PrOPara project workshop Focus Group Manual: Step-wise Approach (Project deliverable 10.(WP4))

This handbook is a deliverable (WP4) from the EU-funded PrOPara project. The PrOPara project aspires to i) assess existing knowledge from research, development and benchmarking studies on alternatives to parasite control on organic ruminant farms, ii) collecting novel data on disease prevalence, risk assessment analysis and parasite control measures, through monitoring (farm surveys and stakeholder participation studies), iii) performing cost-benefit analysis on alternative parasite control measures and iv) developing and delivering technical innovation to facilitate implementation of sustainable parasite control strategies. This handbook serves as a baseline to conduct workshops with stakeholders in France and Scotland. It provides the organisers with a structured approach on 8 steps. The implementation of this approach will allow identification of main alternative GIN practices according to stakeholders’ views, as well as analysing economic impacts and reasons for adopting them or not

Organic farm incomes in England and Wales 2009/10 (OF 0373)

This report presents results of research on the financial performance of organic farms in 2008/09 and 2009/10 financial years. Carried out for the Department for Environment, Food and Rural Affairs (Defra), this research continues project OF0373 and builds on previous work on the economics of organic farming carried out at Aberystwyth University (Projects OF0190, covering 1995/96 to 1998/99 and OF0189, covering 1999/00 to 2004/05)1. This report utilises data collected through the Farm Business Survey in England and Wales. An analysis of the FBS/Defra Data Archive for 2009/10 found a total of 241 businesses with some organic land, and of these holdings, 189 met the criterion for inclusion within this study by having greater than 70% fully organic certified land. In total, data from 185 organic farms were suitable for inclusion in the analysis. The organic holdings were matched with clusters containing a total of 785 comparable conventional holdings. It was not possible to identify comparable conventional businesses for four organic farms, though the gross margin results from one of these businesses could be utilised. Comparable conventional farms (CCF) were clustered to each organic farm to ensure that the comparison between farming types is based on a similar resource base e.g. similar land area, farm type, region and other factors. This enabled each organic farm to be matched to the average for a conventional farm cluster that comprised data from at least three comparable conventional holdings. The full sample analysis utilised data from 185 organic farms and provides the best comparison of organic and comparable conventional farm income data in 2009/10 (2008/09 data is provided for a non-identical comparison). The profitability (Farm Business Income) of most organic farm types was higher than that of comparable conventional farms. Organic lowland dairy and cropping farm types were considerably more profitable than their conventional comparisons, however, the organic LFA dairy and horticulture (not shown) farm types did not perform as well as conventional systems, mainly as a result of high feed and other livestock costs in LFA dairying and the specialisation/intensity of the comparable conventional horticulture systems, but both farm types had small organic samples which may also have affected the results. Data were also analysed using identical samples and gross margins

Socio-economic impacts of alternative GIN control practices. Project deliverable 11 (WP4)

This report is a deliverable (WP4) from the EU-funded PrOPara project. The PrOPara project aspires to i) assess existing knowledge from research, development and benchmarking studies on alternatives to parasite control on organic ruminant farms, ii) collecting novel data on disease prevalence, risk assessment analysis and parasite control measures, through monitoring (farm surveys and stakeholder participation studies), iii) performing cost-benefit analysis on alternative parasite control measures and iv) developing and delivering technical innovation to facilitate implementation of sustainable parasite control strategies. A combined approach of modelling and focus groups for feedback was employed to assess the economic impacts of alternative GIN control strategies in South West France and North East Scotland. This two step method allowed results from the survey and farm modelling to be used during workshops, which also addressed social factors explaining the uptake and acceptance of GIN practices to control parasites. An existing excel based farm model was adapted in order to estimate the economic impacts of a range of alternative GIN practices. The model was adapted using data from a typical farm for organic goat system in France (Occitanie and Auvergne-Rhône-Alpes Regions) and two organic sheep systems (lowland and upland) in Scotland. A structured workshop approach was utilised to address both the social and economic factors related to adoption of alternative GIN practices by farmers. To this purpose, we adapted the Structured Decision Making (SDM) approach commonly used for decisions taking (Gregory and Keeney 1994, Conroy, Barker et al. 2008, Ogden and Innes 2009, Gregory 2012, Johnson, Eaton et al. 2015, Fatorić and Seekamp 2017). Overall, the modelling and farmer feedback showed that control of GIN needs to be farm specific, to suit the individual characteristics of both the farm but also the beliefs of the farmer. The extension of withdrawal periods combined with resistance issues in France have led to the adoption of TST by some farmers, but others are less convinced of its efficiency. The farmers in Scotland seem to have adopted multiple strategies such as use of arable land and mixed grazing to keep GIN levels from severely affecting their profits. However, the diversity of opinions and calls by the French farmers in particular for more trials, shows there is still further work to understand this problem and develop more effective, sustainable solutions

Organic farm incomes in England and Wales 2007/08 (OF 0373)

This report presents result of research on the financial performance of organic farms in 2006/07 and 2007/08, carried out for the Department for Environment, Food and Rural Affairs (Defra). This research builds on previous work on the economics of organic farming carried out at Aberystwyth University (Projects OF0190, covering 1995/96 to 1998/99 and OF0189, covering 1999/00 to 2004/05)1. Overall, identicla samples examined over a two year period 2006/07 to 2007/08 show that most organic sectors achieved higher net farm incomes in 2007/08 LFA cattle and sheep farm Net Farm Income (NFI) had the greatest increase,gaining by 46%, with strong gains in cropping and dairying. Organic mixed farming was the only sector to see reduced profitability, the identical sample falling back by 10%, while lowland cattle and sheep farming NFI increased by only 10%. When compared with comparable conventional farms, all organic farming sectors except poultry were above conventional profitability levels in 2007/08

Organic farm incomes in England and Wales 2006/07 (OF 0373)

