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On Combining Multiple Sources of Information toEnhance NASS Crop Estimates

Nathan B. Cruze

National Agricultural Statistics Service (NASS)United States Department of Agriculture

[email protected]

CNSTAT Panel on Improving Federal Statistics for Policy andSocial Science Research Using Multiple Data Sources and

State-of-the-Art Estimation Methods

December 16, 2015

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

Presentation Outline

1. NASS crop survey cycle–winter wheat

2. The Agricultural Statistics Board–synthesizing many sourcesof information

3. Model-based forecasts of winter wheat yield

4. Recurring themes and challenges of combining multiplesources of information

CNSTAT Panel–Using Multiple Data Sources 2

Winter Wheat Production CycleMonth Crop Stage NASS Surveys NASS Publications Variables of Interest

December Most planting complete December APSWinter Wheat Seedings Acres Planted

Grain Stocks On & Off Farm Stocks

March Emerging from dormancy March APSProspective Plantings Acres Planted

Grain Stocks On & Off Farm Stocks

May Crop actively developingMay AYS

Crop Production Acres Harvested, Yield, ProductionMay OYS

June Crop develops; early harvest

June AYSCrop Production Acres Harvested, Yield, Production

June OYSJune APS Acreage Acres Planted/Harvested

June Area Survey Grain Stocks Ending On & Off Farm Stocks

July Harvest underwayJuly AYS

Crop Production Acres Harvested, Yield, ProductionJuly OYS

August Harvest winding downAugust AYS

Crop Production Acres Harvested, Yield, ProductionAugust OYS

September Harvest complete September APSSmall Grains Summary Acres Planted/Harvested, Yield, Production

Grain Stocks Beginning On & Off Farm Stocks

August-December Marketing activities Small Grains CAPS County Estimates Acres Planted/Harvested, Yield, Production

I Sequence of survey indications–update our understandingI Multiple survey indications in same monthI Administrative data, remote sensing indications, weather dataI One official statistic in publication


The Agricultural Statistics Board (ASB)Agricultural Statistics Board–panel of NASS commodity specialists

I Review current and historical survey ‘indications’

I Review other available information–weather data, crop conditionindications, administrative data

I Consensus on estimates of parameters of interest–planted/harvestedarea, production, yield

I Official statistics in Crop Production Report are a compositeor synthesis of multiple sources of information determined byexpert assessment

Challenge: Capture expert assessment in a manner that is1) easily reproducible2) includes appropriate measures of uncertainty


A Bayesian Hierarchical Model for Crop Yield ForecastingGoal: Synthesis of yield indications from NASS surveys andauxiliary information

I Yield measures output per area harvested (bushels/acre)I Yield for state j : µj , j = 1, 2, ..., JI Yield for speculative region: µ =

∑Jj=1 wjµj

I Weights wj ∝ harvested acres for state j

APS

AYSAYSAYSAYS

OYSOYSOYSOYSOYS

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Crop

Production

Report

Crop

Production

Report

Crop

Production

Report

Crop

Production

Report

Small

Grains

Summary

APS

AYS

OYS

May Jun Jul Aug Sep OctMonth

Survey and Publication Timeline for Winter Wheat


Winter Wheat Speculative Region–Acreage and Dates

1.8%2.5%2.7%

5.1%

8.7% 35.8%

16.9%

11.4%

8.5%6.6%

25

30

35

40

45

50

−120 −110 −100 −90 −80long

lat

StartStart

StartStartStart

StartStartStart

StartStart

ActiveActive

ActiveActive

ActiveActiveActive

ActiveActive

Active

EndEnd

EndEnd

EndEnd

EndEnd

EndEnd

TexasOklahoma

MissouriIllinois

KansasColorado

OhioNebraska

WashingtonMontana

Jun 01 Jun 15 Jul 01 Jul 15 Aug 01 Aug 15 Sep 01

Usual Harvest Dates for Winter Wheat Speculative Region States


Winter Wheat Yield Data–Example State

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NASS Yield Survey Indications: Example Winter Wheat State

Year

Yie

ld (

Bu/

Acr

e)

2002 2004 2006 2008 2010 2012 2014

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AYSOYSSept. APS

● MayJuneJuly

AugSept


Bayesian Hierarchical Model for Speculative RegionNotationI µt–true yield

I yktm–observed yield

I k ∈ {O,A,Q}–survey index

I t ∈ {1, ...,T}–year index

I m ∈ {months}–survey month

I m∗–forecast month

Region data model

yktm∗|µt ∼ indep N(µt + bkm∗, s

2ktm∗ + σ2

km∗), k = O,A (1)

yQt |µt ∼ indep N(µt , s

2Qt

)(2)

