Forecast Report with Analog Years-Malawi

Introduction

This is the Malawi February Maize forecast report. The goal is to assist users in evaluating current forecasts in the context of past forecasts and to translate the forecast component into key assumptions about food security.

The report includes the following key components:

1. A verbal summary of assumptions based on the statistics in this forecast

2. Mean Area, Production, and Yield over the years 2007 to 2017:This provides context for interpreting the grain data

3. Historical Out of Sample Forecast Error Averaged Over a 10 Year+ Period. We show the Mean Absolute Percent Error (MAPE).

4. Yield Forecast, (based on February 2022 Precip, NDVI, Et0 ) expressed as percent of mean yield over the period 2007 - 2017.

5. Forecasts and Forecast Error in Analog Years We show specific forecasts and forecast error for years were climatologically similar to those in this report.

Assumption Statements [NOT YET UPDATED FOR THIS REPORT]

Average Forecast Error For This Point in the Season

At this point in the season historical forecast error, is on average, below 50% for 46 out of the 46 admin units used in the forecast.

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Summary Figures

Mean area, production, and yields for the years 2007 to 2017.

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Out of Sample Forecast Error (MAPE)

Mean Absolute Percent Error (MAPE) calculated based on historical out of sample seasonal forecasts. Lower scores indicate greater accuracy. Forecasts are based on model type MODEL¹

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Yield Forecast for February 2022

Forecast values expressed as percent of Mean Yields over the Years 2007 - 2017.

The figure shows predicted percent of mean (center) as well as lower (left) and higher (right) predicted percent of mean intervals.

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Static and Dynamic Version of Main Forecast

Roll over the polygon borders to get the district name and % of mean forecast value.

Static Version

This map shows the main % of mean forecast value along with district labels for reference.

Discrete Map

This map shows the main % of mean forecast binned into discrete values. Averages are based on the most recent 10 year period of observed yields: 2007 - 2017

This table shows the forecast percentage of mean values in the above table along with the mean yield values from the first figure.

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Table of Mean Yields and Predicted Percent of Mean Values

District	% of mean	% of mean (low)	% of mean (high)
Chitipa	1.08	0.98	1.18
Karonga	1.25	1.04	1.46
Nkhata Bay	1.33	1.16	1.50
Rumphi	1.06	0.95	1.17
Mzimba	1.11	1.04	1.18
Kasungu	0.69	0.59	0.79
Nkhotakota	0.68	0.58	0.78
Ntchisi	0.98	0.84	1.13
Dowa	0.88	0.65	1.12
Salima	0.88	0.77	0.99
Mchinji	0.78	0.67	0.90
Dedza	0.98	0.85	1.11
Ntcheu	0.93	0.83	1.03
Mangochi	1.43	0.84	2.02
Machinga	0.43	0.23	0.63
Zomba	0.93	0.76	1.09
Chiradzulu	0.69	0.54	0.83
Blantyre	0.63	0.42	0.83
Mwanza	0.58	0.25	0.92
Thyolo	0.73	0.53	0.94
Mulanje	1.14	0.91	1.37
Phalombe	0.51	0.39	0.63
Chikwawa	1.09	0.49	1.69
Balaka	1.32	1.11	1.53
Neno	0.07	0.00	0.19

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Analog Year Forecasts

Yield forecasts in analog years. <–DESCRIPTION OF ANALOG YEAR PROCESS–>.
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Analog Year Forecasts Errors

Forecast errors in analog years. If observed data is not available in a given year we cannot calculate forecast errors. Values are expressed a percentage of observed yields in a given year (t):

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\[\frac{(observed_{(t)}-forecast_{(t)})}{observed_{(t)}}\] ***

Positive (+) values indicate an under prediction. Negative (-) values indicate an over prediction.

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1. Extra/Extended Trees. A type of Random Forest Model↩︎