In the report, we present results of research on the financial performance of organic farms in 2006/07 carried out for the Department for Environment, Food and Rural Affairs (Defra). The main aim of this work is to assess the financial performance of organic farms differentiated by farm type, in order to inform Defra policy-making with respect to economics of organic farming, and to provide a basis for assessments by farmers, advisers and other interested parties of the farm-level implications of conversion to and continued organic farming. This research builds on previous work on the economics of organic farming carried out at Aberystwyth University (Projects OF0190, covering 1995/96 to 1998/99 and OF0189, covering 1999/00 to 2004/05). In this report, financial data are shown for the 2005/06 and 2006/07 financial years, including between year comparisons and comparisons with similar conventional farms. For this report only, it has not been possible to produce identical samples for all farm types due to the change in data collection approach between 2005/06 and 2006/07. Identical sample comparisons have only been possible for lowland dairy and lowland and LFA cattle and sheep systems. The identical farm samples comprise farms that are present in both the 2005/06 and 2006/07 datasets. The total number of organic farms for 2006/07, also referred to as the full farm sample data, is shown alongside the identical datasets. In the other cases, data for the full samples in 2005/06 and 2006/07 are presented, but comparisons should be treated with caution due to changes in sample composition. Summarised and detailed financial input, output, income, returns to labour and capital, liabilities and assets and some physical performance measures are presented based on current Farm Business Survey (FBS) data collection and collation guidelines. The full samples of organic farms per robust farm type are sufficiently large to give some reasonable level of confidence in the data although it should be noted that the organic farm samples are not statistically representative of their type. However, the results can be seen as a reasonable indication of farm income levels for comparable organic and conventional farms. Smaller farm samples should be treated more cautiously as there is a possibility for outliers (especially larger farms) to have a significant influence on the average results. An additional element of this work is the inclusion of comparable conventional farm data (obtained from the main FBS sample) for the farm types shown. Each organic farm within this study was matched with an appropriate cluster of conventional farms based on the resource endowment indicators for individual organic farms. The indicators included farm type, FBS region, Less Favoured Area (LFA) status, utilisable agricultural area (UAA), milk quota held (where applicable) and farm business size. The cluster farm data were averaged for each farm type to derive the comparable conventional farm (CCF) data based on the organic farms from the identical and full farm samples. The identical samples of organic farms showed a much higher level of net farm incomes for lowland dairy and lowland cattle and sheep in 2006/07 than in 2005/06, with LFA cattle and sheep showing a small decrease over the period. Overall, organic net farm incomes exceeded conventional in all sectors, with most sectors showing an organic NFI twice that of the comparative conventional NFI. Increased organic prices and only small cost increases have led to substantial increases in organic NFI, especially in the livestock sectors

Review of the market for Welsh organic meat, 2007

The supply situation for Welsh organic meat Organic production in Wales has been developing steadily in the last five years, with particular emphasis on organic cattle and sheep production. This was despite over-supply conditions in some sectors, notably dairy, following the very rapid growth in 1999/2000. Between the end of 2002 and end of 2005, the number of holdings increased by 12% to 688, and the certified land area increased by 29% to 71,000 hectares, of which more than 90% is grassland. Growth in Wales has exceeded other parts of the UK, reaching 5% of agricultural land by end 2005. More rapid growth is projected for 2006 and possibly 2007. Organic cattle and sheep numbers have also increased steadily between 2003 and 2005, with total cattle numbers increasing by 114% to 37,000, and total sheep numbers increasing 105% to 248,000, representing 17 and 36% of the end 2005 UK organic population respectively (compared with 16% of UK holdings and 12% of UK land area). Actual output of Welsh organic lamb and beef is more difficult to quantifying reflecting a continuing need for improved statistical data to support market development and the delivery of public policy. Best estimates are 4000-5000 cattle slaughtered as organic, but potentially available production (some in conversion and/or marketed as conventional) may be as high as 8000 head. For lambs, possible estimates based on availables source range from 25,000 to 57,000, but potentially available production may be as high as 100,000. Better data is available within the industry, but is regarded as highly commercially sensitive and was not made available to the review team. Organic farm gate prices for lamb and beef have remained relatively steady over the period, although the gap with conventional prices has closed as the conventional sector has recovered. Recent increases in demand for organic meat, and the temporary suspension of beef imports from Argentina (now restarted) have resulted in some strengthening of organic prices in 2006. Organic premium prices do not, however, fully compensate for the increased costs of production per kg of meat, so that organic producers, like their conventional counterparts, are being paid less than the real costs of production, and are relying on Tir Mynydd, agri-environmental and Single Farm Payments to subsidise continued production. This leaves the industry vulnerable to any decline in market conditions and will mean continuing pressure on smaller producers to leave the sector. The Welsh organic red meat sector currently relies on two main marketing approaches. The majority of lamb and beef (> 80%) is marketed through multiple retailers, supplied by two producer groups. The need for producer collaboration to ensure a strong price negotiating position with the multiple retailers is recognised and has been yielding benefits. The remainder of Welsh production is marketed on a smaller scale through specialist and local retailers and directly to consumers, through farmers markets, farm shops and via internet sales. There is currently virtually no exploitation of the potential export market (outside the UK) and still some difficulties with marketing light and store lambs as well as dairy bred calves and cull cattle, although various initiatives are in progress to address this. The demand situation for Welsh organic meat From a consumer demand perspective, the overall organic food market is in a healthy state: according to TNS data, it has just passed the £1 billion mark and has put on an extra £200 million in the last two years. Growth in the latest year was 10% and 17% in the previous year. There is still huge opportunity for growth by continuing to convert non-users and simply getting existing users to purchase more often. Household penetration of any organic product is very high at 84%. However, many organic products are purchased by default, and are not planned, as consumers were either satisfying other needs or simply because they liked the product. The positive aspect is that organic is a benefit to products that fall in this category and gives something extra. Current organic users are also interested in most of the ethical issues affecting society today. They regard themselves as connoisseurs of food and wine and as such purchase quality and premium food. As the main contributor to the sales within each of these sectors, this may dilute the expenditure they could make on organic food specifically. Heavy users in total organic represent 20% of buyers and they are responsible for 80% of organic expenditure. You would expect these heavy users to be committed organic purchasers but they only spend 5% of their grocery shopping spend on organic products. None of them are exclusive organic users and they cross-shop across the retail quality tiers (Organic/Premium/Healthy/Standard and Value) extensively. In organic meat the situation is the same. There are 0.3% of meat shoppers who buy only organic and a further 0.1% who buy only organic and premium. The rest shop across all the tiers. This does however identify some of the scope for expansion and these heavy users must be prime targets for increased organic usage. The red meat heavy organic shopper will buy over six times a year but medium users just under twice and light users just over once. This level of frequency is low and would suggest little commitment from the light and medium buyers and a very mixed cross-tier purchasing strategy for the heavy organic buyer. There are 3.2 million households in GB who buy organic meat but there are only 68,000 who only buy organic meat. This figure is lower than that for any of the individual species, indicating that someone who is a loyal organic user of one species is not loyal to organic, when purchasing the other species. (Households who only purchase organic: Beef 108,000, Lamb 269,000, Pork 112,000, Red meat 68,000). Heavy organic meat buyers will have one or two children and be in social class ABC1; they may be younger and older family groups. They are over represented in London, South, Scotland, East England and the South West. Whilst beef is the biggest organic red meat sector, it is only 1.5% of total beef sales; Lamb is the strongest at 2.2% of sales. Pork is a clear third with organic being 1% of sales. Organic meat in Wales is currently worth £2.4 million and is growing at 3% a year. This growth is coming from new entrants into the market. Total GB is growing 10% ahead of Wales but the household penetration in Wales is higher at 13.1% compared to 12.9% for GB. Growth in Wales is coming from all age groups and social classes, with the under 28’s and the C2 groups being particularly strong. This report also looks at the retail market in Great Britain for organic produce; there are additional opportunities within the foodservice sector where a number of specialist organic restaurants are appearing and interest shown by some of the large operators in including an organic alternative on their menus. There are also opportunities for export of organic Welsh lamb; currently some exports to Italy are carried out and there is further potential to exploit and develop this market. The potential for export of light organic lamb is restricted to southern European markets due to the small size and high seasonality of the product. Hybu Cig Cymru is have ongoing discussions with potential buyers in countries such as Portugal, Spain and Greece; however to date, the small volumes required have precluded meaningful developments due to logistical difficulties. Recommendations Despite the generally positive outlook from a demand perspective, there is a need to address some of the factors that might discourage producers from converting, including disruption to the Organic Farming Scheme, price levels that are below costs of production, and lack of markets for some livestock categories, in particular light lamb. To address this, there is a need for: • better statistical data on current and future production levels and market shares; • continued efforts to support producer groups in developing markets for organic meat and in seeking to achieve realistic prices; • continued development of alternative marketing channels, building on Welsh PGI and organic status, including local multiple and smaller retailers, public procurement, distribution hubs and exports; • consumer promotion initiatives and increased Welsh organic meat presence at trade fairs; • improved production systems, supported by effective research and development and knowledge transfer; • improved integration of effort between organic sector businesses and the agencies that support the development of the Welsh meat and organic sectors; • better linkage with the dairy, arable and horticulture sectors to benefit from complementarity relationships between the sectors at production, market development and promotional levels

