Seminar by Dr. Roberto Quiroz, March 24, 2011.

1. Challenges to potato‐based systems under climate
variability/change conditions
Presented by R. Quiroz on behalf of the
Production Systems & the Environment team
Crop Management & Production Systems Division

2. Contents
• Climate change vs. climate variability
• Expected impact on agriculture
• Knowledge gaps
• Selected research challenges for CIP and
where we stand
• Integrating knowledge
• The Peruvian “constructed” example
• CCAFS

4. Projected Climate: Andes

5. Progressive Climate change
Versus
Current climate risks

6. Effects of climate change on agric. crops
Direct Effect (higher CO2): Stimulating radiation and water use
efficiency
Combined Effect
(Direct + Indirect)
Indirect Effect (change in temp. and prec.): Affecting growing
seasons, suitable cultivation area, etc.
Bindi 2008

7. Effects of climate change on agricultural crops
•Most assessments done for the Northern hemisphere where measured
climate and crop data are more abundant and climate and crop models
are better parameterized
•The consequences of climate change on agricultural productivity must
be seen as referential. Most of the assessments are based on models
parameterized with results from experiments under controlled conditions
and not in actual production fields
•The expected impact varies across space. For instance in Northern
areas of Europe and North America CC may produce positive effects
through the introduction of new crop species and varieties, higher crop
production and expansion of suitable areas for crop cultivation
•Adaptation of agriculture to CC is a must and should be supported by
adequate policies
•The Southern hemisphere needs better data and models to enhance
the few existing assessments
•Tradeoffs between expected changes in agriculture and the future
feedback to the environment must be included in the analyses
Main sources:
Maracchi et al., 2005
Olesen & Bindi, 2002;
Reddy & Hodges, 200;
Rosenzweig & Hillel, 1998

8. Effects of climate change on agriculture:
Summary of some important staple crops
Crop Increased CO2 Increased Combined
Temperature effects
Wheat Photosynthesis Yield in 6 ‐10 % at •CO2 benefits offset
Photorespiration temperatures by hi temp
RUE & WUE above optimal •Increase in yield
Yield in ~ 20 % variability
•Presence of weeds
Rice Yield by ~ 24 % Yield at temp > 26
oC •More pests and
WUE by ~ 40 ‐50 % diseases
Potato Yield in ~ 25 % Reduced growing
RUE & WUE season and yield
Maize Variable response; e.g. + Cool areas today
in N‐Europe & ‐ in the may become
South suitable
Soybean Yield in some Northern Northward
areas expansion in Europe
Small H2O savings & USA

9. Effects of the climate change (cont.)
•Land and soil health: Soil organic matter will
decline as temperatures are elevated.
•As a result of decline in OM, soils will
become more acidic, nutrients will become
depleted, microbiological diversity will
diminish and a decline in soil structure will
result in less WHC (Barnard et al., 2005,
Schulze, 2005 and Molope, 2006).

10. Effects of the climate change (cont.)
•Biodiversity: Climate change, in the
medium to long term, will affect
biodiversity.
•Pests and diseases: Plant and animal
diseases and insect distributions are likely
to change.

11. Knowledge gaps and research priorities:
• Experimental analyses and model simulation to quantify:
– effect of increasing CO2 on crops other than cereals, including those of
importance to the rural poor (e.g. local potato cultivars)
– interaction between crop yields and other factors of production (pests,
diseases, weeds, etc.) under climate change conditions
– impact of climate extreme events on crop yields
• Reduce and quantify uncertainties of future prediction (for climate
change and their impacts)
• Develop tools (e.g. farm and cropping system models) to evaluate
adaptation strategies at different spatial levels (cropping, farm, region)
• Evaluate actual applicability of adaptation strategies:
– Cost and benefits (economic, social, environmental)
– Role of new technology (e.g. biotechnologies, fertilizers, etc.)
– Interaction with mitigation strategies
from Chapter 5 – WGII FAR-IPCC, 2007
Source: Bindi, 2008

