1. Event, Date Application of remote sensing to monitor agricultural performance Farai. M Marumbwa & Masego. R Nkepu BDMS

2. Event, Date 1) Relevance of Application: Agriculture plays an important role in most countries of the SADC region where most economies dependent on crop production. It is therefore of great significance to obtain the crop condition information at early stages in the crop growing season. Sometimes it is even more important than acquiring the exact production after harvest time, especially when large scale food supply shortage or surplus happens. Accurate monitoring can actually avert a disastrous situation and help in strategic planning to meet the demands.

3. Event, Date 1) Relevance of Application: Ground based crop monitoring - expensive, prone to large errors, and cannot provide real-time monitoring of crop condition. Satellite systems provide temporally and spatially continuous data cover most of the globe, which makes it possible to monitor the crop continuously AMESD 2007- initiative makes use of Earth observation technologies and data to set-up operational environmental and climate monitoring applications. AMESD _SADC Agricultural Service monitor the state of the crops and rangeland.

4. Event, Date 2) Objectives The main objectives is to develop a method that allows agricultural managers to do an up-to-date assessment of the current growing conditions using Remote sensing data

5. Event, Date 3) Data used Local / regional (in-situ) data Input 1- Crop Masks for staple crops (or zones of interest) Input 2 - Administrative Boundaries Data from GEONETCast – DevCoCast Input 3 - Dekadal S-10 NDVI raster data Input4 – Long term NDVI raster data

6. Event, Date 4) Methodology and data pre-processing The main steps are illustrated below: 1) Extract the crop specific NDVI by crossing the NDVI images with the sadc crop map mask. 2) Extract the decadal crop specific NDVI data for each district from the current season the and the long term averages 3) Cross the crop specific map with Administrative boundaries and extract the average (current season and the long term averages) for each district. 4) Plot the data into line graph from the two tables (Current and long term average) in a graph

7. Event, Date 5) SADC Main cropping region

8. Event, Date Flowchart for the production of the C-NDVI graphs

9. Event, Date 5) Results

10. Event, Date Results

11. Event, Date 5) Results: Discussion The fewsnest NDVI has provides historical database of 27 (1981-2008) years which makes it the best option when calculating Longterm averages SPOT VGT which date back from 1998 good resolution The main weakness of the Fews Ndvi is that the spatial resolution is very coarse 8km. It is recommended that for small scale study Spot VGT NDVI should be used. Sharp drop in NDVI ?????

12. Event, Date The END Thank You.