A stakeholders’ driven development of forest management support tools. Margarida Tomé, Centro de Estudos Florestais, Instituto Superior de Agronomia Universidade de Lisboa Non-Timber Forest Products and Bioeconomy 28–30 November 2017, Rovaniemi, Finland

2. Managing Portuguese forests for
non-timber forest products
A stakeholders’ driven development of
forest management support tools
Margarida Tomé
Centro de Estudos Florestais
Instituto Superior de Agronomia
Universidade de Lisboa

3. With the help of the ForChange group…

4. and of many others…

5. Topics
 Non-timber forest products in Portugal
 Cork oak
– Most important forest management decisions
– Long term optimisation (growth and yield models)
– Short term refining – tools developed on managers’ request
 Stone pine
– Most important forest management decisions
– Long term optimisation (growth and yield models)
– Short term refining – tools developed on managers’ request
 Conclusions

6. Non-timber forest products in Portugal
(results from COST FP1203 survey +
Marlene Marques & Ana Cardeal Msc thesis)

7. Tree products Understory products
Mushrooms & Truffles Animal products
resin
Cork
Pine nuts
Acorns
Amanita
caesarea
Amanita
ponderosa
Boletus
edulis
Chantharellus
cibarius
Terfezia
arenaria
Terfezia
leptoderma
Choiromyces
gangliformis
Tuber
oligospermum
Wild bore
Red deer
Roe deer
Rabbit
Legged partridge
Lúcia-lima
Erva
cidreira
Tomilho limão
Hortelã pimenta
Honey
Lavanda amarela
Tricholoma
equestre
Macrolepiota
procera

8. Tree products Understory products
Mushrooms & Truffles Animal products
resin
Cork
Pine nuts
Acorns
Amanita
caesarea
Amanita
ponderosa
Boletus
edulis
Chantharellus
cibarius
Terfezia
arenaria
Terfezia
leptoderma
Choiromyces
gangliformis
Tuber
oligospermum
Wild bore
Red deer
Roe deer
Rabbit
Legged partridge
Lúcia-lima
Erva
cidreira
Tomilho limão
Hortelã pimenta
Honey
Lavanda amarela
Relevant in
the past
starting to
come back
Tricholoma
equestre
Macrolepiota
procera

9. Tree products Understory products
Mushrooms & Truffles Animal products
resin
Cork
Pine nuts
Acorns
Amanita
caesarea
Amanita
ponderosa
Boletus
edulis
Chantharellus
cibarius
Terfezia
arenaria
Terfezia
leptoderma
Choiromyces
gangliformis
Tuber
oligospermum
Wild bore
Red deer
Roe deer
Rabbit
Legged partridge
Lúcia-lima
Erva
cidreira
Tomilho limão
Hortelã pimenta
Honey
Lavanda amarela
Have potencial
but presently
just used as
animal food
Tricholoma
equestre
Macrolepiota
procera

10. Tree products Understory products
Mushrooms & Truffles Animal products
resin
Cork
Pine nuts
Acorns
Amanita
caesarea
Amanita
ponderosa
Boletus
edulis
Chantharellus
cibarius
Terfezia
arenaria
Terfezia
leptoderma
Choiromyces
gangliformis
Tuber
oligospermum
Wild bore
Red deer
Roe deer
Rabbit
Legged partridge
Lúcia-lima
Erva
cidreira
Tomilho limão
Hortelã pimenta
Honey
Lavanda amarela
Have potential but presently not
very relevant
Several “domestication” programs
becoming relevant
Tricholoma
equestre
Macrolepiota
procera

11. Tree products Understory products
Mushrooms & Truffles Animal products
resin
Cork
Pine nuts
Acorns
Amanita
caesarea
Amanita
ponderosa
Boletus
edulis
Chantharellus
cibarius
Terfezia
arenaria
Terfezia
leptoderma
Choiromyces
gangliformis
Tuber
oligospermum
Wild bore
Red deer
Roe deer
Rabbit
Legged partridge
Lúcia-lima
Erva
cidreira
Tomilho limão
Hortelã pimenta
Honey
Lavanda amarela
Tricholoma
equestre
Macrolepiota
procera
Have high potential but presently
not very relevant due to
 Unsolved property rights
 Subsequent market issues

