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MONITORING AGRICULTURE USING REMOTE SENSING AT THE SEBELE DAR FARM

MONITORING AGRICULTURE USING REMOTE SENSING AT THE SEBELE DAR FARM. PRESENTATION BY MMOLOKI G. MOLWANTWA. Introduction.

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MONITORING AGRICULTURE USING REMOTE SENSING AT THE SEBELE DAR FARM

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  1. MONITORING AGRICULTURE USING REMOTE SENSING AT THE SEBELE DAR FARM PRESENTATION BY MMOLOKI G. MOLWANTWA

  2. Introduction • The issue of monitoring crop performance and productivity is one of the major challenges facing the agricultural sector in Botswana. Often difficulties arise in monitoring the agricultural sector in pastoral or arable commercial farms especially where there is limited in situ data. Remote sensing has been identified to be used widely for monitoring agriculture in areas where there is limited in situ data and also offers farmers the opportunity to evaluate the crop productivity level of large areas more efficiently and cost effectively (Flynn, 2008).

  3. Need of the study • Monitoring is an important aspect in agriculture because with it we can predict seasonal outlook of crops. • In Botswana monitoring crop performance and productivity using remote sensing is not done at a satisfactory level, therefore a need then arises to monitor crop performance and productivity using remote sensing.

  4. Aim • The aim of this study was to use remote sensing products to monitor crop performance in the Department of Agriculture Research Content Farm in Sebele.

  5. Methodology • Study Area (Sebele DAR Farm, Gaborone Botswana)

  6. Methodology cont`d • Shape file

  7. Methodology cont`d • Data requirements • Rainfall products The rainfall products were derived from FEWSNET Rainfall Estimation (RFE) dekadal imagery. • Yield data The yield data was obtained from the DAR archive. This was record of yield from the 2008-09 growing season up to the 2011-2012 cropping season.

  8. Methodology cont`d • Vegetation products The vegetation products were derived from the SPOT-Vegetation NDVI at 1km spatial resolution provided by DevCoCast (www.devcocast.eu). The data is obtained from the BCA remote sensing station which is under African Monitoring of the Environment for Sustainable Development (AMSED) Organisation.

  9. Methodology cont`d • Shape file and NDVI Overlay

  10. Methodology cont`d • Data analysis Regression analysis model for long term average NDVI against long term average rainfall for the years 2008-2010 were made using Microsoft excel whereby the rainfall was an independent variable and NDVI a dependent variable.

  11. Results

  12. Results cont`d Image a November 7 2011 Image b November 13 2011

  13. Results cont`d Image c November 18 2011 Image d December 10 2011

  14. Results cont`d Image f December 15 2011 Image e December 20 2011

  15. Results cont`d

  16. Results cont`d

  17. Results cont`d

  18. Results cont`d

  19. Results cont`d

  20. Conclusion • The aim of this project was to monitor agriculture in DAR farm using remote sensing products. From the results it shows that monitoring has been achieved as rainfall estimates and NDVI can be used to make inferences about crop performance.

  21. Recommendations • The commercial farms particularly those that produce large crop outputs should adopt the use of remote sensing products to monitor crop performance in Botswana.

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