Organic farm incomes in England and Wales 2010/11 (OF 0373)

This report presents results of research on the financial performance of organic farms in the 2010/11 financial year (with 2009/10 data for reference. Carried out for the Department for Environment, Food and Rural Affairs (Defra), this research continues project OF0373. An analysis of the FBS/Defra Data Archive for 2010/11 found a total of 250 businesses with some organic land. To ensure the analysis undertakes a reasonable comparison of organic and conventional farms, the organic sample comprises holdings with at least 70% fully organic land (>=70% of UAA ha). Farms identified as “in-conversion” or those with less than 70% fully organic certified land were excluded from the analysis, providing a sample of 217 organic holdings. In total, data from 212 organic farms were suitable for inclusion in the analysis, as it was not possible to identify comparable conventional businesses for four organic farms, and one organic specialist pig farm was also not utilised (due to minimum sample size restrictions). The data was analysed as two samples; a full sample and an identical sample. The full sample analysis utilised data from all 212 organic farms and provides the best comparison of organic and comparable conventional farm income data in 2010/11, (2009/10 data is only provided for reference). The identical sample identifies year to year changes within systems, though the sample sizes are smaller as not all farms will be part of the FBS dataset for two years

Dominance Motivation, Goal Pursuit and Mania in Bipolar Disorder

The study aimed to test how progress on achievement and power goals, and perceptions of power, fluctuate with mania symptoms in Bipolar Disorder (BD), testing the Dominance Behavioural System (DBS) model. The DBS includes biological, psychological, and behavioural components that serve the goal of control over social and material resources needed for survival and reproduction (Johnson, Leedom, & Muhtadie, 2012c). Daily diary methodology was employed, with 29 individuals meeting the Diagnostic and Statistical Manual-Fourth Edition (DSM-IV) criteria for BD I or II as verified by the Structured Clinical Interview [SCID-I-RV] (First, Spitzer, Gibbon & Williams, 2002). Baseline measures of dominance motivation and ambitious goal setting were taken. Over fourteen days, participants reported daily on their goal progress, symptoms of mania, power, and anger. It was hypothesised there would be a positive relationship between symptoms of mania and dominance motivation. It was also hypothesised that for power but not achievement goals, ii) goal progress would be associated with perceptions of power, iii) symptoms of mania, and iv) that goal frustration would be associated with anger. Pearson’s correlations and multilevel modelling analyses found largely null results with the exception of a positive relationship between progress towards power goals and perceptions of power. Thus, the results did not provide support for the DBS model predictions for relationships between power goals and manic symptoms. Future studies could utilise further measures of dominance motivation and power, and study goal pursuit over a more protracted duration, including comparisons between BD, depressed groups, and healthy controls. Keywords Bipolar Disorder, Dominance Behavioural System, Goals, Powe