12. Tree products Understory products
Mushrooms & Truffles Animal products
resin
Cork
Pine nuts
Acorns
Amanita
caesarea
Amanita
ponderosa
Boletus
edulis
Chantharellus
cibarius
Terfezia
arenaria
Terfezia
leptoderma
Choiromyces
gangliformis
Tuber
oligospermum
Wild bore
Red deer
Roe deer
Rabbit
Legged partridge
Lúcia-lima
Erva
cidreira
Tomilho limão
Hortelã pimenta
Honey
Lavanda amarela
Very important
 Not as much as cork and pine nuts
 Forest management problems
very different from those for cork
oak and pine nuts
Tricholoma
equestre
Macrolepiota
procera

13. Cork oak management in Portugal

14. Cork oak in Portugal
 Cork is the main product from cork oak (Quercus suber)
that occupies in Portugal 736,775 ha (23% of Portuguese
forests and 34.4% of the cork oak world area)
 Annual production is 100,000 Mg  exports of 897 million
€ in 2015 (respectively 49.6% and 62.7% of the world)
 Exports represent 1.2% of total Portuguese exports
 The sector has 670 companies implying 9,000 jobs
 One tree produces up to 160 kg in one harvest
 One stand produces up to 5000 kg ha-1 (depending on site,
stand density and tree size)

15. Cork debarking occurs every k years
Movie by Joana A Paulo

16. 14
After extraction, the cork is usually
stored in the farm in large stacks

17.  The raw cork is then transported to the mill where it will be
processed
 Time between harvest and processing is about 6-8 months
 Before processing cork is treated with boiling water in autoclaves at
ambient pressure during 1 hour

18. Dimension expansion with boiling:
radial – 10-15%
tangencial and axial – 5%

19. Cork oak in Portugal
 Historically managed as agroforestry systems
 Gradually transformed into silvopastoral systems
 Several management systems, namely:
– Optimize cork production (no grazing, higher crown cover)
– Maintain a multifunctional system with grazing underneath
(sparse stand)
 Landowners are quite dynamic, always trying to
adapt management to new market opportunities and
changing environment (e.g. climatic conditions)

20. Main cork oak management decisions
 Tree density
– evaluated by crown cover
 Silvicultural system
– stand regeneration method
 Even-aged or uneven-aged cork
– Cork extracted from all trees at the
same time or in different years
 Cork debarking rotation
– period between two cork extractions
Long term
(strategic)
decisions

G&Y models
Short term
decision

Other type of tools

21. Main cork oak management decisions
 Tree density (evaluated by crown cover) – implies
the selection of the type of system
– a sparse stand compatible with agriculture, pasture or game
– a denser forest that aims at producing cork as main product
Dense stand
More cork, no grazing or game
Sparse stand
combining cork with grazing/game

22. Main cork oak management decisions
 Silvicultural system (stand regeneration method)
– Even-aged
– Multi-layred
– Uneven-aged
Young plantation Stand with 2 layers

23. Main cork oak management decisions
 Cork debarking rotation (period between two cork
extractions)
– The best time to debark is when cork value is maximum
– Cork value depends mainly on two factors: cork thickness
(caliper) and cork quality (porosity and defects)

24. Cork with good quality and different calipers

25. Cork with poor quality and different calipers

26. Cork with similar caliper and different quality

27. Cork value is related to cork caliper

28. Cork prices structure in 2007
2007 prices €/@ (1@ = 15 kg)
Thickness (mm) 1st to 3rd 4th 5th 6th Refuge
< 18 36.0 1.7 1.7 1.7 1.7
18 to 20 36.0 36.0 13.5 1.7 1.7
20 to 25 36.0 36.0 13.5 1.7 1.7
25 to 39 88.5 54.8 13.5 16.7 1.7
> 39 88.5 54.8 13.5 16.7 1.7
small pieces and virgin cork 1.7 1.7 1.7 1.7 1.7
Quality class

29. Main cork oak management decisions
 Cork debarking rotation (period between two cork
extractions)
– The best time to debark is when cork value is maximum
– Cork value depends mainly on two factors: cork thickness
(caliper) and cork quality (porosity and defects)
– Cork caliper can, to a certain extent, be controled by
management – adjusting the period between cork extractions
– But this is not an easy task as there is a very high variability
in caliper within the stand (inter tree variability)

