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PA Methods to Diagnose Crop Performance

PA Methods to Diagnose Crop Performance. ?. Tim Neale 1 and John Heap 2 1 PrecisionAgriculture.com.au 2 SARDI. Seedling. Black box & Can of worms. Vegetative biomass. Water limited yield in all zones?. Another Black box & Can of worms. Grain yield The ultimate integrator. No.

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PA Methods to Diagnose Crop Performance

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  1. PA Methods to DiagnoseCrop Performance ? Tim Neale 1 and John Heap 2 1 PrecisionAgriculture.com.au 2 SARDI

  2. Seedling Black box & Can of worms Vegetative biomass Water limited yield in all zones? Another Black box & Can of worms Grain yield The ultimate integrator No Yes “Diagnostic Agronomy” To the beach! What Went Wrong??

  3. What Went Wrong? (www.WWW.org.au?) • Crop grain yield is the ultimate integrator of constraints • Spatial-temporal patterns (PA) give excellent diagnostic clues • - eg gap between vegetative biomass and grain yield • Real-time sensing vs. historical diagnosis • Yield constraints • Natural or man-made (management) • Natural: Biological, Soil, Other • Treatable or untreatable (practicality, economics) • Simple or complex (eg ARG x subsoil constraints?)

  4. Yield (+ biomass) maps Identify problem zones Observation, knowledge, experience, tests, PA data Diagnose (ID) problem Management decision: Amelioration vs ↓ inputs VRT options, economics, experience, risk PA VRT equipment Management action PA Methods to Diagnose and Manage Crop Performance

  5. Constraints to achieving water limited yield (incomplete list for example only) Biological Weeds Soilborne diseases Foliar diseases Soil biota Insects Soil N P pH Other nutrients Soil depth Soil texture Boron Salinity Water repellance Other Frost Wind Herbicide toxicity Water-logging Compaction Lodging

  6. PA technologies and data to diagnose crop performance (incomplete list for example only) Real-time (sensors) N application (CropCircle, CropSpec, Greenseeker) Herbicide application (WeedSeeker) Technologies Yield/Protein monitors EM38 Satellite imagery Aerial imagery Weed mapping (CropCircle, CropSpec etc) Gamma Data Yield/Protein maps ECa maps Biomass maps Soil maps Weed maps Disease inoculum maps Elevation (DEMs) Thermal maps Slope/aspect Drainage models

  7. Diagnostic Agronomy: Where are we? • Sound agronomic knowledge at paddock level • Good range of diagnostic tests and tools • PA research - good progress on specific problems • Need an integrated approach to diagnose zone constraints • Best diagnostic models are in good agronomists heads

  8. Diagnostic Agronomy: Next steps? • Need to capture and codify agronomist’s knowledge • and diagnostic process – aided by PA data. • Synergistic combination of technologies important • (e.g. gamma + EM38) • Need sound economic mapping and VRT strategies • Human observation and experience critical to success

  9. Potential GRDC investment: • Integrated Diagnostic Model • Identify major constraints • Define/research characteristics of constraints • Construct a diagnostic tool for constraints • (similar to plant taxonomy?) • Field test diagnostic tool • Sampling/testing promotion? • Develop mapping and VRT technologies • Practical techniques and economics

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