Integrated Administration And Control System Research Articles

Crop Type Mapping and Winter Wheat Yield Prediction Utilizing Sentinel-2: A Case Study from Upper Thracian Lowland, Bulgaria

The aim of this study is to predict and map winter wheat yield in the Parvomay municipality, situated in the Upper Thracian Lowland of Bulgaria, utilizing satellite data from Sentinel-2. The main crops grown in the research area are winter wheat, rapeseed, sunflower, and maize. To distinguish winter wheat fields accurately, we evaluated classification methods such as Support Vector Machines (SVM) and Random Forest (RF). These methods were applied to satellite multispectral data acquired by the Sentinel-2 satellites during the growing season of 2020–2021. In accordance with their development cycles, temporal image composites were developed to identify suitable moments when each crop is most accurately distinguished from others. Ground truth data obtained from the integrated administration and control system (IACS) were used for training the classifiers and assessing the accuracy of the final maps. Winter wheat fields were masked using the crop mask created from the best-performing classification algorithm. Yields were predicted with regression models calibrated with in situ data collected in the Parvomay study area. Both SVM and RF algorithms performed well in classifying winter wheat fields, with SVM slightly outperforming RF. The produced crop maps enable the application of crop-specific yield models on a regional scale. The best predictor of yield was the green NDVI index (GNDVI) from the April monthly composite image.

Soil Data Cube and Artificial Intelligence Techniques for Generating National-Scale Topsoil Thematic Maps: A Case Study in Lithuanian Croplands

There is a growing realization among policymakers that in order to pave the way for the development of evidence-based conservation recommendations for policy, it is essential to improve the capacity for soil-health monitoring by adopting multidimensional and integrated approaches. However, the existing ready-to-use maps are characterized mainly by a coarse spatial resolution (>200 m) and information that is not up to date, making their use insufficient for the EU’s policy requirements, such as the common agricultural policy. This work, by utilizing the Soil Data Cube, which is a self-hosted custom tool, provides yearly estimations of soil thematic maps (e.g., exposed soil, soil organic carbon, clay content) covering all the agricultural area in Lithuania. The pipeline exploits various Earth observation data such as a time series of Sentinel-2 satellite imagery (2018–2022), the LUCAS (Land Use/Cover Area Frame Statistical Survey) topsoil database, the European Integrated Administration and Control System (IACS) and artificial intelligence (AI) architectures to improve the prediction accuracy as well as the spatial resolution (10 m), enabling discrimination at the parcel level. Five different prediction models were tested with the convolutional neural network (CNN) model to achieve the best accuracy for both targeted indicators (SOC and clay) related to the R2 metric (0.51 for SOC and 0.57 for clay). The model predictions supported by the prediction uncertainties based on the PIR formula (average PIR 0.48 for SOC and 0.61 for clay) provide valuable information on the model’s interpretation and stability. The model application and the final predictions of the soil indicators were carried out based on national bare-soil-reflectance composite layers, generated by employing a pixel-based composite approach to the overlaid annual bare-soil maps and by using a combination of a series of vegetation indices such as NDVI, NBR2, and SCL. The findings of this work provide new insights for the generation of soil thematic maps on a large scale, leading to more efficient and sustainable soil management, supporting policymakers and the agri-food private sector.

Determining the usefulness of the Copernicus High-Resolution Vegetation Phenology and Productivity Product (HR-VPP) with official agricultural data on cropland in case of the 2018 drought in the Federal State of Saxony, Germany

Abstract Remote sensing-based data on vegetation conditions provide important information for agriculture. In this study, the potential uses of the freely available High-Resolution Vegetation Phenology and Productivity Product (HR-VPP) are tested. This test examines the 2018 drought year in the state of Saxony, Germany, and the capabilities and limitations of the HR-VPP product in use with Integrated Administration and Control System (IACS) data. The results show that field and crop type-specific spatial (re)analyses of a drought are possible and that there is still great potential in this data analysis. Using the data in a new proposed VPP-based Farm-Level Temporal Comparison Indicator (VPP-FLTCI), it was not possible to tease out patterns in why farms applied for state drought aid in 2018 compared to other farms. In the future, even better and more detailed analyses based on the HR-VPP can be expected, as the data series with now a total of 5 years is still very short to generate sufficient references, especially in Central European agriculture, which is characterized by crop rotation.