30. Inter-tree variability in cork caliper
>27 mm – useful
for cork
stoppers
<27 mm – other
less valuable
uses

31. Inter-tree variability in cork caliper

32. Main cork oak management decisions
 Cork debarking rotation (period between two cork
extractions)
– The best time to debark is when cork value is maximum
– Cork value depends mainly on two factors: cork thickness
(caliper) and cork quality (porosity and defects)
– Cork caliper can, to a certain extent, be controled by
management – adjusting the period between cork extractions
– But this is not an easy task as there is a very high variability
in caliper within the stand (inter tree variability)
– Cork thickness is highly affected by weather

33. Weather, namely precipitation, affects cork growth
 Cork growth can be measured
 But the pattern is not always clear…
low precipitation general
decreasing
pattern over
time
high precipitation

34. Main cork oak management decisions
 Cork debarking rotation (period between two cork
extractions)
– The best time to debark is when cork value is maximum
– Cork value depends mainly on two factors: cork thickness
(caliper) and cork quality (porosity and defects)
– Cork caliper can, to a certain extent, be controled by
management – adjusting the period between cork extractions
– But this is not an easy task as there is a very high variability
in caliper within the stand (inter tree variability)
– Cork thickness is highly affected by weather
– Cork value may also vary among years depending on the new
products that are always being developed

35. Cork prices structure in 2007 and 2010
2007 prices €/@ (1@ = 15 kg)
Thickness (mm) 1st to 3rd 4th 5th 6th Refuge
< 18 36.0 1.7 1.7 1.7 1.7
18 to 20 36.0 36.0 13.5 1.7 1.7
20 to 25 36.0 36.0 13.5 1.7 1.7
25 to 39 88.5 54.8 13.5 16.7 1.7
> 39 88.5 54.8 13.5 16.7 1.7
small pieces and virgin cork 1.7 1.7 1.7 1.7 1.7
Quality class
2010 prices €/@ (1@ = 15 kg)
Thickness (mm) 1st to 3rd 4th 5th 6th Refuge
< 18 13.2 0.2 0.2 0.2 0.2
18 to 20 13.2 9.6 6.0 9.2 0.2
20 to 25 13.2 9.6 6.0 9.2 0.2
25 to 39 92.6 51.0 6.0 9.2 0.2
> 39 92.6 51.0 6.0 9.2 0.2
small pieces and virgin cork 0.2 0.2 0.2 0.2 0.2
Quality class

36. Cork oak management in Portugal
Long term optimisation (the SUBER model)

37. The SUBER model
 Is a growth and yield model for cork oak in Portugal
 Based on the simulation of individual tree growth
 Includes a module for cork growth simulation and
cork weight prediction at extraction
 Is implemented in a user friend platform freely
available on the web
http://www.isa.utl.pt/cef/forchange/fctools/
 It allows the comparison, on the long term, of
alternative management approaches

42. sIMfLOR platform - graphical outputs

43. sIMfLOR platform - graphical outputs

44. sIMfLOR platform - graphical outputs

45. SUBER model – application
Case 1 – comparing management approaches
– Stand density: 136 ha-1
– Basal area: 7.76 m2 ha-1 (under bark)
– Quadratic mean dbh: 28 cm
– Crown cover: 40%
 It is a relatively young stand, “more or less” even-aged
 Cork rotation is 9 years, with two extractions in a 9 years period
(two cork ages)

