1. No category

Role of intensive management, Technology and
Tree Improvement in Chilean Productivity:
Applicable Operational Lessons for SE US
Growers
Cristian R. Montes, PhD
Warnell School of Forestry and Natural Resources
University of Georgia
Summary
• Context
• Spatially explicit environmental information used at every step of the
process
• Matching carbohydrate reserves with site environmental information
helped clonal program success.
• Clonal Program success largely dependent on Nursery deployment
program.
• Site biophysical information is used to determine growth constrains
and plant limiting factors
• Growth and yield models are combined with environmental
information to make better decisions
Forest objectives
•5.8 million acres intensive
forest
•3 species mainly clonal:
P.radiata (60%), E.globulus
(70%), E.nitens (3%)
Large Variability in Temperature and rainfall in Chile, therefor, other information
is needed.
Mean Yearly Temperature
Mean Yearly
Rainfall
(°C)
(mm)
-3.7 - 5.5
377 - 900
5.5 - 7.6
900 - 1,300
7.6 - 9.4
1,300 - 1,600
9.4 - 11.1
1,600 - 1,900
11.1 - 12.7
12.7 - 15.5
48° – 59° F range
1,900 - 2,200
2,200 - 3,827
15 – 100 in range
Water holding capacity is being estimated
using pedotransfer functions
0 100
}
10
20
}
80
30
70
40
60
50
50
60
40
Moist Content %
40
30
30
20
20
10
10
0
70
80
}
90
100
0
10
90
Soil
Water
Storage
Capacity
0
20 30 40 50 60 70 80 90 100
% Sand
2 – 10 inch range
Site specific Silviculture works at the zone scale.
Precision silviculture uses spatially explicit
information to make recommendations
We wanted to estimate likely forest in between our
estimates, and predict silviculture response
Extra information is used to interpolate
23
16
11
15
10
16
20
17
28
21
21
24
20
22
Extra information is used to interpolate
(that includes some noise)
23±5
16±2
11±1
15
10±.5
16±2
20±4
17±4
28±2
21±2
21±4
20±5
24
22±2
Containerized
rooted cuttings
Clones out of
somatic
embriogenesis
Expected 15% gain at rotation age
Containerized
rooted cuttings
in Eucalyptus
No need for
somatic
embriogenesis
Expected 40% gain at rotation age
Result from the process
Bare root cutting.
Containerized Cutting:
Two major limitation to clonal production out of somatic
embryogenesis: Making the trees to grow roots
Radiation Intensity
and light quality
research important to
better understand
Clonal production
variables.
Rooting Probability
Light intensity affects clonal chances to
root
Light Intensity (mmol m2 s-1)
Application
of artificial
light in
greenhouse
duplicate
plan growth
rate
No light
Light
Gaining better light-water-nutrient understanding through
detail environmental measurements and modeling.
2nd Peek growth
rate
1st Peek grow
rate
Initial lag
1st Rooting
Plateau
“Lettuce plant”
High glucose concentration
High chlorophyll
Fast initial grow
Doesn’t resistant drought
“Potato” plants
Drought resistant
High starch concentration
Low initial grow
Taste bad if put in the refrigerator
Concept utilized to predict field
performance:
Grow
• On a sunny day,
1g fixed
CH2O, investing
in 2 g
of biomass or in storage.
Stay alive (respiration)
• One seedling would use up to .5 gr C at 77°F
through respiration
• At 95° F it will use up to 1 g de CH2O. (Q10)
Compaction did not affect survival,
drainage did.
Flooded site = 20 times more
respiration compared to unflooded
plant.
At 41° will use 0,2 gr,
Flooded will use 4g,
For a 0,5g during winter fixation
Draining excess moisture or bedding better than subsoiling in some sites
Flow Direction and Flow Accumulation
Used to better design drainage chanels
0.93
(m3/s)
Two chanels 0.4 x 0.4 meters
Dry soils with low hydraulic
conductivity, higher frost damage
probability.
T°
Dying plants
because of
excess
temperature
not because of
rainfall.
T°
High weed, high frost effect.
No weeds, no
High weed, high frost mortality
Predio Nicaragua
Big impact of
harvesting
machinery
In soil compaction
and internal
drainage
Stand Yield for several establishment treatments, showed varying levels of responsiveness
Volume yield (m3/ha)
Treatments:
Full Weed C.
Sub Soil + Weed
None
Age (years)
Response for subsoiling at 5 years of age
Bulk, density
Clay content
(%)
62
0.2 - 0.9
0.9- 1.1
1.1 - 1.3
1.3 - 1.5
0
%Organic Matter
0-3
3-6
6-9
9 - 12
12 - 25
Water deficit(mm)
High : 0
Low : -1276
1.5 - 2.6
Relative Response at
5 years
No Subsoiling
0 – 50%
50 – 200%
200 – 500%
Barria and Montes 20
Precip.
PET
300
Water (mm)
250
200
150
100
50
JA
N
FE
B
M
AR
AP
R
M
AY
JU
N
SE
P
O
C
T
N
O
V
D
EC
JU
L
AU
G
0
Month
300
Water Deficit
-1,276- -940
-940 - -750
-749 - -550
250
Water (mm)
(mm)
Precip.
