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.     

Evaluation of the water use efficiency of alternative farm practices at a range of spatial and temporal scales: A conceptual framework and a modelling approach

Water is the principal limiting resource in Australian broadacre farming, and the efficiency with which farmers use water to produce various products is a major determinant both of farm profit and of a range of natural resource management (NRM) outcomes. We propose a conceptual framework based on multiple water use efficiencies (WUEs) that can be used to gain insight into high-level comparisons of the productivity and sustainability of alternative farming practices across temporal and spatial scales. The framework is intended as a data aggregation and presentation device. It treats flows of water, biomass and money in a mixed farming system; economic inefficiencies in these flows are tracked as they are associated with a range of NRM indicators.We illustrate the use of the framework, and its place in a larger research programme, by employing it to synthesise the results from a set of modelling analyses of the effect of land use choices on long-term productivity and a range of NRM indicators (frequency of low ground cover, deep drainage, N leaching rates and rate of change in surface soil organic carbon). The analyses span scales from single paddocks and years to whole farms and have been carried out with the APSIM and GRAZPLAN biophysical simulation models and the MIDAS whole-farm economic model.In single wheat crops in one study, different land uses in preceding years affect grain yield primarily by affecting the harvest index. When the scale changes to cropping rotations, the critical factor affecting overall water use efficiency is found to be the proportion of stored soil water that is transpired by crops. When ordinated in terms of their water use efficiencies, a set of 45 modelled rotation sequences at another location are differentiated mainly by the proportion of pasture in the rotation; when rotations are ordinated using key NRM indicators, the proportion of lucerne pasture is the main distinguishing factor. Finally, we show that for whole crop-livestock farms at three different locations across southern Australia, the pattern of water use efficiencies in the most profitable farming systems changes in similar ways as cropping proportion is altered. At this scale, land use choices affect multiple water use efficiency indices simultaneously and commodity prices determine the balance of the resulting economic tradeoffs.Limitations to the use of the WUE framework arising from its relative simplicity are discussed, as are other areas of farming systems research and development to which it can be applied.     

GAMEDE: A global activity model for evaluating the sustainability of dairy enterprises. Part II – Interactive simulation of various management strategies with diverse stakeholders

GAMEDE is a stock-flow dynamic simulation model designed with farmers to represent dairy farm functioning and the consequences of the farmer’s daily management decisions for whole-farm sustainability. Sustainability is evaluated according to its three pillars: technico-economic viability, respect for environment, and social liveability. The model provides original information for a better understanding of the processes regulating nitrogen dynamics within the farm, and the factors determining farmers’ decisions and practices. Model implementation experiments have revealed that GAMEDE is also a useful tool to support discussions and to generate knowledge exchange among various stakeholders who play an important role in the development of farm sustainability: farmers, extension agents and researchers.While a majority of researchers and advisers are specialised and a majority of farmers fix their attention on specific and narrow themes of farm management, such a comprehensive model can help stakeholders complement their knowledge to gain a holistic view of the farming system. This holistic and integrated view is crucial: (i) for researchers who wish to explain diversity in farming systems and understand decisional and biophysical processes and their interrelated effects operating in such complex agro-ecosystems, (ii) for advisers whose aim is to define alternative management strategies applicable in practice, i.e. taking into account farm specificities, and (iii) for farmers who must choose practices compatible with their resources, assets, constraints and objectives.Holism can also improve versatility and thus the generic character of models. Issues are narrowly specified and greatly vary both among categories of stakeholders (e.g. scientists versus farmers) and within each category (e.g. among farmers). A comprehensive model that: (i) details all farm management operations, and (ii) represents their effects on different spatio-temporal levels and on the three sustainability dimensions, is more likely to respond to the various issues facing different stakeholders. We argue that capacity of models to respond to stakeholders’ questions has to be considered in future evaluations of decision support systems.     

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.     