Performance of mixed and agroforestry systems:Deliverable 5.1

Introduction: With increasing pressure on agriculture to reduce its environmental impacts, it has been hypothesised that mixed farming and agro-forestry systems (MiFAS), either at a farm or landscape scale, could be an option to mitigate issues of nutrient excess, the import of synthetic fertilisers or feed production. Traditionally, mixed farming was practised to provide nutrients to crops through rotation breaks and to feed livestock, however the use of synthetic fertilisers and economies of scale has led to increased specialisation.It has been proposed that the re-integration of cropping and livestock could close nutrient cycles and reduce imports of external feed or fertiliser nutrients, improve soil quality through returns of organic matter, as well as potential socio-economic benefits; although difficulties may occur, such as a loss of more profitable crops. A further potential climate change mitigation measure is the use of agroforestry within specialised agricultural systems to provide shading, soil stabilisation, drought resistant browse material for direct grazing or cutting, as well as directly offsetting GHG emissions through sequestration of carbon through biomass and or soil organic carbon (SOC). Task 5.1 therefore aimed to assess farm survey data (Task 2.3a) from existing MiFAS (Mixed and Agroforestry Systems), to assess their environmental and economic performance, as well as link to Task 5.2 to include coverage of potential labour issues.Methodology: The assessment of the MiFAS within Task 5.1 employed a quantitative approach to undertake an LCA-based assessment of farm practices to generate results across a wide range of environmental and economic indicators through use of the FarmLCA model that includes both Lifecycle Impact Assessment (LCIA) and the German KTBL standard costs database. As per the guidance of ISO14040 and ISO14044 (ISO, 2006), we followed the recommended four steps to conduct an LCA, including a goal and scope definition phase. Our main goal was to assess the performance of MiFAS, based on data collected through farm surveys in multiple European countries. We also aimed to improve methodologies for assessing these systems and then to make a comparison so that conclusions and recommendations could be made. In terms of scope, we would conduct single farm assessments, with flexible boundaries either at the farm gate or at a smaller scale for some systems. Interactions, such as exchanges of straw or manures were treated as external inputs or outputs to the farm boundary, as these exchanges were assessed within WP3 (D3.4).The second step, the inventory analysis phase, comprised collection of data for each farm and was linked to Tasks 2.2 and 2.3a where the on-farm data collection was developed and supervised. The survey was conducted in each network and were undertaken either as an on-farm interview or in some countries farm management data was already accessible electronically, with a follow up interview with the farmer. In general, data collection started in autumn 2022 and final queries were completed in autumn 2023.Collected data was utilised from 9 networks for assessment through Task 5.1. For Denmark, NW02 was organised around reducing nutrient excesses and involved exchanges of manures with biogas plants and other farms, as well as returns of digestates. Whilst in Scotland, NW04 was focussed on the trialling of winter grazing of cereals by sheep, as well as other material exchanges such as straw and manures, but the network also included mixed farms with beef cattle. The German networks comprised NW5, focussed on peatland restoration of former intensively managed land, whilst the second German network NW06, comprised three farms developing agroforestry. In Switzerland NW07 comprised farms with high-stemmed fruit trees as an agroforestry system with grazing livestock and or crops. In France two networks comprised, NW09 with a focus on outdoor pig production in an agroforestry/woodland setting, and the second French network NW10 located in the SW region, included a range of mixed, arable and specialist livestock farms collaborating with partners to improve exchanges of materials and nutrients. In the east of Europe, the Romanian NW11 comprised farms collaborating to develop agri-tourism within the region of diverse small farms, whilst in Poland NW13, a large single farm comprised a biodynamic mixed farm, linking dairy and arable production.Following data collection, data validation was critical due to the wide range of systems studied and we focused on agronomic data, such as yield, fertiliser use, animal herd and rationing values withqueries passed back to data enumerators for clarification. The assessment tool also included plausibility checks for nutrient requirements for crops or animals of a certain yield or animal type.Each farm was modelled using the FarmLCA tool, which comprises LCA and economics modules and allows the individual nature of each farm and the management of their crops and livestock to be included. Subsequent sub-models estimating relevant emissions, such as enteric methane, nitrous oxides, or other pollutants, were based on methodologies recommended by IPCC and EMEP. The combination of on-farm data for external inputs together with outputs from the sub-models a farm specific LCA inventory was created. This is used to then calculate the impacts for different crops and livestock systems, which can be reported at various levels. Where multiple products were produced by the same plot or livestock, allocation was conducted as per ISO14040 and 14044.The assessment of MiFAS required additional model adaptations, as by their nature, MiFAS may produce multiple products from the same land area concurrently, such as apple trees with pasture beneath them, or arable crops that provide winter grazing for sheep. The main issues included allocation of impacts between the co-products, especially when an input is not attributable to a single output. We therefore undertook specific methodological adaptations for assessing MiFAS within the MIXED project including assessing soil carbon changes and biomass with agroforestry (AF) systems. Quantifying SOC changes is scientifically challenging, and we therefore adapted the IPCC Tier 2 steady state method to include a wider variety of land uses including permanent pasture, orchards, and other trees. For agroforestry, we included a module for biomass carbon calculations and adopted a Tier 1 approach according to the meta-review by Cardinael et al. (2018), to estimate the changes in above and below ground carbon for the first 20 years following land use change. Furthermore, to increase accuracy in AF calculations we used a spatial allocation method to differentiate between trees and pasture and to account for tree size and planting density. For the economic analysis, we utilised an adapted database from the German KTBL database which allows for farms from multiple countries to be grouped together without issues of costs differing due to local situations. Gross margin calculations (partial net margins) are reported based on