Early warning analysis for crop and pastureland in Senegal: a contribu8on to food security. Title

Size: px

Start display at page:

Download "Early warning analysis for crop and pastureland in Senegal: a contribu8on to food security. Title"

Jonah Gilmore
6 years ago
Views:

1 Early warning analysis for crop and pastureland in Senegal: a contribu8on to food security Diop Mouhamadou Bamba 1, Diouf Abdoul Aziz 1,4, Faye Gayane 1, Tote Carolien 2, Royer Antoine 2, El Hadji M. Dieng NGOM 3 Title 1. Centre de Suivi Ecologique (CSE), Dakar, Sénégal; 2. Flemish InsEtute for Technological Research (VITO), Mol, Belgium; 3. DirecEon de l Analyse, de la Prévision et des StaEsEques Agricoles (DAPSA), Dakar, Sénégal; 4. Université de Liège (Ulg), Belgique Author 1, Author 2 1. AffiliaEon, 2. AffiliaEon bamba.diop@cse.sn agricab@vito.be agricab@vito.be

2 Presenta8on overview» The crop monitoring actors and organisaeon in Senegal» The AGRICAB informaeon support» MAPS analysis» GRAPHS analysis» Convergence of evidence» The derive analysis publicaeon» Ten day bulleens» End and mid term campaign review bulleens» Prospects : 2014 AGRICAB development» New muledimension data analysis MATRIX graph» New anomalies impact assessment approach» OperaEonal Yield forecast models on Maize, Millet, Groundnut and Sorghum 2

3 Crop monitoring organiza8on in Senegal» MulEdisciplinary groups composed of - ANACIM meteo analysis - DAPSA yield forecast and crop staesecs - CSE vegetaeon monitoring and remote sensing support - DirecEon of livestock pastureland ground informaeon - Etc CSE (Centre de Suivi Ecologique), is the coordinator of AGRICAB project in Senegal. Created since 1986, CSE is the technical body of the Ministry of environment using spaeal technologies for the monitoring of environment and natural resources 3

4 AGRICAB Crop monitoring informa8on support The challenge of informa8on analysis : The AGRICAB approach MAP analysis over Senegal - NDVI anomalies + dynamic vegetaeon mask - Return frequency of NDVI anomlies - Start of season anomalies - Cluster analysis GRAPH analysis per district/province - NDVI profil evolueon Convergence of evidence - Cluster Analysis with rainfall estimate (RFE/FEWSNET) - Ground data collection 4

vegeta8on mask 0 0 1 1 0 1 0 1 + 0 1 0 0 0 0 1 1 = 0 1 1 1 0 1 1 2 Mean

5 MAP analysis in AGRICAB NDVI Anomalies analysis (by 10 Days from may to august) Focus on vegetated area to avoid false alert Use a dynamic vegeta8on mask = Mean of time series Current 10Days NDVI 0 = Pixel Masked in the current 10Days 5

6 NDVI Anomalies analysis (by 10 Days from may to august) Introduce return frequency value focus on vegetated area to avoid false alert use a dynamic vegetation mask 6

7 Start of Season Anomalies analysis (last 10 Days of month from may to august) iden8fy late/early vegeta8on emergence focus on vegetated area to avoid false alert use a dynamic vegeta8on mask Focus Monitoring on administrative level with late vegetation emergence 7

8 Cluster analysis (by 10 Days from august to october) space and 8me synthesis combine start/dura8on/intensity/extension of an anomaly - Start: only Cluster 3 have a good start of season - Duration of anomalies: till the second 10 days of September for cluster 5 - Intensity: most high anomalies is below 15% of the mean of the time series (cluster5) - Extension: cluster 3 for example is in Region of Tambacounda mainly with 31% of the whole country 8

start/dura8on/intensity/extension of an anomaly Identify area to

9 Reducing the field of inves8ga8on (End of august : Mid- term season) Cluster analysis : combine start/dura8on/intensity/extension of an anomaly Identify area to focus on analysis with graph (NDVI profile), for example the department of Bambey located in Cluster 5 9