InfoCrop: A dynamic simulation model for the assessment of crop yields,losses due to pests,and environmental impact of agro-ecosystems in tropical environments. II. Performance of the model

InfoCrop, a generic crop model, simulates the effects of weather, soils, agronomic management (planting, nitrogen, residues and irrigation) and major pests on crop growth, yield, soil carbon, nitrogen and water, and greenhouse gas emissions. This paper presents results of its evaluation in terms of its validation for rice and wheat crops in contrasting agro-environments of tropics, sensitivity to the key inputs, and also illustrates two typical applications of the model. Eleven diverse field experiments, having treatments of location, seasons, varieties, nitrogen management, organic matter, irrigation, and multiple pest incidences were used for validation. Grain yields in these experiments varied from 2.8 to 7.2 ton ha⁻¹ in rice and from 3.6 to 5.5 ton ha⁻¹ in wheat. The results indicated that the model was generally able to explain the differences in biomass, grain yield, emissions of carbon dioxide, methane and nitrous oxides, and long-term trends in soil organic carbon, in diverse agro-environments. The losses in dry matter and grain yield due to different pests and their populations were also explained satisfactorily. There were some discrepancies in the simulated emission of these gases during first few days after sowing/transplanting possibly because of the absence of tillage effects in the model. The sensitivity of the model to change in ambient temperature, crop duration and pest incidence was similar to the available field knowledge. The application of the model to quantify multiple pests damage through iso-loss curves is demonstrated. Another application illustrated is the use of InfoCrop for analyzing the trade-offs between increasing crop production, agronomic management strategies, and their global warming potential.     

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.     

Epistics: A dynamic model to generate nitrogen fertilisation and irrigation schedules in apple orchards, with special attention to qualitative evaluation of the model

Epistics is a model combining a biophysical and a decisional model designed to generate irrigation and N fertilisation schedules in apple orchards. These techniques were chosen since they are key elements in the management of fruit tree cropping systems. The biophysical model representing water and N dynamics in orchards was based on the water and N dynamics of Stics and was completed using a crop water and N requirement estimation method adapted to orchards. It was linked to an agronomic decision rule in a combined model able to generate N fertilisation and irrigation schedules. The Epistics evaluation process dealt with numerical evaluation of state variables (water and N soil content) and qualitative evaluation of model-generated schedules. The numerical evaluation, which concerned the biophysical model of Epistics, was performed on the basis of (i) soil nitrate and water content at the end of winters 2002 and 2003, and on (ii) nitrate and water dynamics during spring and summer 2003. The mean Root Mean Squared Error (RMSE) between observed and simulated values at the end of winter was 3.3% water per horizon and 56 kg N/ha, which is relatively good owing to the high spatial and temporal variability of soil water and nitrate content. The qualitative evaluation of generated schedules was performed during interviews with farmers. Farmers were asked to evaluate the model with reference to their own practices. A sharp difference between farmers and the model concerned the beginning of the irrigation period. This suggested that the model should take into account the constraints imposed by scab and codling moth control practices and irrigation rounds. The difference between model-generated and farmers’ fertilisation practices suggested that the model may take plot vigour into account in the fertilisation decision rule. Such a study is a first step towards the design of models linking sound agronomic decision rules to crop modelling and representing interactions between practices.     

Assessing the impact of the Nitrate Directive on farming systems using a bio-economic modelling chain

Bio-economic models can be used to assess the impact of policy and environmental measures through economic and environmental indicators. Focusing on agricultural systems, farmers’ decisions in terms of cropping systems and the associated crop management at field scale are essential in such studies. The objective of this paper is to present a study using a bio-economic model to assess the impact of the Nitrate Directive in the Midi-Pyrenees region (France) by analyzing, at the farm scale, farm income and three environmental indicators: nitrate leaching, erosion and water consumption. Two scenarios, the 2003 CAP reform (baseline scenario) and the Nitrate Directive (policy scenario), with a 2013 time horizon, were developed and compared for three representative arable farm types in the Midi-Pyrenees region. Different types of data characterizing the biophysical context in the region (soil, climate), the current cropping systems (rotation, crop management) and farm resources (irrigated land, labor) were collected to calibrate and run the models. Results showed that the implementation of the Nitrate Directive may not affect farm income. However, significant modifications to cropping systems and crop allocation to soil types were simulated. Contrary to expectations, nitrogen leaching at the farm scale did not change. Overall water consumption increased and soil erosion decreased due mainly to a modification in cropping patterns and management by soil type. This study provides an example of unanticipated effects of policy and trade-offs between environmental issues.     

