Introduction

This is the Somalia December Sorghum 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 2012 to 2022: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 December 2021 Precip, NDVI, Et0 ) expressed as percent of mean yield over the period 2012 - 2022.

  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 OND 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.



Summary Figures

Mean area, production, and yields for the years 2012 to 2022.



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



Yield Forecast for December 2021

Forecast values expressed as percent of Mean Yields over the Years 2012 - 2022.

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



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: 2012 - 2022

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


Table of Mean Yields and Predicted Percent of Mean Values
District % of mean % of mean (low) % of mean (high)
Hargeysa 1.29 0.88 1.70
Gabiley 1.29 0.95 1.63
Marka 0.79 0.50 1.08
Afgooye 1.12 0.79 1.46
Qoryooley 1.08 0.89 1.27
Wanla Weyn 1.15 0.94 1.37
Baydhabo 0.89 0.65 1.12
Buur Hakaba 0.95 0.58 1.32
Diinsoor 0.82 0.53 1.11
Qansax Dheere 0.66 0.32 1.00
Tiyeglow 1.22 0.83 1.61
Waajid 0.79 0.33 1.25
Garbahaarey 1.30 1.22 1.38
Baardheere 0.79 0.44 1.14
Bu'aale 0.35 0.00 0.70
Saakow 0.61 0.22 1.01
Afmadow 0.71 0.54 0.88

Analog Year Forecasts

Yield forecasts in analog years. <–DESCRIPTION OF ANALOG YEAR PROCESS–>.
***


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):


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

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



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

---
title: "Forecast Report with Analog Years-Somalia OND"
output:
  html_notebook:
    toc: yes
fig_width: 7
fig_height: 6
fig_caption: true
---

```{r,eval=TRUE,echo=FALSE,warning=FALSE,message=FALSE,results='hide'}
#-------------------Base Setup--------------------------------------------------
rm(list=ls())

#---Set Project Directories
dirBase<-'/Volumes/GoogleDrive/My Drive/'

dirBase2<-'/Volumes/GoogleDrive/Shared drives/CHC Team Drive /'

#-Project Directories
dirProj<-paste0(dirBase2,'project_machine_learning_forecasting/') #project directory

dirViewer<-paste0(dirProj,'viewer/')
dirViewerOutStatic<-paste0(dirViewer,'viewer_static_shapes/')
dirViewerDynamic<-paste0(dirViewer,'viewer_dynamic_shapes/')

dirReport<-paste0(dirProj,'forecast_reporting/')
dirReportRdata<-paste0(dirReport,'forecast_reporting_Rdata/')

library(stringr)
library(ggplot2)
library(dplyr)
library(raster)
library(rgdal)
library(mgcv)
library(tidyr)
library(lubridate)
library(sf)
library(rmapshaper)
library(viridis)
library(scales)
library(plotly)
library(forcats)
library(knitr)
library(kableExtra)
library(shiny)
#========================================================================================

#Parameters
CURRENT_YEAR<-2021
MONTH<-12
DEKAD<-3
MODEL<-'ET'
COUNTRY<-'Somalia'
PRODUCT<-'Sorghum'
#CROP_AREA<-2  #Percent
ANALOG_YEARS<-c(1999,2000,2001,2008,2009,2011,2012,2017)

month_name<-month.name[MONTH] #month the product is based on

#--Load Existing Plots
setwd(dirReportRdata)
load(file=paste0('20_forecast_reporting_main_plots',month_name,COUNTRY,'_',PRODUCT,'.Rdata'))
load(file=paste0('01_forecast_report_agstatmaps_',COUNTRY,'_',PRODUCT,'.Rdata'))
```
# Introduction
This is the `r COUNTRY` `r month_name` `r PRODUCT` 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 `r lis_vars_report$min_ag` to `r lis_vars_report$max_ag`:***This provides context for interpreting the grain data

3. ***Historical Out of Sample Forecast Error Averaged Over a 10 Year+ Period***. We show the ***M***ean ***A***bsolute ***P***ercent ***E***rror (***MAPE***).

4. ***Yield Forecast***, (based on `r lis_vars_report$month_name` `r lis_vars_report$max_evar_year` `r lis_vars_report$var_name` ) expressed as **percent of mean yield** over the period `r lis_vars_report$min_ag` - `r lis_vars_report$max_ag`.

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 OND 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.

***



***

# Summary Figures

#### Mean area, production, and yields for the years `r lis_vars_report$min_ag` to `r lis_vars_report$max_ag`.

***
```{r,echo=FALSE,warning=FALSE,message=FALSE}
p1all
```
***





#### 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 `MODEL`^[Extra/Extended Trees. A type of Random Forest Model]

***
```{r,echo=FALSE,warning=FALSE,message=FALSE}
p2
```
***


# Yield Forecast for `r lis_vars_report$month_name` `r lis_vars_report$max_evar_year` 
Forecast values expressed as percent of Mean Yields over the Years `r lis_vars_report$min_ag` - `r lis_vars_report$max_ag`. 

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

***
```{r,echo=FALSE,warning=FALSE,message=FALSE,fig.width=8,fig.height=8}
#p3<-ggplotly(p=p3,tooltip=c('district','per_of_mean'))
p1
```
***

#### Static and Dynamic Version of Main Forecast
Roll over the polygon borders to get the district name and % of mean forecast value.

```{r,echo=FALSE,warning=FALSE,message=FALSE,fig.align='center',fig.width=8,fig.height=8}
#p1Ls
p1L<-ggplotly(p1L,tooltip=c('admin2','value')) %>% layout(legend = list(orientation = "h", x = 0.4, y = -0.2))
p1L
```
#### Static Version
This map shows the main % of mean forecast value along with district labels for reference.
```{r,echo=FALSE,warning=FALSE,message=FALSE,fig.align='center',fig.width=8,fig.height=8}
p1Ls

```
#### 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: `r lis_vars_report$min_ag` - `r lis_vars_report$max_ag`
```{r,echo=FALSE,warning=FALSE,message=FALSE,fig.align='center',fig.width=8,fig.height=8}
p1Lc

```

#### This table shows the forecast percentage of mean values in the above table along with the mean yield values from the first figure. 
***
```{r,echo=FALSE,warning=FALSE,message=FALSE}
t1<-knitr::kable(dtab, caption = 'Table of Mean Yields and Predicted Percent of Mean Values')
t1<-kable_styling(t1,bootstrap_options = c("striped", "hover","condensed"))
scroll_box(t1, height = '300px', width = '100%',
  box_css = "border: 1px solid #ddd; padding: 1px; ", extra_css = NULL,
  fixed_thead = TRUE)


```
***

# Analog Year Forecasts
Yield forecasts in analog years. <--DESCRIPTION OF ANALOG YEAR PROCESS-->.  
***
```{r,echo=FALSE,warning=FALSE,message=FALSE,fig.width=7.5,fig.height=7.5}
p3
```
***

# 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)_:

***
$$\frac{(observed_{(t)}-forecast_{(t)})}{observed_{(t)}}$$
***

***Positive (+)*** values indicate an _under prediction_. ***Negative (-)*** values indicate an _over prediction_.

***
```{r,echo=FALSE,warning=FALSE,message=FALSE,fig.width=7.5,fig.height=7.5}
p4
```