6. ABIOTIC FACTORS PRECIPITATION
+2 to +5 °C +10 to +30 %
TEMPERATURE
GENETIC CONTROLS
• Fescue cultivar: KY-31, Jesup
• Fescue genotype
• Endophyte strain :
• Common toxic endophyte: ergot
and loline alkaloids producer
• Novel endophyte: loline alkaloids
producer. Example: MaxQ
• Endophyte status: E+; E-
ENDOPHYTE,
Neotyphodium coenophialum
7. ABIOTIC FACTORS PRECIPITATIONTEMPERATURE
1st Approach:
Using an existing fescue variety trial data
across the U.S.
Relationship between local weather
variables and fescue yield of KY-31 (E+; E-)
and Jesup (E+; E-) across the U.S.
GENETIC CONTROLSENDOPHYTE,
Neotyphodium coenophialum
8. ABIOTIC FACTORS ELEVATED PRECIPITATION
+3 °C +30 %
ELEVATED TEMPERATURE
2nd Approach:
Using a manipulative field experiment in
Lexington, KY
Quantifying the ecophysiological
responses of E+ and E- tall fescue to
climate change factors.
GENETIC CONTROLSENDOPHYTE,
Neotyphodium coenophialum
9. Relationship between local weather variables and fescue yield of KY-31
(CTE+; E-) and Jesup (MaxQ+; E-) across the U.S.
• Hypothesis 1: Yield would be highest in warm + wet conditions
• Hypothesis 2: But it would be location dependent
• Hypothesis 3: E+ > E-, especially in hot and dry conditions
• Hypothesis 4: E- KY-31 and E- Jesup would perform differently
10. Forage variety trials:
• Cultivar fescue yields
• Endophyte status (E+, E-) and strain (CTE, MaxQ)
• Establishment and harvest dates
• Seedling rate, fertilization, irrigation
• Soil type
Local climate (local weather stations, national climate database):
• Daily precipitation
• Daily maximum, minimum, mean temperature
11. What fescue yield?
• KY-31 and Jesup fescue yield per day
• Endophyte status (E+, E-) and strain (CTE, MaxQ)
When?
• 2nd year of establishment
• 2nd cut
• Summer harvest: April-May to June-July-August
Compared to…
• Daily precipitation
• Daily maximum and mean temperature
Where?
14. Comparison
Number
of Data
Location (State)
Year
Range
H1: Effect of
temperature and
precipitation
Across
cultivar and
endophyte
status
151
GA, IL, KA, KY, MI, MS, NM, NC, OH,
PA, TN, WI
1977 - 2012
H2: Effect of
Location
H3: Influence of
endophyte
infection
CTE+ 67
GA, IL, KA, KY, MI, MS, NC, OH, PA,
WI
1977 - 2012
E- 34 IL, KA, KY, MI, OH, PA 1992 - 2012
MaxQ+ 40 GA, IL, KA, KY, MI, NM, OH, PA, TN 2002 - 2011
E- 10 GA, KY, MI, OH 1991 - 2012
15. Comparison
Number
of Data
Location (State)
Year
Range
H1: Effect of
temperature and
precipitation
Across
cultivar and
endophyte
status
151
GA, IL, KA, KY, MI, MS, NM, NC, OH,
PA, TN, WI
1977 - 2012
H2: Effect of
Location
H3: Influence of
endophyte
infection
CTE+ 67
GA, IL, KA, KY, MI, MS, NC, OH, PA,
WI
1977 - 2012
E- 34 IL, KA, KY, MI, OH, PA 1992 - 2012
MaxQ+ 40 GA, IL, KA, KY, MI, NM, OH, PA, TN 2002 - 2011
E- 10 GA, KY, MI, OH 1991 - 2012
H4: Influence of
cultivar (E-)
KY-31 34 IL, KA, KY, MI, OH, PA 1992 - 2012
Jesup 10 GA, KY, MI, OH 1991 - 2012
16. SAS 9.3:
• H1, H2: Yield = Location, Precipitation, Temperature and interactions
• H3: KY31 Yield = Location, Precip., Temp., Endophyte and interactions
H3: Jesup Yield = Location, Precip., Temp., Endophyte and interactions
• H4: E- Yield = Location, Precip., Temp., Cultivar and interactions
Linear regression models (Proc GLM)
Method: Backward elimination
• Fit model with all predictors
• Remove predictor with largest p-value
• Refit model
• Repeat until all remaining predictors are statistically significant
24 locations, and only Kentucky
21. ABIOTIC FACTORS PRECIPITATION
• Cultivar: Kentucky-31; Jesup
• Endophyte strain : CTE; MaxQ
TEMPERATURE
1st Approach:
Using an existing fescue variety trial data
across the U.S.
