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Geospatial Opportunities in Inclusive Agro-ecosystems for Sustainable Foods and Future
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C. Biradar, Karan, N., El-Shamaa, K., Atassi, L., Low, F., Singh, R., Omari, J., Bonaiuti, E., Koo, J., and King, B.
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- more crop per drop- in a inch of land and a bunch of crop
Sustainable Food and Future
Increased land, water and system productivity while safe guarding the environmental flows and ecosystem services
Knowledge based prioritization (space & time) for better strategy for investment, intervention, implementation and impact
-water focus
-multi dimensions-integrated systems
Ecological intensificationTarget specific interventionsBridging the gapsInputs use efficiencyAgricultural policyHalt degradation Technology scaling
- food and nutritional security - resilience and risk reduction - agro-ecosystem sustainability- adaption and mitigation- citizen science and collective actions- trade, social security and stability
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Active Satellites
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Mutation induced variability in Medicago
Phenotypic/ Genotypic segregation in Medicago
HyperSpectralsignature of 20 Wheat varieties
Advanced Sensors and Tools
Portable spectral devices(Biradar et al., 2012)(Biradar et al., 2013*)
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AI Meta AnalyticsIoT
AI @ genetics, chemistry, weather, agronomies, trade…
Inclusive Agroecosystems
Demand drivenBetter options
Interoperability of Data for Better Decisions
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Big-data, Machine Learning and AI algorithms
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Imagedownload
Atmosphericcorrection
SaveREDandNIRbandtotmp folder,
namesoff iles:*DOY_RED.tif*DOY_NIR.tif
Readtwotif f iles
fromtmpfolder,doNDVI
calculation
SaveNDVIf iletofolder
“basename_DOY_ndvi.tif”
Addcolumntoexistingcsv
Module-1
Module-3
Module-2
Shapef ileall_f ields_fergana
.shp
Field
:“ ID”
Do10-dayinterpolation,addcolumns
GEOTIFFoffieldIDs
Zonalstatistics
Pre-existingdata
EmptyCSVwith
fieldIDsinfoldernamedafter
recentyear?
Doclassif ication
UpdatecolumnsinCSV
(classif ication,probabilities)
Output:
Saveto“annual”folders
Processing
Inputorexisting
database
Savefinalcsvforclassif ication
“fergana_croptype_YY
YY.csv”
SavefinalcsvforinterpolatedNDVI
“fergana_NDVI_ts_int
erpolated_YYYY.csv”
Saveupdated,historicdata
baseforVCIasCSVorRObject?
“historic_vci_db_YYYY.csv”
Dotablejoin:Update
annualshapefilewithclas s ifications ,
probabilities ,andVCIindicator(seaosnal and
end-or-seaosn each)
1x1kmgridasshapefile
ComputeMINandMAXNDVImulti-annualreferenceforeachgrid
Updatecolumns(VCIdeviation+/-)
SavefinalcsvforVCI
“fergana_vci_YYYY.csv”
Front-end
systemFileGeodatabase
Front-end
Web-mappingservice(hostedorreferenced)Fr
ont
end
Pooleddatasets
andRFmodels(seasonaland
annual)
Mean
Saveatmosphericallycorrectedimagesfileto
folder
“basename_DOY.tif”
Big-data, Machine Learning and AI algorithms
SVM, BT, LR, RF, DT, MLPMulti-mode classification algorithms
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#/km2
Dynamics of Cropping Systems Integrated Agro-Ecosystems Sustainable Intensification and Diversification Input Use Efficiency-Conservation Agriculture Thematic Land-Water-Climate Resilience
Agricultural Intensification
Cropping Intensity
Increase in Arable Land
72%
21%7%
Biradar and Xiao, 2009
Length of the crop fallows, start-date, end-date
(Biradar et al., 2015)
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GPP (Growing Seasonal total, Nov-June)
0
0.01
- 50
51 -
100
110
- 150
160
- 200
210
- 250
260
- 500
510
- 1,0
00
1,10
0 - 2
,000
Bridging the Gaps @ multiple-scales
Untapping the production potential data, knowledge, productivity, resilience
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Quantification of Farming Systems @ multiple-scalesDigital Ag Platform
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Digital Agriculture Platform
On the fly demand drivenquery and cluster analysis
Cadastral, Object & Pixel based
Biophysical and socio-ecological
Machine LearningCrop types, crop
intensity, rotation, fallows, crop stress,
AET-l8, soil moisture-SMAP
Citizen-ScienceSmart Extension
feedback
Direct Access and Markets/Business
Precision decision delivery at farm scales and feedback
Crowdsource, OA, CloudComputing at Farm Scale
Precision-Decision
Image Based, Open Source Precision Decision at Farm scales
Smart Extension/Citizen ScienceCommunity of Practices
Farming Stakeholders
Mu
lti-
Scal
e EO
SAI
NNRF
ML
Location Specific Interventions
Right Time Right Place
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Data visualization and QueryClick on the map to add pin
General Ground Truth
Select Map Tool
Select Point or AOI
Location Information
Capture GPS
Points Averaged Clear
Lat: 31.5712034 Lon: 35.520210
Elevation: 245m
Area Information
Bearing to center
Points Averaged Clear
Collect
Specific information
Land Use Text
Select Farm
Yield Gap
Yield Potential
Value
Value
TextAdvisory
Download
Submit
Extract
Feedback
GeoAgro App:Citizen Science Field Data Collection, Data Management, Precision Agriculture App for Tablets and Smart Phones
Select Farm
•Field Data
•Yield Gaps
•Droughts/floods
•Crop Stress
•Water use
•Real-time AET
•Citizen Science
•Crop Type
•Crop Suitability
•Yield Forecasting
•Pest Risk
•Real-time Advisory
In Beta Testing
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Thank [email protected]
avoid the unmanageable and manage the unavoidable
-IPCC Confronting Climate Change:
in an inch of land and bunch of crop
Where much gain is expected?Is that from genetic? 15-20Is that from management? 50-60Is that from socio-economy? 20-35
Chandrashekhar Biradar, PhDPrincipal Scientist (Agro-Ecosystems)
Head-Geoinformatics Unit