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Chapter_13_PICSA__Long_Before_the_season.qmd
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Chapter_13_PICSA__Long_Before_the_season.qmd
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# PICSA -- Long Before the season
## Introduction
PICSA (Participatory Integrated Climate Services for Agriculture) is an
initiative to share climate information with small-scale farmers. It was
described briefly in Chapter 1 and Fig. 12.1a is a repeat of Fig. 1.6a
to show the different stages of the PICSA activity.
-----------------------------------------------------------------------
***Fig. 12.1a Stages of the PICSA project***
-----------------------------------------------------------------------
{width="4.880428696412948in"
height="3.5892180664916884in"}
-----------------------------------------------------------------------
PICSA has been used in many countries in Africa and beyond, including
Tanzania, Malawi, Lesotho, Ghana, Guyana, Rwanda, Haiti and Bangladesh.
At first glance it may seem like many other initiatives to share climate
information with farmers. It has some distinguishing features, that
explain its inclusion in this guide.
The first distinguishing feature is the large set of activities that are
undertaken prior to the availability of the seasonal forecast. They
build on the information from the historical climatic data. There is a
detailed instruction guide, with 12 sections. The first 7 are shown in
Fig. 12.1b and take place in the "Long-before-the- season", step shown
in Fig. 12.1a.
-----------------------------------------------------------------------
***Fig. 12.1b***
-----------------------------------------------------------------------
{width="5.551943350831146in"
height="3.387387357830271in"}
-----------------------------------------------------------------------
The second distinguishing feature is that PICSA supports farmer's
activities in relation to crops, livestock or other livelihood
activities. It identifies options and is not directed towards any
particular crop or activity. This is indicated in Step d) of Fig. 12.1b.
Third is the emphasis on "options by context" that places the farmer
(and not the "expert") at the centre. Thus, PICSA does not make
recommendations for the farmers. Instead it offers options, with the
idea, shown in steps d and e, in Fig. 12.1b, that the farmers, or
households, may wish to select those that particularly fit their
circumstances. Fig. 12.1c shows an example of options related to
livelihood activities.
---------------------------------------------------------------------------------------------------------------
***Fig. 12.1c*** ***Fig. 12.1d***
------------------------------------------------------- -------------------------------------------------------
{width="3.363928258967629in" {width="2.6359897200349955in"
height="2.814714566929134in"} height="2.800184820647419in"}
---------------------------------------------------------------------------------------------------------------
These options are not pre-selected but are constructed through dialogues
between the farmers and extension workers. Farmers evaluate options via
a set of participatory exercises. One is a resource allocation map
(RAM), step a) in Fig. 12.1b, an example of which is in Fig. 12.1d.
Further information on PICSA is available via the website
[[https://research.reading.ac.uk/picsa/]{.underline}](https://research.reading.ac.uk/picsa/),
Fig. 12.1e. Information includes the field manual, from which Fig. 12.1b
shows the first 7 sections. This is currently available in 4 languages,
namely English, Bengali, French and Spanish.
The idea, in PICSA, is that by step g, in Fig. 12.1b, the farmer has
provisional plans for the season. These could apply to any season. These
plans may then be modified by the extra information for this season,
from the seasonal forecast, discussed in Chapter 13.
-----------------------------------------------------------------------
***Fig. 12.1e The PICSA webpage***
-----------------------------------------------------------------------
{width="6.078515966754156in"
height="2.4789512248468943in"}
-----------------------------------------------------------------------
In this chapter we mainly consider Steps b and c from Fig. 12.1b. This
is a discussion of the historical temperature and rainfall data, both in
relation to climate change (Step b) and then to consider the risks from
different options (Step c).
## Climate change and variability
In PICSA it has been useful for farmers to examine and use the
historical graphs themselves, e.g. Fig. 12.2a. Some have had little or
no formal education, but almost all have been able to follow and
interpret the ideas of these time series graphs.
-----------------------------------------------------------------------
***Fig. 12.2a***
-----------------------------------------------------------------------
{width="5.957272528433946in"
height="3.622808398950131in"}
-----------------------------------------------------------------------
Step b, in Fig. 12.1b is one of comparing farmers (and extension
workers) perceptions of climate change with the evidence from the
historical climatic records.
Many farmers, NGO and extension staff already have strong views on
climate change. However, most have never seen any of the time series
graphs of the type shown in Fig. 12.2b and Fig. 12.2c.
Temperatures usually show a clear trend, illustrated by Tmin in Fig.
12.2b. For rainfall the message is usually one of variability, Fig.
12.2c, rather than trend, being the main concern. This is a surprise for
some, who interprete climate change as implying a change in the pattern
of rainfall. This is partly because rainfall is by far the most
important climatic element for tropical agriculture.
----------------------------------------------------------------------------------------------------------
***Fig. 12.2b Tmax and Tmin, Dodoma*** ***Fig. 12.2c Total annual rainfall, Dodoma***
---------------------------------------------------- -----------------------------------------------------
{width="2.729400699912511in" {width="3.0078958880139983in"
height="3.16913823272091in"} height="3.004401793525809in"}
----------------------------------------------------------------------------------------------------------
Of course, if there is a trend in temperatures, then this ***is***
climate change. The climatic elements are interlinked, hence there must
be a corresponding change in the other elements, including rainfall.
