MODELLING ANIMAL SYSTEMS PAPER

Transcription

1 Journal of Agricultural Science, Page 1 of 17. f 2008 Cambridge University Press 1 doi: /s Printed in the United Kingdom MODELLING ANIMAL SYSTEMS PAPER Use of SIMS DAIRY modelling framework system to compare the scope on the sustainability of a dairy farm of animal and plant genetic-based improvements with management-based changes A. DEL PRADO * AND D. SCHOLEFIELD Institute of Grassland and Environmental Research, North Wyke, Okehampton, Devon EX20 2SB, UK (Revised MS received 26 October 2007) SUMMARY Currently, society awareness, legislations and competing markets demand dairy farming systems which are sustainable. In the near future, farm management and animal genetics will be key elements in developing such sustainability. Although the effect of farm management on some attributes of sustainability has already been studied, the impacts and scope for realizing goals of agricultural multifunctionality through genetic changes are still to be tested. Sustainable and Integrated Management Systems for Dairy Production (SIMS DAIRY ) is a new farm level modelling framework which integrates these concepts to practical actions and brings all of this complexity into an operational and scientific modus operandi. The current paper provides a brief description of the structure of SIMS DAIRY and an example of how it can be used to compare the scope for improving the overall sustainability of a dairy farm by: (i) future system changes aimed at improving genetic characteristics of plants and animals with (ii) current system structural changes aimed at improving nutrient management efficiency. In order to do this comparison, management factors and new genetic traits from plants or/and animals, acting singly or in combination, are evaluated against a baseline dairy farm scenario. Sustainability is measured in terms of targets associated with: (i) the Nitrates Directive, (ii) phosphorus (P) threshold for eutrophication, (iii) the Kyoto Protocol, (iv) the Gothenburg Protocol, (v) an adequate net farm income for standard of living and acceptable standards of (vi) quality of milk, (vii) animal welfare, (viii) level of biodiversity, (ix) landscape aesthetics and (x) soil quality. Results suggest that genetic-based changes offer greater scope than management-based ones to improve sustainability up to an acceptable level. Costs associated with management changes are often too high within current socio-economics circumstances. Optimizing nitrogen (N) mineral fertilizer rate and timing was the only management-based measure that, while improving most of the environmental and biodiversity indices, resulted in improved economic results. Some genetic-based changes offered substantial scope for reducing environmental losses while having economic benefits. However, only those decreasing the crude protein (CP) of the plant and increasing the diet N cow partition into milk seemed to result in non-significant pollution swapping and be achievable in the nearby future. INTRODUCTION Dairy farming in Europe and in particular in the UK is facing numerous pressures related to its lack of * To whom all correspondence should be addressed. Agustin.del_Prado@bbsrc.ac.uk sustainability. Economic, environmental, ecological and sociological aspects of dairy farming need to be balanced to provide for more sustainable systems. Over the last 30 years within the EU the situation has developed such that milk production is regulated via milk quota, while an increasing number of

2 2 A. DEL PRADO AND D. SCHOLEFIELD environmental policies are required to be implemented (Oenema & Berentsen 2004). These environmental policies force farmers to use nutrients more efficiently and to decrease nutrient losses to the wider environment. Currently, the most important environmental policy in the EU affecting nutrient management is the Nitrates Directive (EC 1991). However, there are many more legislations and protocols affecting dairy systems. With the implementation of the EU Water Framework Directive (WFD; EC 2000) farmers will have to further decrease the use of nitrogen (N) and phosphorus (P) and to further decrease N and P losses to surface waters. The Air Quality Directive (EC 2000) also sets limits to the emission of ammonia (NH 3 ) and N oxides into the atmosphere, so as to abate acidification, eutrophication and tropospheric ozone (O 3 ). The increasing concentration of greenhouse gases (GHG) (Kyoto Protocol: UNFCCC 1997), NH 3 and nitric oxide+ nitrogen dioxide (NO x ) (Gothenburg Protocol: UNECE 1999) is also of international environmental concern. Agenda 2000 and the reform of the EU Common Agricultural Policy (CAP) have changed the production-based subsidies system to a single farm payment (SFP) decoupled from production. In order to receive this SFP, farmers have to meet statutory environmental and animal welfare regulations and must maintain their land in good agricultural and environmental condition. Up to now, there have been many studies which have investigated the effect of management, environment or genetic variation (i.e. plant varieties) on the main issues that affect sustainability (e.g. plant genetics and environment on diffuse pollution: Macleod et al. 2007; management and environment on GHG pollution: Schils et al. 2006). However, most of these studies considered parts of the system in isolation and hence were unable to explain the interactions between the parts. Therefore, there is a need to develop approaches that view the system as a whole and that are able to interrogate the system about the potential scope to reach desirable targets. Systems modelling is a useful approach to integrate all the relevant issues that affect the sustainability of a dairy farm and it may also be used to explore the effect of different combination of measures on such sustainability. However, appropriate models to determine objectively the sustainability of dairy farming systems are still lacking. Some integrated modelling approaches have been developed as decision support that considers both biophysical and socio-economic approaches (e.g. Rossing et al. 1997; Vereijken 1997; Van Calker et al. 2006). However, they generally lack process-based mechanisms and thereby lack sensitivity to major factors that affect sustainability and show only a partial reflection of the complex chain of causes and effects (Van Cauwenbergh et al. 2007). They also lack the capability of simulating the introduction of more nutrient efficient plants or animal types, which may need to be evaluated against merely improving nutrient management. In order to fill these gaps, a new model (Sustainable and Integrated Management Systems for Dairy Production (SIMS DAIRY ): del Prado et al. 2006b) has been developed. Plant growth and nutrient use efficiency is generally limited by a combination of genotype and environmental factors. New genotypic varieties with increased nutrient use efficiency have been recently developed and may indirectly result in decreasing