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AfricaGIS 2013 - GSDI 14 - Global Geospatial Conference 2013 Addis Ababa, Ethiopia, November 4-8, 2013 By Antoine DENIS – PhD student - University of Liège.

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Presentation on theme: "AfricaGIS 2013 - GSDI 14 - Global Geospatial Conference 2013 Addis Ababa, Ethiopia, November 4-8, 2013 By Antoine DENIS – PhD student - University of Liège."— Presentation transcript:

1 AfricaGIS 2013 - GSDI 14 - Global Geospatial Conference 2013 Addis Ababa, Ethiopia, November 4-8, 2013 By Antoine DENIS – PhD student - University of Liège - Belgium Can satellites help organic cotton certification ? Remote sensing and GIS techniques for supporting organic cotton certification process in West Africa

2 1.Context & Justification 2.Objectives 3.The IDEA 4.Hypothesis 5.Method 6.Results 7.Discussion and conclusion Can satellites help organic cotton certification ?

3 1. Context & Justification

4 Want organic food/products?  Human health  Environmentally friendly 1. Context & Justification Organic crop ? = NO chemical synthetic pesticide & fertilizer = NO GMO = Crop rotation = Organic fertilizer and pesticide =... Crop Control · Yearly farm inspection · Documentary accounts · + Unannounced inspection · + Laboratory analysis · Cost? Frequency? Remote areas? Certification  Rules & agencies  Labels Organic food on the market To trust or not to trust ?

5 1. Context & Justification

6 Why the Burkina Faso ? Interest from organic certification bodies for developing countries: Huge amount of organic products ($) Remote areas and certification control more difficult Why the cotton? Need a crop certified as organic That can be studied by RS Field big enough

7 Economic Importance of cotton in Burkina Faso Cotton accounts for 50 to 60% of the country’s foreign currency earnings Cotton is the first export product contributing largely to the country’s economic development 1. Context & Justification

8 Development of Organic cotton in Burkina Faso Successful since 2004, bright example of sustainable development that contributes to: Alleviation of poverty Improved food security by enhancing producers’ income with less risk to run into debt 1. Context & Justification Healthy way to crop both for people and the environment resulting in improved human and animal health (absence of chemical pesticides), and improved soil fertility and environment (organic cropping technique).

9 2. Objectives

10 Is it possible, in the context of South-West Burkina Faso, ► To help organic cotton certification process with satellites? ► To discriminate organic and conventional cotton fields with satellites? ► Need to assess the bio-chemico-physical difference between organic and non organic cotton with diverse field measurements 2. Objectives

11 3. The IDEA

12 BIO BIO!

13 Identification of area to control

14 Field declared as organic !

15 Indicator computation

16 Identification of suspect fields Too high nitrogen!

17 Analysis with several indicators

18 4. Hypothesis

19 ► Management differences between organic and conventional crops ► Difference in crop bio-chemico-physical characteristics and general field appearance ► Observable by satellites and transformable into satellites derived indicators

20 Cotton management differences Bio-chemico-physical differences Indicators Less fertilizer in organic fields Less biomass Less canopy cover Field canopy cover Biomass estimation Lower nitrogen content of the plants Leaves chlorophyll content Smaller plantsPlant height Less spatial homogeneous fertilizer application and less efficient pesticide in organic fields Higher spatial heterogeneity Standard deviation of other indicators by field 4. Hypothesis In particular :

21 4. Hypothesis Cotton Yield in Burkina Faso: Organic = 675 kg/ha (std dev = 314 kg/ha) Conventional = 1 100 kg/ha (std dev = 391 kg/ha) (Centre for Development and Environment of the University of Berne (CDE), Pineau et al. 2009)Centre for Development and Environment of the University of Berne (CDE), Pineau et al. 2009

22 5. Method

23 Study site

24 5.Method Study site

25 5.Method Study site

26 Several local varieties for organic and conventional Several varieties Bt GMO Low intensive cultivation Farming operations: manually or workanimals Rainfed 5.Method Cotton cropping method in Burkina Faso

27 Crop cover: hemispherical pictures: 10/field CAN-EYE software was used to derive 2 indexes from the hemispherical pictures: A Plant Area Index (PAI) A Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) index 5.Method Field measurements

28 + GPS Chlorophyll content: CCM200: 10/field Height: meter: 10/field 5.Method Field measurements

29 Field spatial heterogeneity = standard deviation of parameters by field 5.Method Field measurements

30 SPOT 5 (via ISIS program / CNES) 2.5 m color 3 BANDS: Green, Red, NIR Tasking window between 16/08/2011 – 25/10/2011 Nearly permanent cloud cover 1 image on 15/11/2011 only !!! Very late ! + 1 MODIS image: surface temperature emissivity (Sept- Oct) 5.Method Satellite image