12. Research challenges to CIP research
and highlight of progress
H
Cloud Pathway
h
Surface

13. Missing data in climate series

14. It is common to find missing data in weather data sets

15. The Wavelet Transform
∞
WTS(λ ,τ ) = ∫ S (t )ψ λ ,τ (t )dt,
−∞
Where:
1 ⎛ t −τ ⎞
ψ λ ,τ (t ) = ψ⎜ ⎟,
λ ⎝ λ ⎠

16. Weather data infilling
Source: Carbajal et al., 2010

17. Climate data: limited spatial coverage in developing countries

18. Source: Heidinger et al., 2011
Improving TRMM rainfall estimates in mountains

19. Raw versus corrected TRMM

20. R‐Square metric
TRMM Base from Station x*
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
1 0.93 0.89 0.90 0.93 0.93 0.89 0.93 0.89 0.93 0.91 0.90 0.87 0.90 0.93 0.95 0.91 0.91 0.92 0.88
2 0.94 0.93 0.92 0.94 0.91 0.89 0.91 0.92 0.93 0.92 0.90 0.92 0.93 0.91 0.93 0.92 0.93 0.94 0.91
3 0.92 0.87 0.88 0.92 0.91 0.88 0.91 0.89 0.91 0.92 0.90 0.86 0.88 0.91 0.93 0.90 0.89 0.91 0.87
4 0.91 0.86 0.89 0.91 0.91 0.87 0.90 0.90 0.92 0.90 0.88 0.85 0.89 0.91 0.93 0.89 0.89 0.91 0.85
5 0.90 0.85 0.88 0.90 0.93 0.86 0.93 0.85 0.91 0.89 0.88 0.81 0.87 0.93 0.94 0.86 0.86 0.86 0.84
Gauge Noise from Station y*
6 0.88 0.88 0.89 0.88 0.90 0.88 0.88 0.90 0.90 0.90 0.86 0.89 0.89 0.90 0.90 0.89 0.88 0.91 0.88
7 0.93 0.88 0.91 0.93 0.95 0.91 0.95 0.90 0.93 0.92 0.92 0.86 0.90 0.95 0.95 0.89 0.90 0.90 0.88
8 0.94 0.92 0.92 0.94 0.91 0.88 0.92 0.91 0.93 0.91 0.90 0.92 0.92 0.92 0.93 0.91 0.93 0.92 0.91
9 0.94 0.91 0.93 0.94 0.95 0.91 0.95 0.92 0.96 0.93 0.92 0.89 0.93 0.95 0.96 0.94 0.92 0.93 0.90
10 0.90 0.89 0.89 0.90 0.89 0.84 0.88 0.88 0.91 0.88 0.86 0.88 0.90 0.89 0.91 0.88 0.90 0.89 0.88
11 0.91 0.88 0.88 0.91 0.89 0.85 0.90 0.87 0.90 0.88 0.87 0.87 0.89 0.90 0.91 0.87 0.89 0.89 0.88
12 0.93 0.90 0.90 0.93 0.92 0.87 0.91 0.90 0.93 0.90 0.88 0.92 0.92 0.91 0.93 0.90 0.92 0.92 0.89
13 0.96 0.93 0.93 0.96 0.93 0.90 0.93 0.93 0.94 0.93 0.92 0.91 0.94 0.93 0.96 0.94 0.94 0.95 0.92
14 0.92 0.87 0.90 0.92 0.93 0.91 0.94 0.90 0.92 0.91 0.91 0.87 0.89 0.95 0.94 0.90 0.89 0.90 0.88
15 0.93 0.90 0.92 0.93 0.93 0.89 0.92 0.90 0.94 0.92 0.90 0.88 0.92 0.93 0.95 0.91 0.91 0.93 0.89
16 0.97 0.97 0.96 0.97 0.96 0.95 0.96 0.95 0.96 0.96 0.95 0.96 0.96 0.96 0.97 0.96 0.96 0.95 0.96
17 0.93 0.91 0.90 0.93 0.89 0.85 0.89 0.88 0.92 0.90 0.88 0.91 0.92 0.89 0.92 0.90 0.92 0.93 0.89
18 0.93 0.92 0.91 0.93 0.90 0.87 0.90 0.92 0.92 0.90 0.88 0.94 0.92 0.90 0.93 0.92 0.93 0.93 0.92
19 0.92 0.90 0.90 0.92 0.91 0.89 0.91 0.89 0.91 0.91 0.90 0.87 0.90 0.91 0.93 0.87 0.90 0.89 0.89