pollution to the wider environment (e.g. Wilkins et al. 2000). These existing as well as possible new varieties may assist the full realization of the potential of crop improvement to multifunctional agriculture, where both productivity and ecosystems services are major goals (Abberton et al. 2006). Manipulating the scope for future animal traits such as the ability to yield milk and the ability to excrete different proportions of ingested N as urine or dung (irrespective of diet N ingested), although useful in reducing losses per unit of product, may have important trade-off implications for animal welfare (fertility and mastitis), animal replacements and milk quality. Enhanced focus on traits that balance milk yield, milk quality and animal health could potentially improve the sustainability of dairy farming systems. In the current study, SIMS DAIRY is used to compare the scope to improve the overall sustainability of a dairy farm by: (i) future system changes aimed at improving genetic characteristics of plant and animal with (ii) current system structural changes aimed at improving nutrient management efficiency (e.g. manure application techniques, silage making quality and optimized fertilizer distribution). MATERIALS AND METHODS General SIMS DAIRY modelling framework description SIMS DAIRY is a new modelling framework which integrates existing models for N (NARSES: Webb & Misselbrook 2004; NGAUGE: Brown et al. 2005), P (PSYCHIC: Davison et al. 2008; Stromqvist et al. 2008) and farm economics (A. Butler, personal communication), equations to simulate NH 3 losses from manure application (Chambers et al. 1999), predict CH 4 losses (Chadwick & Pain 1997; Giger-Reverdin et al. 2003) and nutrient requirements (Feed into Milk (FiM) (Thomas 2004)), as well as score matrices for measuring sustainability attributes of biodiversity, landscape, product quality, soil quality and animal welfare. The modelling framework has been fully described by del Prado et al. (2006b) and A. del Prado et al.

3 Genetics v. management for sustainable dairy farms 3 (unpublished). SIMS DAIRY has been used for a number of desktop studies in order to investigate abatement options at farm scale for GHG emissions from ruminant livestock systems (Schils et al. 2007), investigate possible trajectories towards UK dairy sustainable farming systems (del Prado & Scholefield 2006), assess the impact of NO 3 leaching abatement measures on N 2 O and CH 4 emissions from a UK dairy system (del Prado et al. 2006a) and evaluate CH 4 mitigation measures for long-term national CH 4 emissions from ruminants (J. Mills et al., unpublished). The model is very sensitive not only to management but also weather, topography and soil characteristics and is capable of optimizing farm management practices to meet user multiweighted criteria and to explore the possible impact of application of mitigation options on (i) emissions of pollutants such as: N 2 O, CH 4,NH 3,NO x,no 3 and P; (ii) economic profitability; (iii) milk quality; (iv) biodiversity; (v) landscape; (vi) soil quality; and (vii) animal welfare. The effect of management practices on N, P and CH 4 losses are predicted within different components and through different processes in the soil plant animal system using a monthly time step and applying the principle of mass conservation. These practices can be defined in terms of management for instance of: (i) manure (i.e. straw-or slurry-based system, storage type, application method, incorporation time and technique, timing of application, rate, manure dry matter (DM) g/kg content and spatial distribution), (ii) mineral fertilizer (e.g. rate, type and spatial distribution), (iii) animal (i.e. milk target/cow, fat content target in milk, protein content target in milk, calving month, grazing time, diet profile and animal breeds) and (iv) forage area (i.e. spatial distribution, sward age, history, tillage, plant varieties and silage making technique). The effect of incorporating either current or theoretical future plant and animal varieties with changed traits on the different components that define the sustainability of a dairy farm can also be simulated with SIMS DAIRY, where relevant functional characteristics of either plants or animals can be modified. The structure of the framework is shown in Fig. 1. The main data inputs required to run the model are: farm management (e.g. animal, grassland and maize), milk target (e.g. milk protein and butterfat contents), site characteristics (e.g. coordinates that define average climatic conditions and soil types), plant/animal traits, management to optimize (e.g. number of grazing days) and criteria used as a basis to optimize (e.g. environmental). SIMS DAIRY scope focuses on strategic and tactical management levels and is capable of optimizing dairy management factors in order to find more sustainable systems (del Prado & Scholefield 2006). The whole framework operates automatically and, except for Management Milk target Site Plant/animal traits Losses Losses INPUTS Run farm 1,2,3,...,N Animal requirements Excreta NGAUGE PSYCHIC ECONOMICS SIMS SCORE RANK FARM 1,2,3,...,N Management to optimize Feeds (supply) Losses Criteria Areas of forage Farm Storage Grass and maize fields Farm Fig. 1. The structure of the SIMS DAIRY modelling framework. the SIMS PSYCHIC submodel which is an external link through a Visual Basic (VB) Dynamic Linking Library (DLL) file, has been coded into a program compiled with Borland Delphi 5. SIMS DAIRY s final users are currently those involved in strategic research (e.g. policymakers); however, it is likely to be extended to include user-friendly input and output screens for farmers advisors to use it. Description of stream of calculations made in SIMS DAIRY These can be subdivided into the calculations related to: (1) animal nutrient and energy flows, (2) manure nutrient flows, transformations and losses, (3) plant and soil nutrient flows, transformations and losses and (4) economic and attributes of sustainability. Animal calculations Prediction of feed voluntary DM intake during the housing and grazing period is calculated as a function of forage intake potential (FIP), concentrate dry matter intake (CDMI), animal condition score (CS), animal weight (W), milk energy output (MEO), week of lactation (WOL) and forage starch (FS) concentration. Calculations are based on the FiM system (Thomas 2004). Total metabolizable energy (ME)