31 Spectral indicators Simple bands: B1, B2, B3 2 bands combination (simple ratio:) B1/B2, B1/B3, B2/B3 Spatial heterogeneity indicators Standard deviation of pixels by field for B1, B2, B3 Coefficient of variation of pixels by field for B1, B2, B3 … 5.Method Satellite indicators

32 Comparative approach Comparison of indicators values of organic and conventional (not a threshold indicator value method) Selection of most discriminating indicators among those systematically computed Univariate and multivariate (Linear Discriminant Analysis) Indicator discrimination power = p.value of the Mann–Whitney–Wilcoxon (MWW) test 5.Method Statistical method

33 6. Results

34 Field measurements

35 6. Results Field measurements

36 6. Results Field measurements

37 Red/NIR 6. Results Satellite indicators: univariate - spectral

38 6. Results Satellite indicators: univariate – spatial heterogeneity Std. dev. Green

39 TYPE_BIO_CONV_OGM ~ Eff_PAI_P57 + SPOT_15112011_CV_L1 6. Results Satellite indicators: multivariate, Linear Discriminant Analysis

40 6. Results Satellite indicators: multivariate, Linear Discriminant Analysis

41 6. Results Satellite indicators: multivariate, Linear Discriminant Analysis

42 6. Results Satellite indicators: multivariate, Linear Discriminant Analysis

43 7. Discussion and conclusion

44 Differences are observed between cotton types For both field and satellite indicators Statistically significant Not enough pronounced with values ranges that largely overlap This prevents the use of these indicators alone to be the base of a robust discrimination But the method enables to target for priority field control, organic fields who present indicator values getting closer to the one of conventional or GM cotton fields Further research: Timely satellite acquisition! Identification of the ideal phenological stage for cotton monitoring 7. Discussion and conclusion General conclusion

45 Regarding the initial hypothesis Mixed results regarding the initial hypothesis: Most of the indicators: organic fields present significant lower general field development and higher spatial heterogeneity CCI indicators don’t show any significant difference between management types and the standard deviation of the canopy cover show a slightly lower spatial heterogeneity for organic fields 7. Discussion and conclusion

46 Satellite Indicators are questionable: A single image was acquired very late in the crop cycle No straight conclusion regarding the general relevance of the use of RS techniques in the study context Use of satellite images seems to be quite compromised given the unfavourable atmospheric conditions which are most of the time cloudy. Need for daily image acquisition for cloud free image? Trees in cotton fields can strongly influence the reflectance and the spatial heterogeneity (from no tree to a complete agroforestry system) 7. Discussion and conclusion Relevance of the use of satellite images in this context

47 Difference between cotton parcel is also due to other factors, difficult to take into account: The phenology stages that can strongly vary from one parcel, farmer or region to another due to varying seeding date, itself depending among other on the local climatic condition, with very localized rainfalls. Varying soil natural fertility Varying level of development of the farmers (fertilizer availability) 7. Discussion and conclusion Remaining obstacles

48 Given Lack of experience of organic cotton farmers Yields already achieved by the organic farmers “elite” which are close to the conventional ones If organic farming techniques are encouraged and tuned (increase of quantity of available organic fertilizers), the current gap between organic and non organic cotton yields would be considerably reduced 7. Discussion and conclusion Skills of organic farmers

49 Thank You !

50 Université de Liège - Belgique Acknowledgement The French “Centre National d’Etudes Spatiales” (CNES) through its « ISIS » program (« Incitation à l'utilisation Scientifique des Images SPOT ») that enabled to acquire a SPOT 5 image at low cost for this study. SPOT "© CNES (2012), distribution Spot Image S.A.", Http://www.isis-cnes.fr/IntroPage.do Http://www.isis-cnes.fr/IntroPage.do The “SOciété Burkinabé des FIbres TEXtiles” (SOFITEX) that allowed the field survey in conventional and GMO cotton fields. The National Union of Cotton Producers of Burkina Faso (UNPCB – Union Nationale des Producteurs de Coton du Burkina Faso) that enabled the field survey in organic cotton fields and accompanied the entire field survey. Helvetas Swiss Intercooperation Burkina Faso, for their important documentation on organic cotton production in Burkina Faso and their advices for the field survey preparation.

51 Contact information Arlon Campus Environnement (ACE) University of Liège (ULg) 185, Avenue de Longwy, 6700 Arlon Belgium Antoine DENISBernard TYCHON TEL0032 63 230 9970032 63 230 829 EmailAntoine.Denis@ulg.ac.beBernard.Tychon@ulg.ac.be Websitehttp://www.campusarlon.ulg.ac.be/


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