21. MF spectra metrics
1.3
1.25
1.2
1.15
D(h)
1.1
1.05
1
0.95
0.9
0.2 0.3 0.4 0.5 0.6 0.7
h
CTRMM_Ayaviri STATION_Ayaviri TRMM_Ayaviri

22. TRMM corrections‐ Ethiopia
Abomsa Adaba
Linear Regression Linear Regression
y = 1.0024x + 2.549
100.00 2 50.00 y = 0.9809x + 1.452
R = 0.8282
Rainfall Estimated
Rainfall Estimated
2
80.00 R = 0.8092
40.00
60.00 30.00
40.00 20.00
20.00 10.00
0.00 0.00
0.0 20.0 40.0 60.0 80.0 100.0 0.0 10.0 20.0 30.0 40.0 50.0
Rainfall Rainfall
Addisbole Addisobs
Linear Regression Linear Regression
70 y = 0.9974x + 1.7439 120 y = 1.0525x + 2.7101
Rainfall Estimated
Rainfall Estimated
60 2
R = 0.8346
2
R = 0.819
100
50
80
40
30 60
20 40
10 20
0
0
0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0
0.0 20.0 40.0 60.0 80.0 100.0 120.0
Rainfall
Rainfall

23. Rainfall estimation from NDVI
Source: Quiroz et al., 2010

24. From RS data to rainfall
(ppm)
HUANCANE
50
40
m.m.
30
20
10
0
1-Jan-99 20-Jul-99 5-Feb-00 23-Aug-00 11-Mar-01 27-Sep-01 15-Apr-02 1-Nov-02
Días
Source: Yarlequé et al., 2007

25. Long‐term reconstruction
LINEAR REGRESSION
70.0
y = 1.0618x + 2.087
Estimated Rainfall (mm)
60.0
R2 = 0.7491
50.0
40.0
30.0
20.0
10.0
0.0
0.0 10.0 20.0 30.0 40.0 50.0 60.0
Gauged Rainfall (m m )
Work in progress

26. Spatial reconstruction
Work in progress

28. Work in progress

29. GCMs – spatial resolution
Source: J. Ramirez - CIAT

31. Multifractal spectrum - Cabanillas station
1.2
D(h, q=‐1.5) 0.60
1.0
D(h, q=0) 0.95
0.8 D(h, q=1.5) 0.75
h (q=‐1.5) 1.10
Dh
0.6
h (q=0) 0.59
0.4
h (q=1.5) 0.27
0.2
Asymmetry 1.59
0.0 Scale range 2 ‐ 69.8
0.0 0.2 0.4 0.6 0.8 1.0 1.2
h
Daily rainfall - Cabanillas station
60
Variable Value
50
Maximum monthly
34.26
rainfall in 1 day
Rainfall (mm)
40
Maximum number of
30 consecutive days with 89.1
rainfall < 1mm
20
Maximum number of
10 consecutive days with 12.2
rainfall >= 1mm
0
1 366 731 1096 1461 1826
Days

32. Multifractal spectrum - Pizacoma station
1.2 D(h, q=‐1.5) 0.62
1.0 D(h, q=0) 0.74
D(h, q=1.5) 0.62
0.8
h (q=‐1.5) 0.49
Dh
0.6
h (q=0) 0.31
0.4 h (q=1.5) 0.13
0.2 Asymmetry 0.97
Scale range 2 ‐ 12.6
0.0
0.0 0.2 0.4 0.6 0.8 1.0 1.2
h
Daily rainfall - Pizacoma station
60 Variable Value
50 Maximum monthly
32.35
rainfall in 1 day
Rainfall (mm)
40 Maximum number of
consecutive days with 101.7
30 rainfall < 1mm
20 Maximum number of
consecutive days with 16.46
10 rainfall >= 1mm
0
1 366 731 1096 1461 1826
Days

33. Schematic representation of the process for assessing
the effect of climate change on agriculture
GCM
scenarios
Downscaling
PP, MOS, RCM, WGs
Biophysical Experimental
Models Data
Soil, water, crops
Crop Adaptation Management
Yield Strategies Responses
(environmental)
Perfect prognosis (PP), Model output statistics (MOS), Weather generators (WGs)

