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Corné Kempenaar

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Wageningen University & Research Center, The Netherlands

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Corné Kempenaar

  1. 1. Mainstreaming precision farming The missing link between data and implements Corné Kempenaar et al. Capigi, Rotterdam, 24 May 2016
  2. 2. Precision farming  Precision farming is whole farm management concept based on measuring and responding to temporal and/or spatial variability in crops and livestock.  Benefits: higher yields, better quality, less inputs, less environmental pressure, less tiring work, new business opportunities  Enabling technologies - at least partly - are available ● ICT, FMIS, GNSS, many sensors, data, implements, robotics
  3. 3. S?
  4. 4. Precision farming and scale Grid • In practice Plant • On station research Leaf • On institute research Disease • On institute and university research
  5. 5. Recommendations EIP Agri Focus group MPF  End user involvement in development DSS for PF applications  Tools are needed that support accounting for regional and farm-specific conditions  Training on and show cases of PF  Further R&D on enabling technologies  Infrastructure and new business models for big data  https://ec.europa.eu/eip/agriculture/en/content/mainstreami ng-precision-farming (2015)
  6. 6. Connecting DSS in PF applications  Examples of variable rate application (VRA) ● Haulm killing ● Lime application ● Topdress Nitrogen ● Soil herbicides ● Fuel use monitoring ● Nematode management and granulate task maps ● Late blight control
  7. 7.  An internet GEO-platform used by farmers and farm advisors to implement precision farming ● Started in 2012  A Public Private initiative of Wageningen UR, Agrifirm and DOBS  Data ownership policy  Basic Apps for free, other Apps for sale in stor  Status ● > 20 Apps ● Ca.30.000 Dutch fields in Akkerweb 2015
  8. 8. My Akkerweb
  9. 9. Akkerweb GEO-platform
  10. 10. VRA Potato haulm killing  VRA of potato haulm killing herbicides on basis of variation in crop biomass on the field  Apps on Akkerweb  Access to crop biomass data ● Satellite, drone, proximal sensors
  11. 11. Haulm killing DSS Weather data, forecast, soil moisture WUR minimum effective dose model WUR PHK VRA App op Akkerweb FMIS: Crop management data Herbicide data Crop biomass data (NDVI, WDVI, S1) Variety data Others, e.g. weeds on field
  12. 12. Reglone 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 0 20 40 60 Reflection parameter CropScan Minimumeffectivedose (l/ha) Decision support algorithm
  13. 13. Biomass sensing Handheld Crop refl. sensor (CropSca Nearby sensors Remote sensing, e.g. Worldview-2 Sensing with UAS and manned airplane
  14. 14. Overview of biomass indices Index Name Formula Authors (year) NDVI Normalized Difference Vegetation Index (Rnir-Rred)/(Rnir+Rred) (Rouse et al. (1974) RVI Ratio Vegetation Index Rnir/Rred Jordan (1969) WDVIr WDVIr, with red light reflection in formula R810-(R810-R560)xR560 Clevers (1989) WDVIg WDVIg, with green light reflection in formula R810-(R810-R660)xR660 Bouwman (1992) REP-LI Red Edge Position: Linear Interpolation method 700+40x(Rre-R700)/(R740-R700) ; and Rre = (R670+R780)/2 Guyot et al. (1988) MTCI Meris Terrestrial Chlorophyll Index (R754-R708)/(R708-R680) Dash, Curran (2008) TCARI Transformed Chlorophyll Absorption in Reflectance Index 3x((R700-R670)-0.2x(R700- R550)x(R700/R670)) Haboudane et al. (2002) TCARI/ OSAVI TCARI with Optimized Soil- Adjusted Vegetation Index 1.16x(R800-R670)/ (R800+R670+0.16) Haboudane et al. (2002) MCARI Modified Chlorophyll Absorption Index (R700-R670)-(0.2x(R700- R550)x(R700/R670)) Daughtry et al. (2000) DCNI Double-peak Canopy Nitrogen Index ((R720-R700)/(R700-R670)/ (R720- R670+0.03)) Chen et al. (2010) NDRE Normalized Difference Red Edge index (R780-R720)/(R780+R720) Eitel et al. (2010) Rnir = reflection at near-infrared wavelengths, Rred at red light wavelengths, other reflections at specified wavelengths.
