than from suckler-beef production (Casey & Holden, 2006a). The extent to which integration can occur requires research. Changes to the CAP may enable other options (Section 3.4.1). CAP could be designed to encourage extensification of intensive systems, with part of basic payments subject to a maximum stocking density. This may bring environmental co-benefits on farms with currently high stocking densities. The scope for extensification within suckler systems requires research. Farms that already have low stocking densities, potentially supporting HNV farmland (Martin et al. 2016; EPA, 2016), should automatically qualify, with little or no adjustment in management. Further extensification in such situations may negatively impact biodiversity (NPWS, 2013) and should be avoided. Similarly, the diversification of enterprises, and potentially exiting bovine production should happen with cognisance of environmental impacts.

As indicated by Scenarios A, B and C, a gradual reduction in numbers would significantly contribute to reducing overall agricultural emissions, enabling additional mitigation measures in combination, to potentially achieve emission reduction targets. It is recognised that the proposed mitigation measures alone are insufficient in meeting targets (Lanigan et al., 2018).

In discussion with experts, several potential options to encourage reductions have been proposed including: sectoral emissions trading, retirement schemes for suckler cows and as discussed, coupling farm payments to environmental / ecosystem service provision. The latter could involve payments to support the measured delivery of alternative ecosystem services, other than agricultural production. Production or consumption taxes were discussed but thought to be controversial with low public acceptance. Additionally, the high proportion of food exported would make implementation challenging. Regarding

social aspects, it is critical that just transition is pursued. Clearly, comprehensive research is required and regrettably beyond the scope of this working paper.

Extended grazing season

Conserved grass, e.g. silage, may contain more lignin or cellulose than fresh grass. The digestion of higher levels of cellulose is associated with increased methane emissions (Boadi et al., 2004). Extending the grazing season may increase the level of fresh grass consumed and reduce conserved grass intake during housed periods. Additionally, a reduction in livestock housing periods may reduce the quantity of stored manure, further reducing methane emissions. Lanigan et al. (2018) estimated reductions of 0.065 Mt

CO2-eq as a result of extended grazing at a negative cost of -€96 t CO2-eq abated per annum facilitated by altered grazing management or improved soil drainage. Issues may arise on vulnerable soils.

Dietary additives and vaccines

Bovine dietary additives, supplements and vaccines may also reduce methane emissions though products are in the early stages of development and require further research. Research into enzyme inhibitors (for example 3-nitrooxypropanol (3-NOP)) has shown promising results. There are potential issues regarding administration within grass-based systems where opportunities for feeding may be limited. The development of vaccines, which induce an immune response to produce antibodies that inhibit methanogens, may negate the need for as regular administration, but appears challenging. Research into the use of additives and vaccines within Irish bovine systems and their potential sides effects is required.

Genetic Efficiency

Breeding indices such as the dairy Economic Breeding Index (EBI) and beef sector equivalents may be used to help increase favourable genetic traits associated with reduced greenhouse gas emissions in livestock. For example, traits associated with feed intake, methane emissions, daily live weight gain and animal health may help increase production efficiency. Improved beef animal maternal traits were estimated to mitigate

0.025 Mt CO2-eq at a cost of -€602 t CO2-eq abated per annum, improved beef terminal traits, 0.061 Mt CO2-eq at -€215 t CO2-eq per annum and improved dairy traits, 0.43 t

CO2-eq at -€200 t CO2-eq per annum (Lanigan et al., 2018). However, any reductions in

emissions are dependent on cattle numbers remaining static as any expansion will offset genetic efficiencies of individual animals.

Herd Health

Endemic diseases and poor animal welfare impact livestock production efficiency. Ensuring herd health allows optimal utilisation of feed, reduces finishing times, enhances output per animal and ensures optimum calving intervals, which combine to achieve a potential reduction of greenhouse gas emissions in absolute terms and per unit of product. Additionally, greater animal survival rates impact absolute emissions. Overall improved herd health was estimated to mitigate a total of 0.131 Mt CO2-eq per annum at a negative cost of -€46 t CO2-eq (Lanigan et al., 2018).

Mitigation Option 1. - Reducing bovine emissions

A reduction in national bovine numbers may be necessary. Lanigan et al. (2018) identified cost effective bovine mitigation options, which would aid emissions reductions. These combined with a continued gradual reduction in the suckler herd in conjunction with stabilisation of dairy cow numbers, would represent an important contribution to national efforts to reach Effort Sharing Regulation targets. Incentives and supports could be refocused to facilitate this and achieve environmental and social objectives, while avoiding adverse impacts to farm enterprises and the wider rural economy. Any encouraged reduction of the suckler herd should only happen where appropriate, with full cognisance of local environmental impacts and just transition.

3.1.2 Greenhouse gas emissions from soils

Soil management can lead to emissions of all three key greenhouse gases (carbon dioxide, nitrous oxide and methane) (Schaufler et al., 2010) and are determined by factors including, soil moisture content, temperature, pH, site characteristics, vegetative cover and the availability of nutrients (Oertel et al., 2016). The sequestration of carbon dioxide is discussed in the next section.

Soils are a principle source of nitrous oxide emissions and direct emissions are estimated based on the availability of nitrogen from fertiliser and manure application rates and from grazing livestock excretion rates (Butterbach-Bahl et al., 2013; Bauwman et al., 2013; Duffy et al., 2019). Duffy et al. (2019) estimated direct nitrous oxide emissions from agricultural soils in Ireland to be 5.1 Mt CO2-eq in 2017 and represented 26.3% of total agricultural greenhouse gas emissions.

Nitrogen fertiliser formulation

The substitution of Calcium Ammonium Nitrate (CAN) fertiliser with protected urea was found to reduce nitrous oxide emissions on grasslands by 70% (Harty et al., 2016) without yield penalties (Forrestal et al., 2017). Protected urea is currently commercially available at approximately the same price as CAN. Teagasc estimated a reduction in

-1 emissions of 0.52 Mt CO2-eq yr from the substitution of CAN with urea, at a cost of €8 per t CO2-eq abated (Lanigan et al., 2018). This measure is included despite incurring a cost, as the potential reduction in nitrous oxide emissions is considerable. The use of protected urea, which contains a urease inhibitor, typically NBPT will also limit ammonia emissions that are associated with the use of urea. However, concerns over the potential fate of NBPT residues within the food chain have been raised. Following a thorough review of literature, Teagasc suggested these concerns were unfounded. Empirical research is currently being conducted by Teagasc to confirm this conclusion. Further information can be found in Appendix 2 (Section A.2.1). The European Union Good Agricultural Practice for Protection of Waters (GAP) regulations allow the Minister for Agriculture, Food and the Marine to specify what nitrogen fertiliser formulations are permitted for use on farms with high stocking rates from January 2021 (Statutory Instruments, 2017). This provides a mechanism for legally requiring the replacement of CAN with protected Urea on certain farms.

Mitigation Option 2. - Nitrogen fertiliser formulations

Protected urea can substitute for CAN within the Irish beef sector with immediate effect, while it is prudent to await the findings of research on residues before progressing to the substitution of CAN with protected urea in dairy production systems, which typically employ more intensive fertiliser strategies. Research indicates that the use of protected urea-based fertilisers is very effective at reducing both nitrous oxide

and ammonia emissions, the latter a major pollutant and an indirect source of additional nitrous oxide. Protected urea is currently commercially available at approximately the same price as CAN. Despite anecdotal concerns, there is currently no evidence that NBPT residues enter the food chain, Teagasc is currently undertaking additional research to confirm this. Nitrates regulations provide an opportunity to ensure some replacement of CAN with protected Urea.

Nitrogen fertiliser replacement by multi-species swards

The use of multi-species swards which include clover and forage herbs have been shown to reduce nitrogen fertiliser requirements. Research in Ireland suggests comparable yields can be achieved with multispecies swards receiving 40 to 90 kg N ha- 1 yr-1 as intensively fertilised monoculture swards receiving up to 250 kg N ha-1 yr-1. Nationally, farm surveys have demonstrated significant scope for the establishment of multispecies swards. Lanigan et al. (2018) estimated annual emission reductions of

0.069 Mt CO2-eq a cost of -€7 per t CO2-eq abated from avoided fertiliser usage where 25% of beef and 15% of dairy farms included clover in swards by 2030.

Mitigation Option 3. - Multi-species swards

Research in Ireland indicates that acceptable yields can be achieved with reduced nitrogen fertiliser with the use of clover. Further research into multi-species swards should be conducted under ‘real-life’ on-farm conditions to understand impacts and develop best management advice. The inclusion of clover and herbs within swards not only reduce fertiliser requirements and nitrous oxide emissions, but also generates a number of co-benefits including the enhancement of livestock health and performance, and potentially, sward drought resistance. Grassland research in Ireland over the last 60 years has concentrated on optimising ryegrass systems. Better understanding of the management of multi-species swards is required.

Soil fertility management

There is a need to maintain soil fertility of productive farmlands within an optimum range to ensure healthy soil function and plant performance. Soil testing and information on soil fertility correction is a fundamental service provided by agricultural advisory services. Research and advisors are unambiguous on the need to improve soil condition and the resulting benefits to profitability and environmental sustainability. However, Teagasc estimates that 88% of Irish grassland soils have suboptimal pH, potassium (K) or phosphorous (P) levels (Plunkett, 2018). It is believed that this has led to the over- application of nitrogen fertiliser to compensate for the lack of fertility in an attempt to maintain productivity – leading to unnecessary costs and adverse environmental outcomes, including additional nitrous oxide emissions. Lanigan et al. (2018) proposed a scenario where appropriate pH management through lime application occurred on an a third of grassland with sub-optimal pH (429,000 hectares). This was estimated to increase the nitrogen availability equivalent to the application of 30,000 t N over the period 2021 to 2030, mitigating 119.6 kt CO2-eq from reduced nitrous oxide emissions. This more than compensates for the emission of carbon dioxide associated with lime application, in this example estimated to be 6.8 kt CO2-eq. The estimated net mitigation over the period 2021 to2030 was 1.12 Mt CO2-eq at a negative cost of -€124 per t CO2- eq. The EPA, in submission to DAFM on nitrate derogation, has recommended that optimal soil pH should be maintained on derogation farms.

Mitigation Option 4. - Soil fertility management

There are few technical barriers to improving soil fertility as techniques such as liming are well established and understood. Soil testing and information on soil fertility correction is a fundamental service provided by agricultural advisory services. It is necessary to review options to enhance the effectiveness of national programmes to improve soil fertility in Ireland. Research may be necessary to explore underlying barriers to implementation.

Low emission slurry spreading

Spreading of animal slurry on agricultural lands is an important means of nutrient recycling. However, the application of slurry also results in the emission of greenhouse gases and ammonia. The technology used for application greatly influences the amount of emissions. Splashplate spreading is the most common technology currently in use in Ireland but results in high rates of emission. Alternative, low emission slurry spreading technologies (LESS) exist including trailing shoe, and band spreading. Appropriate use of LESS systems also reduce indirect nitrous oxide emissions while improving fertiliser replacement value of slurry, potentially reducing nitrogen fertiliser requirements, further nitrous oxide emissions and lower input costs. Lanigan et al. (2018) estimated an annual mitigation of 0.117 Mt CO2-eq at a cost of €187 t CO2-eq assuming 50% of the slurry applied was spread by low emission systems. The implementation of low emission technology is expensive if only greenhouse gas abatement is considered. However, it is included in this report due to the significant co-beneficial impact on ammonia emissions. Grant aid through TAMS is available for farmers to purchase equipment. Uptake of these grants is strong, but additional supports may be necessary to achieve widespread deployment.

Mitigation Option 5. - Low emission slurry spreading

Low emission slurry spreading reduces ammonia and therefore, indirectly nitrous oxide emissions. The associated equipment is expensive. Support for the acquisition of equipment through TAMS is available to farmers. Uptake of these grants is strong, but additional supports may be necessary to achieve more widespread deployment.

Soil structure management and drainage of mineral soils

Soil structure refers to the physical arrangement of soil aggregates and the corresponding porosity within the soil matrix. Soil structure is recognised as underpinning overall soil quality, directly and indirectly influencing many soil processes, including productivity and greenhouse gas emissions. Soil structure is easily damaged by

processes such as compaction on grassland and arable soils. Soil compaction may result from livestock or machinery traffic, notably when soils are wet and create favourable conditions for nitrous oxide emission. There is a risk that extended grazing practices may inadvertently cause compaction. Increased awareness and therefore prevention can be provided through the advisory services.

A significant proportion of agricultural soils in Ireland exhibit restricted drainage. Wet soil conditions provide favourable conditions for enhanced nitrous oxide emissions. Therefore, improved drainage can prevent nitrous oxide production as well as increasing a soils resistance to compaction. Drainage management can be costly and requires expert advice, as where implemented incorrectly can be ineffective. Teagasc proposed a scenario where the improved drainage of 10% of the total area of grassland (with linear uptake in the period 2021- 2030) would reduce nitrous oxide emission by an average of

-1 0.197 Mt CO2-eq yr at a cost of €16.2 t CO2-eq. Though not cost-neutral, drainage has been included due to its potential co-benefits regarding production, soil structure stability and enabling additional greenhouse gas mitigation through extended grazing seasons.

Mitigation Option 6. - Soil structure and drainage

Improved knowledge transfer on soil structure management from farm advisory services is required, particularly where extended grazing is encouraged. Improvements in drainage can reduce greenhouse gas emission but should be undertaken on the basis on expert advice and on a site-specific assessment.

3.2 CARBON STOCKS AND SEQUESTRATION POTENTIAL IN SOILS AND

BIOMASS

3.2.1 Carbon stocks and sequestration potential in grasslands

Irish grasslands are estimated to contain 53% of national soil carbon stocks or 769 (± 163) Tg C in the top 50 cm of soil (Xu et al., 2011). A large proportion of this carbon is

held in relatively stable forms (Kiely et al., 2009). The Royal Irish Academy (RIA, 2016) indicated that Irish grasslands have potential to sequester additional carbon. However, research is required to determine the best management practices to maintain and enhance carbon stocks in mineral soils. Studies suggest that grazing and nutrient input intensity levels can be optimised to enhance stocks, above or below which carbon losses may occur. The grazing management to enhance soil carbon stocks should only be achieved within the carrying capacity of the land with respect to other environmental impacts. Lanigan et al. (2018) outlined potential management options regarding the balance of carbon and nitrogen inputs. Improved management of 450,000 hectares was estimated to sequester 0.262 Mt CO2-eq annually at a cost of €45 per t CO2-eq. It must be noted that there is a limit to the sequestration that can occur, due to soils reaching a maximum carbon storage capacity. Management of grasslands on organic soils is discussed in Section 3.2.4.

Mitigation Option 7. – Carbon stocks and sequestration in mineral soil grasslands

Grasslands are a significant stock of soil carbon in Ireland. It is recommended that long-term in-field research be conducted into the impacts of grassland management on carbon sequestration considering sward species composition, grazing intensity and nutrient inputs. It is additionally recommended that associated activity data and country specific land use and management factors for national inventory reporting are developed.

3.2.2 Forests and Woodland

Carbon sequestration by afforestation

Forests are recognised as an important store of carbon. Afforestation is identified as a key emissions mitigation strategy. A mean annual afforestation rates of 7,674 ha yr-1 was observed between 2012 and 2017 (DAFM, 2018a), though current rates may be as low

as 4,000 ha yr-1. These rates are well below the annual requirements of 10,000 hectares to meet a target of 18% national forest cover nationally by 2050. Lanigan et al. (2018) estimated that an afforestation rate of 7,000 ha yr-1 could mitigate on average, 2.1 Mt

CO2-eq annually, at a cost of €45 per t CO2-eq abated. However, low afforestation rates as well as deforestation and existing plantation reaching harvestable maturity may limit the accountable removals in the period beyond 2030. Teagasc have identified barriers to afforestation including social perceptions and a rigidity in afforestation schemes regarding the permanent re-classification of land as forest notwithstanding current flexibility within the schemes.

Mitigation Option 8. - Afforestation

The redesign of incentive schemes to enable higher afforestation rates may be necessary. Insights from Teagasc social research may provide a guide to redesign and appropriate targeting of afforestation incentives to increase implementation. Current afforestation rates are considerably below national targets, limiting the likely contribution that forestry will make to mitigation of national greenhouse gas emissions in the period to 2050.

Carbon sequestration by agroforestry

Agroforestry refers to the integration of trees within either livestock or crop production systems. Low-density planting of trees within grassland systems increases land functionality, carbon sequestration (both above and below ground), enhances the natural environment and improves nutrient catchment potentially limiting nitrous oxide emission. Agroforestry may also enable extended grazing due to modified soil structural and associated hydrological properties, while providing additional shelter for livestock. Agroforestry systems are generally less productive than conventional systems, but can offer alternative income opportunities with respect to biomass and wood harvest. Carbon sequestration rates are site and species specific but typically range from 1 to 6.5 t C ha-1 yr-1 with 2 t C ha-1 yr-1 considered reasonable for the introduction of agroforestry on temperate grasslands (Aertsers et al., 2013). Research in Northern Ireland showed agroforestry led to more stable forms of soil organic carbon (Fornara et al., 2017).

Agroforestry allows the continuation of conventional agricultural activities, making the system more appealing to farmers. However, existing incentives for agroforestry require the permanent reclassification of land as forestry. This may act as a disincentive to the adoption of agroforestry in Ireland. Additionally, market outlets for associated wood product, may be limited.

Mitigation Option 9. - Agroforestry

Agroforestry has the potential to contribute to greenhouse gas mitigation and adaptation to the impacts of climate. Redesign of agroforestry incentives to maintain classification of land as agricultural could address barriers to adoption. Research into market outlets for associated wood products is required.

Small-scale native woodland plantations

Forestry grants permit small-scale afforestation, with a minimum area of 0.1 hectares for deciduous plantations (DAFM, 2015b). It is suggested that an agricultural scheme to promote small-scale native deciduous plantations on farmland could be established. Such plantations would bring multiple benefits including carbon sequestration and enhancing the natural environment and ecosystem service provision, with commercial value in the long-term. Schemes for native woodland plantations could be specifically directed to farms under intensive management (e.g. dairy farms) or in regions with low woodland coverage. Plantation of a percentage area of each holding (not under existing hedgerows or scrubland) could be required under CAP Pillar II schemes, with the option of reducing the area if other greenhouse gas mitigation measures are successfully adopted. Research is required into the potential area for such plantations, their likely contribution to greenhouse gas mitigation, the environmental impacts - regarding intensification or increased stocking rates on remaining un-planted land, and economic impacts - from potentially reduced production.

Mitigation Option 10. - Small-scale native woodlands

Agricultural grants should be made available for promoting the planting small areas of farmland with native deciduous trees. Such schemes could require planting of an area

percentage of holdings under intensive management. The required area could be reduced on the successful implementation of other greenhouse gas mitigation measures; thereby ensuring on-farm mitigation at some level. Research is required into the likely greenhouse gas mitigation capacity, wider environmental and economic impacts, along with the potential markets for wood products produced.

3.2.3 Carbon sequestration by farm hedgerows

Hedgerows represent an important carbon stock, while also providing high value environmental, ecosystem and aesthetic co-benefits. According to the National Forest Inventory, 276,460 hectares in Ireland were under hedgerows in 2017 (DAFM, 2018a). Preliminary estimates by Black et al. (2014) indicated that hedgerows and non-forest

-1 -1 woodland could potentially sequester 0.66 to 3.3 t CO2 ha yr or provide a net removal

-1 of 0.27 to 1.4 Mt CO2 yr . However, there is limited research concerning management of hedgerows for optimum carbon sequestration. Research indicates that roughly half of hedgerows are appropriately maintained, with a considerable proportion exhibiting undesirable features such as gappiness or low basal density. Practices such as regular cutting, coppicing or hedge-laying will aid rejuvenation, while also enhancing many important co-benefits associated with hedgerows. Knowledge on hedgerows, along with associated best practice in management, needs to be consolidated, expanded on and shared with advisors and farmers. Existing CAP regulations recognises hedgerows as landscape features (under the Good Agricultural and Environmental Condition (GAEC) regulation 7). However, this provision does not ensure appropriate management and maintenance of hedgerows particularly in the context of carbon sequestration. GLAS includes hedge coppicing and laying actions, but the scheme is voluntary.

Mitigation Option 11. - Farm hedgerows

Incentives to encourage improved maintenance of existing hedgerows are required. It is recommended that field research is conducted to generate models on hedgerow biomass production and related carbon sequestration potential under different management scenarios. This would enable the development of a robust inventory

system for hedgerows. However, effective means of gathering activity data needs consideration.

3.2.4 Carbon stocks in organic soils

Classified internationally as Histosols (FAO, 2015), organic soils include peatlands and organo-mineral soils (Wilson et al., 2013) with the former exhibiting a peat layer > 30 cm depth that contains > 20% organic matter. If the peat layer is < 30 cm, the soil is considered organo-mineral (Duffy et al., 2018; Renou-Wilson et al., 2018). Peatlands account for roughly 3% of the global terrestrial area and are recognised as important sinks of carbon dioxide and sources of methane (Kroon et al., 2010).

Organic soils under grassland

Wilson et al. 2013 estimated that between 300,000 and 375,000 hectares of peatland were under grassland in Ireland. Grasslands can be broadly classified into two categories, nutrient poor or nutrient rich (Renou-Wilson et al., 2015). Agricultural production and associated drainage on nutrient poor sites should be encouraged to cease to enhance the carbon sink function. Rushes that are likely to encroach should be permitted under Cross Compliance regulations under these circumstances. Nutrient rich sites are considerable sources of greenhouse gasses (Renou-Wilson et al., 2015; 2014). Theoretically the rewetting of such sites would be desirable, however, the economic sustainability and impacts at a wider catchment scale must be considered. The site- specific nature of restoration at multiple scales is emphasised. The Teagasc MACC analysis estimated that the cessation of drainage on 40,000 hectares would lead to

-1 emission avoidance of 0.44 Mt CO2-eq yr at a cost of €10.9 t CO2-eq avoided. This was assumed to take place on extensive beef production systems (Lanigan et al., 2018). Acquiring activity data on rewetting activity for inventory accounting needs to be developed. The drainage of some sites may have already ceased which requires identification.

Organic soils under forestry

Approximately 39% of forests in Ireland occur on former peatlands (DAFM, 2018a). The afforestation of relatively intact wetlands, causing a lowering of the water table and significant carbon losses, is not desirable. Despite afforestation of unenclosed or unimproved land being supported by afforestation grants, planting on unmodified raised bogs, industrial cutaway peatlands or infertile blanket or raised bogs is prohibited (DAFM, 2015). The impact on emissions of rewetting existing plantation sites following clear-felling, is unclear. At certain sites, rewetting may produce a net sink of carbon, while at others, re-planting may be beneficial. Additional research is required on management options and appropriate actions will be site specific.