4 4 A. DEL PRADO AND D. SCHOLEFIELD and true protein (MP) requirements are defined as the energy and proteins needed for body weight change, pregnancy post 250 days, maintenance, milk production and activity (e.g. grazing). For any given diet comprising silage (grass and/or maize) and concentrates, SIMS DAIRY optimizes the concentrates characteristics in order to ensure that: (i) feed supply matches dairy cow requirements (DM, energy and N), (ii) rumen does not become acidic, (iii) amino acids in the diet are optimum for milk protein synthesis and (iv) the effect of diet composition on the composition of milk is predicted. Manure calculations The N and P contents of both manure produced during housing and excreta deposited during grazing, are then calculated for each type of cow and subsequently in total (applying number of animals of each type) by subtracting N and P in milk and net body change from those ingested by both housing and grazing young and lactating cows. The N and P contents of manure produced during housing and storage are simulated to be applied to the different farm fields. Prior to application, gaseous N and CH 4 losses (from housing and storage) and straw N from bedding are calculated and computed with the total existing pool of N in manure. Manure N losses are simulated according to the approach from Webb & Misselbrook (2004), by which NH 3,N 2 O, NO and N 2 emissions are calculated from the pool of total ammonium nitrogen (TAN) in manure N according to different Emission factors (Efs) for different manure management stages. The manure N that is not lost through NH 3 losses during the housing or storage period is assumed to be applied in the soil. Efs for NH 3 volatilization from slurry were determined for application on grassland and maize land according to: (i) properties of the slurry (DM content), (ii) its application date (for soil moisture content), (iii) incorporation timing after application, (iv) method of application and (v) method of incorporation. Equations from MANNER (Chambers et al. 1999) were used for this purpose. Efs for NH 3 volatilization from farm yard manure (FYM) were also incorporated from MANNER taking incorporation delay and technique as the main driving factors for controlling NH 3 losses from application. Plant and soil calculations The nutrient requirements (N and DM) for cows from different sources must match harvested and grazed N and DM plant yields (after allowing losses from grass and maize conservation). Land on the farm is used to grow grass and maize in order to fulfil those animal requirements not covered by the ingestion of concentrates. This land is defined in terms of use and four classes are allowed: (i) grass area grazed by dairy herd, (ii) grass area grazed by followers, (iii) area for grass conservation and (iv) maize. Using the submodel NGAUGE, N and P flows are simulated on a per hectare basis for each land use, given soil types, sward age, pest management and for given rates of manure and mineral fertilizer applications. DM, N and P plant yields together with losses of N [N 2,N 2 O, NH 3, nitric oxide+nitrogen dioxide (NO x ) and nitrate (NO 3 x ) leaching] and CH 4 are predicted and the surface required for each land use is hence calculated by simply dividing the total predicted animal requirements from grass and maize (previous stages) over plant yield (once silage making losses have been accounted for) of each crop source. The submodel PSYCHIC predicts the risk of P diffuse pollution from a source area by estimating source, mobilization and delivery of P and sediment: P inputs in manure and fertilizers and soil residual P, the mobilization of P and sediment through dissolution and soil detachment and the delivery of dissolved and particulate P and associated sediment, to watercourses in surface and subsurface runoffs. Economic and attributes of sustainability calculations The net farm margin is then calculated by subtracting the total fixed costs and overheads from the gross margin. Variables costs are calculated by the model as a function of management variables (i.e. per unit of applied manure volume). Some management strategies (e.g. those resulting in enhancing landscape), because of large variability in their costs, are user-proposed inputs and hence are not intended to reflect an accurate value. Subsequently, sustainability matrices are scored by the submodel SIMS SCORE, which simulates the effect of both nutrient management variables (e.g. effect of unsaturation of fatty acids in the diet on milk yield) and non-nutrient management variables (e.g. available surface per cow during housing) on the sustainability of the farm in terms of biodiversity, landscape, milk quality, soil quality and animal welfare. The scores assigned reflect poor (0) to very satisfactory (6) sustainability. The farm sustainability is then evaluated by the submodel RANK. Design of simulation study A baseline conventional dairy farm was defined for testing the different management and genetic changes proposed in the current study. The main characteristics of the farm are shown in Table 1. A conventional dairy farm in the South West of England which typically relies on on-farm grass and maize production and bought-in concentrates for sustaining animals. Dairy cows graze around half a year (from April till September) and remain housed during the rest of the year. Some assumptions were made to the dairy system in order

5 Genetics v. management for sustainable dairy farms 5 Table 1. Main characteristics of the typical dairy farm used as baseline Farm management Site Milk yield (l/cow yr) 7000 Location Devon (UK) Fat in milk (g/kg) 40 Soil type Clay loam Protein in milk (g/kg) 34 Drainage status Poor Dairy cows (number) 100 Replacement rate (%) 27 Followers (number) 80 Calving pattern All-year Breed Holstein Silage management Average quality Housing time dairy cows (days/year) 185 Housing time followers (days/year) 160 Diet During housing Grass silage, maize silage, concentrates During grazing Grazed grass, maize silage, concentrates Annual fertilizer management Grass Cut Grazed (dairy) Grazed (followers) Maize Fertilizer N (kg N/ha) Fertilizer P (kg N/ha) Maure management Type of manure Proportion of total applied to land Storage Application technique Grassland management History Sward age (years) Slurry (60 g/kg DM*) Cut-grass Grazed-grass Maize Slurry tank: open Broadcast Cut-grass Grazed-grass Young grazed-grass Long-term grassland >20 * DM, dry matter. to simplify the simulations. For instance, any cow other than dairy was simulated with the assumption that it would be represented by an average follower of a bodyweight size of 300 kg with no body-weight change during the year. The grassland area was split into cut-only fields (three cuts for silage) and cut-andgrazed fields (one cut). The timing and proportion of annual mineral fertilizer applied per month were designed to follow the UK fertilizer recommendations for agricultural crops (RB209) (MAFF 2000) and timing for the manure applied to land followed the distribution patterns described by Smith et al. (2001), in which the proportion of manure applied of the annual total is as follows: from February to April (0. 40), May to July (0. 10), August to October (0. 25) and November to January (0. 25). The NVZ closed periods were taken into account and no manure was spread to grassland fields from 1 September to 1 November. Simulating improved management towards sustainability The specific management factors and plant and animal new traits characteristics modified are shown in Table 2.

