An integrated sustainability assessment of mediterranean sheep farms with different degrees of intensification

In the European Mediterranean basin, pasture-based sheep farming systems are mostly located in marginal/High Nature Value areas. These production systems are multifunctional, and their economic, environmental and social roles are equally important and recognised by policy makers and by society. However, the number of animals and holdings is decreasing, and there is great uncertainty regarding the reproducibility of these farming systems, which depends on many internal and external farm factors and their interactions. The aim of this paper was to perform a comprehensive assessment of sustainability in different sheep farming systems in north eastern Spain using the MESMIS framework. We followed a case-study approach to perform an in-depth investigation of 4 sheep meat and dairy farms with different intensities of reproduction management. Critical points of sustainability, including weaknesses and opportunities, were obtained using a participatory process with stakeholders (farmers and technical advisers) that resulted in the selection of 37 sustainability indicators that were classified according to the systemic attributes defined by MESMIS (productivity, stability, self-reliance, adaptability, equity) and according to the classical sustainability pillars (social, economic and environmental). Some underlying patterns could be observed when analysing sustainability pillars, attributes and indicators. A positive relationship between productivity and intensification level in meat farms was observed; however, economic sustainability was determined not only by on-farm but also by off-farm activities. The economic efficiency of farming (without considering subsidies) was mainly explained by the capture of added value in the dairy systems and the combination of high animal productivity as well as high forage and feed self-sufficiency in the meat systems. Social issues were also central to explaining sustainability at the farm level, including the prospects of generational turnover and the manner in which farmers perceive and rate their activity. A clear trade-off between economic and environmental indicators was observed, i.e., the higher the economic sustainability, the lower the environmental sustainability. Each farm scored differently for diverse attributes, pillars and individual indicators. The scores differed according to size, structure, resource availability and managerial skills, which implies that it would be difficult to apply a holistic sustainability analysis to farming systems instead of individual farms. A number of methodological questions arose during the evaluation process relative to the stakeholders perception of these indicators, their relevance and meaning, the reference values for comparison, or their validity to assess sustainability across spatial and temporal scales. These questions are discussed in the paper.     

Integrated participatory modelling of actual farms to support policy making on sustainable intensification

On the tropical island of La Réunion, population growth, increasing demand in food products, agricultural densification, and the resulting pressure on the environment are representative of what is expected to happen in the majority of the world’s regions in the next decades. Crop-livestock integration is a possible solution for the sustainable intensification of farming systems.GAMEDE, a whole-farm model, was designed and used with six representative dairy farmers on the island to ex-ante assess differences in farm sustainability of various degrees of crop-livestock integration and to support discussions with farmers about these options. The model details the dynamics of the main biophysical and decisional processes affecting labour, gross margin, and energy and nutrient flows within the farm.We propose a method based on typology, modelling and participatory techniques to support policy making. All its methodological stages integrate both quantitative and qualitative data. The large majority of farm model implementation cases reported in the literature refers to constructed synthetic farms. However, in our case, actual farm simulation was particularly useful for capturing farmers’ expert knowledge and providing insights into how agro-ecosystems are really managed. This approach enabled taking farm diversity into account in defining relevant interventions. The reliability of extrapolations and recommendations for policy formulation based on farm-level simulation were verified by a rigorous evaluation of the representativeness of the farm sample, crossing expert data with data stemming from a multi-variate analysis. Our research indicates that actual farms can also be typical.     

Decision-making processes for crop management on African farms. Modelling from a case study of cotton crops in northern Cameroon

Recent work on decision processes on French farms and irrigated systems in Africa has shown that farmers plan their cyclical (recurrent) technical operations, and that one can model this planning process. Taking the case of cotton crop management in North Cameroon, we show that with certain adjustments, modelling of this kind can also be done for rainfed crop farming in Africa. The adjustments are needed to take account of the differences in social status between different fields on one farm and the implications of the fact that farm work is primarily manual. This produces decision models with a similar structure to that described for technical management of an annual crop break in a temperate climate using mechanised implements. Not only do these models give us a detailed understanding of the variability of farming practices, we can also classify them into categories according to weather scenarios yield level as a function of weather scenario. We show that one can attribute farms to these types of model using simple indicators concerning work organisation. By analysing North Cameroon farmers' decision processes for managing cotton crops we can thus produce an effective tool for organising technical supervision of farmers at the regional level: advisers can work with these decision model types by measuring some simple indicators at farm level to predict which types of model are applicable, without the onerous work of constructing individual decision models.     