Cutaway and cutover peatlands

Cutaway (industrial) and cutover (domestic) extracted peatlands are a major source of carbon dioxide. The rewetting of sites offers considerable mitigation potential. Rewetting of a cutaway blanket bog was found to reduce global warming potential by 87% and

-1 mitigate 75 t CO2-eq ha over six years (Wilson et al., 2012). Research suggests rewetting may be highly cost efficient. Renou-Wilson & Wilson (2018) estimated an average cost effectiveness of €4 per t CO2-eq avoided for rewetted industrial cutaway and cutover bogs. It is emphasised that the longer the rewetting process is delayed, the less likely peatlands will be able to sequester carbon. Additionally, climate change may further limit the carbon sequestration capacity potential.

There are multiple options for the after use of these lands including restoration, rewetting and paludiculture, or production on wet soils (Wichmann, 2017). It is important to identify on a case-by-case basis the appropriate after use to optimise environmental outcome and co-design the governance and incentives regime to realise environmental outcomes. Sites that have been drained but not subjected to peat extraction, and therefore retain original surface vegetation, should be prioritised for rewetting. Finally, it is important that the activity data which reflect management and the implementation of measures are captured at appropriate spatial and temporal scales. Innovation in mapping technologies may be useful in this regard.

Mitigation Option 12. - Organic soils

The management of organic soils provides a considerable opportunity for avoiding current carbon dioxide emissions and in certain cases, enable carbon sequestration.

Grasslands can be broadly classified into two categories, nutrient rich and nutrient poor. The potential for rewetting nutrient rich drained organic soils should be considered on a case-by-case basis. Where appropriate, rewetting should be implemented. For nutrient-poor organic soils under grassland, curtailing agricultural activity and allowing existing drains to deteriorate would enable natural reversion to a wetland habitat. It may be possible to incentives this through the habitat provisions under CAP Pillar 2. Means of gathering activity data would need to be developed.

Afforestation should continue to be not permitted on intact peatlands. Existing forest plantations on organic soils require careful management in order to optimise greenhouse gas mitigation and climate resilience of the landscape. Renewed efforts are required to cease all peat extraction and associated drainage of peatlands at industrial, semi-industrial and domestic scale. With consideration of catchment scale impacts, cutover and cutaway sites should be rewetted where appropriate.

3.3 AVOIDING CARBON DIOXIDE EMISSIONS THROUGH REDUCED FOSSIL FUEL USE

3.3.1 Reduction in on-farm energy consumption

Lanigan et al. (2018) identified a number of measures that could be implemented on dairy farms to reduce the consumption of energy and therefore avoid carbon dioxide emissions. These included plate coolers, solar panels, heat recovery systems and variable speed drives on vacuum pumps. Considering plausible sectoral uptake, these were considered cost negative at -€359 t CO2-eq and projected to displace 0.029 Mt

CO2-eq per annum.

3.3.2 Energy from biomass

According to Lanigan et al. (2018), the use of thinnings, sawmill residues and wood waste for electricity and heat production could displace 0.759 Mt CO2-eq annually at a

cost of -€30.7 t CO2-eq. The use of Short Rotation Coppice (SRC) willow and miscanthus for heat production was estimated to displace 0.179 Mt CO2-eq per annum at a cost of -€20 t CO2-eq. These values take account of the greenhouse gas footprint of willow and miscanthus production following the conversion of grassland (15,000 hectares) (Lanigan et al., 2018). Finally, the use of willow for electricity production was estimated to displace 0.187 Mt CO2-eq at a cost of -€10 t CO2-eq displaced. This again assumed the conversion of grassland to willow plantations, with associated beef production continuing by higher stocking rates on alternative land. However, the production of miscanthus and willow to meet such demands is questionable in Ireland with bioenergy crops potentially deemed a less attractive option for farmers. Though dependent on future carbon prices, biomass may have greater value if utilised within the bioeconomy.

3.3.3 The bioeconomy and circular bioeconomy

With rapid global transition away from the use of fossil hydrocarbon resources, demand for land and marine resources to provide substitute raw materials will increase.

The bioeconomy refers collectively to all activities dependent on biological resources, systems and processes, from primary production to processing and refining, with emphasis on renewable inputs (EC, 2018d). It encompasses both terrestrial and marine ecosystems while linking multiple sectors that generate food, feed and bio-based energy, products and services. Ronzon & M’Barek (2018) estimated that the bioeconomy across Europe (EU-28) was worth €2.3 trillion and employed 22 million people in 2015. The enhancement of the bioeconomy is identified as means of driving sustainability (EC, 2012a; EC, 2012b) as outlined with the EU updated bioeconomy strategy (EC, 2018d). As the bioeconomy can engage with multiple sectors within the economy, it has potential to create jobs in rural and industrial areas, while also helping to ensure food security, reduce fossil fuel dependency, conserve or restore natural resources and aid climate change mitigation (EC, 2012a). Outputs of the bioeconomy not only include primary agricultural, forestry and marine products, but also bio-chemicals, bio-fuels, bio-based building materials, cosmetics or pharmaceuticals (EC, 2018d).

A circular economy describes a cyclical economic system, as opposed to the typical, linear model that involves the input of resources, their use and final disposal (Korhonen et al., 2018). A circular economy involves the continual valorisation of one activity’s

waste as the input to another activity and applies at multiple levels, from individual companies, to a national scale, generating environmental, social and economic benefits (Kirchherr et al., 2017).

A circular model of the bioeconomy presents alternative markets opportunities and activities to the agriculture and land use sector generating new income streams and enhance economic stability, therefore bringing social benefits, while also maintaining and enhancing the natural resources on which the sector is based. The circular approach helps drive the bioeconomy, ensuring the renewed and optimal use of resources and therefore the efficiency of the system, while avoiding fossil fuels. The bioeconomy is key to transforming wastes, discards and residues into valuable inputs. This may reduce the carbon footprint of products and ultimately reduces greenhouse gas emissions through carbon dioxide displacement, though is highly scenario dependant.

In Ireland, the bio and circular economies are increasingly recognised as a strategy in achieving decarbonisation, regional development, energy security from renewable sources, along with environmental protection, as outlined in the recent National Policy Statement on the Bioeconomy (Government of Ireland, 2018b). A number of bioeconomy projects have already been established, from research and development facilities such as the Beacon Bioeconomy SFI Research Centre (www.beaconcentre.ie), principally based at University College Dublin, to functioning enterprises. AgriChemWhey is a recently established, Glanbia-led industrial scale bio-refinery at Lisheen, Co. Tipperary (www.agrichemwhey.com). The plant aims to annually process 25,000 tonnes of whey permeate and de-lactosed whey permeate, both important side-streams of the dairy processing industry, into bio-based fertilisers, mineral supplements, lactic acid and poly- lactic acid. The latter materials can be used to make bio-plastics.

With regard to forestry, the substitution of energy intensive non-wood products, such as construction materials, with harvested wood products (HWP), can more effectively reduce greenhouse gas emissions compared to using wood as biomass for energy production (Geng et al., 2017). The implementation of incentives for particular products could be beneficial, for example a reduced VAT rate on HWP. Research by Buchanan & Levine (1999) in New Zealand noted a 20% decreased in carbon emissions with a 17% increase in the substitution of building materials by durable plywood products. This represented a 1.5% reduction in New Zealand’s national emission at the time (Buchanan

& Levine, 1999). Harte (2017) highlighted the increasing use of engineered wood products known as mass timber. Cross-laminated timber (CLT) has high load carrying capacity facilitating high-rise construction. CLT is suggested to increasingly provide an economically viable and sustainable alternative to traditional construction materials. However, both research and use of HWP in construction in Ireland appears limited. None the less, CLT has been used in a number of Irish construction projects (Harte, 2017).

It is worth noting that existing carbon accounting mechanisms may not fully account for potential carbon sequestration within long-lived HWP such as construction materials. Geng et al. (2017) concluded that depending on the end-use, HWP retain carbon and represent an important aspect of forest carbon cycles and balances. There is considerable opportunity for increased HWP use, which provides long-term and stable carbon storage while also providing potential mitigation (avoided emissions) due to substitution of HWP from fossil fuels intensive products, including plastics and concrete.

Duffy et al. (2019) estimated removals from HWP of - 871.6 kt CO2-eq in 2017.

Considering anaerobic digestion, Lanigan et al. (2018) examined the utilisation of grass in conjunction with slurry for heat and energy production as well as gas that can be injected into the national grid. Estimated mean annual displacement of 0.224 Mt CO2-eq at a cost of €115 t CO2-eq was estimated. The anaerobic digestion of grass and slurry and further refinement to produce methane gas for use within the national gas grid was

-1 estimated to displace 0.15 Mt CO2-eq yr at a cost of €280 t CO2-eq, and therefore expensive. Where carbon dioxide emissions are displaced, other greenhouse gases may be emitted. For example, grass is utilised to fuel anaerobic digestion, but may be dependent on high nitrogen fertiliser inputs, the production of which generates significant greenhouse gas emissions, therefore offsetting some benefits of the carbon dioxide avoided. Powlson et al. (2011) highlighted this with regard to carbon sequestration with increased crop growth production that is reliant on high fertiliser inputs. However, the establishment of clover within grass swards will reduce some, or all of the need for fertilisers. The Teagasc MACC analysis considered emissions associated with fertilisers with regard to national inventory accounting (Lanigan et al., 2018). Emissions from the production of fertilisers were therefore omitted, as this takes place outside Ireland. The utilisation of grass for anaerobic digestion, may also offset potential methane emissions if that grass was to be used for ruminant production.

There is considerable opportunity for agriculture and forestry to drive the bioeconomy while, following a circular approach, receiving the outputs from processes higher up the value-chain to use as inputs for agricultural and forestry activity, therefore displacing the need for fossil fuel dependant inputs. Emphasis is placed on closed-loop systems (Toop et al., 2018), even at farm level, where one enterprise’ output is an input to another with a shift away from current agriculture model, which is resource intensive (Ward et al., 2018). There is particular emphasis on soil restoration and its optimisation as a carbon sink (EC, 2018a) as well as eliminating waste at all stages of the production process. The circular approach in agriculture consolidates and incorporates a lot of the mitigation and sequestration measures previously discussed, while additionally contributing significantly to carbon dioxide displacement.

Mitigation Option 13. – Bioeconomy and Circular Bioeconomy

Further scoping, research and development is required to enable the agriculture and land use sector to engage with and foster a robust circular bioeconomy, at local, regional and national levels. This will require identification and quantification of potential primary products, including wastes, discards and residues, appropriate markets or value-chains for these products and the development of the required technology for their utilization. The production of biomass for energy production may be feasible in the short-term, however biomass may have greater economic and carbon dioxide displacement value, within the context of the bioeconomy in the long- term.

3.4 ENABLING GREENHOUSE GAS MITIGATION

3.4.1 Changes to the Common Agricultural Policy

Proposed changes to CAP include greater emphasis on climate change action, with focus on limiting carbon losses from wetlands and peatlands as well as improved nutrient management to reduce nitrous oxide emissions. Simplification of the policy and more performance or result-based support is emphasised. Nine common objectives will be included, of which three will relate to environmental management. These are

environmental care, landscape and biodiversity preservation, and action on climate change. It is proposed that 30% of Pillar II funds will be spent on measures including climate action, with 40% of the overall CAP budget expected to be spent on climate mitigation (Callanan, 2018). Additionally, the European Commission recognises the local and regional nature of environmental and climate issues and the potential of tailored design of schemes for effective support of actions and measures.

Anecdotal evidence suggests that farmers may be willing to extensify production and engage in environmental conservation, which may include greenhouse gas mitigation measures, but are constrained by maximising output to make sufficient returns. Past and existing environmental incentives and supports are suggested to have been, or to be, short-lived, inconsistent or insufficient. In short, there appears to be an appetite to enhance the environmental sustainability of the sector among the farming community, however, anecdotally there is a lack of direction and long-term, consistent supports for environmental conservation measures, which may present barriers to changes in management.

The proposed changes to CAP present a considerable opportunity to enable climate change mitigation. It is vital that changes provide sufficient, consistent and long-term support for environmental conservation and specifically implementation of greenhouse gas mitigation measures.

Enabling Mitigation 1. – Changes to the Common Agricultural Policy

Changes to the CAP include increased control and design by EU Member States in conjunction with a greater emphasis on environmental measures, including climate change mitigation. This presents an important opportunity to enable the implementation of mitigation measures within the Irish agricultural sector and should be fully utilised to do so.

3.4.2 National Land Use Strategy

How Ireland would benefit from a land use strategy?

In Ireland, current land use planning pertains primarily to residential and urban development (OECD, 2017) but there is limited attention to land more generally and in particular, the role of land in the delivery of functions beyond production. Land and ultimately soil, is a vital and finite resource delivering multiple functions and ecosystem services on which all life is based (Hillel, 1991; Haygarth & Ritz, 2009; Oliver & Gregory, 2015). These functions and services include atmospheric or climatic regulation (FAO & ITPS, 2015) and therefore potentially, climate change mitigation. Land management should accommodate and aim to enhance multiple land functions, (Haygarth & Ritz, 2009; O’Sullivan et al., 2015; CCC, 2018), while simultaneously enhancing land-based incomes and rendering the sector less vulnerable to market volatility. However, there may be trade-offs or synergies in delivering multiple land functions (Schulte et al., 2014; Schulte et al., 2015). Management that prioritises one function, often productivity, may impact the delivery of other functions (Schulte et al., 2015; Coyle et al., 2016). The productive function, just one of many land functions and perhaps the most widely recognised, refers to the provision of food, feed, fuel and fibre (Mueller et al., 2013) by means of agricultural and forestry systems. It is recognised that the global intensification of agriculture has been at the expense of other land functions, particularly biodiversity provision (Foley, 2005; Van Vooren et al., 2017). Agriculture occupies a considerable portion of the global land area (Ramankutty et al., 2008) as is the case in Ireland (EPA, 2016), putting pressure on the capacity of land to support other functions and ecosystem services. However, agricultural production may co-exist with the successful delivery of other functions. For example, extensive farming systems (Benayas & Bullock, 2012) may facilitate aesthetic landscape features (Assandri et al., 2018), while the integration of features such as hedgerows or grass strips (Van Vooren et al., 2017) into arable systems, may support biodiversity. The potential for agricultural production to co-exist with enhanced biodiversity is demonstrated by HNV farmland (Stohback et al., 2015; EPA, 2016). An alternative approach is the separation or compartmentalisation of land by function and associated intensification of functions within sections of landscapes (Benayas & Bullock, 2012; Balmford et al., 2018). Both approaches have merits and limitations and may be appropriate in different scenarios. However, intensification of production within certain areas, is likely to limit the range of ecosystem services provided, on which that agricultural production depends.

It must be noted that there are climatic and geo-physical limitations to land functions, as not all land can support all functions to the same capacity (Schulte et al., 2015). Beyond

natural limitations, the degree to which land can optimally support functions is dictated by management. For example, different soil types support certain functions better (Schulte et al., 2015; Coyle et al., 2016), which can be realised or prevented through management. In addition, different land functions require management at different scales. For example, water quality must be managed at all scales, while production of food and fibre, is managed at field and farm scale. To ensure the optimal, balanced and appropriate delivery of multiple land functions and ecosystems services, including greenhouse gas mitigation, in conjunction with satisfying diverse policy requirements, the management of land at farm, catchment and national scales is arguably vital. Additionally, the likely impacts of climate change mitigation measures should be considered within a landscape context (Bourke et al., 2014).

Given the important contribution of LULUCF and following the National Planning Framework (Government of Ireland, 2018a), a strategic approach to land use in Ireland, considering multiple scales and notably, climate change mitigation, may be useful to determine the most appropriate land use options and direct policy and incentives accordingly. More specifically, a national land use strategy could (i) facilitate an assessment of land resources and current management, (ii) determine most holistically sustainable uses of land by testing optimisation scenarios at multiple spatial scales and (iii) guide policy, including aspects of the Common Agricultural Policy, to encourage and achieve desired management. Therefore, a land use strategy approach should not be prescriptive, but act as a decision support tool to guide actions for desired outcomes.

This was previously recommended by the Climate Change Advisory Council (CCAC, 2017; 2018) while the necessity of a collective land management policy specifically concerning greenhouse gas emissions has been emphasised elsewhere (Schulze et al., 2009) including the UK (CCC, 2018). There is also recognition of the need for an integrated land systems approach at a European level to manage land uses and combat processes that limit land functions, including soil degradation (EEA, 2018). The coordination of European policy instruments is identified as an important next step (EASAC, 2017).

A land use strategy in Ireland could incorporate all relevant and evolving environmental legislation such as the Nitrates Directive, Habitats Directive, Air Quality Standards Regulations, National Emissions Ceilings Directive, and National Energy and Climate

Policy. The generation of renewable energy via wind, solar PV or biomass on agricultural land is one opportunity to contribute directly and indirectly to environmental legislation (ISEA, 2014). A land use strategy could also incorporate un-legislated topics of concern such as soil quality conservation. While Irish soils are of relatively high quality (Kiely et al., 2014), it is imperative that this status is maintained.

Finally, a land-use strategy can incorporate relevant stakeholder opinions and knowledge, aiding a just transition, while providing a system for monitoring progress and acting on feedback. It is crucial that the integrated impacts of multiple mitigation measures at farm, catchment and national scales are measured and monitored.

Enhanced ecosystem services delivery as a framework for a land use strategy

A land use strategy could focus on climate change mitigation but would be limited if other ecosystem services and their sustainable delivery were not considered. Therefore, the pursuit of enhancing and facilitating the delivery of multiple ecosystem services could form an appropriate framework for a land use strategy, thereby ensuring mitigation measures are sustainable, bringing multiple co-benefits and aiding long-term environmental conservation. Aspects such as flooding or erosion prevention, nutrient cycling, pollinator conservation and enhanced landscape aesthetics as a basis for recreation and cultural traditions (Costanza et al., 1997; Guerry et al., 2015) should be pursued in conjunction with greenhouse gas mitigation. The pursuit of optimised multiple ecosystem service delivery could therefore also contribute to resiliency and climate change adaptation, while providing a range of co-benefits to society. A research project, funded by the EPA, is about to commence that will conduct ecosystem accounting of a catchment, considering components such as aquatic systems, wetlands, forestry, farming, industry and settlement. This may provide useful insight into how to develop and guide land use aspects of a land use strategy to enhance ecosystem service delivery.

What a national land use strategy could look like

The optimal delivery of multiple ecosystems services is underpinned by maintaining and enhancing diversity, notably biodiversity within the landscape. Diversity is required at genetic, species and habitat scales to ensure resilience to sudden environmental stresses at multiple scales. In this context, mitigation measures such as multi-species swards, hedgerows or agroforestry clearly enhance ecosystem service delivery potential

at a field or farm scale. At a landscape scale, diversity should be encouraged through the conservation of wetlands, mixed species woodlands and other natural and semi-natural habitats. The importance of diversity within afforestation is highlighted (Seddon et al., 2019). While dairy production is economically rewarding at present (Dillon et al., 2018), increased specialisation and the dominance of typically, intensively managed dairy systems within a landscape may be undesirable in the long-term, not only in terms of ecosystem service delivery but also food production capacity and resilience to external shocks, including markets.

The UK Committee on Climate Change (CCC, 2018) highlighted the need for releasing agricultural land for alternative uses such as afforestation, peatland restoration and water catchment management. Diversification within the agriculture sector was also identified as important. Economic supports to farmers for alternative land function provision along with “downstream” factors such as consumer behaviour regarding diets and food waste, were identified as facilitating land use change.

The technical mechanism for achieving a national land use strategy requires research and expert consultation. A number of studies may provide useful insights.

Hochstrasser & Herzig (2018) applied the Land Use Management Support System (LUMASS) model in Ireland on the Suir catchment, showing its potential use. This software platform can support decision making, including initial assessment and design. LUMASS combines spatial data from multiple inventories to identify optimal land use at catchment scales for desired outcomes (Herzig & Rutledge, 2013). The study highlighted the opportunity for stakeholder engagement at each stage of the model’s development, from selecting optimisation criteria, designing scenarios, to the assessment of optimal land utilization. However, the model’s value is dependent on the quality and availability of data and it was suggested that high-resolution spatial datasets are required to capture all ecosystem services.

Functional Land Management described by Schulte et al. (2015) aims to provide a framework for policy design to ensure balanced delivery of different land or soil functions. This has emphasis on soil management and recognises the different spatial scales required to optimally deliver different land functions.

The EPA-funded Environmental Sensitivity Mapping (ESM) project has developed an interactive map that indicates spatial environmental sensitivities, incorporating GIS data layers and is designed specifically to aid planning and support in Strategic Environmental Assessments (González Del Campo et al., 2019). This system should be publicly available in Autumn 2019 and may provide a valuable tool for developing a land use strategy.

The mapping of implemented mitigation measures and monitoring of data at a localised or catchment scale, such as the rewetting of peatlands (Renou-Wilson et al., 2018), is also vital. Hochstrasser & Herzing (2018) emphasised the importance of feedback and an adaptive learning process. Major changes in land management may lead to greater problems. Careful selection of indicators that measure the holistic impacts of changes in land use is critical for effective monitoring.

Enabling Mitigation 2. - A national land use strategy

A national land use strategy could help enable the balanced and sustainable provision of multiple land functions and ecosystem services at both a landscape and national level, including greenhouse gas mitigation. A land use strategy could focus on climate change mitigation, but within a framework of pursuing enhanced delivery of multiple ecosystem services, thereby ensuring mitigation measures also facilitate long-term environmental conservation, provide multiple co-benefits to society, while contributing to resiliency and climate change adaptation. The strategy could help encourage the delivery of optimal land uses, considering environmental, social and economic aspects, by guiding policy and supports to incentivise desirable land management. Therefore, the strategy would not be prescriptive but form a decision support tool. Additional research and development is required to elaborate further on potential policy design and implementation measures to enable appropriate land use and land management decisions across all sectors.

3.4.3 Improved knowledge exchange

The success of AFOLU mitigation policy is largely dependent on farmers’ and other land managers’ perceptions, lived experiences and understanding of climate change. Equally, the active participation and incorporation of land mangers’ knowledge, opinions and experience is critical in the design of sustainable mitigation measures and associated policy.

