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Het landschap wordt beïnvloed door lokale, landelijke, Europese en zelfs mondiale factoren. Die factoren veranderen constant, bijvoorbeeld door een nieuwe regering met een ander beleid (landelijk), een economische crisis (Europees) en door klimaatsverandering (mondiaal). Na een verandering kunnen huidige strategieën onbruikbaar worden bevonden. Voorbeelden zijn de Ecologische Hoofdstructuur (EHS) dat in 2011 voortijdig is stopgezet en het Reconstructieplan de Peel waarvan in 2012 is besloten de uitvoering te stoppen. De toekomst is dus onzeker en valt niet te voorspellen. Wel kunnen we de onzekerheden in kaart brengen met behulp van scenario’s. Hierdoor krijgen we inzicht in verschillende toekomstbeelden en de mogelijke consequenties van deze toekomstbeelden.

Cassava mosaic disease (CMD) seriously affects cassava yields in Africa. This study compared the spatial distribution of CMD using three independent surveys in Rwanda and Burundi. Geostatistical techniques were used to interpolate the point-based surveys and predict the spatial distributions of different measures of the disease. Correlative relationships were examined for 35 environmental and socio-economic spatial variables of which 31 were correlated to CMD intensity, with the highest correlation coefficients for latitude (-0·47), altitude (-0·36) and temperature (+0·36). The most significant explanatory variables were entered in separate linear regression models for each of the surveys. The models explained 54%, 44% and 22% of the variation in CMD. The residuals of the regression models were interpolated using kriging and added to the regression models to map CMD across both countries. Significant differences were calculated in some areas after correcting for interpolation error. An important explanation of the differences is interaction between the CMD pandemic and the dates of the three surveys. Large relative prediction errors obtained in the regression kriging procedure show the need to improve the survey design and decrease measurement error. Improved maps of crop diseases such as CMD could aid targeting of control interventions and thereby contribute to increasing crop yields. This study validated the unique character of each of the survey approaches adopted and underlines the importance of specific interpretation of results for CMD management. The study emphasizes the need for optimization of sampling designs and survey protocols to maximize the potential of regression kriging

Soil organic carbon (SOC) is a key soil property and particularly important for ecosystem functioning and the sustainable management of agricultural systems. Conventional laboratory analyses for the determination of SOC are expensive and slow. Laboratory spectroscopy in combination with chemometrics is claimed to be a rapid, cost-effective and non-destructive method for measuring SOC. The present study was carried out in Limpopo National Park (LNP) in Mozambique, a data- and access-limited area, with no previous soil spectral library. The question was whether a useful calibration model could be built with a limited number of samples. Across the major landscape units of the LNP, 129 composite topsoil samples were collected and analyzed for SOC, pH and particle sizes of the fine earth fraction. Samples were also scanned in a near-infrared (NIR) spectrometer. Partial least square regression (PLSR) was used on 1037 bands in the wavelength range 1.25–2.5 µm to relate the spectra and SOC concentration. Several models were built and compared by cross-validation. The best model was on a filtered first derivative of the multiplicative scatter corrected (MSC) spectra. It explained 83% of SOC variation and had a root mean square error of prediction (RMSEP) of 0.32% SOC, about 2.5 times the laboratory RMSE from duplicate samples (0.13% SOC). This uncertainty is a substantial proportion of the typical SOC concentrations in LNP landscapes (0.45–2.00%). The model was slightly improved (RMSEP 0.28% SOC) by adding clay percentage as a co-variable. All models had poorer performance at SOC concentrations above 2.0%, indicating a saturation effect. Despite the limitations of sample size and no pre-existing library, a locally-useful, although somewhat imprecise, calibration model could be built. This model is suitable for estimating SOC in further mapping exercises in the LNP

Analysis of the economic and environmental outcomes of agricultural systems requires a bottom-up linkage from the farm to market, as well as a top-down linkage from market to farm. This study develops this two-way linkage between the Tradeoff Analysis Model of agricultural systems and a partial equilibrium market model. The resulting model can determine the effects of technology and policy interventions on the spatial distribution of environmental and economic outcomes at market equilibrium quantities and prices. The approach is demonstrated with a case study of tradeoffs between poverty and nutrient depletion in a semi-subsistence agricultural system (Machakos, Kenya). The results suggest that the linkage of market equilibrium analysis to farm level Integrated Assessment Models can be important in the analysis of agriculture–environment interactions.

To feed 9 billion people in 2050, recent estimates indicate that global food production will have to increase by 70% [FAO 2009]. Food security can be realized by expanding the cultivated area and by increasing production per unit land, labor, or capital. Further down the production-consumption chain, increasing ef…ciency and recycling (including postharvest and waste management) and dietary change also are important. To increase agricultural production, area expansion is still possible, mainly in the range of Ukraine-Russia-Kazakhstan, in sub-Saharan Africa (SSA), and in Latin America, but it will negatively affect the services provided by natural ecosystems. The recent agricultural area expansions in Latin America (soybean for savannah and Amazon forest) and Southeast Asia (oil palm for rainforest and peat lands) clearly show the dilemmas: a booming international market for soy and palm oil and for soy meal as animal feed [Smaling et al. 2008], go at the expense of natural vegetation, plant and animal biodiversity, climatic stability, and above-and below-ground carbon (C) stocks. Hence, raising productivity is the main feasible option on the upstream part of the food security chain. This is still possible in most agricultural systems, but the immediate large increases in cereal production of the Green Revolution will not be repeated easily. As Conway [1997] expressed, “What is needed is a doubly green revolution that raises yields while reducing environmental impact.”

