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Modeling Greenhouse Gas Emissions from Spanish Dairy and Beef farms: Mitigation Strategies by Ibidhi Ridha Dirigida por Dr. Sergio Calsamiglia Blancafort 2 AGRADECIMIENTOS Primero y antes que nada, gracias a Dios, por darme la fortaleza para seguir adelante, lograr otra meta más en mi carrera y por haber puesto en mi camino a aquellas personas que han sido mi soporte y compañía durante todo el periodo de estudio. Quiero manifestar mi más profundo respeto y admiración a las siguientes personas e instituciones, quienes colaboraron de una o de otra manera en este proceso, entre ellas: A mi director de tesis, los profesores: Dr. Sergio Calsamiglia Blancafort por su oportunos consejos y correcciones y dedicación, por darme la oportunidad de ampliar mis conocimientos en el amplio campo de Modelización de las emisiones de gases a efecto invernadero en el ganado vacuno de leche y de carne en España : Estrategias de Mitigación, a la Universidad Autónoma de Barcelona, Departamento de Ciencia Animal y de los alimentos. A todos ellos, por haberme permitido aprender tantas cosas, por su ejemplo de profesionalidad y por haber confiado en mí, por todo muchas gracias. Al Instituto Agronómico Mediterráneo de Zaragoza por la concesión de mi beca de master y al CIHEAM, por la posibilidad de obtener el título del Master of Science. Agradezco la buena disposición de todo el personal de la administración del instituto, que me ayudaron mientras realizaba el curso. A nivel personal, el primer agradecimiento va por supuesto para mi familia por estar siempre a mi lado, por darme la fortaleza para seguir adelante. A mi madre, por contar siempre con su amor comprensión y ejemplo, a mi hermano y hermanas por todo el amor y los sueños que compartimos a mi amor Mouna, y con especial amor a mi padre. A todos y cada uno de mis familiares, por preguntarme siempre por esta tesis, por confiar en mi... A todos y cada uno de mis amigos que no necesito nombrar porque tanto ellos como yo sabemos que desde lo más profundo de mi corazón les agradezco el haberme brindado todo el apoyo, cariño y amistad y por estar siempre pendientes de mí, sobre todo, en aquellos momentos en los que más los he necesitado. También, a todo el personal y los compañeros becarios de ambos organismos, SNIBA y UAB, quiero agradecerles muy especialmente el buen trato que siempre me han dispensado y sobre todo con esa especial amabilidad y buen humor, durante el transcurso de esta tesis. Finalmente deseo expresar mi reconocimiento a todos los compañeros, y amigos que atendiendo mi invitación han querido acompañarme en este acto. ¡¡¡Gracias, a todos!!! I List of Abbreviations ADF Acid Detergent Fiber BW Body Weight C Carbon CF Carbon Footprint CH4 Methane CHO Carbohydrate CO2 Carbon dioxide CO2-e Carbon dioxide equivalents CNCPS Cornell Net Carbohydrate and Protein System model CP Crude protein CT Condensed tannins DCD Dicyandiamide DM Dry Matter ECM Energy Corrected Milk EMO Environmental movements EU European Union FA Fatty acid GEI Gross Energy Intake GWP Global-warming potential GHG Greenhouse Gases Ha Hectare IFCN International Farm Comparison Network IFSM Integrated Farm System Model IPCC Intergovernmental Panel on Climate Change IVDMD In vitro dry matter digestibility II Kg Kilogram L Liter LCA Life Cycle Assessment Mcal Megacalorie ME Metabolisable Energy MJ Mega joule MNE Milk N Efficiency N Nitrogen N2 Non reactive nitrogen N, E Nord, Est NEL Net Energy Lactation NFE Net Feed Efficiency NH3 Ammonia NH4+ Ammonium P Phosphorus N2O Nitrous Oxide RDP Rumen Degradable Protein T Tonne Total VFA Total Volatile Fatty Acids UNFCCC United Nations Framework Convention on Climate Change Ym Emission factor III RESUMEN Las emisiones de gases de efecto invernadero (GEI) y sus posibles efectos sobre el medio ambiente se ha convertido en un problema nacional e internacional importante. La producción bovino de leche y de carne, junto con todos los demás tipos de producción animal, se reconocen las fuentes de emisiones de gases de efecto invernadero, pero existe poca información sobre las emisiones netas de las granjas lecheras y de carne. Componente de modelos para predecir todas las fuentes importantes de CH4, N2O y CO2 a partir de fuentes primarias y secundarias en la producción de leche se han integrado en una herramienta de software se llama Integrate Farm System Model (IFSM). Esta herramienta calcula la huella de carbono de la producción de leche y carne como el intercambio neto de todos los gases de efecto invernadero en equivalentes de CO2 por unidad de energía de leche corregida (ELC) producida o kg de peso corporal (PC). IFSM y Cornell Net Carbohydrate and Protein System (CNCPS) se utilizaron en este estudio para evaluar las granjas lecheras típicas españolas para el cálculo de las emisiones de GEI y la evaluación de dieta y su contribución en la producción de metano, respectivamente. Las tres regiones más importantes de producción de vacuno lechero en España fueron seleccionados Mediterráneo área (Cataluña, Valencia y Murcia), Zona Cantábrica (Galicia, Asturias y Cornisa Cantábrica) y la zona Centro (CastillaLa Mancha, Castilla y León, Madrid y Aragón), en además dos otros granjas se han seleccionados (una ecológica y otra de la isla de Baleares). El promedio de la huella de carbono de todas las granjas evaluadas fue de 0,83 kg de unidades de CO2 equivalente / kg de ECM. Las granjas de zona mediterráneas tienen la más alta huella de carbono (promedio 0,98 kg CO2e/kg de ECM), mientras que la Zona Central fue de 0,84 y el más bajo fue en las granjas del área Cantábrica (0.67). Dos extremos granjas se seleccionaron la primera tenía la huella de carbono el más alta y el metano no entérico (granja 197MA), mientras que la segunda tenía la huella de carbono más baja y el metano entérico el más alto (64CA), el primera fue simulada por el modelo IFSM utilizando diferentes escenarios de cambio de gestión, mientras que el segunda se simula con el modelo CNCPS utilizando diferentes estrategias de cambio en la dieta. Hemos encontrado que el cambio de gestión reduce las emisiones de metano hasta en un 30%, mientras que el cambio de dieta redujo hasta un 5%. IV Tres granjas españolas representativas de terneros de cebo (dos granjas sin ensilaje de maíz, una con la raza Holstein y otra mixta, y la tercera con ensilado de maíz) se utilizaron para simular las emisiones de gases de efecto invernadero los mismos modelos. Los valores de la huella de carbono oscilaron desde 6,38 hasta 7,03 kg con un valor medio de 6,86 CO2e por kg de peso corporal. La granja de engorde con ensilaje de maíz tuvo un valor promedio de la huella de carbono de 6,98 Kg CO2 eq / kg de peso corporal, mientras que sin el ensilaje de maíz fue 6,90 Kg CO2 eq / kg de peso corporal. Se concluyó que tanto la industria láctea española y sector de la carne tiene una menor huella de carbono y las estrategias de gestión proporcionan un mayor potencial para reducir las emisiones de metano en comparación con los cambios de escenarios dietéticos. Palabras clave: gas de efecto invernadero, huella de carbono, la granja, el metano, IFSM, CNCPS, metano. V ABSTRACT Greenhouse gas (GHG) emissions and their potential effect on the environment has become an important national and international issue. Dairy and beef production, along with all other types of animal agriculture, are recognized sources of GHG emissions, but little information exists on the net emissions from dairy and beef farms. Component models for predicting all important sources of CH4, N2O, and CO2 from primary and secondary sources in dairy production were integrated in a software tool called the Integrate Farm System Model (IFSM). This tool calculates the carbon footprint of dairy and beef production as the net exchange of all GHG in CO2 equivalent units per unit of energy-corrected milk (ECM) produced or kg body weight (BW). The IFSM and Cornell Net Carbohydrate and Protein System (CNCPS) were used during this study to evaluate typical Spanish dairy farms for GHG emissions calculation and diet evaluation for methane production, respectively. The Three most important regions of dairy cattle production in Spain were selected Mediterranean (Catalonia, Valencia and Murcia), Cantabric Area (Galicia, Asturias and Cantabria) and Central zone (Castilla-La Mancha, Castilla-Leon, Madrid and Aragon), in addition to two other farms (one organic and one from Baleares Island). The average carbon footprint of all evaluated farms was 0.83 kg of CO2 equivalent units/ kg of ECM. Mediterranean farms have the highest Carbon footprint (average 0.98 kg CO2e/kg of ECM), while Cental Zone was 0.84 and the lowest was in Cantabric farms which (0.67). Two extreme farms were selected the first one had the highest carbon footprint and non-enteric methane (197MA), while the second had the lowest carbon footprint and the highest enteric methane (64CA), the first one was simulated by the IFSM model using different management change scenarios, while the second was simulated with CNCPS model using different dietary change strategies. We found that the management change reduced methane emission up to 30% while dietary change reduced it up to 5%. Three representative feedlot beef Spanish farms (two farms without corn silage; one Holstein and another mixed breed, and the third with corn silage) were used to simulate GHG emissions using the same models. The carbon footprint values ranged from 6.38 to 7.03 kg with an average value of 6.86 CO2e per kg BW. The feedlot farm with corn silage had an average carbon footprint value of 6.98 Kg CO2e/ Kg BW while without corn silage was 6.90 Kg CO2e/ Kg BW. 6 Figure 3. Arctic sea-ice level (NASA satellite observations). Figure 4. Sea leve (NASA satellite observations). Chapter 1 7 The Kyoto Protocol is an international agreement linked to the United Nations Framework Convention on Climate Change, which commits its parties by setting internationally binding emission reduction targets. Recognizing that developed countries are the main contributor for the current high levels of GHG emissions in the atmosphere as a result of more than 150 years of industrial activity, the Protocol places a heavier burden on developed nations under the principle of common but differentiated responsibilities. The Kyoto Protocol was adopted in Kyoto, Japan, on 11 December 1997 and entered into force on 16 February 2005. The detailed rules for the implementation of the Protocol were adopted at COP 7 in Marrakesh, Morocco, in 2001, and are referred to as the "Marrakesh Accords". Its first commitment period started in 2008 and ended in 2012. Recently, the "Doha Amendment to the Kyoto Protocol" (Doha, Qatar, on 8 December 2012) was adopted. Eventually, differentiated target plans were fixed, with each country establishing its own feasible aim. The European Union took the most ambitious of the targets, agreeing to a reduction of eight per cent of the six greenhouse gases (CO2, CH4, N2O, HFCs, PFCs and SF6) below 1990 levels. Figure 5. GHG trends and projections in Spain and his position from the Kyoto protocol (Source: Eurostat, 2010). 8 With the growing concern over global climate change and the potential impact of GHG emissions on the environment, there is a need to express the total emission of GHG associated with a product, process, or service in common units. A term that has come to represent this quantification is the carbon footprint. A carbon footprint is defined in many ways depending upon the product, process or service represented. In general, though, the carbon footprint is the total GHG emission, expressed in CO2 equivalent units (CO2e), associated with a product, process or service. The conversion of GHG to CO2e is done using the Global-warming potential (GWP) of each gas where GWP values used for CH4 and N2O are 25 and 298, respectively (IPCC, 2001; EPA, 2007). The carbon footprint of milk or beef production is the net of all greenhouse gases assimilated and emitted in the production system expressed as CO2e divided by the total energy corrected milk or by kg of carcass produced. All emission sources of the three gases are summed and the net CO2 assimilated in the feed production process is subtracted to give the net emission of the production system (Carbon Trust, 2010). 