outputs minus inputs, which include labour.Due to the wide diversity of farm systems within and between networks, we adopted a statistically based two-step clustering approach to group the farms from across all the networks into farm system type groups for comparison. We found that using binary variables related to the presence of AF, livestock, together with proportions of farms with permanent grassland and field crops generated four groups for comparison. To enable a statistical assessment of farm system differences, we also utilised the non-parametric Kruskall Wallis test to enable robust assessment within data that violated the normal assumptions of one-way ANOVAs.Results: For the impact assessment phase, we presented results for each farm per network, followed by results per farm system type, as well as presented at an enterprise level for a limited number of crops and livestock. For the analysis, we adopted a number of performance indicators to characterise the farm systems, assessed their use of nitrogen as a main agricultural nutrient, estimated potential changes in carbon, environmental indicators as part of the LCIA and economic indicators for sales, costs and partial net margins.Farm Networks: The networks assessed provided a wide variety of farm systems across a wide geographical area. The data provided about the farm systems included full farm systems through to specific areas of farms that focussed on a particular topic, e.g. agroforestry. The farms also ranged from very diverse, complex systems with crops, livestock and agroforestry through to large highly specialised units, which were included within the MIXED project due to their participation in landscape scale collaborations. These specialised systems also proved extremely valuable as comparators to the MiFAS type systems. Whilst farms within some of the networks had a common theme, such as the French NW09, others were highly diverse, such as French NW10. Livestock types were also diverse, covering all sectors except broiler chickens.The Danish NW02 farms produced a wide range of arable crops, as well as some farms with large herds of intensively managed cattle or pig systems. Despite the transfer of nutrients via biogas plants, all of the farms had high nitrogen inputs, especially when nitrogen within feed is accounted for (up to 551kg N ha-1). The intensity of farm systems therefore resulted in some very high GHGemissions, especially when livestock were kept. Whilst in Scotland NW04, most of the farms comprised cropping with or without livestock, and emissions were greatest on farms with livestock. The German NW5 farms showed that whilst emissions from the peat land have declined, the current utilisation, such as extensive beef production, was found to have very high emissions, because of the underlying peat, as well as slow growth rates. The German NW06 farms were diverse, including a free-range egg system which was very reliant on external feeds, resulting in high nitrogen related emissions, whilst the other two systems were less integrated and planted more like tree hedgerows. Whilst new agroforestry showed a potential for new carbon storage, the long-term aspects were unclear, so carbon storage on a 100-year basis was unclear.In Switzerland, despite the NW07 farms having larger high-stemmed fruit trees as an agroforestry system, the Tier 1 methodology means that beyond 20 years of age the biomass carbon was assumed to be at equilibrium. However, the Swiss farms did demonstrate the improved circularity from using livestock manures as the primary fertiliser source, with low external nitrogen sourcing. However, when livestock are maintained with and feed imported, emissions increase, though offsetting emissions through biomass storage in new trees can be partially effective. In France NW09, despite the woodland setting for the raising of pigs, the high feed imports and stocking densities, combined with slower growth rates caused high emissions. Although comprising a significant level of woodland, the trees were generally older (around 70 years), therefore biomass carbon was assumed to be in equilibrium as per the Tier 1 guidelines. The French NW10 was a mixture of farm types, and the specialist livestock farms were generally very extensive, whilst the cropping farms were quite intensive, and emissions depended largely on their intensity and the presence of livestock. For the Romanian NW11 farms obtaining high quality data suitable for conducting an LCA was problematic, and therefore a single typical farm for the region was constructed comprising a high intensity of livestock, feeds purchased and diverse fruit trees on pasture or in orchards. The high density of livestock resulted in high emissions within this system. In Poland NW13 as a single very large biodynamic mixed farm had few external inputs, but limitations to its crop yields are a severe handicap to economic performance, as well as causing some higher-than-expected product impacts.The interpretation phase assessed all phases of the analysis, including input data from the farms, methodological challenges, results at the network, system type and enterprise level, as well as making general conclusions from the work undertaken. We found that with such a diverse range of farms in the dataset it was difficult to come to clear conclusions about the performance of different farm system types, therefore, a single farm dataset was formed, and farms were grouped into four system types, integrated cropping and livestock (ICL), specialist arable (SA), specialist livestock (SL) and integrated cropping/livestock and agroforestry (ICLF). Ideally, we would also have liked to compare organic and conventional systems, but the dataset was too small to undertake any valid comparison.In terms of characteristics. we found that the ICL and ICLF farm clusters were larger than the specialist systems, highlighting the focus of the farm networks. Farm areas were much greater for the ICL, SA and SL systems, whilst the ICL and SA types both had a high proportion of field cropping. However, we also observed that the more integrated system had a reasonable proportion of temporary forages, with a little grassland. The SL was dominated by permanent grassland, with similar livestock numbers for both ICL and SL, though livestock stocking density was greatest for ICLF, probably because of the French pig systems.The main nitrogen indicators all showed significant differences between the four farm system types, whilst fertiliser application of nitrogen was lower on SA systems. Nitrogen self-sufficiency and the proportion of nitrogen applied as organic manures was always lower on SA farms, intermediate for ICL and higher on the ICLF and SL farms, as may be expected with higher livestock levels. However, nitrogen export as products was lower on SL, with ICL and SA the highest because of the higher N exported per hectare of cropland.Whilst we found differences in revenue and costs between the farm systems, overall, there was no significant difference between the farm system types. However, when comparing environmental impacts, all environmental indicators showed significant differences