1. RSA: Crop Estimates Overview AfriGEOSS-EOPower-GEOGLAM
Southern Africa Agric Workshop
08 May 2014
Fanie Ferreira
Wiltrud du Rand, Johan Malherbe,
Eugene du Preez, George Chirima

2. National Crop Statistics Consortium
Roles and Responsibilities
Agricultural Research Council
Institute Soil Climate &Water:
Agro-metereology / Climatic conditoins
Summer Grain Institute: objective yield – maize
Field measurements:
Small Grain Institute: objective yield – wheat
Field measurements
SiQ
PICES (Producer Independent Crop Estimate System)
Statistical processing, surveys & interviews
GeoTerraImage
Satellite image processing
Crop type classifications

3. Summer Maize Sequence Area planted x Yield = Production
Maize: 3 M ha x 4 ton/ha = 12 M tons
Mapping update of Field Boundaries
Spot 5 Satellite imagery: complete Nov
Producer Independent Crop Estimate System
February & March – PICES Aerial Survey
Objective Yield Measurement
May – Field Visits
Intentions to plant & Actual Harvested
Oct / Nov – Telephonic survey
Crop Type Classification: July 2013 –May 2014

4. Umlindi Report - Monthly
Various products are derived from ARC-ISCW weather station data and remote sensing data
Data from the Coarse Resolution Imagery Databank (vegetation conditions) and from the National Agro-Climatology Databank (weather/climate conditions). Coarse resolution data received from GeoNetCast
Focus is on periods relevant for agricultural activities and areas important for crop production
Products developed from above-mentioned sources at the ARC-ISCW
Kan hier ook na ArcView revert om hele GT te wys

5. Rainfall amount, deviations and other derivatives for periods relevant to crop production

6. Extreme conditions that may impact crop production

7. Vegetation conditions as determined from various derivatives of the NDVI
Spatial products
Time series and comparisons with yields and vegetation conditions of previous years for various areas

8. Use of satellite imagery
SPOT5
LANDSAT 7/8, DMC ?
Previous seasons 2006/7/8/9/10/11/12/13
In-season 2013/14
Satellite image calibration
Field crop boundary
Throughout presentation will adress issues
PICES provincial level
Satellite image field level

9. Stratification: Mapped Fields

10. SA coverage: 14 million ha
Kan hier ook na ArcView revert om hele GT te wys

11. PICES Infrastructure

12. PICES: crop verification
Vast improvement: survey efficiency
Support image classification
Statistical calculated of area
Additional points used for image training
Selected fields with identified crop types

13. Rainfall

14. Crop Calendar

15. Freestate Province
Winter 2007
Summer 2008

16. Western Freestate

17. Maize Comparison: 2007 vs 2008 Spatial Distribution
Cultivated area
Crop types
District level comparison:
Maize area / district

18. Maize Comparison: 2009 vs 2010 Spatial Distribution
Cultivated area
Crop types
District level comparison:
Maize area / district

19. Maize: Objective Yield
Approx 800 farms visited
Selected from PICES where Maize identified

20. Maize: Objective Yield
Select 5 random points within field
Sample of 11 cobs harvested and measured

21. Maize: Objective Yield

22. Telephonic Survey Randomly selected farms
Indentify farmer and confirm location
Telephonic Survey during Oct / Nov
Phone farmer to conduct interview
Previous season
Area ( hectares ) of white maize
Area ( hectares ) of yellow maize
Actual yield harvested
Next season
Intention to plant: hectares for next season

23. Summary: Crop Estimates #1
DAFF – Crop Estimates Committee (CEC)
Field Boundaries
Annual updates of Centre Pivot irrigation fields
Stratification layer for PICES & other surveys
Deliver provincial stratification
PICES Survey (Cropped Area Estimate)
Geographic Random Selected Points
Used to calibrate crop type classification
Deliver report to DAFF CEC
Provincial summary of crops planted in ha

24. Summary: Crop Estimates #2
Objective Yield Measurement for Maize
Field visits during May (after senescence started)
Deliver report to DAFF CEC:
Yield per province for Maize
Telephonic Survey
White / Yellow Maize split per province
Area (ha) per crop per province
Crop Type Classification
Trends: Cultivation and Crop Rotation Practices
Deliver report: District Crop Area Table

25. Summary: Crop Estimates #3

26. Summary: Crop Estimates #4

27. Conclusion Aircraft Technology Statistical Geographic Sampling Frame
Satellite imagery
GiS
PICES Team

28. Thank you