3. The Contribution of Agriculture and Animal Production to the Greenhouse Effect Climate change is one of the greatest concerns facing our society (Steffen et al., 2007). During recent years, the livestock sector has been revealed as one of the main contributors to climate change, representing 18% of the GHG emissions (Steinfeld et al., 2006). This number has further been disaggregated, showing that the dairy sector (including meat by-products) is responsible for 4.0% of global emissions (Gerber et al., 2010a). If emissions are divided between dairy products and by-products (i.e. beef), dairy products represent 2.7% of global GHG emissions (Gerber et al., 2010a). GHG emissions associated with agricultural products, especially animal products, differ from those of other sectors (e.g. transport or energy). For most sectors, fossil carbon dioxide (CO2) dominates GHG emissions, while in agriculture methane (CH4) and nitrous oxide (N2O) are the most important contributors. In addition, biogenic CO2 from land use change contributes significantly in agriculture. Figure 6 shows GHG emissions for different sectors. In this schematic representation, the agricultural contribution is only 13.5%, but this is because diesel consumption by tractors (reported under energy supply), transportation of feed (reported under transport) and production of fertilizers (reported under industry) are not included in agricultural emissions. Moreover, emissions from land use change are reported under forestry. If all these Chapter 1 9 contributions were included, the agricultural sector would actually be accountable for about one third of global anthropogenic GHG emissions. Hence, agriculture is likely responsible for 30-35% of the global GHG emissions (Foley et al., 2011). Figure 6. Greenhouse gas emissions per sector (FAO, 2010). Methane is the highest contributor to GHG produced by ruminants. The amount of this gas emitted by ruminants depends essentially on the make-up of their digestive system and diet. Ruminant species have higher emission rates due to the type of fermentation that generates methane in the rumen as part of their digestion process. In Spain, the main species of ruminants include cattle, sheep and goats. Among the pseudo-ruminants (horses, mules, asses) and the monogastric species (swine), methane emission rates are far lower. Figure 7 shows the contribution, relative to emissions, of each of the activities of animal production. In 2011, the main source of CH4 in this activity were non-dairy cattle ( beef, dry cattle and replacement heifers ) with 43% of emissions, followed by sheep with 30% and dairy cattle with 17% of total emissions activity. Taken together, the rest of animals represent almost 10 % of the emissions produced. 2.80% 13.10% 17.40% 25.90% 7.90% 13.50% 19.40% Waste and waste water Transport Industry Forestry Energy supply Agriculture Residential and commercial buildings 10 Figure 7. Distribution of emissions according to livestock activity in Spain (UNFCCC, 2013). II. Sources of Greenhouse Gas Emissions from Dairy and Beef Farm In all dairy and beef production systems, the most important sources of GHG including carbon dioxide (CO2) , methane (CH4) , nitrous oxide (N2O) and indirect greenhouse gases/pollutants (NH3) are the animal themselves, animal housing, storage and treatment areas for manure, and spreading of manure and chemical fertilizers (Steinfeld et al., 2006). Figure 8. Overview of the main greenhouse gas emissions at farm level (Berglund et al.2009). 17% 43% 30% 10% Dairy Cattle Non-Dairy Cattle Sheeps Other Chapter 1 11 1. Methane Emission (CH4) 1.1. Enteric Emission: Enteric Fermentation Enteric fermentation in ruminants is the largest source of CH4 emission from dairy farms (Chianese et al., 2008a). In the rumen, CH4 is formed from hydrogen and CO2 due to archaea methanogens. Hydrogen originates from the fermentation of carbohydrates by bacteria and protozoa, and the amount depends on the ratio of different volatile fatty acids: The pathway for the synthesis of acetate and butyrate produces H and the pathway for the synthesis of propionate consumes H. The processes are detailed in Morgavi et al. (2010a). The CH4 produced is released to the atmosphere by belching. The amount of CH4 produced from enteric fermentation depends on various factors including animal type and size, digestibility of the feed, and the intake of dry matter, total carbohydrates, and digestible carbohydrates (Chianese et al., 2008a). Figure 9. Schematic microbial fermentation of feed polysaccharides and H2 reduction pathways in the rumen (Morgavi et al., 2010a). 12 1.2. Non-Enteric Emission: Manure Storage During manure storage, CH4 is also generated through a reaction similar to that described for enteric fermentation. The cellulose in the manure is degraded by microbes, and the products of this process serve as substrates for methanogenesis (Kreuzer and Hinderichsen 2006). Temperature and storage time are the most important factors influencing CH4 emissions from stored manure because substrate and microbial growth are generally not limited (Boadi et al., 2004). Although the processes are similar, there are important differences between the two. The temperature in the storage varies, in contrast to the relatively constant temperature in the rumen, and the manure in storage is more heterogeneous (e.g., the substrate is less well mixed and some carbohydrates are already partially decomposed compared with the consistency of the rumen) (Lassey 2007; Saggar et al. 2004a). 2. Nitrous Oxide Emission (N2O) These emissions result from nitrogen turnover in agricultural soil from the use of synthetic fertilizers and manure, crop residues left after harvesting and excreta deposited during grazing. N2O is produced naturally in soils through nitrification and denitrification processes (Figure 10). Several factors influence the production of N2O, such as soil type, drainage, degree of soil compaction and climate (Henriksson et al., 2011; Bouwman and Boumans, 2002). A high precipitation, freeze and thaw periods, clay and organic soils, high pH, application of nitrogen, soil compaction and tillage lead to increased N2O emissions, while draughts and drainage leads to reduced N2O emissions (Berglund et al., 2009; Bouwman and Boumans, 2002). None of these aspects are generally considered when estimating N2O in carbone footprint (CF) studies. Chapter 1 13 Figure 10. Schematic overview of nitrification and denitrification processes (Berglund et al., 2009). 3. Carbon Dioxide (CO2) Multiple processes emit CO2 from dairy and beef farms. Carbon dioxide emissions are primarily due to the manufacturing and operation of farm machinery and vehicles, the manufacturing of fertilizers and agrochemicals, and the manufacturing of farm buildings and electrical power generation. Additional emissions are associated with a change in land management practices, which can influence carbon stored in the soil, resulting in either CO2 emissions or CO2 sequestration, as soil organic carbon. Land use change can also be a significant source of CO2 as a result of the loss of soil carbon, as well as above-ground biomass associated with land degradation and/or deforestation (Chianese et al., 2009a). 4. Ammonia (NH3) This gas is not considered as a direct GHG, however, it is a precursor to N2O (direct active GHG) and NO (Clemens et al., 2001). An important part (65%) of all NH3 emissions from terrestrial systems come from animal farming systems (National Research Council, 2002) from the anaerobic digestion of food proteins. In cow’s rumen, microbes use NH3 to produce proteins. But often, NH3 is in excess in the rumen goes into the blood and it is excreted in urine in form of urea. Once excreted, urea is degraded releasing NH4 to the atmosphere. When NH3 is in the atmosphere, a part of this gas comes back to the ground, which can cause environmental problems such as soil acidification or changes in the soil structure, and the other part remains in the atmosphere and reacts with some atmospheric acids to produce aerosols, which can be a problem for air quality and for animals and human health (McGinn et al., 2007). III. Methodology for Quantification of Greenhouse Gas Emissions from the Livestock Sector and Modeling Emissions Over the last 100 years several different methods have been developed with the purpose of measuring and estimating methane emissions from ruminants. These methods, such as respiration chambers, SF6 tracer technique, in vitro gas production technique, CO2 technique 14 and N2O technique, have various scopes for application, advantages and disadvantages, but none of them is perfect. Some direct, experimental methods are expensive and others are of limited capacity of testing large number of animals. The description and critical evaluation of such methods have been described by Ida et al. (2012). Direct measurement of greenhouse gas emissions can be expensive and complicated (Ellis et al. 2007). However, prediction equations and models can be used to estimate emissions of enteric CH4 and CH4, NH3 and N2O from manure, without undertaking costly experiments for each estimation. 1. Intergovernmental Panel on Climate Change Reporting Protocols The IPCC published guidelines for calculating national GHG inventories (IPCC, 1997a). These were subsequently updated in 2000, 2003 and 2006 and allowed for quantification of national emissions based on readily available activity data, such as power usage, fossil fuel consumption, fertilizer sales, animal numbers and land use change, as well as associated emission factors for each activity. In terms of agriculture, the simplified approach of the IPCC protocols was applied with large variations in different agricultural practices within and among countries, which made direct national scale measurements of farm emissions impossible. IPCC guidelines provide the best widely applicable defaults for compiling national GHG inventories and, as such, are the main methodologies by which sectorial emissions can be compared among countries. However, the robustness of these inventories is dependent on country specific emission factors and verification of emissions inventories via modeling and/or direct measurement (IPCC, 1997b). Consequently, the IPCC operates with three different levels to estimate GHG. These three levels depend on the quality of the database established in the country in question, and are known as Tiers 1, 2 and 3, where Tier 1 is the simplest calculation method and Tier 3 the most complex and data-dependent method. For example, in case of methane, the three methods are based on the proportion of the cow’s gross energy intake (GE) excreted. Thus Tier 1 utilizes an emission factor (Ym) of 6.5% and an assumed GE, which results in an estimated methane production of 109 kg/cow/year in Western Europe. When using Tier 2, and especially Tier 3, more information is required to determine Ym, e.g., in relation to the digestibility and nutrient content of the feed. Chapter 1 15 However, the structure of the IPCC reporting protocols are not conducive to integrated systems analysis as a result of the sector based approach. Specifically, emissions that arise in agricultural systems are reported in three sectors for IPCC purposes according to the 1996 guidelines (IPCC, 1997a); agriculture, land use change and forestry, and energy. Further, indirect emissions related to agricultural production may also arise in the industrial processes and waste categories. In addition, if data from these three sectors are combined to generate a whole farm balance, any emissions generated outside the national boundaries are not included. Because of the limitations of IPCC methodology for modeling farm level emissions, whole farm modeling is widely used. Whole farm GHG emissions models may be categorized as systems analysis models or life cycle assessment models (reviewed by Crossona et al., 2011). 1.1. Life Cycle Assessment Methodology Life cycle assessment (LCA) is a method used to compile and assess total environmental impacts and emissions from the entire life cycle of a product or service. The life cycle of a product includes acquisition of raw materials, processing, use, and final disposal (ISO 14040, 2006). LCA methodology has gained wide acceptance, and although many assumptions are made in its execution, modern assessments are at least minimally comparable if they follow the pattern laid out by the International Organization for Standardization (ISO 14040, 2006). An ISO 14040 compliant LCA consists of 4 parts: goal and scope definition, life cycle inventory, impact assessment, and interpretation. Best practices for important assumptions that must be made in LCA analysis are also included in the ISO standards, such as methods to allocate environmental impacts between products resulting from the same production system. LCA methodology is well-adapted to evaluate agricultural systems because it provides an objective method of defining the production system and quantifies the impact in terms of the outputs of a production system (Casey and Holden 2006; Thomassen et al., 2008a). Availability of a farm-produced commodity to be consumed by humans or to enter another production process is generally the scope of modeling in agricultural LCA. This means use and end-of-life scenarios are not considered for agricultural production systems. Typical LCA of manufactured product is termed a “cradle to grave” analysis because all impacts on the environment from the life of that product have been included. Without use phase or end-of- 22 this model is more comprehensive, convenient and can be selected to be used during the simulation of dairy and beef farms. Chapter 1 23 Table 1. General characteristics of whole-farm GHG models. DairyWise FarmGHG SIMSDAIRY FarmSim Holos DairyGHG IFSM Model type Empirical Empirical Semimechanistic Semi-mechanistic Empirical Empirical and mechanistic Empirical and mechanistic CH4 and N2O emissions x x x x x x x CO2 emissions x x x x x x C sequestration x x x NH3 emissions x x x x x x Pre-chain emissions x x x x x x Economics x x x x Carbone Footprint (Kg CO2 e/ Kg of product ) x x x Target animals Dairy farms Dairy farms Dairy farms All type All type Dairy farms Dairy and beef farms 24 IV. Mitigation Measures for the carbon Footprint of Dairy and Beef Cattle There are several strategies that may be employed in the beef and dairy cattle industry to reduce GHG. These strategies may be categorized into dietary and management strategies. Each one will be discussed in the following section. 