A Tour of USDA NASS’s Decision Support

System

Nathan B. Cruze, Clyde Fraisse*, Ana Wagner*,

Habtamu Benecha, Noemi Guindin

United States Department of Agriculture

National Agricultural Statistics Service (NASS)

*University of Florida

Department of Agricultural and Biological Engineering

Government Advances in Statistical Programming Workshop

Washington, DC–September 23, 2019

“. . . providing timely, accurate, and useful statistics in service to U.S. agriculture.” 1
Outline and Disclaimer

1. Motivating need for Decision Support System (DSS)

◮ Relevant, timely, consolidation of multiple data sources

◮ National Academies of Sciences, Engineering, and Medicine

(2017a,b,c, 2019)

2. Project origins, open source software, and public data inputs

3. Added value for National Agricultural Statistics Service

(NASS) estimation programs

Disclaimer: The findings and conclusions of this presentation are

those of the authors and should not be construed to represent any

official USDA or U.S. Government determination or policy.

This research was supported in part by the intramural research program of the U.S. Department of Agriculture, National Agriculture

Statistics Service.

GASP 2019–Tour of NASS Decision Support System 2
Mother Nature Does NOT Respect Due Dates!

Hurricane season 2017: Harvey (August 25), Irma (September 10)

Crop Production 

ISSN: 1936-3737

Released September 12, 2017, by the National Agricultural Statistics Service (NASS), Agricultural Statistics Board, United States 

Department of Agriculture (USDA).

Special Note

Hurricane Harvey made landfall on Friday, August 25 near Rockport, Texas. The resulting rainfall caused flooding in 

parts of southeastern Texas and southwestern Louisiana. As a result, data collection activities for the September Crop 

Production report were impacted in these areas and the full impact of this weather event may not be fully reflected in this 

report. Therefore, NASS will collect harvested acreage information in both Texas and Louisiana for a number of crops in 

preparation for the October Crop Production report. Harvested acreage information will be collected from all producers 

surveyed in Louisiana for corn, Upland cotton, rice, sorghum, soybeans, and sugarcane; and in Texas for corn, 

Upland cotton, alfalfa hay, other hay, rice, sorghum, and soybeans.

Hurricane Irma made landfall on Sunday, September 10. NASS will also collect harvested acreage information in 

preparation for the October Crop Production report in Alabama, Florida, Georgia, and South Carolina. Harvested acreage 

will be collected in these four States from all producers surveyed for Upland cotton, peanuts, and soybeans. 

Corn Production Up Less Than 1 Percent from August Forecast

Soybean Production Up 1 Percent 

Cotton Production Up 6 Percent

Figure: September 2017 Crop Production Report

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“To be...

8. Idle cropland - idle all during 2019

857

.

857

.

857

.

857

.

857

.

9. Two crops planted in this field or two

uses of the same crop □ Yes □ No □ Yes □ No □ Yes □ No □ Yes □ No □ Yes □ No

[Specify second crop or use.]

Acres

844

.

844

.

844

.

844

.

844

.

10. Acres left to be planted

610

.

610

.

610

.

610

.

610

.

11. Acres irrigated and to be irrigated [If double

cropped, include acreage of each crop irrigated.]

620

.

620

.

620

.

620

.

620

.

16. Winter Wheat

(include cover crop)

Planted

540

.

540

.

540

.

540

.

540

.

17. For grain or seed

541

.

541

.

541

.

541

.

541

.

20. Oats

(include cover crop)

Planted and to be planted

533

.

533

.

533

.

533

.

533

.

21. For grain or seed

534

.

534

.

534

.

534

.

534

.

24. Corn

[exclude popcorn

and sweet corn]

Planted and to be planted

530

.

530

.

530

.

530

.

530

.

25. For grain or seed

531

.

531

.

531

.

531

.

531

.

29. Other uses of

grains planted

(Abandoned, silage,

green chop, etc.)

Use

Acres . . . . .

30.

Hay

[Cut and to be 

cut for dry hay.]

Alfalfa and Alfalfa Mixtures

653

.

653

.

653

.

653

.

653

.

31. Grain

656

.

656

.

656

.

656

.

656

.

33. Other Hay

_ _ _

.

_ _ _

.

_ _ _

.

_ _ _

.

_ _ _

.

34.

Soybeans

Planted and to be planted

600

.

600

.

600

.

600

.

600

.

35. Following another harvested crop

602

.

602

.

602

.

602

.

602

.

81. Other crops Acres planted or in use

848

.

848

.

848

.