A model of decision-making and information flows for information-intensive agriculture

This paper describes the development of a systems based model to characterise farmers’ decision-making process in information-intensive practices, and its evaluation in the context of Precision Agriculture (PA). A participative methodology was developed in which farm managers decomposed their process of decision-making into brief decision statements along with associated information requirements. The methodology was first developed on a university research farm in Denmark and further revised during testing on a number of research and commercial farms in Indiana, USA. Twenty-one decision-analysis factors were identified to characterise a farm manager’s decision-making process. Then, a general data flow diagram (DFD) was constructed that describes the information flows “from data to decision”. Illustrative examples of the model in the form of DFDs are presented for a strategic, a tactical and an operational decision. The model was validated for a range of decisions related to operations by three university farm managers and by five commercial farmers practicing PA for cereal, corn and soybean production in Denmark and in Indiana, USA.     

The impact of water and agriculture policy scenarios on irrigated farming systems in Italy: An analysis based on farm level multi-attribute linear programming models

The objective of this paper is to evaluate the impacts of agriculture and water policy scenarios on the sustainability of selected irrigated farming systems in Italy, in the context of the forthcoming implementation of the directive EC 60/2000. Directive EC 60/2000 (Water Framework Directive) is intended to represent the reference norm regulating water use throughout Europe. Five main scenarios were developed reflecting aspects of agricultural policy, markets and technologies: Agenda 2000, world market, global sustainability, provincial agriculture and local community. These were combined with two water price levels, representing stylised scenarios for water policy. The effects of the scenarios on irrigated systems were simulated using multi-attribute linear programming models representing the reactions of the farms to external variables defined by each scenario. The output of the models consists of economic, social and environmental indicators aimed at quantifying the impact of the scenarios on different aspects of sustainability relevant for irrigated farming systems. Five Italian irrigated farming systems were considered: cereal, rice, fruit, vegetables and citrus. The results show the diversity of irrigated systems and the different effects that water pricing policy may produce depending on the agricultural policy, market and technological scenarios. They also highlight a clear trade-off between socio-economic sustainability and environmental (water, nitrogen, pesticide) sustainability. Water pricing will have, in most cases, less impact than agricultural markets and policy scenarios, though it appears to be an effective instrument for water regulation in the least intensive irrigated systems considered. This emphasises the need for a differentiated application of the Water Framework Directive at the local level as well as a more careful balance of water conservation, agricultural policy and rural development objectives.     

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.     

The economic value of glyphosate-resistant canola in the management of two widespread crop weeds in a Western Australian farming system

Nitrogen fertilisation is a source of potential groundwater pollution and is a key issue in the current debate about the environmental impacts of agricultural production. It is also a key element in the management of cropping systems by farmers. Therefore, cropping system design entails the understanding and evaluation of farmers' fertilisation practices. Biophysical models describing the soil–plant system can serve this purpose. A comparison between model outputs and farmers' practices was made of a set of 128 apple (Malus domestica Borkh.) plots from 31 members of a farmers' co-operative in south-eastern France. Farmers' fertilisation practices were compared with theoretical practices generated by a series of soil–plant system models of increasing complexity, each model giving the amount of nitrogen that should be applied to the plot according to the knowledge included in the model. The model that reproduced farmers' fertilisation practices most closely was the most complex, taking all plant requirements, soil organic matter and residue mineralisation, denitrification and irrigation supply into account. A Monte Carlo method showed that the differences between farmers' practices and model outputs were not random. Spatial analysis showed a strong spatial organisation of these differences, mainly due to three farms. This congruence between farmers' practices and model outputs suggests the existence of some indicators that depict the N nutrition status of the orchard as a basis for rules indicating how much nitrogen should be applied. The spatial analysis suggests the existence of farmer and neighbourhood effects, which need to be explained. Models appear to be useful tools to study farmers' practices by removing biophysical effects (soil, variety, etc.). This raises new questions concerning agricultural research at the interface between the biophysical and social sciences.     