PET
200
150
100
-549 - -300
-299 - -150
50
Month
JU
N
AY
M
AP
R
AR
M
FE
B
JA
N
V
EC
D
O
C
T
N
O
SE
P
AU
G
JU
L
-149 - 0
Good
agreement
between
WDI
and yield in
Chile
First Year Survival (%)
Water Deficit Index (mm)
Water Deficit Class (mm)
Spot
No Control
No Control
No Weeds
No Weeds
No Water Deficit
Strong Water deficit
More trees per hectare = more yield
Crecimiento Corriente en
Volumen (m3/ha/año)
entre los 5 y 5
6 años
los ensayos de espaciamiento
Volume
growth between
andpara
6 years
60
Volume growth (m3/ha)
50
40
30
20
Chan Chan Bajo
Cuy inco Alto
Villa Alegre Grande
Las Lumas
Hijuela 3
Molino del Sol
Pinares de Forel
Pichinguileo
Huelón
San Gregorio Mingre
SanGregorio Mingre
Rari
Quiv olgo 1
Reñico
San Luis 2
Collico
Guacamapu
Water Deficit Index
Pr:
Pr:
Pr:
Pr:
Pr:
Pr:
Pr:
Pr:
Pr:
Pr:
Pr:
Pr:
Pr:
Pr:
Pr:
Pr:
Pr:
ICA VHA (m3/ha)
10
Crecimiento Corrien
0
0
500
1000
1500
2000
Densidad de Plantación (árb/ha)
Planting density (trees/ha)
2500
3000
Plantation density to affect more on highly productive sites
Crecimiento
los 500
árboles dominantes
entre los 5 y 65años
Volume growth
forde500
dominant
trees between
and 6 years
35
ICA VHA 500
(m3/ha)
Water Deficit Index
Volume growth (m3/ha)
30
25
20
15
Pr:
Pr:
Pr:
Pr:
Pr:
Pr:
Pr:
Pr:
Pr:
Pr:
Pr:
Pr:
Pr:
Pr:
Pr:
Pr:
Pr:
Chan Chan Bajo
Cuy inco Alto
Villa Alegre Grande
Las Lumas
Hijuela 3
Molino del Sol
Pinares de Forel
Pichinguileo
Huelón
San Gregorio Mingre
SanGregorio Mingre
Rari
Quiv olgo 1
Reñico
San Luis 2
Collico
Guacamapu
10
5
Crecimiento Corrient
0
0
500
1000
1500
2000
Densidad de
Plantación
(árb/ha)
Planting
density
(trees/ha)
2500
3000
Area Foliar v/s Incremento Volumen
Ensayos Poda
26
24
22
20
18
16
14
12
10
8
6
4
2
0
0%
28
27
26
25
24
23
22
21
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0
0.0
Water Deficit (-mm)
3
(m3(m
Increment
Volume
/ha/year)
Volume
growth
(m3/ha)
Incremento
/ha)
Volumen
Leaf Area Efficiency varied between Sites, depending on water availability
ENS: 501 (El Peral)
ENS: 502 (Laguan)
ENS: 503 (Los Remolinos)
ENS: 504 (Pilpilco A.)
ENS: 505 (Santa Estela)
ENS: 506 (Pidima)
10%
0.5
1.0
20%
1.5
30%
2.0
40%
2.5
50%
3.0
60%
3.5
LeafLeaf
Area LAI_final
After
Pruning
%
Area
Removed
(m2/m2)
70%
4.0
4.5
80%
5.0
The more
the density,
la bigger
theAleaf
area up
to a fix amount
TENDENCIA
MAYOR
IAF
MAYOR
DENSIDAD
INICIAL
4
CLON KC59
CLON LX876
FAM S1
INDICE DE AR
Leaf Area Index (m2/m2)
) 2 /m
2
3
2
1
800
1200
1600
2000
DENSIDAD
INICIAL
(árb/ha)
Initial
Density
(trees/ha)
2400
Same yield, different
canopy
sizeCOPA v/s LX876
KC59 MAYOR INCREMENTO
A IGUAL
LARGO
60
45
/ha/año)
3
Volume growth (m3/ha)
CLON: KC59
CLON: LX876
30
15
KC59: ICA_VHA = 16.7769+1.4769*LCopa
LX876: ICA_VHA = 6.1915+1.5296*LCopa
INCREMEN
0
0
5
10
15
LARGO
COPA
(km/ha)
Canopy
length
(trees/ha)
20
25
The
one with
more juvenile
leaves
was able
hold
them even at high
LARGO
COPA
JUVENIL
KC59
NOtoES
AFECTADO
PORdensities
DENSIDAD
LARGO COPA JU
Juvenile crown length (m2/m2)
6
5
CLON KC59
CLON LX876
FAM S1
4
3
2
1
0
800
1200
1600
2000
DENSIDAD
INICIAL
(árb/ha)
Initial
Density
(trees/ha)
2400
Current annual increment
(feet3 acre-1 y-1)
Light Interception is the driver for Biomass
Production
R2=0.90
160
140
120
100
80
60
40
20
500
1000
1500
2000
2500
Annual IPAR(MJ m2 y-1)
(Adapted, Will et al., 2005)
3000
Digital Terrain Models
help evaluate plantations in
context of the landscape
Slope
0-6
6 - 12
12 - 17
17 - 22
22 - 27
27 - 31
Aspect
31 - 34
34 - 39
Flat (-1)
North (0-22.5)