However, rainfall is so variable from year to year, that any change is
often not detectable. In addition, unlike temperatures, that are rising,
the changes in the patterns of rainfall will not be so simple -some
places will become wetter and others dryer.
Hence, in most sites where PICSA has been used, many of the activities,
i.e. the options for farming households, are designed to manage the
rainfall risks, (i.e. variability), rather than change.
There is an important corollary to this idea. It is easy to blame
climate change "on the West" and hence assume it is a problem for others
to solve. But rainfall variability, as shown in Fig. 12.2c, is a problem
faced locally by successive generations. Hence discussing options to
manage the risks is sensible for individual farmers to consider.
This type of discussion, within PICSA, is constructive for both the
intermediaries (NGO and extension staff) and farmers in encouraging an
openness to consider changes in their activities, i.e. to consider what
options might be useful to manage the (rainfall) risks. This idea is
well phrased in the ICRISAT study, (Cooper, et al., 2008). They claim
that managing the current climate risks has a double benefit. It is
useful itself, as well as preparing farmers for future climate change.
An initial one-week workshop is often used to introduce PICSA in a new
country, or in a new district, within a country. The graphs, such as
Fig. 12.2b and Fig. 12.2cc are usually part of the materials from the
first day.
## Producing the initial graphs - no data issues
Currently a key input in PICSA is a series of time-series graphs on
aspects of the rainfall that are of direct interest, and support farmers
in their choice of options. They don't just look at the graphs, but also
use them, as shown in Fig 12.2a, to calculate risks for themselves.
For many farmers, also for intermediaries, this is the first time they
have seen this type of time-series graph. Hence, as mentioned above, it
usually serves two purposes. The first is as a practical demonstration,
that (for the rainfall) the main issue is one of variability, rather
than change. Hence "the ball is in their court" to manage their climatic
risks, rather than being part of the general topic of climate change.
The second is as a tool to calculate the risks for alternative options.
These graphs are usually prepared by staff from the corresponding NMS.
Currently the work is often by NMS headquarters staff, but perhaps LMS
staff based locally may be able to do some of this work in the future.
The production is simple when there are no "data issues" and is
described in this section.
A checklist may useful, and an initial version is in Table 12.3a. Start
with this list, and then edit to produce your own.
---------------------------------------------------------------------------------
***Table 12.3a
Initial
checklist***
-------------- ----------------------------------- ------------------------------
***Step*** ***Action*** ***R-Instat Dialogue***
1 Read the data into R-Instat File \> Open from File
2 Check the data as input Climatic \> Tidy and Examine
\>\
One Variable Summarise
3 Make a date variable Climatic \> Dates \> Make Date
4 Infill if dates are absent Climatic \> Dates \> Infill
5 Make further date variables Climatic \> Dates \> Use Date
(possibly shifted)
6 Probably delete the initial year, Right-click, then Delete and
month, day variables, plus further Reorder variables
"housekeeping"
7 Define the data as climatic Climatic \> Define Climatic
Data
8 Omitting Check Data, because data Could add:\
are ok! Climatic \> Check Data \>
Inventory,\
Climatic \> Check Data \>
Display Daily,\
Climatic \> Check Data \>
Boxplot, etc.
9 Add rain-day variable Climatic \> Prepare \>
Transform
10 Save data as a R-file File \> Save As
11 Get annual/seasonal rainfall and Climatic \> Prepare \>
rain day totals Climatic Summaries
12 Get annual/seasonal mean Climatic \> Prepare \>
temperatures Climatic Summaries
13 Graph the max and min temperature Describe \> Specific \> Line
Plot
14 Graph the rainfall and rain day Climatic \> PICSA \> Rainfall
totals graphs
---------------------------------------------------------------------------------
This checklist involves largely going systematically down R-Instat's
climatic menu, shown in Fig. 12.3a, from ***Tidy and Examine***, to the
***PICSA menu*** for the graphs of the annual summaries.
The data used for illustration are from a single station, Dodoma, in
Tanzania, but the checklist works equally well with data from multiple
stations. If the data file only has rainfall, then omit steps 12 and 13.
The data from Tanzania were supplied as an Excel file as shown in Fig.
12.3b. This was exported from Clidata (Tolatz, 2019) and is in the
"right shape" for R-Instat, i.e. each row of data is for one day and the
four elements are in successive columns.
---------------------------------------------------------------------------------------------------------------
***Fig. 12.3a*** ***Fig. 12.3b***
------------------------------------------------------- -------------------------------------------------------
{width="2.1945570866141733in" {width="3.8149748468941382in"
height="2.2015015310586175in"} height="2.8353357392825895in"}
---------------------------------------------------------------------------------------------------------------
Use ***File \> Open from File*** to input your data.
To practice with these data, use ***Open from Library \> Instat \>
Browse \> Climatic \>Tanzania*** and open the ***Dodoma18.xlsx*** file,
Fig. 12.3c.
We first make a deliberate mistake. If you are following the exercise,
then we strongly recommend that you make this mistake also. It is very
common!
