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

schauber@pik-potsdam.de

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

Estimating and forecasting maize yields in Ghana

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

Correlations between yield and weather are not at all obvious

Precipitation anomaly Temperature anomaly

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

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.

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

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

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

Estimating and forecasting maize yields in Burkina Faso

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

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.

A comprehensive review of methods for yield forecasting

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.

Literature on yield forecasting is biased in crops and regions

Methods applied and inputs used seem not balanced either

Data sources are mostly analyzed separately

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.

do you have any questions?

schauber@pik-potsdam.debernhard.schauberger@lsce.ipsl.fr

if not, can you help me in Ghana?

backup slides

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)

There is some variation in maize yields in Ghana between regions

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

Documents

300 Classic 9×9 Sudoku puzzles300 Classic 9×9 Sudoku puzzles This document includes: 60 Easiest Puzzles 60 Easy Puzzles 60 Hard Puzzles 60 Expert Puzzles 60 Extreme Puzzles Sudoku

Puzzles pitch

Cockeram Drove Batcombe

FROM SPARKS TO FIREWORKS OF CREATIVITY ......Tongue twisters Crosswords/Anagrams Word puzzles Reading puzzles Writing puzzles Logical problem solving Computer games Number puzzles

Tan a Me Drove

Mathematical Puzzles

300 Jigsaw Sudoku puzzles - Web Newdoku - Free …300 Jigsaw Sudoku puzzles This document includes following content: 12 3×3 puzzles 48 4×4 puzzles 48 5×5 puzzles 48 6×6 puzzles

challenging puzzles

400 Puzzles

IQ Puzzles

Puzzles 101

50 Puzzles

Maths Puzzles

Math puzzles

Java puzzles

Techniques for Solving Logic Puzzles. Logic Puzzles Logic puzzles operate using deductive logic. Logic puzzles operate using deductive logic. A well-designed

Canterbury Puzzles

ROSE COTTAGE 164 The Drove, Barroway Drove, Downham … · ROSE COTTAGE 164 The Drove, Barroway Drove, Downham Market, Norfolk PE38 0AL Three Bedroom Detached Bungalow Approx. Six

Ai Puzzles

Tangrams Puzzles