There is limited research available which has sought to explore farmers’ attitudes to and awareness of climate change issues in Ireland. A survey conducted in 2014 explored farmers’ awareness of and willingness to act on climate change as part of assessing potential use of an information technology (IT) tool to aid greenhouse gas mitigation (Tzemi & Breen, 2018). The study acknowledges some limitations but is indicative of the situation at that time. Approximately 53% of the farmers surveyed (n = 746) felt that anthropogenic activity was contributing to climate change. However, 27.5% believed that the consequences of climate change will only be relevant in the long-term, while 28.9% considered that there will be no consequences at all. Interestingly, arable (tillage) farmers were found to have the most optimistic outlook about impacts, which may relate to expectations of higher yields as a result of warming temperature. Approximately 30% of farmers disagreed that livestock production was a source of emissions, though 69% considered deforestation to be a key contributor. It was suggested that the global media emphasis on the negative impacts of deforestation had influenced perceptions, with potentially less awareness of Ireland’s emissions profile (Tzemi & Breen, 2018).

Since the survey by Tzemi & Breen was conducted, the frequency of extreme weather events has increased (Met Éireann, no date), and likely to have influenced farmers’ lived experience of climate change. In addition, there has been increased media focus on the contribution of the agriculture sector on Ireland’s national emissions profile. Research is required to explore how land managers’ and farmers’ perceptions may have changed. Effective advisory services and courses within educational institutions are vital in addressing potential knowledge deficits.

As mentioned, farmers appear keen to engage in environmental conservation measures, including those relating to greenhouse gas mitigation (Section 3.4.1). Anecdotal evidence indicates an awareness among farming communities of changing weather patterns, notably precipitation intensity and frequency that has necessitated adjusted

farm management. This may signify a shift in awareness and opinions of farmers in recent years. Anecdotal evidence also suggests that proposed climate change mitigation policy may be prescriptive and implemented in a top down manner. This indicates that farmers’ knowledge and experience has been largely omitted, with little active participation from farming communities in the design of mitigation measures and actions to respond to climate change. One-way transfer of knowledge undermines the future success of climate mitigation policy, in that it inherently implies that farmers’ and land managers’ experience and knowledge of the land is not valued. In practice, indigenous knowledge of land at a localised or field scale can be valuable in informing science and best practice. Additionally, local knowledge regarding social and cultural values within communities can help to inform the communication of scientific evidence, and in turn inform the design of policy.

Knowledge exchange between farmers, rural communities and policy makers is critical to enabling the development and implementation of holistic and sustainable policy responses, that can address the wider social and environmental impacts of climate change on rural Ireland.

Afforestation faces considerable social and behavioural barriers, some irrespective of economics (Farelly & Gallagher, 2015; Ryan & O’Donoghue, 2016; Ryan et al., 2018). Issues associated with the required re-classification of agricultural land have been briefly mentioned (Section 3.2.2). Ryan and O’Donoghue (2016) found that 84% of farmers surveyed would not consider afforestation while more recent work suggested that some younger farmers on larger farms, may be willing to consider afforestation, if financial returns are greater than those obtained from agriculture (Ryan et al., 2018). The research also highlighted a reluctance of some older farmers on smaller holdings to consider afforestation, regardless of economic incentives. Current afforestation grants and premia are outlined in Appendix 2 (Section A.2.2).

Finally it is suggested that farmers are receiving mixed messages regarding management decisions. The market currently provides the clearest signal, which encourages production expansion, with climate change mitigation an emerging lesser factor. This in-coherence of messages in conjunction with a potential knowledge deficit on specific mitigation measures, may exacerbate any barriers to adoption and temper the potential willingness for action. In addition to financial support to enable mitigation,

the provision of information and direction is now required to facilitate management changes.

Enabling Mitigation 3. - Improved knowledge exchange

A study conducted in 2014 indicated that a lack of awareness regarding climate change existed among farmers. The adoption of mitigation measures is dependent on knowledge of both, reasons behind and direct measure implementation. Research is required to explore how farmers’ and other land managers’ perceptions may have changed in recent years. The role of agricultural advisory services, media and educational institutions is crucial where knowledge deficits exist. Equally, it appears there has been limited opportunity for active participation from farmers and other land managers within the policy design process. Their knowledge, opinions and lived experience is crucial in the design of acceptable, holistic mitigation strategies while addressing wider social and environmental issues in rural Ireland. Research and resources to enable effective knowledge exchange is required.

3.4.4 Agriculture and just transition

Mitigation options within the AFOLU sector have been discussed. This section explores the underlying interconnected challenges to implementing mitigation options in the context of a just transition, which requires greater consideration of the social and environmental impacts in the development of responses to climate change.

Ireland’s agriculture and agri-food sector has the potential to be a leader in climate action by demonstrating how to transform food production in the face of climate change and addressing environmental degradation, both locally and globally. This will require greater collaboration between policy makers, farmers and rural communities to develop solutions that add to Ireland’s food security while supporting a low carbon future that prioritises environmental and human health (WHO, 2018). However, collaboration will require the focus of policy in the agricultural sector to be broadened beyond the scope of economics alone. This will involve considering how agriculture policy aligns with for example,

environmental policy, national health policy and social policy as well as overseas development policy. Understanding the synergies between these policy areas may present a novel means of addressing the challenges facing agriculture in a low carbon future and enable a just transition.

To identify how the opportunities may be realised an understanding of what constitutes a just transition is required, along with an understanding of the barriers and challenges in the sector.

Just Transition

Increasingly there is a call, in society and in academic literature, for a movement towards a just transition (Bulkeley et. al., 2014; Whyte, 2018). This is not a move away from climate justice, but, an enhancement of the framework for understanding how the transition to a low-carbon society needs to be equitable and cause no harm. Climate justice is one of three forms of justice that need to be considered in a just transition to a low carbon future (Figure 8), with energy and environmental justice forming additional key components (McCauley & Heffron, 2018).

Climate justice links human rights and development to achieve a human-centred approach, safeguarding the rights of the most vulnerable and sharing the burdens and benefits of climate change and its resolution equitably and fairly. Climate justice is informed by science, responds to science and acknowledges the need for equitable stewardship of the world’s resources (MRCJ, 2013).

Energy justice applies human rights across the energy life cycle. The focus is on insuring that people have access to energy to maintain a decent quality of life, while guaranteeing the production and distribution of energy is done in a manner that causes no harm (Jenkins et al., 2016).

Environmental justice is concerned with the inclusion of citizens in the development, implementation and enforcement of laws, regulations and policies regarding the environment; central to this is equity. It is important to note that justice is considered not merely as an outcome of policy, but within the policy process itself (Jenkins et al., 2016; Whyte, 2018).

Environmental Justice

Climate Justice Energy Justice

Just Transition

Figure 8 Components of the Just Transition

In addition to these three justices, is the concept of restorative justice (Heffron & McCauley, 2018; Whyte, 2018). This refers to the recognition and acceptance of past grievances and mistakes in order to progress and collaboratively develop solutions. Consent and reconciliation are needed to have an equitable distribution of the burdens and opportunities of responding to climate change (Bulkeley et al., 2014; Heffron & McCauley, 2018; Whyte, 2018). A dialogue that is respectful, deliberate and considerate, and aimed at understanding the barriers and challenges faced by at-risk individuals and communities is valuable to the longevity of climate action (Bulkeley et al., 2014; Dekker, 2018; Heffron & McCauley, 2018; Whyte, 2018). Imposing solutions may do more harm to these individuals and communities and increases the risk of U-turns on policies that are essential for mitigation and adaptation. The policy development process should consider underlying causes of individuals’ and communities’ increased risk and vulnerability to climate change as well as the impacts of proposed mitigation or adaptation measures.

A just transition moves beyond protecting the rights of vulnerable individuals, to understanding the causes of vulnerability and how responding to climate change is an

opportunity to engage in restorative justice (McCauley & Heffron, 2018). It is necessary to actively engage with vulnerable and underrepresented groups considering gender, ethnicity and socio-economic status in developing responses to climate change.

There are two dimensions to the Just Transition in the Irish context: (i) achieving a just transition in the domestic sphere; and (ii) contributing to a just transition internationally. Achieving a just transition is not without challenges locally and internationally. Ireland has the potential to be a leader and to show how a nation can transition from relatively carbon intensive food production and land management to an equitable low-carbon society by designing and implementing policy that leaves no one behind.

A just transition calls for understanding the root causes of problems, and how individuals and communities want to address their challenges (Heffron & McCauley, 2018; Whyte, 2018). At the core of this, is fostering a culture of respect and dignity. Respectful and open dialogue about individuals and communities ‘hopes and concerns’ is imperative. Table 8 presents a process of engagement to develop responses to climate change that achieve resilience. Collaboration with stakeholders who recognise the existence of a threat and are ready to take action is crucial.

Table 8 Resilience process - complex system (Source: Dekker, 2018)

Pre- Conditions Recognition of a threat

Perceptions of:

• Vulnerability created by threat • Risks stemming from threat

Knowledge of:

• Threat • Causal pathways • Mechanisms

Desire to take action

Agreement of Threat Action to become resilient

Resilience Process Analysis of Threats:

• Risks • Assessment of impacts • Knowledge exchange (i.e. information about threat outcomes) • Collaboration/ Collective action • Integrated action

Debate on Action:

• Assessment of possible actions • Assessment of risk associated with actions • Role clarification (stakeholders and their agendas) • Options: short and long run

Visioning of state of being resilient

Implementation

Monitoring and evaluation

• Resilience achieved as an endpoint or an on-going process

Engaging in this process within the Irish agricultural sector will be challenging, as the dialogue around climate change and agricultural policy can be politically difficult. Anecdotal evidence indicates that wider dialogue and policies have not fully considered the importance of the social and cultural aspects of agriculture.

There is an alienation of farmers, whereby they are perceived to be the cause of the problem, as opposed to being part of the solution as a key contributor with their knowledge and expertise. Agriculture is at the core of rural communities and identity across Ireland, it is more than the production of food.

In terms of changes in policy and in order to climate-proof policy in Ireland, it is important to consider current events but with recognition of the historical context of agriculture. Additionally, the social and cultural values placed on the land must be considered. Anecdotal evidence also suggests that the sector is viewed as an unattractive or unviable profession, especially with respect to future generations, as in other countries (Sulemana & James, 2014; Zou et al, 2018). This needs to be researched in the Irish context. Farming is a challenging livelihood, where income can be relatively low (Dillon et al., 2018), work is labour intensive and potentially conducted in isolation. External

challenges to the system, including market shocks, social isolation and climate related impacts such as flooding and drought, pose a significant risk to health and well-being (Sulemana & James, 2014; Campbell et al, 2018; WHO, 2018; Zou et al., 2018). Finally, agriculture and its role in public health, regarding the production of wholesome food and the provision of public goods including landscape aesthetics, associated cultural values and habitats for biodiversity, need consideration.

Agriculture, food security and climate change: a global and local public health problem

The publication of the IPCC’s 1.5 Degree Report (IPCC, 2018) and the EAT Lancet Commission Report (Willett et. al., 2019) has drawn attention to food production and consumption. The reports have called for a global shift towards eating less meat. They also highlight the challenges inherent in the global food system, which in its current state, is carbon and water intensive. The COP24 Special Report: Health and Climate Change (WHO, 2018) also highlights the challenges ahead, while acknowledging that there are inherent opportunities in addressing the agriculture and food sector with balanced supply side and demand side actions. Critically, the World Health Organisation has highlighted the key reason for climate action, and one in which agriculture is central - human health:

‘“Climate action is development action”; as social resilience and economic productivity depend on the good health of populations, health must be central to climate change policy’ (WHO, 2018).

The decarbonisation of agriculture is a global challenge, one that is complicated by climate change and urbanisation (Campbell et al., 2018; WHO, 2018). At present, 50% of the world’s population live in cities, this is projected to increase to 70% by 2050 (UN Habitat, 2016). Urbanisation is being driven by factors such as education and employment opportunities in urban areas. With urbanisation, comes the challenge of feeding urban populations, as well as providing affordable housing and transport options. On the surface, access to food, housing and transport appear to be separate policy issues however, they are interdependent. Research in public health policy highlights the importance of the Social Determinants of Health (SDHs) in health outcomes (Galvão et al., 2009; Barton, 2009). The SDHs are mentioned here to highlight the importance of health to economic productivity, both in rural and urban areas. Incomes are a

determinant of health, as individuals and families will choose to prioritise their spending based on their needs depending on, and relative to their income security. This may result in individuals and families forgoing wholesome food for processed food due to the costs of housing and transport placing a greater demand on their income (Galvão et al., 2009; Barton, 2009; Dekker, 2014a; 2014b).

Growing urban populations are increasingly dependent on a declining number of farmers to provide food that is both healthy and affordable (UN Habitat 2016; WHO, 2018; Zou et. al., 2018). There are new and growing nutritional challenges such as obesity, malnutrition and food wastelands stemming from access to affordable healthy food within urban contexts (Campbell et al., 2018; WHO, 2018). These challenges are connected to the distribution of food, which highlights the vulnerability of human health to changes in food availability and supply chains (Campbell et al., 2018; WHO, 2018). While Ireland’s foreign policy acknowledges that the distribution of food globally is unequal, and that under-nutrition is the main contributor to childhood death, food distribution and affordability is not considered in the national context (Godfray et. al., 2010).

There is an opportunity to demonstrate leadership on climate proofing agriculture and food policy. However, in Ireland, there are challenges that have not featured strongly in national discussions around climate change and agriculture. These include population health, national food security, water supply and security, and impacts on international trade. As an open market economy, Ireland is not food independent and climate change has the potential to disrupt food supply chains. In 2018, Storms Emma and the “Beast from the East” demonstrated Ireland’s dependence on imports for food and the vulnerability of the water supply system. The drought during summer 2018 further illustrated Ireland’s water insecurity and its impact on the ability of farmers to sustain production levels and guarantee their incomes. Climate change will impact the health status of people in Ireland (WHO, 2018).

The “Healthy Ireland – A Framework for Improved Health and Wellbeing 2013-2025” Report, notes emerging concerns regarding malnutrition, specifically obesity, which is primarily linked to diabetes but can mask other nutrient deficiencies and malnutrition (DOH, 2012). Solutions to these challenges may include promoting active lifestyles, community gardens, urban agriculture and social farming. Less discussed, though is our dependence on a decreasing number of farmers to deliver nutritious and affordable food,

and the costs added along the food supply chain to bring food to people. Policy responses should be holistic to achieve resilience in the agriculture and food systems locally and globally in a just manner. If people are to be healthy, they need to be able to afford food that is wholesome.

Responding to the increased demand for food will need a diverse and resilient range of policy solutions that include (Godfray et al., 2010): extensification, diversification, social farming, urban agriculture and spatial planning that considers alternative land-uses, infrastructure (energy, water and telecommunications) and transportation connectivity. Development and implementation of policy solutions will need active engagement, which will be challenging, but necessary.

Challenges and opportunities

It will be critical to understand how policies at the EU and international level have changed and shaped the Agriculture and Agri-food Sector in Ireland. The Common Agricultural Policy sought to guarantee fair prices to producers (farmers) and provide affordable food across Europe. This generated change within Ireland’s agricultural practices, from mixed to specialised systems. To change policy, consideration of the sector’s history and the socio-cultural values associated with the land is needed.

Farming provides a strong social identity for those involved. Farm holdings may have been in families for generations, with a sense of connection felt by the individual and families concerned, to the land. Farming is about more than an income. Such social aspects may not be properly considered in policy, which currently tends to focus on short-term quantifiable economic impacts (McCauley & Heffron, 2018). The long-term social, environmental and economic impacts are harder to quantify and therefore are absent.

Translating the loss of social connection and the mental health impacts of losing one’s job into economic terms is currently not considered within policy making processes. The World Health Organisation has acknowledged that depression is the leading cause of absenteeism from work (WHO, 2017). Causes of depression are broad, as are the impacts of depression. It affects not just an individual but their entire family, community and society. Using a just transition framework to design policy will allow consideration of social impacts of policy (McCauley & Heffron, 2018).

Policy regarding the transition of the fossil fuel sector in Alberta, Canada, provides an example, where the health and well-being of workers was a priority (Marshall et. al., 2018). Policy makers engaged with workers from the outset to understand their vision, hopes and concerns about the future and their role in it. Backlash was minimal from workers as they acknowledged that the future of energy was not coal, but renewables. Having the opportunity to contribute to the design of policy that would affect their livelihoods, rather than having a policy imposed upon them, enabled the government to make progress on the transition out of coal. Ireland could similarly demonstrate leadership by working with the agriculture and agri-food sector to make it more resilient while still supplying food locally and globally.

Achieving a just transition requires an all of government plan

Acknowledging that a just transition is concerned about doing no harm it is worth highlighting Article 25 of the UN Declaration on Human Rights before discussing actions for a just transition.

“Everyone has the right to a standard of living adequate for the health and wellbeing of himself [herself] and of his [her] family, including food, clothing, housing and medical care and necessary social services, and the right to security in the event of unemployment, sickness, disability, widowhood, old age or other lack of livelihood in circumstances beyond his [her] control” - Article 25(1) of the UN Declaration of Human Rights

Policy actions and measures to mitigate the impacts of climate change on the agriculture sector need to consider the broader context. Climate action is not just about reducing emissions. It is about society, understanding, how this affects us all and how we are going address this problem that has been created by us (Dekker, 2018; Heffron & McCauley, 2018; McCauley & Heffron, 2018; Whyte, 2018).

Ireland can demonstrate leadership by working with the agriculture and agri-food sector to make it more resilient while still supplying food locally and globally. For example, the Smart Farming programme, developed by the Irish Farmer’s Association with funding from the EPA and support from Teagasc, aims to inform farmers on ways to protect the environment while saving on costs associated with energy, fertiliser and water use.

All farmers in Ireland need to be effectively represented in policy development that impacts on their livelihoods. As mentioned, families have farmed for generations and have an intimate knowledge of their land. Farmers understand the consequences of legislation and regulations and how these translate at farm level. They know what is needed to progress. The transition to low carbon agriculture and a food secure Ireland is a process. It will take time and dialogue, during which conflict will arise but will serve as an opportunity to learn and inform policy-making.

The Irish Government has the policy tools and resources to enable a just transition. One is the Citizens’ Assembly, which has been recognised internationally as an ideal model of public participation in national government policy development. Though not necessarily dealing with climate change, the agricultural European Innovation Partnership (EIP-AGRI) aims to facilitate and support the engagement of farmers and land managers with other stakeholders in designing localised solutions to environmental issues (EC, no date; DAFM, 2019). Greater emphasis could be placed on greenhouse gas mitigation in conjunction with addressing other environmental concerns. Opportunities also exist to work with the National Dialogue on Climate Action to explain the national transition, explore opportunities for the sector and contribute to the further development of policy.

As local authorities across the country develop their climate action plans with the support of Climate Action Regional Offices, there is an opportunity for active and continuous public participation that supports the agricultural sector and achieves social and economic growth in rural Ireland. At present, Fingal and South Dublin County Councils, have indicated that they will support farmers with GLAS measures in their Climate Action Plans. Local authorities need not limit their action to GLAS and should consider the potential of urban agriculture and social farming. Social Farming has been active in County Kerry for a number of years (www.kerrysocialfarming.ie) and has been successful in providing social supports for individuals with disabilities. The programme could serve as a template for social farming across the country. Both social farming and urban agriculture can contribute to climate change mitigation, adaptation and provide social benefits for citizens by fostering a connection with nature, an understanding of food production and promoting constructive dialogue.

In summary, the Irish Government has the capacity to achieve a just transition within the agriculture sector and can be a global leader in the field. This will require greater collaboration with farmers and communities in the policy making process in order to capture their valuable knowledge and lived experience. This will take an investment of time and dialogue, that will pay dividends going forward. The active engagement process employed by the Citizens’ Assembly provides an example of how to collaborate and incorporate a diverse range of views in a meaningful way and will enable a just transition.

Enabling Mitigation 4. – just transition in agriculture

The need for just transition in responding to climate change is highlighted internationally. The Just Transition provides a framework for understanding how the transition to a low-carbon society is to be equitable and minimise the negative impacts for all stakeholders, notably farmers and rural communities. The development of climate related policy should consider underlying causes of individuals’ and communities’ increased risk and vulnerability to not only the impact of climate but, of proposed mitigation measures. Participatory approaches for engagement are fundamental to this. Approaches adopted by the Citizens’ Assembly and under the European Innovation Partnership (EIP) programme may be useful in facilitating engagement and ultimately strong stakeholder ownership of mitigation policies and therefore help achieve just transition.

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4 DISCUSSION AND CONCLUSIONS

This review of existing literature and collation of expert opinion has identified a clear and urgent need for changes in management and associated policy within the AFOLU sector. The need for change also presents considerable opportunity, not only in combatting climate change, but also making the sector more sustainable, bringing multiple co- benefits to society.

Agricultural systems, the predominant land use, are: causing significant greenhouse gas emissions; in certain cases, adversely impacting Ireland’s natural environment; often generate poor economic returns and are subject to external drivers creating incentives for expansion. The environmental, economic and social sustainability of many current agricultural systems and practices in Ireland needs to be assessed. Farmers, as key land managers, have successfully responded to policy, market and institutional signals in the past, and will form a key part of the solution. Conflicting communication, policy and incentives within the agricultural sector, further exacerbate issues. Afforestation rates are substantially below target, while organic soils are a significant source of carbon emissions.

The following mitigation options to should be considered:

2. Management options for wetlands, especially peatland, require urgent assessment and implementation. Time is of the essence, as it will take a number

of years for peatland ecosystems to re-establish and built resilience to projected changes in climate. The drainage of peat for multiple land uses, including peat extraction, must cease. Areas for rewetting should be identified and associated land management programmes started. Identification of agricultural land on which drainage has already ceased is required for inventory accounting. Bord na Móna’s plans for peatlands under its management are an important opportunity for leadership, learning and public engagement.

3. Low afforestation rates need to be addressed with recognition and consideration of behavioural barriers. The type of afforestation, in terms species and environmental impacts, needs to be considered. Agroforestry appears to be a resilient system that permits agricultural production with limited afforestation, bringing multiple co-benefits and with further research, should be encouraged.

4. Expanding on approaches in the National Planning Framework, there is merit in the development of a national land use strategy. This should not be prescriptive but would enable design of policy to promote the sustainable delivery of multiple and competing land functions, while ensuring long-term environmental sustainability.

5. Cost-effective mitigation measures, identified in the Teagasc Marginal Abatement Cost Curve analysis, should be implemented as appropriate. These mitigation

options that would deliver reductions of 2.9 Mt CO2-eq per year, by 2030. The Common Agricultural Policy provides the mechanism for aiding this as it moves to greater national control.

6. There is a need for specific research into mitigation options that are detailed in this paper. Research requirements concern existing mitigation measures, the development of new measures, their technical implementation, impacts or trade- offs and associated development and refinement of inventory accounting methodologies.

7. Adoption and successful implementation of climate change mitigation policy and measures depends on farmers’ acceptance based on their lived experience,

knowledge and understanding. Additional research and resources to enable effective knowledge exchange are required.

8. Noting the success of participatory approaches to engagement, for example the Citizens’ Assembly, a process of co-design could be implemented to facilitate engagement and ultimately strong stakeholder ownership of mitigation policies, which would help to achieve a just transition.