Dairy farm nutrient management model. 1. Model description and validation

Intensive dairy farming results in significant phosphorus (P) emission to the environment. Field data indicates that farm-gate P surplus is highly positive in Finland and strategies to mitigate the surplus are needed. The objectives of this study were to build a P cycle model for dairy farms (1) and to validate the model with independent field data (2). The dairy farm nutrient management model (“Lypsikki”) described in this paper includes three sub-models: (1) soil and crop, (2) dairy herd and (3) manure management. The model is based on empirical regression equations allowing estimations of crop and milk yields in response to increased fertilisation and nutrient supply, respectively. In addition, the model includes a dynamic simulation model of the dairy herd structure and calculation of the farm-gate nutrient surplus. The model was validated with independent annual (average for 1-4 years) farm-gate P surplus data from 21 dairy farms. Model simulations were conducted using two levels of soil productivity, mean (M) and low (L). The model validation indicated a strong relationships between model-predicted and observed farm-gate P surplus: (M: R² = 0.77 and L: R² = 0.80). The line bias between the model-predicted and observed data was negligible and insignificant (P > 0.6) suggesting a robustness of the model. The mean biases were relatively high and significant (M: 4.7 and L: 1.8 kg/ha, P < 0.001), but evidently related to overestimation of crop yields that has to be taken into account when using the model on a single farm. The prediction error of the model (observed minus predicted P surplus) was significantly correlated to the difference between simulated and observed P import in feeds (M: R² = 0.55 and L: R² = 0.51). This suggests either that all the dairy farms did not fully exploit the possibilities in the crop production or that all the model assumptions are not correct. The effects of purchased feed and fertiliser P and exported milk P (per cow or cropping area) on farm-gate P surplus were of the same magnitude in both observed and simulated data. This implies that the model developed can be used as a management decision tool to find strategies to mitigate P surplus on dairy farms.     

Adaptation and evaluation of the integrated farm system model to simulate temperate multiple-species pastures

Whole-farm simulation provides a tool for predicting the effects of farm management strategies on farm productivity and profitability. One such model, the Integrated Farm System Model (IFSM), was modified to allow representation of up to four forage species coexisting in a pasture mixture. The model was calibrated to simulate net herbage accumulation (NHA) observed during six periods of a 2002 experiment in a 3-species pasture in Pennsylvania, USA, composed of orchardgrass (Dactylis glomerata L.), white clover (Trifolium repens L.), and chicory (Cichorium intybus L.). The model also predicted sward botanical composition, total annual NHA, crude protein (CP), and neutral detergent fiber (NDF). Sensitivity analysis showed that predictions of NHA were most sensitive to both chicory and orchardgrass specific leaf area and partitioning of photosynthate to the shoot, as well as chicory photosynthetic efficiency. The model was evaluated against data from the same 3-species pasture in 2003 as well as a 2-species pasture (lacking chicory) from the same experiment in 2002 and 2003. Predictions of total annual NHA in 2- and 3-species pastures were within ±18% of observed values, though predictions of within-season NHA were less accurate. Predictions of botanical composition tended to remain within ±15% of observed values by species. Predictions of within-season CP and NDF concentrations in the whole sward tended to remain within ±22% and ±15%, respectively. Given the generality and realism required of IFSM, the degree of precision in the modified pasture submodel is acceptable for achieving IFSM’s primary goal of comparing the effects of different management scenarios on forage productivity and the long-term profitability and environmental impact of farms.     

The environmental performance of milk production on a typical Portuguese dairy farm

The activities associated with raw milk production on dairy farms require an effective evaluation of their environmental impact. The present study evaluates the global environmental impacts associated with milk production on dairy farms in Portugal and identifies the processes that have the greatest environmental impact by using life cycle assessment (LCA) methodology. The main factors involved in milk production were included, namely: the dairy farm, maize silage, ryegrass silage, straw, concentrates, diesel and electricity. The results suggest that the major source of air and water emissions in the life cycle of milk is the production of concentrates. The activities carried out on dairy farms were the major source of nitrous oxides (from fuel combustion), ammonia, and methane (from manure management and enteric fermentation). Nevertheless, dairy farm activities, which include manure management, enteric fermentation and diesel consumption, make the greatest contributions to the categories of impact considered, with the exception of the abiotic depletion category, contributing to over 70% of the total global warming potential (1021.3 kg CO₂ eq. per tonne of milk), 84% of the total photochemical oxidation potential (0.2 kg C₂H₄ eq. per tonne of milk), 70% of the total acidification potential (20.4 kg SO₂ eq. per tonne of milk), and 41% of the total eutrophication potential (7.1 kg eq. per tonne of milk). The production of concentrates and maize silage are the major contributors to the abiotic depletion category, accounting for 35% and 28%, respectively, of the overall abiotic depletion potential (1.4 Sb eq. per tonne of milk). Based on this LCA case study, we recommend further work to evaluate some possible opportunities to improve the environmental performance of Portuguese milk production, namely: (i) implementing integrated solutions for manure recovery/treatment (e.g. anaerobic digestion) before its application to the soil as organic fertiliser during maize and ryegrass production; (ii) improving manure nutrient use efficiency in order to decrease the importation of nutrients; (iii) diversifying feeding crops, as the dependence on two annual forage crops is expected to lead to excessive soil mobilisation (and related impacts) and to insignificant carbon dioxide sequestration from the atmosphere; and (iv) changing the concentrate mixtures.     