Use Cases of the Integrated Administration and Control System’s Plot-Level Data: Protocol and Pilot Analysis for a Systematic Mapping Review

In recent years, land use data from the EU’s Integrated Administration and Control System (IACS) have become increasingly available for research purposes. IACS data contain annual plot-level information on cultivation and location of the land farmed by agricultural beneficiaries, covering the majority of farmland in the EU. The data thus provide information on land use that is spatially and temporally highly disaggregated. Researchers from a broad range of disciplines already rely on IACS data for various applications and EU member states must make an anonymised, geospatially-referenced version of the data publicly available in the future. However, there is no complete and structured overview of the data’s use, their benefits and limitations that future research could rely on. This calls for conducting a systematic review of the applications of IACS plot-level data in scientific literature. Preparing the ground for such a review, this paper presents a review protocol for identifying and analysing publications using plot-level IACS data from Austria, Czechia, France, Germany, and Sweden in a systematic map. To test and refine the protocol and to illustrate the planned review, we conduct a pilot analysis of 12 selected academic publications and present the results. The pilot analysis demonstrates that the review protocol is adequate and that novel insights will be gained by mapping out the already existing work that uses IACS data.

Land Parcel Identification System (LPIS) data allows identification of crop sequence patterns and diversity in organic and conventional farming systems.

Farmers grow crops in specific sequences to lower disease pressure and boost crop productivity, particularly in organic farming where artificial pesticides and chemical fertilisers are prohibited. Knowledge about crop sequences used in organic and conventional farming will aid the development of future farming systems through optimising crop diversity and pre-crop effects for improved resource efficiency. This study aims to investigate crop diversity and patterns in organic and conventional crop sequences in Sweden. Large-scale LPIS field data managed by the European Union (EU) Integrated Administration and Control System (IACS) were used to monitor crop sequences on arable land in Sweden over 10 consecutive years (2005–2014). Individual fields (land parcels) could be followed on 40% of Sweden’s total arable area (349,891 fields extracted) over the 10 years. The LPIS data was combined with information from a database on which fields were farmed organically. Crop distribution, diversity of crop sequences and pre-crops to the main cereal crops (winter wheat, spring barley) were analysed in organic and conventional farming systems in the five agricultural productivity zones of Sweden. The results showed that in the most productive zone in southernmost Sweden, small-grain cereals (particularly winter wheat) were the most common crops (62%), followed by oilseeds (11%), ley and forage crops (9%) and sugar beet (8%), when excluding permanent grassland. In the least productive zone (at higher altitudes and/or latitudes), ley and forage crops dominated (67%), followed by spring cereals (barley, oats) (23%). Crop diversity was higher in the two more productive zones (mean 4.6 crop types) than in two less productive zones (3.4) and organic farms showed 9% higher crop diversity than conventional farms in the most productive zones. Overall, in all zones, the pre-crop to winter wheat was generally a different crop type (3 out of 5 times) e.g., young ley (1–2 years) or grain legume, while the pre-crop to spring barley was most often (4 out of 5 times) another cereal. For both these crops, pre-crop type was more diverse in organic than conventional systems. These findings demonstrate that LPIS data can offer valuable insights into agronomic trends and on-farm practices regarding crop choice and that analysis of field-level LPIS data on crop sequences at large scale can reveal information about organic and conventional cropping in different productivity zones across countries. This information can be used to understand the practical limitations in the use of crop diversity to maximise pre-crop effects. This could in turn support advisory service and policy makers to facilitate more sustainable, productive and resource efficient crop production.