9. Identification and review of existing incentives and schemes that may be in conflict with greenhouse gas mitigation objectives is required, as coherence in policy is vital.

10. Ireland needs to engage with national and international experts to demonstrate and validate its environmental sustainability or ‘green’ credentials regarding food production.

11. Ireland should continue to support research into balance and neutrality concepts while promoting international research and policy development on this topic. Specifically, regarding the development of metrics that appropriately account for the lifetimes of short-lived greenhouse gases, such as methane.

Finally, it must be emphasised that many climate mitigation measures within the AFOLU Sector generate additional co-benefits, including biodiversity, ecological interaction, enhanced air quality, landscape protection, recreation and tourism potential, economic diversity and human wellbeing. Such benefits are associated with economic, environmental and social sustainability and represent the result of good land stewardship.

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APPENDIX 1.

Supporting Notes on Key Land Use types in Ireland

A.1.1 Wetlands

In Ireland, peatlands have the highest storage capacity, especially pristine raised bogs. The limiting factor for peat depth tends to be a combination of local topography, hydrology and climate conditions. Drainage of peatlands, for whatever purpose, is a significant source of carbon dioxide emissions. The rate of carbon loss due to drainage can be an order of magnitude greater than rates of carbon sequestration of pristine or restored peatlands. Management of the water table, including rewetting, can curtail these emissions.

Although uncommon, pristine peatlands in Ireland appear to be still in a growth phase, with peat formation conditions occurring in most years. However, dry, warm conditions have been observed to lead to carbon losses from peatlands. The average rate of carbon uptake in a pristine blanket peatland was 0.3 t C ha-1 yr-1 (Kiely et al, 2018, Renou-Wilson et al., 2011). The large majority of peatlands in Ireland are in a degraded state, often as a result of historic disturbance and exploitation. Nevertheless, degraded peatlands remain the largest store of carbon is the Irish landscape. There are two broad categories of degraded peatlands, areas which are subject to on-going active management and drainage; and areas where there is no longer active management, but have been drained and exploited in the past. There are limited data on the condition of degraded bogs, and general conclusions on whether they are losing carbon, or have reverted or recovered to a growth phase are not possible. Re-establishment of much of the biodiversity of the habitat is possible on rewetted degraded peatland in a relatively short period. Recovery of peat formation takes longer to establish and the carbon stocks are more vulnerable to inter-annual variability than pristine peatlands. Therefore, it is generally inappropriate to suggest that restoration and rewetting of drained organic soils would deliver significant carbon sequestration on timescales relevant to the requirement to mitigate national greenhouse gas emissions. Of greater importance is the opportunity to avoid the loss of carbon due to drainage. Pristine and restored peatlands are a source of methane emissions. The rate of emission of methane can depend on a wide variety of factors including water table, plant species and temperature. Where restoration of peatland habitat is the objective, methane emissions are unavoidable but can be viewed

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as a necessary characteristic of the ecosystem, and in the long term will be offset by the gradual sequestration of atmosphere carbon dioxide into new peat. Although, water table management cannot be guaranteed to restore carbon sequestration function to peatlands, it is a very effective means of reducing carbon losses.

Rewetting to manage carbon losses does not require restoration. It may be possible, and indeed preferable at some sites, to actively manage the water table to curtail carbon losses, whilst maintaining a shallow depth of drained soil. This aerated, surface layer can effectively oxidise the methane emerging form the rewetted layers below. This may be a useful option for management of drained organic soils where restoration is not feasible.

A.1.2. Grasslands

Grasslands are the second largest carbon stock, due to the large area and high soil carbon content. There is evidence that grazing pastures managed to maintain typical livestock stocking rates (1.0 to 2.0 Livestock Units ha-1) tend towards higher soil carbon content relative to low intensity grazing systems. There are limits to the sustainable level of intensification, with high stocking rates leading to a decline in soil carbon (Muhammed, et al., 2018, Abdalla et al., 2018). Additional caution is required in moving towards optimised production intensity within grassland-based livestock systems. In many instances the improved production is achieved with greater use of synthetic nitrogen fertilisers. This will lead to the emission of nitrous oxide. There are options to reduce the emission of nitrous oxide, including alternative formulations of fertilisers, mixed swards incorporating clover, and actions to improve other aspects of soil fertility such as pH, potassium and phosphorous.

Teagasc have reported decline in soil quality and currently Irish soils have significantly sub-optimal soil fertility (Teagasc, 2017). An expert statement from the Royal Irish Academy suggests that Irish agricultural soils have considerable potential for additional carbon sequestration which may be realised through improved land management (Kiely, 2016 as cited in RIA, 2016).

Traditionally, grasslands are managed to provide grazing and fodder for livestock. Much of the area of grassland are actively grazed, which enables high rates of nutrient and carbon recycling to soils. However, a large area of grassland is harvested to provide winter fodder, mainly silage. This harvesting of grass removes biomass from the fields.

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Some is recycled through later spreading of slurry. This may be of increasing concern if other economic uses of grass biomass emerge, such as anaerobic digestion generation of biogas from feedstock such as grass.

Approximately 300,000 hectares of agricultural grasslands occur on drained organic soils, converted from peatlands, and as such as subject to significant losses of carbon. Currently, the default IPCC emission factor for carbon losses from nutrient poor drained organic soils is applied to estimate the emissions from these areas in the national inventory (Duffy et al., 2018). However, there is evidence that these lands are managed with relatively shallow water table, leading to observed lower emission rates than the IPCC default value (Renou-Wilson et al, 2015).

A.1.3. Croplands

Croplands tend to have the lowest soil carbon content, as oxidation of soil carbon is enabled by repeated disturbance and exposure to air during tillage. This project has established a robust methodology for tracking the management of croplands on a land parcel scale on an annual basis. However, additional research and monitoring is required to provide country specific analysis of the impact of land management practices on emissions. Greenhouse gas mitigation options for croplands include improved nutrient management, straw incorporation and the establishment of catch or cover crops (Eory et al., 2015; RICARDO-AEA, 2016; Lanigan et al. 2018). As mentioned, catch crops are established after harvest of principle crops to provide ground coverage and intercept availed soil nutrients, thus preventing leaching and run-off. Teagasc identified cover crops and straw incorporation as having potential, at estimated costs of €86 and €279 t

CO2-eq mitigated respectively. Cover crops may reduce nitrogen leaching (Premrov et al., 2014) and therefore, indirect nitrous oxide emissions, while facilitating carbon sequestration (Poeplau & Don, 2015). Cross Compliance regulations require the establishment of sown green cover within six weeks of ploughing arable land and sown or naturally regenerated green cover within six weeks of spraying non-selective herbicide. The voluntary Green Low-Carbon Agri-Environmental Scheme (GLAS) includes an option for catch crop establishment on a minimum of area of 4 hectares and maximum of 32 hectares.

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A.1.4. Forest land

Forest land has the highest biomass per hectare. As noted, historically Ireland was largely denuded of native forest, and over 90% of existing forest area have been established since the beginning of the 20th century. The annual Forest Statistics from DAFM assign a large carbon stock of approximately 380 Mt C to forest land (DAFM, 2018a). However, country specific research has established that forest soils tend towards carbon contents that are similar to those of managed grasslands (Duffy et al., 2018). Therefore, although it is correct to assign a large soil carbon stock to forest soils, the carbon is not as a result of afforestation, but consistent with maintaining an existing carbon stock in the transition from other land use to forest land. Afforestation is a central element of current land use policy, and it is the biomass component of the carbon stock which provides the main carbon removal option. There is a need for detailed scenario analysis to provide insight into the potential development of the forest carbon stock in response to emerging demands for biomass resources for energy, materials and other ecosystem services. For example, in the long-term, over the management cycle of a commercial plantation, the average carbon stock is approximately 60 t C ha-1. If the total forest area increases to 1.2 million hectares, then the total sustainable national carbon stock in biomass would reach 70 Mt C, (260 Mt CO2-eq) with a capacity of delivering 2.5 Mt C yr-1 of biomass to markets. This figure is greater than the current biomass stock, due to the larger area in this scenario, and the assumption of moving gradually to a uniform age profile. Currently, the national forest has an age profile skewed to less mature trees.

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Figure 9 Annual State and private afforestation 1922-2016

Figure 10 Forest age class distribution by ownership (Source National Forest Inventory, 2012), extract from DAFM Annual Forest Statistics, 2018

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Irish forestry development is principally guided by Ireland’s Forest Policy (DAFM, 2014) and is expanded on by the Forestry Programme 2014-2020 (DAFM, 2015c) with regard to EU Regulation 1305/2013 concerning rural development. The former policy document outlined a forestry expansion aim of 10,000 ha yr-1 to 2015 and 15,000 ha yr-1 from 2016 to 2046, which would increase forest cover to 18% (1.25 million hectares). Forestry cover is currently 11% (DAFM, 2018a). The Afforestation Grant and Premium Scheme 2014- 2020, under the Forestry Programme, aims to support the increase of national forest cover to 18% by 2046, with aimed establishment rates for new forest of 10,000 ha yr-1 (DAFM 2015d). Within this schemes’ timeframe (to 2020), 30% of the area afforested aims to be under deciduous trees with general encouragement of planting on better land. Mean afforestation rates of 7,674 ha yr-1 occurred between 2012 and 2017 (DAFM, 2018a). It is highly unlikely that the 18% forestry cover target by 2046 will be achieved.

APPENDIX 2.

Supporting Notes on AFOLU Mitigation Strategies

A.2.1. REDUCTION OF AGRICULTURAL GREENHOUSE GAS EMISSIONS

A.2.1.1. Greenhouse gas emissions from livestock

A Gradual Reduction in Bovine Numbers

The impact of bovine numbers on agricultural and national greenhouse gas emissions has been outlined (Sections 2.2 and 3.1.1.). According to Duffy et al. (2019), methane emissions from bovines alone (enteric fermentation and manure deposition and management), accounted for 85.6% of total national methane emissions (excluding LULUCF), 61.3% of total agricultural emissions and 19.7% of total national greenhouse gas emissions in 2017 (excluding LULCF). A current trend of increasing agricultural emissions was principally attributed to increasing bovine numbers (EPA, 2018a; EPA, 2018b), with emission projections to 2030 determined accordingly (Lanigan et al., 2018). The EPA has identified increasing bovine numbers as also largely responsible for increasing ammonia emissions and failure to meet national emission targets (EPA, 2018d). Additionally, and though not directly attributed to bovines, agriculture is identified

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as negatively impacting Irish river water quality (EPA, 2016; EPA, 2018e) as well as threatening biodiversity (DCHG, 2017a). Bovine systems form the principle agricultural activity in Ireland (Dillon et al., 2018; CSO, 2018a) and are therefore likely to be a key driver of current environmental degradation.

The 2017 national farm survey (Dillon et al., 2018) indicated an average increase in national farm income, attributed largely to gains made in the dairy sector. Cattle rearing, principally from suckler herds, was identified as generating the lowest average farm income (€12,529) in 2017 and remained relatively unchanged since 2016Roughly half of cattle farms earned < €10,000 in 2017 with heavy reliance on subsidies. Direct payments contributed to 114% and 96% of income for cattle rearing and cattle finishing enterprises respectively. This is compared to the dairy sector, where direct payments contributed to 22% of farm income. Therefore, not only do direct payments account for the entire farm income of cattle rearing enterprises, but for every €100 received, €14 was lost to subsidise production. When direct payments were discounted, cattle rearing on average was found to make a loss per unit of product, or the cost of production exceeded market income (Dillon et al., 2018). Cattle finishing was estimated to generate small profits, though for every €100 received in income, €4 represented market income and €96 - direct payments. Overall, Buckley et al. (2019) indicated that only roughly 25% of beef enterprises (including both cattle rearing and finishing) were economically viable.

It is therefore suggested that overall, current national bovine numbers are environmentally and, economically unsustainable. A gradual reduction may be necessary, and further expansion of the national herd should not occur.

Where production does not exceed environmental limitations at farm and catchment scales, there is justification for maintenance of the dairy herd at current levels. Expansion within the dairy herd should only take place where within environmental limitations. It is difficult to envision further expansion of the dairy herd within environmental constraints. As beef production is largely dependent on direct payments (Dillon et al., 2018; Dillon et al., 2019), it may be appropriate to focus a potential gradual number reduction within the beef sector. Following the introduction of the EU Milk Quota in 1984, expansion of the national beef herd occurred (Hennessy & Kinsella, 2013). The abolition of the Milk Quota in 2015 facilitated the expansion of the dairy herd (Läpple & Hennessy, 2012), with little re-adjustment within the overall beef sector evident (CSO,

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2018a). However as discussed and outlined in Figure 1 (Section 2.2), a decline in suckler cow numbers has occurred in tandem with increasing dairy cow numbers, though expansion of the dairy herd is proportionally greater than reductions in the suckler herd. Lynch et al. (2016b) projected a likely decline in suckler cow numbers nationally and a 5% reduction in Irish beef exports by 2030 compared to 2015 under a modelled baseline projection. It is emphasised that any gradual reduction within the beef herd and associated release of land, may in the first instance support alternative land use, including afforestation, and should not facilitate dairy herd expansion and where so, only if environmentally appropriate. There is recognition that dairy systems, which are typically intensive, generate greater emissions than beef systems (Lynch et al., 2016a; Buckley et al., 2019; Tzemi & Breen, 2019). With regard to extensification, suckler cow number reductions may not be appropriate where low stocking rates, or extensive systems already exist. The potential importance of such systems in supporting important habitats and associated biodiversity (NWPS, 2013; Sheridan et al. 2017) has been discussed (Section 2.2).

From a greenhouse gas emissions perspective and in the absence of other agricultural mitigation measures, the theoretical impact of a gradual reduction in suckler cow numbers and stabilisation of the dairy herd has been explored through three simple scenarios for the period out to 2030 (Section 3.1.1). Scenario A indicated that a 15% reduction in the suckler herd equated to an increase in total agricultural emissions by 2.9% relative to 2005 in 2030. Scenario B explored the impact of a 30% reduction in the suckler herd by 2030 which equated to a reduction in total agricultural emissions by 0.9% relative to 2005. Finally, Scenario C indicated that a reduction of the suckler herd to pre- Milk Quota levels (1984 levels), equated to a 45% reduction in suckler cow numbers relative to 2018 levels, and could lead to a 6.7% reduction in total agricultural emissions relative to 2005 in 2030. For all scenarios it was assumed that dairy cow numbers remained at 2018 levels (1.4 million animals) though milk yields and associated feed intake was projected to increase to 2030. Emission factors were adjusted accordingly. The associated reductions in follower heifers and in cattle of different age classes has been accounted for along with changes in methane and nitrous oxide emissions from manure management and deposition. Total sheep and pig numbers were adjusted to Teagasc baseline scenario (S1) projections (Donnellan et al., 2018; Lanigan et al., 2018) while nitrogen fertiliser use was assumed to stabilise at projected 2018 levels.

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Total agricultural emissions in 2005 were 18.7 Mt CO2-eq (Duffy et al., 2019). In the absence of sector-specific emission reduction targets, Lanigan et al., (2018) analysed a reduction in agricultural emission of 20% relative to 2005, equal to an emissions reduction of 3.7 Mt CO2-eq by 2030. The same study identified cost effective agricultural mitigation options which would deliver 2.9 Mt CO2-eq by 2030. This combined with a gradual reduction in suckler cow numbers would represent an important contribution to national efforts to reach Effort Sharing Regulation targets.

Policy intervention may be required to enable continued suckler cow number reductions. Despite previous incentives, such as decoupling within the CAP to facilitate number reductions, little change has been observed in overall beef livestock numbers (Figure 10).

7000 6500 6000 5500 5000

(000's) 4500

4000

Livestock Numbers Numbers Livestock

2009 2011 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2010 2012 2013 2014 2015 2016 2017 Year

Figure 11 Beef livestock (non-dairy cows and cattle) numbers (Extracted from EPA emission inventory CSO Tables)

Buckley et al. (2019) explored the economic and environmental impact of the various beef production systems prevalent in Ireland. The study noted a positive correlation between emission efficiency and economic performance. Using IPCC guidelines, the top

-1 economic performing farms emitted less (9.6 kg CO2 kg beef ) than bottom performing

-1 -1 enterprises (14.9 kg CO2 kg beef ). However, despite slightly better nitrogen use efficiency, nitrogen surpluses were found to be higher on better performing farms (89.7 kg N ha-1) compared to bottom performers (47.8 kg ha-1) due to the system intensity. As discussed, less-intensive systems may also support wider eco-system services. Using High-Nature-Value (HNV) farming as an indicator, regions associated with lower agricultural economic performance are identified as having greater value (Finn, 2016;

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EPA, 2016). The important role of less-intensive systems in supporting wider eco-system services, including landscape aesthetics is perhaps highlighted by the Burren Programme (NESC, 2016). Indeed, the maintenance of traditional farm landscapes was found to be publicly supported (Howley et al., 2012). From a local environmental and landscape perspective, reductions in bovine numbers on less-intensive systems may not be appropriate, notably if changes facilitate more intensive systems such as dairy production.

In addition to local environmental considerations, the likely negative socio-economic and cultural, impacts of a reduction in bovine numbers must be recognised. Progressive elimination of the Irish suckler herd to 2030 was estimated to cause a 14% reduction (from a projected baseline) in beef exports (Lynch et al., 2016b). Lanigan et al. (2018) warned of disproportional social and economic implications of a reduction in the national herd. Indeed, beef farmers receive some of the lowest farm incomes (Dillon et al., 2018). From a social perspective, 23% of beef farmers were identified as living alone and therefore, at risk of isolation. Roughly, 32% of enterprises had an age profile above 60 with no other member of the household less than 45 years old. Both isolation and age profile were negatively correlated to economic performance (Buckley et al., 2019). Indeed, the average age of beef rearing and beef finishing farmers was 56 and 57 years respectively (Dillon et al., 2018). A survey of small farms (where standard output = ≤ €8,000 per annum), of which cattle enterprises represent 61%, indicated that 32% were farmed by people ≤ 65 years while 28% were single person households (Dillon et al., 2015). The encouragement of a reduction in suckler cow numbers must be only with full cognisance of a just transition, and government accordingly (see Section 3.4.4).

Hennessy et al. (2018) discuss the contribution of the suckler beef sector to, not only the export industry but also rural economies. Roughly €1.5 billion was estimated to be spent per annum on agri-products by cattle farmers predominantly in rural economies with positive multiplier effects greater than other sectors. Suckler farming clearly has social benefits, notably in isolated rural regions (Hennessy et al., 2018). Any encouragement of changes in cattle production warrants carful research into socio-economic impacts.

Elimination of the suckler herd was envisioned to impact land markets and other agricultural activity, potentially facilitating increased dairy production (Lynch et al., 2016b). Additionally, if a proportion of farms exit beef production for afforestation, an

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intensification of remaining beef farms may occur, therefore maintaining beef numbers and generating localised pollution. Casey & Holden (2006a) suggested that the integration of the dairy and beef systems is important in combatting livestock emissions. From and LCA perspective, emissions allocation can potentially be reduced by over 30% if beef animals are produced from dairy rather than suckler cows. Styles et al. (2017) projected increased emissions associated with the intensification of milk production in the UK that led to reduced dairy-beef output and increase reliance on suckler herds for beef production. The optimisation of dairy-beef production systems is clearly desirable, though analysis of the extent to which this is possible and its likely contribution to greenhouse gas mitigation is required. Additionally, contract rearing of dairy herd stock by beef enterprises may be beneficial. Barriers to integration between the two systems may include necessity, proximity of enterprises, land availability, biosecurity and attitudinal motivation or preferences.

Extended grazing season

Production of methane by enteric fermentation is caused by methanogenic bacteria working principally in the rumen, on hydrogen (H2), a product of primary and secondary digestion. Hydrogen is associated with the production of volatile fatty acids (VFAs) notably, acetate. Other volatile fatty acids include propionate and butyrate. Feed materials that have a higher level of cellulose or structural carbohydrate, lead to a greater proportion of acetate production and potentially greater methane emissions (Boadi et al., 2004). As mentioned, grass accounts for 80 to 90% of the diet of beef and dairy cattle in Ireland (EPA, 2016). Conserved grass (i.e. silage or hay) typically has a higher proportion of cellulose compared with freshly grassed grass, and therefore may generate higher methane emissions, though impacts may vary (Martin et al., 2010). Teagasc recommend extending the grazing season, which allows greater utilization of fresh grass and less silage intake (Lanigan et al. 2018). Grazing season length is also identified within the Carbon Navigator package (DAFM, 2018). Extending the grazing season may require the implementation or renovation of land drainage systems, as soil compaction from livestock treading is likely if soils are wet (Houlbrooke & Laurenson, 2013; Drewry, 2006) later in the season. However drainage must only be conducted where appropriate. The drainage of organic soils is discouraged regarding carbon dioxide emissions (Renou-Wilson et al., 2015) as discussed later. Extending the grazing season may also reduce the quantity of slurry stored, further reducing emissions, though

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increased dung and urine deposition on grassland particularly when soil moisture content is high, may lead to increased nitrous oxide emissions (Ball et al., 2012).

Dietary additives and vaccines

Numerous measures regarding bovine diets have been investigated along with associated challenges (refer to Martin et al., 2010). Teagasc explored the inclusion of lipids or fatty acids to dairy cow diets, which was estimated to mitigate 0.035 Mt CO2-eq per annum, at a cost of €76.0 t CO2-eq abated (Lanigan et al., 2018). There are a number of other directly fed additives that may contribute to reducing methane emissions. The United Kingdom MACC identified nitrate and probiotics such as yeast culture (Saccheromyces cerevisiae) as potentials (Eory et al., 2015). Research indicates that seaweed extracts may greatly reduce methane production (Machado et al., 2014; Patra et al., 2017) while the use of 3 - nitrooxypropanol (3-NOP), an enzyme inhibitor, has also shown promising results (Haisan et al., 2014; Haisan et al., 2016; Joyanegara et al., 2018). Romero-Perez et al. (2015) observed methane emission reductions in beef cattle by 59% from feeding of 3-NOP in mixed rations at a rate of 2.0 g day-1 animal-1. Reduced methanogenesis was sustained during the trial for 112 days.

Patra et al. (2017) note potential issues with such additives regarding inconsistent results or digestion and feed intake restrictions. In an Irish context, administration of additives may be daily and by bolus within total mixed rations. This may not suite grass-based systems, where opportunities to feed additives may be limited. Long-lived boluses that could be administered during milking, or during ad libitum feeding of intensive beef finishing, may have potential. Methane-inhibiting vaccines may negate the need for daily administration. The development of vaccines, which induce the production of methanogen inhibiting antibodies, potentially in the saliva and transported to the rumen, has proven extremely challenging but suggested to be possible (Wedlock et al., 2013; Subharat et al., 2016; Patra et al., 2017). Interestingly, Hook et al. (2010) noted challenges associated with variation in methanogen populations as a result of livestock geographic location and associated feed. Research currently being conducted in New Zealand may provide useful insights (New Zealand Agricultural Greenhouse Gas Research Centre, no date).