Relationship between local weather
variables and fescue yield of KY-31 (CTE+;
E-) and Jesup (MaxQ+; E-) across the U.S.
GENETIC CONTROLS
• Endophyte status: E+; E-
LOCATION
• Not enough data?
• Not extreme enough conditions?
• Not paired?
ENDOPHYTE,
Neotyphodium coenophialum
22. ABIOTIC FACTORS ELEVATED PRECIPITATION
+3 °C +30 %
ELEVATED TEMPERATURE
2nd Approach:
Using a manipulative field experiment in
Lexington, KY
Quantifying the ecophysiological
responses of E+ and E- tall fescue to
climate change factors.
• Fescue genotype
• Endophyte strain
GENETIC CONTROLS
• Endophyte status: E+; E-
ENDOPHYTE,
Neotyphodium coenophialum
23. Quantifying the ecophysiological responses of E+ and E- tall fescue to
climate change factors.
• Obj. 1: Plant genetic controls over fescue response to climate change
Hypothesis: Different degree of sensitivity across genotype
• Obj. 2: Variability across fescue genotypes to endophyte infection
Hypothesis: Different effects produced by endophyte presence
• Obj. 3: Variability in fescue-endophyte symbiosis to climate change
Hypothesis: Different responses to climate change factors
31. Jan Feb Mar Apr May Jun Jul Aug Sep DecOct Nov
2012
Net photosynthesis rate (LICOR-6400): n=10
32. Net photosynthesis rate (LICOR-6400): n=10
Leaf water potential (Scholander-type pressure chamber): n=3
Jan Feb Mar Apr May Jun Jul Aug Sep DecOct Nov
2012
33. Net photosynthesis rate (LICOR-6400): n=10
Leaf water potential (Scholander-type pressure chamber): n=3
Aboveground growth and production:
• Growth rate n=31
Jan Feb Mar Apr May Jun Jul Aug Sep DecOct Nov
2012
34. Net photosynthesis rate (LICOR-6400): n=10
Leaf water potential (Scholander-type pressure chamber): n=3
Aboveground growth and production:
• Growth rate n=31
• Tiller production/mortality n=4
Jan Feb Mar Apr May Jun Jul Aug Sep DecOct Nov
2012
35. Net photosynthesis rate (LICOR-6400): n=10
Leaf water potential (Scholander-type pressure chamber): n=3
Aboveground growth and production:
• Growth rate n=31
• Tiller production/mortality n=4
• Aboveground biomass n=3
Jan Feb Mar Apr May Jun Jul Aug Sep DecOct Nov
2012
36. Net photosynthesis rate (LICOR-6400): n=10
Leaf water potential (Scholander-type pressure chamber): n=3
Aboveground growth and production:
• Growth rate n=31
• Tiller production/mortality n=4
• Aboveground biomass n=3
• Overall mortality
Jan Feb Mar Apr May Jun Jul Aug Sep DecOct Nov
2012
37. Net photosynthesis rate (LICOR-6400): n=10
Leaf water potential (Scholander-type pressure chamber): n=3
Aboveground growth and production
Endophyte physiology:
• Status (Immunoblot test kit): n=1
Jan Feb Mar Apr May Jun Jul Aug Sep DecOct Nov
2012
38. Net photosynthesis rate (LICOR-6400): n=10
Leaf water potential (Scholander-type pressure chamber): n=3
Aboveground growth and production
Endophyte physiology:
• Status (Immunoblot test kit): n=1
• Alkaloids production (HPLC; GC): n=3
Jan Feb Mar Apr May Jun Jul Aug Sep DecOct Nov
2012
39. Net photosynthesis rate (LICOR-6400): n=10
Leaf water potential (Scholander-type pressure chamber): n=3