A field parcel-oriented approach to evaluate the crop cover-management factor and time-distributed erosion risk in Europe

The crop cover-management (C-) factor in arable landscapes describes the soil erosion susceptibility associated with seasonally cultivated crops. Previous informatic and computational limitations have led many modelling studies to prescribe C-factor values and assume spatial and temporal stationarity. However, the multiple influencing factors ranging from parcel-scale crop cultivation and management to regional-scale rainfall regimes motivates new methods to capture this variation when identifying at-risk areas. Here, we define a multi-component method to derive the C-factor by associating time series of canopy and residue surface cover from Sentinel-2 and climate-specific rainfall erosivity with Integrated Administration and Control System (IACS) field parcel data from European Union member states. A scalable and standardised method is emphasised to increase the future interoperability and inter-comparability of soil erosion modelling studies deploying the C-factor. Additionally, field parcel simulation units with associated crop declarations provide a new reference scale to link predictions of soil erosion risk with specific management decisions and declarations by farmers. After implementing the method on a homogenised subsample of 8600 field parcels covering available IACS regions, several key findings are outlined: 1) time series information provides new opportunities to predict the time-criticality of erosion in specific crop cultivations, 2) the varying (a-)synchronicity between seasonal crop canopy cover and heavy rainstorms means that spatial variability is inherent within the C-factor across Europe, and 3) the addition of agricultural management practices (e.g. tillage practice descriptions) to open-access IACS repositories can facilitate more comprehensive evaluations of the C-factor and soil erosion risk.

Soil Reflectance Composites—Improved Thresholding and Performance Evaluation

Reflectance composites that capture bare soil pixels from multispectral image data are increasingly being analysed to model soil constituents such as soil organic carbon. These temporal composites are used instead of single-date multispectral images to account for the frequent vegetation cover of soils and, thus, to get broader spatial coverage of bare soil pixels. Most soil compositing techniques require thresholds derived from spectral indices such as the Normalised Difference Vegetation Index (NDVI) and the Normalised Burn Ratio 2 (NBR2) to separate bare soils from all other land cover types. However, the threshold derivation is handled based on expert knowledge of a specific area, statistical percentile definitions or in situ data. For operational processors, such site-specific and partly manual strategies are not applicable. There is a need for a more generic solution to derive thresholds for large-scale processing without manual intervention. This study presents a novel HIstogram SEparation Threshold (HISET) methodology deriving spectral index thresholds and testing them for a Sentinel-2 temporal data stack. The technique is spectral index-independent, data-driven and can be evaluated based on a quality score. We tested HISET for building six soil reflectance composites (SRC) using NDVI, NBR2 and a new index combining the NDVI and a short-wave infrared (SWIR) band (PV+IR2). A comprehensive analysis of the spectral and spatial performance and accuracy of the resulting SRCs proves the flexibility and validity of HISET. Disturbance effects such as spectral confusion of bare soils with non-photosynthetic-active vegetation (NPV) could be reduced by choosing grassland and crops as input LC for HISET. The NBR2-based SRC spectra showed the highest similarity with LUCAS spectra, the broadest spatial coverage of bare soil pixels and the least number of valid observations per pixel. The spatial coverage of bare soil pixels is validated against the database of the Integrated Administration and Control System (IACS) of the European Commission. Validation results show that PV+IR2-based SRCs outperform the other two indices, especially in spectrally mixed areas of bare soil, photosynthetic-active vegetation and NPV. The NDVI-based SRCs showed the lowest confidence values (95%) in all bands. In the future, HISET shall be tested in other areas with different environmental conditions and LC characteristics to evaluate if the findings of this study are also valid.

Effects of wildflower strips, landscape structure and agricultural practices on wild bee assemblages – A matter of data resolution and spatial scale?