6 6 A. DEL PRADO AND D. SCHOLEFIELD Table 2. Management factors and plant and animal characteristics modified in order to improve the sustainability of UK dairy farms Genetic changes Current management practices Manure application technique Manure system Silage making Fertilizer distribution Diet Plant N in plant (GN ) Recovery of soil N (G h ) Shoot:root ratio (G u ) Plant N mineralization (G plantmin ) PUFA content (GPUFA ) Animals N partition into milk (same milk) (Gmilk ) Fertility (G FERT ) Urine:dung ratio (G urinedung ) Among the changes in current management, SIMS DAIRY tested the following alternative practices to those used in the baseline scenario: (i) different manure application techniques (e.g. deep injection, shallow injection and band-spread), (ii) different timing of incorporation of manure, (iii) different manure system (e.g. straw-based system), (iv) different g/kg DM content in manure, (v) different quality of silage making, (vi) different mineral fertilizer distribution (with the target to produce the same herbage or maize per hectare), (vii) different timing and spatial distribution of manure and (viii) different diet strategies (e.g. changing concentrates/litre) milk produced or adding fat supplementation). The following changes were investigated at the plant genetic level: (i) the crude protein (CP) of the plant (G N ), (ii) recovery of soil-available N by the plants (G h ), (iii) harvest index (G u ), (iv) plant dead N mineralization (G plantmin ), (v) polyunsaturated fatty acids (PUFA) content in the plant (G PUFA ) and at the animal genetic level: (vi) diet N partition into milk (producing the same amount of milk) (G milk ), (vii) fertility (G FERT ) and (viii) urine:dung ratio (G urinedung ). Changes were either simulated singly or introduced in a stepwise process by which each step introduced a new different management or genetic change; this change being optimized by the SIMS DAIRY modelling framework. The changes, acting singly, or in combination, are evaluated on farms. The SIMS DAIRY optimization procedure, for most parameters, was set to adjust the hectares needed for forage instead of adjusting the number of cows that certain reductions in plant production per hectare could trigger. This is not the case for mineral fertilizer, where changes are obtained by adjusting the fertilizer rate and supposing the same amount of forage surface and CP plant production per hectare. These assumptions do not intend to emulate the most probable decision made by farmers but are used only as an example of the most probable way to achieve sustainability. In order to assess environmental sustainability seven indices were used as the criteria of SIMS DAIRY optimization. One of these indices (environmental index) accounts for the impacts of farming on eutrophication potential per hectare, acidification potential per hectare, global warming potential per tonne of milk and water use per hectare and comprises predicted outputs of CH 4,N 2 O, NO x,nh 3 and NO 3 leaching and water use (index adapted from Van Calker et al. 2006). The score assigned reflect poor (0. 0) to very satisfactory (1. 0) environmental sustainability. The rest of the indices are as follows: (i) economic: farm net profit 0.03/litre of milk), (ii) quality of milk: defined as milk with enhanced PUFA composition, (iii) animal welfare status, (iv) level of biodiversity and landscape aesthetics and (v) soil quality. A set of desirable sustainable targets for the predicted farms was also defined. Targets associated to impacts on water quality were set to comply with the Nitrates Directive (<11. 3 mg/l in the leachate) and to reduce the P threshold for eutrophication (<100 mg/l). A GHG reduction (12. 5%) from the baseline farm was proposed in order to comply with Kyoto Protocol targets both individually and as a whole. Gothenburg Protocol set emission ceilings for 2010 for NH 3 and NO x (Gothenburg Protocol: UNECE 1999) in Europe. According to this protocol, a reduction from the baseline farm was proposed at 17 and 41% for NH 3 and NO x, respectively. An adequate net farm income for standard living and acceptable standards (score of 4 out of 6) of quality of milk, animal welfare, level of biodiversity and landscape aesthetics and soil quality were also set as sustainable targets. The acceptable net farm income was assumed to be net profit of 0.03/litre of milk. This value was arbitrarily chosen for the purposes of this modelling exercise as no data were available to support any specific value. It must be pointed out that, although not explored in the current study, the results are not intended to cover the whole range of possible conditions in the UK. Results cannot be extrapolated to the different climatic and soil conditions in the UK because sustainability indicators, especially environmental losses are likely to vary, not only in total but also in different