Aboveground growth and production
Endophyte physiology
Aboveground %C and N (Flash EA1112 elemental analyzer): n=3
Jan Feb Mar Apr May Jun Jul Aug Sep DecOct Nov
2012
40. Net photosynthesis rate (LICOR-6400): n=10
Leaf water potential (Scholander-type pressure chamber): n=3
Aboveground growth and production
Endophyte physiology
Aboveground %C and N (Flash EA1112 elemental analyzer): n=3
Statistical analysis:
• Repeated measures, split-plot 2*2 factorial
• SAS 9.3, Proc Mixed
• Fixed effects: time, heat, precipitation, endophyte, genotype
Jan Feb Mar Apr May Jun Jul Aug Sep DecOct Nov
2012
Heat (+3 °C)
0 1
Precip. (+30%)
0 Control +Heat
1 +Precip +Heat+Precip
45. Control +Heat +Precip +Heat+Precip
TillerProduction(counts)
0
10
20
30 Geno 14
Geno 45
Geno 16
Geno 19
AB
A
A
A
B
B
C
B
A
A
B
B
B
B
B
B
Genotype: < 0.0001
Heat: 0.0271
Precipitation: NS
Genotype × Heat × Precipitation: < 0.0001
LeafWaterPotential(MPa)
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5 Geno 14
Geno 45
Geno 16
Geno 19
Genotype: 0.0001
Heat: < 0.0001
Precipitation: < 0.0001
Genotype × Heat × Precipitation: 0.0235
Control +Heat +Precip +Heat+Precip
B
B C
AB
C
C
C
C
A
A
A
A
BC
BC B
B
LeafWaterPotential(MPa)
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5 Geno 14
Geno 45
Geno 16
Geno 19
Genotype: 0.0001
Heat: < 0.0001
Precipitation: < 0.0001
Genotype × Heat × Precipitation: 0.0235
Control +Heat +Precip +Heat+Precip
b
b b
a
b
ab
a
b
a
a
a
a
b
b b
a
Control +Heat +Precip +Heat+Precip
TillerProduction(counts)
0
10
20
30 Geno 14
Geno 45
Geno 16
Geno 19
c
ab
a
b
a
a
a
a
a
a
a
b
b
b
a
b
Genotype: < 0.0001
Heat: 0.0271
Precipitation: NS
Genotype × Heat × Precipitation: < 0.0001
46. Endophyte Infection
P-value Mean ± S.E.
Photosynthesis
(µmol.m-2
.s-1
)
0.0121
E+
E-
16.8 ± 0.3 (a)
15.7 ± 0.3 (b)
Growth Rate (cm.day-1
) 0.0008
E+
E-
0.3 ± 0.0 (a)
0.2 ± 0.0 (b)
Tiller production
(counts)
0.0010
E+
E-
8 ± 1 (a)
4 ± 1 (b)
Annual aboveground
biomass (g.plant-1
)
0.0158
E+
E-
6.5 ± 0.4 (a)
5.4 ± 0.3 (b)
AnnualAbovegroundBiomass(g.plant
-1
)
0
2
4
6
8
10
12
E+
E-
b
a
b b
b b
b
b
Endophyte: 0.0158
Endophyte × Genotype: 0.0447
Symbiotic Genotype
CTE14 CTE45 NE16 NE19
TillerProduction(counts)
0
2
4
6
8
10
12
14
16
18
a
a
b
b b
b
b
b
Endophyte: 0.0007
Endophyte × Genotype: < 0.0001
47. Symbiotic Genotype per Treatment
C
TE14C
TE45N
E16N
E19
C
TE14C
TE45N
E16N
E19
C
TE14C
TE45N
E16N
E19
C
TE14C
TE45N
E16N
E19
Photosynthesis(mol.m
-2
.s
-1
)
0
5
10
15
20
25
30
E+
E-
Control +Heat +Precip +Heat+Precip
Endophyte: 0.0121
Heat: < 0.0001
Precipitation: 0.0124
Genotype × Endophyte × Heat × Precipitation: 0.0387
A
ab
a
A
a
A
A
b
A
a
a
AB
B
b
B
ab
A
ab
A
ab
A
b
a
A
A
b
A
b
a
A
A
b
Symbiotic Genotype per Treatment
C
TE14C
TE45N
E16N
E19
C
TE14C
TE45N
E16N
E19
C
TE14C
TE45N
E16N
E19
C
TE14C
TE45N
E16N
E19
Photosynthesis(mol.m
-2
.s
-1
)
0
5
10
15
20
25
30
E+
E-
Control +Heat +Precip +Heat+Precip
Endophyte: 0.0121
Heat: < 0.0001
Precipitation: 0.0124
Genotype × Endophyte × Heat × Precipitation: 0.0387
A
ab
a
A
a
A
A
b
A
a
