How forecasting puzzles in West Africa drove me to writing ...€¦ · Bernhard Schauberger Growing...

Bernhard Schauberger

Growing global from Ghanaor

How forecasting puzzles in West Africa drove me to writing a review

[email protected]

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I will present two distinct topics around yield forecasting

Bernhard Schauberger: Ghana & Review

Estimating or forecasting maize yields in Ghana & B.Faso

A comprehensive review of methods for yield forecasting

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Estimating and forecasting maize yields in Ghana

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Crop modeling in Ghana relies on the following inputs


District-level crop yields from 1993 to 2017, provided by the Ministry of Agriculture in Ghana.

Yields

Weather

Planted area per crop, district and year => lowest 10% removedGrowing seasons from various resources (MIRCA2000, Ministry, etc.)

=> Northern savannah: May-September=> Rest of Ghana: April-August (major), Sept.-December (minor)

Further data

The task is to estimate maize yields in Ghana, at best even before harvest time.

Temperature and radiation, from ERA-Interim (0.5°)Precipitation from CHIRPS (0.08°)All mapped to districts

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Correlations between yield and weather are not at all obvious


Precipitation anomaly Temperature anomaly

Yie

ld a

no

mal

y

Plots for the northern part of Ghana (savannah) are shown.(In the south these look very similar.)

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I have applied several techniques to elicit weather influences on maize


Empirical model for whole Ghana is not very robust.Out-of-sample explained variance is only 26%.

Yield variation is, in general, very low.

I have tried...

...an empirical regression model (successfully applied in 30+ countries),

decision trees,

quantile regression,

random forests,

logistic regression,

Support Vector Machines,

and combinations of them.

None of them works satisfyingly.

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Decision trees offer the best estimation and forecast so far


Dry days inJuly

Precipitation anomaly Precipitation anomaly Precipitation anomaly

July mean temperature July mean temperature July mean temperature

PET inSeptember

more than average

less than average

lower than average

higher than average

Lo

ssN

o l

oss

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Maize yield failure in Ghana could be insured (but more work is required)


Thanks for great support by Abel Chemura and Christoph Gornott (both at PIK Potsdam)

Insurance tree for Northern Ghana

Next steps: include household-level data and remote sensing information (S2A, Landsat8)

Error rates are high ~50%

Observed Predicted FALSE TRUE FALSE 270 79 TRUE 81 83

CDD 7

PET 9

No payout(probability for losses is low)

less than average

more than average

higher than average

less than average

Total precipitation

less than average

higher than average

No payoutPayout

Minimum Temp. July

less than average

higher than average

No payoutPayout

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Estimating and forecasting maize yields in Burkina Faso

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An empirical model for maize in Burkina Faso seems robust


Out-of-sample performance is acceptable even before harvest:R2

OOS = 0.45 (-1 month) R2

OOS = 0.23 (-2 months)

Observed yieldsEmpirical model Out-of-sample model

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A (preliminary) binomial logit model to predict losses seems trustworthy


The figure shows ROC curves for different input months into a logistic regression.

Already in July losses (20% lowest yields) are foreseeable.

Exogenous variables comprise only weather (precipitation, temperature, PET, SPEI).

Inclusion of remote sensing and household data is esteemed to increase accuracy.

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A comprehensive review of methods for yield forecasting

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A comprehensive search for articles has been performed


TOPIC = (crop AND agric*) OR (crop AND yield)TITLE = (harvest OR yield OR production OR insur* OR food OR "early warning") AND (forecast OR estimat* OR predict* OR outlook OR pre*harvest OR monitor*)YEAR = 2004+ This resulted in 1,118 papers (on Nov 06, 2018).

Search terms for Web of Science

● only true forecasts during the season● out-of-sample assessment provided● no simulated crops as reference● in English● food crops● no greenhouse studies (except for e.g. tomatoes)

Filter criteria

Articles are manually scanned for 24 variables (crop, country, model type, inputs, performance, lead time, etc.).

Article processing

This results in around 500 relevant articles. Of these, 60 have already been processed.

The task is to create an overview about methods applied for yield forecasting.

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Literature on yield forecasting is biased in crops and regions


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Methods applied and inputs used seem not balanced either


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Data sources are mostly analyzed separately


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There will be several lessons to learn from this review


Thanks for the great support by Jonas Jägermeyr (U Chicago) and Christoph Gornott (PIK).

There is diversity in methods, crops & countries, but with a bias.

There are well-performing methods for many different settings. These could be combined in a large framework or used for operational forecasting schemes.

The majority of studies does not perform an out-of-sample assessment, which leaves their true performance unknown.

A merging of remote sensing and weather data seems to deliver the largest accuracy, but is not often done. Additional information about soil adds quality.

The terms forecast, prediction, projection, prediction, outlook or prognosis are often used differently.

There is no one-fits-all technique that can be applied for each crop and each region.

Since management has a major influence on yield variation, it would be worthwhile to forecast management.

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do you have any questions?

[email protected]@lsce.ipsl.fr

if not, can you help me in Ghana?

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backup slides

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The empirical model is an OLS regression function


Exogenous variables: PET, precipitation, KDD, dry daysAll are split between vegetative and reproductive season.For more details: Schauberger et al., Global Change Biology (June 2017)

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There is some variation in maize yields in Ghana between regions


How forecasting puzzles in West Africa drove me to writing ...€¦ · Bernhard Schauberger Growing...

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