7 Genetics v. management for sustainable dairy farms 7 Table 3. Units, predicted results and assessment of sustainability for the different variables studied Parameter Units Results Sustainable? Surface ha N.A.* Total milk l N.A.* NO 3 leaching (load) g NO 3 -N/l milk 6. 5 N.A.* kg NO 3 -N/ha N.A.* NO 3 leaching (concentration) mg NO 3 -N/l (mean) 6. 4 Yes mg NO 3 -N/l (peak) N.A.* P concentration in leachate mg P/l (mean) No CH 4 losses g CH 4 /l milk N.A.* kg CH 4 /ha N.A.* N 2 O losses g N 2 O-N/l milk 0. 6 N.A.* kg N 2 O-N/ha 5. 5 N.A.* NH 3 losses g NH 3 -N/l milk 5. 9 N.A.* kg NH 3 -N/ha N.A.* NO x losses g NO x N/l milk 0. 4 N.A.* kg NO x N/ha 3. 8 N.A.* GWP (CH 4 +N 2 O) t CO 2 -eq/ha 1. 1 N.A.* Environmental index N.A.* /litre of milk No Milk quality 2. 5/6 No Biodiversity 1. 0/6 No Landscape 3. 0/6 No Soil quality 0. 9/6 No Animal Welfare 1. 5/6 No * N.A., not applicable. forms of pollutants. Economic results would also be very sensitive to herd size and thereby farms with larger herds than those simulated in the current study would be likely to result in greater net farm margins. RESULTS AND DISCUSSION Baseline farm scenario results Table 3 shows the results from the simulated baseline farm scenario. For all the variables studied, except for the mean concentration of NO x 3 leaching (in relation to the Nitrates Directive threshold), the baseline farm scenario failed to meet previously defined desirable targets for sustainability. The baseline results for GHG and acidifying gases were used as starting values and were not subject to assessment at this point. Of all the rest of variables studied, only the net farm profit reached at least half of the value required to meet the desirable sustainable targets (0.016/0.03 /litre of milk). The predicted score for biodiversity was very low (1/6), partly due to frequent grazing and cutting. Frequent grazing and cutting have been found by other authors (e.g. Benton et al. 2003) to result in lack of spatial and structural heterogeneity, which is widely recognized as a key factor influencing farmland birds and overall biodiversity. Other factors that greatly influenced this low biodiversity score were the fertilizer and stocking rates. The high level of N fertilizer rates simulated in the current study s baseline scenario (>300 kg N/ha at the farm level), together with predicted N from mineralized soil organic matter and animal excreta, has been found by many studies (e.g. Firbank 2005) to encourage the growth of competitive species, resulting in the loss of many species which are of substantial ecological significance. Stocking rates greater than 2. 0 LU/ha combined with a heavy textured soil and very wet conditions have been found to increase the likelihood of soil compaction and structural damage (Whitmore 2001). This was the main factor affecting the predicted low soil quality score. Soil structural damage caused also contributed to the low score for biodiversity as it can have both direct and indirect negative consequences for grassland fauna and biodiversity. Sanderson (1989), for example, found that compaction of soil by cattle made the habitat unsuitable for some invertebrates. The challenge to reverse the negative trend of biodiversity levels would be substantial as studies suggest that changing to more extensive practices does not necessarily yield to restore grassland biodiversity in the short term and methodologies for incorporation of patches for biodiversity within the farm are not as well developed for grassland fields as they are for arable systems (Smart et al. 2003).

8 8 A. DEL PRADO AND D. SCHOLEFIELD Management-based changes acting singly As expected, changes in management practices led to improvements in the overall sustainability over the baseline dairy farm. Table 4 shows the predicted percentage change in the different sustainability indices for the five most environmentally sustainable overall management practices acting singly. SIMS DAIRY optimization predicted that the three best management changes to improve the value of the environmental index were: (i) changing to an FYM-based system where the manure was rapidly incorporated into the soil (+36%), (ii) optimization of the mineral N fertilizer rate and timing (+30%) and (iii) optimization of diet (+28%). Of these three changes, optimization of mineral N fertilizer was the only measure that, while improving the environmental index, did not result in trade-offs to any of the other indices or pollutants in isolation. By tactically matching the plant N requirements to the rate and temporal distribution of mineral N fertilizer (Fig. 2), the changed farm reduced environmental losses by 29% in GHG production or 98% in NO x output while improving economic output by 11%, through saving costs on fertilization, and increasing the biodiversity index by 27%. This predicted increase was mainly a result of reducing the inorganic N flows in the soil (data not shown). It must be pointed out that SIMS DAIRY incorporates different relationships by Herrmann et al. (2003) that relate nutrient input (mineral fertilizer and manure) with biodiversity (del Prado et al., unpublished). Based on these relationships, biodiversity level increases with decreasing amount of nutrient (N and P) inputs (McAdam et al. 2001). For this particular baseline farm the optimized fertilizer distributions indicated that higher doses in early spring would improve the N use efficiency of grass. In terms of maize fields, manure application in February or March released enough available N to reach a more efficient match for maize plant N uptake requirements (Fig. 2). Currently, there is an ongoing process to revise the existing Fertiliser Recommendations for Agricultural and Horticultural Crops (RB209). Changes will be made to improve advice on reducing losses to the environment through fertilizer management. However, although it has been assumed in the baseline scenario that current farmers would follow the RB209 guidelines, there is little evidence to support this assumption and there is great suspicion that most farmers are still far behind these guidelines. Changing to an FYM-based system where the manure was rapidly incorporated into the soil reduced losses related to eutrophication and acidification potential but increased losses of GHG (N 2 O and CH 4 ), most of these emissions coming from storage prior to spreading (data not shown). The use of additional straw in animal housing and the