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

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

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

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

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

Extreme conditions that may impact crop production

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

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 survey @ provincial level Satellite image analysis @ field level

Stratification: Mapped Fields

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

PICES Infrastructure

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

Rainfall

Crop Calendar

Freestate Province Winter 2007 Summer 2008

Western Freestate

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

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

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

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

Maize: Objective Yield

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

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

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

Summary: Crop Estimates #3

Summary: Crop Estimates #4

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

Thank you fanie.ferreira@geoterraimage.com