848

.

848

.

◮ June Area Survey

◮ Example Ohio instrument

◮ June 1 reference date

◮ Two-week data collection

◮ Respondents also report

intentions (‘to be’)

◮ Acreage report published

June 28, 2019

Intentions may change...

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...or not to be”

Heavy rains impacted subsequent planting activity

◮ User interest in planted area totals published June 28, 2019

◮ Announced re-contact efforts1 with release of Acreage report

State

Corn Soybeans

2018 Final 2019 June2 2019 August3 2018 Final 2019 June2 2019 August3

(1,000 Acres) (% Change) (% Change) (1,000 Acres) (% Change) (% Change)

Illinois 11,000 0% -3% 10,800 -5% -7%

Indiana 5,350 3% -5% 5,950 -11% -9%

Kansas 5,450 8% 17% 4,750 -1% -3%

Michigan 2,300 0% -13% 2,300 -9% -24%

Missourri 3,500 -3% -7% 5,850 -9% -13%

Ohio 3,500 -6% -20% 5,000 -6% -16%

South Dakota 5,300 -9% -15% 5,650 -22% -38%

References and Data–Accessed September 15, 2019

(1) Reference: June 28, 2019 USDA NASS Agricultural Statistics Board Notice

(2) Reference: American Farm Bureau Federation–Groundtruthing USDA’s June Acreage Report

(3) Author calculations based on Corn Data and Soybean Data in NASS August 2019 Crop Production

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Mother Nature Controls Key Factors of Crop Production

Anecdotes provided by state Farm Bureau agents:

◮ Illinois–“prevented-planting of corn...planting soybeans”

◮ Michigan–“corn...will go to silage, not grain”

◮ Ohio–“[crops are] behind, struggling...in need of replant”

◮ Indiana–“Anticipated yields...less than the 10-year average”

◮ Kansas–“...will require near optimal temperatures

and...precipitation...an earlier than normal frost could be

devastating”

Economic decisions, progress, condition, trend yield, and phenology

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University of Florida/NASS Collaboration

AgroClimate Tools

◮ Origins with Southeast Climate Consortium (SECC)

◮ Currently managed by University of Florida

◮ Decision tools for farmers

Collaboration to customize tools for NASS internal use

◮ Nebraska pilot began in 2015

◮ Nationwide expansion summer 2017

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Public Data Inputs and Software

Statistics in DSS derived from NASS data and these inputs:

1. Oregon State University PRISM Climate Data

2. NOAA National Centers for Environmental Prediction Real-Time

Mesoscale Analysis (RTMA) Data

3. USDA Natural Resources Conservation Service Soil Survey

Geographic Database (SSURGO)

Free or open-source software underpinnings:

◮ MySQL and PostgreSQL with required PostGIS 2.4.5 extension

◮ Apache Server, PHP, WordPress

◮ R v3.4.3: reshape, reshape2, ggplot2, rJava, zoo, stringr, sp,

RPostgreSQL, RMySQL, rgdal, RCurl, raster, plyr, ncdf4, maptools,

mailR, Jsonlite, RJSONIO, doMC, compare, foreach, AgroClimate

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DSS Structure

Browser-based, menu-driven

◮ Intuitive, user-friendly

◮ Read-only connection

◮ Spatial and systems

databases

◮ R scripts derive and

aggregate statistics

◮ Curated data matched

to NASS deliverables

◮ Visualize, summarize,

export
Monitoring Capabilities

• The Weekly and Advanced Summary 

Report Tools summarize weather and 

climate information at the county, district 

and state level and are customized to 

match the time-frame for NASS’s Weekly 

Crop Progress and Condition Reports.

• The Weekly Map Tool displays variables at 

State and Regional Field Office level. The 

summary of the weather information - for 

the week ending on Sunday - is available 

every Monday morning at 9:00 am EST.

Home Monitoring Tools External Monitoring Tools Forecasts & Outlooks ENSO Contact Log Out

Precipitation and Temperature – Map

Rainfall and Temperature – Stations

Drought Index – ARID

GDD – Map

GDD – Stations

Vegetation Indices

County Yield Statistics

Weekly Summary Report

Advanced Summary Report

Weekly Maps

GASP 2019–Tour of NASS Decision Support System 10
Menu for Summary Reports: Derived Statistics Monitoring Tools – Advanced Summary Report