Northeast (22.5-67.5)
East (67.5-112.5)
Southeast (112.5-157.5)
South (157.5-202.5)
Southwest (202.5-247.5)
West (247.5-292.5)
Northwest (292.5-337.5)
North (337.5-360)
39 - 54
Monthly radiation integral is used to understand differences in
productivity within and between sites
Winter
Summer
Monthly Radiation
MJ/m2/month
High : 40.03
Low : 5.833
Carrasco & Montes et al 2002
Monthly Radiation
MJ/m2/month
High : 40.03
Low : 5.833
Light use Efficiency is the way we
can change things
100
% Intercepted Radiation
90
e = 4.1
80
70
60
Beer-Lambert equation
e = 24.7
50
I = 1 – (e (-k *L))
IAF = 4
40
30
20
10
IAF = 1
0
0
1
2
3
Leaf Area Index
4
(m2/m2)
5
6
7
Incorporating process based thinking into
empirical G&Y equations
1.0
0.6
0.4
max
min
0.2
optimum
Growth rate
0.8
0.0
40
50
60
70
80
90
Mean temperature (°F)
100
3: A hybrid state-space growth and yield model for loblolly pine.
1.0
1.0
0.8
0.8
Growth rate
Growth rate
Incorporating process based thinking into
empirical G&Y equations
0.6
0.4
0.6
0.4
0.2
0.2
0.0
0.0
0
5
10
VPD (mBar)
15
20
Critical
Moisture
0
20
40
60
80
Soil moisture ratio (%)
100
Resource index between years
Growth Index
Resource Index
1350
1300
1250
1200
1150
1100
1992
1994
1996
1998
Year
2000
2002
2004
Domminant Height (ft)
60
50
40
30
20
Control
Irrigation
Fertilization
Irr. + Fert
10
0
7
9
11
13
15
17
19
21
Stand Age
Montes, 2012
50
2004
40
2000
30
1997
20
1994
Domminant Height (ft)
60
10
Irr. + Fert
1991
0
0
2000
4000
6000
8000
10000
Cumulative Resource Index * LAI
Montes, 2012
50
2004 (Irr+ Fert)
40
2004 (Control)
Domminant Height (ft)
60
30
20
10
Control
Irrigation
Fertilization
Irr. + Fert
0
0
2000
4000
6000
8000
10000
Cumulative Resource Index * LAI
Montes, 2012
% Error
Expected Yield for E.globulus
at 10 years (m3/ha)
Montes et all 20
Yield probabilities for Eucalyptus globulus over the plantation area
P (V >300 m3/ha)
P (V >200 m3/ha)
P (V >150 m3/ha)
Montes et all 20
Final yield probability at fix thresholds
P (V >150 m3/ha)
P (V >200 m3/ha)
P (V >250 m3/ha)
P (V >300 m3/ha)
Radiata pine
productivity
with explicit
uncertainty
showing areas with
poor
representation
Montes and Fuenzalida
Estimated volumetric
Volumetric
(afterafter
3 years)
post fertilization
gain threegain
years
fertilization
farm: Conuco
5944000
32
38
34
36
32
32
36
5943500
34
30
34
36
5943000
32
34
Break even Volume
gain
34
32
32
5942500
30
30
34
34
28
5942000
699000
699500
700000
700500
Montes et all 2012
Summary
• Context
• Spatially explicit environmental information used at every step of the
process
• Matching carbohydrate reserves with site environmental information
helped clonal program success.
• Clonal Program success largely dependent on Nursery deployment
program.
• Site biophysical information is used to determine growth constrains
and plant limiting factors
• Growth and yield models are combined with environmental
information to make better decisions
Acknowledgements
Ivan Appel, Beatriz Barria, Hebert Ojeda, Pedro Burgos, Juan
Quiroga, Sandra Fuenzalida, Ricardo Gonzalez, Carlos
Contardo, Damian Almendras, Rodrigo Burgos, Francisco
Garate, Claudio Balocchi, Francisco Flores, Mariela Yanez,
Eduardo Rodriguez, Rodolfo Calquin, Cecilia Munoz, Carlos
Jaque, Marcela Millar, Juan Enrique Junemann, Milton Flores,
Luis Flores, Carlos Gonzalez, Carlos Vergara, Carlos Martin,
Marco Justiniano, Liliana Villalobos, Rodrigo Ruiz, Rafael
Rubilar, Jorge Toro, Rodrigo Palma, Pabla Vejar, Juan Anzieta,
Felipe Vargas, Susan Crumacher