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Despite some of these technologies being commercially available, they appear not to be used currently in Ireland and warrant research, notably regarding side effects. However, dietary additives and the use of vaccines may have considerable potential.

Genetic efficiency

Breeding for bovine production efficiency may reduce greenhouse gas emission per unit of output (Schils et al., 2013; Pickering et al. 2015). Pickering et al. (2015) suggest genomic breeding values offer a sustainable means of reducing ruminant methane emissions. In Ireland, there are a number of breeding indices that currently operate to increase genetic merit for productive efficiency and therefore economic gain but have potential to be used to reduce greenhouse gas emissions (Quinton et al., 2018). These include the Economic Breeding Index (EBI) for dairy cattle (ICBF, 2013) and the Terminal (T), Maternal Replacement (MR) and Dairy Beef (DB) Indices for beef cattle (McHugh & McGee, 2016). Both the Terminal Index and Replacement Index are incorporated in the Beef Data Genomics Programme (BDGP) (DAFM, 2018c). Established under EU Commissions approval, BDGP provides direct grant aid to farmers with the partial aim of specifically reducing greenhouse gas emissions (DFAM, 2018). Quinton et al. (2018) found both the beef MR and T Indices reduced system gross emissions (kg CO2-eq breading cow) and the emissions intensity (kg CO2-eq kg meat breading cow). The MR Index was important for both the gross emissions and emissions intensity, notably traits including cow survival (reducing the number of replacements) and cow live weight (less feed for maintenance of mature cows), though benefits may be offset by shorter calving intervals, increasing feed requirements. The T index was important for emission intensity regarding traits for meat production efficiency.

Lanigan et al. (2018) estimated that selection of favourable traits including feed consumption and methane emissions through the Maternal Replacement Index, could

-1 reduce greenhouse gas emissions by 0.81 kg CO2-eq breeding cow yr € index, or form a system perspective, emission intensity could reduce by 0.0089 kg CO2-eq kg meat breeding cow yr-1 € index. This was under the assumption that 65% of beef farmers enter into the BDGP. Regarding improved terminal traits, Lanigan et al. (2018) suggested emissions per unit of beef could potentially be reduced by 17% by enhancing traits such as daily live-weight gain.

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Regarding dairy production, the use of EBI, may allow better utilisation of grass from earlier calving dates, improved herd health and reduced gulling and therefore, replacement rates. However, as Lanigan et al. (2018) point out, caution is required as any mitigation is dependent on the national herd numbers or the production level remaining static, as if not, absolute emissions would increase regardless of greater efficiency per cow. In terms of specific traits, Quinton et al. (2017) suggested compiling indices specifically concerning emissions but note that these may not be aligned with economic gains, with clear trade-offs between economics and greenhouse gas reductions. None the less, certain efficiency traits will reduce greenhouse gas emissions and potentially increase economic returns.

Herd health

Maintenance of animal heath is a recently recognised mitigation strategy (Schulte et al., 2012, Eory et al., 2015). Theoretically, the absence of disease ensures optimal livestock productive efficiency, thereby requiring less input per unit of product and untimely, generating less greenhouse gas emissions. Ruminants, often more exposed than monogastrics (Eory et al., 2015), are subject to numerous endemic, sometimes sub- clinical, diseases including Mastitis, Johnes disease, Salmonella, Bovine Viral Diarrhoea (BVD) and lameness (ADAS, 2015, Lanigan et al. 2018). In United Kingdom, ADAS (2015) estimated Johne’s disease to cause a 25% increase in greenhouse gas emissions per 1000 l of milk produced and BVD, a 130% increase in emissions per 100 kg of beef carcass weight. Mostert et al. (2018) found foot legions to lead to greater greenhouse gas emissions per unit of milk. The impact of digital dermatitis, white line disease and solar ulcers were collectively found to increase emissions per ton of fat and protein corrected milk by 14 kg t CO2-eq (Mostert et al., 2018). Hospido & Sonesson (2005) found mastitis to contribute to greenhouse gas emissions through the discarding of affected milk. In addition to endemic diseases, it is now recognised that improvements in general animal welfare may also contribute to reducing greenhouse gas emissions (Herzog et al., 2018).

A.2.1.2. Greenhouse gas emissions from soils

Nitrogen fertiliser formulation

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Grazed grasslands are highlighted as a nitrous oxide source as a result of enhanced microbial activity, associated with carbon input from decaying surface vegetation and dense root-mats (Schaufler et al., 2010), with rhizosphere plant and microbe interaction an important factor in emissions (Butterbach-Bahl et al., 2013). Harty et al. (2016) demonstrated that the type of fertiliser applied significantly impacts nitrous oxide emissions in temperate grasslands. Three forms of nitrogen fertiliser are currently used in Ireland (Duffy et al., 2018). Calcium ammonium nitrate (CAN) is the principle form, accounting for 85% of nitrogen fertiliser sales in 2016, while urea and protected urea accounted for 14 and 0.5% sales respectively (Duffy et al., 2018). This is in contrast to global nitrogen fertiliser consumption, which is urea dependant (Cantoarella et al., 2018). Lanigan et al. (2018) advocated the replacement of CAN with urea, with the latter protected by nitrogen stabilizers. The form of nitrogen contained in urea (ammonium or

+ - NH4 ) is less readily available for denitrification compared to nitrate (NO3 ) contained in CAN (Roche et al., 2016) and its use may lesson nitrous oxide emissions. Nitrogen stabilizers include nitrification or urease inhibitors (Watson et al., 2009), typically dicyandiamide (DCD) and N-(n-butyl) thiophosphoric triamide (NBPT) respectively (Harty et al., 2016; Roche et al., 2016). Urea is associated with higher ammonia volatilisation (Forrestal et al., 2017) with 16% of the N applied worldwide being converted to ammonia (Cantarella et al., 2018). Ammonia may indirectly contribute to nitrous oxide emissions while also being an important air pollutant.

In light of the EU Emissions Ceiling Directive, requiring 1% and 5% reductions in ammonia emissions by 2020 and 2030 respectively (EPA, 2016), the protection of urea with a urease inhibitor is important, with NBPT commonly used (Harty et al., 2016; Cantarella et al., 2018) and the most promising (Forrestal et al., 2016). Forrestal et al. (2016) observed a 78.5% reduction in ammonia emissions from the use of NBPT compared with straight urea in Irish grasslands. However, NBPT has been found to cause toxicity crops. Artola et al. (2011) reported chlorosis or yellowing of leaf tips, changes in amino acids composition and alteration in plant metabolic rate causing higher levels of urea in plant tissue in wheat (Triticum aestivum L.). However, these effects were suggested to be transitory. Watson & Miller (1996) observed leaf scorch or tip necrosis in perennial ryegrass 7 to 10 days following NBPT and urea application, along with reduced urease activity in shoots. Again, effects were transitory, while reduced nitrogen loss though ammonia volatilisation from NBPT application is suggested to lead to improved dry matter production and far outweigh initial negative impacts (Watson &

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Miller, 1996). There are concerns over the potential fate of NBPT and that it may remain present in food products such as milk. Issues were noted with a nitrogen inhibitor called dicyandiamide (DCD) as fully outlined by Teagasc. Further information can be found at; https://www.teagasc.ie/publications/2019/protected-urea.php.

However, it is understood that concerns over NBPT are anecdotal, that the potential for residues to be present in food products is unlikely and that there is no current evidence of this occurring. A four- year project has commenced under Teagasc to examine the fate of NBPT, including the likelihood of its entry into food chains. Finally, it must also be noted that the application of urea leads to carbon dioxide emissions. In 2017 this accounted for roughly 0.2% of agricultural emissions (Duffy et al., 2019).

Despite no significant difference observed between CAN and urea for Spring cereal systems (Roche et al., 2016), Harty et al. (2016) reported less nitrous oxide emissions on grasslands with the use of urea compared with CAN, estimated a reduction potential of 70% in nitrous oxide emissions by substituting CAN with urea and suggested urea treated with NBPT, could reduce overall fertiliser costs. Additionally, Forrestal et al. (2017) found no significant difference in annual grass yields between the use of CAN and urea, though noted a slight decrease in efficiency of between 4 and 8% associated with urea through loss of nitrogen by conversion to ammonia. None the less, the substitution of CAN with protected urea is seen as a viable option, with urea protected with NBPT currently costing roughly the same as CAN (Lanigan et al., 2018). Additionally, refined or Tier 2 emission factors for Ireland (Harty et al., 2016; Roche et al., 2016) for use in inventory calculations, will aid more accurate national accounting.

Nitrogen fertiliser replacement by multi-species swards

The application of inorganic nitrogen fertilisers in Ireland was estimated to account for 38% of national nitrous oxide emissions (Lanigan et al., 2018). Roughly half the amount of nitrogen applied to soils is used by growing crops, with the remainder open to loss through multiple processes including denitrification (Watson et al., 2009). The most effective means of reducing impacts of fertilisers is to reduce the amount applied, which is also easily quantified for national inventory purposes. The use of multi-species grass swards is a means of reducing artificial nitrogen inputs (Schils et al., 2013), as the inclusion of legumes, typically clover (Trifolium L.) allows natural nitrogen fixation. Indeed, the use of clover has successfully formed a principle component of organic

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farming systems for years (Lampkin, 1990) and is identified as a measure within both the Teagasc (Lanigan et al., 2018) and United Kingdom (Eory et al., 2015) MACCs.

Research in Switzerland demonstrated legume-grass swards fertilized with 50 kg N ha-1 yr-1 gave the roughly the same yield as a grass monocultures that received 450 kg N ha-1 yr-1 (Nyfeler et al., 2009). Legumes in general can produce up to 400 kg N ha-1 yr-1 though this rarely occurs in agricultural pastures, with average nitrogen fixation in temperate, grassed pastures estimated to be between 80 and 100 kg N ha-1 yr-1 (Ledgard, 2001). Research in Ireland (SmartGrass Project) showed a reduction in direct

-1 -1 -1 nitrous oxide emissions from nitrogen fertiliser of 19 g N2O-N t DM ha yr associated with grass-clover swards compared to monoculture grass swards, with both swards receiving 90 kg N fertiliser ha-1 yr-1 (Murphy et al., 2018). The SmartGrass project also indicated that grass-clover swards receiving between 40 and 90 kg N ha-1 yr-1 will generate comparable yields to monoculture ryegrass swards receiving intensive nitrogen fertilisation (≈ 250 kg N ha-1 yr-1).

Species rich swards, which may include herbs such as chicory (Cichorium intybus L.) and plantain (Plantago lanceolota L.) in addition to clover, have potential co-benefits, such as reduced nitrogen leaching (Romero et al., 2017) anthelmtic properties, therefore enhancing livestock health (Lüscher et al., 2014; Grace et al., 2018) and improved drought resistance (Sanderson et al., 2005), the latter important for climate change adaptation. Additionally, the nutritional value and digestability of grass and forage may be enhanced (Lüscher et al., 2014). Total nitrogen within swards was found to be significantly greater in legume-grass mixes compared to grass monocultures at sites across Europe with mixtures containing a third clover, obtaining optimal total sward nitrogen and 57% more than grass monocultures (Suter et al., 2015). Nitrogen fixation by clover is regulated by the soil nitrogen sink (Suter et al., 2015) and can be negatively affected by the use of fertiliser (Nyfeler et al., 2009). However relatively low nitrogen application rates are unlikely to discouraged clover. The majority of farmland surveyed across a three regions in Ireland was classified as intensive or improved grasslands respectively (Sheridan et al., 2017). Despite some clover being present in swards, the former consist predominantly (≥ 70%) of perennial (Lolium perenne) or Italian rygrass (Lolium multiflorum) while the later are dominated (< 70%) by perennial ryegrass. Clearly there is considerable opportunity to increase the area of multi-species swards.

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Soil fertility management

Increasing nitrogen use efficiency and therefore negating the need for fertiliser application by optimizing soil pH is deemed an important mitigation measure in MACCs for both the United Kingdom (Eory et al., 2015) and Ireland (Lanigan et al., 2018). LCA analysis indicated that nitrogen use efficiency is key factor in reducing the carbon footprint of milk production in Ireland (O’Brien et al., 2014). Theoretically, if soil pH is sub-optimal, greater quantities of nitrogen fertiliser is required to offset reduced soil nutrient cycling to maintain plant performance and ultimately, crop yields. The benefits of applying lime to manipulate soil pH are well recognised (Holland et al., 2018). Soil pH, a measure of the concentrations of hydrogen ions (H+) considerably effects nutrient availability by influencing mineralisation or the breakdown of organic matter by both soil macro- and micro-organisms (Parkinson, 2003). Kemmitt et al. (2006) observed a significant positive correlation between soil pH with not only, crop productivity and microbial respiration but, nitrification, soil microbial biomass carbon and nitrogen.

In Ireland, lime applications are and have been historically considered crucial for agricultural activity. Irish soils are prone to acidity from leaching associated with high precipitation levels (Collins et al., 2004). However, liming using limestone or calcium carbonate (CaCO3) to correct soil pH, generates carbon dioxide. In Ireland, emissions from liming were estimated to be 332.7 kt CO2, accounting for 1.7% of total agricultural emissions in 2017 (Duffy et al., 2019). It is also worth noting that, as improving soil pH leads to increase microbial activity and therefore nutrient turn-over, a potential increase in carbon breakdown and emissions may occur (Moxely et al., 2014). However, Paradelo et al. (2015) found that this is only temporary and offset by higher plant productivity, which generates greater carbon returns to the soil. Liming may also alter soil structure by enhancing clay particle bonds and aggregation, thereby providing greater protection to soil organic carbon (Paradelo et al., 2015). Of course liming will impact all soil nutrient availability, including trace elements. Holland et al. (2015) outline how liming will impact both potassium (K) and phosphorus (P) levels, potentially leading to both increases and reductions in availability. Potassium, depending on the soil cation exchange capacity, is normally readily available and formed from the weathering of parent material and clay (Parkinson, 2003). Parkinson (2003) also noted how high levels of hydrogen ions associated with low pH, flood the cation exchange system and restricts potassium availability. Climate is recognised as responsible for low levels of potassium in Irish soils,

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with leaching a result of precipitation exceeding evapotranspiration (Collins et al., 2004). Phosphorus is derived from the weathering of parent material, though this process is extremely slow, with mineral forms also be held in soil organic matter (Parkinson, 2003). According to Collins et al. (2004) mineralisation of organic matter, a biological process therefore dependant on factors including soil pH, may account for 30 to 70% of available phosphorous.

Currently in Ireland there is considerable concern about soil fertility. Teagasc estimated that 88% of grassland soils have sub-optimal pH, phosphorous or potassium levels with only 12% considered to have optimal soil fertility (Plunkett, 2018). Considering potassium and phosphorous, Plunkett (2018) also outlined that 61 and 64% of grassland soils exhibit suboptimal optimal levels respectively. The correction of potassium and phosphorous in conjunction with soil pH is deemed important to optimise to the soil system performance and negate the need for compensatory nitrogen inputs to maintain production.

Low emission slurry spreading

As previously discussed, there is urgent need to reduce ammonia emissions in tandem with greenhouse gas emissions in Ireland (EPA, 2018d). A survey conducted in 2011, indicated that the majority (97%) of farms surveys spread slurry using splashplate systems (Hennessy et al., 2011). The use of trailing shoe, shallow injection and band spreading application equipment were found to reduce ammonia emission by 57%, 73% and 26% respectively compared to splashplate type broadcasting (Misselbrook et al., 2002). A review conducted by Webb et al. (2010) identified trailing shoe and open injection as most efficient compared with a trailing hose, with potentially less variation associated with trailing shoe application. The exposure of slurry to ambient air causes volatilisation and an associated release of ammonia. Methods that ensure the direct delivery of slurry either into the ground or sward limit the opportunity for volatilisation. Misselbrook et al. (2002) noted a significant (P = < 0.05) trend between increasing sward height and decreasing ammonia emissions, highlighting the effects of cover, following slurry deposition. Similarly, ploughing and therefore incorporating slurry immediately after application on arable soils, may also reduce emissions (Webb et al., 2010)

However reduced volatilisation enhances the level of available nitrogen and therefore may lead to increased nitrous oxide emissions. Indeed, nitrous oxide and ammonia

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emission fluxes may be antagonistic and trailing shoe application techniques have been associated with increased direct nitrous oxide emissions. Bourdin et al. (2014) indicated that though trailing shoe led to reduced ammonia volatilisation and indirect nitrous oxide emissions compared to splash plate applications, an increase in direct nitrous oxide emissions may offset any benefits. It was suggested that the timing of application was important, with Spring applications generating less ammonia and greenhouse gas emissions as a result of soil and climatic condition favouring crop growth. Drier conditions are generally associated with higher ammonia volatilization, carbon dioxide fluxes and lower nitrous oxide fluxes, with nitrous oxide directly linked to soil water-filled pore pace (Louro et al., 2013). Webb et al. (2010) suggested increased nitrous oxide emissions associated with ammonia emission reduction techniques are not inevitable, with rapid incorporation of slurry identified as beneficial. Overall, low emission slurry spreading techniques were identified in the Teagasc greenhouse gas MACC analysis as an effective mitigation measure (Lanigan et al., 2018).

Low emission technology is expensive though grant aid for up to 40% of costs to a maximum of €40,000 through TAMS II under the Low Emissions Slurry Spreading (LESS) Scheme (DAFM, 2018d), is available for farmers to purchase equipment.

Soil structure management and drainage of mineral soils

Soil structure describes the product of soil particles being bound together to form units known as aggregates and the resulting spaces or void within or between the aggregates known as pores (Russell, 1973; Ghildyal & Tripathi, 1987; Kay, 1990). Soil structure is suggested to underpin soil quality as it influence soil physical, chemical and biological characteristic (Mueller et al., 2013), These include soil hydrology, aeration, soil temperature, therefore soil microbial activity, nutrient availability, solute transport, gaseous exchange and root growth (Roger-Estrade et al., 2009). Therefore soil structure influences greenhouse gas emissions. Ball et al. (1999) outlined the impacts of changes in soil structure caused by tillage on carbon dioxide, nitrous oxide and methane emissions. Nitrous oxide and carbon dioxide fluxes were determined by air-filled porosity and gas diffusivity. Ploughing, which helps alleviate compaction and aerate the soil, was suggested to reduce nitrous oxide emissions while methane oxidation was described as being effected by long-term compaction. Nitrous oxide fluxes were found to be greatest on compacted soils following fertilisation and rainfall (Ball et al., 1999). Indeed, soil

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compaction is recognised as an important driver of nitrous oxide emissions (Schmeer et al., 2014).

Soil compaction describes a reduction in volume of a certain mass of soil, therefore altering soil porosity, as a result of applied pressure (McKibben, 1971; Marshall et al., 1996). Compaction can result from both machinery operations and livestock traffic (Bilotta et al., 2007; Batey, 2009). Processes such as pugging (indentations caused by livestock hooves) and poaching (the rendering of soil conditions to a soup-like state) result from livestock treading (Drewry, 2006) and are notable problems in wet soil conditions (Hamza & Anderson, 2005) when soils are weak and prone to damage (Batey et al., 2009; Ball et al., 2012). Induced livestock trampling was observed to considerably increased nitrous oxide emissions from applied urine-N by (Ball et al., 2012) while anoxic zones created within hoof indentations were found to lead to nitrogen losses by denitrification (Batey & Killham, 1986).

Soil structural degradation through processes such as compaction is of particular concern in temperate climates, due to persistently wet soil conditions (Creamer et al., 2010; Newell-Price et al., 2013). Every effort should be made to avoid compaction through the careful timing of machinery operations and livestock grazing (Creamer et al., 2010). The prevention of soil compaction is indirectly identified within Irish MACC under grassland management for carbon sequestration (Lanigan et al., 2018) but forms a specific measure for the United Kingdom (Eory et al., 2015). As mentioned, there is potential conflict between extending the grazing season and preventing soil compaction. Trees planted within grassland may have the potential to help strengthen soil structure stability and resilience, capture nutrients not utilised by grass and therefore help to extend the grazing system (McAdam et al., 2006).

With regards to soil drainage, roughly 60% of Irish agricultural soils were estimated to exhibit problems associated with wetness (Collins & Cummins, 1996). Issues associated with soil moisture and greenhouse gas emissions have been discussed, notably regarding nitrous oxide, where reduced aeration and water logging enhance nitrous oxide production. Improved soil drainage may reduce nitrous oxide emissions, while also potentially facilitating an extended grazing season. The drainage of wet mineral soils was identified within the Teagasc MACC analysis as a mitigation option, with the proposed use of gravel and shallow mole drains as well as deep drains in conjunction with sub-

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soiling (Lanigan et al. 2018). Tuohy et al. (2016a) explored the efficiency of different mole systems on clay loam soils, with gravel mole drainage found to be preferable. However, the need for careful consideration of site-specific factors when designing and installing drainage systems was emphasised (Tuohy et al., 2016b).

Regarding potential soil organic carbon losses by drainage of mineral soils, research by Kumar et al. (2013) suggests losses may occur. However this work was conducted on arable soils in the USA under maize (Zea mays L.) in conjunction with different tillage practices. The extent to which drainage of temperate mineral grasslands will lead to reduced soil organic carbon stocks is unclear. O’Sullivan et al. (2015) highlighted conflict between the production function, facilitated by land drainage on imperfectly-drained soils, and carbon storage in Ireland, suggesting drainage reduces carbon storage potential in certain scenarios. Nachimuthu & Hulugalle (2016) discussed the loss of dissolved organic carbon by deep drainage through the soil profile and highlighted a lack of associated data. However, improved soil functioning in terms of crop productivity and microbial activity, through improved drainage, may theoretically lead to greater carbon inputs and potentially accumulation.