Adaptation to Agenda 2000 (CAP reform) and optimisation of the farming system of French suckler cattle farms in the Charolais area: a model-based study

French suckler farmers need advice on the implications of the Agenda 2000 CAP reform for their farms and, in particular, on the incentives it offers for a more extensive mode of production. To support the dialogue between advisers and farmers, and thus help farmers with their decision-making, we constructed a linear programming (LP) model that optimises the farming system of the northern Massif Central Charolais suckler cattle farms, which may be either mixed (crop-livestock) or specialised (livestock). This model, called Opt'INRA, incorporated all of the production activities presently encountered in this zone, together with the constraints of the CAP premium attributions. We used it to study how, on the basis of their 1999 data, two farms, representing two situations frequently encountered in the Charolais area (a mixed crop-livestock farm and a specialised livestock farm), could best adapt to Agenda 2000.According to the model, for both of the farms studied, the economic impact of Agenda 2000 is relatively low, albeit negative. The adaptation of the system when possible does not lead to a significant increase in the gross margin of this farms. Agenda 2000 did not encourage farmers to extensify their farming system. On the other hand, this CAP reform discourages them from intensifying.     

Agricultural land use and flood risk management: Engaging with stakeholders in North Yorkshire

Recent changes in agricultural and flood defence policies create new opportunities for involving rural land use, in particular agriculture, in flood risk management. This paper presents the results of a case study on land management and flooding in the Laver and Skell catchments in North Yorkshire. The perceptions of local stakeholders were explored through interviews with farmers and discussions among stakeholders that were held, supported by the Floods and Agriculture Risk Matrix (FARM) tool, during a stakeholder workshop. These stakeholder perceptions are reviewed against scientific evidence. Temporary storage of runoff water on farmland was found to have potential to mitigate flooding, but the participating stakeholders thought that this was beyond farmers’ responsibility of good farming practice. During the stakeholder workshop, it was therefore agreed among all participants that targeting funding is needed, as well as stakeholder engagement and demonstration farms, in order to successfully involve farmers in flood risk management.     

Perceived environmental uncertainty in Dutch dairy farming: The effect of external farm context on strategic choice

This paper explores the way in which dairy farmers perceive their environment (PE), i.e., the external context of their farm, and the uncertainty (PEU) this poses to them. The environment is defined using the STEP concept (society, technology, economy and politics) and Porter’s five forces model. The relationship between the perception of the external farm environment and the strategy farmers choose for their farm is quantified to gain insight into the effect of the external farm environment on decision-making. Data from a survey of 103 Dutch dairy farmers was analyzed using regression analysis. The results indicate that environmental uncertainty is not related to complexity or dynamism, but to the illiberality (i.e., intolerance, hostility) of the external farm environment. The institutional environment is considered especially illiberal, thus causing high uncertainty. Farmers with high PEU are more likely to choose a diversification strategy, while low perceived uncertainty results in a process-control strategy for the farm. A growth strategy is not affected by perceived environmental uncertainty. The PE and PEU approach is new in agricultural research and shows that the farmer’s view on the external environment is a key issue for decision-making on farms The significant relationship between the perceived uncertainty caused by the external farm environment and farm strategies shows that to get a good understanding of the farm, farming system boundaries should be expanded to incorporate the effect of the external farm environment on decision making. Reduction of uncertainty will enhance decision-making because instead of farming within an uncertain context, risk management practices can be used.     