Agricultural systems in Central Europe were redesigned during the last century to attain maximum yields. The results often lead to homogeneous landscapes with only few structures of ecological value and have concurrently exacerbated habitat fragmentation. Perennial wildflower strips have become a significant agri-environmental measure (AEM) to counteract the ecological consequences for wild bees and other pollinators in agricultural landscapes. The effectiveness of AEMs depends on the landscape context, but information about geodata sources and spatial scales relevant for the analysis of landscape effects on wild bees is lacking. This study uses data from various sources on land cover and agricultural practices to assess their applicability in an evaluation of perennial wildflower strips as AEM for wild bees in Saxony-Anhalt, Germany. We investigated the relationships of wild bee species diversity and abundance to the landscape context at spatial scales from 200 m to 10 km considering several factors: land cover/land use, protected areas, crop types, agri-environment schemes/greening, intensity of agriculture, and intensity of grassland farming. In general, our results revealed that landscape effects were more relevant for solitary than social wild bees on flower strips, pointing to a higher limitation of solitary wild bees in nesting resources as compared to social wild bees. Numbers of wild bee species and individuals benefitted from bare soil and ecological focus areas in the surroundings up to 3 km distance, whereas the share of Red List solitary bee species was positively influenced by a variety of factors (e.g., wood structures and grasslands) especially at large scales up to 10 km. The comparison of models based on different land cover data sources showed that the lack of geodata resolution can mask landscape effects on wild bees. Altogether, our results suggest a high potential of data from the Basic Digital Landscape Model (DLM), together with the Integrated Administration and Control System (IACS), to indicate effects of landscape structures and agricultural practices on the species composition and distribution of wild bee assemblages in Germany.

Sentinel-1 Time Series for Crop Identification in the Framework of the Future CAP Monitoring

In this upcoming Common Agricultural Policy (CAP) reform, the use of satellite imagery is taking an increasing role for improving the Integrated Administration and Control System (IACS). Considering the operational aspect of the CAP monitoring process, the use of Sentinel-1 SAR (Synthetic Aperture Radar) images is highly relevant, especially in regions with a frequent cloud cover, such as Belgium. Indeed, SAR imagery does not depend on sunlight and is barely affected by the presence of clouds. Moreover, the SAR signal is particularly sensitive to the geometry and the water content of the target. Crop identification is often a pre-requisite to monitor agriculture at parcel level (ploughing, harvest, grassland mowing, intercropping, etc.) The main goal of this study is to assess the performances and constraints of a SAR-based crop classification in an operational large-scale application. The Random Forest object-oriented classification model is built on Sentinel-1 time series from January to August 2020 only. It can identify crops in the Walloon Region (south part of Belgium) with high performance: 93.4% of well-classified area, representing 88.4% of the parcels. Among the 48 crop groups, the six most represented ones get a F1-score higher or equal to 84%. Additionally, this research documents how the classification performance is affected by different parameters: the SAR orbit, the size of the training dataset, the use of different internal buffers on parcel polygons before signal extraction, the set of explanatory variables, and the period of the time series. In an operational context, this allows to choose the right balance between classification accuracy and model complexity. A key result is that using a training dataset containing only 3.2% of the total number of parcels allows to correctly classify 91.7% of the agricultural area. The impact of rain and snow is also discussed. Finally, this research analyses how the classification accuracy depends on some characteristics of the parcels like their shape or size. This allows to assess the relevance of the classification depending on those characteristics, as well as to identify a subset of parcels for which the global accuracy is higher.

Agricultural Landscapes in Brandenburg, Germany: An Analysis of Characteristics and Spatial Patterns

The increasing demand for agricultural commodities for food and energy purposes has led to intensified agricultural land management, along with the homogenization of landscapes, adverse biodiversity effects and robustness of landscapes regarding the provision of ecosystem services. At the same time, subsidized organic agriculture and extensive grassland use supports the provision of ecosystem services. Yet little is understood about how to evaluate a landscape’s potential to contribute to protecting and enhancing biodiversity and ecosystem services. To address this gap, we use plot-level data from the Integrated Administration and Control System (IACS) for Germany’s federal state of Brandenburg, and based on a two-step cluster analysis, we identify six types of agricultural landscapes. These clusters differ in landscape structure, diversity and measures for agricultural land management intensity. Agricultural land in Brandenburg is dominated by high shares of cropland but fragmented differently. Lands under organic management and those with a high share of maize show strong spatial autocorrelation, pointing to local clusters. Identification of different types of landscapes permits locally- and region-adapted designs of environmental and agricultural policy measures improves outcome-oriented environmental policy impact evaluation and landscape planning. Our approach allows transferability to other EU regions.

