Title. From a global ecosystem model to national studies on biomass estimation & livestock insurance (WP3.2 Livestock systems)

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1 Title From a global ecosystem model to national studies on biomass estimation & livestock insurance (WP3.2 Livestock systems) Julien Minet 1, Abdoul Aziz Diouf 1,2, Issa Garba 1,3, Marie Lang 1,4,5, Bakary Djaby 1,3, Author1, Jason Sircely Author2 4,6, Bernard Tychon 1, and Richard Conant 4,6 1. Affiliation, 2. Affiliation 1.ULG, 2. CSE, 3. AGRHYMET, 4. ILRI, 5. VITO, 6. CSU agricab@vito.be agricab@vito.be AGRICAB Final Meeting, Antwerpen, Belgium, March 23-24,

2 Overview To develop fodder biomass production models using remote sensing data in the Sahel In 3 African countries: Senegal, Niger, Kenya + Global use case (G- Range model) By calibration and improvement of models By studying an index-based livestock insurance (IBLI) scheme (pastoralism insurance) in Kenya. To share these experiences in national and regional workshops (Capacity building) 2

3 Overview 3 PhD students + 1 Post-doc Issa Garba Jason Sircely Abdoul Aziz Diouf Marie Lang WP managed first by Bakary Djaby, then Julien Minet 3

4 Parametric and non-parametric models for fodder production assessment in Niger (I. Garba) Generalized regression model Link remote-sensing indices (NDVI from SPOT-Vegetation) to biomass data Photo1 : site visité à Gadabédji /Dakoro (Maradi) Photo 2: site visité à Batté (Belbédji/Zinder) collected in the field Better results with ecoregions analysis (relative RMSE from 40% (global) to %) 4

5 Parametric and non-parametric models for fodder production assessment in Niger (I. Garba) Similarity analysis Field data collection is expensive and sometimes hampered by security issues Biomass production assessment could be done based on similarity analysis 5

6 Livestock Insurance (Marie Lang) Starting point: The Index Based Livestock Insurance (ILRI) Started in January 2010 in Marsabit District, Northern Kenya. Objective: provide to pastoralists an insurance contract covering losses in livestock (camel, cattle, sheep and goat) due to drought. Index insurance: modelling of livestock mortality by linear regression using freely available NDVI data (MODIS). Example: PhD thesis: improve the RS aspect of the model New indicators (fapar, DMP, VHI, meteo, RFE ). Alternative statistical techniques (GLM, CART ). 6

7 Livestock Insurance (Marie Lang) Phase 1: linear modelling with vegetation indicators Input data: SPOT VGT (NDVI, DMP and fapar), 1 km 2 resolution, April 1998 to May VHI (AVHRR), 1 km 2 resolution, 1984 to

8 Livestock Insurance (Marie Lang) Phase 1: linear modelling with vegetation indicators Modelling of mortality using NDVI at location level Non significant correlations between mortality and NDVI when the sample is split according to the vegetation condition. Weak R2 (~0.15) 8

9 Health of animals Livestock Insurance (Marie Lang) Phase 2: classification and regression trees YES Condition A on vegetation NO Condition B on vegetation Condition on precipitation Vegetation Condition on density of animals Mort Rate 2 Condition A on wealth rank Condition B on wealth rank Mortality Weather Infrastructure (water points) Mort Rate 1 Mort Rate 3 Mort Rate 4 Wealth ranking First results expected early May Location 9

10 G-range (Jason Sircely, Rich Conant, Randy Boone) G-Range model activities under AGRICAB G-Range: a global rangeland ecosystem simulator Global, spatial implementation of CENTURY Model development goals Provide a robust model implementation (de-bugging and further refinement) Validation and documentation Model training goals Hold workshops Make model accessible 10

11 G-range (Jason Sircely, Rich Conant, Randy Boone) Model development results Robust model implementation Resolved a variety of programming challenges Sensitivity analysis G-Range provides fidelity to calibration datasets (global CENTURY outputs, additional global datasets) 11

12 G-range (Jason Sircely, Rich Conant, Randy Boone) Model development results Validation using: Field data Remote sensing (MODIS) Documentation: Model description draft completed 12

13 G-range (Jason Sircely, Rich Conant, Randy Boone) Model training results Workshops held: CSU, Fort Collins, USA: 1 month, 2 participants RCMRD, Nairobi, Kenya: 2.5 days, ~30 participants ARC, Khartoum, Sudan: 1 week, 7 participants ULg, Arlon, Belgium: 1 week, 7 participants Model accessibility: Training materials prepared, utilized in workshops Model and driving data available online: 13

14 G-range (Jason Sircely, Rich Conant, Randy Boone) Current & upcoming applications Global climate x [CO 2 ] scenarios 7 global circulation models x 2 representative carbon pathways Simulations completed, writing in progress Forecasting of ecosystem services Forage production and resilience in the Greater Horn of Africa (with the Technical Consortium) Forage production and supplementation requirements in Sudan (with ARC, Sudan) Parameterization refinement in progress 14

15 Capacity building activities AGRICAB workshop in Ouagadougou, January

16 Capacity building activities 1. Training workshop on forage biomass modelling, Belgium, Dec Workshop on yield forecasting using remote sensing data, Niamey, Niger, Feb Workshop on fodder production forecasting using remote sensing data, Niamey, Niger, Feb Training workshop on the G-Range ecosystem model, USA, Oct Regional training on forecasting of forage resources in Ouagadougou, Burkina Faso, Jan Regional training on the G-range model in Nairobi, Kenya, Feb G-Range ecosystem model training in Khartoum, Sudan, Feb G-Range ecosystem model training in Arlon, Belgium, May 2014 About 115 people were trained on forage/biomass modelling with/without remote sensing data 16

17 Summary Research activities & capacity building Collaboration between ULg, ILRI, CSE, Agrhymet, VITO & CSU Livestock systems: Technical report (Deliverable D32.1) published on Agricab website in February Thanks for your attention 17