---------------------------------------------------------------------------------------------------------------
***Fig. 12.3c Importing from Excel*** ***Fig. 12.3d Data imported incorrectly***
------------------------------------------------------- -------------------------------------------------------
{width="3.363928258967629in" {width="2.6359897200349955in"
height="2.814714566929134in"} height="2.800184820647419in"}
---------------------------------------------------------------------------------------------------------------
In Fig. 12.3c the data frame preview indicates something is wrong,
because there are m values present. This is not always so obvious,
because only 10 lines are shown, which may not include missing values.
Press ***Ok*** to import that data. The problem is now shown, in
R-Instat, by the (c) after SUNHRS(c) and TMPMIN(c) and TMPMAX(c). These
variables are numeric but have been imported as character (text)
variables, because there are some non-numeric characters in these
columns.
You can correct this problem in R-Instat, but it is simpler to correct
when you import the data, or in Excel. Here it is easy to correct when
the data are imported.
So, use ***File \> Close Data File***[^49]. Then recall the last
dialogue to give Fig. 12.3c again and insert ***m*** as the ***Missing
Value String***, Fig. 12.3c. The preview changes to show NA instead of
m. Press ***Ok*** and the variables are imported correctly.
Use the climatic menu, Fig. 12.3e and item 2 in the checklist, see Table
12.3a. Start with the ***Tidy and Examine*** menu, Fig. 12.3e. The data
here are already tidy, hence move straight to the ***One Variable
Summarise Dialogue***. If your data are in a "different shape" some of
the other dialogues in this menu may be needed.
---------------------------------------------------------------------------------------------------------------
***Fig. 12.3e The Tidy and Examine Menu*** ***Fig. 12.3f One Variable Summarise***
------------------------------------------------------- -------------------------------------------------------
{width="2.9702985564304463in" {width="2.7105074365704285in"
height="2.4269542869641296in"} height="2.8048304899387575in"}
---------------------------------------------------------------------------------------------------------------
The results are in Fig. 12.3g. They are promising, because of the
following:
a) There are no missing values in the Year, Month, Day variables. It
would be a problem if there were.
b) Amazingly the rainfall variable is also complete. This is great, but
rare. There are missing values in the other elements, partly because
they started later than the rainfall.
c) However, the Station name was imported as a character, and not a
factor variable.
--------------------------------------------------------------------------------------------------------------
***Fig. 12.3g Results from Summary -- Step 2 in ***Fig. 12.3h Name as Factor***
checklist***
------------------------------------------------------ -------------------------------------------------------
{width="4.383143044619422in" {width="1.6977471566054243in"
height="2.081823053368329in"} height="2.714501312335958in"}
--------------------------------------------------------------------------------------------------------------
The Station name is not a problem here, because there is only one
station. However, it is still made into a Factor variable for
completeness, Fig. 12.3h. This is important when there are multiple
stations in the same file.
---------------------------------------------------------------------------------------------------------------
***Fig. 12.3i*** ***Fig. 12.3j***
------------------------------------------------------- -------------------------------------------------------
{width="2.7324212598425195in" {width="3.1415496500437445in"
height="1.6530107174103237in"} height="3.01915135608049in"}
---------------------------------------------------------------------------------------------------------------
The ***Climatic \> Date*** menu, Fig. 12.3i is used for Steps 3 to 5 in
the checklist, Table 12.3a. First calculate a single Date variable, i.e.
a Variable of Type (D). Here it is calculated from the 3 variables,
giving the YEAR, MONTH and DAY as shown in Fig. 12.3j.
---------------------------------------------------------------------------------------------------------------
***Fig. 12.3k Infilling where dates are omitted*** ***Fig. 12.3l Adding variables from the date***
------------------------------------------------------- -------------------------------------------------------
{width="3.102471566054243in" {width="2.859963910761155in"
height="2.9833847331583554in"} height="4.37336176727909in"}
---------------------------------------------------------------------------------------------------------------
Next -- Step 4 - is to check for any gaps in the data, using ***Climatic
\> Dates \> Infill Missing Dates***, Fig. 12.3k. They are absent dates
in the file, for example a year being absent. This is separate from the
dates being complete, but with missing values in the data.
In this case the result was that there was nothing to infill. Proceed to
Step 5 with ***Climatic \> Dates \> Use Date***, Fig. 12.3l. Dodoma is
in the southern hemisphere, with a single rainy season from November to
April. Hence, in Fig. 12.3l, the year is shifted to start in July. Five
variables are generated, as shown in Fig. 12.3l.
Now a little "housekeeping", Step 6 in the checklist. ***Right-click***
and ***delete*** the 3 original YEAR, MONTH DAY variables and re-order
the remaining variables, Fig. 12.3m, so the measurements are last. This
is a convenient, and not an essential step.
--------------------------------------------------------------------------------------------------------------
***Fig. 12.3m*** ***Fig. 12.3n***
------------------------------------------------------ -------------------------------------------------------
{width="2.459254155730534in" {width="3.5132097550306214in"
height="1.949679571303587in"} height="2.354465223097113in"}
--------------------------------------------------------------------------------------------------------------
The data are shown in Fig. 12.3n. They are now ready for Step 7, which
is to define the data as climatic, as shown in Fig. 12.3o.
