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Dr Andrew ROBSON Kingaroy, Queensland, Australia, 4610

Accurate Regional to Field Scale Yield forecasting of Australian Sugar Cane and Peanut Crops using Remote Sensing and GIS. Dr Andrew ROBSON Kingaroy, Queensland, Australia, 4610. Ajrob720@yahoo.com.au b.h. 07 41600735. mob. 0417322137. Importance of accurate yield forecasting:.

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Dr Andrew ROBSON Kingaroy, Queensland, Australia, 4610

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  1. Accurate Regional to Field Scale Yield forecasting of Australian Sugar Cane and Peanut Crops using Remote Sensing and GIS Dr Andrew ROBSON Kingaroy, Queensland, Australia, 4610 Ajrob720@yahoo.com.au b.h. 07 41600735. mob. 0417322137

  2. Importance of accurate yield forecasting: • At a regional level: provide essential pre-harvest information to support decisions regarding harvesting, transporting, marketing and forward selling. - At the farm level:provide information to growers that support improved management strategies that optimise productivity (i.e. PA).

  3. Sugarcane

  4. SPOT5 : 10 m spatial resolution 4 band spectral resolution cost ~ $1/ km2 2-3 revisit day improved possibility of capture 3600 km2 tile encompasses the majority of crops in each region. Most suitable source of imagery: Regional Scale Full GIS layers of every crop provided by mills Bundaberg and Isis Herbert Burdekin

  5. IKONOS: Due mainly to cost 3 * 50km2 (~$22/ km2) 3.2 m spatial resolution, 0.8 m PS 4 band spectral resolution (NIR, red, green, blue) Identify sub metre constraints such as weeds, soldier fly, rat, cane grub, land forming etc. Most suitable source of imagery: Paddock Scale Poor irrigation Insect Weed infestation Differing Cultivar

  6. Imagery captures between January to early March generally restricted by cloud cover, for all regions. This limits the opportunity to implement alternative management strategy within the same season of imagery capture due to size of cane and growth stage limiting its capacity to respond. However, remote sensing was identified to be highly effective for identifying differences in crop vigour, assisting with coordinated plant and soil sampling to identify the likely driver of reduced production. This information was then used to coordinate remedial action for the following season. Optimal timing of imagery capture

  7. At the Crop scale:Indentifying variability Vegetation Index (VI) (plant structure/ pigment/ water content etc) False colour image-IR, Red, Green VI- highlights variation in crop vigour Classified VI image

  8. Development of Yield maps from sampling points Correlation between GNDVI and TCH (Tonnes of Cane Per Hectare) False colour image of a cane crop with sample points In crop sampling Average GNDVI = 0.61 Predicted Yield = 77.9 TCH GNDVI (NIR-Green)/(NIR+Green) Surrogate yield map derived by correlation algorithm

  9. Development of generic SPOT5 Yield algorithm Entire Cane blocks where the average GNDVI value was extracted. 600ha. Average crop GNDVI Vs Yield from 2008 (n = 39) and 2010 (n= 112).

  10. Validation of generic algorithm at the in- crop level. Classified yield map from in crop samples Classified yield map from generic algorithm Selected point locations within Bundaberg crops. Accuracy of prediction

  11. Production of surrogate yield maps: Farm Scale 61 blocks predicted average yield 90 TCH, Act. 93.4 TCH (97%) cv. Q208 Pred. 47 tch Act. 51 tch 93% cv. Q208 Pred. 82 tch Act. 89 tch 92% cv. Q208 Pred. 72 tch Act. 71 tch 101% cv. Q200 Pred. 71 tch Act. 70 tch 102% cv. KQ228 Pred. 112 tch Act. 108 tch 104% cv. KQ228 Pred. 97 tch Act. 107 tch 91% cv. Q138 Pred. 94 tch Act. 97 tch 97% cv. KQ228 Pred. 91 tch Act. 101.5 tch 90% cv. KQ228 Pred. 99 tch Act. 105 tch 94% cv. MXD Pred. 75 tch Act. 65.5 tch 114% cv. KQ228 Pred. 101 tch Act. 113 tch 89% cv. MXD Pred. 81 tch Act. 85.5 tch 95% cv.Q208 Pred. 70 tch Act. 64.5 tch 110% cv. Q135 Pred. 70 tch Act. 71 tch 99% cv. Q208 Pred. 50 tch Act. 50 tch 100% cv. Q200 Pred. 87 tch Act. 85 tch 103%

  12. Regional Prediction of Sugarcane Yield Bundaberg/ Isis/ Herbert growing regions Using 2008/ 2010 algorithm : Yield = 3.1528 * EXP (5.6973 * GNDVI) • - 2010Nth Bundaberg (3544 crops): Pred. 79.7 TCH, act. 81.8 TCH (97%) • 2010ISIS (2772 crops): Pred. 84 TCH, act. 84 TCH (100%) - 2011 Nth Bundaberg (3824 crops): Pred. 80.1 TCH, act. 73.3 TCH (109%) - 2011 ISIS (4205 crops): Pred. 98.4 TCH, act. 83.3 TCH (118%) - 2012 Nth Bundaberg (3217 crops): Pred. 88 TCH, act. 88.9 TCH (99%) - 2012 ISIS (4000 crops): Pred. 92.5 TCH, act. 96 TCH (96%) - 2011 Herbert (6481 crops:): Pred. 56.9 TCH, act. 55 TCH (103%) - 2012 Herbert (15463 crops): Pred. 75 TCH, act. 72 TCH (104%) * TCH- Tonnes of Cane Per Hectare