rapid incorporation of manure into the soil have been recognized in experimental studies as potential techniques to reduce NH 3 emissions (Webb et al. 2005) but increase N 2 O and CH 4 emissions (Yamulki 2006), i.e. pollution swapping. However, there is still not much experimental evidence that incorporation of manure into the soil will always result in increasing N 2 O emissions compared with broadcast spreading. This will possibly be regulated by site-specific conditions (i.e. weather and soil conditions) (Thorman et al. 2007). A better solution, as proposed by Thorman et al. (2007), would possibly be either bypassing the storage phase, or to use a N 2 O mitigation measure specifically targeted at storage, e.g. the addition of high C substrates (Yamulki 2004). Changing to an FYM-based system also had positive implications for the animal welfare score. The use of straw substantially improved the bedding conditions for dairy cows. Unfortunately, it caused a reduction in net farm income (x18%), mainly from increased cost through manure handling and incorporation into the soil. Although not reflected in the current study, this type of system implies harder working conditions as management complexity is generally greater in FYM-based than in cubicle-based systems. For instance, FYM is assumed to be cleared out in suitable weather after the cows have gone out to grass and sometimes FYM must be removed by a contractor. Diet changes to minimize overall environmental index included a shift towards more maize and less concentrates and grass silage, and changes in the concentrate characteristics. These changes affected the nutrient composition of the diet by increasing total starch, fat and low degradable protein, and decreasing energy and CP. As a consequence of decreasing dietary CP content and increasing low degradable protein content, urinary N excretion decreased and the cow N use efficiency increased, which is in line with experimental studies (e.g. Kebreab et al. 2001; Colmenero & Broderick 2006). The inclusion of fat supplements with a high degree of unsaturation in their fatty acids profile increased the milk quality score (+81%) and animal welfare index (+40%). Although some studies have found that elevated levels of unsaturated fat supplementation may have a negative impact on the rumen function and DM intake (Jenkins & Jenny 1989; Pantoja et al. 1994) other studies have not found such evidence (Palmquist & Conrad 1978). Therefore, since SIMS DAIRY cannot predict any of these consequences, simulated results assume that the level of fat supplementation would be such that it would not adversely affect the rumen function and DM intake. These fat supplements were rich in PUFA and enhanced the PUFA composition in the milk. This milk characteristic has a potentially positive effect on the health of the milk consumers (Parodi 1997). In experimental studies (e.g. Dewhurst et al. 2003),

9 Table 4. Predicted change (%) in the different sustainability indices after optimizing management changes acting singly Eutrophication potential GHG Acidification potential Indices Management change NO 3 /ha x NO 3 (mg/l) P (mg/l) CH 4 /ha N 2 O/ha GWP* NH 3 /ha NO x /ha Environmental Economic Milk Biodiversity Soil Manure application technique Deep injection x1 x x FYM incorporation x9 x x x18 0 x Manure system FYM system x19 x x x21 0 x Slurry dilution x4 0 5 x Rigid cover x1 0 x1 x x Silage making Silage quality x2 x4 x3 x Fertilizer distribution Mineral fertilizer x33 x33 x2 0 x29 x14 x12 x Distribution and amount Organic fertilizer Areas of slurry x1 x1 x2 x1 0 2 x spreading Timing of slurry x13 x spreading Diet Diet optimized x19 0 x11 x18 x47 28 x10 81 x * GWP=global warming potential (CO 2 eq/ha yr). Animal welfare Genetics v. management for sustainable dairy farms 9

10 10 A. DEL PRADO AND D. SCHOLEFIELD Aug Jul Jun Cut Cut-opt May Apr Mar kg N/ha Aug Jul Jun Graz Graz-opt May Apr Mar kg N/ha Aug Jul Jun May Apr Mar GrazY kg N/ha GrazY-opt Aug Jul Jun May Apr Mar Maize kg N/ha Maize-opt Fig. 2. Mineral fertilizer distribution and rate for the farm areas under cut grass (Cut), grazed grass by dairy cattle (Graz), grazed grass by followers (GrazY) and maize (maize) for baseline and SIMS DAIRY s optimized (-opt) farms, respectively. concentrations of conjugated linoleic acids (CLA), trans-vaccenic acid (TVA) and nx3 PUFA have been found to increase after PUFA cow supplementation. This positive impact on dietetic quality of milk results in an improved image of this product by the consumer as indicated by Dewhurst et al. (2003). Moreover, high levels of some of these PUFA in the diet have been described as having a positive effect on fertility (Ambrose et al. 2006). Predicted GHG were reduced through the reduction of CH 4 output from rumination. This effect has been described by numerous studies (e.g. Machmu ller et al. 1998) as one of the best ways to mitigate CH 4 outputs from the rumen as some fats alter the ruminal microbial ecosystem and, in particular, the competition for metabolic hydrogen (H 2 ) between the CH 4 and propionate production pathways (Czerkawski 1972). Addition of fat supplements in the diet, in addition to increasing dietary energy and suppressing methanogenesis, can under certain circumstances decrease gaseous nitrogen emission from the manure (Machmu ller et al. 2006). However, this cannot be simulated by SIMS DAIRY since experimental studies (e.g. Machmu ller et al. 2006) still find no evidence to relate the effect of this type of supplementation on NH 3 or N 2 O emission from manure storage and application to the soil. The predicted acidification potential, especially through NH 3 mitigation, was