--------------------------------------------------------------------------------------------------------------
***Fig. 12.3o Defining a data frame as climatic*** ***Fig. 12.3p The Check Data Menu***
------------------------------------------------------ -------------------------------------------------------
{width="3.073804680664917in" {width="2.909319772528434in"
height="3.739970472440945in"} height="1.7850229658792651in"}
--------------------------------------------------------------------------------------------------------------
The ***Climatic \>*** ***Define Climatic Data*** dialogue should largely
be filled automatically. Check carefully this it has the variables you
will be using in the future analyses. Check, especially, the variable
for the Station names and those for the dates. Then ***press the Check
Unique button***.
In this check, green is a good colour as is shown in Fig. 12.3o. This
check verifies that the combination of Station name and Date can become
key fields in this data frame. If not, then it is likely that you have
some duplicates in the data. Duplicates are days (rows) in the file
where a date has been given twice. Then you need to return to the
***Climatic \> Tidy and Explore*** menu as we discuss in Section 12.6.
In the checklist, Table 12.3a you now usually move to the ***Climatic \>
Check Data*** menu, Fig 12.3p. This is described in detail in Chapter 5,
and we return to this menu in Section 12.6. It is omitted here to
proceed quickly to the production of the variables and graphs needed for
PICSA.
-------------------------------------------------------------------------------------------------------------
***Fig. 12.3q*** ***Fig. 12.3r***
------------------------------------------------------- -----------------------------------------------------
{width="3.1966568241469817in" {width="2.34839457567804in"
height="2.9785728346456692in"} height="3.0432392825896764in"}
-------------------------------------------------------------------------------------------------------------
Hence, move to Step 9 in the checklist, i.e. the ***Climatic \>
Prepare*** menu, Fig. 12.3q. The ***Climatic \> Prepare \> Transform***
dialogue, Fig. 12.3r is used first as explained below.
Complete Fig. 12.3r as shown and press ***Ok***. This produces a new
variable, called ***rainday***, that takes the value 1 when it is rainy
-- defined here as a day with more than 0.85mm. It is zero otherwise, as
shown in Fig. 12.3s. This is used for graphs of the number of rain days.
-------------------------------------------------------------------------------------------------------------
***Fig. 12.3s Saving the data*** ***Fig. 12.3t Save the RDS file***
------------------------------------------------------- -----------------------------------------------------
{width="2.8975043744531934in" {width="3.153951224846894in"
height="1.8040365266841645in"} height="0.9491251093613299in"}
-------------------------------------------------------------------------------------------------------------
These data are now saved as an R file, i.e. with an RDS extension. Use
***File \> Save As \> Save Data As***, Fig. 12.3s to give the dialogue
shown in Fig. 12.3t. ***Browse*** to where you want to save the data.
Once you click ***Save*** on that dialogue, you return automatically to
Fig. 12.3t and ***click Ok*** to make the Save.
Once the data are saved, then re-open these saved data, to continue the
work on a future occasion. The first 10 steps do not have to be repeated
These steps, so far, have been described in detail. In practice, once
they become routine, they typically take 5 minutes or less. If problems
are found during the process, then we strongly recommend you consider
making corrections in the database, or (less comfortably) in an Excel
file, and then start the checklist again.
If you are following these steps with the Dodoma data, then it is time
to substitute a further data set. The data used, so far, was of very
good quality, but there were still some issues, that we discuss in
Section 12.6. Hence use ***File \> Open From Library \> Instat \> Browse
\> Climatic \> Tanzania*** again and choose the file called
***Dodoma18c***. The data are the same, except 2 more variables are
added, with the corrected temperature data. These new variables are
called Tmax and Tmin.
-------------------------------------------------------------------------------------------------------------
***Fig. 12.3u Rainfall and temperature summaries*** ***Fig. 12.3v Day range sub-dialogue***
------------------------------------------------------- -----------------------------------------------------
{width="3.0173523622047242in" {width="2.9682020997375327in"
height="3.719714566929134in"} height="2.7967705599300086in"}
-------------------------------------------------------------------------------------------------------------
The next steps, 11 and 12 in the checklist, both use the ***Climatic \>
Prepare \> Climatic Summaries*** dialogue, Fig. 12.3u
Two important decisions are i) whether the summaries are to be for the
whole (shifted) year, or perhaps just for the rainy season? Then ii) how
you will handle missing values in the data.
In Fig. 12.3u we choose to get all summaries for the ***rain variable***
for the 6 months of the rainy season, from November to April. Hence
click on the ***Day Range*** button in Fig. 12.3u.
In the sub-dialogue in Fig. 12.3v, choose the range to be ***November
1st to April 30th***. After pressing ***Return*** you see that the day
range is now 6 months, from the shifted day number 124 to day 305, i.e.
about 182 days.
Now, in Fig. 12.3u, check the ***Omit Missing Values checkbox***. Then
click on the ***Summaries button***, and then on the ***Missing Tab***,
Fig. 12.3w. Set it, as shown in Fig. 12.3w, to about 160 days. This
permits a few missing values, but not a complete month missing.
Then, in the same sub-dialogue, click on the ***Summaries tab*** and
just have the ***N-Non Missing*** and the ***Sum*** checked, as shown in
Fig. 12.3x.