  13. Bundaberg: Comparison of years 2010 ave. GNDVI: 0.567 2011 ave. GNDVI: 0.5697 2012 ave. GNDVI: 0.584

  14. Generation and Distribution of Yield Maps at the Regional Scale. Can be used to identify sub- regional seasonal and temporal trends

  15. Peanut

  16. Development of Yield maps from sampling points In crop sampling Correlation between (NDVI) and Yield (Tonnes Per Hectare) False colour image of a peanut crop with sample points NDVI (NIR-Red)/(NIR+Red) Surrogate yield map derived by correlation algorithm

  17. Development of generic algorithm for predicting yield Pod yield vs NDVI (n=352) data from 6 growing seasons and 8 varieties

  18. Identifying where the Crops are. South Burnett 5000km2 coverage Assigning blocks as peanut crops Classification of peanut ‘pixels’ False colour image Example: Predicted area 6000 ha Ave. NDVI: 0.55

  19. Predicting Average and Total Yield. Average NDVI extracted = 0.55 2.4 t/ha 0.55 Correlation between NDVI and crop yield Predicted average Yield: 0.2717*EXP (3.9659*0.55) = 2.4 T/ha Predicted total yield: 2.4 T/ha * 60000 ha = 14,400 t peanut

  20. Yield Predictions: Crop scale NDVI images of dry land peanut crops overlayed on a false colour image. 1500 m Dryland crop (cv. Walter) Area=317 ha Predicted 849 t : Delivered 874 t (97%) Crop locations within Australia Dryland crop (cv. Walter) Area=18.9 ha Predicted 76.6 t: Delivered 77.4 t (99%)

  21. Yield Prediction at the Block Scale Total area of Peanut (143.6 ha) average predicted yield = 3.43 t/ha: Actual average yield = 3.39 t/ha total predicted yield = 492.3 tonnes; Actual total yield = 487 tonnes Pred. yld 4.3 t/ha (Act. 4.1 t/ha) Pred. yld 6.87 t/ha (Act. 5.3 t/ha) Pred. yld 4.3 t/ha (Act. 3.9 t/ha) Pred. yld 1.2 t/ha (act. 1.8 t/ha) Pred. yld 4.0 t/ha (3.6 t/ha) Pred. yld 4.3 t/ha (act. 4.4 t/ha) Pred. yld 3.6 t/ha (act. 2.9 t/ha) Pred. yld 2.1 t/ha(act. 1.8 t/ha)

  22. Production of Peanut Yield Maps at the regional Level.

  23. Surrogate maps provide can assist with harvest segregation for quality based on pod maturity and aflatoxin risk.

  24. Maturity: Case Study Total area 17.61ha Total Yield 101.27t Ave t/ha 5.75t/ha Correlation between NDVI and pod maturity (% Black kernel)

  25. Soil temperature variability across zones Tiny tag sensor Soil temperatures measured at strategic locations Portable weather station

  26. the predicted crop maturity datewas 158 days using ambient temperature. • Substitution of soil temperature data measured over the three week period a predicted maturity range of 2 days between the black and red colour zones (155-157 days from sowing) • extrapolated for the entire pod-filling period, a maturity range of 7 days between the black to red colour zones (138- 145 days from sowing) APSIM thermal time model 1/t = (T-Tb)/θ where, 1/t is the development rate T is daily mean temperature (°C) Tb is base temperature (°C) below which the rate is zero θ is a constant identifying the thermally modified time for each development stage.

  27. Opportunity for Harvest Segregation Based on Aflatoxin Risk. The optimum conditions for aflatoxin production are low soil moisture, as well as high soil temperature (25C to 32C). Stressed plants/ less shading are likely to be exposed to a longer period of high aflatoxin risk. R2: 131.94476, -14.62327 X2: 131.93914, -14.63059 X3: 131.94083, -14.63234

  28. Tree Crops

  29. Avocado- Field sampling based on NDVI maps False colour image of Avocado block sampling locations overlayed Classified NDVI image of Avocado block sampling locations overlayed

  30. Development of commercially relevant maps Correlation between N1/RENDVI and % commercial yield Map of %commercial yield generated from the correlation between N1RENDVI.

  31. Avocado: Regional Forecasting Predicted Avocado pixels (SAM Classification) Derived yield maps (Avocado blocks) 1,383 ha * Predicted average yield of 13.1 TPH = 18,117 tonnes Predicted Avocado blocks (Polygons)

  32. ‘Geo tagging’ individual trees so in field assessments and measurements can be spatially linked (i.e. with a PDA). Can be performed at low cost using GoogleEarth, with points then exported into ArcGIS/ Excel for further analysis. Mapping orchard constraints at the tree level

  33. Mapping Disease False colour image of Avocado orchard with Geo-tagged trees Derived tree health map following field survey Ciba Geigy Avocado tree health rating scale

  34. - Imagery is an effective and efficient TOOL for: - For identifying within crop/ Orchard variability. Assist with the adoption of PA, fertigation, disease and pest monitoring. - Within season yield forecasting at the Block/ Farm and Regional level. - Useful for multiple applications but only as good as the data collected. Requires involvement from all facets of farming (i.e. grower, agronomist, seed and fertilizer reps, research bodies etc) to ensure maximum use, feasibility and adoption. GoogleEarth provides a very effective method for image distribution. Conclusions:

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