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A.2.2. CARBON STOCKS AND SEQUESTRATION POTENTIAL IN SOILS AND BIOMASS

A.2.2.1. Carbon sequestration in grasslands

A study of 14 managed grasslands across central Europe indicated that grasslands were generally a sink of carbon and a source of nitrous oxide with mixed trends noted concerning methane. Despite this, only one site of a subset of nine sites demonstrated a positive net greenhouse gas balance (emissions exceeded sequestration) and was associated with ploughing and reseeding (Hörtnagl et al., 2018). The soil pool is the principle store of carbon within grasslands (as opposed to standing biomass) with soil organic carbon derived from inputs of dead vegetative material (grass) or if grazed, animal excreta. Irish grasslands provide second largest stock of soil carbon after wetlands (Eaton et al., 2008). As mentioned, a review undertaken by the Royal Irish Academy highlighted the potential for grassland soils to further sequester carbon in Ireland but noted issues with the measurement, reporting and verification required for IPCC inventories (RIA, 2016). Xu et al. (2011) estimated national soil organic carbon stocks to be 383 (± 38) and 1,475 (± 181) Tg within 0 to 10 and 0 to 50 cm soil depths respectively, of which grasslands represented roughly 53%. Total grassland soil organic carbon stocks were estimated to be 203 (± 35) and 769 (± 163) Tg for 0 to 10 and 0 to 50 depths respectively. In terms of density, this equated to 55 (± 11) t SOC ha-1 within 10 cm depth, or 207 (± 49) t SOC ha-1 within 50 cm depth. At field scale, Keily et al. (2009) observed mean soil organic carbon contents of 6.6% at 0 to 10 cm depth a grassland site in Cork while carbon fractionation at eight sites in Southern Ireland indicated that the majority of soil organic carbon held within grasslands is relatively stable. Roughly 50% was classified as within the passive pool, contained within sand, stable aggregates and non-recalcitrant carbon contained with silt and clay. The remaining 50% was composed of carbon held within microbial and particulate organic matter (the active pool ≈ 25%) and recalcitrant forms of carbon held in silt and clay particles (the slow pool ≈ 25%). However, Jones et al. (2017) highlighted that that the carbon sink function of grasslands is not perpetual and grassland management should aim to limit carbon losses as much as increasing the quantity of carbon stored.

The rate of carbon sequestration in grassland soils can be twice that of arable soils (Kayser at al., 2018). Jones & Donnelly (2004) outlined factors determining the rate of carbon sequestration in temperate grasslands including the input rate of material

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(organic matter), how quickly the material is broken down, the soil depth to which deposition takes place and the physical protection offered to organic matter within the soil matrix (e.g. within aggregates). Management greatly impacts soil organic carbon contents, with soil disturbance associated with carbon losses (Jones & Donnelly, 2004; Rumpel et al., 2015; Conant et al., 2017). For example, the conversion of grassland to arable production, significantly reduces soil organic carbon content (Soussana et al., 2004). Teagasc recommended that leys or temporary grasslands should remain for at least five years before ploughing (Lanigan et al., 2018). Indeed, the disturbance of established pasture to improve swards by reseeding, though agronomically beneficial, is likely to cause soil carbon losses (Kayser et al., 2018). Considering grassland management, a review of international literature indicated that the inclusion of legumes, fertilization, improved grass-species and grazing management lead to increased soil organic carbon (Conant et al., 2017). In general, practices that encourage pasture growth or enhance production, may lead to increased carbon sequestration (Jones & Donnelly, 2004).

Fertilization may affect sward and associated root growth, sward and therefore litter composition and in turn the metabolism of the microbial community, all impacting soil organic carbon stocks (Poeplau et al., 2018). Rumpel et al. (2015) discussed the impact of sward nutrient status. The organic matter generated from an extensively managed sward, where little fertilization has occurred or where grass is allowed to mature, will be broken down slowly within the soil, remain in unstable forms for longer and therefore open to losses. Under an intensively managed system, the sward is likely to have a higher nitrogen content, be composed of leafy material and the associated organic matter will be rapidly broken down and locked-up within stable organ-mineral soil complexes more rapidly. Poeplau et al. (2018) found fertilization had a positive effect on soil organic carbon within upper 30 cm soil depth and found 1.15 kg nitrogen (contained within NPK fertiliser) correlated with a 1 kg carbon sequestered. However, the carbon footprint of the fertilisers required to enhance carbon sequestration must be considered. In terms of organic manures, Jones et al. (2006) observed mixed results with the application of manures to a Scottish grassland, where increases in carbon sequestration was offset by increased nitrous oxide emissions with variation between different manures types. However, regular application of organic manure to grasslands is likely to increase carbon sequestration and it was suggested that nitrous oxide emissions may be limited if manures are applied when grass growth requires increased nitrogen (Jones et al. 2006).

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Equally, dung and urine deposition from grazing livestock may provide an important carbon input (Rumpel et al., 2015).

In terms of grazing intensity on soil organic carbon, research in Canada found that moderate grazing increased soil organic carbon content by 12% within the upper 15 cm soil depth compared to no grazing, though also highlighted regional climatic variation and outlined mixed results reported by other studies (Hewins et al. 2018). Indeed, Abdalla et al. (2018) also noted regional impacts of grazing and suggested that in cool moist zones, which included temperate climates, reductions in soil organic carbon are associated with grazing. A meta-analysis conducted by McSherry & Ritchie (2013) indicated variability, region specific impacts and that increased grazing intensity of C3 dominated grasslands (as found in temperate climates) may negatively impact soil organic carbon. A review conducted in the United Kingdom suggested that soil organic carbon may increase with moderate grazing though higher stocking density may negatively impact grass production and therefore soil organic carbon (Moxley et al., 2014). Adaptive multi- paddock (AMP) grazing, a system emerging in the USA, involves grazing for short periods of time at high sticking density while facilitating plant recovery, promoting plant communities and soil protection. Evidence suggests that AMP systems may lead to higher carbon sequestration compared to continuous grazing (Stanley et al., 2018). It appears an optimal “moderate” grazing intensity exists, below and above which soil organic carbon stocks may be depleted. Data specifically concerning temperate grasslands appears limited while short term experiments may not be sufficient to capture associated changes in soil organic carbon (EIP-AGRI, 2017). Work under the European Commission funded “Grass for Carbon” focus group (EIP-AGRI, 2017) as well as the Irish Government funded AGRI-SOC project (DAFM, no date) may provide useful insights.

It is worth noting that apart from enhanced nitrogen fixation by clover, multispecies swards containing herbs, may theoretically sequester more carbon due to greater rooting depths obtained. This was examined in Ireland as part for the SmartGrass Project and results are to be published shortly. However, gains in sequestration from sward composition modification, such as the inclusion of herbs, will be relatively minor compared to gains from complete land use change, for example the conversion of arable to grassland system.

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A.2.2.2. Forests and woodland

Afforestation

The mitigation potential of forestry is widely recognised including in Ireland (DAFM, 2015c; 2015d). The national Climate Mitigation Plan suggests Irish forestry and the use of associated products could potential sequester 20 to 22% of agricultural emissions annually (DCCAE, 2017). Irish forestry is estimated to currently represent a sink of 312

Mt C, while removing 3.8 Mt CO2-eq annually between 2007 and 2016 (DAFM, 2018a). The latter estimate does not include associated emissions including forest fires, drainage of organic soil under forestry or fertiliser use. The CARBWARE model (Black, 2016), used to estimate national carbon stock changes, includes these factors and estimated net emissions from forested land to be - 3,728.1 kt CO2-eq (carbon dioxide = - 4,013.7, methane = 104.8, and nitrous oxide =180.8 kt CO2-eq) in 2017 (Duffy et al., 2019).

In order to achieve the National Forest Policy Objective of 18% forest cover by 2046, an afforestation rate of 15,000 ha yr-1 was identified as required (DAFM, 2014). Current annual rates may be as low as 4,000 ha yr-1. This shortfall in combination with the legacy of a large area historic afforestation reaching harvestable status means a decline in the annual forestry carbon sink is likely to occur from 2025 onwards.

It must be noted that deforestation also occurs in Ireland (Duffy et al., 2019), with 6,432 hectares reconverted from forestry between 2006 and 2017 from factors including deforestation (DAFM, 2018a). Grants currently available for the conversion of agricultural land under the afforestation scheme (DAFM, 2015d), includes 12 measures (GPCs) ranging from establishment of Sitka spruce (Picea sitchensis L.) or Lodgepole pine (Pinus contorta L.) stands (GPC 2) to deciduous native woodland (GPCs 9 & 10) or trees for fibre production (GPC 12). As discussed, agroforestry is also supported (GPC 11). GPC measures are designed to cover the cost of establishment. Additional premia are provided on the establishment of new forests and payable for 15 years. Separate schemes are available to support aspects such as forestry roadway construction, thinning broadleaves or native woodland conservation. Grants and premia require the reclassification of agricultural land to forest land however, land afforested since 2009 is still eligible for Basic Farm Payment Scheme and premia are exempt from income tax.

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Despite grants and premia, there are considerable barriers to afforestation and may limit the contribution that forestry will make to mitigation. Apart from geophysical constraints, lack of adequate financial incentive or familiarity with forestry along with negative perceptions may discourage farmers from entry into schemes (Farelly & Gallagher, 2015; Ryan & O’Donoghue, 2016; Ryan et al., 2018). Farmers’ awareness regarding afforestation has been discussed (Section 3.4.1.). Ryan and O’Donoghue (2016) suggested a number of policy recommendations including: the full recognition and proportional reward for the public service that farmers provide by planting trees, the option for guaranteed Government advanced purchase of timber before maturity, government forestry insurance schemes, the combining of land use options, linking carbon neutrality with agricultural activity, improved extension, funds for planting subsequent rotations, provision for land reversion to agricultural land and greater certainty on impacts of afforestation on payment schemes. Ryan et al. (2018), additionally identify the mutually exclusive relationship between agriculture and forestry policy as a barrier to afforestation.

In conjunction to attitudinal barriers, Farrelly & Gallagher (2015) highlighted potential conflict over land use. An assessment of the land available for afforestation indicated that, of the 4.65 million hectares technically, suitable for afforestation, 896,880 hectares is designated as habitat conservation areas while 2.42 million hectares are under productive agriculture. The remaining available land (1.3 million hectares) is considered marginal agricultural land, (Farrelly & Gallagher, 2015) wet grasslands and various non- intact or modified peatland types, issues with which are discussed later. This highlights issue of the availability of land to accommodate multiple and sometime competing, functions (Schulte et al., 2014), whether agriculture, forestry or biodiversity conservation in this case. To maximise sequestration in the medium term, conifer stands on mineral soils are favoured. However, this is undesirable from biodiversity, water quality and other multifunctional land provision perspectives. The importance of diversity within afforestation has been highlighted (Seddon et al., 2019). Management at a landscape level may therefore be important to ensure multifunctionality, with the designation of areas for different function including, optimal carbon sequestration by forestry.

Carbon sequestration by Agroforestry

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Agroforestry, widely noted for both climate change mitigation (Aertsens et al., 2013; Lorenz & Lal, 2014; De Stefano & Jacobson, 2018) and adaptation (Verchot et al., 2007; Mbow et al., 2014; Hernández-Morcillo et al., 2018) potential, is defined as the integration of cultivated woody perennials (e.g. trees) within either livestock or crop production systems on the same unit of land, bringing associated ecological and economic interactions (Nair, 1993). A variety of agroforestry systems exist (Feliciano et al., 2018) with notable adoption in tropical climates (De Stafans & Jacobson, 2018; Feliciano et al., 2018), though identified as a mitigation action for Europe (RICARDO- AEA, 2016). Silvopastoralism refers to the integration of trees within grazed grassland or pastoral systems. Due to the proportion of grassland (EPA, 2016), there is significant opportunity for silvopastoralism in Ireland (McAdam et al., 2006; McAdam & McEvoy, 2009).

Though caution in accounting is required (Nair, 2012) and a scarcity of data exists (Lorenz & Lal, 2014; Feliciano et al., 2018) notably for temperate climates (Eory et al., 2015), agroforestry is generally associated with increase carbon accumulation in terms of both below ground soil stocks and within the above ground, standing biomass (Jose & Bordhan, 2012; Feliciano et al., 2018). Tree roots allow the accumulation of carbon within deeper soil horizons (Lorenz & Lal, 2014). Recent meta-analyses found agroforestry generally lead to greater soil organic carbon compared to agriculture in multiple regions (De Stafans & Jacobson, 2018; Chatterjee et al., 2018) except in temperate climates (Chatterjee et al., 2018). Indeed higher rates of sequestration were observed in tropical regions (Feliciano et al., 2018). Considering silvopastoral systems only, Feliciano et al. (2018) report sequestration by above ground biomass of between 0.15 t C ha-1 yr-1 in Africa, to 2.65 t C ha-1 yr-1 in Asia. Below ground soil carbon sequestration ranged from 0.06 t C ha-1 yr-1 in North America to 6.54 t C ha-1 yr-1 in Latin America, with the greatest mean absolute change for all regions, associated with conversion of grassland to silvopastoralism (+ 4.4 t C ha-1 yr-1). Regarding methane and nitrous oxide net emissions, Kim et al. (2016) found areas under agroforestry were roughly the same as adjacent agricultural land.

Considering Europe, agroforestry legislation (EU Regulation 1698/2005) was introduced to encourage agroforestry (de Jalón et al., 2018) with rates of sequestration from implementation on both arable land and pasture were estimated as roughly 10 t CO2-eq ha-1 yr-1 (Aertsens et al. 2013). Roughly 15.4 million hectares are currently under

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agroforestry across the Europe Union (de Herder et al., 2017) with adoption was found to be determined by multiple positive and negative perceptions, the latter associated with increased labour, management costs and administration (de Jalón et al., 2018). None the less, an assessment of European agroforestry with the goal of its promotion for rural development (AGFORWARD Project) was recently completed and highlighted the interest in, and the potential of such systems (Burgess et al., 2018). It is worth noting that livestock agroforestry forms the most prominent system (15.1 million hectares) within the European Union (de Herder et al., 2017). Agroforestry in temperate regions within arable systems was found to enhance soil conditions in terms of soil organic carbon content and nutrient concentrations attributed to leaf litter (Pardon et al., 2017). For grasslands, agroforestry is identified as a strategy for sustainable land management in Northern Ireland (DAERA, 2018) with research conducted at Agri-food and Biosystems Institute (AFBI) Loughgall, Co. Armagh. Ash (Fraxinus excelsior L.) were planted 5 m apart or 400 trees ha-1 which allowed successful grazing of sheep. Indeed, careful considerations of factors such as tree inter- and intra-row spacing, row orientation and likely agricultural field operations is required (de Jalón et al., 2018). However, agricultural operations can successfully continue.

To allow trees to fully establish, grazing by sheep is recommended for up to seven years, after which cattle can be introduced (DAERA, 2018). Though empirical research in the Republic of Ireland is limited, Short et al. (2005) showed successful grazing of cattle between oak (Qeurcus robur L.) during an experiment at Johnstown Castle, Co. Wexford. Trees were planted in rows with 0.75 m intra-row and 2 m inter-row spacing (6,600 tress ha-1). Regrettably, carbon sequestration was not measured. The research in Northern Ireland demonstrated no significantly difference in soil carbon storage between 26 years of silvopasture and permanent pasture. However, greater quantities of carbon were observed in the micro-aggregate (53 to 250 μm) and the silt and clay fractions (< 53 um) in soils under a silvopastoral system. Greater macro-aggregate (> 2mm) carbon pools were found under grassland. Long-term storage of carbon is associated with that held in silt, clay and micro-aggregate fractions and it was concluded that silvopastoralism lead to more stable carbon pools that may have greater resilience to future climate change (Fornara et al., 2017).

In addition to carbon sequestration, agroforestry may generate other benefits. Lorenz & Lal (2014) describe how the integration of trees in agricultural systems may positively

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alter the soil environment regarding hydrology, aeration, organic matter deposition, all effecting soil organisms including microbial communities. In an Irish context this may allow extended grazing seasons, as soils may have enhanced drainage and increased structural stability (DAERA, 2018). A significant issue concerning extended grazing is soil compaction (Harris, 1971) as a result of livestock treading (Drewry, 2006) associated with increased soil moisture content later in the year, making the soil more prone to structural degradation (Hamza & Anderson, 2005; Houlbrooke & Laurenson, 2013). The extension of the grazing season was identified as a measure to reduce livestock emissions (Lanigan et al., 2018) as previously discussed. Deeper tree roots may also intercept un-utilised nutrients from the associated agricultural system (McAdam et al., 2006), for example nitrogen that in turn, enhances tree growth, biomass accumulation and potential carbon sequestration capacity.

Depending on policy framework, agro-forestry gives flexibly with regard to land use allowing a form of forestry and agricultural activity, and deemed more attractive to farmers compared to afforestation, therefore potentially encouraging uptake. In Europe, lack of knowledge and financial support were identified as key barriers to agroforestry (Hernández-Morcillo et al., 2018). Currently in Ireland, the only grant aid for the establishment of agroforestry (GPC 11) is part of the afforestation scheme (DAFM, 2015). Though farmers are still eligible for the Basic Payment Scheme (formally termed Single Farm Payment) under this scheme (DAFM, 2015), entry requires land to be permanently classified as forestry, which may discourage farmers (Farrelly & Gallagher, 2015; Ryan et al., 2018). Despite these issues, a number of holdings have successfully adopted agroforestry with roughly 55 to 60 hectares estimated to be in, either planned conversion or established agroforestry in Ireland at present.

A.2.2.3. Carbon sequestration by hedgerows

Hedgerows are recognised as an important carbon sink and have mitigation potential if the area of hedgerow is increased (Falloon et al., 2004; Black et al., 2014). Carbon sequestration by hedgerows is associated with either storage within soils or above ground woody biomass (Thiel et al., 2015). Research in Belgium indicated soil organic carbon to 20 cm soil depth was 8% higher at 1 m, compared to 30 m away from a hedgerow in an arable field (Van Vooren et al., 2018) while a review of literature

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suggested soil carbon stocks within hedgerows were 22% higher than in arable land without hedgerows (Van Vooren et al., 2017).

It is difficult to ascertain recent changes in levels due to reclassification of scrubland altering figures (DAFM, 2018a) though a decline of 4,548 hectares (1.6%) was noted between 2006 and 2012. An EPA report (Green et al., 2016), estimates that nationally there is 689,000 km of hedgerow in Ireland (Green et al., 2016). A survey of 118 farms over three regions, found on average, 13% of farmland area consisted of semi-natural habitat, the majority of which consisted of hedgerow (Sheridan et al., 2017). Hedgerows were estimated to account for roughly 9% of area on farms. At a landscape level, Bourke et al. (2014) estimated a hedgerow density of 10.37 km per 1 km2. It is worth noting that the area of semi-natural habitat on farmland in Ireland is higher than European averages (Sheridan et al., 2017) with for example, natural habitat accounting for just 2.1% of farmland in the Netherlands (Manhoudt & de Snoo, 2003). This highlights the importance and potential contribution of semi-natural habitat and specifically hedgerows in Ireland. In terms of carbon sequestration, difficulties in terms robust accounting methodology and reporting to inventories were noted (Black et al., 2018) with lack of monitoring data restricting analysis and inclusion (Duffy et al., 2018).

With regard to current hedgerow condition, an in-depth assessment of 50 farms in South- Eastern Ireland, indicated that 49% of hedgerows were maintained as stock proof while there was no evidence of newly planted hedgerows observed on any of the farms surveyed (Sheridan et al., 2011). When specifically examining intensive grassland farms (with stocking rates ≥ 1.5 Livestock Units ha-1) semi-natural habitats accounted for 7.4% on average and hedgerows were again found to be the most abundant semi-natural habitat type (Larkin et al., 2018). Of the 290 hedgerows surveyed on these farms, 129 (44.5%) were ranked as “significant” considering factors including species diversity, landscape importance and connectivity. The hedgerow condition score is a semi- quantitative assessment considering unfavourable categories such as basal density, invasive species and gappiness, with a maximum score of 24. A mean score of 12.05 was obtained with 85.5% of hedgerows exhibiting at least one unfavourable category. Hedgerow condition is known to be important for ecological reasons (Garratt et al., 2017) but little research appears to have been conducted into management for optimal carbon sequestration. The planting of bio-diverse woody vegetation within a hedgerow was suggested to increase soil carbon pools (Thiel et al., 2015). Theoretically, the

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encouragement of growth and rejuvenation should enhance sequestration potential, with basal thinning and the maturing of individual hedge trees and shrubs undesirable. Management intervention such regular cutting in the short term and coppicing or hedgelaying in the long term are suggested as likely to be beneficial.

In addition to potential carbon sequestration, hedgerow networks and associated microenvironments within agricultural landscapes provide ecological benefits. These include habitat provision, floral and faunal diversity, pest control, soil conservation, habitat linkage and physical protection (Forman & Baudry, 1984; Hannon & Sisk, 2009; Van Vooren et al. 2017; Garratt et al., 2017). Hedgerows may also act as barriers to livestock disease endemics. Of the 689,000 km of hedgerow in Ireland, 15% and 26% are classified as “shared” and “internal farm” boundaries respectively (Green et al., 2016) therefore dividing farm holdings. Livestock diseases may pose a greater risk with global warming (Purse et al., 2005). Therefore, hedgerows may play an important role, not only in climate change mitigation, but also adaptation.

A.2.2.4. Carbon sequestration in organic soils

Organic soils under grassland

Renou-Wilson et al. (2014; 2018) loosely categorised organic soil grasslands as either occurring on is nutrient-rich or nutrient-poor. However, within these categories, the site- specific nature of emissions is emphasised. Renou-Wilson et al. (2014; 2015) identified not only nutrient status and drainage, but also grassland management as factors contributing to greenhouse gases. The average net ecosystem exchange over two years for grassland on a nutrient poor site with, shallow and deep drains was - 94 and - 56 g C

-2 -1 m yr respectively (Co. Donegal). Mean annual methane emissions of 18 ± 15 kg CH4 ha-1 yr-1 were observed at the shallow drained site. Considering the mean net ecosystem carbon balance, it was concluded that grasslands on nutrient-poor peats under extensive management with low nutrient inputs and where mean water table levels are maintained within 25 cm soil depth, have negligible impacts (Renou-Wilson et al., 2015). It is recommended that agricultural activity, including grazing and fertiliser application, should cease and drains be allowed to naturally deteriorate, therefore restoring such sites to wetlands and enhancing their carbon sink function. Rushes (Juncus effusus) are likely to initially encroach. The control of rushes is required under current Cross Compliance

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regulation, with breaches impacting Basic Farm Payments. Therefore exceptions, under the circumstances of peatland restoration must be permitted.

In contrast to nutrient-poor sites, grasslands on nutrient-rich drained organic soils, which include fens, have considerable climatic impact (Renou-Wilson et al., 2015). For example, Kroon et al. (2010) examined emission from a drained fen soil under grass in the Netherlands, supporting intensive dairy production. A final average greenhouse

-1 -1 gases balance of 16 CO2-eq Mg ha yr was observed, with carbon dioxide, nitrous oxide and methane accounting for 30, 45 and 25% respectively. It was concluded that this soil was considerable source of greenhouse gases (Kroon et al., 2010). In Ireland

-2 -1 -2 -1 emissions of 233 g C m yr and 0.16 g N2O-N m yr were observed from a nutrient- rich grassland in Co. Longford (Renou-Wilson et al., 2015). It was recommended that grasslands on nutrient-rich organic soil should be rewetted with consideration of specific site conditions.