Bio-economic evaluation of farmers’ perceptions of viable farms in western Kenya

Arable land in western Kenya is under considerable pressure from increasing human population. Rural households depend on farming for at least part of their livelihood, and poverty rates are among the highest in Kenya. Land is often depleted of nutrients, and for most farmers, access to inputs and markets is poor. There is a need to identify options that are manageable within the context of the farmer’s resource base and the household’s objectives that could improve farm household well-being. In this study we integrated qualitative informal participatory approaches with quantitative mathematical programming and biophysical simulation modelling. Households in four sub-locations in Vihiga District were clustered and pilot cases identified. Meetings were held with farmers to elicit their perceptions of what their ideal farm would look like, and how its performance might compare with their own farm’s performance. With farmers’ help, a range of scenarios was analysed, relating to changes in current enterprise mixes, changes in current farm sizes, and changes in prices of staples foods and cash crops. A considerable mismatch was found between farmers’ estimates of their own farm’s performance, and what was actually produced. There seems to be a threshold in farm size of 0.4 ha, below which it is very difficult for households to satisfy their income and food security objectives. Even for larger farms whose households are largely dependent on agriculture, the importance of a cash crop in the system is critical. There is a crucial role for extension services in making farmers aware of the potential impacts on farm revenue of modest changes in their farm management systems. We are monitoring nine households in the district, whose farmers have made some changes to their system in an attempt to increase household income and enhance food security.     

The FARMSCAPE approach to decision support: farmers', advisers', researchers' monitoring,simulation, communication and performance evaluation

FARMSCAPE (Farmers', Advisers', Researchers', Monitoring, Simulation, Communication And Performance Evaluation) is a program of participatory research with the farming community of northeast Australia. It initially involved research to explore whether farmers and their advisers could gain benefit from tools such as soil characterisation and sampling, climate forecasts and, in particular, simulation modelling. Its current focus is facilitating the implementation of commercial delivery systems for these same tools in order to meet industry demand for their access. This paper presents the story of what was done over the past decade, it provides performance indicators of impact, it reflects on what was learnt over this period and it outlines where this research is likely to head in the future.Over the past 10 years, the FARMSCAPE team employed a Participatory Action Research approach to explore whether farmers could value simulation as a decision support tool for managing their farming system and if so, could it be delivered cost-effectively. Through farmer group engagement, on-farm trials, soil characterisation, monitoring of crops, soils and climate, and sessions to apply the APSIM systems simulator, FARMSCAPE represented a research program on decision support intervention. Initial scepticism by farmers and commercial consultants about the value of APSIM was addressed by testing its performance both against measured data from on-farm trials and against farmers' experiences with past commercial crops. Once this credibility check was passed, simulation sessions usually evolved into participants interactively inquiring of the model the consequence of alternative management options. These ‘What if’ questions using APSIM were contextualised using local climate and soil data and the farmer's actual or proposed management rules.The active participation of farmers and their advisers, and working in the context of their own farming operations, were the key ingredients in the design, implementation and interpretation of the FARMSCAPE approach to decision support. The attraction of the APSIM systems simulator to farmers contemplating change was that it allowed them to explore their own system in a manner equivalent to learning from experience. To achieve this, APSIM had to be credible and flexible. While direct engagement of farmers initially enabled only a limited number of beneficiaries, this approach generated a commercial market for timely and high quality interactions based on soil monitoring and simulation amongst a significant sector of the farming community. Current efforts are therefore focused on the training, support and accreditation of commercial agronomists in the application of the FARMSCAPE approach and tools.The FARMSCAPE approach to decision support has come to represent an approach to guiding science-based engagement with farm decision making which is being tested nationally and internationally.     

GAMEDE: A global activity model for evaluating the sustainability of dairy enterprises Part I – Whole-farm dynamic model

 Crop-livestock farms are complex systems. The interactions operating in such systems involve decisional, biophysical, structural, and environmental factors. Moreover, as farmers face a large range of management options, tools are needed to support their decision-making to enable them to reach production levels meeting their objectives and compatible with their human and physical resources, while controlling their effects on the environment. Gamede, a whole-dairy-farm model, has been developed to explore this complexity and to represent dynamically the effect of management decisions on biomass and nitrogen flows and on numerous sustainability indicators, such as milk and forage crop productivity, labour requirements, nitrogen balance, and nitrogen efficiency.This article describes the integration of six modules accounting for biophysical processes in a dairy farm (forage production; forage conditioning; herd demography; milk, excreta and animal biomass productions; grazing, quality of fertilisers; and nitrogen gaseous emissions) together with a decision system accounting for the farmer’s strategy and technical operations. Most of the six biophysical modules incorporate mathematical models from the literature, but the decision system stems from our own original work.Six commercial farms with different structures, agro-climatic conditions and management strategies were used for validation. The model can explain the differences found in their sustainability indicators at the year scale. The intra-year variability of the main biomass stocks and flows is also well explained. This quantitative validation was completed by a qualitative validation from researcher, adviser and farmer points of view, including simulations of prospective scenarios.     