The functionality of landscape is shaped by humans to obtain goods and services that are beneficial for society. Examples of these landscape functions are food production, recreation, flood protection, and habitat provision (Verburg et al., 2009; de Groot et al., 2006). To comply with the increasing demand for land, landscape (multi-)functionality is increasingly used in environmental development policies and plans (Renting et al., 2009). It is therefore important to quantify and map landscape functions and to recognise relations between landscape functions and their environment. Because numerous different landscape functions exist, there is not one single method to identify and map these landscape functions (Gulickx et al., in prep), which makes their assessment complex and difficult. Next to scientific research, environmental policy makers and planners develop maps to establish the spatial consequences of their policies. Both disciplines use their own methodologies, whilst they can enhance each other’s methods to achieve improved results. This research developed three landscape function maps for two case study areas in the Netherlands. For each map a different methodology was used, one based on environmental policy, one on environmental research, and one on a combination of the two (Figure 1). Interactive interviews were conducted with the governing coalition, including engineers, local decision makers, and the water board to agree on terminology and objectives. The methods were compared and consolidated throughout three workshops. The authors generated a ‘combined landscape function’ map using the combined methodology, which were compared with the ‘policy landscape function’ and ‘scientific landscape function’ maps for both case studies. The results and methodologies were discussed in a feedback workshop and useful recommendations were specified to enhance collaboration between environmental policy makers and planner, and environmental scientists. This research shows that close collaboration between scientific environmental research and environmental policy making and planning can lead to an improved product.

This review starts with a brief discussion on agricultural land use in the Netherlands, followed by an overview of the expected climatic changes in the Netherlands and the consequences of land use change on current agricultural practices. Subsequently, adaptation strategies as suggested in the literature are summarized and discussed.

Bananas are the world’s fourth food crop but in many countries in Asia and Latin America, it is also an important export crop. Africa is also importing it increasingly. Among European stakeholders, there's interest in African bananas. Before export from this continent can take off, a number of questions need addressing. Wageningen UR is developing practical knowledge for a multidisciplinary approach

Current European Common Agricultural Policy (CAP) has been moving from production support subsidies to direct decoupled income support. The emergence in policy making of the concept of multifunctional agriculture leads to the recognition that a farmer produces more than food: he produces jointly both commodity and non-commodity goods. Environmental contracts were developed in order to encourage the provision of non-commodity goods such as landscape or biodiversity. Next to these contracts, other activities as for example recreation can be observed. They are the result of farm diversification. The role of location in farmers¿ decision making to diversify is pointed out in literature but geographical information is generally reduced to the location within a political delimitation unit the empirical work. Objective of this paper is two-fold. Firstly, it addresses the role of location, in term of site specific natural conditions as well as neighbouring emerging dynamics in farmer¿s decision making to diversify. Attention is paid to number of activities as well as the specific types of activities, notably green services, daily recreation and other farm-linked services. Secondly, this paper introduces income from agriculture explicitly allowing testing short term price sensitivity. It was found that attractive landscape is a driver for diversification as these landscapes offer more opportunities. Furthermore, diversification is responsive to price. Thirdly, role of density of past multifunctional activities in the neighborhood influences farm diversification: multifunctional activities create an externality effects as new activities emerge next to already existing ones. This dynamic may lead to the emergence of `multifunctional hotspots¿ in landscape.

