Table 1 Overview of NIVA progress on main challenges
Challenge | Baseline (project start) situation | NIVA achievements up to date |
Absorbing innovations to simplify the governance |
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Reducing socio-economic and administrative burden to farmers |
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Reducing the gap between IACS data use and potential broader uses |
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Table 2 Overview of NIVA progress on main objectives
Objective | Baseline (project start) situation | NIVA achievements up to date |
Integrate and reuse IACS evolutions based on open standards and common services |
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Build on farmers’ acceptance of the Smart Monitoring methodology |
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Reducing the gap between IACS data use and potential broader uses** |
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Create a permanent exchange platform for discussion and exchange |
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Table 3 Main innovations as achieved in the Large Scale Pilots
Use Case number | Use case title | Building on | Main Innovations | Main challenges |
UC1A | Earth Observation Monitoring and Traffic Lights | EO Processing chain of SEN4CAP for the AMS, Open EO | Expanding the coverage of SEN4CAP crop/permanent pasture classifiers, also extending to smaller sized parcels Integrating EO processing chain into PA processes for eligibility at the parcel level | Limited reusability of SEN4CAP, not ready to be integrated in an IACS system Improve marker uncertainty to reduce uncertain/yellow cases, also dealing with Sentinel limitations and need to add high-high resolution for specific cases Customizing the system for specifics of other MS |
UC1B | Agri-environmental monitoring | SEN4CAP for EO processing, scientific definitions of indicators and dedicated models | Operationalizing indicator calculations addressing 3 CAP objectives: Climate change, Environmental care, landscape and biodiversity | More advanced indicator calculations require multiple data sources (incl. FMIS, soil samples, etc) Eligibility criteria for eco-schemes not fully clear yet |
UC1C | Farmer Performance | FMIS data and E-Crop exchange standard (FMIS <–> IACS) | Automated data exchange of farm level relevant data between FMIS and IACS systems Assessment of farmer performance, based on integration of FMIS, IACS, potentially other data | Every FMIS is different, lack standardization=> difficult to provide (easily) reusable tools Authentication and agreement by farmers |
UC2 | Prefilled application, GSAA/Land link | SEN4CAP and UC1a EO processing chains /traffic lights | Automated tools for data harvesting from external registers in case of low/unknown system integration levels | -early crop prediction requires investment from PA (training data from the fields) – sufficient quality of parcel delineation – external registers may be very different depending on countries. |
UC3 | Farm Registry | -New policies on agriculture (new CAP) -Commission strategies 2030 (F2F and SB) -Seamless claim | -Reference Data Model – Common definitions and common code lists – Basis for a crossborder Farm Registry – Provide comparable data form different PA’s in order to exchange information and to obtain statistics and indicators | Situation per Member state is different, big standardization challenge Part of the data model is dependent on yet unknown implementation of the new CAP Contents of the data model go beyond IACS data Authentication and agreement by farmers |
UC4A | Geotagged photos | GSAA app for positioning | Farmer acceptance of geo-tagged photo app, integration with PA systems | Many different Mobile phones, many options for mistakes by farmers Other geotagged applications already in the market => need to prove added value of the NIVA one |
UC4B | Machine data in Geo-spatial ‘on-line’ aid application GSAA as added value data | E-Crop exchange standard, and AgGateway, and IsoXML | absorption/acceptance of machinery-borne data in PA systems Alternative evidence in case of yellow light | Many different farm machines, lack of standardization, many data links Given eCrop complexity and flexible standard => need of adapted profiles according required data. Need to convince machine data suppliers to adopt standards tested and recommended by UC4B. |
UC5A | Land Parcel Identification System LPIS update & change detection | Already running LPIS update systems | Automate detection for objects (EFA, LF, buildings) and update of eligible parcel now requiring manual work Machine and/or deep learning techniques | High costs and management requirements of orthophotos Some objects cannot be automated (e.g. difference between high grass and maize) |
UC5B | Scheme Eligibility and Payment eligibility: Click & Pay | Operationalisation of the principle of automated payment | Use of smart contracts, integrating downstream components Simulation of farm typology and entitlements | All downstream processes are operational Skills of the farmers in understanding the process |
Table 4 NIVA links to other projects and initiatives
Project or initiative | Use in NIVA |
SEN4CAP, ESA project on Remote Sensing processing chain for the CAP |
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EGNSS4CAP: GSAA app for positioning |
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H2020 OpenEO: an open source collection of tools for EO data processing |
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CEF components, DG-Connect/DG-DIGIT components and protocols for common definitions |
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H2020 IoF2020, large scale pilots on IoT devices |
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H2020 OpenIACS, using LinkedOpenData and CEF components for IACS data |
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H2020 MEF4CAP, on evaluation frameworks for the CAP |
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Learning Network of directors of PA’s |
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CAPIGI |
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EIP-agriculture |
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H2020 ENVISION |
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