--------------------------------------------------------------------------------------------------------------
***Fig. 12.3w Missing values tab*** ***Fig. 12.3x Summaries calculated***
------------------------------------------------------ -------------------------------------------------------
{width="2.949481627296588in" {width="3.1188287401574804in"
height="1.9575754593175854in"} height="2.7169477252843395in"}
--------------------------------------------------------------------------------------------------------------
Pressing Ok in the dialogue in Fig. 12.3u results in a new data frame
with 4 variables and 85 rows, Fig. 12.3y, because there are 85 years
(seasons) of data. The summary for the first year (1934/35 season) is
missing. This may come as a slight surprise, because there are no
missing values in the rainfall data. However, the record starts on 1
January 1935, which therefore does not have November or December of the
1934/35 season.
--------------------------------------------------------------------------------------------------------------
***Fig. 12.3y*** ***Fig. 12.3z***
------------------------------------------------------ -------------------------------------------------------
{width="1.984459755030621in" {width="3.9305555555555554in"
height="3.0939971566054245in"} height="3.126576990376203in"}
--------------------------------------------------------------------------------------------------------------
Return to the same dialogue (***Climatic \> Prepare \> Climatic
Summaries***) to add 3 more summaries.
a) Change the ***rain*** to the ***rainday*** variable in the main
dialogue, Fig. 12.3u. Press Ok.
b) Change the element to ***Tmin*** and change the ***Summary*** to the
***Mean***, (rather than the Sum). Press ***Ok***.
c) Change the element to ***Tmax***. Press Ok.
The results are in Fig. 12.3z. They show, for example, that in the
1962-63 season (November to April) there was a total of 422mm rain from
41 rain days, hence an average of just over 10mm per rain day. The value
of Tmin could not be given, because there were only 147 non-missing days
in that season. The mean for Tmax was 29.4˚C.
Finally, in this initial checklist, give the corresponding graphs. For
the temperature data there is not yet a special climatic dialogue, so
use ***Describe \> Specific \> Line Plot***, as shown in Fig. 12.3aa.
--------------------------------------------------------------------------------------------------------------
***Fig. 12.3aa*** ***Fig. 12.3ab***
------------------------------------------------------ -------------------------------------------------------
{width="2.9165726159230094in" {width="3.211813210848644in"
height="3.036790244969379in"} height="3.0356714785651793in"}
--------------------------------------------------------------------------------------------------------------
In Fig. 12.3aa the ***Multiple Variables*** option is used to facilitate
plotting Tmax and Tmin together. ***Points are added***, as is a ***line
of best fit***. The ***Data Options*** button is also used to
***filter*** the data to just ***s_year \> 1957***.
The resulting graph is shown in Fig. 12.3ab. It indicates an increase of
temperatures, with Tmin having a higher slope than Tmax. The analysis of
the temperature data and the presentation of the corresponding PICSA
graphs is considered further in Section 12.5.
------------------------------------------------------------------------------------------------------------
***Fig. 12.3ac Remove filter*** ***Fig. 12.3ad The Climatic \> PICSA dialogues***
----------------------------------------------------- ------------------------------------------------------
{width="2.43494750656168in" {width="3.483146325459318in"
height="2.9829297900262466in"} height="2.312853237095363in"}
------------------------------------------------------------------------------------------------------------
Before doing a rainfall graph use ***Right-click*** and the last option
is to ***Remove Current Filter***, Fig. 12.3ac.
Then use ***Climatic \> PICSA \> Rainfall Graph***, Fig 12.3ad. Graph
the variable called sum_rain, Fig. 12.3ae. Then use the ***PICSA
Options*** button to add a horizontal line for the mean, Fig. 12.3af.
------------------------------------------------------------------------------------------------------------
***Fig. 12.3ae*** ***Fig. 12.3af***
----------------------------------------------------- ------------------------------------------------------
{width="2.8456485126859143in" {width="3.093505030621172in"
height="2.7895516185476814in"} height="2.1058464566929134in"}
------------------------------------------------------------------------------------------------------------
The resulting graph is shown in Fig. 12.3ag. Return to the ***Climatic
\> PICSA \> Rainfall Graph*** dialogue and substitute the
***sum_rainday*** variable to give a similar graph of the seasonal
number of rain-days.
-----------------------------------------------------------------------
***Fig. 12.3ag PICSA graphs of the seasonal totals and number of rain
days***
-----------------------------------------------------------------------
{width="5.721399825021872in"
height="4.006120953630796in"}
-----------------------------------------------------------------------
The results are in Fig. 12.3ag. They show that the mean rainfall total
was about 560mm from an average of just over 40 rain days. This is an
average of about 7 rain days per month, roughly one day in 4. This is
sufficiently low that it is likely that there are often long dry spells
during the season.
We have produced our first "PICSA-style" graphs. Further graphs are
produced in the next Section, together with ways of making the graphs
appropriate for the extension staff and for farmers.
## The rainy season
In this section we consider the production of further rainfall summaries
with the ***Climatic \> Prepare*** menu and the corresponding graphs
from the ***Climatic \> PICSA*** menu.
