W.A.H. Rossing

Exploring multifunctional agriculture. A review of conceptual approaches and prospects for an integrative transitional framework

In the last decade the multifunctional agriculture (MFA) concept has emerged as a key notion in scientific and policy debates on the future of agriculture and rural development. Broadly speaking, MFA refers to the fact that agricultural activity beyond its role of producing food and fibre may also have several other functions such as renewable natural resources management, landscape and biodiversity conservation and contribution to the socio-economic viability of rural areas. The use of the concept can be traced to a number of wider societal and political transformation processes, which have influenced scientific and policy approaches in different ways amongst countries and disciplines. This paper critically discusses various existing research approaches to MFA, both from natural and social sciences. To this aim different strands of literature are classified according to their focus on specific governance mechanisms and levels of analysis into four main categories of research approaches (market regulation, land-use approaches, actor-oriented and public regulation approaches). For each category an overview of the state-of-the-art of research is given and an assessment is made of its strengths and weaknesses. The review demonstrates that the multifunctionality concept has attracted a wealth of scientific contributions, which have considerably improved our understanding of key aspects of MFA. At the same time approaches in the four categories have remained fragmented and each has limitations to understand MFA in all its complexity due to inherent constraints of applied conceptualizations and associated disciplinary backgrounds. To go beyond these limitations, we contend, new meta-level frameworks of analysis are to be developed that enable a more integrated approach. The paper concludes by presenting the main lines of an integrative, transitional framework for the study of MFA, which analyses multifunctional agriculture against the background of wider societal change processes towards sustainability and identifies a number of key elements and research challenges for this.

rotat, a tool for systematically generating crop rotations

Abstract This paper reports part of a methodology for a model-based exploration of land use motivated by the lack of sustainability of small farming systems in southern Uruguay. Explorative land use studies aim to gain insight into future possibilities for agricultural development. They support strategic thinking during the design of new farming systems. The crop rotation plays a central role in a farming system and represents a logical starting point in the design process. The combination and sequence of crop species determine characteristics of farming systems such as crop yields, soil erosion, occurrence of soil-borne pests, diseases and weeds, and dynamics of nitrogen and labour. Here, we present a software tool called rotat , designed for generating crop rotations based on agronomic criteria in a transparent manner. The program combines crops from a predefined list to generate all possible rotations. The full factorial number of possible combinations of crops is limited by a number of filters controlled by the user. These filters are designed to eliminate crop successions which are agronomically unfeasible and for farm-specific reasons not practical or desirable. The filters represent expert knowledge in a quantitative and explicit way. The use of this computer program as a stand-alone tool in the process of designing crop rotations is illustrated with a published case study from an ecological pilot farm in Flevoland (The Netherlands). Using this software we were able to design 840 rotations based on the same crops and designing criteria that were used for the example farm. Many of these rotations might be interesting alternatives to the one actually implemented. Coupled with a sound procedure to evaluate the performance of such a large number of rotations ‘a priori’, rotat can reduce the risk of ignoring promising options and the arbitrariness present in previous studies dealing with design of rotations. The usefulness of rotat for designing production activities in explorative land use studies based on linear programming is discussed.

Farming options for The Netherlands explored by multi-objective modelling

Abstract Intensive agriculture in The Netherlands has a price in the form of environmental degradation and the diminution of nature and landscape values. A reorientation of farming is needed to find a new balance between economic goals and rural employment, and care for clean water and air, animal well-being, safe food, and the preservation of soil, landscape and biodiversity. The search for farm systems that meet such multiple goals requires a systematic combination of (a) agrotechnical, agroecological and agroeconomic knowledge, with (b) the stakeholders’ joint agreement on normative objectives, to arrive at conceptual new designs followed by (c) empirical work to test, adapt and refine these under real commercial farming conditions. In this paper explorative modelling at the whole farm level is presented as a method that effectively integrates component knowledge at crop or animal level, and outlines the consequences of particular choices on scientific grounds. This enables quantitative consideration of a broad spectrum of alternative farming systems, including very innovative and risky ones, before empirical work starts. It thus contributes to a transparent learning and development process needed to arrive at farm concepts acceptable to both entrepreneurs and society. Three case studies are presented to illustrate the method: dairy farming on sandy soils; highly intensified flower bulb industry in sensitive areas in the western Netherlands; and integrated arable farming. Trade-offs between economic and environmental objectives were assessed in all three cases, as well as virtual farm configurations that best satisfy specified priority settings of objectives. In two of the three cases the mutual reinforcement and true integration of modelling and on-farm empirical research appeared difficult, but for obvious reasons. Only in the flower bulb case was the explorative approach utilized to its full potential by involving a broad platform of stakeholders. The other two case studies lacked such formalised platforms and their impact remained limited. Three critical success factors for explorative modelling are identified: to cover a well-differentiated spectrum of possible production technologies; early timing of modelling work relative to empirical farm prototyping; and involvement of stakeholders throughout.

Model-based explorations to support development of sustainable farming systems: case studies from France and the Netherlands.

Abstract Sustainable land use requires development of agricultural production systems that, in addition to economic objectives, contribute to objectives in areas such as environment, health and well-being, rural scenery and nature. Since these objectives are at least partially conflicting, development of sustainable farming systems is characterized by negotiation about acceptable compromises among objectives. Four phases can be distinguished in the course of farming systems development: diagnosis, design, testing and improvement, and dissemination. During the last decade an approach coined ‘prototyping’ has emerged as a promising method for empirical farming systems development in Western Europe. Limitations of the approach include: (1) the limited number of systems that can be evaluated, resulting in a lack of perspective on conflicts among objectives, and (2) the expertise-based nature of rules used during systems design which unduly narrows the range of available options and obscures understanding of systems behaviour. In the paper, explorative studies based on transparent models of agronomy and management are put forward to supplement empirical prototyping and to remedy its shortcomings. To illustrate the potential of model-based explorations, two case studies are presented. The first case study deals with diagnosis and design of wheat-based rotations in the Paris Basin of France, aimed at alleviating tactical problems of poor resource-use efficiency within the constraints imposed by existing crop rotations. The second case study addresses design of sustainable bulb-based farming systems in the Netherlands with the purpose of investigating strategic options at crop rotation and farm level to resolve conflicts between economic and environmental objectives. In the discussion, methodological elements of model-based explorations and interaction with stakeholders are addressed, and opportunities for enhanced development of sustainable farming systems are identified.

Monte Carlo estimation of uncertainty contributions from several independent multivariate sources.

An efficient random sampling method is introduced to estimate the contributions of several sources of uncertainty to prediction variance of (computer) models. Prediction uncertainty is caused by uncertainty about the initial state, parameters, unknown (e.g. future) exogenous variables, noises, etcetera. Such uncertainties are modelled here as random inputs into a deterministic model, which translates input uncertainty into output uncertainty. The goal is to pinpoint the major causes of output uncertainty. The method presented is particularly suitable for cases where uncertainty is present in a large number of inputs (such as future weather conditions). The expected reduction of output variance is estimated for the case that various (groups of) inputs should become fully determined. The method can be applied if the input sources fall into stochastically independent groups. The approach is more flexible than conventional methods based on approximations of the model. An agronomic example illustrates the method. A deterministic model is used to advise farmers on control of brown rust in wheat. Empirical data were used to estimate the distributions of uncertain inputs. Analysis shows that effective improvement of the precision of the model’s prediction requires alternative submodels describing pest population dynamics, rather than better determination of initial conditions and parameters.

Influence of Host Diversity on Development of Epidemics: An Evaluation and Elaboration of Mixture Theory

ABSTRACT A spatiotemporal/integro-difference equation model was developed and utilized to study the progress of epidemics in spatially heterogeneous mixtures of susceptible and resistant host plants. The effects of different scales and patterns of host genotypes on the development of focal and general epidemics were investigated using potato late blight as a case study. Two different radial Laplace kernels and a two-dimensional Gaussian kernel were used for modeling the dispersal of spores. An analytical expression for the apparent infection rate, r, in general epidemics was tested by comparison with dynamic simulations. A genotype connectivity parameter, q, was introduced into the formula for r. This parameter quantifies the probability of pathogen inoculum produced on a certain host genotype unit reaching the same or another unit of the same genotype. The analytical expression for the apparent infection rate provided accurate predictions of realized r in the simulations of general epidemics. The relationship between r and the radial velocity of focus expansion, c, in focal epidemics, was linear in accordance with theory for homogeneous genotype mixtures. The findings suggest that genotype mixtures that are effective in reducing general epidemics of Phytophthora infestans will likewise curtail focal epidemics and vice versa.

Invasion of Phytophthora infestans at the landscape level: how do spatial scale and weather modulate the consequences of spatial heterogeneity in host resistance?

Strategic spatial patterning of crop species and cultivars could make agricultural landscapes less vulnerable to plant disease epidemics, but experimentation to explore effective disease-suppressive landscape designs is problematic. Here, we present a realistic, multiscale, spatiotemporal, integrodifference equation model of potato late blight epidemics to determine the relationship between spatial heterogeneity and disease spread, and determine the effectiveness of mixing resistant and susceptible cultivars at different spatial scales under the influence of weather. The model framework comprised a landscape generator, a potato late blight model that includes host and pathogen life cycles and fungicide management at the field scale, and an atmospheric dispersion model that calculates spore dispersal at the landscape scale. Landscapes consisted of one or two distinct potato-growing regions (6.4-by-6.4-km) embedded within a nonhost matrix. The characteristics of fields and growing regions and the separation distance between two growing regions were investigated for their effects on disease incidence, measured as the proportion of fields with ≥1% severity, after inoculation of a single potato grid cell with a low initial level of disease. The most effective spatial strategies for suppressing disease spread in a region were those that reduced the acreage of potato or increased the proportion of a resistant potato cultivar. Clustering potato cultivation in some parts of a region, either by planting in large fields or clustering small fields, enhanced the spread within such a cluster while it delayed spread from one cluster to another; however, the net effect of clustering was an increase in disease at the landscape scale. The planting of mixtures of a resistant and susceptible cultivar was a consistently effective option for creating potato-growing regions that suppressed disease spread. It was more effective to mix susceptible and resistant cultivars within fields than plant some fields entirely with a susceptible cultivar and other fields with a resistant cultivar, at the same ratio of susceptible to resistant potato plants at the landscape level. Separation distances of at least 16 km were needed to completely prevent epidemic spread from one potato-growing region to another. Effects of spatial placement of resistant and susceptible potato cultivars depended strongly on meteorological conditions, indicating that landscape connectivity for the spread of plant disease depends on the particular coincidence between direction of spread, location of fields, distance between the fields, and survival of the spores depending on the weather. Therefore, in the simulation of (airborne) pathogen invasions, it is important to consider the large variability of atmospheric dispersion conditions.