10 GRAPH analysis in AGRICAB NDVI/RFE profil evolu8on per province/district and per land use 10

11 Convergence of evidence Profil of RFE Ground Visit Profil of RFE Profils of NDVI and RFE are similar 11

12 Convergence of evidence with RFE and Ground visit Profil of RFE Profils of NDVI and RFE are not similar Ground Visit is necessary 12

Convergence of evidence Ground visit In case or quantity and

ground visit are organised in the focused zone by the

Introduction of mobile technologies using tablet for :

application for data entry, Test with Agricultural Statistics

13 Convergence of evidence Ground visit In case or quantity and quality of rainfall can t explain the anomalies detected, ground visit are organised in the focused zone by the multidisciplinary groupe of monitoring of campaign Introduction of mobile technologies using tablet for : Location of area to visit (With mobile GPS) Introduction of application for data entry, Test with Agricultural Statistics Task on AGRICAB project Sending directly data collected to a central data base using 3G Network 13

Convergence of evidence Ground visit The mobile technology was largely tested in agricultural statistics task for data entry with Methodologies of points and segments for area estimation in AGRICAB

Its is also adopted by the Ministry of agriculture for the next year because : - ReducEon of budget of agricultural staesecs (no printed queseonnaires) - Early data, reduceon of Eme to get field data

14 Convergence of evidence Ground visit The mobile technology was largely tested in agricultural statistics task for data entry with Methodologies of points and segments for area estimation in AGRICAB project. Its is also adopted by the Ministry of agriculture for the next year because : - ReducEon of budget of agricultural staesecs (no printed queseonnaires) - Early data, reduceon of Eme to get field data to 5 months (no data entry at office, no cars colleceng papers in the whole of country, quality control on real Eme) In the focused zone, ground visit gave informaeon that there was flood in the rice crop area. So the rainfall is good, but crops was lost in a big part by root asphyxia, the reason of NDVI anomalies in this zone. The avantage of the AGRICAB approach is that we have reduced the field of invesegaeon for more effisciency and make sure informaeon with convergence of evidence 14

15 Integra8on of the analysis in monitoring bulle8ns 10- day bulle8ns Mid and end of campaign bulle8ns 10- day bulle8n - NDVI Anomalies analysis - Return frequency - SoS analysis 15

16 Mid and end of campaign bulle8ns - Cluster analysis - Graphs Analysis - Convergence of evidence in focused zone 16

17 Prospects: 2014 AGRICAB development New multidimension data analysis MATRIX graph to better compare the on going year with all the historical time series at a glance New anomalies impact assessment approach to better quantify the anomalies BY surface, villages, population, production to monitor their evolution OVER TIME 17

18 Operational Yield forecast models on Maize, Millet, Groundnut and Sorghum - Integration of indicators generated by SPIRITS software (like cumulate fapar) in CST (CGMS Statistical Tool) - Integration of indicators generated in GMFS project in CST - Development of the best model by crops : scenario or regression analysis by CST 18

19 Concluding remarks Although, CSE Senegal succeeded establishing, since the mid- 1980s, an agriculture monitoring system relying on remotely sensed data, AGRICAB helped to : make free sozware like SPIRITS, VgtExtract introduce new indices; improve analysis combining start/dura8on/intensity/extension of an anomaly of Vegeta8on and rainfall with cluster analysis Increase capacity building of CSE and other member of agricultural monitoring system in Senegal (Ministry of Agriculture and Met Agency mainly) fund the introduc8on of mobile technologies for ground survey to improve the agricultural sta8s8cs system in Senegal 19

20 THANK YOU FOR YOUR ATTENTION 20

Crop Modelling & Yield Forecasting

Crop Modelling & Yield Forecasting AGRICAB FINAL MEETING 23-24/03/2015 Hendrik Boogaard Alterra, WUR hendrik.boogaard@wur.nl WP31 Crop Production Systems Agricultural statistics Agro-meteorological modeling