also reduced, which agrees with experimental studies, such as that from Misselbrook et al. (2005). Misselbrook et al. (2005) showed that manipulating the concentration and form of protein in the diet of lactating cows influenced the amount and form of N excretion and subsequent NH 3 emissions from the barn floor and manure management. However, the predicted eutrophication potential increased through increase in both NO 3 x and P losses and, moreover, the economic returns were 10% worse than the baseline farms due mainly to the increased costs of fat supplementation. The costs associated with fat supplementation may discourage farmers from adopting this change. However, this trend could be overturned if an increased milk payment to farmers is established through production of higher value milk directly from the farm (del Prado & Scholefield 2006) or/and sustainable deliverables to the environment. Other manure-based changes were either too costly (e.g. deep injection) or did not improve the environmental index (e.g. timing of manure spreading:+ 1%). Most of the changes in manure management generally led to a certain degree of pollution swapping. For instance, those changes aimed at reducing NH 3 emissions from application of manure to the soil resulted in increased NO 3 x leaching losses (e.g. deep injection). Many other studies have also found this pollution swapping effect (e.g. Webb et al. 2001). Improving the quality of silage making resulted in decreasing most of the environmental losses, especially NO x, and increased (+7%) the economic returns and the level of soil quality (through a shift from surface dedicated to grazing to surface dedicated to high quality silage) and animal welfare. Genetic-based changes acting singly SIMS DAIRY optimization of genetic-based changes resulted in varieties (data not shown) that increased: (i) the recovery of the available soil N by the plants

11 Genetics v. management for sustainable dairy farms 11 Table 5. Predicted change (%) in the different sustainability indices after optimizing genetic changes in terms of plant and animal functional traits acting singly Eutrophication potential GHG Acidification potential Indices Animal welfare Biodiversity Soil Environmental Economic Milk x NO 3 (mg/l) P (mg/l) CCH 4 /ha N 2 O/ha GWP* NH 3 /ha NO x /ha NO 3 /ha Genetic change Plant N in plant (G N ) x16 x16 x4 x1 x20 x10 x19 x x11 x16 Recovery of soil N (G h ) x42 x42 x6 0 x31 x x11 0 Shoot: root ratio (G u ) x2 x2 x1 x Plant N mineralization x7 x7 0 x1 x2 x1 x2 6 5 x x16 (G plantmin ) PUFA content (GPUFA) x19 0 x x7 x7 x4 x6 x6 x7 x Animals N partition into milk (same milk) (Gmilk) Fertility (G FERT ) x3 x3 0 x2 x3 x2 x4 x Urine:dung ratio (G urinedung ) x4 x4 1 x1 x21 x11 x23 x3 36 x * GWP=Global warming potential (CO 2 eq/ha yr). (G h ), (ii) harvest index of plants (G u ), (iii) the PUFA content in the plant (G PUFA ), (iv) the diet N partition into milk (G milk ) and the fertility of the cow (G FERT ) and decreased: (i) the CP of the plant (G %N ), (ii) plant dead N mineralization (G plantmin ) and (iii) the urine:dung ratio (G urinedung ). Table 5 shows the predicted percentage change in the different sustainability indices after introducing genetic changes in terms of plant and animal functional traits acting singly. SIMS DAIRY optimization predicted that the best changes to improve the value of the environmental index (percentage improvement compared with the baseline scenario as shown in brackets) were by manipulating the following traits: (i) G urinedung (+36%), (ii) G %N (+35%), (iii) G milk (+17%) and (iv) G h (+9%). Both G urinedung animal trait and G %N plant trait resulted in reductions in all of the other indices or pollutants in isolation. Whereas G %N was more effective in reducing NO 3 x leaching losses than G urinedung, the opposite result was found in terms of reducing the NH 3 losses. G milk was also an effective trait to decrease values from most of the indices or pollutants in isolation; however, reductions were always below 10%. Incorporating the G h plant trait resulted in great reductions in both NO 3 x leaching (x42%) and N 2 O emissions (x31%). However, it resulted in some amount of pollution swapping with NH 3 and NO x. The main differences in terms of the non-environmental indices were found in the economic performance of the farm. Both G %N and G milk resulted in economic improvements of almost 40% and G h improved the economic performance in a smaller way (16%). The biodiversity index was only improved by introducing the G h trait, by which the amount of annual inorganic N flows in the soil were reduced and, hence, potentially with greater opportunities for improving the botanical diversity. This fact, however, needs further testing as the temporal variation of inorganic N flows in the soil within a year may have different implications for different potential plant species. As expected, traits related to increasing the PUFA content in plant (G PUFA ) and that related to increasing the fertility of the animal (G FERT ) improved the index of animal welfare. G PUFA increased greatly the quality of milk index. The scope for these traits would be obviously related to the amount these new varieties can increase these PUFA-rich content without trade-offs in other aspects affecting the sustainability of the farm (e.g. yield production). Variation between grass varieties and species in content of PUFA and in susceptibility to biohydrogenation in the rumen has already been identified (Elgersma et al. 2003). However, so far, the effects of this genetic variation are small compared with variation through the growing season (Dewhurst et al. 2001).