Usually between 6 and 8 graphs are prepared and discussed on the first
day of the main PICSA workshop. They all have the same format as shown
in Fig. 12.3ag and are designed to look consistent. The x-axis is the
years (or seasons) and the y-axis is for something of interest. They
usually include a rainfall total, Fig. 12.3ag together with the start,
end and length of the rainy season. There are then one or two graphs of
events within the season, for example the length of the longest dry
spell or the most extreme daily rainfall.
Definitions can be changed easily. PICSA encourages "options by context"
and this can apply to households and or crops having different
definitions for the start of the rains, and for any other
characteristic.
The graphs are also used on the "practice with farmers day", during the
workshop, usually day 4. Following the workshop, the agreed graphs are
then used by the extension staff or farmer's representatives to share
with individuals or groups of farmers.
We continue with the data from Dodoma, used in Section 12.3.
------------------------------------------------------------------------------------------------------------
***Fig. 12.4a Start of the rains*** ***Fig. 12.4b Adding a dry-spell condition***
----------------------------------------------------- ------------------------------------------------------
{width="3.4121161417322834in" {width="2.573474409448819in"
height="3.154821741032371in"} height="2.9682469378827645in"}
------------------------------------------------------------------------------------------------------------
Use ***Climatic \> Prepare \> Start of the Rains***, Fig. 12.4a. A range
of definitions of the Start of the rains is discussed in Section 7.3. In
2019, in Malawi and Tanzania the definitions used were:
- Malawi: First occasion from 1 October with 25mm or more in 3 days.
- Tanzania: First occasion from 15 November with 20mm in 4 days, of
which 2 days were rainy.
We here use the same as Tanzania. Hence click on the ***Day Range*** in
Fig. 12.4a and set the earliest date to ***15 November***. Make the
latest date ***29 February***. Then ***complete the dialogue*** as shown
in Fig. 12.4a and press ***Ok***.
This generates 2 new variables, the first with the day number in the
(shifted) year and the second giving the corresponding date.
Return to the ***Climatic \> Prepare \> Start of the Rains*** dialogue
and add the dry-spells condition, Fig. 12.4b. Change the default of 9
days to 10 days as the maximum allowable spell[^50]. Also change the
names of the resulting variables, or the events produced before will be
overwritten.
------------------------------------------------------------------------------------------------------------
***Fig. 12.4c End of the rains*** ***Fig. 12.4d***
----------------------------------------------------- ------------------------------------------------------
{width="3.0006211723534557in" {width="2.9240430883639545in"
height="3.02583552055993in"} height="2.478840769903762in"}
------------------------------------------------------------------------------------------------------------
+-----------------------------------+----------------------------------+
| ***Fig. 12.4c End of the rains*** | ***Fig. 12.4d*** |
| | |
| ***Climatic \> Prepare \> End of | |
| the Rains*** | |
+===================================+==================================+
| {width="3.0006211723534557in" | ng){width="2.9240430883639545in" |
| height="3.02583552055993in"} | height="2.478840769903762in"} |
+-----------------------------------+----------------------------------+
In Chapter 7 we discuss the use of these alternative definitions of the
start. Here we simply choose one of them -- for the next PICSA graph. We
quickly also get data on the end and length of the season.
In many countries we use a simple water-balance definition of the end of
the rains/season. This does not work well in Southern Africa, as
explained in Section 7.4. Hence here we use the method proposed by
(Mupamgwa, Walker, & Twomlow, 2011).
So, complete the ***Climatic \> Prepare \> End of the Rains*** dialogue
as shown in Fig. 12.4c. In the ***Day Range*** use ***15 February to 30
June***.
Then use ***Climatic \> Prepare \> Length of the Season***, Fig. 12.4d
and complete as shown.
The results have added variables to the annual data frame, Fig. 12.4e.
The year indicated shows that in the 1937/38 season there was a planting
opportunity on day 174, (i.e. 21 December). But, if the dry-spell
definition is included then the start was on 23rd January. The last
heavy rainfall was on 31^st^ March, which was defined as the end of the
rains/season, giving a season length of 101 days.
Graphs can be produced assuming the user accepts the definitions.
---------------------------------------------------------------------------------------------------------------
***Fig. 12.4e The annual data frame*** ***Fig. 12.4f A graph of the Start***
------------------------------------------------------- -------------------------------------------------------
{width="3.2341983814523183in" {width="2.7932720909886264in"
height="2.942286745406824in"} height="2.8815354330708662in"}
---------------------------------------------------------------------------------------------------------------
Use ***Climatic \> PICSA \> Rainfall Graph*** for the variable
***start_rain*** and complete as shown in Fig. 12.4f. Then complete the
***PICSA options*** for the ***Y-axis*** and the ***Lines***, as shown
in Fig. 12.4g and 12.4h.
--------------------------------------------------------------------------------------------------------------
***Fig. 12.4g*** ***Fig. 12.4h***
------------------------------------------------------ -------------------------------------------------------
{width="2.811955380577428in"
{width="2.9353149606299214in"
height="2.2885793963254595in"} height="2.232117235345582in"}
--------------------------------------------------------------------------------------------------------------
The resulting graph is in Fig. 12.4i. There can be similar graphs for
the end of the rains and the length of the season.
