Presentation on theme: "Kingaroy, Queensland, Australia, 4610"— Presentation transcript:
1 Kingaroy, Queensland, Australia, 4610 Accurate Regional to Field Scale Yield forecasting of Australian Sugar Cane and Peanut Crops using Remote Sensing and GISDr Andrew ROBSONKingaroy, Queensland, Australia, 4610b.h mob
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).
4 Most suitable source of imagery: Regional Scale SPOT5 :10 m spatial resolution4 band spectral resolutioncost ~ $1/ km22-3 revisit day improved possibility of capture3600 km2 tile encompasses the majority of crops in each region.Full GIS layers of every crop provided by millsBundaberg and IsisHerbertBurdekin
5 Most suitable source of imagery: Paddock Scale IKONOS :Due mainly to cost 3 * 50km2 (~$22/ km2)3.2 m spatial resolution, 0.8 m PS4 band spectral resolution (NIR, red, green, blue)Identify sub metre constraints such as weeds, soldier fly, rat, cane grub, land forming etc.Poor irrigationInsectWeed infestationDiffering Cultivar
6 Optimal timing of imagery capture 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.
7 At the Crop scale: Indentifying variability Vegetation Index (VI)(plant structure/ pigment/ water content etc)False colour image- IR, Red, GreenVI- highlights variation in crop vigourClassified VI image
8 Development of Yield maps from sampling points False colour image of a cane crop with sample pointsCorrelation between GNDVI and TCH (Tonnes of Cane Per Hectare)In crop samplingAverage GNDVI = 0.61Predicted Yield = 77.9 TCHGNDVI(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 samplesClassified yield map from generic algorithmSelected point locations within Bundaberg crops.Accuracy of prediction
16 Development of Yield maps from sampling points In crop samplingCorrelation between (NDVI) and Yield (Tonnes Per Hectare)False colour image of a peanut crop with sample pointsNDVI (NIR-Red)/(NIR+Red)Surrogate yield map derived by correlation algorithm
17 Pod yield vs NDVI (n=352) data from 6 growing seasons and 8 varieties Development of generic algorithm for predicting yieldPod yield vs NDVI (n=352) data from 6 growing seasons and 8 varieties
18 South Burnett 5000km2 coverage Identifying where the Crops are.South Burnett 5000km2 coverageAssigning blocks as peanut cropsClassification of peanut ‘pixels’False colour imageExample:Predicted area 6000 haAve. NDVI: 0.55
19 Predicting Average and Total Yield. Average NDVI extracted = 0.552.4 t/ha0.55Correlation between NDVI and crop yieldPredicted average Yield:0.2717*EXP (3.9659*0.55) = 2.4 T/haPredicted total yield:2.4 T/ha * ha = 14,400 t peanut
20 Yield Predictions: Crop scale NDVI images of dry land peanut crops overlayed on a false colour image.1500 mDryland crop (cv. Walter) Area=317 haPredicted 849 t : Delivered 874 t (97%)Crop locations within AustraliaDryland crop (cv. Walter) Area=18.9 haPredicted 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/hatotal predicted yield = tonnes; Actual total yield = 487 tonnesPred. 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 4.0 t/ha (3.6 t/ha)Pred. yld 1.2 t/ha (act. 1.8 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 Correlation between NDVI and pod maturity (% Black kernel) Maturity: Case StudyTotal area haTotal Yield tAve t/ha t/haCorrelation between NDVI and pod maturity (% Black kernel)
25 Soil temperature variability across zones Tiny tag sensorSoil temperatures measured at strategic locationsPortable weather station
26 the predicted crop maturity date was 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 ( days from sowing)extrapolated for the entire pod-filling period, a maturity range of 7 days between the black to red colour zones ( days from sowing)APSIM thermal time model1/t = (T-Tb)/θwhere, 1/t is the development rateT is daily mean temperature (°C)Tb is base temperature (°C) below which the rate is zeroθ 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 (25C to 32C).Stressed plants/ less shading are likely to be exposed to a longer period of high aflatoxin risk.R2: ,X2: ,X3: ,
32 Mapping orchard constraints at the tree level ‘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.
33 Mapping Disease Ciba Geigy Avocado tree health rating scale False colour image of Avocado orchard with Geo-tagged treesDerived tree health map following field surveyCiba Geigy Avocado tree health rating scale
34 Conclusions: - 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.