between systems. For greenhouse gas (GHG) emissions we found that per hectare, the SA farms had lower emissions,with SL at an intermediate level and the two integrated systems showing the greatest impacts. Using the alternative functional unit of per kg of nitrogen exported, the results showed the greatest emissions for the SL system, likely in part due to the low productivity extensive systems, whilst the integrated systems were at an intermediate level.In terms of fossil and nuclear energy (FNE) use, SL farms were lowest per hectare, but again, when assessed by kilogram of N exported, became the highest energy user. The cropping systems showed the greatest energy use per hectare, but SA farms were the lowest per kg nitrogen exported. In terms of mineral resource use, the SA and SL farm types had lower use per hectare, whilst per kg of N exported, SA farms had the lowest impacts, ICL was intermediate with the SL and ICLF farms the largest resource users.Considering acidification impacts, both indicators (FA and TA) showed SL farms to have low impacts reflecting the far lower levels of N inputs per hectare, whilst for impacts per kg N exported, SA systems showed lowest impacts due to high N outputs compared to the livestock centric ICLF and SL systems. Eutrophication (FEU and MEU) results per hectare reflected the low Phosphorus inputs of the SA and SL systems, whilst for MEU, the SL system was lowest per hectare but greatest per kg N exported. The integrated systems were intermediate for both functional units.When we assessed data at an enterprise level, we found wheat and beef to be present in many networks. In total we found 36 wheat crops, and results of comparing the underlying farming system indicated very different management between the farm types. The highest levels of mineral nitrogen were used on ICL and SA farm types who also achieved the highest yields. This probably explains why the GHGs and energy use, were lower for the ICLF and SL farm types, however due to heterogeneity within the data, for most of the environmental impact indicators there were no significant differences.Beef animals were reared on 21 farms within the networks and included animals from both dairy and suckler cows. We found that stocking density was highest on the ICLF and ICL farms, whilst rations were not significantly different, with all systems receiving a high median level of forage. However, the environmental impacts were significantly different between farm types, with the SL farm types showing the highest impacts. Contribution analysis highlighted the greater impacts of the SL system for most impact categories, with greater GHGs likely because of enteric emissions and the greater emissions embedded within the transferred in-stock, such as weaned calves from generally higher GHG suckler cow systems.Changes in the soil carbon were generally very small, probably due to reporting of only the passive soil pool as the more active soil pools are short term and therefore inappropriate to report within the 100 year GHG basis (GWP100). Soil carbon changes were also more limited due to the single time frame of the detailed data collection, preventing more consideration of specific management changes that may have affected SOC. One factor that became apparent within the modelling, was that in the absence of fundamental system changes, the temperature effect on soil C degradation is already apparent. As temperature increases, we see greater SOC loss under the same management and as the model uses a 20-year period for assessing SOC, the increasing temperature within the climate datasets shows SOC is generally being lost in the carbon dynamic tables.The biomass modelling was entirely new for the project and the Tier 1 method, together with adaptations for tree size and planting density provided some insight into the potential of agroforestry. We found that there was a great difference in tree biomass potential carbon storage depending on the age structure of the trees, partly as a direct result of the modelling assumptions, i.e. no additional storage in AF systems after 20 years as most AF systems are built around early maturing trees, like fruit, nut or short-rotation coppice (SRC) trees. Furthermore, whilst the initial planting of AF trees adds new above and belowground biomass carbon storage, this is potentially at the cost of soil carbon initially and it may take up to 30 years before an increase in SOC is observed (e.g. Paul et al., 2022), however, the ecosystem services of AF go beyond carbon storage and still represents a viable climate change mitigation option.In conclusion, we were able to assess a very diverse range of farm systems in varying geographical locations to at least partly, answer the question of whether MiFAS systems provide environmentaland potentially economic benefits. The answer is sometimes and depending on the indicator and functional unit applied. The ICL and ICLF systems, as well as the SL were more self-sufficient in nitrogen supply, but SA farms had better external nitrogen utilisation. In terms of GHGs, the SA farms emitted the least at both per hectare and per kg nitrogen exported from the farm, with SL emitting the highest and the ICL and ICLF farms at an intermediate level. For the other environmental indicators, the SL farms were usually the lowest per hectare because of their extensive characteristics, whilst for the per kg nitrogen FU, SA farms were lowest and SL the highest. Economically, all farm types showed a net loss, with the low input SL farms showing the smallest loss and ICL the greatest, though these differences were not significant.However, these results are influenced by the farms within each type, and there were clear trade-offs between per area and per product impacts. The results also showed that the impacts are very related to the specific situation on the farm and that strategies such as agroforestry alone will not solve issues, but a whole farm approach to reducing impacts through reduction and efficient use of fertilisers and feeds, combined with additional strategies will have the greatest impact. Some of the ICLF systems were situated with existing woodlands and due to its age, new carbon sequestration was unlikely, whilst the system was also supported by considerable external feed inputs, therefore the system does not appear to be a solution from an LCA impact perspective. However, the more extensive versions of this systems provided direct benefits as well as other factors such as welfare which may be much improved compared to intensive indoor production.The results from this analysis should be viewed with caution as the systems assessed were only representative within a range of networks available within the MIXED project. Farms had specific management strategies, which may provide considerable benefits either at a local or even wider spread adoption, such as winter grazing of cereals by sheep, exchanges between farms, as well as agroforestry. However, the results could be strongly influenced by certain aspects and generalisations should not be made. From a policy perspective, the results point to variation in impacts due to t