Modelling worker physical health and societal sustainability at farm level: An application to conventional and organic dairy farming

Farm-level modelling can be used to determine how farming systems and individual farm-management measures influence different sustainability indicators. Until now however, worker physical health and societal sustainability have been lacking in farm models. For this paper, we first selected attributes of physical health (working conditions) and societal sustainability (food safety, animal welfare and health, and landscape quality). Second, possible sustainability indicators for these attributes were identified, and those selected were included in an existing dairy farm LP-model that was subsequently used to analyse possible differences in societal sustainability within and between a conventional and organic dairy farming system. Results for physical health and societal sustainability were similar for conventional and organic dairy farming systems in the basis situation, as well as in the situation where additional management measures were applied to improve societal sustainability, but improved animal welfare did result in the organic system due to prescribed grazing, and due to assumed summer feeding in the conventional system. Results show that additional management measures considerably improved societal sustainability of the conventional as well as the organic system. LP-modelling appeared to be a suitable method for comparing farming systems and determining the effect of management measures on physical health and societal sustainability. The level of societal sustainability is determined mainly by applied management measures, and is related to the particular farming system in only a very limited way. This implies that societal sustainability is mainly dependent on the cost-effectiveness of management measures and on the attitude of the dairy farmer.     

An LP-model to analyse economic and ecological sustainability on Dutch dairy farms: model presentation and application for experimental farm “de Marke”

Farm level modelling can be used to determine how farm management adjustments and environmental policy affect different sustainability indicators. In this paper indicators were included in a dairy farm LP (linear programming)-model to analyse the effects of environmental policy and management measures on economic and ecological sustainability on Dutch dairy farms. For analysing ecological sustainability, seven indicators were included in the model: eutrophication potential, nitrate concentration in groundwater, water use, acidification potential, global warming potential, terrestrial ecotoxicity, and aquatic ecotoxicity. Net farm income was included for measuring economic sustainability. The farm structure of “De Marke” formed the basis for three optimisations: (1) basis situation without environmental policy, (2) situation with Dutch environmental policy for 2004, and (3) situation with farm management measures applied at “De Marke”. The Dutch environmental policy was included to comply with the EC nitrate directive. It resulted in lower fertiliser use and consequently in a decrease in sales of maize. This led to a decrease in net farm income of ca. €2500. Including this policy improved most used ecological indicators (except for ecotoxicity) and showed to be an effective tool to reduce the environmental impact of dairy farming. Adapting the model with farm management measures applied at experimental farm “De Marke” resulted in even better ecological performance compared to the situation with environmental policy. Nonetheless this increase in ecological performance led to a considerably lower net farm income (€14,500).     

Systematic design and evaluation of crop rotations enhancing soil conservation, soil fertility and farm income: a case study for vegetable farms in South Uruguay

Rapid changes in the social and economic environment in which agriculture is developing, together with the deterioration of the natural resource base threatens sustainability of farm systems in many areas of the world. For vegetable farms in South Uruguay, survival in the long term depends upon the development of production systems able to reduce soil erosion, maintain or improve physical and biological soil fertility, and increase farmer’s income to socially acceptable levels. We propose a model-based explorative land use study to support the re-orientation of vegetable production systems in South Uruguay. In this paper we present a new method to quantitatively integrate agricultural, environmental and socio-economic aspects of agricultural land use based on explicit design objectives. We describe the method followed to design and evaluate a wide variety of land use activities for Canelón Grande (South Uruguay) and we illustrate the usefulness of this approach in an ex-ante evaluation of new farming systems using data from 25 farms in this region. Land use activities resulted from systematic combination of crops and inter-crop activities into crop rotations, different crop management techniques (i.e., mechanisation, irrigation and crop protection) and animal production. We identified and quantified all possible rotations and estimated inputs and outputs at crop rotation scale, explicitly considering interactions among crops. Relevant inputs and outputs (i.e., soil erosion, balance of soil organic matter and nutrients, environmental impact of pesticides, labour and machinery requirements, and economic performance) of each land use activity were quantified using different quantitative methods and following the target-oriented approach. By applying the methodology presented in this paper we were able to design and evaluate 336,128 land use activities suitable for the different soil types in Canelón Grande and for farms with different availability of resources, i.e., land, labour, soil quality, capital and water for irrigation. After theoretical evaluation, a large subset of these land use activities showed promise for reducing soil erosion, maintaining soil organic matter content of the soil and increasing farmer’s income, allowing improvement of current farming systems in the region and providing a widely diverse set of strategic options for farmers in the region to choose from. This method can be used as a stand-alone tool to explore options at the field and farm scale or to generate input for optimisation models to explore options at the farm or regional scale.     