Region process model

µt ∼ indep N(z′tβ, σ

2η

)(3)

Diffuse prior distributions

I Data model parameters: Θd ≡(bkm∗, σ

2km∗)

I Process model parameters: Θp ≡(β, σ2

η

)


Bayesian Hierarchical Model–Speculative Region YieldLikelihood function–assuming conditional independence

[yO , yA, yQ |µt ,Θd ] =∏

k∈{O,A,Q}

[yk |µt ,Θd ] (4)

Posterior distribution

[µt ,Θd ,Θp|yO , yA, yQ ] ∝∏

k∈{O,A,Q}

[yk |µt ,Θd ][µ|Θp][Θd ][Θp] (5)

Full conditional of regional yield, µt

[µt |yO , yA, yQ ,Θd ,Θp] ∼ N

(∆2

∆1,

1

∆1

)(6)

∆1 =∑

k=O,A

1

σ2km∗ + s2ktm∗

+I{Q}s2Qt

+1

σ2η

(7)

∆2 =∑

k=O,A

yktm∗ − bkm∗σ2km∗ + s2ktm∗

+I{Q}yQt

s2Qt

+z′tβ

σ2η

(8)


Auxiliary InformationCovariates reflect conditions approaching active harvest dates

µtj = βj1 + βj2zj2 + βj3zj3 + βj5zj4 + βj5zj5

I State-specific constant

I zj2: Linear time trend

I zj3: Monthly precipitation(NOAA)

I zj4: Monthly avg. temperature(NOAA)

I zj5: Crop condition–% good +% excellent week # (NASS)

StartStart

StartStartStart

StartStartStart

StartStart

ActiveActive

ActiveActive

ActiveActiveActive

ActiveActive

Active

EndEnd

EndEnd

EndEnd

EndEnd

EndEnd

TexasOklahoma

MissouriIllinois

KansasColorado

OhioNebraska

WashingtonMontana

Jun 01 Jun 15 Jul 01 Jul 15 Aug 01 Aug 15 Sep 01

Usual Harvest Dates for Winter Wheat Speculative Region States


Comparing ASB Estimates and Model Outputs

State 1 State 2 State 3 State 4 State 5 State 6 State 7 State 8 State 9 State 10 Region

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May

June

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t

May

June

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t

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t

May

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July

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Yie

ld

Source●

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ASBModel95% CI.u95% CI.l

Year 2012 Comparisons: Published Yield and Model−based Yield Indications


Emphasis Placed on Each Information SourceWeighted average decomposition of forecasts by information source

µ̂tj ≈∑

k∈{O,A,Q,Covariates}

ck(SOURCE )k (9)

ck ∝ (variance)−1k

May June July August September

0.00

0.25

0.50

0.75

1.00

CO IL KS

MO

MT

NE

OH

OK TX

WA

SP

EC

CO IL KS

MO

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NE

OH

OK TX

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CO IL KS

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CO IL KS

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Unit

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ght Source

AYS Covariates OYS Sept. APS


Many Data Sources–Recurring Themes and Challenges1. NASS tradition–official statistic derived from many data

sources2. Pertains to all spatial scales, as well as livestock and economic

programs3. NASS is convening an expert panel through CNSTAT to

strengthen crop county estimates and cash rental rates

ChallengesI Timeliness and availability of auxiliary information by areal

unit, commodity, variables of interestI Coverage/accuracy of administrative or remote sensing dataI Benchmarking and variance estimationI DisclosureI Tight publication deadlines–simplicity preferred!I Impact on end users


Select References

Adrian, D. (2012). A model-based approach to forecasting corn and soybeanyields. Fourth International Conference on Establishment Surveys.

Cruze, N. B. (2015). Integrating survey data with auxiliary sources ofinformation to estimate crop yields. In JSM Proceedings, Survey ResearchMethods Section. Alexandria, VA: American Statistical Association.

Nandram, B., Berg, E., and Barboza, W. (2014). A hierarchical Bayesianmodel for forecasting state-level corn yield. Environmental and EcologicalStatistics, 21(3):507–530.

Wang, J. C., Holan, S. H., Nandram, B., Barboza, W., Toto, C., andAnderson, E. (2012). A Bayesian approach to estimating agricultural yieldbased on multiple repeated surveys. Journal of Agricultural, Biological, andEnvironmental Statistics, 17(1):84–106.


Thank you!Questions?

[email protected]


Documents

On Combining Multiple Sources of Information to …sites.nationalacademies.org/cs/groups/dbassesite/... · On Combining Multiple Sources of Information to ... 3.Model-based forecasts