The WBG Ag Observatory - World Bank
21
The WBG Ag Observatory Harnessing ‘Ag Weather’ Big Data and Machine Learning for productive and resilient agriculture
Transcript of The WBG Ag Observatory - World Bank
The WBG Ag Observatory
Harnessing ‘Ag Weather’ Big Data and Machine Learning for productive and resilient agriculture
The WBG Ag Observatory’s Goals
1. To support the World Bank and partners to access and deploy high resolution and near real-time geospatial Ag-meteorological data
2. To enable and empower WBG analytical and operational programs for strategic, proactive and timely decision-making
3. To get promising disruptive technology applications into the hands of our clients
• Global Crop Monitoring (GEOGLAM)
• UN-FAO Global Information & Early Warning System (GIEWS)
• Climate Change Knowledge Portal (CCKP)
• USAID FEWS NET • USDA GADAS• NASA Earth Observatory
(EOS)
Public Sector – Open Access (free)
Private Sector – Proprietary (client subscription/contract)
+
Tracking food and feed systems
aWhere: Complete Global Ag-Met Information at 9 km X 9 km>1.5 million big data generated virtual weather stations
> 7 billion data points updated every six hours
Daily Observed(>10 years baseline)• Precipitation• Min/Max Temperature• Min/Max Relative Humidity • Max/Mean Wind Speed• Solar radiation
Hourly Forecast• 7 days of
hourly forecast • Updated 4x
daily
Agricultural Intelligence• PET & Crop stress indices• Plant growth, crop
calendars• Soil moisture, net water• Pest & disease indices• Crop Suitability• Yield & Production
The WBG Ag Observatory partners with “aWhere”data that actively monitors most of the earth’s agricultural lands
Source: World CroplandsUSGS/GFSAD30 Project, 2017
Data logger(cellular)Soil moisture
(5 depths)
Wind, solar, relative
humidity, temperature,
& rainfall
Ground Weather Stations
Field
Satellite Radar
Ground Radar
Weather Stations
© Copyright 2017, aWhere. All Rights Reserved
Ground Stations + New Radar + Satellite Platforms
The WBG Ag ObservatoryGamechanger Disruptive Technologies & Innovation?
“Big Data + Machine Learning”
Field
Satellite Radar
Ground Radar
Ground Weather Station
• 1.5 million Virtual Met Stations• Every 9 km across the terrestrial
surface of the earth• 7 billion data points updated
every 6 hours
Providing agricultural intelligence for the World Bank Group and Partners
21%
C
28%
38%
17%
35%
15%
17%
Satellite Radar
Ground Radar5%
C
24%
C
Precipitation
Temperature, Humidity
Weather Station
Soils
Crop Production
Statistics
aWhere
Virtual
Weather
Stations
Soils
Crop Statistics
Virtual Weather Stations
generate contiguous weather
surfaces that offer local insights
such as detecting “pocket
drought” events that traditional
weather stations often miss given
a 5 km radius of representation
Drought
© Copyright 2019, aWhere. All Rights Reserved
Providing agricultural intelligence for the World Bank Group and Partners
aWhere VirtualWeather Station
PocketDrought
• Drier than normal pocket between 4 ground stations.
• 4 ground stations recorded higher than normal precipitation.
• The Pocket Drought is missed based on ground stations.
aWhere’s weather surface technology addresses ground station spatial limitation by providing a
CONTIGUOUS WEATHER SURFACE.
Local Agricultural Insight
“Pocket Drought” undetected by ground weather stations
© Copyright 2019, aWhere. All Rights Reserved
Advanced IndicesBuilding value-add from aWhere Observed Contiguous Weather
P/PET difference from 11-year Normal 2/25/19 to 3/26/19
P/PET = precipitation / potential evapotranspiration= the most solid indicator of conditions conducive
for vegetative propagation
P/PET is the most powerful AG Index
Many areas of Africa are either drier than normalor wetter than normal – Climate variability is increasing!
Validated Penman-Monteith equation
P/PET difference from NORMAL• A single fast index to highlight AG anomalies• Only aWhere calculates P/PET globally daily• Daily maps available free on www.aWhere.com
P/PET
0.8
1.0
0.6
0.4
Maize
Millet
Forage
Moving towards localized & timely insight for farmersUsing a data driven approach, each farmer receives information relevant to their current situation
Examples of how the WBG is testing the Ag Observatory’s Disruptive Tech Approach
to High Resolution Ag Meteorology
Robust, disaggregated, and potentially linked, near real-time metrics for FAMINE RISK Assessments?
Influx of people and livestock from Niger
aWhere
For illustrative Purposes only (data on displaced people and conflicts is continuously evolving)
Biomass anomaly as of 30 September 2017 - NDVI analysis
Biomass Collapse in the Sahel Threatens Livelihoods – Is there a mega drought in progress?
Source: Sigsahel, 2017
The actual situation (below) shows the extreme variability that is more and more the normal
126.867 “Virtual Weather Stations” (VWS) –Africa has less than 300 GWS that report to WMO (WBG, 2018)
Ag Observatory Real Time Analysis: No Mega Drought But Several Pocket Droughts
Pocket Drought: current rainfall far below normal
Ag Observatory Conclusion to Biomass Collapse Question
“Not a mega drought but Pocket Droughts + OverstockingResults in Biomass Collapse due to Overgrazing”
Suggested Solution: Sell your livestock online before they eat everything & die! For example: Agrikaab
Source: AriFarm
12 sample areas representative of 80% ofEthiopian Lowlands (ecology of interest)
Location SETS for each study location: • must be within 0.30 statistical distance • within 150km• 120 locations most similar to sample location
Example: Environmental Trend AnalysisP/PET trends for Ethiopian Lowlands
© Copyright 2019, aWhere. All Rights Reserved
30 day rolling P/PET for 12 lowland sites
2018 was wetter than Normal, but the distribution was unusual for 2 regions
Abnormally wet spring
Guiding Ethiopia on how to adapt to Climate Change
Report for one week during the 2013, 2014, 2015, 2016 seasons for Aspergillus around Belgrade, Serbia
• is an inspection company tasked with managing supply chain quality
• aWhere provides assessment of disease risk by running a disease model on its platform
• Note: pest and disease are mainly weather driven\enabled
Example: Pest and Disease RiskObserved weather drives RISK MODELS (e.g. DSSAT, APSIM)
2013 2014 2015 2016
2016 Aspergillus risk well below 2015, similar to 2013
Aspergillus affecting
Serbian Maize in
September 2018
Early Warning of Disease and Pest RiskGlobal observed weather drives models to identify production risks in advance of pest and disease outbreaks and inform aggregation to preserve quality and improve food safety (e.g. reduce aflatoxin contamination)
© Copyright 2019, aWhere. All Rights Reserved
Kenya Ag Observatory
Ethiopia Ag Observatory
Zimbabwe & Zambia Ag Observatories…
