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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. [email protected] b.h. 07 41600735. mob. 0417322137. Importance of accurate yield forecasting:. - PowerPoint PPT Presentation
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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. 07 41600735. mob. 0417322137
Importance of accurate yield forecasting:
- At the farm level: provide information to growers that support improved management strategies that optimise productivity (i.e. PA).
- At a regional level: provide essential pre-harvest information to support decisions regarding harvesting, transporting, marketing and forward selling.
Sugarcane
Most suitable source of imagery: Regional ScaleSPOT5 :
– 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.
Bundaberg and Isis BurdekinHerbert
Full GIS layers of every crop provided by mills
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
InsectPoor irrigation Weed infestation Differing Cultivar
• 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
Classified VI imageVI- highlights variation in crop vigour
False colour image- IR, Red, Green
Vegetation Index (VI)(plant structure/ pigment/ water
content etc)
At the Crop scale:Indentifying variability
Development of Yield maps from sampling points
Average GNDVI = 0.61Predicted Yield = 77.9 TCH
y = 1512.19x - 842.05
R2 = 0.84
40
60
80
100
120
140
0.60 0.61 0.62 0.63 0.64 0.65GNDVI
TC
H
GNDVI (NIR-Green)/(NIR+Green)
Correlation between GNDVI and TCH (Tonnes of Cane Per Hectare)
False colour image of a cane crop with sample points
Surrogate yield map derived by correlation algorithm
In crop sampling
Entire Cane blocks where the average GNDVI value was extracted. 600ha.
Development of generic SPOT5 Yield algorithm
Average crop GNDVI Vs Yield from 2008 (n = 39) and 2010 (n= 112).
y = 3.15e5.70x
R2 = 0.59
0
50
100
150
200
250
0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7GNDVI
TCH
2008 & 2010 data
Predicted Yield (TCH)
Lower 2.5% Prediction Interval
Upper 97.5% Prediction Interval
Validation of generic algorithm at the in- crop level.
Classified yield map from in crop samples
Classified yield map from generic algorithm
Actual yield Vs Predicted yield using the 2010 Bundaberg predictive algorithm
0
20
40
60
80
100
120
140
160
0 20 40 60 80 100 120 140 160
Act. TCH
Pre
d.
TC
H
Selected point locations within Bundaberg crops. Accuracy of prediction
Production of surrogate yield maps: Farm Scale
cv. KQ228Pred. 101 tchAct. 113 tch89%
cv. MXDPred. 75 tchAct. 65.5 tch114%
cv. MXDPred. 81 tchAct. 85.5 tch95%
cv.Q208Pred. 70 tchAct. 64.5 tch110%
cv. KQ228Pred. 91 tchAct. 101.5 tch90%
cv. KQ228Pred. 112 tchAct. 108 tch104%
cv. Q138Pred. 94 tchAct. 97 tch97%
cv. Q135Pred. 70 tchAct. 71 tch99%
cv. Q208Pred. 50 tchAct. 50 tch100%
cv. KQ228Pred. 97 tchAct. 107 tch91%
cv. Q208Pred. 82 tchAct. 89 tch92%
cv. Q200Pred. 71 tchAct. 70 tch102%
cv. KQ228Pred. 99 tchAct. 105 tch94%
cv. Q208Pred. 72 tchAct. 71 tch101%
cv. Q208Pred. 47 tchAct. 51 tch93%
61 blocks predicted average yield 90 TCH, Act. 93.4 TCH (97%)
cv. Q200Pred. 87 tchAct. 85 tch103%
Regional Prediction of Sugarcane Yield Bundaberg/ Isis/ Herbert growing regions
Using 2008/ 2010 algorithm : Yield = 3.1528 * EXP (5.6973 * GNDVI)- 2010 Nth Bundaberg (3544 crops): Pred. 79.7 TCH, act. 81.8 TCH
(97%)
- 2010 ISIS (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
Bundaberg: Comparison of years
2010 ave. GNDVI: 0.567
2011 ave. GNDVI: 0.5697
2012 ave. GNDVI: 0.584
Generation and Distribution of Yield Maps at the Regional Scale.
