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.