a
AB
B
b
B
ab
A
ab
A
ab
A
b
a
A
A
b
A
b
a
A
A
b
E+ = E-
E+ = E-
*
E+ = E-
48. Symbiotic Genotype per Treatment
Control +Heat +Precip +Heat+Precip
Treatment
Control +Heat +Precip +Heat+Precip
ErgotConcentration(ppm)
0.0
0.2
0.4
0.6
0.8
CTE14
CTE45
Symbiotic Genotype: < 0.0001
Heat: < 0.0001
Precipitation: 0.0093
Symbiotic Genotype × Heat × Precipitation × Time: 0.0024
B
A
AB
A
C
AB
AB
B
Xheat = 0.3 ± 0.0 (a) Xambient heat = 0.2 ± 0.0 (b)
Xprecip = 0.2 ± 0.0 (b) Xambient precip = 0.3 ± 0.0 (a)
Symbiotic Genotype per Treatment
Control +Heat +Precip +Heat+Precip
Treatment
Control +Heat +Precip +Heat+Precip
ErgotConcentration(ppm)
0.0
0.2
0.4
0.6
0.8
CTE14
CTE45
Symbiotic Genotype: < 0.0001
Heat: < 0.0001
Precipitation: 0.0093
Symbiotic Genotype × Heat × Precipitation × Time: 0.0024
a
a
b
b
a
a
b
b
XCTE14 = 0.3 ± 0.0 (a) XCTE45 = 0.1 ± 0.0 (b)
49. Control +Heat +Precip +Heat+Precip
TillerProduction(counts)
0
5
10
15
20
a
ab
ab
ab
bc
c
ac
c
AnnualAbovegroundBiomass(g.plant-1
)
0
2
4
6
8
10
12
E+
E-
Endophyte: 0.0158
Heat: NS
Precipitation: NS
Endophyte × Heat × Precipitation: 0.0062
a
ac
abc
bc bbc
abc
abc
Endophyte: 0.0007
Heat: 0.0271
Precipitation: NS
Endophyte × Heat × Precipitation: 0.0220
50. ABIOTIC FACTORS ELEVATED PRECIPITATION
+3 °C +30 %
ELEVATED TEMPERATURE
GENETIC CONTROLS
Relative
importance
depends on
evaluated
parameters
ENDOPHYTE,
Neotyphodium coenophialum
51. ABIOTIC FACTORS ELEVATED PRECIPITATION
+3 °C +30 %
ELEVATED TEMPERATURE
• Fescue genotype: Important
• Endophyte presence and strain :
Important
• Fescue-Endophyte combination:
CTE14 and NE19
GENETIC CONTROLS
• E+ > E-
• Leaf water potential, %C: No
effect
• Parasitism? Very few support
Endophyte × Heat
>
Endophyte ×
Precipitation
What difference between
fescue genotypes?
What difference
between strains?
ENDOPHYTE,
Neotyphodium coenophialum
Heat × Precip ≠ Heat + Precip
52. 1st approach (multiple locations):
• No endophyte or cultivar effect on fescue yield
• Precipitation and temperature effect
• Strong location effect
2nd approach (one location):
• Tall fescue genotype as important as endophyte presence
• Ecophysiological parameters varied
Response to future changes in climate will depend on fescue genetics,
endophyte presence and strains, the environmental changes, and crop
management
53.
54. Dr. Rebecca McCulley
Dr. Dinkins, Dr. Phillips and Dr. Egli
Dr. Bush, Huihua Ji, Kristen McQuerry, Gene
Olson, Forage extension teams for 1st study
Department of Plant and Soil Sciences, UK
Team Endo-Fight: Jim Nelson, Lindsey
Slaughter, Ben Leffew, Elizabeth Carlisle and
Dan Weber
Other “Peeps”: Alexandra & Caleb Williams,
Alex Hessler, Marion Robert, Mizuki Tateno,
Jessi Ghezzi, Bret Sparks, Ezequiel De
Oliveira, Grant Mackey, etc.
#1 Advisor
Numerous studies have shown that fescue is very sensitive to abiotic factors (ex. Across the U.S. = different climates) and when it is infected, it performs better especially under hot and dry conditions, although it is not universal and the mechanisms are complex and not clearly identified.
