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Bernhard Schauberger
Growing global from Ghanaor
How forecasting puzzles in West Africa drove me to writing a review
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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
Bernhard Schauberger: Ghana & Review
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
Bernhard Schauberger: Ghana & Review
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
Bernhard Schauberger: Ghana & Review
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
Bernhard Schauberger: Ghana & Review
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)
Bernhard Schauberger: Ghana & Review
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
Bernhard Schauberger: Ghana & Review
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
Bernhard Schauberger: Ghana & Review
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
Bernhard Schauberger: Ghana & Review
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
Bernhard Schauberger: Ghana & Review
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Methods applied and inputs used seem not balanced either
Bernhard Schauberger: Ghana & Review
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Data sources are mostly analyzed separately
Bernhard Schauberger: Ghana & Review
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There will be several lessons to learn from this review
Bernhard Schauberger: Ghana & 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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backup slides
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The empirical model is an OLS regression function
Bernhard Schauberger: Ghana & Review
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
Bernhard Schauberger: Ghana & Review