Organic soils under forestry

Forestry occurs on organic and organo-mineral soils, accounting for 212,000 and 30,500 hectares respectively in 2016 (Duffy et al., 2018). A decline in afforestation was observed on peats over the last 40 years though rates of afforestation on minerotrohic type peats has remained relatively constant (DAFM, 2018a). The disturbance and drainage of pristine or relatively intact peatlands for forestry is undesirable and should be avoided for reasons discussed. Despite afforestation of unenclosed or unimproved land being supported under GPD 1 of the afforestation scheme, planting on unmodified raised bogs, industrial cutaway peatlands or infertile blanket or raised bogs in the midlands, is forbidden within the scheme (DAFM, 2015). However, Coilte may still technically be able to plant blanket bogs, though it is assumed that this would not occur.

The afforestation of cutaway peatlands to meet national forestry targets was suggested and reviewed by Black et al. (2017). Renou-Wilson et al. (2008) had outlined Bord na Móna estimates of between 16,000 to 20,000 hectares as being potentially available, with research on cutaway peatland afforestation conducted under the BOGFOR project. Such forestry may be utilised for either biomass for energy generation or commercial timber production. However, issues were noted regarding the heterogeneity of excavated peatlands and not all sites are suitable. Specific sites such as gravity-drained fens are preferable with aeration, peat depth and nutrient status identified as limiting factors. Sitka

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spruce (Picea sichencsis L.) and Norway spruce (Picea abis L.) are suitable on shallow peats (≤ 1 m) while lodgepole pine (Pinus contorta L.), larch (Larix L.) and Scots pine (Pinus sylvestris L.) are more tolerant of deeper peats (< 2 m). Interestingly, birch (Betula L.) is tolerant of deeper peats, poorer drainage, and may facilitate Sitka spruce inter-planting (Black et al., 2017). Black et al. (2017) also recommend that cutaway peatland afforestation decisions should be conduct at a landscape level to ensure the most appropriate land use (i.e. forestry, rewetting, grassland, short rotation coppice, amenity facilities) of different areas, all with consideration to biodiversity and arguably climate change mitigation.

The full impact of peatland afforestation from a climate change perspective is unclear as it is suggested that some of the carbon dioxide released may be absorbed by the growing tree biomass (DAHG, 2015). Net forest carbon balances consider multiple factors including above- and below-ground biomass, deadwood, litter and soil emissions (DAFM, 2018a). However, the carbon absorbed by trees represents short-lived capture and the longevity of its storage is dependent on the end use of the wood (Artz et al., 2013). Wilson et al. (2013) suggest at a national scale, forestry on peatlands is a carbon sink, though raise issue with the aesthetic value, economics and carbon sequestration beyond the first rotation. The latter highlights the need to consider long-term impacts and whether the ecosystem represents a net carbon sink or source (Artz et al., 2013). Considering the first rotation, Black & Gallagher (2010) modelled net greenhouse gases balances of forestry on blanket peat at different stages of development. Following initial drainage and planting (2 to 4 year), the site becomes a carbon source, emitting between 7 to 14 t C ha-1 yr-1. Once trees are established and other vegetation colonises (4 to 8 years), the site becomes a carbon sink of between -2 to 3 t C ha-1 yr-1. The site then remains a carbon sink, with maximum carbon uptake of - 12 to 30 t C ha-1 yr-1 before tree thinning (12 to 20 years). Renou-Wilson & Wilson (2018) suggested a lack of published data on emissions form forested peatlands while it must be remembered that carbon sequestered in peat is relatively stable. Intact and functioning peatlands represent a net carbon sequestering ecosystem in the long term, while long-term impacts afforestation are not clear (Artz et al., 2013). Considering multiple rotations, afforested peatland ecosystems may represent carbon sources.

The impacts of rewetting existing forestry sites once clear-felled, as with other disturbed peatlands, is site specific. Following deforestation, it was recommended to block drains

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to raise the water table and maintain anaerobic conditions to prevent further peat decomposition (Black & Gallagher, 2010). More recent research showed that a rewetted forest site as a net source of carbon with emissions ranging from 1.02 to 5.60 t C ha-1 yr-1 (Renou-Wilson et al., 2018). It was suggested that caution is required as brash left from forest harvesting may contribute to increased atmospheric and hydrological carbon dioxide emissions. None the less, rewetting, with proper management of the water tables and clear felled sites to accepted guidelines, is outlined as an option, but with prioritisation of peatland following other uses (Renou-Wilson et al., 2018).

Cutaway and cutover peatlands

Cutaway (industrial) and cutover (domestic) peatlands (Wilson et al. 2013) are a substantial source of carbon dioxide with increased emissions associated with water table lowering and soil temperature increases (Renou-Wilson & Wilson, 2018). Since the 1920s, peat has been extracted as a source of fuel, with commercial extraction predominantly conducted by Bord na Móna (Duffy et al., 2018), notably for electricity generation. Wilson et al. (2012) described the industrial peat extraction process, which initially requires the excavation of drainage channels to facilitate harvesting machinery. This lowers the water table, allowing carbon to be oxidised and causing the release of carbon dioxide. Following this, the removal of surface vegetation eliminates photosynthetic capacity and carbon capture, further shifting the carbon dioxide balance of the site, from being a sink to a source. Peat will then be extracted until exhausted, after which, there are a number of land use options including afforestation, agriculture or simply natural flooding or encroachment of scrub (Wilson et al. 2012).

In 2017, the area under industrial and domestic peat extraction was estimated to be 56,341 hectares (Duffy et al., 2019). Bord na Móna aims to cease peat-fired electricity production by 2030 (Bord na Móna, 2018). The organisation implemented and is increasing the level of co-firing at their Edenderry power station (Co. Offaly) with ESB aiming to convert a further two power-stations to co-firing in 2019, with biomass supplementing peat in energy production (DCHG, 2017b). However, the provision of sufficient levels of biomass in Ireland to support co-firing is questionable, with recent sourcing of wood pellets from Africa (Bord na Móna, 2018). As Bord na Móna ceases peat extraction, significant areas of peatland will be available for alternative land use. Indeed, Wilson et al. (2012) estimated roughly 30,000 hectares.

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The managed rewetting of former cutaway and cutover peatlands offers a considerable opportunity in preventing further carbon losses (Wilson et al. 2012; Renou-Wilson et al., 2018), is recognised by IPCC accounting (IPCC, 2014b) and is identified in the National Peatlands Strategy as a potential option for climate change mitigation (DAHG, 2015). However, no specific actions regarding the rewetting of drained peatland were included within the strategy. Rewetting will either reduce carbon dioxide emissions, or at certain sites, generate carbon sequestration, though may increase methane emissions (Renou- Wilson & Wilson, 2018). Wilson et al. (2012) estimated that the rewetting of a former industrially extracted blanket bog at Bellacorick (Co. Mayo), reduced the global warming

-1 potential of the site by 87%, while mitigating 75 t CO2-eq ha over a six-year period. Restored surface vegetation was estimated to sequestered 279 ± 246 g C m2 yr-1. More recent monitoring of multiple sites as part of the NEROS project (Renou-Wilson et al., 2018), indicated mean emission from drained nutrient-rich and nutrient-poor cutaway sites to be 1.51 and 0.91 t C ha-1 yr-1 respectively, while mean emissions of 1.37 t C ha-1 yr-1 were observed from a cutover site. Interestingly, a rewetted nutrient-rich cutaway site was a source with average emissions of 0.32 t C ha-1 yr-1. The IPCC Tier 1 emission factor for rewetted nutrient-rich peatlands also indicated a positive value (IPCC, 2014b). Both rewetted nutrient-poor industrial cutaway and domestic cutover sites were sinks with average emissions of - 1.04 and - 0.49 t C ha-1 yr-1 respectively (Renou-Wilson et al., 2018).

Rewetting is suggested to be a relatively cheap method of carbon dioxide avoidance and described as a “low-hanging fruit” for climate change mitigation (Wilson et al., 2013). The emissions avoided over 50 years from rewetting ranged from between 100 and 151 t

-1 CO2-eq ha depending on site. Emphasis is on rewetting plans being in place before extraction ceases (Wilson et al., 2012) and urgency regarding the commencement of the process, as if delayed the capacity for carbon storage and potential sequestration diminishes (Renou-Wison et al., 2018). Peatlands that have only been drained and not excavated, should be prioritised for rewetting (Renou-Wilson et al., 2018). Additional management of rewetted sites is required, to ensure that water tables remains sufficiently high. For example the removal of encroaching trees (e.g. birch or willow) may be necessary (Wilson et al., 2012). Regarding co-benefits of rewetting and biodiversity, the re-establishment of bog flora on rewetted former industrial sites may be problematic, though achieved successfully on rewetted domestic cutover sites (Renou-Wilson et al., 2019). In all cases, the need for proper, site-specific management is highlighted.

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A.2.3. AVOIDING CARBON DIOXIDE EMISSIONS THROUGH REDUCED FOSSIL FUEL USE

A.2.3.1. Energy from biomass

Teagasc examined wood thinnings and sawmill reside for electricity and heat production, short rotation coppiced (SRC) willow (Salix spp.) and Miscanthus (typically Miscanthus giganteus L.) for heat generation, and SRC willow for electricity generation within the MACC (Lanigan et al. 2018). All of these measures had negative costs per tonne of

CO2-eq displaced.. It was suggested that SRC willow may contribute to a national requirement for 12% of heat generation by renewable sources by 2020. This is under the EU Renewable Energy Directive (EC, 2009). Willow will produce roughly 35 tonnes (moisture content = 25%) of biomass annually with 1.0 hectare potentially generating 172 GJ yr-1 at a moisture content of 20% (Caslin et al., 2015a). Following three years of establishment, Miscanthus is suggested to give a yield of ≥ 10 t ha yr-1 (dry matter) (Caslin et al., 2015b) with figures of up to 30 t ha yr-1 reported from research in Germany (Lewandowski & Heinz, 2003). Indeed, Miscanthus is generally considered as having the greatest potential as a biomass crop, due to high perennial yields and a relatively low greenhouse gas footprint (Arnoult et al., 2015). However, it may be deemed less attractive in Ireland due to previous shortfalls or uncertainty within the market. Lack of demand may have been partly a result of Miscanthus ash clogging the furnaces grates currently used, due to the high potassium contents of the crop. Biomass ash contains high levels of potassium and calcium which together, reduce the melting point of ash, causing slagging or fouling of heat surfaces, while potassium and chloride emitted during combustion as potassium chloride (HCL) may cause corrosion (Lewandowski & Kicherer, 1997).

There is also uncertainty around uptake of Miscanthus as well as willow (Lanigan et al., 2018). It is worth noting that agroforestry may contribute to the generation of biomass for either heat or electricity production (Jose & Bordhan, 2012), particularly as trees planted are not generally intended for commercial timber production. However, it is suggested that the use of biomass for energy production is suitable in the short term in pursuit of compliance with legislative targets, but this may undervalue biomass in the long term, with regard to its potential integration within the bio-economy.

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ARTICLE

DOI: 10.1038/s41467-018-03406-6 OPEN The underappreciated potential of peatlands in global climate change mitigation strategies

J. Leifeld 1 & L. Menichetti1,2

Soil carbon sequestration and avoidable emissions through peatland restoration are both strategies to tackle climate change. Here we compare their potential and environmental costs regarding nitrogen and land demand. In the event that no further areas are exploited, drained

1234567890():,; peatlands will cumulatively release 80.8 Gt carbon and 2.3 Gt nitrogen. This corresponds to a

contemporary annual greenhouse gas emission of 1.91 (0.31–3.38) Gt CO2-eq. that could be saved with peatland restoration. Soil carbon sequestration on all agricultural land has comparable mitigation potential. However, additional nitrogen is needed to build up a similar carbon pool in organic matter of mineral soils, equivalent to 30–80% of the global fertilizer nitrogen application annually. Restoring peatlands is 3.4 times less nitrogen costly and involves a much smaller land area demand than mineral soil carbon sequestration, calling for a stronger consideration of peatland rehabilitation as a mitigation measure.

1 Agroscope, Climate and Agriculture Group, Reckenholzstrasse 191, 8046 Zurich, Switzerland. 2 Now Ecology, SLU (Sveriges Lantbruksuniversitet), Ulls Väg 16, 75651 Uppsala, Sweden. Correspondence and requests for materials should be addressed to L.M. (email: [email protected])

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n intact peatland ecosystems, oxygen deficiency resulting from 0.018 kg N per kg C sequestered8,9, which is considerably less Ihigh-water tables causes the formation of organic soils1. These than that of mineral soils at 0.094 kg N per kg C23. Hence, the are distinguished from mineral soils by their high carbon (C) well-recognized long-term climate cooling effect of global peat- and nitrogen (N) density, often with an organic matter content of lands24 comes at a relatively low N cost. >90% and thicknesses of up to several meters2,3. Peatlands only Here, we begin by revisiting data on global peatland distribu- account for ~3% of the terrestrial surface4, predominately tion and degradation, then analyze current and future GHG occurring in boreal and temperate ecosystems, with a smaller release and its uncertainty from degrading peatlands (neglecting proportion in tropical regions. Nevertheless, they may store ~644 peat fires). This provides a global estimate of the magnitude of – Gt of C4 6 or 21% of the global total soil organic C stock of possible GHG savings for organic soils, assuming that peatland ~3000 Gt (0–3 m, ref. 7). In addition, peatlands are large stores of restoration renders a GHG neutral ecosystem. We compare this organic N: Northern peatlands, characterized by wide C/N ratios potential to management-induced organic matter accumulation between 12 and 2178, have accumulated 8–15 Gt N9, whereas the in mineral soil and assess both pathways from the perspectives of N stock in tropical peatlands has not yet been reviewed. C and N cycling, time, and land demand. Our analysis assigns At present, human activity is either draining or mining ~10% higher GHG emissions to degraded peatlands than previous of global peatlands10, transforming them from long-term C sinks reports, mostly owing to a larger area of managed organic soils in into sources by acting on three C loss pathways: CO2 from the tropics. Cumulative GHG emissions from already drained microbial peat oxidation, dissolved C leaching, and CO2, CO and peatlands are greater than mineral soil carbon sequestration fi 11 CH4 from peat res and combustion of mined peat . In addition, potentials on all agricultural land. Restoration of degraded peat- 11 fi drained peatlands release relevant amounts of N2O and there- lands provides an ef cient mitigation measure in the land-use fore drainage induces peatland degradation and alters peatlands, sector, owing to a smaller area and nitrogen demand when globally, from a net sink to a net source of greenhouse gas (GHG) compared with mineral soils. in the land-use sector12,13. Consequently, peatland protection and 10,14 restoration are seen as proximate mitigation measures . Results Restoration through rewetting can significantly reduce GHG 15 Global peatland area and GHG emissions. Our maps of the emissions , restore vegetation communities, and recover biodi- peatland distribution (Figs. 1 and 2) reveal a contribution of 83.3, versity16, while still allowing for extensive management such as 17–19 4.0, and 12.7% from the boreal (and polar), temperate, and tro- paludiculture . pical zone, respectively, to the total peatland area. Our area A substantial fraction of anthropogenic GHG emissions could estimate of in total 463.2 Mha puts a higher weight on tropical be compensated for by improving management of mineral soils. peatlands compared with previous estimates (8–11%, refs. 4,5), Indeed, various options for C sequestration, such as residue owing to an updated climate classification (Methods) and inclu- management and improved crop rotations in arable land or sion of recent data6. It should be noted that the area estimate of species introduction in grasslands, have been discussed (e.- 20,21 global peatland is in general highly uncertain. The assignment of g., refs. ). However, stoichiometric constraints limiting C actual emission factors for drained organic soils11, classified by storage are more severe in mineral soils compared to peatlands. climate and land use, to the overall peatland area is considered a In organic soils, mostly phenolic or lignocellulosic remains from vulnerability indicator and illustrates that potential hot spots for mosses or vascular plants with low concentrations of N and other GHG emissions are mostly located in the tropics (Fig. 1). The nutrients predominate. In mineral soils, microbial products with global peatland area estimated as drained for forestry, cropland or narrow C/N ratios are quantitatively dominant components of 22 grassland is ca. 50.9 Mha (Table 1). The distribution of actual and organic matter , which makes C storage in mineral soil nutrient potential emissions differs substantially, particularly in the tro- costly. C storage in peatlands is much less N limited: they accu- pical zone of South America and Africa, indicating a low degree mulate organic matter over millennia, needing an average of only of degradation, whereas actual rates are closer to the potential

Potential estimated emissions –1 –1 (t CO2-eq. ha a ) 0–1.6 1.7–7.8 7.8–21.9 21.9–40.2 40.2–58.2 Esri, DeLorme, GEBCO, NOAA NGDC, and other contributors

Fig. 1 Global peatland distribution and annual potential emissions from peatland degradation. The colored area indicates the areal distribution of peatlands globally. Legend colors refer to per hectare greenhouse gas emissions if those peatland would be drained. High potential emissions are associated with intensive land use and warm climate, low potential emissions with low land-use intensity in cold climate

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Actual estimated emissions –1 –1 (t CO2-eq. ha a ) 0–1.6 1.7–7.8 7.8–21.9 21.9–40.2 40.2–58.2 Esri, DeLorme, GEBCO, NOAA NGDC, and other contributors

Fig. 2 Global peatland distribution and estimated annual actual emissions from peatland degradation. Data are calculated by correcting the potential emissions in Fig. 1 for the share of degraded peatland (see Supplementary Fig. 1), thereby highlighting regions with a large degree of disturbance. The map shows the average emission per area peatland for any given land use and climate category, not the average emission per area of disturbed peatland

Table 1 Area and emission overview of global peatlands

Climate Total CLa GLa FLa GL/FL CL/GL/FL Degrading Actual Peat C Degrading peat C peatland peatland emissionsb

Area (Mha) Gt CO2 eq. Gt C

Tropical 58.7 8.5 11.3 34.6 1.6 2.7 24.2 1.48 (0.04–2.79) 119.2c, e 49.1 Temperate 18.5 3.5 5.0 8.9 0.7 0.6 10.6 0.16 (0.10–0.21) 21.9d, e 12.5 Boreal 360.9 6.8 85.6 249.5 17.9 1.1 15.5 0.26 (0.16–0.36) 427.0d, e 18.3 Polar 25.0 0.1 14.9 9.7 0.1 0.3 0.7 0.01 (0–0.02) 29.6d, e 0.8 Oceanic <0.1 <0.1 <0.1 <0.1 <0.1 <0.1 <0.1 0 (0–0) <0.1 <0.1 Total 463.2 50.9 1.91 (0.31–3.38) 597.8 80.8 aPeatland area distributed by land use. Land-use classes are cropland (CL), grassland (GL) and forest land (FL). Rows with more than one land-use represent areas where a clear assignment to one type is not possible (see Methods) b Annual means, values in parentheses express the range from minimum to maximum. Emissions include CO2,CH4,N2O, and DOC cBased on refs. 5,6 dBased on ref. 8 eAssignment to climate region by applying C densities in refs. 5,6,8 to our newly delineated areas ones in Southeast Asia and Europe, owing to their higher degree would be cumulatively released upon degradation of currently of degradation25,26. Degraded peatlands store globally ~80.8 Gt utilized tropical peatlands, together with 0.6 Gt N from currently – −1 soil C and emit ~1.91 (0.31 3.38) Gt CO2-eq. a (0.52 degrading temperate, boreal, and polar peatlands (total N stock – −1 + + 8 (0.08 0.92) Gt CO2-C-eq. a ) mostly as CO2 (Table 1 and temperate boreal polar peatlands 9.8 Gt, median C/N 49.0 ). Methods). Our analysis categorizes degraded peatlands by climate In reverse, these quantities of C and N stocks in degrading and shows that, in accordance with previous studies27,28, the peatlands represent avoidable emissions during the calculated largest GHG emitters are drained tropical peatlands (Table 1, time span and become an asset upon restoration. Supplementary Fig. 1). Even the most recent global estimate on the contemporary net − −1 Depending on the quantity of C stocks, GHG emission factors, biogenic terrestrial CO2 sink of 5.3 (±4.5) Gt CO2 a does not and contribution of C to the overall emission, a drained peatland fully consider peatland emissions29. Hence, our analysis suggests continues to emit for decades to centuries. The ongoing organic the magnitude of that sink is probably undervalued. We use, in matter loss will exhaust the actual degrading peat deposits within contrast to any of the previous estimates, the most recent the next centuries (Fig. 3), depending on the intensity of the Intergovernmental Panel on Climate Change (IPCC) emission emissions and their C density. We calculated a N stock in tropical factors11, a map of peatland area updated to 2016 and including peat (intact and degrading) of ~4.0 Gt N, based on a median C/N areas not previously considered, a reﬁned allocation of drained ratio of 29.7 (Methods) and a tropical peat deposit of 119.2 Gt C. organic soils to land use and climate regions and a revised This latter estimate is based on our revised, high-resolution area estimate of degrading peatland areas based on such allocation assignment and comprehensive estimates on carbon stocks per (Methods). For the ﬁrst time with regard to such estimates, our 4,5,6 6 area land in tropical peat . A discovery as in ref. suggests that calculation accounts for all GHGs (CO2,N2O, CH4,CO2 from the tropics might present still uncharted peat stocks, for example oxidizing leached dissolved organic C (DOC) (Methods). The in the Amazon basin. Of this N in tropical peatlands, 1.7 Gt upper end of the range of our estimate for annual GHG emissions

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Emissions from actual degrading peatlands (uncertainty) Emissions from actual degrading peatlands (ave.) Emissions from increased degrading peatlands (uncertainty) Emissions from increased degrading peatlands (ave.) 300 Mitigation potential of agricultural soils (uncertainty)

200

100 C (Gt)

–100

Equivalence point (incr., max emissions) max (incr., point Equivalence Equivalence point (act., max emissions) max (act., point Equivalence Equivalence point (incr., mean emissions) mean (incr., point Equivalence Equivalence point (act., mean emissions) mean (act., point Equivalence –200 emissions) min (incr., point Equivalence 0 100 200 300 400 500 Time (years)