34. Data Source:http://trmm.gsfc.nasa.gov/

35. Fig.1. Schematic of intermittent random cascade method

37. Gauged versus downscaled monthly precipitation in the Andes: February

38. Better knowledge of soils in target systems
&/or

39. Examples of emerging techniques for
SOC measurements
45 0-2.5 cm
2.5-5 cm
40 5-10 cm
10-20 cm
LIF intensity (a.u.) / C (g kg )
-1
20-30 cm
35
30
25
20
15
10
5
460 480 500 520 540 560 580 600 620 640 660
λ (nm)

40. LIBS System
Source: Da Silva et al., 2008

41. LIF Emission spectrum
soil
3
calcinate and treated soil λexcitation = 458 nm
Humification Degree:
HLIF = LIF Area/total carbon
Intensity (a.u.)
2
1
0
400 450 500 550 600 650 700
λ (nm)
Milori et al., SSSAJ, 2006.; González-Pérez et al., Geoderma, 2007

42. SOM characterization with
13C‐NMR
Nuclear Magnetic Resonance
EMBRAPA Lab

43. Results from EMBU-Kenya
CARBON STOCKS# (kg m‐2)
Area 1 Area 2 Area 3
sites Forest Tea Coffee + Coffee Native Rotation Native Rotation
depth (cm) eucalyptus vegetation crops vegetation crops
0‐2.5 1.8 ±0.1 0.6 ±0.0 0.6 ±0.0 0.5 ±0.0 0.3 ±0.0 0.7 ±0.1 1.0 ±0.0 0.5 ±0.1
2.5‐5 1.3 ±0.1 0.3 ±0.0 0.6 ±0.1 0.5 ±0.0 0.2 ±0.0 0.7 ±0.1 0.8 ±0.0 0.5 ±0.1
5‐10 2.4 ±0.1 1.2 ±0.1 1.3 ±0.3 1.0 ±00 0.5 ±0.0 1.3 ±0.1 1.4 ±0.0 0.9 ±0.1
10‐20 4.1 ±0.6 2.1 ±0.0 2.1 ±0.1 1.8 ±0.2 0.8 ±0.1 2.1 ±0.4 2.8 ±0.1 2.0 ±0.3
20‐30 3.1 ±0.3 2.1 ±0.0 1.9 ±0.1 1.8 ±0.2 0.8 ±0.2 1.7 ±0.2 1.8 ±0.1 1.3 ±0.1
Total (0‐30) 12.7 ±1.2 6.3 ±0.1 6.4 ±0.5 5.6 ±0.4 2.6 ±0.4 6.5 ±0.9 7.8 ±0.3 5.1 ±0.6

44. LIF results:Kenya
Humification degree or carbon stability (HLIF) of whole soils obtained
through Laser Induced Fluorescence (LIF) spectroscopy.
90
80
70
60
LIF inde x (a.u.) 50
(x1000) 40
30 0 - 2.5
20
2.5 - 5
10 20 - 30
0 5 - 10
5 - 10
depth (cm )
forest (1)
10 - 20
tea (1)
0 - 2.5
coffee + eucalyptus (1)
coffee (1)
natural vegetation (2)
rotation (2)
natural vegetation (3)
20 - 30
rotation (3)
Land us e
# HLIF can be estimated through the ratio area under fluorescence emission
(excitation range 350 - 480 nm) / total organic carbon content.

45. Know your genetic material
Selection of contrasting drought & heat tolerance
genotypes
Native Andean potato
• S. tuberosum Andigenum cultivar group
• S. ajanhuiri
• S. juzepczukii
• S. curtilobum
Source: Division 3

46. Potato climate requirements
Temperature Requirements: A. Effect of temperature on the metabolic reaction rate B. Effect of soil temperature on the emergency rate of
potato plants
Reaction Rate Optimal t°
mean daily temperatures 18 to 20°C %
Emergency Rate
night temperature below 15°C (required for
tuber initiation)
temperatures below 10°C and above 30°C Temperature ( °C )
Temperature ( °C )
inhibit tuber growth C. Effect of temperature on photosynthesis and
respiration in potato
D. Relationship between total dry matter and intercepted
solar energy under different environmental conditions
Respiration/photosynthesis rates
(gCO2 cm -2 hoja min -1
Cummulative DM (gcm-2)
Cold weather + water
B = 2.0
• Water Requirements: Total
photosynthesis
Net hesis
Warm weather + water
B = 1.2
ynt
tos Warm weather w/o water
pho B = 0.8
– 500 to 700 mm for a 120 to 150 d Respiration
growing season Air temperature ( °C ) Intercepted solar radiation