Spatio-temporal analysis of the effects of biogas production on agricultural lands

With the subsidization of energy crops in Germany by the Renewable Energy Act, the production of biogas became increasingly dependent on agricultural land for biomass generation. The effects of increasing biogas plants’ capacities on the increase of maize and other energy crops area over the years have raised sustainability concerns in the German society. A periodic assessment of the impacts of biomass production is necessary to determine efficient and environmentally sustainable recommendations for future planning processes. Previous studies have neglected the effects of biogas plant capacity, which is directly linked with the amount of biomass required and, thus, is supposed to be a driver for the impacts of biogas production on agricultural lands. Combining the Integrated Administration and Control System (IACS) and Landsat satellite data provides a means to study present and past agricultural landscape. The aim of the study was to map the agricultural lands of Northern Hesse from 2000 to 2015, evaluate the change in maize area and structural diversity (i.e. field edge density) of agricultural fields, and assess if biogas production is a major driver of possible changes. Northern Hesse was consciously chosen, as it represents a typical European low-mountain range region with substantial geographical and agricultural variability. Our results show that from 2000 to 2015 on average, the shares of grassland and summer crops areas decreased by 5.7 percentage points (p.p.) and 5.8 p.p., respectively, while the shares of winter crops and maize areas increased by 9.2 p.p. and 2.2 p.p., respectively. Potential drivers of the change in maize area and structural diversity of agricultural fields, such as the mean capacity of biogas plants (kW), and topographical attributes, such as mean elevation (m) and slope (%) were employed as explanatory variables on a municipality level in multiple linear regression models. The mean biogas capacity and elevation were the most significant drivers of change in maize area (p < 0.05), while biogas capacity and slope were the significant drivers of the change in structural diversity. Maize area increased with an increasing capacity of biogas plants at lower elevations of the study area (p < 0.05). A decrease in the structure of fields, which is an indicator for species and habitat diversity, was observed and this was very much associated with the extent of maize and winter crop production. However, the drivers of the changes in the maize area and structure of fields were of low dimensions with model R2 of 0.21 and 0.35 respectively. Overall, our study demonstrates the potential of spatial data from different origins in mapping land-cover over longer time periods for monitoring bio-economy effects on agricultural landscape.

Agricultural landscape-scale C factor determination and erosion prediction for various crop rotations through a remote sensing and GIS approach

In arable land management, different crop rotation patterns and sequences, such as changing agricultural land use to erosion prone crops, or crops providing less ground cover, can greatly influence soil loss rate through their impact on soil cover status (C factor value). The influence of crop rotation on C value and on erosion rate is often determined on an experimental plot scale, so the results are often erroneous when extrapolated to large heterogeneous landscapes, where they fail to capture the spatiotemporal variability beyond the experimental sites. In the present study we have endeavored to investigate the impact of various crop rotation patterns on C value and on subsequent soil erosion rate, at a landscape level, by combining 28 time-series satellite images (from 2013 to 2016) along with annually updated land-use data, via the integrated administration and control system (IACS), from the Uckermark district of north eastern Germany. In total, 21 different crop sequences were investigated. Winter wheat (WW), winter rape (WR), and maize (Mz) were found to be the predominant arable crops grown in the study area. The highest average annual C values were estimated from crop sequences involving Mz and sugar beet (SB), both as pre-crops and succeeding crops. The highest value of 0.39 was computed from SB/Mz rotation. On the other hand, crop rotation involving WR gave significantly lower annual C values in all the years considered, with the lowest average annual C value of 0.07 calculated on WR parcels preceded by winter cereals. It was also apparent that crop rotation patterns influenced C value in a temporally variable manner. Among the self-sequencing patterns, WR/WR reduced the C value significantly compared with Mz/Mz and to a lesser extent compared with WW/WW. Continuous cultivation of Mz increased the potential soil loss rate by as much as 72 % compared to WR/WR and by 51 % compared to WW/WW. It was also possible to determine the spatial distribution of the impact of crop rotation on soil erosion risk within the study area. The results obtained agreed with the results of other international and regional studies. Overall, the output from this research could contribute towards further efficient investigation of the impact of agronomic practices on the environment in a large agricultural landscape, without the need to set up multi-location experimental plots.