Performance of mixed and agroforestry systems:Deliverable 5.1

Introduction: With increasing pressure on agriculture to reduce its environmental impacts, it has been hypothesised that mixed farming and agro-forestry systems (MiFAS), either at a farm or landscape scale, could be an option to mitigate issues of nutrient excess, the import of synthetic fertilisers or feed production. Traditionally, mixed farming was practised to provide nutrients to crops through rotation breaks and to feed livestock, however the use of synthetic fertilisers and economies of scale has led to increased specialisation.It has been proposed that the re-integration of cropping and livestock could close nutrient cycles and reduce imports of external feed or fertiliser nutrients, improve soil quality through returns of organic matter, as well as potential socio-economic benefits; although difficulties may occur, such as a loss of more profitable crops. A further potential climate change mitigation measure is the use of agroforestry within specialised agricultural systems to provide shading, soil stabilisation, drought resistant browse material for direct grazing or cutting, as well as directly offsetting GHG emissions through sequestration of carbon through biomass and or soil organic carbon (SOC). Task 5.1 therefore aimed to assess farm survey data (Task 2.3a) from existing MiFAS (Mixed and Agroforestry Systems), to assess their environmental and economic performance, as well as link to Task 5.2 to include coverage of potential labour issues.Methodology: The assessment of the MiFAS within Task 5.1 employed a quantitative approach to undertake an LCA-based assessment of farm practices to generate results across a wide range of environmental and economic indicators through use of the FarmLCA model that includes both Lifecycle Impact Assessment (LCIA) and the German KTBL standard costs database. As per the guidance of ISO14040 and ISO14044 (ISO, 2006), we followed the recommended four steps to conduct an LCA, including a goal and scope definition phase. Our main goal was to assess the performance of MiFAS, based on data collected through farm surveys in multiple European countries. We also aimed to improve methodologies for assessing these systems and then to make a comparison so that conclusions and recommendations could be made. In terms of scope, we would conduct single farm assessments, with flexible boundaries either at the farm gate or at a smaller scale for some systems. Interactions, such as exchanges of straw or manures were treated as external inputs or outputs to the farm boundary, as these exchanges were assessed within WP3 (D3.4).The second step, the inventory analysis phase, comprised collection of data for each farm and was linked to Tasks 2.2 and 2.3a where the on-farm data collection was developed and supervised. The survey was conducted in each network and were undertaken either as an on-farm interview or in some countries farm management data was already accessible electronically, with a follow up interview with the farmer. In general, data collection started in autumn 2022 and final queries were completed in autumn 2023.Collected data was utilised from 9 networks for assessment through Task 5.1. For Denmark, NW02 was organised around reducing nutrient excesses and involved exchanges of manures with biogas plants and other farms, as well as returns of digestates. Whilst in Scotland, NW04 was focussed on the trialling of winter grazing of cereals by sheep, as well as other material exchanges such as straw and manures, but the network also included mixed farms with beef cattle. The German networks comprised NW5, focussed on peatland restoration of former intensively managed land, whilst the second German network NW06, comprised three farms developing agroforestry. In Switzerland NW07 comprised farms with high-stemmed fruit trees as an agroforestry system with grazing livestock and or crops. In France two networks comprised, NW09 with a focus on outdoor pig production in an agroforestry/woodland setting, and the second French network NW10 located in the SW region, included a range of mixed, arable and specialist livestock farms collaborating with partners to improve exchanges of materials and nutrients. In the east of Europe, the Romanian NW11 comprised farms collaborating to develop agri-tourism within the region of diverse small farms, whilst in Poland NW13, a large single farm comprised a biodynamic mixed farm, linking dairy and arable production.Following data collection, data validation was critical due to the wide range of systems studied and we focused on agronomic data, such as yield, fertiliser use, animal herd and rationing values withqueries passed back to data enumerators for clarification. The assessment tool also included plausibility checks for nutrient requirements for crops or animals of a certain yield or animal type.Each farm was modelled using the FarmLCA tool, which comprises LCA and economics modules and allows the individual nature of each farm and the management of their crops and livestock to be included. Subsequent sub-models estimating relevant emissions, such as enteric methane, nitrous oxides, or other pollutants, were based on methodologies recommended by IPCC and EMEP. The combination of on-farm data for external inputs together with outputs from the sub-models a farm specific LCA inventory was created. This is used to then calculate the impacts for different crops and livestock systems, which can be reported at various levels. Where multiple products were produced by the same plot or livestock, allocation was conducted as per ISO14040 and 14044.The assessment of MiFAS required additional model adaptations, as by their nature, MiFAS may produce multiple products from the same land area concurrently, such as apple trees with pasture beneath them, or arable crops that provide winter grazing for sheep. The main issues included allocation of impacts between the co-products, especially when an input is not attributable to a single output. We therefore undertook specific methodological adaptations for assessing MiFAS within the MIXED project including assessing soil carbon changes and biomass with agroforestry (AF) systems. Quantifying SOC changes is scientifically challenging, and we therefore adapted the IPCC Tier 2 steady state method to include a wider variety of land uses including permanent pasture, orchards, and other trees. For agroforestry, we included a module for biomass carbon calculations and adopted a Tier 1 approach according to the meta-review by Cardinael et al. (2018), to estimate the changes in above and below ground carbon for the first 20 years following land use change. Furthermore, to increase accuracy in AF calculations we used a spatial allocation method to differentiate between trees and pasture and to account for tree size and planting density. For the economic analysis, we utilised an adapted database from the German KTBL database which allows for farms from multiple countries to be grouped together without issues of costs differing due to local situations. Gross margin calculations (partial net margins) are reported based on outputs minus inputs, which include labour.Due to the wide diversity of farm systems within and between networks, we adopted a statistically based two-step clustering approach to group the farms from across all the networks into farm system type groups for comparison. We found that using binary variables related to the presence of AF, livestock, together with proportions of farms with permanent grassland and field crops generated four groups for comparison. To enable a statistical assessment of farm system differences, we also utilised the non-parametric Kruskall Wallis test to enable robust assessment within data that violated the normal assumptions of one-way ANOVAs.Results: For the impact assessment phase, we presented results for each farm per network, followed by results per farm system type, as well as presented at an enterprise level for a limited number of crops and livestock. For the analysis, we adopted a number of performance indicators to characterise the farm systems, assessed their use of nitrogen as a main agricultural nutrient, estimated potential changes in carbon, environmental indicators as part of the LCIA and economic indicators for sales, costs and partial net margins.Farm Networks: The networks assessed provided a wide variety of farm systems across a wide geographical area. The data provided about the farm systems included full farm systems through to specific areas of farms that focussed on a particular topic, e.g. agroforestry. The farms also ranged from very diverse, complex systems with crops, livestock and agroforestry through to large highly specialised units, which were included within the MIXED project due to their participation in landscape scale collaborations. These specialised systems also proved extremely valuable as comparators to the MiFAS type systems. Whilst farms within some of the networks had a common theme, such as the French NW09, others were highly diverse, such as French NW10. Livestock types were also diverse, covering all sectors except broiler chickens.The Danish NW02 farms produced a wide range of arable crops, as well as some farms with large herds of intensively managed cattle or pig systems. Despite the transfer of nutrients via biogas plants, all of the farms had high nitrogen inputs, especially when nitrogen within feed is accounted for (up to 551kg N ha-1). The intensity of farm systems therefore resulted in some very high GHGemissions, especially when livestock were kept. Whilst in Scotland NW04, most of the farms comprised cropping with or without livestock, and emissions were greatest on farms with livestock. The German NW5 farms showed that whilst emissions from the peat land have declined, the current utilisation, such as extensive beef production, was found to have very high emissions, because of the underlying peat, as well as slow growth rates. The German NW06 farms were diverse, including a free-range egg system which was very reliant on external feeds, resulting in high nitrogen related emissions, whilst the other two systems were less integrated and planted more like tree hedgerows. Whilst new agroforestry showed a potential for new carbon storage, the long-term aspects were unclear, so carbon storage on a 100-year basis was unclear.In Switzerland, despite the NW07 farms having larger high-stemmed fruit trees as an agroforestry system, the Tier 1 methodology means that beyond 20 years of age the biomass carbon was assumed to be at equilibrium. However, the Swiss farms did demonstrate the improved circularity from using livestock manures as the primary fertiliser source, with