47. Best 20 breeding potato clones vs. Drought
Best 20 – Irrigated tolerant to drought
Potato Drought Tolerance 2000.0
Screening
1800.0
1600.0
1400.0
1200.0
1000.0
800.0
600.0
400.0
200.0
• Materials: breeding clones & 0.0
16
18
43
20
39 .4
.5
.1
37 3
.5
.1
n
21
88
.1
9
1
1
8
3
0
3
ea
.
.2
.3
.1
.3
.4
.3
.1
64
02
01
00
28
24
17
01
01
00
01
00
01
landraces from CGS
13
08
36
29
73
74
65
M
71
14
91
36
55
50
17
37
72
72
37
37
72
60
06
81
35
94
29
38
39
38
59
39
39
59
39
39
38
39
39
59
• Methods:
– Replicated plots in La Molina
– Irrigation suspended 5-6 weeks
after planting
– Harvest 90-110 days
• Results: collection of 192 drought-
tolerant breeding clones and
landraces
Drought tolerance screening in CIP potato CGS R. Cabello, E. Chujoy

48. RS data for helping select tolerant potato cultivars
NDVI
0-0.1
0.1-0.2
0.2-0.3
0.3-0.4
0.4-0.5
0.5-0.6
Fresh yield (t/ha)
<16
>24
60 60
50 50
Fresh yield (t/ha)
Fresh yield (t/ha)
40 40
30 30
20 20
10 10
0 0
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
Plot Plot
Normal irrigation
Deficit irrigation Terminal drought

49. Understanding root
architecture, growth, and
H2O transport with non-
destructive non- invasive
tools

50. Integrate knowledge
• Modeling
• Scenario assessment
• Tradeoffs
• Generate & promote appropriate
technologies & management practices
• Inform decision makers for appropriate
policies

51. Adapting models for CC scenarios
S. Tuberosum - tuberosum - andigena S. Ajanhuiri S. juzepczukii
Light
Light
Interception
LUE (—)
DM
PAR
Photosynthetic
Apparatus
Kg DM.ha¨¹.d ¨¹ T GC LAI
Light Reflectance
Tubers
Roots Stems Leaves

52. Figure 1. Tuberization dynamics in the high Andes
5
4.5
4
Tuberization rate, g/m2-degree day
3.5
3
Alp TDM'
Gen TDM'
2.5 Aja TDM'
Saj TDM'
Tot TDM'
2
1.5
1
0.5
0
0 500 1000 1500 2000 2500
Degree-days

53. Global risks of potato tuber moth in potato:
2000 - 2050

54. Absolute generation index change due to
effect of climate change: 2000 - 2050
Expansion to the North and to higher altitudes!

56. Putting pieces together for a hypothetical example:
Changes in potential potato (improved and native) in Peru: 2000-2050

57. Late Blight (LB)
Warmer temperatures with
some humidity in higher
grounds will increase the
presence of potato late blight.
High incidence of LB in the
future (2050) above 3000
masl (highlighted in the map)
where it is virtually absent
today

58. Potato tuber moth (PTM)
PTM is actually present in
interandean valleys and the
coastal areas of the Andes
PTM is expected to climb as
well due to climate change

59. As temperature and presence of pest increase in the
Andes Potatoes are planted in higher grounds
1975:
(4000-4150msnm)
2005:
(4150-4300msnm)
S. De Haan & H. Juarez, CIP (2008)

60. Peatlands and other
land uses in the
Andean high
plateau

61. Potential loss of soil carbon stocks due to cropping
peatlands and grasslands in Peru & Bolivia
Peatlands to potato
350
300
Gigagrams (10x9)
250
200
150
100
50
0
2000 Scenarios 2050
Bolivia Peru
Grasslands to potato
12000
10000
Gigagrams (10x9)
8000
6000
4000
2000
0
2000 Scenarios 2050
Bolivia Peru

62. A strategy for change
• Stress‐tolerant varieties
• Sustainable soil
management practices
• Farmers as environmental
stewards
(incentives/rewards)

63. A Systems Approach
Agricultural Systems and
Agriculture as part of the
Broader System of
Climate Change Mitigation
International Potato Center