46. Dense stand, no grazing
Net present value ha-1=16238
Combined with game
Net present value ha-1=14174
Uneven-aged option
Net present value ha-1=13539
0
5
10
15
20
2000 2010 2020 2030 2040 2050
Year
Basalarea(m2
ha-1
)
G Gu
50
70
90
110
130
2000 2010 2020 2030 2040 2050
Year
Standdensity(ha-1
)
5
6
7
8
9
10
2000 2010 2020 2030 2040 2050
Year
Meancrowndiameter(m)
30
35
40
45
50
55
2000 2010 2020 2030 2040 2050
Year
Percentcrowncover
0
100
200
300
2003 2008 2012 2017 2021 2026 2030 2035 2039 2044 2048
Year
Corkweight(15kg)
Virgin Mature
2028
0
20
40
60
80
100
<7.5
15
25
35
45
55
65
75
85
95
Diameter class (cm)
Numberoftrees(ha-1)
2052
0
20
40
60
80
100
<7.5
15
25
35
45
55
65
75
85
95
Diameter class (cm)
Numberoftrees(ha-1)
0
5
10
15
20
2000 2010 2020 2030 2040 2050
Year
Basalarea(m2
ha-1
)
G Gu
50
70
90
110
130
2000 2010 2020 2030 2040 2050
Year
Standdensity(ha-1
)
5
6
7
8
9
10
2000 2010 2020 2030 2040 2050
Year
Meancrowndiameter(m)
30
35
40
45
50
55
2000 2010 2020 2030 2040 2050
Year
Percentcrowncover
0
100
200
300
2003 2008 2012 2017 2021 2026 2030 2035 2039 2044 2048
Year
Corkweight(15kg)
Virgin Mature
2028
0
10
20
30
40
50
<7.5
15
25
35
45
55
65
75
85
95
Diameter class (cm)
Numberoftrees(ha-1)
2052
0
10
20
30
40
50
<7.5
15
25
35
45
55
65
75
85
95
Diameter class (cm)
Numberoftrees(ha-1)
2003
0
10
20
30
40
<7.5
15
25
35
45
55
65
75
85
95
Diameter class (cm)
Numberoftrees(ha-1)
Present status
of the stand
0
5
10
15
20
25
2000 2010 2020 2030 2040 2050
Year
Basalarea(m2
ha-1
)
G Gu
50
70
90
110
130
2000 2010 2020 2030 2040 2050
Year
Standdensity(ha-1
)
5
6
7
8
9
10
2000 2010 2020 2030 2040 2050
Year
Meancrowndiameter(m)
30
35
40
45
50
55
60
65
70
2000 2010 2020 2030 2040 2050
Year
Percentcrowncover
0
100
200
300
2003 2008 2012 2017 2021 2026 2030 2035 2039 2044 2048
Year
Corkweight(15kg)
Virgin Mature
2028
0
10
20
30
40
50
<7.5
15
25
35
45
55
65
75
85
95
Diameter class (cm)
Numberoftrees(ha-1)
2052
0
10
20
30
40
50
<7.5
15
25
35
45
55
65
75
85
95
Diameter class (cm)
Numberoftrees(ha-1)

47. SUBER model – applications
Case 2 – cork debarking rotation
 Some questions:
– Is 9 years the best cork extraction rotation?
– Is it wise to concentrate cork extraction on every ith year?
– What is the impact of increasing the intensity of debarking?
 Again, the SUBER model was used to give answer to these
questions

48. 10000
11000
12000
13000
14000
15000
16000
17000
6 7 8 9 10 11 12 13
Cork extraction rotation (years)
Netpresentvalue(euros)
50% 40% buisiness as usual

50. Workshops with forest managers and landowners

51. Cork oak management in Portugal
Short term refining tools (users’ demand)

52. Stakeholders’ question
 Field sampling of cork quality before extraction has
been implemented some years ago – from a
cooperation between university and practitioners - to
support the cork price negotiation

53. Sampling is carried out before cork extraction

54. Each of the 5 trees closer to the plot center is identified and
measured and cork is sampled by extracting one “cala”

55. Field assesment of cork quality
One
“cala”

56. Cork price evaluation
 Cork samples are boiled
 Cork caliper is measured, before and after boiling
 Cork quality is evaluated
 A report is produced including:
– Proportion of cork samples by cork caliper class X cork quality
– An estimation of cork value (price)
 The field assessment of cork quality supports the
landowner in the cork price negotiation

57. Stakeholders’ question
 Field sampling of cork quality before extraction has
been implemented some years ago to support the
cork price negotiation
 Stakeholders asked if the cork sampling results could
be used to help them to decide if it would be wise to
delay cork extraction 1 or 2 years and increasing its
caliper
 The webCorky tool was developed to answer this
question

58. The webCorky tool
 Is based on the cork module of the SUBER model

59. Example with a cork with 9 years
Cork growth model – complete years
1st half year
last half year
8 complete years
(cork growth index)
total cork
thickness

60. The webCorky tool
 Is based on the cork module of the SUBER model
 Helps the forest manager/landowner to decide on
the best time to debark
 After its implementation cork sampling is sometimes
undertaken at an earlier stage (1 or 2 years from the
“average” cork rotation)
 Is available from the web – FCTOOLS site or
http://home.isa.utl.pt/~joaopalma/modelos/webcorky/

61. The webCorky tool

62. The webCorky tool

63. The webCorky tool

64. Stone pine management in Portugal

65. Stone pine in Portugal
 Pine nuts is the main product from stone pine (Pinus pinea) that
occupies in Portugal 175,742 ha (6% of Portuguese forests and
20% of the world area) with an increase of 45% since 1995
 Exports in 2013 were 364 Mg  12.8 million € (26.9% and 9.6%
of the world total)  35 € per kg
 Exports represent 0.017 % of total Portuguese exports
 The sector has 23 companies related to the processing of nuts
 One tree produces up to 500 kg in one harvest (with a large
variation among years – masting)
 One stand produces up to 8000 kg ha-1 (depending on site, stand
density and tree size)

