1. Diego Rocha 7 to 18 February, 2011

2.  The application of the Agrometeorological spectral model, based on Report No. 33 of FAO for Estimating the harvest productivity can help on improving the planning, monitoring and control of crops.

3.  The aim of this application is apply the FAO # 33 agrometeorological model for estimating sugarcane crops by using the NDVI S-10, DMP SPOT Vegetation products.

4. Flowchart of methodology

5. STUDY AREA

6.  Local / Regional (in-situ) data Sugarcane plantation in the city of Coruripe in Alagoas Sate, Brazil. The sugarcane parameters used are: BF = Factor breath (0,5 for temp. ≥ 20°C and 0,6 for temp <20°C (GOUVÊA, 2008)); APF = Agricultural Productivity Factor (2,9) (RUDDORF, 1985); Ky= yield response factor (DOORENBOS E KASSAM, 1979). Kc= culture of coefficient

7.  MATERIALS:  Remote Sensing Data : Vegetation-2, SEVERI  Satellite digital data : Spot-5, Meteosat 9  Products : NDVI S10 and Production of Dry Matter (DMP) for South America, Land Saf ETo  Data acquisition : 2009  Spatial resolution : 1Km (Spot-5) and 3-4 Km Meteosat-9  Source : EUMETCast service installed at LAPIS (Laboratory of Analysis and Processing of Satellite Images) at http://www.lapismet.com at University of Federal of Alagoas (UFAL) and SPOT Vegetation VITO at http://free.vgt.vito.be/

8. (2) Yp =CGF*BF*APF*DMP CGF = Compensation of Growth Factor; BF = Factor breath (0,5 for temp. ≥ 20°C and 0,6 for temp <20°C); APF = Agricultural Productivity Factor (2,9) propose by RUDORFF (1985); DMP = Production of dry matter, on this point, the DMP Spot-Vegetation data initially processed are inserted. (4) EQUATIONS (5) Kc= Coeficient of Culture

9.  Agrometeorological spectral model proposed based on the report number 33 from FAO (DOORENBOS and KASSAM, 1979). Ye = Yield estimated by the model; Yp= Maximum yield potential; Ky= yield response factor (DOORENBOS e KASSAM, 1979); ETr/ETp= Relative evapotranspiration.

10.  Results

11.  Tabulation of results

12. In blue the relationship between the first and last ten-day period analyzed corresponding to April and August, respectively, of an idea of the behavior of the sugarcane during phenological period. And in red we have the relationship between periods of ten-days of lesser and greater productivity estimates that match the first ten days of April to the last of May respectively

13. Estimated production during the study period in only one pixel sample.

14.  Yield estimated by the model June 2° Decade

15.  Conclusions Can use the ILWIS to perform agricultural modeling studies using products received by the SPOT Vegetation DevCoCast system. The study presents a good application potential, if properly implemented could help in forecasting and crop monitoring with good temporal scale.

16. Thanks to all team for data, support and opportunity