Precision agriculture: PPP&P What is needed?

Transcription

1 Precision agriculture: PPP&P What is needed? Corné Kempenaar et al NSO workshop AgriEO, 4-5July 2017, The Hague, NL

2 Precision Agriculture / Farming Precision agriculture is a farm management concept based on measuring and responding to temporal and spatial variability in crops, livestock and the environment Sensing data -> decision making ->application Operational,..., tactical, strategical Enabling technologies are available: GNSS, sensors, implements, robotics, IoT Expected benefits of PA: More with Less and Better

3 Benefits of use of GNSS in Precision Agriculture (=PA 1.0, over 80% adoption in arable farming NL) AeroVision BV UNIFARM, 2012 Global Navigation Satellite Systems in agriculture

4 PA is now focussing on responding to spatial variation Grid Plant Leaf Disease In practice On station research On institute research On institute and university research

5 Sensors all over

6 STOA study 2016: PA Impact on EU Policy ++ + Business development in agri-food chains Food security & food safety Transparency of agri-food chains Sustainable production = - Farm holding size and number Multi-functional agriculture Jobs on farms in primary production Competitiveness of EU-farming Skilled workforces Demographic and rural development Climate change and action

7 Barriers for implementation PA Skills & knowledge needed Investments in High Tech and IoT are needed Much technology push, little models/ algorithms for PA-applications available Independent data on cost-benefit often miss Interoperability and standardization issues

8 Farmer s views 8

9 Potato production and storage cycle

10 Where is the PA business case? Yield gap 40% Potential yield potato: kg/ha Lintul model: planten April 15, 2017, 30 year weather profiles starting mid June Inputs, ca 20 cultivation measures, tot. variable costs 1900 per ha Labour costs: 30 hours, from tillage to harvest, Incl. monitoring and decision PA service and mechanisation costs to be covered by More with Less and better

11 Potato production and storage cycle: Potato haulm killing

12 VRA potato haulm killing herbicides Input: herbicide, biomass maps (NDVI, WDVI), soil conditions Decision support: Statistical model and expert judgement

13 My Akkerweb

14 Selection of the field

15 Processed satellite NDVI image download

16 Calculate apply map

17 VRA potato haulm killing herbicides Input: herbicide, biomass maps (NDVI, WDVI), soil conditions Decision support: Statistical model + expert judge. Output: Variable rate dose different scales Using historic data (satellite or drone data) Using machine mounted sensors and spraying on the go (e.g. N-Sensor) Results at scale m 2 : Good level of haulm killing and weed control Reduction in herbicide use (38%, = 26 /ha )

18 Potato production and storage cycle: topdress N

19 VRA potato topdress N Input: Planting date, N-fertilizer type, biomass maps (WDVI, NDRE, CI, S n ) of remote or nearby optical sensors, expected yield Decision support: Statistical model, mechanistic model, expert judgement Output: Variable rate dose different scales scale of 50 m 2 : Reduction in N-fertilizer(15-20%, per ha) No significant yield increase or decrease

20 UAV and camera SenseFly ebee Airinov multispec 4C 10 bits and 550, 660, 735, 790 nm Flying height 50 m 5 cm pixels 20

21 Avarna / N0 Seresta / N0 Valthermond, 15 June 2016, NIR (0-1) 21

22 Field and crop data 22

23 Load drone image (13 June 2017) 23

24 Calculate N-uptake (kg N in crop) 24

25 Calculate N-dose (based on 50 ton/ha) 25

26 Finalize N-product task map 26

27 Potato production and storage cycle: Late blight control

28 Where are needs for satellite data in PA? Controlled Traffic Farming Soil and crop monitoring Variation mapping Biomass and yield assessments Stress, disease and weed detection/mapping Variable rate applications (besides PHK and N top dress) VRA fungicides VRA growth regulators Spot treatment weeds

29 How to make it work? Deliver good quality data Deliver added value (value for money) Better cooperation between data providers and experts on agronomic models Yield prediction models, vra models, quality parameters are needed

30 Ongoing R&D in PA (selection) Development of new technologies Sensor and ICT development Quality, plant stress, diseases Towards tactical and strategic decision support Big data analytics Autonomous vehicles and robotics Ongoing R&D projects More with sat. data (e.g. PL 2.0 PPP) Detection of diseases (e.g. PL 2.0 PPP) G4AW IoF2020 E-Potato in HT2FtW call (soil and quality)

31 Bioscope system (satellite + drone data) FIELD BOUNDARY SPECIFICATION ADVICE DELIVERY PORTAL IMAGE ACQUISITION IMAGE PROCUREMENT IMAGE PROCESSING CONVERSION to INDEX MAPS ADVICE APPLICATION in the FIELD

32 Benefits current PA practices Better soil and crop monitoring solutions Reduction scale 50 m % Higher yields 0-5% Others See STOA report presented earlier

33 Thank you! Tel or Skype: corne.kempenaar