Many research and scientific organizations emphasize the importance of science for society in their strategic plans. This is certainly true for land use studies being discussed in this chapter as new environmental policies are introduced at European and national level. Such policies reflect concerns of society so that a structural link between science and policymaking would appear to be logical and desirable. Rather than following traditional top-down and disciplinary research approaches, emphasis is increasingly being placed on interactive, interdisciplinary work in Communities of Practice (CoPs) in which scientists work together with various stakeholders and policymakers in a joint learning mode. But this requires new research approaches including long-term engagement during the entire policy cycle asking for a new attitude of scientists. Few experiences have been reported so far. Three Dutch case studies are therefore discussed to illustrate the functioning of CoPs by focusing on up- and downscaling (called multiscaling hereafter), a key element of land use research. Five types of multiscaling were used in the three case studies. Three were technical: (1) use of model- or design-based (geo)statistical techniques, (2) extrapolation of data obtained from experimental plots to larger areas, and (3) use of quasi-3D process models to upscale grid data in a Geographic Information System (GIS) to regions. Two were policy oriented: (1) nutrient balances for farms to allow upscaling from fields to farm and (2) a research framework for regions, based on the DPSIR approach, which sequentially covers aspects mentioned in environmental laws as being important for sustainable development. Quite diverse and unrelated questions about land use issues by different members of the CoP cannot be a fruitful basis for research programs. Scientists have therefore an important role to play within a CoP in orchestrating a demand analysis that puts questions into context and defines existing knowledge as well as knowledge gaps. Defining research on the basis of a demand analysis in a CoP creates innovative ideas, creates commitment of participants, and allows definition of needed research that is functional. This includes cutting edge research publishable in literature and requires for land use studies updating of valuable existing soil survey information to a level that can be used in modern modeling techniques including functional characterization of soil series, development of pedotransfer functions, and definition of phenoforms. Particular attention is needed for introducing modern monitoring techniques for soil and water because the high cost of traditional methods implies that little monitoring is done now with detrimental effects for the calibration and validation of simulation models that increasingly secure a live of their own. The scientific community needs to take a fresh look at its paradigms. Next to the establishment of CoPs, we therefore advocate development of Communities of Scientific Practice (CSP) within the research community that define different functions for members of the scientific community in terms of (1) communication within CoPs by shaping the demand analysis and to the outside world and (2) defining research needs and its execution, using knowledge chains including basic research.

This chapter focuses on the subject of ecoregional research for development. The character of international agricultural research has changed profoundly in the last decades of the twentieth century. This can be illustrated by developments within the Consultative Group on International Agricultural Research (CGIAR). The need for a support of comprehensive systems' analysis of the CGIAR agenda required new research; therefore, the development agencies of the Dutch and Swiss governments initiated the Ecoregional Methodology Fund in 1995 as part of its development-oriented activities. This Fund was expected to support the development of new methodological tools for research that is ecoregional in scope with the intention of promoting new approaches to natural resource management and rural development in ecoregions. This chapter summarizes and discusses the accomplishments and the future challenges of Ecoregional Methodology Fund. In doing so, the chapter presents a sketch of the changing social and political environment in which the work has been done, ranging from the UN Millennium Development Goals (MDGs) to the strategy of the CGIAR Science Council and the Gleneagle summit. It also elaborates the changing role of research, vis-a-vis the policy cycle. Implications for research, including new forms of interaction, design of operational tools, and new forms of education and communication are also discussed in the chapter.

Typically, the guiding principle for fertilizer management is a rather fixed cropping calendar in which applications are given at fixed moments in time or at specific crop development stages. The recommendation is based on average weather and crop development. However, in most conditions the average year is rare and more commonly we see that weather conditions deviate and crop development follows different patterns. It is therefore a logical option to adapt management during the growing season according to changing weather conditions and crop development. Different techniques are available to treat fertilization more dynamically including simulation models, decision support systems and real time weather records and forecasts. We present three very different case studies in which these tools have been used. The first case refers to a Costa Rican program on precision agriculture in banana plantations. A network of weather stations provides information to a meteorologist who makes specific weather forecasts for the banana region. A new fertilizer recommendation system is developed that uses the weather conditions and forecasts in combination with yield mapping to establish the proper fertilizer recommendations. A second case refers to an arable farm in the south-west of the Netherlands where actual nitrogen stocks and nitrogen uptake by wheat are simulated for different parts of the field using dynamic real-time simulation modeling. Finally, we simulated nitrogen leaching for various farms in the northern part of the Netherlands. Here, we specifically looked at the environmental impact as a result of specific weather conditions in combination with past and current management. The three case studies illustrate the potential for more dynamic cropping calendars but at the same time show some of the practical operational problems. This includes the need to look into the environmental aspects but also the management goals of the farmer.

Gestimuleerd door ervaringen tijdens de 5e European Conference on Precision Agriculture in Uppsala stellen de auteurs hier de vraag, waarom in ons land de ontwikkeling van precisie-landbouw achterblijft. Een bedrijfsmatige veldstudie van precisie-landbouw wordt hierbij besproken tegen de achtergrond van de nitraatrichtlijn

After almost two decades of precision agriculture (PA) research it is time to look at where we are. Significant progress has been made with different elements of research but at the same time we see that PA is not broadly being embraced in global agriculture. However, there is a clear need for advanced techniques, such as PA, to not only improve the agricultural production process but to also control losses of agricultural chemicals to the environment. Key problems in the adoption of PA are the narrow technological focus and lack of an integrating systems approach to research. Insight is lacking to translate the observed spatial variability in resources and crop performance into practical site and time specific management recommendations. We studied two agricultural systems. One system is a typical case for arable farming in the Netherlands. The system is increasingly being constrained by agricultural policies that limit use of chemicals on the farm. The other system under consideration is a Costa Rican banana plantation. The plantation is also constrained in its management decisions but here it is the European market that limits the management options on the farm. The two examples illustrate the potential value of PA if studied in an integrated manner. They also show that high-tech is not a prerequisite for successful implementation but that, rather, a joint learning process with the producers is the key to success.