A short-term mechanistic model of forage and livestock in the semi-arid Succulent Karoo: 1. Description of the model and sensitivity analyses

This series of two papers describes a mechanistic model that simulates within years the productivity of vegetation and livestock on the communal semi-arid rangeland of the Succulent Karoo of South Africa. The model enables users to evaluate short-term management decisions on the production of milk and meat and to develop sets of equations and rules for long-term models designed to examine the effects of different strategies on the sustainability of the ecosystem.A soil moisture module partitions daily rainfall between runoff, infiltration and drainage and also simulates the loss of soil moisture by evaporation and transpiration. Forage production by different types of plant is modelled in relation to soil moisture and the present potential for growth. Three factors are assumed to influence the animal’s preference for a specific type of plant or part of a plant: relative abundance, ease of harvesting and digestibility. The model combines three mechanisms of food intake regulation: the rate at which the animal is able to eat forage, physical capacity of the digestive system, and, in young animals, their growth potential. Metabolisable energy intake is partitioned between maintenance, accretion/depletion of body protein and fat, conceptus growth and milk production. Reproductive and survival rates are simulated in relation to predicted liveweight and liveweight changes for the different age classes of livestock.     

Use of available information at a European level to construct crop nitrogen response curves for the regions of the EU

Nowadays European agriculture is evolving in a context where policy-making and environmental concerns play a key role. To better assess agro-environmental policies, the AROPAj agricultural supply model needs to take into account the technical characteristics of crop management for different farms. A method to build up specific relationships between yield and nitrogen fertilization that takes into account agronomic techniques is proposed in this paper. The nitrogen response curve is based on an exponential function that integrates economic properties consistently from an agronomic point of view. In AROPAj, individual production systems (farm types) do not have a given location within a specified region and in databases technical information is scarce. The method involves determining technical and physical characteristics, inputs that allow the STICS crop model to assess the yield response to nitrogen of each crop on every farm type. From this information, a nitrogen response curve can be drawn up for each crop of each one of the farms. It can take into account both nitrogen from purchased fertilizer and nitrogen from animal effluents produced on farm. The method was designed to be adaptable to any European region, and tests carried out on two French regions covering a wide range of situations (crops, soils, climates and techniques) showed it was able to cope with varying prices and environments. The agronomic consistency of STICS inputs and curve shapes was also checked. When incorporated into the AROPAj economic model, the response curves can be used to render farms more sensitive to agricultural policy scenarios, by allowing their optimal fertilization level to be adjusted.     

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).     

Assessment of energy use in arable farming systems by means of an agro-ecological indicator: the energy indicator

Efficient energy use by the agriculture sector is one of the conditions for sustainable agriculture because it allows financial savings, fossil resources preservation and air pollution decrease. We propose an Energy indicator (I_En) to evaluate environmental impacts due to energy consumption of arable farming systems to help farmers to manage their energy inputs at the field level according to the guidelines of integrated agriculture. I_En is based on the energetic analysis of four types of energy: two for indirect energy (pesticides and fertilisers) and two for direct energy (machinery and irrigation systems). In a second step, the assessed values of energy consumption are converted by means of an abacus into a mark between 0 and 10. I_En needs only data that are available on farms or easily assessed, and will be implemented with a set of seven other agro-ecological indicators to assess environmental sustainability of farms.