Greening the Gas Grid—Evaluation of the Biomethane Injection Potential from Agricultural Residues in Austria

In order to implement the Paris Climate Agreement, the current Austrian coalition government has included trend-setting targets in its policy statement. “Green gas” plays a key role in this context, as the natural gas grid shall also gradually become renewable. This article analyses the technical biomethane injection potential for agricultural residues based on Integrated Administration and Control System (IACS) data on a municipal level. While a technical biogas potential of 16.2 TWhCH4 from catch crops, farm manure, straw and beet leaves is available, only about half of it can be fed into the gas grid because of technical and economic reasons. Austria’s biomethane injection potential of 7.4 TWhCH4 is mainly produced in arable farming regions. In order to further increase this potential, the investment costs of biogas upgrading plants must be reduced, the use of biogenic waste and energy crops must be further promoted and an investor-friendly legal framework must be created.

Farm-level indicators for crop and landscape diversity derived from agricultural beneficiaries data

The EU follows the broad aim to develop viable food production systems, through supporting the sustainable management of natural resources and climate action and to strive for more balanced territorial development. These core values are reflected in the changing Common Agricultural Policy. This policy is one of the key drivers of agricultural practice across the EU, as it has become more oriented towards the concepts of sustainability in recent years. The EU operates a farm-level monitoring system, the Farm Accountancy Data Network (FADN), which acts as a check to establish if such successful change is being achieved, currently the primary focus is on the economic dimension of sustainability. We argue that to complement existing monitoring tools, cost-effective measurement of indicators for other sustainability dimensions at the farm scale is required. This article explores to what extent EU agricultural beneficiaries data as collected under the Integrated Administration and Control System (IACS) can be a complementary source of information for this task. To this end, candidate indicators for the environmental dimension from available studies were reviewed and it was assessed whether the data requirements for the indicators could be met through IACS data. The identified five environmental indicators were produced from an IACS data set from Germany. The indicator data was quantitatively analysed and compared with target values reported in the literature, and interpreted with regard to meaningfulness and possible improvements. Our results show that IACS data is indeed a potential source for farm level indicators for the environmental dimension, primarily with regard to landscape diversity (landscape features, parcel size) and the diversity of crop production. The spatial nature of IACS allows for a link to other environmental data and to agroecosystem models which may provide additional indicators, e.g. nutrient balances. However, important information on management of field operations and livestock herds is still insufficient in both FADN and IACS.

Multi-Temporal Agricultural Land-Cover Mapping Using Single-Year and Multi-Year Models Based on Landsat Imagery and IACS Data

The spatial distribution and location of crops are necessary information for agricultural planning. The free availability of optical satellites such as Landsat offers an opportunity to obtain this key information. Crop type mapping using satellite data is challenged by its reliance on ground truth data. The Integrated Administration and Control System (IACS) data, submitted by farmers in Europe for subsidy payments, provide a solution to the issue of periodic field data collection. The present study tested the performance of the IACS data in the development of a generalized predictive crop type model, which is independent of the calibration year. Using the IACS polygons as objects, the mean spectral information based on four different vegetation indices and six Landsat bands were extracted for each crop type and used as predictors in a random forest model. Two modelling methods called single-year (SY) and multiple-year (MY) calibration were tested to find out their performance in the prediction of grassland, maize, summer, and winter crops. The independent validation of SY and MY resulted in a mean overall accuracy of 71.5% and 77.3%, respectively. The field-based approach of calibration used in this study dealt with the ‘salt and pepper’ effects of the pixel-based approach.