PRISM

PRISM+SSURGO

PRISM

RTMA PRISM
Field Office Review and Weekly Reports

Compare/contrast April 29, 2019 Crop Progress and Condition

Features Wyoming Illinois

Weekly Narrative X X

Crop/Livestock Progress X X

Crop/Livestock Condition X X

# Days for Fieldwork X X

Soil Moisture X X

State/District Weather X

Weather Maps X

Opportunities: standardize, provide additional useful data

◮ Link: Wyoming Crop Progress for April 29

◮ Link: Illinois Crop Progress for April 29

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Wyoming District Weather: April 22-April 28, 2019

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Wyoming Weather Map: April 22-April 28, 2019
Weekly Map Menu–Exporting Texas Precipitation
Texas Precipitation: August 21-September 3, 2017

◮ See also Boryan et al. (2017) ◮ Benecha et al. (2019)

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Southern Region Precipitation: September 4-17, 2017

◮ See also Hurricane Irma: NASS Flood Assessment Report
2019 Illinois Precipitation and Planting Progress

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2019 Illinois Precipitation and Planting Progress

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2019 Illinois Precipitation and Planting Progress

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2019 Illinois Precipitation and Planting Progress

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2019 Illinois Precipitation and Planting Progress

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2019 Illinois Precipitation and Planting Progress

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2019 Illinois Precipitation and Planting Progress

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2019 Illinois Precipitation and Planting Progress

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2019 Illinois Precipitation and Planting Progress

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2019 Illinois Precipitation and Planting Progress

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2019 Illinois Precipitation and Planting Progress

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2019 Illinois Precipitation and Planting Progress

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2019 Illinois Precipitation and Planting Progress

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2019 Illinois Precipitation and Planting Progress

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2019 Illinois Precipitation and Planting Progress

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2019 Illinois Precipitation and Planting Progress

Illinois 2019: Corn and Soybeans Planted Progress Data

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Planned Extensions: Alerts and Modeling –

GASP 2019–Tour of NASS Decision Support System 34
Trend: Platte County, Non-Irrigated Corn Yield, 2015
Planned Extensions: Crop Simulation Modeling

Figure: Image Source–Pioneer Agronomy Sciences

◮ Simulate progress as functions of weather, soil, management

◮ Crop phenology and health of crop at critical points in time

GASP 2019–Tour of NASS Decision Support System 36
Linking to External Monitoring Tools from One Dashboard

Home Monitoring Tools External Monitoring Tools Forecasts & Outlooks ENSO Contact Log Out

◮ NASS Cropland Data Layer

◮ UNL U.S. Drought Monitor

◮ NWS Climate Prediction

Center ENSO Diagnostics

◮ NOAA NWS National

Hurricane Center

◮ UNL High Plains Regional

Climate Center
Conclusions

The DSS provides timely and relevant auxiliary data

1. Tailored for internal use at NASS

2. Offered first to field offices for use in routine duties

3. Standardization of Crop Progress and Condition reports

4. Weather data complements and augments other approaches

◮ Survey data

◮ Administrative data

◮ Remote sensing of disasters

◮ Modeling

5. Planned enhancements coming soon

GASP 2019–Tour of NASS Decision Support System 38
Additional References

Benecha, H., Sartore, L., and Cruze, N. (2019). Model-Based Crop Yield Forecasting: Covariate Selection and

Related Issues. In Proceedings of the Joint Statistical Meetings, Survey Research Methods Section. American

Statistical Association, Alexandria, VA. To Appear.

Boryan, C., Yang, Z., Sandborn, A., Willis, P., and Haack, B. (2017). Operational Agricultural Flood Monitoring

With Sentinel-1 Synthetic Aperture Radar.

https://www.nass.usda.gov/Research_and_Science/Disaster-Analysis/2017/Hurricane-Harvey/

Hurricane_Harvey_Flood_Monitoring_Methodology_Paper.pdf. Accessed: 2019-09-20.

National Academies of Sciences, Engineering, and Medicine (2017a). Federal Statistics, Multiple Data Sources, and

Privacy Protection: Next Steps. The National Academies Press, Washington, DC.

National Academies of Sciences, Engineering, and Medicine (2017b). Improving Crop Estimates by Integrating

Multiple Data Sources. The National Academies Press, Washington, DC.

National Academies of Sciences, Engineering, and Medicine (2017c). Innovations in Federal Statistics: Combining

Data Sources While Protecting Privacy. The National Academies Press, Washington, DC.

National Academies of Sciences, Engineering, and Medicine (2019). Methods to Foster Transparency and

Reproducibility of Federal Statistics: Proceedings of a Workshop. The National Academies Press, Washington,

DC.

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