--------------------------------------------------------------------------------------------------------------
***Fig. 12.4i PICSA graph for the start*** ***Fig. 12.4j The start with the dry-spell condition***
------------------------------------------------------ -------------------------------------------------------
{width="2.963582677165354in" {width="3.1734350393700788in"
height="2.6766152668416447in"} height="2.6980872703412073in"}
--------------------------------------------------------------------------------------------------------------
Then it is time to reflect in three different ways:
a) Is this the ***right definition to use for the start***? For
example, should the 15 Nov be the earliest possible starting date,
given that quite a lot of seasons had a starting opportunity very
close to this earliest date. Or should the dry spell have been
included.\
It is easy to try the graph with the dry spell included. Just return
to the ***Climatic \> PICSA \> Rainfall Graph*** dialogue,
substitute the start_dry variable and press Ok to give the graph in
Fig. 12.4j. The mean starting date is now about a week later and
there are considerably more years that do not have a successful
start until January. Which graph more closely reflects the farmer's
situation?
b) Does the graph indicate there may be ***problems with the data***?
Often the first results indicate possible data issues. In this case
there is nothing that stands out, but to show what might be done, we
examine the extreme value in Fig. 12.4i. This was a start only on
6^th^ February in the 1960/61 season.
+------------+--------------+-----------------------------------------+
| ***Fig. | | ***Fig. 12.4l PICSA graph with |
| 12.4k The | | options*** |
| start in | | |
| the | | |
| extreme | | |
| year*** | | |
| | | |
| * | | |
| **Climatic | | |
| \> Check | | |
| Data \> | | |
| Display | | |
| Daily*** | | |
+============+==============+=========================================+
| ![] | {width="3.490313867016623in" |
| ge1284.png | 85.png){widt | height="3.295974409448819in"} |
| ){width="0 | h="1.1892607 | |
| .981046587 | 174103238in" | |
| 9265092in" | heigh | |
| height="4 | t="4.1505664 | |
| .193491907 | 91688539in"} | |
| 261592in"} | | |
+------------+--------------+-----------------------------------------+
Use the ***Climatic \> Check Data \> Display Daily*** dialogue to give
the results in Fig. 12.4k. This shows that November and December did
have very poor rains in that season. With the definition of the start
used in Malawi (25mm in 3 days) the start would have been on 23^rd^
January, but the insistence, in the Tanzania definition, of at least 2
rain days ruled this out. Hence the start was indeed on 6^th^ February.
c) Is the graph as clear as possible for the intended PICSA audience?
This is what we address here.
An example of a graph with additional options is in Fig. 12.4l. The
elements changed, compared to the default, are shown in Fig. 12.4m.
-----------------------------------------------------------------------
***Fig. 12.4m Setting PICSA graph options***
-----------------------------------------------------------------------
{width="5.721399825021872in"
height="4.006120953630796in"}
-----------------------------------------------------------------------
1) From the titles tab in Fig. 12.4m, a sub-title shows what has been
plotted. The caption gives credit to TMA for supplying the data. The
units are now specified on the y-axis.
2) On the x-axis the labels are given every 10 years.
3) On the y-axis the data start at zero. I like that!
In Fig. 12.4n the x-axis labels have been changed to every 4 years. The
minor-grid lines are now omitted (using the Panel tab in the
sub-dialogue). The sub-title has also been moved to become part of the
caption[^51].
---------------------------------------------------------------------------------------------------------------
***Fig. 12.4n*** ***Fig. 12.4o***
------------------------------------------------------- -------------------------------------------------------
{width="2.9957917760279966in" {width="3.0032381889763777in"
height="2.755722878390201in"} height="2.7912871828521433in"}
---------------------------------------------------------------------------------------------------------------
Further "within the season" graphs can be given as needed. The season
length was calculated earlier, Fig. 12.4e and is plotted in Fig. 12.4o.
The median length at Dodoma was about 4 months and varies between 2 and
6 months.
The total rainfall within the season is sometimes requested. This is the
rainfall between the start dates and the end dates. This again uses the
***Climatic \> Prepare \> Climatic Summaries***, as shown in Fig. 12.4p.
---------------------------------------------------------------------------------------------------------------
***Fig. 12.4p Climatic \> Prepare \> Summaries*** ***Fig. 12.4q Day Range from start to end***
------------------------------------------------------- -------------------------------------------------------
{width="2.4361231408573927in" {width="3.6065048118985126in"
height="3.1956178915135607in"} height="2.3646872265966756in"}
---------------------------------------------------------------------------------------------------------------
The difference here, from the summaries given earlier, is the choice of
the dates, from the ***Day Range*** button in Fig. 12.4q. In Fig. 12.4r
they are specified as ***Variable Day*** and use the summary data for
the start and end of the rains, that was found earlier. In the dialogue,
in Fig. 12.4q the missing values checkbox is now unticked to dis-allow
any years when there are missing values for the rainfall during the
season.
--------------------------------------------------------------------------------------------------------------
***Fig. 12.4s*** ***Fig. 12.4t***
------------------------------------------------------ -------------------------------------------------------
{width="2.741344050743657in" {width="3.2065726159230095in"
height="2.8988910761154854in"} height="2.9150656167979in"}
--------------------------------------------------------------------------------------------------------------
The results are in the last 2 variables of the summary data, shown in
Fig. 12.4s. In this summary, the variable called ***count_rain*** is the
number of days used for the sum and is almost the same as the length --
which is also given in Fig. 12.4s. It should be the same, because it is
simply counting the number of days used for that calculation, i.e.
between the start and end dates. It is thus effectively another way of
finding the length[^52].