Quantifying gross vs. net agricultural land use change in Great Britain using the Integrated Administration and Control System

Land use change has impacts upon many natural processes, and is one of the key measures of anthropogenic disturbance on ecosystems. Agricultural land covers 70% of Great Britain's (GB) land surface and annually undergoes disturbance and change through farming practices such as crop rotation, ploughing and the planting and subsequent logging of forestry. It is important to quantify how much of GB's agricultural land undergoes such changes and what those changes are at an annual temporal resolution. Integrated Administration and Control System (IACS) data give annual snapshots of agricultural land use at the field level, allowing for high resolution spatiotemporal land use change studies at the national scale. Crucially, not only do the data allow for simple net change studies (total area change of a land use, in a specific areal unit) but also for gross change calculations (summation of all changes to and from a land use), meaning that both gains and losses to and from each land use category can be defined. In this study we analysed IACS data for GB from 2005 to 2013, and quantified gross change for over 90% of the agricultural area in GB for the first time. It was found that gross change totalled 63,500 km2 in GB compared to 20,600 km2 of net change, i.e. the real year-on-year change is, on average, three times larger than net change. This detailed information on nature of land use change allows for increased accuracy in modelling the impact of land use change on ecosystem processes and is directly applicable across EU member states, where collection of such survey data is a requirement. The modelled carbon flux associated with gross land use change was at times >100 Gg C y−1 larger than that based on net land use change for some land use transitions.

Making the best of both worlds: Can high-resolution agricultural administrative data support the assessment of High Nature Value farmlands across Europe?

Worldwide, the role of farmlands for biodiversity conservation and the delivery of multiple ecosystem services has been widely acknowledged. In the European Union (EU), societal demands to include environmental conservation concerns within the Common Agricultural Policy (CAP) have resulted in the recognition of the importance of maintaining High Nature Value farmlands (HNVf).HNVf constitute complex social-ecological systems, which owe their nature conservation value to the maintenance of specific, mostly low-intensity farming systems, supporting high levels of species and habitats dependent on agricultural practices. Even though HNVf assessment in space and time is essential to evaluate the effectiveness of Rural Development Programmes, the diversity of rural landscapes across EU, the scarcity of data on farming systems, and the lack of common methodological guidelines has hampered the implementation of HNVf mapping and monitoring across Europe. Thus, there is a pressing need to develop and test methodological approaches that may support HNVf assessment across the EU.The Integrated Administration and Control System (IACS) which is mandatory for all EU Member States constitutes a system for the management and control of CAP payments to farmers. Essentially, IACS comprises high-resolution, spatially explicit information on the type and intensity of agricultural land-use. Even though such data exhibits high thematic, spatial and temporal resolution, IACS has seldom been used, due to significant access restrictions. Here, the potential to use IACS data to support the assessment of HNVf was evaluated within the German Federal State of Lower Saxony by implementing a recently developed methodological framework. Sets of indicators known to be essential for identifying potential HNVf and underlying farming systems (expressing landscape structure and composition, farming systems, and crop diversity), were derived from IACS. Spatial patterns of indicators were analyzed at two different scales to delineate the potential distribution of HNVf across Lower Saxony.Results highlighted that most regions in Lower Saxony were characterized by intensive farming practices including high livestock density, high share of intensive crops and low density of linear elements. Only 3% of the Utilized Agricultural Area (UAA) of Lower Saxony potentially constituted HNVf, with the majority of HNVf coinciding with mosaics of arable and/or permanent crops and semi-natural features under less intensive farming practices. Semi-natural grasslands, partially under agri-environment scheme management contracts, covered roughly 1% of the UAA and were mostly intermingled with other farmland habitats in extensively managed agricultural landscapes.In the context of the EU-wide HNVf assessment, IACS constitutes an important source of data, characterized by a high spatial, thematic and temporal resolution of data collected annually. Whilst having the potential for use in HNVf assessment, some challenges remain, especially due to significant access restrictions. Nevertheless, IACS constitutes a powerful tool to evaluate the extent and condition of HNVf across the EU countryside. Making use of IACS data in such a way could provide a stepping-stone towards achieving a more effective balance between the management and control of CAP support payments and the growing societal demands related to the maintenance and enhancement of farmland biodiversity and ecosystem services.