1. Dietary Strategies 1.1. Strategies to Reduce Enteric Methane from Cattle 1.1.1. Concentrate (Proportion, Nature) It is well established that increasing the level of concentrate in the diet leads to a reduction in CH4 emissions (g/kg DM intake) compared with feeding forage based diets. Starch fermentation promotes propionate production in the rumen creating an alternative hydrogen sink to methanogenesis, lowers ruminal pH, inhibits growth of rumen methanogens, and decreases rumen protozoal numbers limiting the transfer of hydrogen from protozoa to methanogens (Grainger and Beauchemin, 2011). Sauvant and Giger-Reverdin (2007) conducted a meta-analysis of literature data and showed that the relationship between concentrate proportion in the diet and CH4 production is curvilinear (Figure 12). It is clear from this figure that an increase in proportion of concentrate in the diet decreases CH4 emission. The use of cereal forages that contain high quantities of starch has been proposed as a means to increase the starch content of the diet and lower CH4 emissions (Beauchemin et al., 2008). Feeding forages high in starch favours the production of propionate over acetate, which should reduce enteric CH4 emissions. Furthermore, intake of whole crop silages is often higher than that of grass forages, and thus shorter residence times in the rumen could reduce CH4/kg of feed intake. In a study with growing beef cattle, Mc Geough et al. (2010a) compared diets (i.e., 240 g concentrate and 760 g silage/kg DM) containing one of four maize silages to a high concentrate diet (i.e., up to 834 g concentrate and 166 g grass silage/kg DM). Maize silages were harvested at increasing stages of maturity such that starch content increased from 315 to 386 g/kg DM and neutral detergent fiber content decreased from 485 to 434 g/kg DM. Cattle Chapter 1 25 fed the high concentrate diet (i.e., starch content of 369 g/kg DM) produced 19% less CH4 (g CH4/kg DM intake) than cattle fed the maize silage diets. For maize silages, CH4 output relative to DM intake tended to linearly decline to a 10.9% reduction in response to increasing the starch to neutral detergent fiber ratio. Thus, increasing the starch content of forages can help decrease CH4 emissions, but CH4 emissions of forage fed cattle is still considerably higher than concentrate fed cattle. Figure 12. Effect of the concentrate proportion on the CH4/kg production Sauvant and GigerReverdin., (2007). 1.1.2. Level of Intake An increase in feeding level induces lower CH4 losses as percent of GEI. Johnson and Johnson (1995) noted that CH4 losses as percentage of GEI declined by 1.6 percentage units for each multiple increase of intake. This is caused mainly by the rapid passage of feed out of the rumen. As a result of the increased passage rate, the extent of microbial access to organic matter is decreased, which in turn reduces the extent and rate of ruminal dietary fermentation. Boadi et al. (2004) reported a 29% decrease in CH4 production of cattle when the fractional passage rate of particulate matter was increased by 63%. According to Giger-Reverdin et al (2003) and Sauvant and Giger-Reverdin (2007), the acceleration of passage rate observed in the rumen with high levels of ingestion favors propionate production, which is a competitive 26 pathway for the use of hydrogen. However, the extent to which intake levels affect passage rate of roughages is proportionally less than with concentrate or mixed diets. Figure 13. Influence of feeding level on the metabolizable energy share lost as methane (Giger-Reverdin et al 2003; Sauvant and Giger-Reverdin 2007). 1.1.3. Forage (Type and Quality) Improving forage type reduces enteric CH4 emissions in ruminants. According to the prediction model of Benchaar et al. (2001), the substitution of timothy hay by lucerne decreases CH4 emissions by 21% (expressed as % of digestible energy). Moreover, the lower CH4 loss observed with legumes compared with grasses can be attributed to the lower proportion of structural carbohydrates in legumes and faster rate of passage, which shift the fermentation pattern towards higher propionate production. Boadi and Wittenberg (2002) also demonstrated that forage quality has a significant impact on enteric methane emissions. Cattle given hay of high (61.5 % IVOMD), medium (50.7% IVOMD) and low (38.5% IVOMD) quality differed (P < 0.01) in dry matter intake, as animals consumed 9.7, 8.9 and 6.3 kg/d, respectively. Moreover, differences existed in enteric methane emissions (P < 0.01), as 47.8, 63.7 and 83.2 CH4 L/ kg digestible organic matter intake was produced from cattle consuming the high, medium and low quality forages, respectively. The same authors subsequently demonstrated this same effect on pasture (Boadi et al., 2002). Steers grazing during the early period of the grazing season had 44% and 29% Chapter 1 27 less energy lost as methane (P < 0.01) than steers grazing during the mid and late grazing periods, respectively. The impact of pasture forage quality and availability on enteric methane emissions from cattle in grass-based production systems has been studied by Ominski et al. (2004). Enteric methane emissions measured early and late in the grazing season were influenced by pasture type and season of grazing. Further, it appeared that emissions were influenced by pasture dry matter availability and quality, in that emissions were highest (11% of GEI) when pasture quality and availability were low. Emissions were lower when pasture quality was high and availability was low (6.9% of GEI) or when quality was low and availability was high (7.19.4% of GEI).Unfortunately, neither pasture ever attained a status of high forage quality and high pasture availability. It can be concluded that enteric emissions are highest when the animal is presented with poor quality forage and has limited opportunity to select higher quality forage as a consequence of reduced dry matter availability. 1.1.4. Addition of Lipids Dietary fat shows a promising nutritional alternative to depress ruminal methanogenesis without decreasing ruminal pH as opposed to concentrates (Sejian et al., 2011b). Their effect has been summarised by equations provided by Giger-Reverdin et al. (2003) and by Eugene et al., (2008) who reported a mean decrease in CH4 of 2.2% per percentage unit of lipid added in the diet of dairy cows, independently of the nature of fatty acid (FA) supply. Lipids cause the depressive effect on CH4 emission by toxicity to methanogens, reduction of protozoa numbers and therefore protozoa associated methanogens, and a reduction in fibre digestion. Beauchemin et al. (2008) recently assessed the effect of level of dietary lipid on CH4 emissions over 17 studies and reported that with beef cattle, dairy cows and lambs, for every 1% (DMI basis) increase in fat in the diet, CH4 (g/kg DMI) was reduced by 5.6 %. Martin et al., (2009) also confirmed that the effect of lipids on methanogenesis is proportional to their level of supply. The effect of FA is also dependent on their nature. Medium-chain FA, mainly provided by coconut oil, are more depressive (7.3% decrease per percentage unit of added lipids). According to Soliva et al., (2004) oils containing lauric Acid (C12:0) and myrstic acid (C16:0) are particulary toxic to methanogens. When taken alone they have similar effects, but a combination of these two acids has a synergistic effect leading to a sharp decrease in CH4 28 (60%) in vitro. In another study of fat effects on enteric CH4, the supplements rich in polyunsaturated FA such as linoleic acid (C18:2 from soybean and sunflower) and linolenic acid (C18:3 from linseed) also have a negative effect on CH4 production (4.1% and 4.8% decrease per percentage unit of added lipids) (reported by Martin et al., 2010). 1.1.5. Additives 1.1.5.1. Ionophores Ionophores are highly lipophilic substances able to delocalize the charge of ions and facilitate their movement across membranes (Boadi et al. 2004). Monensin is the most commonly used and studied ionophore, with others such as lasalocid, tetronasin, lysocellin, narasin, salinomycin and laidomycin also being used commercially. The ionophore monensin has been used as a feed additive in cattle production to improve feed conversion efficiency and N metabolism, and for the prevention of bloat and post-calving ketosis. Monensin can be delivered as a premix added to the diet, as a slow release capsule inserted into the rumen or, increasingly in pasture-based systems, in the water supply in paddocks using a form of monensin designed for in line water dispenser systems. Duffield et al. (2008) offered the most thorough analysis of monensin effects on milk production and DM intake, and concluded that monensin decreased DM intake by 0.3 kg/d, increased milk yield by 0.7 kg/d, and improved milk production efficiency by 2.5%. Appuhamy et al. (2013) conducted a meta-analysis of literature data and showed that 32 mg/kg DM of monensin reduced CH4 emissions and CH4 conversion rate (Ym) in beef steers fed total mixed rations by 19 g/animal per d (P < 0.001) and 0.33 (P = 0.047), respectively. In dairy cows the reductions were 6 ± 3 g/animal per day (P = 0.065) and 0.23 ± 14% (P = 0.095) for monensin given at a dose of 21 mg/kg DM. Moreover, Beauchemin et al. (2008) studied the effects of monensin on CH4 emissions and found evidence of a dose response with monensin at <19 mg/kg DM intake not reducing CH4 emissions, but at 24–35 mg/kg DM intake, it reduced CH4 (as g/kg DM intake) by 3–8%. Chapter 1 29 1.1.5.2. Plant Extracts This category includes a variety of plant secondary compounds, specifically essential oils, tannins and saponins. The term plant secondary compound is used to describe a group of chemical compounds found in plants that are not involved in the primary biochemical processes of plant growth and reproduction (Agrawal and Kamra, 2010). Many of these compounds function as defense a mechanism which ensures survival of their structure and reproductive elements protecting against insect or pathogen predation or by restricting grazing herbivores. Several thousands of plant secondary compounds have been reported in various plants and many of them have found their use in traditional Indian and Chinese medicine (Kumar et al., 2007). Furthermore there is a growing interest in the use of plant secondary compounds as a CH4 mitigation strategy (Jouany and Morgavi, 2007). Preparations from plants are seen as a natural alternative to chemical additives that have been banned in the EU or may be negatively perceived by consumers. Essential oils have antimicrobial properties that are capable of affecting rumen fermentations. A number of studies have recently evaluated the ability of essential oils to reduce enteric CH4 production (Table 2). Evans and Martin (2000) examined effects of increasing concentrations of thymol (50, 100, 200, and 400 mg/L of culture fluid) on in vitro 24 h batch culture fermentation of D-glucose by mixed rumen bacteria (Table 2). Methane concentration was not affected when thymol was supplied at 50, 100, and 200 mg/L of culture fluid. However, at 400 mg/L, thymol increased the pH of the medium and decreased CH4 (−94%) acetate (−44%) and propionate (−78%) concentrations. A higher pH and a reduction in VFA concentrations are an indication of an overall inhibition of rumen microbial fermentation, and these changes would not be nutritionally beneficial to the host animal if the same effects were expressed in vivo. Macheboeuf et al. (2008) evaluated in batch cultures (16 h incubation) the effects of thyme (T. vulgaris; 470 g/kg thymol, 200 g/kg terpinene and 200 g/kg p-cymene) on rumen fermentation. A minimum of 300 mg/L of thymol provided as is, or via thyme oil, was required to inhibit CH4 production with a concomitant decrease in total VFA production, acetate and propionate production (Table 2). Busquet et al. (2005b) were the first to report effects of garlic essential oil and two of its compounds (i.e., diallyl disulphide and allyl mercaptan) on CH4 production (Table 2). When added at 300 mg/L in 17 h in vitro batch culture fermentations, allyl mercaptan decreased CH4 production by 19.5% without altering digestibility. At the same concentration, garlic and diallyl disulphide reduced CH4 30 production by −74 and −69%, respectively, but DM digestibility and VFA concentration were also depressed. Condensed tannins (CT) have been shown to reduce CH4 production by 13%–16% (DMI basis) (Woodward et al., 2004; Grainger et al., 2009), mainly through a direct toxic effect on methanogens. However, high CT concentrations (> 55 CT/kg DM) can reduce the voluntary feed intake and digestibility (Beauchemin et al., 2008; Grainger et al., 2009). Plant saponins may also potentially reduce CH4, and some saponin sources are clearly more effective than others (Beauchemin et al., 2008). Similar reduction in methane production by saponins were reported in vitro (Lila et al., 2003; Hu et al., 2005) and in vivo (Santoso et al., 2004a). In addition, many authors reported that the effect of tannins/saponins on methanogenesis dependent on the dose and the source of tannins /saponins (Table 3). Chapter 1 31 Table 2. Effects of essentials oils and extracts of plants on methane production.  