Exploring profit – Sustainability trade-offs in cropping systems using evolutionary algorithms

Models that implement the bio-physical components of agro-ecosystems are ideally suited for exploring sustainability issues in cropping systems. Sustainability may be represented as a number of objectives to be maximised or minimised. However, the full decision space of these objectives is usually very large and simplifications are necessary to safeguard computational feasibility. Different optimisation approaches have been proposed in the literature, usually based on mathematical programming techniques. Here, we present a search approach based on a multiobjective evaluation technique within an evolutionary algorithm (EA), linked to the APSIM cropping systems model. A simple case study addressing crop choice and sowing rules in North-East Australian cropping systems is used to illustrate the methodology. Sustainability of these systems is evaluated in terms of economic performance and resource use. Due to the limited size of this sample problem, the quality of the EA optimisation can be assessed by comparison to the full problem domain. Results demonstrate that the EA procedure, parameterised with generic parameters from the literature, converges to a useable solution set within a reasonable amount of time. Frontier ‘‘peels’’ or Pareto-optimal solutions as described by the multiobjective evaluation procedure provide useful information for discussion on trade-offs between conflicting objectives.

Exploring the potential for improved internal nutrient cycling in dairy farming systems, using an eco-mathematical model

Nutrient management at Dutch dairy farms is changing rapidly from strong reliance on external inputs to more prudent utilization of internal resources. This paper explores opportunities and constraints arising from this shift towards eco-technological management. A mathematical model of inorganic and organic nitrogen (N) flows in a dairy farming system was formulated based on ecological concepts, integrating processes of nutrient input, recycling, immobilization and mineralization. Recycling is defined as the mineralization of N within the year of its incorporation into herbage, which occurs through release from faeces, animal urine and non-harvested biomass. We simulated changes in inorganic and organic N per hectare, and the consequent emission (E), mineralization (M s ) and recycling (R) of N for different initial amounts of inorganic and organic N. Results demonstrate that in the long term, the system evolves to equilibrium amounts of inorganic and organic N, which are strongly determined by the imposed management practices, such as fertilizer input and grassland management. In the short term, moving away from the equilibrium is possible for particular initial amounts of inorganic and organic N. In the equilibrium state, E was reduced by lowering inorganic fertilizer input rate, increasing grassland productivity and improving animal N conversion efficiency, i.e., only by production-related parameters. Only in the short term E was affected by adjustments in quality-related parameters: lower N content, lower digestibility of herbage, reduced degradability of non-harvested biomass and faeces, and parameters determining the functioning of soil biota (degradation rate, efficiency, C/N ratio). Qualityrelated parameters had no effect on internal nutrient cycling in the equilibrium state, because adjustments in M s were completely compensated by changes in R. A comparison of farming systems demonstrated that farming systems can be designed in such a way that improvement of internal nutrient cycling supports the same production with lower inputs and lower emissions.

Uncertainty analysis applied to supervised control of aphids and brown rust in winter wheat. Part 2. Relative importance of different components of uncertainty

The components of an existing model for supervised control of aphids (especially Sitobion avenae) and brown rust (Puccinia recondita) in winter wheat contain uncertainty. Their contribution to uncertainty about model output is assessed. The model simulates financial loss associated with a time sequence of decisions on chemical control as a function of crop development, population growth, and damage. Four sources of uncertainty were quantified: model parameters, incidence sample estimates, future average daily temperature, and white noise. Uncertainty about the first two sources is controllable because it decreases when more information is collected. Uncertainty about the last two sources is uncontrollable, given the structure of the model. Uncertainty about model output, characterized by its variance, is calculated by repeatedly drawing realizations of the various sources of uncertainty, and calculating financial loss after each draw. By processing new realizations of these sources one by one, the contribution of each component to total variance can be assessed using an adapted Monte Carlo procedure. For most relevant initial conditions and decision strategies the sources of uncontrollable uncertainty cause more than half of the uncertainty about model output. White noise in the relative growth rates of aphids and brown rust is the most important source of uncertainty. Resources for improvement of the model are most effectively allocated to studies of the population dynamics of aphids and brown rust.