66. Stone pine in Portugal
 The management objective of stone pine stands is
almost always the optimization of cone production
per hectare
 Most of the adult stands are naturally regenerated
– Even-aged
– Uneven-aged
– Two-storied
 Some of the adult stands are mixed with cork oak
and/or maritime pine

67. 67

68. Stone pine in Portugal
 The management objective of stone pine stands is
almost always the optimization of cone production
per hectare
 Most of the adult stands are naturally regenerated
– Even-aged
– Uneven-aged
– Two-storied
 Some of the adult stands are mixed with cork oak
and/or maritime pine
 New plantations are mostly even-aged pure stands,
very often grafted

70. Stone pine in Portugal
 The management objective of stone pine stands is almost
always the optimization of cone production per hectare
 Most of the adult stands are naturally regenerated
– Even-aged
– Uneven-aged
– Two-storied
 Some of the adult stands are mixed with cork oak and/or
maritime pine
 New plantations are mostly even-aged pure stands, very
often grafted
 Collecting the cones is not easy…

71. Collecting the cones is hard…
Manual (most common) Mechanical (introduced recently)
From: UNAC 2014 From: UNAC 2014

72. Getting the pine nuts

73. Main silvicultural treatments
 Initial density in new plantations
 Selection of genetic material
 Decision about grafting/not grafting
 Thinnings
 Pruning
 Intensification: irrigation and/or fertilization
 Cone harvesting methods
 Regeneration method in existing stands (including new
plantations when they get old) and time/age to start
regeneration treatments

74. Traditional management in Portugal
Operation Correia & Oliveira 2002 Louro et al. 2002 Costa et al. 2008
Initial density 625 ha-1 500 to 600 ha-1 208 to 400 ha-1
Beating up Between one and three years after installation if applicable (plantations)
Pruning Remove branches without
female flowers
(no prescription)
Removing 1/3 of branches:
-Between 8 and 12 years
-Between 20 and 25 years
Removing branches that
do not produce female
flowers:
-Between 35 and 40 years
-Between 50 and 60 years
Removing 1/3 of branches:
-Between 5 and 6 years
-Between 10 and 12 years
-Between 20 and 25 years
Thinning -15 to 20 years: 440 ha-1
-20 to 25 years: 352 ha-1
-25 to 30 years: 281 ha-1
-35 to 40 years:225 ha-1
-5 to 6 years
-20 to 25 years
(no information about
densities after thinning)
Final density 100 ha-1 225 ha-1 100 to 120 ha-1
Final harvest 80 years 80 to 100 years No information

75. Developing new thinning guidelines
 Permanent plots started to be established in 2004
 There is now a large data set that is being used
– To review the silviculture guidelines
– To develop growth and yield models

76. Stand density –permanent plots data
 Higher cone production in trees with large diameter and crown width
 The heterogeneity is mainly due to masting
 In each year plots with very big sparse trees are always the ones with
higher cone production per hectare
0
100
200
300
400
500
600
0 15 30 45 60 75 90 105 120 135 150
Treeconeproduction(kg)
Diameter at breast height (cm)
0
100
200
300
400
500
600
0 2 4 6 8 10 12 14 16 18 20 22 24 26
Treeconeproduction(kg)
Crown diameter (m)

77. 0
1000
2000
3000
4000
5000
6000
7000
8000
2 6 10 14 18 22 26 30
Coneweight(kgha-1)
Basal area class (m2 ha-1)
Even-aged
0
1000
2000
3000
4000
5000
6000
7000
8000
2 6 10 14 18 22 26 30
Coneweight(kgha-1)
Basal area class (m2 ha-1)
Uneven-aged – two storied
0
1000
2000
3000
4000
5000
6000
7000
8000
2 6 10 14 18 22 26 30
Coneweight(kgha-1)
Basal area class (m2 ha-1)
Uneven-aged – selection forests
0
1000
2000
3000
4000
5000
6000
7000
8000
2 6 10 14 18 22 26 30
Coneweight(kgha-1)
Basal area class (m2 ha-1)
All stand structures
 Cone production increases till a basal area of 14 and start to decrease
after 18 m2ha-1
 Thinning should take place to avoid basal areas > 18 m2ha-1
Stand density –permanent plots data

