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The WBG Ag Observatory
Earth Observation for Sustainable Agricultural Development Awareness Event
September 27, 2018
Harnessing Big Data, Artificial Intelligence 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 (in AGR, ENV, Water, CC) for productive and resilient food systems and landscapes
2. To enable and empower WBG analytical and operational programs to harness next generation and disruptive technologies for strategic, proactive and timely decision-making!
3. To get promising disruptive technology applications into the hands of our clients to enhance productivity and resilience to climate-related agroecosystem disturbances
The Ag Observatory Harnesses Existing Public Sector Open-Source Platforms
e.g. FEWSNET, GEOGLAM, GIEWSNET
Source: FEWS-NET, 2018 Source: GEOGLAM, 2018
• Global Crop Monitoring (GEOGLAM)
• UN-FAO Global Information & Early Warning System (GIEWS)
• 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
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 Observatory
Gamechanger Disruptive Technologies & Innovation?
“Big Data + Artificial Intelligence + 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
The WBG Ag Observatory uses aWhere’s data that actively monitors most of the earth’s agricultural lands
Source: World CroplandsUSGS/GFSAD30 Project, 2017
Client-driven Information Access: Empowering local youth for ag-tech innovation
Immediate access: http://developer.awhere.com/
Code Samples and Widgets
Outreach: Hackathons
www.hack4farming.comGhana, Kenya, India, Colombia, Zambia, USA
Application Programming Interface (API) allowing programmatic access to ag-weather information covering the whole of the Agricultural Earth
Moving towards localized & timely insight for farmersUsing a data driven approach, each farmer receives information relevant to their current situation
Example on how WBG is testing the Ag Observatory’s Disruptive Tech Approach
to High Resolution Ag Meteorology across regions and countries
Kenya: What is the potential impact of an El Niño for the upcoming season?
Source: Ag Observatory‘s own elaboration based on FAOSTAT crop data and NOAA historical ONI
Kenya: cropping season contextThe end of the mains 2018 cropping season in Kenya is underway, while farmers are currently preparing for the second season shorts rains.
Source: CCKP, 2018
With no lean season, Kenya is a key stakeholders on ensuring food security on the region.
Source: FAO/GIEWS, 2018
Start of season 1
Start of season 2
Kenya: overview 2015 El Niño effects during short rains!
October 2015 November 2015 December 2015
During some of the monthswith the highest ONI, anomalies for the current short rains season were the largest, and likely most impactful, in November.
Kenya: rainfall patterns in corn producing regions, November 2015
(mm)
Source: MapSPAM, 2018 for corn growing areas
Highest corn producing areas (80%)
Corn producing areas
Administrative Unit
No. aWhereVirtual Weather Stations
Total current Precipitation (mm)
Average LTN Precipitation (mm)
Difference: Current Precipitation vs. LTN Precipitation (mm)
Difference: Current Precipitation vs. LTN Precipitation (%)
Nairobi 8 184.81 71.88 112.92 157.1Central 129 192.8 110.46 82.34 74.54Eastern 736 187.23 113.82 73.42 64.5Western 93 225.63 137.24 88.39 64.41North-Eastern 385 185.87 117.09 68.79 58.75Coast 472 134.33 84.63 49.7 58.73KENYA 3074 152.66 100.41 52.26 52.05Rift Valley 1080 109.21 80.01 29.21 36.5Nyanza 171 182.66 151.22 31.44 20.79
Administrative Unit
No. aWhere Virtual Weather Stations
Total current Precipitation (mm)
Average LTN Precipitation (mm)
Difference: Current Precipitation vs. LTN Precipitation (mm)
Difference: Current Precipitation vs. LTN Precipitation (%)
Nairobi 4 186.21 75.50 110.71 146.64
Eastern 115 270.72 141.00 129.72 92.00
Central 66 196.84 112.22 84.62 75.40
Western 77 215.55 131.83 83.72 63.50
KENYA 752 170.73 113.05 57.68 51.02
Rift Valley 366 120.13 88.74 31.39 35.37Nyanza 121 186.40 150.69 35.71 23.70
Kenya: data drill !
(Live Demo)
Additional example on how WBG is testing the Ag Observatory’s Disruptive Tech Approach
to High Resolution Ag Meteorology across regions and countries
Zambia cropping season (FAO)
Zambia produces most crop throughout the months of November-June, while limited areas take on wheat production during the dry and cooler season. Precipitation is determinant of crop growth from Nov. through March.
Source: CCKP, 2018
Source: FAO/GIEWS, 2018
Start of main season
End of main season
Zambia: main 2018 season outlook
November December
January February March
The figures show the anomalies in rainfall when comparing a given month to a 10 year baseline data. Both drought and excess of rainfall were observed throughout the season, underlining the increasing weather variability farmers must be equipped to cope with.
Rainfall in January failed across most of the country, when crops, particular maize, were only in the early vegetative growth stage. The map below and histogram to the right shows just how dry it was compared to expected. The current P/PET (lower right) shows that enough moisture was still available for crops in the Northern part of the country, while the most of the South was under great stress.
Rainfall (P) compared to LTN P (mm)
Zambia: January 2018 Red = current rainfall Blue = 2007-2017 average rainfall
Current vs. LTN (10 yr) Anomalies
P/PET
What are the most relevant crop-producing regions?
Source: USGS/GFSAD30, 2018
Arable land from Global Food Security Analysis-Support Data at 30 Meters (GFSAD30) (2015)
Source: MapSPAM, 2018
Source: aWhere, 2018
Highest corn producing areas (80%) Spatial Production Allocation Model (2005)
P/PET
Zambia: Corn producing areas across the country, January 2018
Source: MapSPAM (2018) for crop data
When comparing rainfall anomalies of the entire country with the areas of highest corn production, it is possible to see an even more pronounced lack of rainfall in the latter. Such insights are crucial for better decision-making across the supply-chain.
(mm)
Red = current rainfall Blue = 2007-2017 average rainfall
Total Precipitation
Administrative Unit
No. aWhereVirtual Weather Stations
Total current Precipitation (mm)
Average LTN Precipitation (mm)
Difference: Current Precipitation vs. LTN Precipitation (mm)
Difference: Current Precipitation vs. LTN Precipitation (%)
Current P/PET LTN P/PET
Difference: Current P/PET vs LTN P/PET
Central 508 58.35 253.86 -195.51 -77.01 0.40 2.00 -1.60Lusaka 144 62.47 246.39 -183.93 -74.65 0.40 1.92 -1.52Copperbelt 166 101.72 306.58 -204.86 -66.82 0.70 2.51 -1.82Southern 764 68.52 200.27 -131.76 -65.79 0.40 1.43 -1.04ZAMBIA 2906 97.91 234.19 -136.28 -58.19 0.68 1.81 -1.13Eastern 488 107.25 232.32 -125.07 -53.84 0.85 1.92 -1.07Western 343 99.12 211.38 -112.27 -53.11 0.54 1.43 -0.89North-Western 122 162.85 245.35 -82.50 -33.63 1.00 1.81 -0.81Northern 248 184.98 266.79 -81.81 -30.66 1.55 2.26 -0.71Luapula 123 199.85 245.86 -46.01 -18.71 1.63 2.09 -0.46
Zambia: Corn producing areas by state, January 2018
When considering agrometeorological conditions at province level, Central, Lusaka & Copperbelt have been the most affected by lack of rainfall.
Considering household conditions are likely stressed and still recovering from below normal rainfall, an potential El Niño developing from Dec.-Jan. could further deteriorate food security and income levels