Spatial information in European agricultural data management. Requirements and interoperability supported by a domain model

Data compatibility and system interoperability are fundamental for crosswalks and collaboration between domains. The most frequently used references for information sharing are time and location. In order to understand the requirements, fundamental processes, and core information concepts of a domain, a comprehensive, but standardised documentation is needed. In spatial data infrastructures models presented in Unified Modelling Language (UML) are widely used to facilitate the uptake of standards and valorise best practices of various communities.The European agricultural decision makers must deal with many components described in the Common Agricultural Policy (CAP) in order to optimize data integration and achieve transparency. Geospatial information plays a key role in the implementation of this policy, which comprises the establishment and maintenance of the Integrated Administration and Control System (IACS).In the past the IACS of the MS has principally served a single high level business case: to run a correct administration of the CAP. However, the recent reform and the synergies with environmental and societal policies create increasing expectations for IACS, which impact upon system interoperability and data usability. These objectives can be achieved by establishing a framework that is standard based, allows flexible extensions, and that supports efficient implementation and information exchange between the stakeholders.This paper presents the development process and describes the structure of a domain model we propose for IACS. This standard driven model was designed to translate requirements into technical elements, ensuring interoperability, and providing flexible extensions at the same time. For this purpose we set-up a formal requirement model, formalized use cases, and integrated ISO/TC 211 and INSPIRE UML based class diagrams. As such, this paper proposes a methodology to help guide how the policy can be implemented.The importance of traceability from IACS and third party business rules to the information concepts was proven by simulation runs. The developed modelling approach yielded a reference for conformance testing, indicated critical points of potential errors, and highlighted the impact of eventual changes. Storing all concepts and implementation options of IACS in a unique framework helps to eliminate redundant efforts, provides a strong basis for developing various applications, underpins interoperability with other domains and enhances transparency of the CAP.

Analysing Data of the Integrated Administration and Control System (IACS) to Detect Patterns of Agricultural Land-Use Change at Municipality Level

European landscapes have featured considerable changes towards intensification and marginalisation. These major trends are expected to continue in the future. Besides, the cultivation of bioenergy crops has become an important factor in agricultural land use. A thorough understanding of land-use processes for management purposes is needed. In this study, the spatial and temporal pattern of agricultural land use and land-use change was classified at the scale of municipalities from 2005 to 2010. The study region was the German federal state Hesse. By using data of the Integrated Administration and Control System (IACS) of the European Union and with the help of k-means cluster analysis, five types of agricultural land-use patterns and dynamics (TLPDs) were detected. These TLPDs represent different sub-regions. Sub-regions with favourable physical conditions for cultivation are dominated by arable land. A progressive land-use change occurred by conversion of grassland to arable land. In sub-regions, where physical conditions are rather unfavourable, especially in mountainous areas, grassland is the predominant land use. But on the remaining arable land, there is a slight change in favour of maize. The knowledge of sub-regions with spatially and temporally different agricultural land use could be utilised to develop land management instruments like site-specific agri-environmental schemes.

The dynamic effects of government-supported farm-investment activities on structural change in Austrian agriculture

The objective of our paper is to analyse the effects of government-supported farm-investment activities on structural change in agriculture. Our method comprises combining direct covariate matching with a difference-in-difference (DiD) estimator. In order to capture the dynamics and the heterogeneity of structural effects, we have developed time and farm-group specific models. We apply our model in Austria, where we analyse the Integrated Administration and Control System (IACS) data of 98,000 farms within the time period of 2000–2011. Our results show that farms adapt their numbers of livestock very quickly, whereas, the increase in agricultural area seems to be fairly decoupled from the investment activity itself. Effects tend to be farm-group specific; e.g. farm size initially increases (and drops) on pig farms to a greater extent than on cattle farms. Furthermore, government-supported farm-investment activities not only influence structural change but also tend to increase production intensity and reduce diversification on arable land – perhaps counteracting, therefore, the goals of agri-environmental schemes. However, our results indicate that investing (cattle) farmers are more likely to enter the organic farming programme and tend rather to remain in animal husbandry.