1. 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
b.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).

3. Sugarcane

4. Most suitable source of imagery: Regional Scale
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.
Full GIS layers of every crop provided by mills
Bundaberg and Isis
Herbert
Burdekin

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 PS
4 band spectral resolution (NIR, red, green, blue)
Identify sub metre constraints such as weeds, soldier fly, rat, cane grub, land forming etc.
Poor irrigation
Insect
Weed infestation
Differing 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, Green
VI- highlights variation in crop vigour
Classified VI image

8. Development of Yield maps from sampling points
False colour image of a cane crop with sample points
Correlation between GNDVI and TCH (Tonnes of Cane Per Hectare)
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 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 tch
90%
cv. KQ228
Pred. 99 tch
Act. 105 tch
94%
cv. MXD
Pred. 75 tch
Act tch
114%
cv. KQ228
Pred. 101 tch
Act. 113 tch
89%
cv. MXD
Pred. 81 tch
Act tch
95%
cv.Q208
Pred. 70 tch
Act 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 = * EXP ( * GNDVI)
Nth Bundaberg (3544 crops): Pred TCH, act TCH (97%)
2010 ISIS (2772 crops): Pred. 84 TCH, act. 84 TCH (100%)
Nth Bundaberg (3824 crops): Pred TCH, act TCH (109%)
ISIS (4205 crops): Pred TCH, act TCH (118%)
Nth Bundaberg (3217 crops): Pred. 88 TCH, act TCH (99%)
ISIS (4000 crops): Pred TCH, act. 96 TCH (96%)
Herbert (6481 crops:): Pred TCH, act. 55 TCH (103%)
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:
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. Pod yield vs NDVI (n=352) data from 6 growing seasons and 8 varieties
Development of generic algorithm for predicting yield
Pod 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 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 * 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 = 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 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 Study
Total area ha
Total Yield t
Ave t/ha t/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 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 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: ,
X2: ,
X3: ,

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. 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 trees
Derived tree health map following field survey
Ciba 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.