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:

- 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.