12 12 A. DEL PRADO AND D. SCHOLEFIELD 7 Pence/Litre of milk Environmental index Net farm profit (pence/litre of milk) Baseline 1-Genetic 1-Management 2-Genetic 2-Management 3-Genetic 3-Management 4-Genetic Number and type of changes Fig. 3. Comparison of net farm profit and environmental predicted indices results for the baseline farm and those farms under different number (from 1 to 7) and type of changes (management-derived or genetically derived). Vertical columns and rhomboids represent means of up to eight predicted results for net farm profit and environmental indices, respectively. Vertical bars represent maximum and minimum values. 4-Management 5-Genetic 5-Management 6-Genetic 7-Genetic Environmental index As current trends in grass breeding already focus on increasing the amount of DM yield per unit of fertilizer applied by either reducing the CP concentration of the herbage (G %N ) or by increasing the proportion of the available soil N recovered (G h ) (Wilkins & Humphreys 2003), it is realistic to think that some of the traits predicted in this study are feasible to be available for farmers in the nearby future. There is, however, less scope for altering the harvest index (G u ) of grasses (except perhaps a little between root: shoot ratio) (Wilkins & Humphreys 2003), which, considering the small effect found in the current study on either environmental or/and economic performance, should discourage any plant breeding in that direction. The likelihood of finding either plant varieties which could reduce rates of mineralization of dead plant material (G plantmin ) or cow varieties which may favour excretion as dung over urine (G urinedung ) is still unknown. It is worth mentioning that most of the studies on decreasing dung over urine so far (e.g. Castillo et al. 2000; Kebreab et al. 2001; Marini & Van Amburgh 2005) have focused the effect of diet on this urine: dung output ratio and so this paper considers such a trait as purely theoretical. Management- and genetic-based changes acting in combination: a general comparison The results of the single and combined management and genetic changes are summarized in Fig. 3. This figure shows the mean, and maximum and minimum values for each number and type (management or genetic) of combinations in terms of economics and environmental index performance of the farms. As expected, as the number of changes increased, the environmental losses decreased (as the environmental index increased) both with management and with genetic derived changes. However, the net farm income increased only when the changes were genetically based ones and not management-based ones. Obviously, this scenario is assuming that no increased cost would be involved in, for example, new varieties of grass seeds or animal insemination. The environmental performance increased quite constantly as increasing number of managementbased changes was considered. This increase in environmental performance for the genetic-based changes, however, was large only for the first four cumulative combinations and thereafter, no substantial improvement was observed (Fig. 3). Net farm incomes resulting from combinations of managementbased changes were in most cases, very similar both in average and maximum minimum values (Fig. 3). Only when five management changes were combined, did net farm income decrease considerably. The fact that the net farm income greatly increased when the genetic-based changes were upgraded from three to four implies that the scope for obtaining the most balanced system in terms of environmental losses and economic performance through

13 Genetics v. management for sustainable dairy farms 13 genetic-based changes would be generated from four of the genetic combinations. The fact that the ranges of minimum maximum values are large in all the genetic-based changes with fewer than four combinations would support this selection of combinations of four. Management- and genetic-based changes acting in combination: best combinations Examples of the predicted results from the three best combinations in terms of overall environmental performance (environmental index) from one to two measures together, three to four and over five measures for management and genetic changes, respectively, are shown in Fig. 4. Results from these combinations are compared with those results from the baseline scenario and the targeted sustainable scenario (as defined in the material and methods section). Figures were plotted as radar graphs (Fig. 4). Two different graphs were plotted for each combination: one with seven variables for pollutants and another with six variables for other sustainability attributes (net farm income, milk quality, biodiversity, landscape, soil quality and animal welfare). The expected values for the sustainable scenario were transformed to a 1 unit basis and the predicted results from the different combinations were proportionally transformed to this value of 1. Values <1 implied meeting the threshold of individual sustainability indices. For clarity, all values o1. 5 were assumed to be Thereby, the closer the value was to the centre of the diagram, the more sustainable the farm scenario was for that particular variable. The different pollutants met the required individual targets for sustainability after different number of combinations of changes: for these particular climatic and soil conditions, farms always resulted in mean NO x 3 -N concentration in the leachate well below the Nitrate Directive threshold and P concentrations around the 100 mg/l value threshold. However, it must be pointed out that ecological targets in accordance with the WFD may actually require lower total N concentrations than mg/l (Camargo & Alonso 2006) and possibly much lower P concentrations than 100 mg/l. Moreover, evidence suggests that a further quantification of the different bioreactive forms of P is required to link P losses to actual eutrophication (Turner & Haygarth 2000). Potential P sources vulnerable to mobilization and delivery to surface or groundwaters were involved in some changes, either genetic-based or management-based. However, P losses could not be substantially decreased as no changes affected mobilization and transport of P in the dairy farm system in the short-medium term. In terms of GHG, both management and genetic measures showed great potential to reduce N 2 O emissions to below the sustainability threshold values. However, in terms of management combinations, this threshold was actually only reduced through measures that included fertilizer as a single measure or fertilizer and diet optimization together. Most of the combinations from genetic measures shown in Fig. 4 resulted in decreasing N 2 O emissions further than the targeted values for sustainability. The reduction of CH 4 losses below the sustainable threshold was achieved when fatty supplements were used in the diet only or when genetic changes that increase PUFA content in the grass were introduced. Targets of GHG values as a combined result of CH 4 and N 2 O were met with almost any of the combinations plotted in Fig. 4, both management and genetic-based. Ammonia sustainability threshold was reduced in most cases especially when over two measures, either management-based or genetic-based, were used. The NO x threshold value was only reduced by means of management measures that combined fertilizer optimization and silage process improvements. The studied management or genetic changes did not have a substantial effect on sustainable indices such as those related to landscape aesthetics, soil quality or biodiversity. Whereas landscape was not affected at all, soil quality and biodiversity were mainly affected by combinations which had an effect on soil flows of N and P (biodiversity) and grazing time and intensity (soil quality and biodiversity). Management-based changes, such as those incorporating diet and fertilizer changes, resulted in complying with all the environmental targets except for the P threshold and offered large scope to meet the desired milk quality standards. However, unless this type of milk is priced as a premium product, these management-based changes resulted in financial shortages. CONCLUSIONS In the current study, management-based changes in the baseline farm scenario offer smaller scope to satisfy both environmental and economic targets than genetic-based ones. Most management-related changes imply some technical improvements and, hence, large costs. Among the management-based changes, optimizing N mineral fertilizer rate and timing is the only management-based measure that, while improving most of the environmental and biodiversity indices, can improve economic results. Diet-based changes, although they can considerably reduce environmental losses and increase milk quality, will result in economic penalties to the farm unless a market niche is created for milk naturally enhanced with PUFA. Optimization in manure management results in overall environmental benefits but increases both the risk of pollution swapping and economic penalties.