1. Agriculture Sector V&A Assessment, Planning and Implementation - Hands-on Exercise

2. Overview Exercise 1: Types of information Exercise 2: Seasonality
Exercise 3: Historical climate trend
Exercise 4: Historical crop production trend and climatic impacts
Exercise 5: Projected future

3. Exercise 1: Types of information

4. Year-to-year variability of annual temperature in Revelle City was larger from 1990 to 2000 than in all previous decades on record.
Annual rainfall increase for Manabe Heights compared to the average is projected by some models to reach 200mm at the end of the century.
In Croll’s Crossing, corn has the shortest growing season from May to August.
Fourier City receives the largest amount of rainfall in October.
For Keeling Creek, Global Climate Model scenarios project a rise in annual average temperature of between 1.5°C and 3.5°C by 2100.
In Douglass Ville, average mean annual temperature has increased by 0.3°C between 1920 and 1990.
June is typically the hottest month in Tyndall Town measuring 27°C. 2
1. Seasonality in a year: seasonal climate and crop calendar
2. Long-term trend: historical climate and historical crop production
3. Projected future: future climate and possible changes in crop production 3 1 1 3 2 1

5. Exercise 2: Seasonality

6. Q1 / Figure 1: Average monthly temperature (°C ) in Tyndall Town over the last 20 years
June is the hottest month (28°C). There are actually two temperature peaks in a year in this location. The second peak is October.
January is the coldest (24°C).
The annual average is about 26.4°C.
About 4°C difference from the coldest month and the hottest month.
Main features that you will want to immediately note are the overall shape of the temperature curve, temperature peaks (hottest and coldest month), annual average temperature, and the temperature variation through the year.

7. Q2 / Figure 2: Average monthly rainfall (mm) in Tyndall Town over the last 20 years
It rains the most in August (450mm).
November to May is dry.
The total annual rainfall is about 1100mm and it is mostly concentrated in just three months from July to September.
Again, the overall shape of the rainfall curve as well as peaks are important to note. Looking for distinct wet and dry seasons and how they compare to the total annual rainfall will give you crucial information for relating seasonal climate to crop calendar data.

8. Q3 / Figure 3: Cropping calendar (J: January, F: February…)
The table appears not to list all crops grown in the area. As there is hardly any rain in the dry season, all rainfed crops are probably grown in the second half of the year like rice and millet in the table.
For rainfed crops, sowing in June to August, and harvest in October to December is probably the most common cropping calendar, given the rainfall cycle.
Irrigation capacity allows cropping in dry season, such as irrigated rice from February to May as in the table.
Important information for you to include is if crops are grown throughout the year, which months feature a concentration of different crops’ growing seasons, and when crops are in different stages of the crop growth cycle.

9. Q4 / Figure 4: Climate risk calendar (J: January, F: February…)
Water-related extreme events should occur around the rainy season (June to October) like drought and storms in the table.
Other extreme events may be in different seasons (e.g. winds in April as in the table).
Temperature-related extreme events seem to be of less importance in the area.
Wind is another climate variable that deserves attention in the area.
From the climatic risk calendar you will want to gain a good understanding of which types of climatic risks are commonly observed and how they are distributed throughout the year. Main determinants of extreme weather events are precipitation and temperature. Is this the case in Tyndall Town? Are there additional factors that deserve your attention?

10. Q5 / Figure 5: Table to combine climate risks and crop calendars
Storms can affect both rice and millet.
Droughts or late on-set of rainy season may not affect millet because it is sown later in the season.
Irrigated rice is vulnerable to other extreme events like strong winds that rainfed crops are not exposed to.
You connect the dots by systematizing the gathered data in a simple table. On that basis, you can discern patterns and develop some initial thoughts on what the emerging picture implies for the project you are planning.

11. Exercise 3: Historical climate trend

12. Q7 / Figure 6: Annual average temperature (°C) in Croll’s Crossing
Problems: There are just a few years of observation in 1930s, followed by 10 years of gap. No record since 1990.
A warming trend (~0.25°C from 1945 to 1990)
Annual temperature may fluctuate within a 1°C range. Year-to-year variability may be larger in 1970 to 1990 than earlier decades.
Daily maximum temperature and daily minimum temperature records would be nice to have.
Observation in recent decades will be essential to understand current climate variability.
What are the major weaknesses of the graph and how can they be explained?
Despite its limitations, the annual average temperature graph provides you with a crucial piece of information – which one?
On a closer look, what can you infer regarding temperature variability?
What would be valuable additional sets of data to complement the annual average temperature curve?

