Governing CAP 2020 Technology and models ready today

Transcription

1 Governing CAP 2020 Technology and models ready today 23 rd MARS Conference Gormanston, Ireland, 29/11/2017 Livio Rossi, Remote sensing Business Development, e-geos Fabio Slaviero, International Business Development, ABACO

2 A smarter and modern CAP Link what we know with what we grow Reward environmental care 2 Faster delivery of payments Diminish administrative burden Smart monitoring Seamless CAP experience

3 Technical cornerstones Platforms Data Methods Validated Profile Business Workflow Territory Resource Planning Sky sensors Field data Linked Data Analytics & Intelligence Farm Performance Farm Activity Monitoring 3

4 SITI AGRI Knowledge Information Aggregation Quality of information Services & Apps Field Scale data Modular IACS Certification Monitoring, Intelligence, Analytics Smart support, DSS 4

5 Skyborne: Copernicus Suitable for the monitoring reduction approach, however we need different data and monitoring methodology for: Small and jagged parcels Permanent crops and specific RD controls Cross Compliance Landscape elements Parcel boundaries S2 Lazio, Italy 1,37ha = 118 pixel field data and other sources shall be captured and combined 5? 0,5 ha = 33 pixel

6 Skyborne: drone sensors illegal illegal Thermal for stables illegal discharge Solar radiation calculation X day, season, year 60cm 40cm 40cm Morphometric measuring and landslide/erosion alert Pro-rata pastures 2D classification 6 Pro-rata pastures 3D classification Red.edge higher contribution in forestry

7 Skyborne: drones Cost/benefit analysis in Italy (MinAgri): 1 km 2 requires: 1h for acquisition + 5h for prep and process (5.8cm pixels) Capital cost for equipment and software Convenient only for high-value inspections 7

8 Data Processing: Pasture and Pro-rata AGEA New semi-automatic classification (4 classes pro-rata %), for excluding not grazeable land portions (Moran index, spectral analysis, DSM) LPIS polygon 8

9 Data Processing: Pasture and Pro-rata AGEA New semi-automatic classification (4 classes pro-rata %), for excluding not grazeable land portions (Moran index, spectral analysis, DSM) Example Calabria South of Italy Example Lazio Central Italy only with tare polygons 9

10 Fieldborne: geo-photo AGEA/AVEPA Geo-tagged Ground photos: 2 Italian innovative Systems Mobile-enabled App for surveyors and/or farmers Validated pictures with anti-fraud control GPS tracking in different modes, including walking On-line and off-line (matching through sequential codes) Suitable for: RFV, diversification, durum wheat detection, EFA, permanent crops change, RD measures, 10 Orange: parcel limits and LPIS code Green: other parcel Blue sticks parcel corner

11 Fieldborne: SITI4land Geotagged photo + Augmented reality is mature technology Novel input to IACS AR ( Augmented Reality ) on the field Suitable for: capture & geotagged photo, visualise, report agricultural practices, GSAA 11

12 Farmers : SITI4farmer Farm Management Systems have grown Novel input to IACS Schedule and log farming activities Crop monitoring & control Nutrients management Committments log 12 Get data: less follow-up actions!!!

13 Monitoring is two-ended

14 and multi-dimensional 14

15 SMART Monitoring SITI GEOINT Markers and signals then. Use intelligence and machine-learning to combine them and extract knowledge Suitable for: LPIS assessment, traffic-light approach, fraud prevention and detection, continuous monitoring, automated checks, quality control, certification, 15

16 Example: Traffic-light markers 16 Object Classification: Under Assessment Likely not compliant Expert Judgement Compliant Not compliant Let s check this object

17 Example: Traffic-light markers Small gap on physical characteristics High likelihood on NDVI Plausibility rules set the object compliance (e.g. if >60%, then green light) 17

18 Example: Farm Performance 18 Certification systems Low carbon Organic Greening compliance

19 Bob wants to receive Agricultural Support. He identifies himself as 45678Y and express his interest (EoI) to receive agricultural support Government The Government wants to support agriculture, defines policy, allocate funds and open windows 19 Example: SMART claim SITI Easy Claim <Smart Aid Contract> <contract> If I 45678Y have land and corresponding payment rights (PR) by May 2018 and I grow plausible crops with land cover, I express my interest to receive XX per Right from Government funds on my bank account 12456X when the window opens. If I do not improve the Environment Performance I ll be deducted 10%. </contract> Paying Agency A trusted party defines the contract and monitors commitments SITI GEOINT SITI AGRI Knowledge The contract is verified by monitoring plausibility of conditions and/or events Bob receives money on 12456X

20 Example: SMART any claim Bob wants to receive Advance payment. He identifies himself as 45678Y SITI Easy Claim <Smart Advance Contract> <contract> I, 45678Y, have submitted a BPS/SAPS claim, I d like to receive advance payment. Paying Agency A trusted party confirms the claim has been submitted and will return green or red light SITI GEOINT Bob receives money on 12456X Paying Agency to remit funds when monitoring gives green light. SITI AGRI Knowledge Bank Checks contract registered at the Agency If red light, I authorise to withdraw money from my bank account or default the guarantee. </contract> The contract is verified by monitoring conditions 20

21 SMART Claim SITI GEOINT Plausibility Profile by scheme Signals Mar Apr May Jun/Jul Aug Oct Fieldborne 21 Skyborne Pay based on what you find No sanctions

22 2018 is the new Start now! Livio Rossi, e-geos: Fabio Slaviero, ABACO: