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1. Introduction
▪ Speakers: Who we are
▪ Audience: Who we think you are
2. Secondary datasets
▪ Weather
▪ Soil properties
▪ Cropping calendar
▪ Management practices
▪ Evaluation data
3. Other data sources
▪ Phone surveys
▪ Household surveys
▪ Satellite remote sensing
4. Final remarks
5. Q&A
Agenda
Type your questions that have not been addressed during the presentation in the Q&A Section
A researcher who uses crop modeling as one of the tools.
(Ideally) you already have:
1. Defined research questions
2. Defined research location
3. Some knowledge usual management practices
4. Identified data gaps that secondary data can address, potentially
Some research questions can’t be addressed by secondary data!
You are...
Net Income Resource useEnvironmental
Plant growth (grain, biomass,
roots, etc.)
Plant
development (time to flowering,
maturity, etc.)
Yield
Soil conditions (physical & chemical
properties by layer)
Weather (daily rainfall,
solar radiation, max &
min temperatures, …)
Management events (sowing, irrigation,
fertilizer, organic matter,
tillage, harvest)
Genetics (cultivar-
specific parameters
controlling growth
and development)
Crop Model Simulation
Source: G. Hoogenboom (2019)
What data do you need?
1. Experimental plot
2. Field, with multiple management zones
3. Farm, with multiple fields
4. Landscape/watershed
5. Country
6. Region
7. Global
At what scale?
1. Experimental plot
2. Field, with multiple management zones
3. Farm, with multiple fields
4. Landscape/watershed
5. Country
6. Region
7. Global Stay tuned for our next webinar on the grid-based crop modeling
Our scope in this webinar
TAMU Global Weather Stationshttps://beaumont.tamu.edu/ClimaticData
NOAA Global Historical Climate Network Dailyhttps://www.ncdc.noaa.gov/ghcn-daily-description
NASA POWER (0.5 degree)https://power.larc.nasa.gov
CHIRPS and CHIRTS (0.05 degree)https://www.chc.ucsb.edu/data
ERA5 and AgERA5 (0.1 degree)https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels
https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-agrometeorological-indicators
Commercial ProvidersaWhere https://awhere.com
Weather Company https://www.ibm.com/products/weather-company-data-packages
Meteoblue https://www.meteoblue.com/
Weather
TMAXTMINRAINSRAD
1. Stations (point) iAims Climatic Data
(https://beaumont.tamu.edu/ClimaticData/)2. Global Historical Climate Network Daily (NOAA)(ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/)
Weather data
Weather dataNASA Power (https://power.larc.nasa.gov/) (MD) 0.5 x 0.5 degree ca 60 km
resolution
Weather data
CHIRPS Rainfall0.05° resolution approx. 5kmClimate Hazards Group InfraRed Precipitation with Station data (CHIRPS)1981 to near-present
(https://www.chc.ucsb.edu/data/chirps)
0.05° resolution approx. 5kmCHIRTSdaily (TMAX and TMIN)1983-2016 https://www.chc.ucsb.edu/data/chirtsdaily
Weather data
TAMSATDaily Rainfall Africa 4 km resolution1983 to the delayed present
https://www.tamsat.org.uk/
Weather data
ERA5Reanalysis dataset0.1°x0.1°, ca 9 km1981-presentHourly, Daily, Monthly
Temperature, Wind, Precipitation, Solar Radiation, etc
https://cds.climate.copernicus.eu
Weather data
AGERA5Reanalysis dataset0.1°x0.1°, ca 9 km1979-2018 (Ongoing to present)Daily
Temperature, Wind, Precipitation, Solar Radiation, Relative Humidity, Vapor Pressure, etc
https://cds.climate.copernicus.eu
Weather data
SM2RAIN data sets: Daily Rainfall
SM2RAIN-ASCAT 2007-8/2019 12.5 km res
GPM+SM2RAIN 2015-2018 0.25 degree res
SM2RAIN-CCI 1998-2015 0.25 degree res
http://hydrology.irpi.cnr.it/download-area/sm2rain-data-sets/
Weather data
Daymet Canada, USA, Mexico, Puerto Rico, and Hawaii. 1 km resolution
1980-2019Minimum and maximum temperature, precipitation, vapor pressure, radiation, snow water equivalent, and day length
https://daymet.ornl.gov/
Weather data
Commercial ProvidersDaily weather, historical data, forecasts, apps, other products
https://www.awhere.com/
https://www.meteoblue.com
https://business.weather.com/products
ISRIC WISE Global Soil Property Database+10K soil profiles from 150 countries
https://www.isric.org/explore/wise-databases
ISRIC SoilGridshttps://soilgrids.org (GIS; Global, grid, 250m)
https://doi.org/10.7910/DVN/1PEEY0 (DSSAT; Global, grid, 10km)
FutureWater HiHydroSoilGlobal maps of soil hydraulic properties (grid, 1km)
https://www.futurewater.eu/2015/07/soil-hydraulic-properties
GYGA Root Zone Depthhttps://www.isric.org/projects/afsis-gyga-functional-soil-information-sub-saharan-
africa-rz-pawhc-ssa (GIS; Africa, grid, 1km)
HC27 Generic Soil Profile Databasehttps://doi.org/10.7910/DVN/90WJ9W (DSSAT and APSIM; Global, grid, 10km)
Soil properties
Texture,bulk density, organic matter, nitrogen by layer
Global Crop Planting and Harvesting Windowshttps://nelson.wisc.edu/sage/data-and-models/crop-calendar-dataset
(Global, compilation of six sources, 19 crops, grid, 10km)
+
Daily weather data of the seasonSee Slide #7: Weather
+
RulesPlanting automatically using (simulated) soil moisture and temperature
conditions, harvest at (simulated) maturity
Or...