13. Q8 / Figure 7: Total annual rainfall (mm) in Croll’s Crossing
A decreasing trend (-340mm from 1890 to 1990 or about -25%)
Annual rainfall may fluctuate within 1000 mm range. Changes in the size of year-to-year variability are not very apparent.
Problems: A few missing years in the long time series but no record in recent 20 years.
It would be nice to know if it is raining less frequently or the amount of rain in one rain event is smaller.
Observation in recent decades will be essential to understand current climate variability.
What is the main message to be derived from the graph?
Can you infer anything on annual rainfall variability from the graph?
Again, what additional data would help to get a more complete picture of historic rainfall trends in Croll’s Crossing?

14. Q9 / Figure 8: Historic extreme weather events by number of affected people in Croll’s Crossing
Droughts, floods, and storms
Large floods were observed in the last 10 years, not in early decades. Large drought events happened decades ago and in recent times too.
Droughts tend to affect more people than other extreme events. It does not necessarily mean that droughts affect agricultural production more than other extreme events.
It would be nice to have statistics on loss in agricultural production in relation to extreme weather events for the local area (and not for the whole country).
What have been common extreme events affecting Croll’s Crossing in the past? When did they occur?
What can we say about the frequency of different types of extreme weather events and their distribution over time? Can a pattern and/or trend be discerned from this data?
From the available data, what can we say about the intensity and impact about these extreme events?
Does the available data provide a clear picture of the historic relationship between agriculture and the occurrence of extreme weather events? What additional data would be important to complement the data you have?

15. Exercise 4: Historical crop production trend and climatic impacts

16. Q 10 / Figure 9: Yield year-to-year variability (t/ha) in Revelle City
Grouping:
a) under 1t/ha,
b) to 2 t/ha,
c) more than 2 t/ha)
Possible reasons for trend/jump in crop yields: e.g. introduction of fertilizers, subsidies, market price,
Linear trend
Other trends of non-linear trend
It may be possible to group the time series into three periods. From one group to another, there seem to be an important shift in the yield level. How do you group the time series?
What might be the possible causes of the shift, and of overall trend in the yield?

17. However equally poor yield year (2000) did not have a low rainfall
Q 11 / Figure 11, 12: De-trended crop yield vs. annual rainfall in Revelle City
Years of very bad yields correspond to low rainfall years (2001 and 2010).
However equally poor yield year (2000) did not have a low rainfall
Year 1994 had a very good yield despite very dry condition
Annual rainfall amount can explain part of the poor yields but not all.
Need to look at rainfall data at shorter time scales (monthly, daily)
What are the most important messages to be gathered from the relationship of these two types of data?
What additional data would be helpful/necessary to gain a deeper understanding of the relationship between climatic conditions and agricultural productivity?
Need to look at other climate variables (average temperature, daily max temperature, daily min temperature, wind speed, radiation, etc)
Would be helpful to look at production statistics not by year but by cropping season (if there are two or more seasons in a year)

18. Exercise 5: Projected future

19. Q 12 / Figure 13: Projected annual average temperature change (°C) in Keeling Creek with respect to average; comparison of 3 GCMs and 2 RCPs
More warming for RCP8.5 scenario than RCP4.5 scenario. More warming for GCM1, least warming for GCM2.
Variation among GCMs and emission scenarios is not large in , but it increases with time and up to 2°C in (GCM1-RCP8.5: 3.5°C vs GCM2-RCP4.5: 1.6°C)
All GCMs and emission scenarios agree on continuous warming through the century
Need to know the effect of higher temperature on major crops grown in the area. And then think about what can be done – shift sowing date? Heat-tolerant variety? etc
What kinds of changes are consistent across the projections based on different GCMs and emission scenarios? Where and how do the projections differ?
Given the projected temperature change, what kind of additional information will be useful to design a climate change adaptation plan in agriculture?

20. Increase in rainfall in general
Q 13 / Figure 14: Projected total annual rainfall change (mm) in Keeling Creek with respect to average; comparison of 3 GCMs and 2 emission scenarios
Increase in rainfall in general
Changes tend to be larger later in the century
100 to 250mm wetter in
Decrease in rainfall is possible in the near future ( )
All GCMs and emission scenarios agree on wetter condition by the end of the century ( )
Need to know how wetter condition might affect crop growth. If one storm will bring larger amount of water, it would be nice to know how much water soil is able to absorb and whether excess water can cause drainage problem, etc (many possible answers)
What kinds of changes are consistent across the projections based on different GCMs and emission scenarios? Where and how do the projections differ?
Given the projected rainfall change, what kind of additional information will be useful to design a climate change adaptation plan in agriculture?