low external nitrogen sourcing. However, when livestock are maintained with and feed imported, emissions increase, though offsetting emissions through biomass storage in new trees can be partially effective. In France NW09, despite the woodland setting for the raising of pigs, the high feed imports and stocking densities, combined with slower growth rates caused high emissions. Although comprising a significant level of woodland, the trees were generally older (around 70 years), therefore biomass carbon was assumed to be in equilibrium as per the Tier 1 guidelines. The French NW10 was a mixture of farm types, and the specialist livestock farms were generally very extensive, whilst the cropping farms were quite intensive, and emissions depended largely on their intensity and the presence of livestock. For the Romanian NW11 farms obtaining high quality data suitable for conducting an LCA was problematic, and therefore a single typical farm for the region was constructed comprising a high intensity of livestock, feeds purchased and diverse fruit trees on pasture or in orchards. The high density of livestock resulted in high emissions within this system. In Poland NW13 as a single very large biodynamic mixed farm had few external inputs, but limitations to its crop yields are a severe handicap to economic performance, as well as causing some higher-than-expected product impacts.The interpretation phase assessed all phases of the analysis, including input data from the farms, methodological challenges, results at the network, system type and enterprise level, as well as making general conclusions from the work undertaken. We found that with such a diverse range of farms in the dataset it was difficult to come to clear conclusions about the performance of different farm system types, therefore, a single farm dataset was formed, and farms were grouped into four system types, integrated cropping and livestock (ICL), specialist arable (SA), specialist livestock (SL) and integrated cropping/livestock and agroforestry (ICLF). Ideally, we would also have liked to compare organic and conventional systems, but the dataset was too small to undertake any valid comparison.In terms of characteristics. we found that the ICL and ICLF farm clusters were larger than the specialist systems, highlighting the focus of the farm networks. Farm areas were much greater for the ICL, SA and SL systems, whilst the ICL and SA types both had a high proportion of field cropping. However, we also observed that the more integrated system had a reasonable proportion of temporary forages, with a little grassland. The SL was dominated by permanent grassland, with similar livestock numbers for both ICL and SL, though livestock stocking density was greatest for ICLF, probably because of the French pig systems.The main nitrogen indicators all showed significant differences between the four farm system types, whilst fertiliser application of nitrogen was lower on SA systems. Nitrogen self-sufficiency and the proportion of nitrogen applied as organic manures was always lower on SA farms, intermediate for ICL and higher on the ICLF and SL farms, as may be expected with higher livestock levels. However, nitrogen export as products was lower on SL, with ICL and SA the highest because of the higher N exported per hectare of cropland.Whilst we found differences in revenue and costs between the farm systems, overall, there was no significant difference between the farm system types. However, when comparing environmental impacts, all environmental indicators showed significant differences between systems. For greenhouse gas (GHG) emissions we found that per hectare, the SA farms had lower emissions,with SL at an intermediate level and the two integrated systems showing the greatest impacts. Using the alternative functional unit of per kg of nitrogen exported, the results showed the greatest emissions for the SL system, likely in part due to the low productivity extensive systems, whilst the integrated systems were at an intermediate level.In terms of fossil and nuclear energy (FNE) use, SL farms were lowest per hectare, but again, when assessed by kilogram of N exported, became the highest energy user. The cropping systems showed the greatest energy use per hectare, but SA farms were the lowest per kg nitrogen exported. In terms of mineral resource use, the SA and SL farm types had lower use per hectare, whilst per kg of N exported, SA farms had the lowest impacts, ICL was intermediate with the SL and ICLF farms the largest resource users.Considering acidification impacts, both indicators (FA and TA) showed SL farms to have low impacts reflecting the far lower levels of N inputs per hectare, whilst for impacts per kg N exported, SA systems showed lowest impacts due to high N outputs compared to the livestock centric ICLF and SL systems. Eutrophication (FEU and MEU) results per hectare reflected the low Phosphorus inputs of the SA and SL systems, whilst for MEU, the SL system was lowest per hectare but greatest per kg N exported. The integrated systems were intermediate for both functional units.When we assessed data at an enterprise level, we found wheat and beef to be present in many networks. In total we found 36 wheat crops, and results of comparing the underlying farming system indicated very different management between the farm types. The highest levels of mineral nitrogen were used on ICL and SA farm types who also achieved the highest yields. This probably explains why the GHGs and energy use, were lower for the ICLF and SL farm types, however due to heterogeneity within the data, for most of the environmental impact indicators there were no significant differences.Beef animals were reared on 21 farms within the networks and included animals from both dairy and suckler cows. We found that stocking density was highest on the ICLF and ICL farms, whilst rations were not significantly different, with all systems receiving a high median level of forage. However, the environmental impacts were significantly different between farm types, with the SL farm types showing the highest impacts. Contribution analysis highlighted the greater impacts of the SL system for most impact categories, with greater GHGs likely because of enteric emissions and the greater emissions embedded within the transferred in-stock, such as weaned calves from generally higher GHG suckler cow systems.Changes in the soil carbon were generally very small, probably due to reporting of only the passive soil pool as the more active soil pools are short term and therefore inappropriate to report within the 100 year GHG basis (GWP100). Soil carbon changes were also more limited due to the single time frame of the detailed data collection, preventing more consideration of specific management changes that may have affected SOC. One factor that became apparent within the modelling, was that in the absence of fundamental system changes, the temperature effect on soil C degradation is already apparent. As temperature increases, we see greater SOC loss under the same management and as the model uses a 20-year period for assessing SOC, the increasing temperature within the climate datasets shows SOC is generally being lost in the carbon dynamic tables.The biomass modelling was entirely new for the project and the Tier 1 method, together with adaptations for tree size and planting density provided some insight into the potential of agroforestry. We found that there was a great difference in tree biomass potential carbon storage depending on the age structure of the trees, partly as a direct result of the modelling assumptions, i.e. no additional storage in AF systems after 20 years as most AF systems are built around early maturing trees, like fruit, nut or short-rotation coppice (SRC) trees. Furthermore, whilst the initial planting of AF trees adds new above and belowground biomass carbon storage, this is potentially at the cost of soil carbon initially and it may take up to 30 years before an increase in SOC is observed (e.g. Paul et al., 2022), however, the ecosystem services of AF go beyond carbon storage and still represents a viable climate change mitigation option.In conclusion, we were able to assess a very diverse range of farm systems in varying geographical locations to at least partly, answer the question of whether MiFAS systems provide environmentaland potentially economic benefits. The answer is sometimes and depending on the indicator and functional unit applied. The ICL and ICLF systems, as well as the SL were more self-sufficient in nitrogen supply, but SA farms had better external nitrogen utilisation. In terms of GHGs, the SA farms emitted the least at both per hectare and per kg nitrogen exported from the farm, with SL emitting the highest and the ICL and ICLF farms at an intermediate level. For the other environmental indicators, the SL farms were usually the lowest per hectare because of their extensive characteristics, whilst for the per kg nitrogen FU, SA farms were lowest and SL the highest. Economically, all farm types showed a net loss, with the low input SL farms showing the smallest loss and ICL the greatest, though these differences were not significant.However, these results are influenced by the farms within each type, and there were clear trade-offs between per area and per product impacts. The results also showed that the impacts are very related to the specific situation on the farm and that strategies such as agroforestry alone will not solve issues, but a whole farm approach to reducing impacts through reduction and efficient use of fertilisers and feeds, combined with additional strategies will have the greatest impact. Some of the ICLF systems were situated with existing woodlands and due to its age, new carbon sequestration was unlikely, whilst the system was also supported by considerable external feed inputs, therefore the system does not appear to be a solution from an LCA impact perspective. However, the more extensive versions of this systems provided direct benefits as well as other factors such as welfare which may be much improved compared to intensive indoor production.The results from this analysis should be viewed with caution as the systems assessed were only representative within a range of networks available within the MIXED project. Farms had specific management strategies, which may provide considerable benefits either at a local or even wider spread adoption, such as winter grazing of cereals by sheep, exchanges between farms, as well as agroforestry. However, the results could be strongly influenced by certain aspects and generalisations should not be made. From a policy perspective, the results point to variation in impacts due to t