Fig. 3 The world’s cumulative GHG emissions from degrading peatlands. Cumulative emissions of the current area are in pink, and for a doubling of that area within 60 years (future scenario, see Methods) in blue (positive values). Their end points represent the theoretical maximum that can be emitted from the degrading peat C stocks. Units are Gt CO2-C-eq. Negative areas display the cumulated C sink in mineral soils assuming the C saturation equation proposed by ref. 31 (gray). Vertical lines denote equivalence points where mineral soil sink potentials are exhausted relative to peatland-related emissions (minimum, average and maximum estimates, for numbers see text; maximum equivalence point for actual degrading peatland is 1021 years and not displayed) from drained organic soils is higher than previous studies look into the time line of emissions reveals that, because of the −1 10 reporting 0.35 Gt CO2-C-eq. a (ref. , all drained peatlands, high-annual GHG release and the size of the peat C pool, the −1 30 CO2 only, area 46 Mha), 0.24 Gt CO2-C-eq. a (ref. , drained equivalence point after which cumulative mineral soil C for cropland and grassland, CO2 from cropland, N2O from sequestration becomes counterbalanced by cumulative GHG −1 cropland and grassland, area 26 Mha), or 0.25 Gt CO2-C-eq. a emissions from degrading peatlands would be reached after on 28 – (ref. , drained for agriculture, CO2 and N2O, area 25 Mha) per average 238 (140 1021) years (Fig. 3). This will apply even if year. This difference arises despite smaller EF’s in the most sequestration strategies were implemented at full capacity in current report11, as compared to ref. 10, mainly because of a mineral soils and considering that no additional peatland areas higher fraction allocated to tropical peatlands known for their would be degraded. After this point, GHG emissions from soils much higher emission factors, and including all GHGs and land- would globally represent a net GHG source to the atmosphere. In use types. a scenario of doubling peatland exploitation area gradually during the next six decades (Methods), the equivalent point would be reached after only 104 (67–374) years, and cumulative C and N Mitigation potentials in mineral and organic soils. Annual release from that larger area would add up to 161.6 and 4.6 Gt, mitigation potentials for cropped organic soil restoration, con- respectively. sidering all GHGs, were previously estimated at 0.08–0.35 Gt −1 CO2-C-eq. a , mostly stemming from avoided CO2 emis- 20,21 – sions . This potential is here adjusted upward (0.08 0.92 Gt Assessment of soil related mitigation measures. Minimizing CO2-C-eq. per year), although estimates mostly agree regarding anthropogenic GHG emissions via peatland restoration is cheap the lower boundary. We consider the upper side of this range as in terms of N when compared with mineral soil C sequestration maximum mitigation potential achievable with full global peat- and thus more cost effective. Owing to their narrow C/N ratio of land restoration, and we account for the full uncertainty range in on average 10.723, N availability will represent a constraint to the following calculations. compensate for anthropogenic GHG emissions in mineral soil How does the global GHG saving potential of peatland together with making such measurements more pricey. This can restoration compare with that of mineral soil abatement be illustrated by comparing the avoidable N release from cur- strategies? Current estimates indicate sequestration potentials in 31 rently degrading peatlands with the N requirement for mineral agricultural soils of up to 64 Gt C over the next century i.e., of soil C sequestration; The latter would entail a total of 2.2–5.9 Gt − the same order of magnitude as avoidable emissions from the N or, averaged over 63 years, 35.2–93.9 Mt N a 1, to be immo- currently degrading peatland C pool. The 64 Gt potential bilized in agricultural mineral soils to offset GHG emissions of considers mitigation measures on all 4923 Mha agricultural the same magnitude from other sources as new organic matter land32 and it is the most optimistic scenario. We adopted the – 31 storage. For perspective, with a quantity of 2.2 5.9 Gt N, global principle of ref. for calculating sequestration potentials of peatlands sequestered 81–216 Gt C. The additional N require- agricultural mineral soils and, within our simulation, the – ment for organic matter sequestration in mineral soils is sub- maximum additional C storage of 24 64 Gt C in 4923 Mha stantial considering that 117 Mt N were applied globally as mineral soil would be reached after 63 years (Methods). We did fertilizer in 201633, and stands in sharp contrast to halting not consider any increase of the current agricultural area, since emissions via organic soil restauration where no additional N any land-use change would imply net GHG release from newly must be ﬁxed but quite the converse, release of 2.3 Gt N can be converted areas. Our calculation suggests that, in a hypothetic avoided. Taking the mean equivalence point of 238 years in the scenario in which substantial peatland restoration is not business as usual scenario of Fig. 3, this number converts to incorporated, comprehensive mineral soil measures alone would annually 9.7 Mt N, whose release can be circumvented by peat- only be able to compensate for most of the future emissions from land restoration. degrading organic soils but not provide a net soil sink. A detailed

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Of course, mineral soil C sequestration is not a purpose in Tasmanian Department of Primary Industries, Parks, Water, and Environment43. fi Our updated peatland extension map accounted for an increase of 0.8% compared itself, but a co-bene t of practices applied to improve soil health 4 and, hence, productivity34. Still, and although not all C to ref. . We utilized our map only as a statistically representative geographical sample to recalculate proportions of emissions and land use based on other spatial sequestration measures in mineral soil require N fertilization data. explicitly, the unavoidable microbial transformation of wide-C/ N-ratio plant litter into narrow-C/N-ratio SOM implies co- 35 The peatland land use and degradation maps. All the work with spatial data has sequestration of C and N . It is also reasonable to assume that been performed with ArcGIS (ArcGIS 10.4, ESRI, Redlands, CA, USA). All cal- this additional N input comes with costs in form of energy for N culations adopt a Goode homolosine projection in order to have approximately fertilizer production36 and N losses to the environment37, equal area per pixel. including additional N O38 (which will need other mitigation We assigned peatland areas to the three major IPCC land-use classes (Forest 2 Land FL, Grassland GL, and Cropland CL) by overlaying our map with the interventions). In addition, a vast area of agricultural land on European Space Agency Global Land Cover Map (ESA-GLCM, release 19/12/ mineral soils needs improved management with continuous 2008), available at http://www.esa.int/spaceinimages/Images/2008/12/ maintenance owing to the non-permanence of mineral soil C Envisat_global_land_cover_map (accessed 21 Jan 2016). The ESA-GLCM raster fi storage21,39. By integrating temporal dynamics instead of just distinguishes 23 classes (Supplementary Table 1) that we reclassi ed with the IPCC emission factors and climate regions (boreal, temperate, tropical), associated with comparing annual rates, we show that even in the case of the three broad land-use types CL, FL, and GL11. Classes 11, 14, and 20 were successful full implementation of C sequestration measures in all assigned to CL, classes 40, 50, 60, 70, 90, 100, 110, 130, 160, and 170 were assigned 4923 Mha agricultural land on mineral soil, GHG emissions from to FL, and classes 120, 140, and 150 were assigned to GL. Class 190, associated with only the 50.9 Mha of currently degrading peatlands would mixed urban areas, was assigned to GL. We assigned the mixed class 30 to a combination of the FL, GL and CL emission factors, and class 180 to a combination override that sink on a midterm time scale. Hence, the global of FL and GL. Classes 200–230 could not be assigned to any land-use type and perspective on soil-born GHG emissions reveals that a net were disregarded. In order to improve land classification we combined to this mitigation cannot be achieved without including the organic soil source also CL areas from the EarthStat database44. Since this map indicates the option. In a scenario of further peatland exploitation, a positive probability of the presence of CL between 0 and 1, we discretized it with a threshold of 0.5, considering all areas with higher probability as being CL. We then global soil GHG balance, i.e., a net source, would likely be reached updated the former land use map by superimposing the CL classification to all within the next one hundred years. these areas. Our comparative simulation of mineral soil C sequestration We combined the resulting peatland land use map with a global climate and GHG mitigation from organic soil underpins the high classification according to Köppen45, broadly reclassified to the generic climate + = relative mitigation efficiency of peatland restoration and protec- classes polar boreal, temperate, and tropical, obtaining a map with 5 × 3 15 emission classes (Supplementary Fig. 2). These climatic zones are therefore not tion. The majority of utilized peatlands is located in the tropical based on latitude, but on ecological and climatic considerations. We then assigned (47% of the global total degrading area) and boreal+polar (32% an emission factor to each class based on ref. 11, after having aggregated the of the global total degrading area) zones, followed by a relevant emission factors by climate and land use (polar and boreal climates were assigned the same emission factors) (Supplementary Table 2). We adopted GHG emission percentage of utilized peatland in temperate zones (21%), and 11 factors from ref. by including CO2,N2O, CH4, and dissolved organic carbon many of these areas are managed at high intensity. A full recovery DOC in the overall calculation. Given the high uncertainty of the data we decided of these areas implies that the corresponding loss of production on a conservative statistical treatment. Uncertainty in the emission factors is from agriculture and forestry should be compensated by an considered as uniform probability distribution inside each of the three major increased productivity on mineral soil. Paludiculture allows for classes (CL, FL, and GL), and uncertainty boundaries are therefore expressed as a range. The range considers explicitly maximum and minimum instead of maintaining a non-intensive land use, but cannot fully replace confidence intervals. The resulting map shows the potential peatland distribution food production. However, the 50.9 Mha of degrading peatlands and spatially assigns potential GHG emissions from peatland degradation to the represents only ~1% of total agricultural land. Under the potential global peatland area (Fig. 1). assumption that productivity on degrading organic soils is similar Inside each country we first assumed that all the cropland areas on peatland are 40 fully degrading, and therefore all the CL areas have been assigned land degradation to that of mineral soil and that all of them were agriculturally factor 1. We then calculated a degradation ratio (between 0, non-degraded, to 1, used, it is unlikely that the environmental impact of increasing fully degraded) using the area of degraded peatland by country reported by ref. 10. productivity per land unit on mineral soil by 1% overrides the In case that this degradation ratio was indicating a smaller area than what indicated detrimental consequences of continuous peatland use listed here. by the cropland areas, we assumed the latter to be the correct estimate. In case, that These insights into mitigation options in organic and mineral the relative cropland land use on peatland was smaller than the degradation ratio for a specific country, we subtracted the cropland/peatland ratio from the soils in terms of N and land demand, and time line do not call degradation ratio reported by ref. 10 and applied the remaining degradation ratio into question efforts undertaken in mineral soils, but indicate uniformly among all the other land use types. that, for effective climate change mitigation in the land-use sector Degradation refers to drainage, the latter being a precondition for further at the global scale, both strategies must be adopted and that we management. We alternatively use both drained and degraded, based on the fi premise that drainage (i.e., a human activity) is a precondition for management must prevent further peatland degradation. Integrating signi cant which always induces peat degradation (i.e., a changing ecosystem property) as peatland preservation and restoration measures in global policies, indicated by changing GHG fluxes (i.e., the measure for degradation). We particularly in the tropics, therefore appears to be a critical step generated this way a degradation map with values between 0 and 1 expressing the for an effective global climate change mitigation strategy. (probabilistic) fraction of degraded peatland in that area (Supplementary Fig. 1). We multiplied the peatland degradation map (Supplementary Fig. 1) with the potential emission map to calculate a probabilistic map of actual emissions from peatland degradation (Fig. 2). Total emissions are calculated on a per pixel base and Methods then aggregated by climate and land use to produce data in Tab. 1. The peatland Data sources for the peatland extension map. The map we built represents an area reported by ref. 10, and the cartographic data we assembled are statistically upper estimate of the possible global peatland area and its spatial distribution, in comparable (Spearman’s p-value 0.02). The peatland area reported by ref. 10, which order to offer a precautionary estimate. There are still many uncharted peatlands contributes to our degradation estimates with the fraction of degraded peatland per are in the world, particularly in the tropical area6, and we therefore assembled the 4 country, is distinctly smaller than the cartographic area depicted in Fig. 1 but it is global map of potential peatland areas starting from the map published in ref. assumed to be representative of the share of degrading peatlands at global scale. (please note that such map relies on different sources than the calculations reported Given the generous estimate on which the original map from ref. 4 is based, since it in the same paper and considers a substantially larger area), and updated it with the includes in many areas (where data are missing) also histosols and gleysols, we most recent data from the literature. We added all the areas considered in the rescaled the peatland areas on a global area estimate based on what is reported in Ramsar convention (http://www.ramsar.org/sites-countries/the-ramsar-sites) and 4 5 fi the table from ref. , updated with the tropical area reported by ref. plus the area included country-speci c data: The data from Sweden have been received from the reported by ref. 6. Swedish Geological Union. The data for Estonia were received from the Estonian Land Board, Department of Geology. The data for Kirghizistan have been taken from ref. 41. The important areas of Malaysia, Sumatra, and Borneo were updated Soil carbon to nitrogen ratios. We used the ISRIC WISE database23 to extract C/ with the maps from ref. 42. The data for Tasmania were received directly from the N ratios for mineral soils. We excluded all Histosols, and any soils with C/N ratios

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− of either <5 or >100 and soil organic C concentration > 200 mg g 1, assuming that 16. Gonzalez, E., Henstra, S. W., Rochefort, L., Bradfield, G. E. & Poulin, M. Is they carry analytical errors (C/N ratios) or represent organic soils not classified as rewetting enough to recover Sphagnum and associated peat-accumulating Histosols. The median C/N ratio of this reduced data set (n = 22,959) is 10.7. species in traditionally exploited bogs? Wetl. Ecol. Manag. 22,49–62 (2014). For organic soils, we took boreal and temperate peat C/N values from ref. 8. 17. Gaudig, G. et al. Sphagnum farming in Germany - a review of progress. Mires Literature values for C and N contents of tropical peats were compiled to get a generic Peat 13, 8 (2013). C/N value for tropical peats (Supplementary Data 1). Samples represent various 18. Croon, F. W. Saving reed lands by giving economic value to reed. Mires Peat countries (Malaysia, n = 14; Indonesia, n = 41; Thailand, n = 1; Peru, n = 1; Brunei, 13, 10 (2013). n = 12; Panama, n = 5; Micronesia, n = 2; Mexico, n = 1; Bangladesh, n = 1; 19. Wichmann, S. Commercial viability of paludiculture: a comparison of = = = Democratic Republic of the Congo, n 1; Colombia, n 5; Guyana, n 2; Rwanda, harvesting reeds for biogas production, direct combustion, and thatching. = = = n 12; Papua New Guinea, n 12; Bolivia, n 2), and various land-use types Ecol. Eng. 103, 497–505 (2017). = = = (forest/plantation, n 40; natural, n 24; cropland and rice paddy, n 20; other, 20. Smith, P. et al. Greenhouse gas mitigation in agriculture. Philos. Trans. R. Soc. = = incl. grazing, degraded, deforested, and extraction sites, n 19; unknown, n 9). Lond. Ser. B 363, 789–813 (2008). 21. Paustian, K. et al. Climate-smart soils. Nature 532,49–57 (2016). Future scenario and mineral soil mitigation potential. Global GHG emissions in 22. Kirkby, C. A. et al. Carbon-nutrient stoichiometry to increase soil carbon the future projection in Fig. 3 were calculated based on a combination of the updated sequestration. Soil Biol. Biochem. 60,77–86 (2013). – C stocks from refs. 4 6, with the global peatland degradation ratio reported by ref. 10. 23. Batjes, N. H. Harmonized soil profile data for applications at global and Global peatland distribution came from our map. The mitigation potential of agri- continental scales: updates to the WISE database. Soil Use Manag. 25, 124–127 cultural soils was calculated based on Eq. 1 reported by ref. 31. We determined (2009). maximum and minimum values according to their calibrated values, and then added 24. Frolking, S. & Roulet, N. T. Holocene radiative forcing impact of northern a prudential uncertainty term corresponding to 1/5 of the value on both ends. Total peatland carbon accumulation and methane emissions. Glob. Change Biol. 13, area of arable land was considered 1563 Gha and for meadow and pasture land was 1079–1088 (2007). 32 3360 Gha . The average global soil C content for the calculation was 1.35% and was 25. Rieley, J. & Page, S. in Tropical Peatland Ecosystems (eds. Osaki, M. & Tsuji, 23 derived from the ISRIC WISE database . As a result, we obtain a mineral soil N.) (Springer, Japan, 2016). – 31 sequestration of 24 64 Gt C within 63 years, similar to the range reported by ref. 26. Alm, J., Byrne, K. A., Hayes, C., Leifeld, J. & Shurpali, N. J. in Soil Carbon in – (32 64 Gt over 87 years). We calculated the future scenario considered in this study Sensitive European Ecosystems (eds. Jandl, R., Rodeghiero, M. & Olsson, M.) − (increased degrading peatlands in Fig. 3) according to a Michaelis Menten function (Wiley, New York, 2011). describing the increase. The function was set to reach half saturation, i.e., a doubling 27. Hooijer, A. et al. 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FAOSTAT (United Nations, 2016). 33. FAO. World Fertilizer Trends and Outlook to 2018 (United Nations, 2015). 34. Ladha, J. K., Reddy, C. K., Padre, A. T. & van Kessel, C. Role of nitrogen fertilization in sustaining organic matter in cultivated soils. J. Environ. Qual. References 40, 1756–1766 (2011). 1. Moore, P. D. The future of cool temperate bogs. Environ. Conserv. 29,3–20 35. Conant, R. T., Paustian, K., Del Grosso, S. J. & Parton, W. J. Nitrogen pools (2002). and fluxes in grassland soils sequestering carbon. Nutr. Cycl. Agroecosyst. 71, 2. Frolking, S. et al. Modeling northern peatland decomposition and peat 239–248 (2005). accumulation. Ecosystems 4, 479–498 (2001). 36. Bernstein, L. et al. in Climate Change 2007: Mitigation. Contribution of 3. Tarnocai, C. et al. Soil organic carbon pools in the northern circumpolar Working Group III to the Fourth Assessment Report of the Intergovernmental permafrost region. Glob. Biogeochem. Cycles 23, GB2023 (2009). Panel on Climate Change (eds Metz, B. et al.) (Cambridge University Press, 4. Yu, Z. C., Loisel, J., Brosseau, D. P., Beilman, D. W. & Hunt, S. J. Global Cambridge, 2007). peatland dynamics since the Last Glacial Maximum. Geophys. Res. Lett. 37, 37. Sutton, M. A. et al. Nitrogen & global change. Preface. Biogeosciences 9, L13402 (2010). 1691–1693 (2012). 5. Page, S. E., Rieley, J. O. & Banks, C. J. Global and regional importance of the 38. Li, C., Frolking, S. & Butterbach-Bahl, K. Carbon sequestration in arable soils tropical peatland carbon pool. Glob. Change Biol. 17, 798–818 (2011). is likely to increase nitrous oxide emissions, offsetting reductions in climate 6. Dargie, G. C. et al. Age, extent and carbon storage of the central Congo Basin radiative forcing. Clim. Change 72, 321–338 (2005). peatland complex. Nature 542,86–90 (2017). 39. Ajani, J. I., Keith, H., Blakers, M., Mackey, B. G. & King, H. P. Comprehensive 7. Scharlemann, J. P. W., Tanner, E. V. 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Peninsular Malaysia, Sumatra and Borneo in 2015 with changes since 1990. 10. Joosten, H. The Global Peatland CO2 Picture: Peatland Status and Drainage Glob. Ecol. Conserv. 6,67–78 (2016). Related Emissions in All Countries of the World (Wetland International, Ede, 43. TASVEG. in TASVEG Version 2. Tasmanian Vegetation Monitoring and The Netherlands, 2010). Mapping Program (Dept. Primary Industries, Parks, Water and Environment, 11. IPCC. 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Tasmanian Gov., Hobart, 2009). Gas Inventories (IPCC, Wetlands, 2014). 44. Ramankutty, N., Evan, A. T., Monfreda, C. & Foley, J. A. Farming the planet: 12. Frolking, S. et al. Peatlands in the Earth’s 21st century climate system. 1. Geographic distribution of global agricultural lands in the year 2000. Global Environ. Rev. 19, 371–396 (2011). Biogeochem. Cycles 22, GB1003 (2008). 13. Leifeld, J. Prologue paper: soil carbon losses from land-use change and the 45. Peel, M. C., Finlayson, B. L. & McMahon, T. A. Updated world map of the global agricultural greenhouse gas budget. Sci. Total Environ. 465,3–6 Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci. 11, 1633–1644 (2013). (2007). 14. Smith, P. et al. in Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds. Edenhofer, O. et al.) Acknowledgements (Cambridge Univ. Press, Cambridge, 2014). We acknolwedge the help of C. Wüst-Galley with ArcGIS and K. Budge for critically 15. Wilson, D. et al. Greenhouse gas emission factors associated with rewetting of reading the manuscript. Shape files delineating organic soil areas were kindly provided by organic soils. Mires Peat 17, 4 (2016). Zicheng Yu (Department of Earth and Environmental Sciences, Lehigh University,

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Bethlehem, Pennsylvania, USA), the Swedish Geological Union, the Estonian Land Board Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in (Department of Geology), the Tasmanian Department of Primary Industries, Parks, published maps and institutional afﬁliations. Water and Environment, Jukka Miettinen (Centre for Remote Imaging, Sensing and Processing CRISP, National University of Singapore), and by Maria Aljes, Northwest German Institute of Forest Research, Göttingen, Germany. Open Access This article is licensed under a Creative Commons Author contributions Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give J.L. developed the idea, compiled tropical peat data, and wrote the text. L.M. wrote the appropriate credit to the original author(s) and the source, provide a link to the Creative text, compiled boreal and temperate peat data, and performed the calculations. Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless Additional information indicated otherwise in a credit line to the material. If material is not included in the Supplementary Information accompanies this paper at https://doi.org/10.1038/s41467- article’s Creative Commons license and your intended use is not permitted by statutory 018-03406-6. regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/ Competing interests: The authors declare no competing interests. licenses/by/4.0/.

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TR060 – Biological and Biomedical Sciences

Foundation Scholarship 2020/21

Contact: Dr. Glynis Robinson ([email protected]) Special General Paper (Paper 1) Selected Theme: Climate change: Ecological impacts on biota, past, present and future.

There is widespread evidence from paleoclimatic proxies (tree rings, ice cores, corals, marine sediments, geomorphological features and plant/animal macrofossils) of climatic perturbations that are represented by glacial and interglacial periods across geological time. Recent evidence from direct measurements have shown a universal and global pattern of increasing surface temperatures driven largely by the anthropogenic emission of greenhouse gases and large-scale changes in land-surface attributes.

Changing climatic patterns such as the long-term trajectories of temperature and precipitation in addition to the frequency, magnitude and duration of extreme climatic events can have significant impacts on the ability of plants and animals to grow, reproduce and ultimately survive in a changing world. Such impacts have the potential to alter the distribution of individual species and could also influence the structure and function of communities, ecosystems and biomes which can have both positive and negative feedbacks on the global climate system. The influence of anthropogenic activity can have further impacts on climatic feedbacks through changing patterns of land cover and land use, which also has implications for the ecosystem services on which humanity relies.

Students should review the material on the reading list below, and should extend the learning process beyond this to gain a comprehensive understanding of the drivers of climate change over various temporal and spatial scales and how this is reflected in key climatic variables over time. They should also pay attention to the impacts of climate change on plant and animal species, and consider the implications of these impacts on ecological function at multiple spatial scales. Further reading should explore the mitigation and adaptation of future climate change, paying particular attention to ecosystems that are particularly vulnerable to change or that have significant mitigation potential.