Total VFA: Total Volatile Fatty Acids; NR, not reported; +, increase; −, decrease; =, no change Essential oil/ extract of plant Dose Test Methane reduction Total VFA Reference Garlic oil Thymus vulgaris 300mg/L 0.5 mM In vitro In vitro 74% 12% − = Busquet et al. (2005b) Macheboeuf et al. (2008) Thymol oil 400mg/L In vivo (cattle) 94% − Evans an Martin (2000) α-Cyclodextrin–horseradish oil complex 80 g/d In vivo (steers) 90% + Mohammed et al.(2004) Peppermint (Mentha piperita) 0.33 mg/L In vitro 19.9% = Agarwal et al. (2009) Garlic oil (diallyl disulfide) 2 g/ kg (DM) In vivo (sheep) No effect = Klevenhusen et al. (2011) Cashew nut shell liquid (CNSL) 4 g/100 kg BW In vivo (cattle) 20% NR Shinkai et al. (2010) Origanum vulgare L. leaves 500 g/d In vivo (Dairy cattle) 35% = Tekippe et al. (2011) Extract of oregano 250 mg/d In vivo (sheep) 9.8% + Wang et al. (2009) Crina Ruminants (thymol, eugenol, vanillin limonene) 1000 mg/d In vivo (beef cattle) No effect = Beauchemin andMcGinn.(2006) 38 VI. Management Strategies 1. Improvement of Genetic Merit of Cows Genetic improvement is a relatively cost-effective mechanism by which to achieve reductions in greenhouses emissions. The larger North American Holstein genotype has been found to produce between 8 to 11% less methane as a percentage of GE intake, on both a total mixed ration and pasture-based diet, than a small New Zealand Holstein (Robertson et al., 2002), presumably due to differences in level of feed intake. However, larger cows have greater maintenance requirements. For the same level of production, a smaller cow is obviously a more efficient converter of feed into milk. This is why selection programmes in both New Zealand and Australia, in particular have focused on increasing the rate of genetic gain in traits that contribute to profitability per unit of feed eaten (Pryce et al.,2007). Moreover, cows which were ~88% North American Holstein selected on increased milk fat and protein production (Select line cows) were found to grow faster and had increased kg milk per kg dry matter intake during their productive life when on a high energy dense diet, compared with cows selected to represent the UK average for milk fat and protein production on the same diet (Bell et al., 2010). Select genetic line animals have a high genetic potential for mobilizing body energy reserves for production, which has been found to have deleterious effects on health and fertility (Dillon et al 2006), particularly later in life (Wall et al.,2010). However, it was found that Select line cows responded to a diet containing a low proportion of forage, rather than a high forage diet, by having a significantly shorter calving interval (Bell et al., 2010). Select line animals on a low forage diet also produced lower CO2-eq. emissions per energy corrected milk compared with non-select and cows on a high forage diet over their lifetime. In addition, Okine et al. (2002) calculated annual CH4 emissions from Canadian high Net Feed Efficiency (NFE) steers to be 21% lower than that for low NFE steers. Selection for high NFE in beef cattle also decreased manure N, P, K output due to a reduction in daily feed intake and more efficient use of feed, without any compromise in growth performance (Okine et al. 2002). 2. Increased Animal Productivity Increasing productivity, such as growth rate, annual milk or meat production, fertility and efficiency of feed conversion, by breeding or precision management, will reduce net Chapter 1 39 GHG emissions, because fewer animals, and hence less feed, land, water, fossil fuels, and fertilizers, are needed to produce the same amount of product. Furthermore, the daily nutrient requirement of all animals within the dairy herd comprises a specific quantity needed to maintain the animals’ vital functions (the maintenance requirements) plus extra nutrients to support the cost of growth, reproduction or lactation. As shown in figure 14 (Capper et al., 2009b), the maintenance energy requirement of a 650 kg lactating cow does not change as a function of production but remains constant at 10.3 Mcal/d (NEL). A high-producing dairy cow requires more nutrients per day than a low-producing dairy cow, but all nutrients within the extra feed consumed are used for milk production. The total energy requirement per kg of milk produced is therefore reduced: a cow producing 7 kg/d requires 2.2 Mcal/kg milk, whereas a cow yielding 29 kg/d needs only 1.1 Mcal/kg milk. Figure 14. The dilution of maintenance effect conferred by increasing milk production in a lactating dairy cow (Capper et al. 2009b). 40 Capper et al. (2009b) reported that the improved dairy productivity between 1944 and 2007 resulted in a 79% decrease in total animals (lactating and dry cows, heifers, mature and young bulls) required producing a set quantity of milk. Feed and water use were reduced by 77% and 65%, respectively, while cropland required for milk production in 2007 was reduced by 90% compared with 1944 . In contrast, Zehetmeier et al., (2011) found that increasing the milk yield from 6000 to 8000 kg/cow per year, the GHG emissions remained approximately constant. Whereas further increases in milk yield (10000 kg milk/cow per year) resulted in slightly higher (8%) total GHG emissions. Figure 15. Carbon footprint per cow and per Ib of milk for 1944 and 2007 US .dairy production systems (adapter from capper et al., 2009b). Similar results in beef production were reported by Capper (2010a) where the improvement in productivity allowed reducing the use of resources and the emission of greenhouse gases. In this regard, beef carcass yield per animal in USA increased over the past 30 yr from 274 kg in 1977 to 351 kg in 2007, which in combination with reduced time to slaughter over the same time period (606 d vs. 482 d), reduces resource use per unit of meat. Chapter 1 41 In addition, Crosson et al (2010) found that the improvement in animal performance (live weight gain; g/d) from 855 to 1047 (g/d) reduced by 50 % CO2e/kg beef carcass (figure 16). Figure 16. Implications of level of animal performance (live weight gain; g/d) on GHG emissions for Irish suckler beef production systems (from Crosson et al., 2010). 3. Intensification of Production The intensification of production is an additional strategy to maintain a balance between production and the environmental impacts. The FAO (2006) concluded that it is essential to continue with the process of intensification of animal production in order to provide food and reduce the environmental impacts of livestock production. These observations contrast with the growing public view that assumes extensive pasturebased systems are more appropriate in terms of their contribution to the production of greenhouse gas emissions. Impacts of intensification of dairy and beef production systems were also investigated by a number of authors, in many cases through comparison of organic and conventional production regimes. For example, in modelling Dutch dairy systems, Thomassen et al. (2008) found that conventional production systems had lower emissions/kg milk than organic production systems. Capper (2010b) showed that emissions are higher in traditional systems of meat production in the finishing phase on pasture (grazing system), intermediate in the 42 beef production of feedlot systems without the use of new technologies (natural or ecological systems), and lower in the feedlot systems using the technology available today (conventional systems) (Figure 17). Figure 17. The comparative carbon footprint of conventional, natural, and grass-fed beef (Capper, 2010b). In contrast, Haas et al. (2001) found no difference between organic and conventional intensive production systems. However, this latter study also found that conventional extensive production systems had lower emissions/kg milk than conventional intensive or organic production systems. This is supported by Basset-Mens et al. (2009b), who reported that increased production intensity in New Zealand production systems, in terms of output/ha, increased emissions/kg product. 4. Manure Management and Treatment Manure management options focus mainly on reduction of N2O and CH4 emissions by anaerobic digestion and manure treatment. 4.1. Manure Storage and Separation Chapter 1 43 Greenhouse gas emissions from stored manure are primarily in the form of CH4 (due to anaerobic conditions). Volatilization losses of NH3 are large and N2O emissions could also occur. One simple way to avoid cumulative GHG emissions is to reduce the time manure is stored (Philippe et al., 2007; Costa et al., 2012). Increasing the time of manure storage increases the period during which CH4 (and potentially N2O) is emitted, as well as the emission rate, creating a compound effect (Philippe et al., 2007). Storage treatments that provide aeration such as mechanical aeration or intermittent aeration have been shown to reduce CH4 emissions. Temperature is a critical factor regulating processes leading to NH3 and CH4 emissions from stored manure (Sommer et al., 2006). Decreasing manure temperature to < 10 °C, by removing the manure from the building and storing it outside in cold climates, can mitigate CH4 emissions (Monteny et al., 2006). 4.2. Anaerobic Digestion Anaerobic digestion is the process of degradation of organic materials by Archaea in the absence of oxygen, producing CH4, CO2, and other gases as by-products, and it is a promising practice for mitigating GHG emissions from collected manure. In addition, when correctly operated, anaerobic digesters are a source of renewable energy in the form of biogas, which is 60 to 80 percent CH4, depending on the substrate and operation conditions (Roos et al., 2004). Moreover, Dhingra et al. (2011) showed that anaerobic digesters reduce GHG emissions between 23 percent and 53 percent. Moreover, the digested manure (digestate) has a number of unique characteristics including a higher pH which could promote NH3 losses but it has little effect on the total nitrogen content of manure. A negligible amount of N may be emitted as NH3, lower DM content and viscosity which could reduce NH3 losses by infiltrating more rapidly into soil. Further, the digestate may contain relatively more NH4-N and less organic C resulting in a lower C: N ratio, all of which are properties that tend to increase the ratio of N2O:N2 produced by denitrification. Further the leakage of Nitrous oxide cannot be avoided which increases the contamination of ground water by nitrite. Digestate also contains less metabolizable organic C, which limits available C for soil microorganisms and decreases N2O emissions (VanderZaag et al, 2011). In addition the digestate (treated manure) has a higher fertilizer value (more inorganic N) than untreated manure and thus less N fertilizer is needed, which reduces N2O emissions. However, the 44 organic N in manure has residual effects after the year of application, which also needs to be accounted for (reported by Flysjö et al, 2011b). 4.3. Acidification Acidification is a means of mitigating NH3 losses because NH3 volatilization is pH dependent and decreases with acidity. The efficacy of this strategy has been documented for decades. For example, Stevens et al. (1992) found that acidifying cattle slurry with nitric acid to a pH of 6.5 decreased NH3 volatilization by 75% after surface application to a cut sward. Stevens et al. (1992) also reported synergistic effects by combining acidification with dilution and separation. Despite these positive results, farm adoption of acidification had been minimal for practical reasons (e.g., on farm handling of strong acids, manure foaming). Recently, Kai et al. (2008) reported a new acidification technology that makes this strategy feasible, and it has been approved as a ‘Best Available Technology’ in Denmark, as their results show that acidifying cattle slurry in the barn from pH 7.5 to 6.3 decreased NH3 loss by 67% when manure was band spread on winter wheat. In spite of these promising results, there has been no published research on effects of applying acidified manure on direct N2O emissions. Because acidification preserves more N in the manure and N2O production is favored at a low pH, acidification could cause an increase in direct N2O emissions. However, if the preserved manure N was used to reduce synthetic N use, and emissions associated with synthesizing N, then there could be a reduction in N2O emissions. However, much uncertainty remains about the effects of acidification on N2O emissions. 5. Use of Nitrification Inhibitors Several amendment options show potential to decrease N2O emissions from soil, such as nitrification inhibitors. Nitrification inhibitors are chemical compounds that retard the formation of nitrate (NO3−) from ammonium (NH4+) based fertilizers in soils, or from urine, thereby reducing the amount of nitrous oxide emissions (Di and Cameron, 2002). There are two main commercially available nitrification inhibitors to use at the farm level; nitrapyrin and dicyandiamide (DCD). These coating substances have been shown to be effective in reducing N2O emissions by approximately 80 % (de Klein et al., 2001). Nitrification inhibitors can also effectively reduce N2O emissions from animal urine by 61% – 91%, with Chapter 1 45 pasture yield increases of 0% – 36% (Di et al., 2007; Kelly et al., 2008; Smith et al., 2008). In this respect, VanderZaag et al., (2011) found that nitrapyrin decreased total denitrification losses by >50% from cattle slurry injected into grassland in winter, and DCD (dicyandiamide) reduced the denitrification rate in grassland receiving cattle slurry. Reduced denitrification probably decreased N2O flux, although N2O flux was not measured in that study. In another study where N2O flux was measured, DCD reduced N2O loss by 60% from surface applied cattle slurry on a poorly drained grassland in Spain (Merino et al., 2002). 