Advances in farming systems analysis and intervention

In this paper, we recognize two key components of farming systems, namely the bio-physical ‘Production System’ of crops, pastures, animals, soil and climate, together with certain physical inputs and outputs, and the ‘Management System’, made up of people, values, goals, knowledge, resources, monitoring opportunities, and decision making. Utilising upon these constructs, we review six types of farming systems analysis and intervention that have evolved over the last 40 years, namely: (1) economic decision analysis based on production functions, (2) dynamic simulation of production processes, (3) economic decision analysis linked to biophysical simulation, (4) decision support systems, (5) expert systems, and (6) simulation-aided discussions about management in an action research paradigm. Biophysical simulation modelling features prominently in this list of approaches and considerable progress has been made in both the scope and predictive power of the modelling tools. We illustrate some more recent advances in increasing model comprehensiveness in simulating farm production systems via reference to our own group's work with the Agricultural Production Systems Simulator (APSIM). Two case studies are discussed, one with broad-scale commercial agriculture in north-eastern Australia and the other with resource poor smallholder farmers in Africa. We conclude by considering future directions for systems analysis efforts directed at farming systems. We see the major challenges and opportunities lying at the interface of ‘hard’, scientific approaches to the analysis of biophysical systems and ‘soft’, approaches to intervention in social management systems.     

Finding niches for whole-farm design models – contradictio in terminis?

Whole-farm design models quantitatively analyze the effects of a variety of potential changes at the farm system level. Science-driven technical information is confronted with value-driven objectives of farmers or other social groupings under explicit assumptions with respect to exogenous variables that are important drivers of agricultural systems (e.g., market conditions). Hence, farm design is an outcome of objective specification and the potential of a system. In recent publications, whole-farm design modelling has been proposed to enhance (farm) innovation processes. A number of operational modelling tools now offers the opportunity to assess the true potential of whole-farm design modelling to enhance innovation. In this paper, we demonstrate that it is not trivial to find niches for the application of goal-based farm models. Model outcomes appeared not to match questions of farm managers monitoring and learning from their own and other farmers’ practices. However, our research indicates that whole-farm design modelling possesses the capabilities to make a valuable contribution to reframing. Reframing is the phenomenon that people feel an urge to discuss and reconsider current objectives and perspectives on a problem. Reframing might take place in a situation (i) of mutually felt dependency between stakeholders, (ii) in which there is sufficient pressure and urgency for stakeholders to explore new problem definitions and make progress. Furthermore, our research suggests that the way the researcher enters a likely niche to introduce a model and/or his or her position in this niche may have significant implications for the potential of models to enhance an innovation process. Therefore, we hypothesize that the chances of capitalizing on modelling expertise are likely to be higher when researchers with such expertise are a logical and more or less permanent component of ongoing trajectories than when these researchers come from outside to purposefully search for a niche.     