The resulting graph is shown in Fig. 12.4t.
An attractive way these results can be used is shown in Fig. 12.4u. The
data, from Fig. 12.4s, can be transferred to an interactive app which
(unlike R-Instat) is available for a smart-phone. In Fig. 12.4u the user
can move the slider, shown at 600mm to find the risks for any given
seasonal rainfall required.
-----------------------------------------------------------------------
***Fig. 12.4u***
-----------------------------------------------------------------------
{width="6.148199912510936in"
height="2.9562587489063867in"}
-----------------------------------------------------------------------
It is an obvious graph and is often proposed for PICSA. However, it is
complicated, as it is composed of three elements, namely the start, the
end, and then the totals within this period, that varies from season to
season. We often find it is not very different to the graph with fixed
end points, such as Fig. 12.3ag that gave the totals from November to
April.
These graphs, from the start to the end may be more relevant for sites
where there is a bimodal pattern of rainfall. Even then that would be
"in competition" with simpler graphs giving the totals for fixed
periods, say from October to December and then for March to May.
We return to the possible use of the facility for variable dates in the
Section 12.5.
This same facility for flexible choice of the starting and ending dates
is available in other dialogues and the longest dry-spell length during
the season is an obvious graph.
The ***Climatic \> Prepare \> Spells*** dialogue is shown in Fig. 12.4v.
The ***Day Range*** is completed as shown earlier in Fig. 12.4r. The
graph of the spell lengths, using Climatic \> PICSA \> Rainfall Graphs
is then shown in Fig. 12.4 w. The median for the longest spell length in
the season is 2.5 weeks and about 1 year in 7 has a dry spell of 25 days
or more.
--------------------------------------------------------------------------------------------------------------
***Fig. 12.4v Dry spells during the season*** ***Fig. 12.4w Graph of maximum spell lengths***
------------------------------------------------------ -------------------------------------------------------
{width="2.5910148731408573in" {width="3.4119761592300963in"
height="3.0473589238845142in"} height="3.088657042869641in"}
--------------------------------------------------------------------------------------------------------------
The Climatic \> Prepare \> Extremes dialogue facilitates a study of
extreme events. It is used in Fig. 12.4x to find the maximum single day
rainfall each year, together with when the maximum occurred.
The graph, in Fig. 12.4y shows the median is 66mm on a day and just a
few years have a day with more than 100mm. The colours in Fig. 12.4y
indicate which month the maximum value occurred, and indicate that it
can be in any of the months of the rainy season[^53].
--------------------------------------------------------------------------------------------------------------
***Fig. 12.4x*** ***Fig. 12.4y***
------------------------------------------------------ -------------------------------------------------------
{width="2.415457130358705in" {width="3.5962543744531934in"
height="3.559381014873141in"} height="3.2320866141732285in"}
--------------------------------------------------------------------------------------------------------------
## More with the rainfall and temperature data?
With rainfall propose more detailed analyses and other types of
presentation.
Vertical lines instead of joined lines[^54]?
-----------------------------------------------------------------------
***Fig. 12.5a***
-----------------------------------------------------------------------
{width="6.148199912510936in"
height="2.9562587489063867in"}
-----------------------------------------------------------------------
[Also do risks for temperatures, where no special facilities exist.
First frost and last frost in Leshoto as examples. Also in more detail
in Chapter 8.]{.mark}
[Also ask about Bangladesh where the risks may be of too much rain,
rather than too little?]{.mark}
## Coping with data issues
## Combining risks for different crops
Table 12.7a is taken from the PICSA Field Guide, (Dorward, Clarkson, &
Stern, 2016) and shows the sort of results we are aiming for. The first
step is for the Ministry of Agriculture or elsewhere to provide the
information in the first 4 columns of Table 12.7a. This specifies
various crops (options for PICSA farmers) together their length and
water requirement. For example, the local variety of maize is a 120-day
crop and needs 480mm water. Alternatively, a possible variety of sorghum
is 110 days and needs 300mm.
The calculations in this section provide the risks from specified dates
of planting. In the last column of Table 12.7a the chance of success
from a late planting is just one year in 5 for the local maize, compared
to 3 years in 5 for the sorghum.
--------------------------------------------------------------------------------------
**Table
12.7a
Example
crop
table**
---------- ------------- ------------ ---------- ----------- ------------ ------------
**Crop** **Variety** **Days to **Crop **Chance of
maturity** water need success if
(mm)** season
starts:**
**on x **on x **on x
(Early)** (Middle)** (Late)**
Maize Local 120 480 5/10 4/10 2/10
Maize Pioneer xxx 100 350 7/10 5/10 4/10
Sorghum Seed Co xxx 110 300 8/10 7/10 6/10
--------------------------------------------------------------------------------------
Overall, for a chosen planting date, there are 3 separate risks, as
follows:
1. Planting may not be possible by that date, i.e. the season starts
later.
2. There may not be time to grow the crop, i.e. the season ends too
early