6. Grazing Management Recent research has shown that restrictive grazing practices can reduce direct and indirect emissions of NO2 by up to 10 % (de Klein et al., 2006; Luo et al., 2008; Schils et al., 2006). In the referenced studies, animals were allowed to graze for 3-15h per day, and were kept off pasture either indoors or on a feed pad for the remaining of the day. Schils et al. (2006) reported that a combination of the reduced grazing time and fertilizer use in Netherland study farms reduced emissions by around 50 % when reported per unit of output scale, and around 10 % on a whole farm basis. The improved nitrogen utilization increased farm efficiency while reducing nitrogen losses and production was held constant. Luo et al. (2008) and de Klein et al. (2006) reported whole farm reductions in the level of emissions of 7-11 % for restricted grazing regimes, following subsequent land application of effluent collected when animals were kept on feed or stand-of pads compared with conventional grazing. 46 Conclusion Assessing the carbon footprint (CF) of agricultural products has gained a lot of attention in recent years. Conducting a CF assessment involves a number of methodological choices which have a significant impact on the final result. In some cases, it could be difficult to determine whether a difference in the CF of two dairy products is caused by ‘real’ differences in impacts or simply by discrepancies in CF methodology. This is a challenge for farmers that need robust methods to properly identify and analyze improvement options, but also for policy-makers and consumers who need robust science as a basis for their decisions on regulations and on purchases. To be able to address these challenges, it is pivotal to gain a better understanding of the relationship between methodological choices and CF results. In relation to dairy and beef products, some key methodological challenges are identified in the present thesis: estimating CH4, N2O and CO2 emissions, some simulation test of management and nutrition on the farm and the production system. There is no ‘silver bullet’ in the mitigation of GHG emissions for dairy and meat productsI Instead many improvements which individually show little changes may together result in significant reductions. The difference in the CF of milk and meat between relatively similar dairy and beef farms indicates that there is scope for reducing GHG emissions by improving management practices. Mitigation strategies need to take the individuality of farms into account. Slight improvement at the farm level can result in relatively large reductions in the CF of dairy and meat products, because emissions before farm gate constitute the main source of the GHG emissions. Finally, dairy and beef companies have an important role to play, representing the link between production and environment to encourage sustainable farming practices at the same time as promoting more sustainable environment. Even though the principal objective for a business is to make a profit, a strong engagement in the promotion of sustainable production (including mitigating climate change) is becoming more important for their image and therefore success especially in a long-term perspective. Chapter 2 47 CHAPTER 2: OBJECTIVES The general objective of this study was: 1. To quantify and analyze the greenhouse gas emissions and carbon footprint from typical Spanish dairy and beef farms. 2. To evaluate the diet contribution on enteric methane emissions. 3. To evaluate the feasibility of management scenarios to reduce methane emissions. 4. To evaluate the impact of dietary modifications on methane emissions. 54 Table 7. Characteristic of the simulated beef farms in Spain. Characteristic / Code farm 800 CAT 2400CYL 5000 ARA Province Catalonia Castilla -León Aragon Size of farm(Ha) 0 0 150 Number of animals 800 2400 5000 Number of cycle during the year 1 2 2 With or without corn silage Without corn silage Without corn silage With corn silage Breed Holstein Limousin Charlais Spanish (Cross) Limousin Charlais Spanish (Cross) Growth period (month) 15 9 9 12 9 9 12 Starting and end weight (kg) 120-450 250-650 250-650 45-450 250-650 250-650 45-450 Average daily weight gain (Kg) 1.57 1.6 1.6 1.4 1.6 1.6 1.4 Chapter 4 55 CHAPTER 4: RESULTS AND DISCUSSION 1. Gas Emissions by Spanish Dairy Farms The results of the greenhouse gas emissions modeling of dairy cattle farms from Mediterranean Area, Cantabric Area, Central Zone and other two farms (one organic and one from Baleares Island) performed by the IFSM are shown in tables 8, 9, 10 and 11, respectively. Gas emissions of an average Spanish dairy cow were 281.6, 4.5 and -3269 kg/cow/year for methane, nitrous oxide and the net emission of carbon dioxide including assimilation by land and feed production, respectively. Each kilogram of Spanish milk emits 0.83 kg of CO2e. Several studies have determined C footprint for dairy production. Capper et al., (2008) found that a cow in the United States with a milk production of about 9050 kg/cow/year has a carbon footprint of about 1.52 kg CO2e/kg of milk. Another study conducted in Canada by Verge et al. (2007) on cows with 9400 kg/cow/year of milk production has a carbon footprint of 0.98 kg of CO2e/kg of milk. In addition, Thomassen et al. (2008) reported a carbon footprint of 1.28 kg CO2e/kg of ECM in a Netherlands dairy farm with an annual production of 7991 kg/cow. It could be concluded from the previously reported results that Spain has lower C footprint than those reported in USA, Canada and the Netherlands. By region the C footprint of the selected dairy farms used in this study was shown in Figure 19. It could be concluded that the C footprint was the highest by Mediterranean Area farms with average value of 0.98 kg of CO2e/kg of ECM followed by Central zone farms with average value of 0.84 kg of CO2e/kg of ECM, while the lowest values were detected in Cantabric Area farms when the average value was 0.68 kg of CO2e/kg of ECM. Moreover, it is clear from the figure 20 that C footprint produced by organic farm was quiet high (0.89 kg of CO2e/kg of ECM).In addition Baleares Island farm (115BI) has a relatively low carbon footprint (0.67 kg of CO2e/kg of ECM). Similar results were obtained by Bellflower et al. (2012) in dairy farms with a carbon footprint varies from 0.79 to 0.87 kg of CO2e/kg of ECM. Other published study have assessed the greenhouse gas emissions from dairy production systems using the IFSM (Rotz et al., 2011) farms with average value of 0.37 kg of CO2e/kg of ECM. 56 The breakdown of total GHG emissions into component gases was examined in absolute terms and relative to each other for all farms (Figure 20). Methane emissions were the biggest share of GHG, accounting for more than 50% of the total emissions. The prediction of CH4 emission using the dairy IFSM was estimated to be the highest in Mediterranean Area; it ranged from 291.5 in the 440MA farm to 335.5 in the 197MA farm with an average of 328 kg/cow. About 70% of emissions were from enteric fermentation and manure. In the Central Zone, CH4 emissions ranged from 223.4 in the 400CZ farm to 334.8 in the 365CZ farm with an average of 273.3 kg/cow. In the Cantabric Area farms, methane emission ranged from 156.6 in the 64CA farm to 284.2 in the 240CA with an average of 243.3 kg/cow. Nitrous oxide emissions were relatively low in all farms, but considering a greater effect on global warming, these low levels have a larger effect on overall GHG emissions. Chapter 4 57 Table 8. Greenhouse gas (GHG) emissions from four representatives Mediterranean Area. ECM= ECM = Energy Corrected Milk with 3.5% fat and 3.1% protein concentrations. CO2e = CO2 equivalent. Mediterranean Area farms Greenhouse gas emission 197MA 106MA 376MA 440MA Ammonia (kg of NH3/cow) Animals and housing Manure storage Field-applied manure Total 87.3 10.9 25 123 34.6 18.5 84.7 138 85.4 16.4 118 120 82 16.1 21.2 119 Methane (kg of CH4/cow) Animals and housing Manure storage Field-applied manure Total 214 120 0.2 335 180 152 0.4 333 217 135 0.1 352 196 96 0.1 292 Nitrous oxide (kg of N2O/cow) Animals and housing Manure storage Cropland Total 3.6 0.0 4.8 8.4 1.8 0 2.4 4.2 3.6 0 2.4 6 3.3 0.8 3.1 7.3 Carbon dioxide (kg of CO2/cow) Animals and housing Manure storage Net Feed Production Fuel combustion Net Emission 6107 450 -10776 313 -3218 6161 361 -10600 169 -3700 6850 503 -10680 252 - 3327 6444 357 -10155 221 -3353 Total GHG (kg of CO2e) Animal emissions Manure emissions Feed production emissions Secondary sources 423374 351776 117890 325326 287653 181554 35518 212946 934701 840921 129715 711710 1035996 840220 203571 990553 Carbon footprint (kg of CO2e/kg of ECM) 1.09 0.84 1.06 0.93 58 Table 9. Greenhouse gas (GHG) emissions from four representative Cantabric Area farms. ECM= ECM = Energy Corrected Milk with 3.5% fat and 3.1% protein concentrations. CO2e = CO2 equivalent. Cantabric Area farms Greenhouse gas emission 170CA(L) 240CA 170CA(C) 64CA Ammonia (kg of NH3/cow) Animals and housing Manure storage Field-applied manure Grazing Total 2.1 12.3 10.4 50.5 75.3 48.5 52.8 11.6 0. 0 113 39 18 35 0 92 4 14 1 34 53 Methane (kg of CH4/cow) Animals and housing Manure storage Field-applied manure Grazing Total 34.4 28.1 0.1 196.4 259 194.3 89.8 0.1 0.0 284.2 187 90 0.2 0 278 30.8 14.2 0.0 125.4 171.6 Nitrous oxide (kg of N2O/cow) Animals and housing Manure storage Cropland Total 0.0 0.0 5.7 5.7 1.2 0.0 1 2.2 1.1 0.0 1.2 2.3 0.0 0.0 2.7 2.7 Carbon dioxide (kg of CO2/cow) Animals and housing Manure storage Net Feed Production Fuel combustion Net Emission 1014 109 -10301 239 -3635 6618 317 -10198 168 -3261 6721 321.1 -10274 186.1 -3232 751 50 -7431 60.5 -2576 Total GHG (kg of CO2e) Animal emissions Manure emissions Feed production emissions Secondary sources 579231 81097 171791 261419 527130 309848 31837 309567 385684 234341 29464 206836 161834 18596 34270 96734 Carbon footprint (kg of CO2e/kg of ECM) 0.67 0.73 0.75 0.59 Chapter 4 59 Table 10. Greenhouse gas (GHG) emissions from four representative Central Zone farms. ECM= ECM = Energy Corrected Milk with 3.5% fat and 3.1% protein concentrations. CO2e = CO2 equivalent. Central Zone farms Greenhouse gas emission 189CZ 312CZ 365CZ 400CZ Ammonia (kg of NH3/cow) Animals and housing Manure storage Field-applied manure Total 71.9 4.8 21.8 98.5 80.2 19.2 18.0 117.3 27.6 45.0 57.0 129.6 73.1 5.5 20.3 98.9 Methane (kg of CH4/cow) Animals and housing Manure storage Field-applied manure Total 204.8 32.8 0.1 237.7 198.0 99.3 0.2 297.5 168.2 166.0 0.6 334.8 178.5 44.8 0.1 223.4 Nitrous oxide (kg of N2O/cow) Animals and housing Manure storage Cropland Total 2.9 0.0 0.8 3.7 3.2 0.8 1.6 5.6 0.0 1.4 0.6 2.0 3 0.6 1 4.5 Carbon dioxide (kg of CO2/cow) Animals and housing Manure storage Net Feed Production Fuel combustion Net Emission 5903 137 -9291 89.3 -3250 6474 415.2 -10191 237 - 3302 6240 649 -10400 372 -3465 5806 131 -8843 119 -2905 Total GHG (kg of CO2e) Animal emissions Manure emissions Feed production emissions Secondary sources 535607 347872 4523 167418 899141 743003 89771 699179 917103 840654 35363 771649 1044694 5143399 0.0 607864 Carbon footprint (kg of CO2e/kg of ECM) 0.79 0.92 0.90 0.75 60 Table 11. Greenhouse gas (GHG) emissions from simulated other farms ECM= ECM = Energy Corrected Milk with 3.5% fat and 3.1% protein concentrations. CO2e = CO2 equivalent. Other farms Greenhouse gas emission 115BI 119OG Ammonia (kg of NH3/cow) Animals and housing Manure storage Field-applied manure Grazing Total 65.2 2.2 18.8 10.1 86.3 48.2 57.8 19.8 8.0 133 Methane (kg of CH4/cow) Animals and housing Manure storage Field-applied manure Grazing Total 172 14.2 0.2 10 196 171 27.1 0.2 25.5 224 Nitrous oxide (kg of N2O/cow) Animals and housing Manure storage Cropland Total 0.0 0.4 5.6 6.0 1.3 1.3 3.6 4.4 Carbon dioxide (kg of CO2/cow) Animals and housing Manure storage Net Feed Production Fuel combustion Net Emission 6274 80.6 -8874 244.8 -2600 5649 43.1 -8540 180 -2171 Total GHG (kg of CO2e) Animal emissions Manure emissions Feed production emissions Secondary sources 278220 27844 94534 212499 491549 33095 115822 0.0 Carbon footprint (kg of CO2e/kg of ECM) 0.67 0.89 Chapter 4 61 Figure 19. Carbon Footprint (Kg CO2e /kg ECM) of the selected dairy farms. 0 0,2 0,4 0,6 0,8 1 1,2 Carbon Footpint (Kg CO2e/ Kg ECM) 62 Figure 20. Annual emissions of important gases (kg/cow/year) in Mediterranean farms (197MA, 106MA, 376MA and 440MA), Cantabric (170CA, 240CA, 170CA and 64CA) farms, Central zone (400CZ, 365CZ, 312CZ and 189CZ) farms and to two other farms one organic and one from Baleares Island (OG and BI). 