Increasing profits and reducing risks in crop production using participatory systems simulation approaches

The development of cropping systems simulation capabilities world-wide combined with easy access to powerful computing has resulted in a plethora of agricultural models and consequently, model applications. Nonetheless, the scientific credibility of such applications and their relevance to farming practice is still being questioned. Our objective in this paper is to highlight some of the model applications from which benefits for farmers were or could be obtained via changed agricultural practice or policy. Changed on-farm practice due to the direct contribution of modelling, while keenly sought after, may in some cases be less achievable than a contribution via agricultural policies. This paper is intended to give some guidance for future model applications. It is not a comprehensive review of model applications, nor is it intended to discuss modelling in the context of social science or extension policy. Rather, we take snapshots around the globe to ‘take stock’ and to demonstrate that well-defined financial and environmental benefits can be obtained on-farm from the use of models. We highlight the importance of ‘relevance’ and hence the importance of true partnerships between all stakeholders (farmer, scientists, advisers) for the successful development and adoption of simulation approaches. Specifically, we address some key points that are essential for successful model applications such as: (1) issues to be addressed must be neither trivial nor obvious; (2) a modelling approach must reduce complexity rather than proliferate choices in order to aid the decision-making process; (3) the cropping systems must be sufficiently flexible to allow management interventions based on insights gained from models. The pro and cons of normative approaches (e.g. decision support software that can reach a wide audience quickly but are often poorly contextualized for any individual client) versus model applications within the context of an individual client's situation will also be discussed. We suggest that a tandem approach is necessary whereby the latter is used in the early stages of model application for confidence building amongst client groups. This paper focuses on five specific regions that differ fundamentally in terms of environment and socio-economic structure and hence in their requirements for successful model applications. Specifically, we will give examples from Australia and South America (high climatic variability, large areas, low input, technologically advanced); Africa (high climatic variability, small areas, low input, subsistence agriculture); India (high climatic variability, small areas, medium level inputs, technologically progressing; and Europe (relatively low climatic variability, small areas, high input, technologically advanced). The contrast between Australia and Europe will further demonstrate how successful model applications are strongly influenced by the policy framework within which producers operate. We suggest that this might eventually lead to better adoption of fully integrated systems approaches and result in the development of resilient farming systems that are in tune with current climatic conditions and are adaptable to biophysical and socio-economic variability and change.     

Modelling farm-level economic potential for conversion to organic farming

This paper discusses linear programming simulations at individual farm-level of potential income changes that may result from conversion to organic farming. The model is based on both conventional farm accountancy data and additional conventional and organic farm data from sector expertise and literature. The model is applied for Belgian agriculture. Simulations show that economic potential for conversion is higher than generally perceived, provided that farmers are willing to change farm management practices. However, the economic conversion potential (ECP) is not positive for all farms, not even when an optimal conversion process is assumed and it depends on farm type and farm characteristics. Additionally, due to higher risk and liquidity problems during the transition period, the positive results need to be put into perspective. Nevertheless, the differentiated ECP calculations can give new insights supporting farm-level policy choices with respect to conversion to organic farming.     

Communicating complexity: Integrated assessment of trade-offs concerning soil fertility management within African farming systems to support innovation and development

African farming systems are highly heterogeneous: between agroecological and socioeconomic environments, in the wide variability in farmers’ resource endowments and in farm management. This means that single solutions (or ‘silver bullets’) for improving farm productivity do not exist. Yet to date few approaches to understand constraints and explore options for change have tackled the bewildering complexity of African farming systems. In this paper we describe the Nutrient Use in Animal and Cropping systems - Efficiencies and Scales (NUANCES) framework. NUANCES offers a structured approach to unravel and understand the complexity of African farming to identify what we term ‘best-fit’ technologies - technologies targeted to specific types of farmers and to specific niches within their farms. The NUANCES framework is not ‘just another computer model’! We combine the tools of systems analysis and experimentation, detailed field observations and surveys, incorporate expert knowledge (local knowledge and results of research), generate databases, and apply simulation models to analyse performance of farms, and the impacts of introducing new technologies. We have analysed and described complexity of farming systems, their external drivers and some of the mechanisms that result in (in)efficient use of scarce resources. Studying sites across sub-Saharan Africa has provided insights in the trajectories of change in farming systems in response to population growth, economic conditions and climate variability (cycles of drier and wetter years) and climate change. In regions where human population is dense and land scarce, farm typologies have proven useful to target technologies between farmers of different production objectives and resource endowment (notably in terms of land, labour and capacity for investment). In such regions we could categorise types of fields on the basis of their responsiveness to soil improving technologies along soil fertility gradients, relying on local indicators to differentiate those that may be managed through ‘maintenance fertilization’ from fields that are highly-responsive to fertilizers and fields that require rehabilitation before yields can improved. Where human population pressure on the land is less intense, farm and field types are harder to discern, without clear patterns. Nutrient cycling through livestock is in principle not efficient for increasing food production due to increased nutrient losses, but is attractive for farmers due to the multiple functions of livestock. We identified trade-offs between income generation, soil conservation and community agreements through optimising concurrent objectives at farm and village levels. These examples show that future analyses must focus at farm and farming system level and not at the level of individual fields to achieve appropriate targeting of technologies - both between locations and between farms at any given location. The approach for integrated assessment described here can be used ex ante to explore the potential of best-fit technologies and the ways they can be best combined at farm level. The dynamic and integrated nature of the framework allows the impact of changes in external drivers such as climate change or development policy to be analysed. Fundamental questions for integrated analysis relate to the site-specific knowledge and the simplification of processes required to integrate and move from one level to the next.     