  15. 15. Satellite image of potato crop (Aug. 2015)
  16. 16. Sat. image April ’16 (wheat crop)
  17. 17. Selection of field in crop rotation
  18. 18. Go to Apps
  19. 19. Processed satellite image download
  20. 20. My satellite images
  21. 21. Select sensor data
  22. 22. Calculate apply map
  23. 23. Biomass map from UAS, DSS gives task map (2012) Acknowledge ment: TerraSphere, Vd Borne
  24. 24. Result VRA on the go, Leipzig, 2009 Yara N-Sensor plus conventional sprayer MLHD PHK dosing algorithm Reglone, normal, 1x Ware potatoes BBCH GS 91, 7 august, sunny, hot (30 °C) Pictures were taken four days after treatment Fixed rate: 2,5 L Reglone/ha @ 300 L water per ha Variabele rate: av. 1,5 L Reglone/ha @ 200 L water per ha
  25. 25. Development of BIOSCOPE service: biomass maps every 10 days from different sources
  26. 26. VRA Topdress N in potato  VRA of nitrogen based on site specific N-content of the aboveground biomass, and crop growth and nitrogen uptake models  Apps on Akkerweb  Access to N-content data
  27. 27. Topdress N potato DSS Weather data and prediction WUR potato growth and NUE models WUR N topdress App on Akkerweb FMIS: Farm and crop management data N mineralization prediction and N soil data N in crop biomass data and prediction Crop and variety data Others, expected yield data and fertilizer data
  28. 28. Crop sensing and Calculation of N-uptake from proximal sensor(in kg N/ha) Step 1
  29. 29. Calculation of task map N-rate in kg N/ha and applicationStep 2
  30. 30. VRA Soil herbicides in potato  VRA of herbicides based on site specific variation in soil properties  Apps on Akkerweb  Access to soil maps and other data
  31. 31. Soil herbicide DSS Weather data and prediction; Soil moisture WUR minimum effective dose models WUR herbicide App on Akkerweb FMIS: Farm and crop management data Herbicide data Soil data and maps (clay, o.m. and/or pH) Crop and variety data Others, e.g. weed (prediction) data
  32. 32. Soil sensors systems in practice
  33. 33. Soil maps O.M. and lutum with Veris sensor, clay soil in N NL
  34. 34. Select parcel
  35. 35. Select soil Herbicide App
  36. 36. Task map Stomp based on O.M. and clay variation within the parcel
  37. 37. Boxer task map, FF, Abbenes, 20 May 2016
  38. 38. NemaDecide Geo supply chain Potato cyst nematodes, root knot nematodes, root lesion nematodes NemaDecide Geo Potato Info app NemaDecide adviceCropping scheme RVO Crop polygons Taskmap nematostats Stripbuilder app Database Soil sampler Digital sampling request Digital sampling result Lab result
  39. 39. Digital request soil analysis nematodes
  40. 40. Sampling result (to farmer/account)
  41. 41. Request NemaDecide advice
  42. 42. NemaDecide advice
  43. 43. Task map granulate with safety zones
  44. 44. Concluding remarks Precision farming 2.0 still in pioneer phase PF 2.0 and 3.0 require: • Accurate data soil, climate, crop and livestock conditions • Data infrastructure to visualize, analyse and use the (big) data • DSS that translate spatial and temporal variation in actions (generic concepts, local calibration) • Implements/robotics for site or animal specific interventions • Smart integration of technology at farm and chain level, in combination with advanced robotics
  45. 45. Thanks for your attention www.precisielandbouw.eu www.akkerweb.nl corne.kempenaar@wur.nl c.kempenaar@cahvilentum.nl Tel. (+31) 317 480498 Skype: corne.kempenaar

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