0 100 200 300 400 500 600 115BI 199OG 189CZ 312CZ 365CZ 400CZ 170CA 64CA 170CA 240CA 197MA 106MA 440MA 376MA Methane Ammonia Nitrous oxide Chapter 4 63 1.1.Diet Evaluation with the CNCPS Model and its Contribution to Enteric Methane Emissions. Table 12 shows the results of diet evaluation of each farm with the CNCPS model and its contribution to enteric methane emission per unit of milk produced. It could be concluded from this table that the average values of enteric methane emissions per Kg of milk were 12.5, 13.5 and 12.4 g/Kg milk by Mediterranean Area, Cantabric Area farms, Central zone and two other farms, respectively. Table 12. Diet contribution in methane production (Kg of milk) from the selected dairy farms. Code farm Number of milking cows Milk yield (kg /day) Enteric methane(g/kg milk) With CNCPS Mediterranean Area farms 197MA 106MA 376MA 440MA 82 59 180 220 32.0 36.5 34.5 33.0 12.3 11.2 13.1 13.3 Cantabric Area farms 170CA(C) 64CA 170CA(L) 240CA 85 42 102 112 36.5 28.5 36.0 40.0 12.2 16.2 13.4 12.4 Central Zone Farms 400CZ 365CZ 312CZ 189CZ 220 200 190 130 36.5 40.0 34.0 38.0 14.4 11.2 13.2 10.7 Other Farms 115BI 119OG 65 101 34.0 19.0 12.8 24.7 70 0 5 10 15 20 25 30 35 Improving Productivity Manure Type collection Bedding Type Aneorobic Digestor Storage Type of Manure Maximum reduction % of methane Maximum reduction % of methane Figure 27. Summary of the various changes in management on the methane emission of the 197MA farm. Data illustrated in Figure 28 indicate that the application of different management scenarios changes may have a large effect on methane emission reduction. The change in manure type collection (figure 24), anaerobic digestor (Figure 26) and storage type of manure (Figure 27) had the highest percent of methane emission reduction (30 %), while improving productivity and bedding type reduced methane emission from 1 to 10 %, respectively. According to Dhingra et al. (2011), which found that the use of an anaerobic digestor can reduce GHG emissions between 23 percent and 53 percent when compared with households without biogas, depending on the condition of the digester, technical assistance and operator ability. In addition, Sommer et al. (2009) simulated several manure management scenarios using data from four European countries and suggested that solids and liquid separation followed by incineration of the solids can reduce overall GHG emissions by 49% to as much as 82%. In the same context, Rotz et al., (2010) found that enclosing manure storage with a flare to burn the escaping biogas, almost eliminated CH4 emission from the storage, but CO2 emission increased. With the enclosed storage, the net result of this change was a 39% reduction in the net GHG emission and C footprint. Chapter 4 71 1.4. Dietary Change Scenarios to Mitigate Greenhouses Gases Emissions The CNCPS model was used to analyze four potential changes in management on the 64CAfarm to determine how these changes affected their enteric methane emission. The diet composition of the 64CA farm is presented in Table 13. Table 13. Ingredients diet of lactating cows. Composition Kg DM /day Forages Rye Grass Silage Grass Pasture 9.00 5.60 Concentrate Corn grain Canola Meal Soybean meal Wheat Ground Barley Corn Dist Solubles Wheat Bran Molasses Cane Sodium bicarbonate Calcium Carbonate Salt 1.53 1.15 1.4 0.87 0.72 0.2 0.58 0.14 0.04 0.07 0.04 Table 14. Diet evaluation and contribution en methane emission in the 64CA farm. 1.4.1. Modification of the Ratio Forage / Concentrate Results displayed in Table 15 show the change in methane production by increasing the proportion of concentrates in the diet by 10 %. It was found such increase in concentrate led to a reduction in methane production from 16.37 to 13.07 g/Kg milk and decreased total Intake DM (Kg/day/cow) 21.59 Number of lactating cows 42 Milk yield kg / cow / day 28.5 CH4 (g / kg milk) 16.37 Total methane production (kg / cow / year) 170 % Forage / concentrate 68/32 72 methane emission (cow/year) by 5%. Feeding grain tends to increase ruminal propionate while lowering acetate levels from microbial fermentation (Grainger and Beauchemin, 2011). Previous work indicated that methane emissions increase as rumen acetate levels increase. This agrees with by Benchaar et al. (2001) who replaced beet pulp with barley, decreasing methane emissions by 22%. Table 15. Effect of increasing the proportion of concentrates on the methane emission 1.4.2. Improved forages quality Data illustrated in table 16 shows effects of improving the nutritional value of Rye grass silage fed to lactating cows by harvesting at an earlier stage of physiological maturity on methane production. It was found that replacing Rye Grass Silage 1 (9 CP, 65 NDF and 8 LNDF) with Rye Grass Silage 2 (21 CP, 50 NDF and 7 LNDF) had a small effect on methane production (from 16.37 to 16.28 g/ Kg milk, respectively). A trial was conducted with lactating cows to evaluate methane production on two types of pasture (McCaughey et. al., 1999). An alfalfa-grass pasture (13% CP, 53% NDF) and a grass pasture (9% CP, 73% NDF) were used. Methane production was about 9% higher for cows on the grass pasture which is lower quality forage. Moreover, our results are in agreement with that reported by Boadi and Wittenberg (2002) which demonstrated that forage quality has a significant impact on enteric methane emissions. Cattle given hay of high (61.5 % IVOMD), medium (50.7% IVOMD) and low (38.5% IVOMD) quality differed (P < 0.01) enteric methane emissions (P < 0.01), as 47.8, 63.7 and 83.2 CH4 L/ kg digestible organic matter intake was produced from cattle consuming the high, medium and low quality forages, respectively. Table 16. Effect of improvement of the quality of ryegrass silage on methane emission % Forage / Concentrate 68/32 (control) 58/42 Milk yield kg / cow / day 28.5 31.2 CH4 (g / kg milk) 16.37 13.07 Total methane production (kg / cow / year) 170 148.84 Ryegrass silage Ryegrass silage farm Ryegrass silage improved Milk yield kg / cow / day 28.5 28.7 CH4 (g / kg milk) 16.37 16.28 Total methane production (kg / cow / year) 170 170.34 Chapter 4 73 1.4.3. The Inclusion of Fat in the Diet Another practice of interest is to supplement diets with fats, which has been shown to lower enteric CH4 production of dairy (Martin et al., 2008; Grainger et al., 2010) cattle. Several meta-analysis studies of numerous fat sources fed to sheep and cattle over a broad range of experimental conditions (Beauchemin et al., 2008; Eugène et al., 2008; Grainger and Beauchemin, 2011; Martin et al., 2010) showed that the inclusion of 40g fat/Kg dietary DM decline methane emission by an average 8–24%. In contrast, our study showed that including of linseed fat oil and canola fat oil in the diet of lactating cows lead to a higher methane emissions and milk production as compared with the control group (Table 17). Such result represents a critical point in this model as it is well accepted that increasing fat oil in the diet of lactating cows followed by reduction of methane gas emission. Table 17. Effect of fat sources on methane and milk production Fat type Control Linseed Fat Oil Canola Fat Oil Fat Amount (g) 0 200 500 200 500 CH4 (g / kg milk) 16.28 16.52 17.01 16.49 16.99 Milk yield (kg /day) 28.5 29.4 30.8 29.3 30.1 1.4.4. The Addition of Ionophore The results displayed in table 18 summarize the effect of addition of ionophore monensin on methane production. The addition of 300g/day monensin weakly reduced the methane emissions from 16.37 to 16.32 g/kg milk. This was in agreement with Appuhamy et al. (2013) that conducted a meta-analysis of literature data and showed that 32 mg/kg DM of monensin reduced CH4 emissions and CH4 conversion rate (Ym) in beef steers fed total mixed rations by 19 g/animal per d (P < 0.001) and 0.33 (P = 0.047), respectively. In dairy cows the reductions were 6 g/animal per day (P = 0.065) and 0.23 (P = 0.095) for monensin given at a dose of 21 mg/kg DM. In addition, Beauchemin et al. (2008) studied the effects of monensin on CH4 emissions and found evidence of a dose response with monensin at 24–35 mg/kg DM intake reducing CH4 emissions (as g/kg DM intake) by 3–8%. 74 Table 18. Effect of the addition of Rumensin 80 on methane production -2 -1 0 1 2 3 4 5 6 Modification of the Ratio Forage / Concentrate Improved forages quality Inclusion of fat in the diet Addition of the ionophore Rumensin 80 Maximum % reduction of methane Maximum % reduction of methane Figure 28. Summary of the various dietary changes on the methane emission of the 64CA farm. Ionophore Without monensin With monensin Milk yield kg / cow / day 28.5 28.6 CH4 (g / kg milk) 16.37 16.32 Total methane production (kg / cow / year) 170 170.36 Chapter 4 75 -5 0 5 10 15 20 25 30 35 Maximum reduction % of Methane Figure 29. Comparison of reduction potential of different management and dietary strategies in the two selected dairy farms (197MA and 64CA). From the aforementioned results in figure 29, it could be concluded that the dietary changes showed a weak reduction in methane emission. Of the four dietary changes, only those of modification of the ration forage / concentrate provided a substantial reduction in the methane emission (5 %). As comparing with changes in management scenarios, it could be recommended that management scenarios is more suitable to be used for methane emission reduction, as it provided a great reduction percent accordingly reduce C footprint with low cost. 2. Beef Farms 2.1. Gases Emissions per Kg of Body Weight Each Kg live weight emits 6.86 kg of CO2e in Spanish beef farms. Other studies have been carried out by other researchers regarding gas emissions from beef farms and reported that each Kg live weight emits 8.0 kg of CO2e in Australia (Peters et al., 2010), 8.7 kg CO2e per kg LW (Williams et al., 2006) in United Kingdom, 14.3 –18.3 kg CO2e per kg LW in France (Veysset et al., 2010), and 14.8 kg CO2e per kg LW in USA (Pelletier., 2010). It is 76 clear that Spanish beef farms have the lowest values of C footprint as compared with the results which previously reported in other countries. The data displayed in table 19 show GHG emissions from 800CAT feedlot farm. The total ammonia, methane, nitrous oxide, net carbon dioxide and carbon footprint were 21.3, 24.6, 0, -1426 Kg /head and 6.38 Kg CO2e / Kg BW, respectively. Chapter 4 77 Table 19. Greenhouse gas (GHG) emissions from 800CAT feedlot farm. BW= Body Weight sold; CO2e = CO2 equivalent. 800CAT farm Greenhouse gas emission Holstein Ammonia (kg of NH3/head) Animals and housing Manure storage Field-applied manure Total 5.9 15.4 0.0 21.3 Methane (kg of CH4/head) Animals and housing Manure storage Field-applied manure Total 24.4 0.2 0.0 24.6 Nitrous oxide (kg of N2O/head) Animals and housing Manure storage Total 0.0 0.0 0.0 Carbon dioxide (kg of CO2/head) Manure storage Fuel combustion Net emission 15 20 -1426 Total GHG (kg of CO2e) Animal emissions Manure emissions Feed production emissions Secondary sources 442842 50361 0.0 1196765 CF Without biogenic CO2 (kg/kg BW) 6.38 78 The data illustrated in table 20 display GHG emissions from 2400CYT feedlot farm without corn silage. The total ammonia, methane, nitrous oxide, net carbon dioxide and carbon footprint were 44, 52, 6, -1614 Kg/head and 7.03 Kg CO2e/Kg BW, respectively in Limousin breed, while they were 21.4, 24.8, 2.6, -1527 Kg/head and 6.90 Kg/ Kg BW, respectively in Charlais breed and 20.5, 25.2, 2.6, -1682 Kg /head and 7.01 Kg CO2e / Kg BW, respectively in Spanish cross-breeds. Table 20. Greenhouse gas (GHG) emissions from simulated 2400CYT feedlot farm without silage corn (Pasteros). BW= Body Weight sold; CO2e = CO2 equivalent. Data in Table 21 show GHG emissions from 5000ARA feedlot farm with silage corn. The total ammonia, methane, nitrous oxide, net carbon dioxide and carbon footprint were 7.9, 2400CYT farm (Without silage corn) Greenhouse gas emission Limousin Charlais Spanish (Cross) Ammonia (kg of NH3/head) Animals and housing Manure storage Field-applied manure Total 15 2 9 44 15.7 1.2 4.6 21.4 15.8 0.9 3.8 20.5 Methane (kg of CH4/head) Animals and housing Manure storage Field-applied manure Total 45 7 0 52 21.6 3.2 0.0 24.8 21.7 3.5 0.1 25.2 Nitrous oxide (kg of N2O/head) Animals and housing Manure storage Total 5 1 6 2.3 0.3 2.6 2.3 0.3 2.6 Carbon dioxide (kg of CO2/head) Manure storage Fuel combustion Net emission 0 39 -1614 0.0 36.9 -1527 0.0 39.5 -1682 Total GHG (kg of CO2e) Animal emissions Manure emissions Feed production emissions Secondary sources 394373 67259 0 1138564 392836 48154 0 1115980 524810 83098 0 1513609 CF Without biogenic CO2 (kg/kg BW) 7.03 6.90 7.01 Chapter 4 79 12.9, 1.1, -900 Kg/head and 6.99 CO2e Kg/Kg BW, respectively in Limousin breed, while they were 7.9, 12.8, 1, -796 Kg/head and 6.77 Kg CO2e /Kg BW, respectively in Charlais breed and 7.3, 12.9, 1.1, -935 Kg/head and 6.95 Kg CO2e/ Kg BW, respectively in Spanish (Cross). Table 21. Greenhouse gas (GHG) emissions from simulated 5000ARA feedlot farm with silage corn (Pasteros) BW= Body Weight sold; CO2e = CO2 equivalent. 5000ARA farm (With silage corn) Greenhouse gas emission Limousin Charlais Spanish (Cross) Ammonia (kg of NH3/head) Animals and housing Manure storage Field-applied manure Total 4.6 1.9 1.5 7.9 4.6 1.9 1.5 7.9 4.6 1.6 1.1 7.3 Methane (kg of CH4/head) Animals and housing Manure storage Field-applied manure Total 11.6 1.3 0.0 12.9 11.6 1.1 0.0 12.8 11.6 1.3 0.0 12.9 Nitrous oxide (kg of N2O/head) Animals and housing Manure storage Total 1 0.1 1.1 0.9 0.1 1.0 1.0 0.1 1.1 Carbon dioxide (kg of CO2/head) Manure storage Fuel combustion Net emission 20 80.1 -900 20.2 80.0 -796 20 79 -935 Total GHG (kg of CO2e) Animal emissions Manure emissions Feed production emissions Secondary sources 399998 63410 2526 788391 400128 66072 2526 788391 533158 45700 2526 788391 CF Without biogenic CO2 (kg/kg BW) 6.99 6.77 6.95 86 Climate Analysis Indicators Tool., 2009. CAIT Version 7.0. World Resources Institute, Washington, DC, USA, www.cait.wri.org/. Coglianese, G., 2001.Social Movements, law and Society: The institutionalization of the environmental movement. University of Pennsylvania, Law Review, 150, 85,118. Colmenero, O.J.J., Broderick, G.A., 2006. Effect of dietary crude protein concentration on milk production and nitrogen utilization in lactating dairy cows. J.Dairy Sci.89,17041712. Costa, A., Chiarello, G.L., Selli, E. & Guarino, M. 2012. Effects of TiO2 based photocatalytic paint on concentrations and emissions of pollutants and on animal performance in a swine weaning unit. J. Environ. Manage. 96: 86–90. Croney C. C, Apley M, Capper J. L, Mench J. A and Priest S., 2012. BIOETHICS SYMPOSIUM: The ethical food movement: What does it mean for the role of science and scientists in current debates about animal agriculture? J ANIM SCI, 90:1570-1582. Crosson, P., Foley, P.A., Shalloo, L., O’Brien, D., Kenny, D.A., 2010. Greenhouse gas emissions from Irish beef and diary production systems. Advances in animal biosciences. Food, feed, energy and fibre from land – a vision for 2020. In: Proceedings of the British Society of Animal Science and the Agricultural Research Forum ,. Cambridge University Press, Cambridge, UK, p. 350. Crossona P , Shalloob L, O’Brienb D , Laniganc G.J, Foleyd P.A, Bolandd T.M , Kennya D.A., 2011. A review of whole farm systems models of greenhouse gas emissions from beef and dairy cattle production systems. Animal Feed Science and Technology 166– 167, 29– 45 Della Porta, D., M., Diani, Social movements: An introduction, Blackwell Publishing , 2006, p. 1. Del Prado, A., Scholefield, D., Chadwick, D., Misselbrook, T., Haygarth, P., Hopkins, A., Dewhurst, R., Davison, P., Lord, E., Turner, M., Aikman, P., Schröder, J., 2006. A References 87 modeling framework to identify new integrated dairy production systems. EGF: 21st General Meeting on “Sustainable Grassland Productivity”, Badajoz, Spain. 3–6 April. De Klein C.A.M. and Eckard R.J., 2008. Targeted Technologies for nitrous oxide abatement from Animal Agriculture. Special Edition: Proceedings of the 3rd Greenhouse Gases and Animal Agriculture Conference. Australian Journal of Experimental Agriculture 48 (2) 14-20. doi: 10.1071/EA07217. De Klein, C.A.M., Sherlock, R.R., Cameron, K.C., van der Weerden, T.J., 2001. Nitrous oxide emissions from agricultural soils in New Zealand—a review of current knowledge and directions for future research. J. N.Z. R. Soc. 31, 543–574. De Klein, C.A.M., Smith, L.C., Monaghan, R.M., 2006. Restricted autumn grazing to reduce nitrous oxide emissions from dairy pastures in Southland, New Zealand. Agric. Ecosyst. Environ. 112, 192–199. Desnoyers, M., Giger-Reverdin, S., Bertin, G., Duvaux-Ponter, C., Sauvant, D., 2009. Meta analysis of the influence of Saccharomyces cerevisiae supplementation on ruminal parameters and milk production of ruminants. J. Dairy Sci. 92, 1620–1632. Dhingra, R., Christensen, E., Liu, Y., Zhong, B., Fu, C., Yost, M. & Remains, J. 2011. Greenhouse gas emission reductions from domestic a naerobic digesters linked with sustainable sanitation in rural China. Environ. Sci. Technol. 45: 2345–2352. Di, H.J., Cameron, K.C., 2002. The use of a nitrification inhibitor, dicyandiamide (DCD), to decrease nitrate leaching and nitrous oxide emissions in simulated grazed and irrigated grassland. Soil Use Manage 18, 395–403. Di, H.J., Cameron, K.C., Sherlock, R.R., 2007. Comparison of the effectiveness of a nitrification inhibitor, dicyandiamide, in reducing nitrous oxide emissions in four different soils under different climatic and management conditions. Soil Use Manage 23, 1–9. 88 Dillon P (2006) Achieving high dry-matter intake from pasture with grazing dairy cows. In: Elgersma A, Dijkstra J, Tamminga S, editors. Fresh Herbage for Dairy Cattle, the Key to a Sustainable Food Chain. Wageningen UR Frontis Series Volume 18: Springer, Dordrecht, the Netherlands. pp. 1-26. Dijkstra, J., France, J., Assis, A.J., Neal, H.D.S.C., Campos, O.F., Aroeira, L.J.M., 1996.Simulation of digestion in cattle fed sugar cane:prediction of nutrient supply for milk production with locally available supplements.J.Agric.Sci.127,247-260. Duffield, T.F., Rabiee, A.R., Lean, I.J., 2008. A meta-analysis of the impact of monensin in lactating dairy cattle, Part 2. Production effects. J. Dairy Sci. 91, 1347–1360. Ellis JL, Kebreab E, Odongo NE, McBride BW, Okine EK, France J., 2007. Prediction of methane production from dairy and beef cattle. Journal of Dairy Science 90, 34563467. EPA. 2007. Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2005. Annex 3: Methodological Descriptions for Additional Source or Sink Categories. US EPA, Washington, DC, USA. Evans J.D , Martin S.A., 2000. Effects of thymol on ruminal microorganisms. Curr. Microbiol., 41, pp. 336–340. Eugène M., Benchaar C., Chiquette J., Massé D., 2008. Meta-analysis on the effects of lipid supplementation on methane production of lactating dairy cows. Can. J. Anim. Sci., 88, 331-337. Fabbri, C., Valli, L., Guarino, M., Costa, A., Mazzotta, V., 2007. Ammonia, methane, nitrous oxide and particulate matter emissions from two different buildings for laying hens. Biosyst. Eng. 97, 441–455 FAO, 2010. Greenhouse gas emissions from the dairy sector: A life cycle assessment. References 89 Flysjö A, Henriksson M, Cederberg C, Ledgard S and Englund J-E., 2011b. The impact of various parameters on the carbon footprint of milk production in New Zealand and Sweden. Agricultural Systems. 104:459-469. Foley, P.A., Crosson, P., Lovett, D.K., Boland, T.M., O’Mara, F.P., Kenny, D.A., 2011. A systems approach to quantifying greenhouse gas emissions from pastoral suckler beef production systems. Agriculture, Ecosystems & Environment. Foskolos A., 2012.Strategies to Reduce Nitrogen Excretion from Ruminates: Targeting the rumen. PhD thesis. Autonomous University of Barcelona. Fox, D.G, L.O. Tedeschi, T.P. Tylutki, J.B. Russell, M.E. Van Amburgh, L.E. Chase, A.N. Pell, and T.R. Overton., 2004. The Cornell net carbohydrate and protein system model for evaluating herd nutrition and nutrient excretion. Anim. Feed Sci. Technol. 112:2978. Franzluebbers, A., Follett, R., 2005. Greenhouse gas contributions and mitigation potential in agricultural regions of North America: introduction. Soil Tillage Res. 83 (1), 1–8. Gerber, P., Vellinga, T., Opio, C., Henderson, B., Steinfeld, H., 2010 a. Greenhouse gas emissions from the dairy sector. A life cycle assessment. Food and Agricultural Organization of the United Nations: Animal Production and Health Division. Viale delle Terme di Caracalla, Rome, Italy. Giger-Reverdin S., Morand-Fehr P., Tran G., 2003. Literature survey of the influence of dietary fat composition on methane production in dairy cattle. Livest. Prod. Sci., 82, 7379. Grainger. C ,. Beauchemin. K.A ., 2011 .Can enteric methane emissions from ruminants be lowered without lowering their production?. Anim. Feed Sci. Technol. 166, 308–320. Grainger, C., Clarke, T., Auldist, M.J., Beauchemin, K.A., McGinn, S.M., Waghorn, G.C., Eckard, R.J., 2009. Mitigation of greenhouse gas emissions from dairy cows fed pasture 90 and grain through supplementation with Acacia mearnsii tannins. Can. J. Anim. Sci. 89 (2), 241–251. Grainger, C., Williams, R., Eckard, R.J., Hannah, M.C., 2010b. A high dose of monensin does not reduce methane emissions of dairy cows offered pasture supplemented with grain. J. Dairy Sci. 93, 5300–5308. Grubb, M., Brack, D. and Vrolijk, C., The Kyoto Protocol. A guide and assessment, Earthscan, London, 1998, p. 3. Haas, G., Wetterich, F., Kopke, U., 2001. Comparing intensive, extensified and organic grassland farming in southern Germany by process life cycle assessment. Agric. Ecosyst. Environ. 83, 43–53. Henriksson M, Flysjö A, Cederberg C and Swensson C. 2011. Variation in carbon footprint of milk due to management differences between Swedish dairy farms. Animal. 5:14741484. Hess HD, Monsalve LM, Lascano CE, Carulla JE, Diz TE, Kreuzer M (2003). Supplementation of a tropical grass diet with forage legumes and Sapindus saponaria fruits: effects on in vitro ruminal nitrogen turnover and methanogenesis. Aust. J. Agric. Res. 54 (7), 703-713. Hristov, A.N., Huhtanen, p., 2008.Nitrogen efficiency in Holstein cows and dietry means to mitigate nitrogen losses from dairy operations. Proc. Cornell. Nutri. Conf. Feed Manuf., Cornell, pp.125-135. Hu W, Wu Y, Liu J, Guo J and Ye J. 2005. Tea saponins affect in vitro fermentation and methanogenesis in faunated and defaunated rumen fluid. J Zhejiang Univ Sci 787–792. Ida M. L. D. Storm, Anne Louise F. Hellwing, Nicolaj I. Nielsen and Jørgen Madsen., 2012. Methods for Measuring and Estimating Methane Emission from Ruminants. Animals. 2, 160-183; doi: 10.3390/ani2020160. References 91 IPCC. 2007. The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC Secretariat, Geneva. IPCC, 1997a. Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories Workbook, vol. 2. Cambridge University Press, Cambridge, UK. IPCC, 1997b. Expert Group Meeting on Methods for the Assessment of Inventory Quality. Meeting Report. Bilthoven, the Netherlands (5–7 November 1997). IPCC, 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Prepared by the National Greenhouse Gas Inventories Programme. Japan: IGES. IPCC, 2007a. The Climate Change 2007 Synthesis Report. In: Pachauri, R.K., Reisinger, A. (Eds.), Contribution of Working Groups I, II and III to the Forth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, Geneva, Switzerland. IPCC, 2001. Climate change 2001: The scientific basis. Contribution of Working Group I to the third assessment report of the Intergovernmental Panel on Climate Change. J.T. Houghton, Y. Ding, D.J. Griggs, M. Noguer, P.J. van der Linden, X. Dai, K. Maskell, and C.A. Johnson, eds. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. ISO. 2006a. Environmental management – Life cycle assessment – Principles and framework. ISO 14040: 2006(E). International Organization for Standardization. Geneva. Switzerland Johnson, K. A. and Johnson, D. E., 1995. Methane emissions from cattle. J. Anim. Sci. 73: 2483–2492. Johnson, R.R. 1976. Influence of carbohydrate solubility on nonprotein nitrogen utilization in the ruminant. Journal of Animal Science 43:184-191. 92 Jouany JP and Morgavi DP., 2007. Use of ‘natural’ products as alternatives to antibiotic feed additives in ruminant production. Animal 1, 1443–1466. Kai, P., Pedersen, P., Jensen, J., Hansen, M., Sommer, S., 2008. A whole-farm assessment of the efficacy of slurry acidification in reducing ammonia emissions. Eur. J. Agron. 28, 148–154. Kelly, K.B., Phillips, F.A., Baigent, R., 2008. Impact of dicyandiamide application on nitrous oxide emissions from urine patches in northern Victoria. Aust. J. Exp. Agric. 48, 156– 159. doi:10.1071/EA07251. Kim, S. and Dale, B.E. 2005. Life cycle assessment of various cropping systems utilized for producing biofuels: Bioethanol and biodiesel. Biomass and Bioenergy 29, 42-439. Klevenhusen F., Zeitz J.O., Duval S., Kreuzer M., Soliva C.R., 2011. Garlic oil and its principal component diallyl disulfide fail to mitigate methane, but improve digestibility in sheep. Anim. Feed Sci. Technol., 166-167, 356-363. Kreuzer M, Hinderichsen IK., 2006. Methane mitigation in ruminants by dietary means: the role of their methane emission from manure. International Congress Series 1293, 199208. Kulessa, M.E., 2007. Setting Efficient EU Climate Policy Targets: Mission Possible? Forum “The Climate Policy of the European Union”, Intereconomics, p. 65 Kumar, R., Kamra, DN., Agarwal N., and Chaudhary, L C., 2007 . In vitro methanogenesis and fermentation of feeds containing oil seed cakes with rumen liquor of buffalo. AsianAust. J. Anim. Sci., 20, pp 1196-1200. Lassey KR., 2007. Livestock methane emission: From the individual grazing animal through national inventories to the global methane cycle. Agricultural and Forest Meteorology 142, 120-132. References 93 Lee, C., Hirostov, A.N., Dell, C.J., Feyreisen, G.W., Kaye, J., Beegle. D., 2012. Effect of dietary protein concentration on ammonia and greenhouse gas emitting potential of dairy manure. J. Dairy Sci.95, 1930-1941. Lila ZA, Mohammed N, Kanda S, Kamada T and Itabashi H., 2003. Effect of sarsaponin on ruminal fermentation with particular reference to methane production in vitro. Journal of Dairy Science 86, 3330–3336. Little, S., Linderman, J., Maclean, K., Janzen, H., 2008. HOLOS – a tool to estimate and reduce greenhouse gases from farms. Methodology and algorithms for versions 1.1.x. Agriculture and Agri-Food Canada, Cat. No. A52-136/2008E-PDF, 158 pp. Luo, J., Ledgard, S.F., Lindsey, S.B., 2008. A test of a winter farm management option for mitigating nitrous oxide emissions from a dairy farm. Soil Use Manage. 24, 121–130. Macheboeuf D., Morgavi D.P., Papon Y., Mousset J.-L., Arturo-Schaan M., 2008. Doseresponse effects of essential oils on in vitro fermentation activity of the rumen microbial population. Anim. Feed Sci. Tech. 145, 335-350 MARM, 2009. Anuario de Estadística 2008. Ministerio de Medio Ambiente y Medio Rural y Marino. Secretaría General Técnica, Subdirección General de Estadística. Madrid, Spain, p. 1150. MARM, 2011. Anuario de Estadística 2010. Ministerio de Medio Ambiente y Medio Rural y Marino.http://www.magrama.gob.es/estadistica/pags/anuario/2010/ae_2010_avance. Martin C., Ferlay A., Chilliard Y., Doreau M., 2009. Decrease in methane emissions in dairy cows with dietary linseed content. Proc. Ann. Meet. Brit. Soc. Anim. Sci., Southport, Royaume-Uni, 21. Martin C., Morgavi D.P., Doreau M., 2010. Methane mitigation in ruminants: from microbe to the farm scale. Animal, 4, 351-365. 