Simulate multiple planting dates within the window, choose the one fits best
with the research design and assumptions
Cropping calendar
1) Simulation
Cropping CalendarPlanting and harvesting windows 5 min resolution19 cropsGridded maps of planting dates, harvesting dates, etc
https://nelson.wisc.edu/sage/data-and-models/crop-calendar-dataset/
Sacks, W.J., D. Deryng, J.A. Foley, and N. Ramankutty (2010)https://nelson.wisc.edu/sage/data-and-models/crop-calendar-dataset/
Crop calendar
Crop calendar by region
Cropping CalendarAlternative methods:http://www.fao.org/agriculture/seed/cropcalendar/welcome.do
IWMI Global Crop Monitoring ToolMonitoring likely harvesting areas, weekly
MODIS (500m) and Sentinel 2 (10m) on Google Earth Engine
https://wrd_iwmi.users.earthengine.app/view/global-cropland-monitoring-tool
ICARDA Crop Monitoring Tool for IndiaMonitoring of harvesting progression, weekly, compared to 2019
MODIS (500m) and Sentinel 2 (10m) on Google Earth Engine
https://geoagro.users.earthengine.app/view/harvestfallowno2
Smartphone Pictures from Farmershttps://bigdata.cgiar.org/inspire/inspire-challenge-2017/seeing-is-believing-using-
smartphone-camera-data and https://doi.org/10.1016/j.agrformet.2018.11.002
Cropping calendar
2) Monitoring
https://geoagro.users.earthengine.app/view/harvestfallowno2
Cropping Calendar
https://wrd_iwmi.users.earthengine.app/view/global-cropland-monitoring-tool
Cropping Calendar
Cropping CalendarAlternative methods:
• Rule-based planting date selection using the window and weather data• Asking farmers/citizens (Crowdsourcing, citizen science) to take pictures (PBI)• Phone surveys (point)
Photo credit: Francisco Ceballos/IFPRI
TillagePIK Global Gridded Dataset on TillageGlobal, circa 2005, grid, 10km, six tillage systems for 42 crops
http://doi.org/10.5880/PIK.2019.009
Remote sensing-based tillage detection methodshttps://www.sciencedirect.com/science/article/abs/pii/S00344257183051
https://link.springer.com/article/10.1007/s13593-020-0610-2
Variety characteristicsCGIAR Open Data
https://gardian.bigdata.cgiar.org
Plus,
● Literature review (journal articles)
● Interviews and surveys with local experts (breeders, agronomists)
● Recommendations by extension services
Management practices
Fertilizers
• Fertilizer use
• Literature review (plus interviews/surveys) (point)
• Recommended rates of fertilizer (NARS, Seed companies, Ministries of Ag)
• Breeding information (Variety management recommendations)
• Local experts (breeders, agronomists, extensionists)
Irrigation “Extent”
FAO AQUASTATCountry-level statistics (e.g., area under irrigation) until 2015
http://www.fao.org/nr/water/aquastat/data/query/index.html?lang=en
FAO Global Map of Irrigation Area (GMIA)Area equipped for irrigation around the year 2005, grid, 10km
http://www.fao.org/aquastat/en/geospatial-information/global-maps-irrigated-
areas/latest-version
A Global Approach to Estimate Irrigated AreasGMIA plus remote sensing-based estimates for 2012, grid, 1km
https://doi.org/10.5194/hess-22-1119-2018
Management practices
Micro-level Surveys
Phone surveysAlso known as CATI (Computer-assisted telephone interviewing).
Many COVID-19 impact surveys being developed in CGIAR and others.
Piggyback opportunity? 😉
https://blogs.worldbank.org/impactevaluations/mobile-phone-surveys-
understanding-covid-19-impacts-part-i-sampling-and-mode
Nationally-representative household surveysSome large-scale surveys, such as LSMS-ISA, include questions on the
input use (e.g., fertilizer types and rates) from recent years.
https://evans.uw.edu/policy-impact/epar/research
And…
other useful data sources
Commercial data products
VanderSat Soil Moisture Mapping
Sensing 5 cm-depth, extrapolates to 40 cm, global, 100m, daily
https://www.vandersat.com/soil-moisture-monitoring
IBM Seasonal Probabilistic Forecasts
RAIN, TMAX, TMIN. Monthly update, site-specific, 6-month daily forecasts.
https://business.weather.com/products/seasonal-forecasts
And…
other useful data sources
Other types of remote sensing applications
1.Empirical/statistical modeling
2.Hybrid modeling (statistical + process-based crop modeling)
3.Data assimilation (e.g., LAI from NDVI) for yield forecasts/prediction
4.Soil moisture from remote sensing: https://www.vandersat.com/soil-moisture-monitoring
(sensing 5 cm-depth; extrapolates up to 40 cm depth) (100 meter resolution, daily)
Remote sensing inputs
https://www.atlasai.cohttps://gro-intelligence.com
Use secondary data with a caution● Start with your research question.
● Evaluation is still required for your site, environment, and research
questions. And, please read the documentation!
● Look for literatures with comparative analyses and performance
evaluations.
It takes a community● One can’t fully review the applicability and specificity of secondary
datasets.
● Your colleagues in the community (e.g., CGIAR CoPs, DSSAT
Listserv, APSIM Mailing List, Facebook Groups) might be the best
resource.
Future webinars● May 18 | Harmonization of COVID-19 Phone Surveys
● TBA | GARDIAN and CGLabs
● TBA | Grid-based Crop Modeling: Data and applications
Final remarks
Feeding the future. Byte by byte.