Economic and environmental evaluation of three goal-vision based scenarios for organic dairy farming in Denmark

The objective of this study was to explore the sustainability of future organic dairy farming systems in Denmark, by evaluating the economic and environmental consequences of three scenarios at the farm level based on different visions of future sustainability leading to different farm-based goals. The future sustainable organic dairy farming systems were deduced from participative sessions with stakeholders, and used to define specific scenarios and related key parameters. Parameterization of the scenarios was based on model simulations and the invoking of expert knowledge. Each scenario was designed to fulfil different aspects of sustainability. The business as usual scenario (BAU) was driven by economic incentives and implemented new technologies and measures to enhance productivity and efficiency. This scenario was expected to be the mainstream strategy of future organic dairy production in Denmark. In the animal welfare scenario (ANW), economic efficiency was subordinate to animal welfare, and measures to improve animal welfare, such as lower milk yield, extra grazing area and a deep-litter barn, were incorporated. The environmental scenario (ENV) was designed to minimize N losses into the environment, reduce emission of greenhouse gases and the use of fossil energy, and was based on self-sufficiency regarding nutrients and feed. The economic evaluation of the scenarios was based on quantification of farm profitability (i.e. net profit), whereas environmental evaluation was based on the quantification of the N-surplus per ha, emission of greenhouse gases, and use of fossil energy per kg energy-corrected milk (ECM).Compared to prolonging the current main stream strategy (BAU), the evaluation of scenarios revealed that investing in animal welfare comprised trade-offs regarding farm profitability, climate change and the use of fossil energy. In ANW, net profit per farm was almost 39 k€ lower than in BAU, whereas emission of greenhouse gases and energy per kg ECM was 8% and 3% higher, respectively. Minimizing environmental impact in ENV reduced local as well as global environmental impact without an economic trade-off. Greenhouse gas emission per kg ECM was 5% lower and fossil energy use was 11% lower than in BAU. The N-surplus of ENV was 80 kg per ha, whereas the N-surplus was approximately 116 in both BAU and ANW. Prolonging the current main stream strategy (BAU) resulted in a high local environmental impact, a moderate global environmental impact and a high economic risk related to changes in milk price or costs.     

Risk and economic sustainability of crop farming systems

In economic terms, resilience in farming has to do with the capacity of a farm business to survive various risks and other shocks. Despite its importance, resilience has seldom been directly considered in evaluations of economic sustainability. A whole-farm stochastic simulation model over a 6-year planning horizon was used to analyse organic and conventional cropping systems using a model of a representative farm in Eastern Norway. The relative economic sustainability of alternative systems under changing assumptions about future technology and price regimes was examined in terms of financial survival to the end of the planning period. The same alternatives were also compared in terms of stochastic efficiency. To model the risk of business failure adequately there is a need to deal with the risk of bankruptcy, and a modification of traditional analysis was used for that purpose. The organic farming system was found to be somewhat less economically sustainable than the conventional system, especially if the organic price premiums and the organic area payments were to be phased out. The results illustrate possible conflicts between pursuit of risk efficiency and economic sustainability. The model developed could be used to support farmers’ choices between farming systems as well as to help policy makers develop more sharply targeted policies.