94 McGeough, E.J., O’Kiely, P., Foley, P.A., Hart, K.J., Boland, T.M., Kenny, D.A., 2010a. Methane emissions, feed intake, and performance of finishing beef cattle offered maize silages harvested at 4 different stages of maturity. J. Anim. Sci. 88, 1479–1491. McGinn SM, Beauchemin KA, Coates T, Colombatto D., 2004. Methane emissions from beef cattle: effects of monensin, sunflower oil, enzymes, yeast, and fumaric acid. J Anim Sci 82:3346–3356. McGinn SM, Flesch TK, Crenna BP, Beauchemin KA, Coates T (2007) Quantifying ammonia emissions from a cattle feedlot using a dispersion model. Journal of Environmental Quality 36, 15851590. Merino, P., Estavillo, J., Graciolli, L., Pinto, M., Lacuesta, M., Mu˜noz-Rueda, A., Gonzalez Murua, C., 2002. Mitigation of N2O emissions from grassland by nitrification inhibitor and Actilith F2 applied with fertilizer and cattle slurry. Soil Use Manage. 18, 135–141 Mills, J. A. N., E. Kebreab, C. M. Yates, L. A. Crompton, S. B. Cammell, M. S. Dhanoa, R. E. Agnew, and J. France. 2003. Alternative approaches to predicting methane emissions from dairy cows. J. of Animal Sci 81:3141-3150. Misselbrook K,T.H.,Powell,J.M.,Broderick,G.A.,and Grabber,J.H., 2005.Dietry manipulation in dairy cattle ;Laboratory Experiments to assess the influence on ammonia emissions .Journal of dairy science,88(5),1765-1777. Mohammed N , Ajisaka N, Lila Z.A, Mikuni K, Hara K , Kanda S , Itabashi H., 2004 .Effect of Japanese horseradish oil on methane production and ruminal fermentation in vitro and in steers. J. Anim. Sci., 82, pp. 1839–1846. Monteny, G.J., Bannink, A. & Chadwick, D. 2006. Greenhouse gas abatement strategies for animal husbandry. Agric. Ecosyst. Environ. 112: 163–170. Morgavi, D.P., Forano, E., Martin C. and Newblod, C.J. 2010a.Microbial ecosystem and methanogenesis in rumiants.Animal, 4:1024-1036. References 95 Moss, A.R., Jounay, J.P., Newbold, J., 2000. Methane production by ruminants: its contribution to global warming. Ann. Zootech. 49, 231–253. Newbold, C.J., Rode, L.M., 2006. Dietary additives to control methanogenesis in the rumen. In: Soliva, C.R., Takahashi, J., Kreuzer, M. (Eds.), Greenhouse Gases and Animal Agriculture: An Update. Elsevier International Conference Series 1293. Elsevier, Amsterdam, The Netherlands, pp. 138–147. Oenema, O., Velthof, G., Kuikman, P., 2001. Technical and policy aspects of strategies to decrease greenhouse gas emissions from agriculture. Nutr. Cycl. Agroecosyst. 60, 301– 315. Olesen, J. E., K. Schelde, A. Weiske, M. R. Weisbjerg, W. A. H. Asman, and J. Djurhuus. 2006. Modelling greenhouse gas emissions from European conventional and organic dairy farms. Agric. Ecosys. Environ. 112: 207220. Ominski, K.H., Wittenberg, K.M., and Boadi, D., 2004. Examination of economically and environmentally sustainable management practices in forage-based beef production systems, presented at the CCFIA Final Workshop, Winnipeg. Okine, E. K., Basarab, J. A., Baron, V. and Price, M. A. 2002. Methane and manure production in cattle with different net feed intake. J. Anim. Sci. 80 (Suppl. 1): 206 . Pen B,Sar C,Mwenya B,Kuwaki K,Morikawa R,Takahashi J., 2006. Effects of Yucca schidigera and Quillaja saponaria extracts on in vitro ruminal fermentation and methane emission.Animal Feed Science and Technology 129,175-186. Petersen, S.O. and Sommer, S.G. 2011. Ammonia and nitrous oxide interactions: Roles of manure organic matter management. Anim. Feed Sci. Technol. 166–167: 503–513. Peters, Gregory M., Rowley, Hazel V., Wiedemann, Stephen, Tucker, Robyn, Short, Michael D., Schulz, Matthias., 2010. Red meat production in Australia: life cycle assessment and comparison with overseas studies. Environ. Sci. Technol. 44, 1327–1332. 102 Woodward, S.L., Waghorn, G.C., Laboyrie, P., 2004. Condensed tannins in birdsfoot trefoil (Lotus corniculatus) reduced methane emissions from dairy cows. Proc. N.Z. Soc. Anim. Prod. 64, 160–164. Zehetmeier M, Baudracco J, Hoffmann H and Heißenhuber A., 2011. Does increasing milk yield per cow reduce greenhouse gas emissions? A system approach. animal , pp 154166. Appendices 103 APPENDICES Survey of dairy cattle farms Encuesta de explotaciones de ganado bovino lechero Objetivo Estimar la producción total de gases de efecto invernadero en explotaciones de diversas áreas geográficas españolas y sistemas de producción Nombre …………………………………………….. E-mail ……………………………………………… Número de teléfono ……………………………………………… Provincia …………………………………………….. Superficie total de la granja …………………………………………..Ha Tamaño del rebaño bovino (total bovino lechero) ………………………………………....cabeza Datos sobre los cultivos y el suelo Tipo de suelo ………………………………………………. Topografía de la granja Casi el nivel (A, 0-3% pendiente)  Pendiente suave (B, 3-8%)  En pendiente (C, 8-15%)  Moderadamente empinada (D, 15-25%)  Empinado (E o F, > 25%)  Nivel de fósforo en el suelo Bajo  Optimo  Alto  Muy alto  Excesivamente alto  Área de cultivo Alfalfa  Área de alfalfa  Aplicación de fertilizantes  Tipo de fertilizantes  Cantidad  Fertilización con estiércol ……………………………………………………….Ha Si  No  ………………………………………………..…Kg/Ha Si  No  ……………………………………………………………… ………….% 104  Porcentaje aplicada de estiércol que está disponible Pasto  Área de pasto  Aplicación de fertilizantes  Tipo de fertilizantes  Cantidad  Fertilización con estiércol  Porcentaje aplicada de estiércol que está disponible Maíz  Área de Maíz  Aplicación de fertilizantes  Tipo de fertilizantes  Cantidad  Fertilización con estiércol  Porcentaje aplicada de estiércol que está disponible Cereales de grano pequeño Cebada, Avena, Trigo, Centeno  Área de cultivos  Aplicación de fertilizantes  Tipo de fertilizantes  Cantidad  Fertilización con estiércol  Porcentaje aplicada de estiércol que está disponible ……………………………………………………….Ha Si  No  ………………………………………………..… …………………………………………………… Kg/Ha Si  No  ……………………………………………………………… ………….% ……………………………………………………….Ha Si  No  ………………………………………………………… ………………………………………………..…Kg/Ha Si  No  ……………………………………………………………… ………….% ……………………………………………………….Ha Si  No  ………………………………………………………… ………………………………………………..…Kg/Ha Si  No  ……………………………………………………………… ………….% Datos sobre la maquinaria (debe indicarse también si la maquinaria es alquilada) Operación Número de máquinas Cosecha / alimentación Siega Rastrillo ………………………………………………. ………………………………………………. Appendices 105 Empacar Secado de los forrajes Cosecha Mezcladora de alimentos Laboreo / plantación Manejo del estiércol Labranza Disqueo Aireación Sembradora Pulverización Riego Diversos Tractores de transporte Suministro / cargadores de estiércol Cargadores de balas Bomba de estiércol / agitador Bomba auxiliar de estiércol Número de remolques de transporte Número total de tractores ………………………………………………. ………………………………………………. ………………………………………………. ………………………………………………. ……………………………………………… ………………………………………………. ………………………………………………. ……………………………………………… ……………………………………………… ……………………………………………… ………………………………………………. ………………………………………………. ……………………………………………… ………………………………………………. ……………………………………………… ……………………………………………… ……………………………………………… ……………………………………………... ………………………………………………. ……………………………………………….. ……………………………………………… Datos sobre el pastoreo Área de pastoreo …………………………………………Ha Área de pastoreo en primavera Área de pastoreo en verano Área de pastoreo en otoño …………………………………………Ha …………………………………………Ha …………………………………………Ha Animales pastaban Novillas  Novillas y vacas secas  Vacas secas  Vacas en lactancia  Todas las vacas  Todos los animales  Tiempo en el pasto Cuarta parte del día durante temporada de pastoreo  Medios días durante la temporada de pastoreo  Días completos durante la temporada de pastoreo  Días completos durante todo el año  Datos sobre el almacenamiento de alimentos Estructura de almacenamiento; Silos Forraje de alta calidad No hay almacenamiento  Silo sobre el suelo  Silo bunker-trinchera  Silo “salsicha”  Silo en bolas  106 Capacidad del silo Número de silo Forraje de baja calidad Capacidad del silo Número de silo Grano de cultivos ensilado Capacidad del silo Número de silo Grano de alta humedad Capacidad del silo Número de silo ……………………………………………Toneladas …………………………………………………….. No hay almacenamiento  Silo sobre el suelo  Silo bunker-trinchera  Silo “salsicha”  Silo en bolas  ……………………………………………Toneladas …………………………………………… No hay almacenamiento  Silo sobre el suelo  Silo bunker-trinchera  Silo “salsicha”  Silo en bolas  ……………………………………………Toneladas …………………………………………… No hay almacenamiento  Silo sobre el suelo  Silo bunker-trinchera  Silo “salsicha”  Silo en bolas  ……………………………………………Toneladas …………………………………………… Heno seco Cubierto en cobertizo  Exterior, en pìlas  Exterior, (sin tocar a suelo) cubierto con plástico  Exterior, (sin tocar a suelo) sin cobertura  Exterior, contacto con el suelo, sin cobertura  Tratamientos de conservación  Secado del heno de alta humedad  Preservación del heno Ninguno  Secado al aire en pequeñas pacas rectangulares  Deshidratado artificial en pequeñas pacas rectangular  Secado al aire en grandes pacas redondas  Deshidratado en grandes pacas redondas/cuadradas  Acido propiónico / otras soluciones de ácidos orgánicos  Buffer / solución diluida de ácido  Microbiano  Inoculante  Appendices 107  Tratamiento del ensilaje  Cultivo de cereales Otros Conservantes  Acido fórmico (corte directo del ensilaje)  Inoculante bacteriano (inactivo)  Aditivo enzimático (inactivo)  Ningún  Amoníaco anhidro  Datos sobre el ganado de vacuno lechero y el alimentación Raza Holstein  Brown swiss  Ayrshire  Guernsey  Jersey  Otros  Producción lechera anual ……………………………………. Litros / vaca Número de animales en lactación ………………………………………… Porcentaje de animales de primera lactación ………………………………………… Terneras de reposición de más de un año ………………………………………… Terneras de reposición de menos de un año ………………………………………… Características de los animales Peso corporal medio de vacas maduras…………….Kg Contenido medio de grasa en la leche……………. % Contenido medio de proteína en la leche…………..% Estrategia de parto Todo el año  Partos en primavera  Partos en otoño  Tipo de sala de ordeño …………………………………………………… Alojamiento de las vacas Ninguno  Lote seco  Estabulación fija  Estabulación libre con cama caliente  Estabulación libre con cubículos  Estabulación con ventilación natural  Estabulación con ventilación mecánica  Estabulación libre con suelo de de bajas emisiones  Alojamiento de las novillas Ninguno  Estabulación fija  Estabulación libre con cama caliente  Estabulación libre con cubículos  108 Estabulación libre, con ventilación natural  Estabulación libre, con ventilación mecánica  Estabulación libre con suelo de de bajas emisiones  Manejo de la alimentación Método de alimentación Grano No alimentados con granos  Alimentación manual  Cargador y el carro mezclador  Mezcladora estacionaria y transportador  Alimentación individual computarizado  Ensilaje No alimentados con ensilaje  Alimentación manual  Cargador y el carro mezclador  Mezcladora estacionaria y transportador  Alimentación individual computarizado  Heno No alimentados con heno  Alimentación manual  Auto-alimentado en el alimentador del heno  Pacas a moler  Composición de la dieta Porcentaje mínima de heno seco en la dieta de la vaca Nivel de alimentación en proteína Nivel de alimentación en fósforo …………………………% forraje …………………………% de recomendación de NRC …………………………% de recomendación de NRC Ratio: forraje / grano Alto  Bajo  Suplemento de proteína cruda Ninguno  Harina de colza  Gluten de maíz  Semilla de algodón  Harina de soja 44%  Harina de soja 48%  Urea  Otros (indicar)  Suplemento energético Concentrado restringido  Cereal  Grasa animal / vegetal  Otros;  Appendices 109 Datos sobre el manejo de estiércoles Método de recogida de estiércol Sin estiércol recogido  Raspado manual con canalones de limpieza  Rascador con rampa de carga  Raspador con bomba de estiércol  Sistema de descarga  Tipo de estiércol Sólido (20% MS)  Semi-sólido (12-14% MS)  Estiércol (8-10% MS)  Estiércollíquido (5-7% MS)  Incorporación de estiércol al suelo respecto a la labranza Mismo día  Dentro de dos días  Dentro de una semana  No incorporación  Distancia media de transporte de estiércol ……………………………………….. Km Almacenamiento No hay almacenamiento  4 meses de almacenamiento  6 meses de almacenamiento  12 meses de almacenamiento  Tipo del almacenamiento Apilar  Cuenca perforada en la tierra  Tanque de acero de baja carga  Tanque de cemento  Tanque o cuenco cubierto  Tanque cerrado  Características Diámetro medio………………………………. M Profundidad media ……………………………..m Capacidad de almacenamiento ………………….t Digestor anaeróbico Si  No  Tipo de cama Ninguno  Estiércol sólido  Arena  Aserrín  Paja  Paja picada  Cantidad de cama por animal maduro …………………………………………………/Kg/Día Importación / exportación Cantidad importada a la granja Tipo de estiércol importado ………………………………………….T Bovino  Aves  110 Cantidad exportada de granja Forma de estiércol Porcino  Otro  ……………………… % de estiércol sólidos recogidos Estiércol crudo  Sólidos separados  Compost  Además, necesitaríamos las dietas de todos los grupos animales (lotes de producción, secas, grupos de novillas,…) indicando el número medio de animales en cada lote. En lo posible, sería conveniente conocer la MS de los silos, la proteína de los forrajes y la composición detallada (ingredientes) de los concentrados (en %). Muchas gracias por sus colaboraciones