Several studies have investigated on genetic controls, responding to abiotic factors or endophyte infection: plant (cultivar + fescue genotype)
Prior papers have shown relationships with abiotic factors highest fescue yield in warm and wet conditions
Based on soil type, sometimes yield is not very high, even if it is warm and wet conditions H2
Forage variety trials are usually ran by forage extension team in universities (equivalent here: Dr. Smith). Variety trials are evaluated for agronomic purposes (farmer use, etc.). TF is a common one so all these reports represent a large database. Information about …
All the site locations vary susbtantially among management, year of establishment, etc. and there are often more than one cut per year. In KY for example, 3 to 5 cuts per year
At each site, multiple data so selection was needed
Jesup / KY-31 because there were the most commun
Time: preliminary analysis/models on KY-31 and compare 1st year of establishment, 2nd or 3rd, as well as the number of cut. I found most significance on the 2nd year of establishment and primarly on the 2nd harvest of the year, that way I knew the time period. Summer harvest to capture endophyte effect (we thought it was the best strategy).
For some states, variety trials have been only made on KY-31 CTE+ but not for other. example: NC
So I was not able to perform an analysis of variance since those data were not paired, which was a limitation
For some states, variety trials have been only made on KY-31 CTE+ but not for other. example: NC
So I was not able to perform an analysis of variance since those data were not paired, which was a limitation
For some states, variety trials have been only made on KY-31 CTE+ but not for other. example: NC
So I was not able to perform an analysis of variance since those data were not paired, which was a limitation
What I did, was performing regression with SAS 9.3, proc GLM.
Yield increase when it wetter but, contrary to what I have expected, it decreased with temperature. But those behaviors do not acoount for a lot of data, considering the low R2.
Multiple locations, big climate variation and at each site, management and soil type was very different.
Why those results? Mostly because they were not paired so with much more data, I would be able to perform again this work and also compare CTE and Maq performance on the same cultivar.
A better approach would be the one I am about to explain:
NE… This comparison did not allow me to evaluate the endophyte genotype effect or the fescue genotype effect when infected so for my 1st objective (fescue genotype importance, I only used E-).
NE… This comparison did not allow me to evaluate the endophyte genotype effect or the fescue genotype effect when infected so for my 1st objective (fescue genotype importance, I only used E-).
NE… This comparison did not allow me to evaluate the endophyte genotype effect or the fescue genotype effect when infected so for my 1st objective (fescue genotype importance, I only used E-).
Treated as a hay field and cut/harvested 3 times a year.
Because time effect on the measured variables was overwhelming, I will not explain that today, and everything I will show will be across the year.
Clearly +3oC and quite dry in 2012
Opposite effect of Heat and Precip
LWP: Across the three time periods, when averaged across genotype, heat effect and precipitation effect
Small letters refer to difference betzeen genotype within a trt and capital letters between trt within genotype.
The point is that influence differently the genotypes. Rank is 19 > 45 = 16 > 16 except for +Heat where LWP 19 dropped, whereas other genotypes maintaned their level.
ABG was also affected but the 3-way itneraction was not that clear. Tiller production: better on Control, climate factors modify all the genotypes but 14 and 19 were more sensitive.
E+ > E-
Photosynthesis and growth rate are not influenced by the interaction E+ performed better than E- for all genotypes when averaged across the trt
I expected to find many endo x geno interactions but only abgd and tiller production. Enhancement by endophyte infection but only for certain fescue genotype and endophyte strains (14). Tiller, smthg about the combination of both fescue and endophyte genotypes since 14 was very much influenced by endophyte presence.
Explain graph and small/capital letters.
Prior work have shown that E+ does more photosynthesis than E- under high temperature conditions but it not what I found. Essentially the same across trt. Varies by genotype sometimes higher, sometimes lower although it is not significant. Actually, E+ > E- only in +precip plots and only for NE16.
Average.
Don’t include genotype.
HxP =/ H + P
All those factors have relative importance depending on the parameters you are evaluating.
Pasture idea. My 2nd study shows that response depends a lot of how the weather will change. I did not investigate on CO2 influence but it has been demonstrated that pasture responds to CO2. My study also showed that we should know about fescue genotype and endophyte type to predict the exact responses. But my ist study demonstrated that it is not only cte or ne but mainly the local management/soil type. Farmer level;
Actual genetic that farmers have in their field is important (both fescue and fungal). Not the same response if it is increase of +3 or +5 oC for example.