Can be used to identify sub- regional seasonal and temporal trends
Peanut
Development of Yield maps from sampling points
NDVI (NIR-Red)/(NIR+Red)
Correlation between (NDVI) and Yield (Tonnes Per Hectare)
False colour image of a peanut crop with sample points
Surrogate yield map derived by correlation algorithm
In crop sampling
y = 10.037x - 2.128
R2 = 0.7692
0.0
1.0
2.0
3.0
4.0
5.0
6.0
0.3 0.4 0.5 0.6 0.7
NDVI
Po
d Y
ield
(t/
ha)
Pod yield vs NDVI (n=352) data from 6 growing seasons
and 8 varieties
y = 0.2104e4.455x
R2 = 0.7377
0
2
4
6
8
10
12
14
16
0.28 0.38 0.48 0.58 0.68 0.78 0.88
NDVI
Mea
sure
d Y
ield
(t/
ha)
Dryland
Irrigated
Development of generic algorithm for predicting yield
Assigning blocks as peanut crops
Classification of peanut ‘pixels’
South Burnett 5000km2 coverage
False colour image
Example: Predicted area 6000 haAve. NDVI: 0.55
Identifying where the Crops are.
Average NDVI extracted =
0.55
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
Correlation between NDVI and crop yield
0
2
4
6
8
0.3 0.5 0.7 0.9NDVI
Yld
(t/
ha
)
0.55
2.4 t/ha
Predicting Average and Total Yield.
Yield Predictions: Crop scale
Dryland crop (cv. Walter) Area=317 ha Predicted 849 t : Delivered 874 t (97%)
NDVI images of dry land peanut crops overlayed on a false colour image.
Dryland crop (cv. Walter) Area=18.9 ha Predicted 76.6 t: Delivered 77.4 t (99%)
1500 m
Crop locations within Australia
Yield Prediction at the Block Scale
Pred. yld 2.1 t/ha (act. 1.8 t/ha)
Pred. yld 3.6 t/ha (act. 2.9 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 4.0 t/ha (3.6 t/ha)
Pred. yld 6.87 t/ha (Act. 5.3 t/ha)
Pred. yld 4.3 t/ha (Act. 4.1 t/ha)
Pred. yld 4.3 t/ha (Act. 3.9 t/ha)
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
Production of Peanut Yield Maps at the regional Level.
Surrogate maps provide can assist with harvest segregation for quality based on pod
maturity and aflatoxin risk.
r = -0.73**
20
30
40
50
60
0.66 0.68 0.7 0.72 0.74 0.76 0.78
NDVI Value (Colour Zones)
% B
lack
ker
nel
Correlation between NDVI and pod maturity (% Black kernel)
Total area 17.61haTotal Yield 101.27tAve t/ha 5.75t/ha
Maturity: Case Study
Soil temperature variability across zones
Tiny tag sensor
Portable weather stationSoil temperatures measured at strategic locations
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 (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)
1/t = (T-Tb)/θ
where, 1/t is the development rateT 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.
APSIM thermal time model
X3: 131.94083, -14.63234
X2: 131.93914, -14.63059
R2: 131.94476, -14.62327
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.
Tree Crops
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
Development of commercially relevant maps
y = 421.68x - 150.07
R2 = 0.8243
20
30
40
50
60
70
80
90
100
0.36 0.41 0.46 0.51 0.56 0.61
N1/RENDVI
% C
om
me
rcia
l Yie
ld
West
South-west
Both sites
Linear (Both sites)
Map of %commercial yield generated from the correlation between N1RENDVI.
Correlation between N1/RENDVI and % commercial yield
Avocado: Regional Forecasting
1,383 ha * Predicted average yield of 13.1 TPH = 18,117 tonnes
Predicted Avocado pixels (SAM Classification)
Predicted Avocado blocks (Polygons)
Derived yield maps (Avocado blocks)
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.
Mapping Disease
Derived tree health map following field survey
Ciba Geigy Avocado tree health rating scale
False colour image of Avocado orchard with Geo-tagged trees
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.