78.  Cone quality may be evaluated by mean cone weight (big cones origine
more pine nuts with bigger dimensions)
 Mean cone weight decreases with basal area, mostly after 14 m2ha-1
0
50
100
150
200
250
300
350
400
450
500
2 6 10 14 18 22 26 30
Aveageweightofacone(g)
Basal area class (m2 ha-1)
Even-aged
0
50
100
150
200
250
300
350
400
450
500
2 6 10 14 18 22 26 30
Aveageweightofacone(g)
Basal area class (m2 ha-1)
Uneven-aged – two storied
0
50
100
150
200
250
300
350
400
450
500
2 6 10 14 18 22 26 30
Aveageweightofacone(g)
Basal area class (m2 ha-1)
Uneven-aged – selection forests
0
50
100
150
200
250
300
350
400
450
500
2 6 10 14 18 22 26 30
Aveageweightofacone(g)
Basal area class (m2 ha-1)
All stand structures
Stand density –permanent plots data

79.  Cone production decreases with number of trees per ha
 Higher productions occur in very sparse stands (15-30 trees ha-1),
despite the stand structure
Stand density –permanent plots data

80. Stand density – some conclusions from pp
 The plots with higher production in even-aged stands have
a basal area between 14-18 m2 ha-1, but cone quality
decreases after 14 m2 ha-1
 The number of trees needed to get higher cone
productions per hectare in Portugal is much less than the
referred in the traditional guidelines
 For the higher dg classes in average it is possible to
achieve more cone production in even-aged stands than in
uneven-aged ones. Cone production decreases even more
if a stand structure near selection forest is considered
 On average, it is possible to attain more cone production
per hectare in Portugal than in Spain

81. Thinning guidelines
 Stands must be thinned when basal area is close to
14 m2 ha-1
 Basal area after thinning should be around 8 m2 ha-1
to avoid an excessive number of thinnings

82. Stone pine management in Portugal
Long term optimisation (the PINEA.pt model)

83. The PINEA.pt model
 The Pinea.pt model is an individual tree model
 It is implemented in standsSIM, na interface similar
to the one for the SUBER model
 A first version developed with diameter growth from
incremente bores is available
 A new version of the PINEA.pt model is on-going

84. Application
 Simulation of an even-aged yound stand
– N=270 ha-1
– hdom=5.5 m
– ddom=18.4 cm
– G=5.89 m2
 This stand was simulated for 50 years, using several
thinnings based on basal area
– Apply thinning whenever G>Glim*1.1, in order to maintain a G
value around Glim (Glim=5, 10, 15, 20, 30 m2ha-1)

85. Application – comparision of G developement
Basalarea(m2ha-1)
Age (years from planting)

86. Application – comparision of N development
Age (years from planting)
Numberoftreesperha(m2ha-1)

87. Application – comparision of dg development
Age (years from planting)
Quadraticmeandbh(cm)

88. Application – comparision of cone production
Age (years from planting)
Coneweight(kgha-1)

89. Application – comparision of cone production
Age (years from planting)
Coneweight(kaha-1)

90. Application – comparision of VAL
Netpresentvalue(euros)
Threshold basal area (m2 ha-1)

91. Stone pine management in Portugal
Short term decisions (users’ demand)

92. Sampling to estimate nuts “yield”
 The product sold by the landowners is the cones
 The price is fixed between the buyer and the
landowner taking the “yield” into account
 The “yield” is the ratio (healthy pine nuts
weight)/(cones weight)
 The landowners asked for the development of a
sampling scheme to estimate the nuts “yield”
 One of the difficulties is the estimation of healthy
pine nuts weight, as they are inside the shells and it
is not an easy task to extract them!

95. Sampling to estimate nuts “yield”
 The on-going research aims at developing a
methodology to estimate healthy pine nuts weight
without “opening” the pine nuts shells
 The methodology is based on:
– Sampling trees with a procedure similar to the one used for
cork quality assessment (how many plots? how many trees
per plot?)
– the analysis of Xrays of all the shelled pine nuts from each
sampled tree

96. Sampling to estimate nuts “yield”

97. Sampling to estimate nuts “yield”

98. Sampling to estimate nuts “yield”

99. Sampling to estimate nuts “yield”
 The on-going research aims at developing a
methodology to estimate healthy pine nuts weight
without “opening” the pine nuts shells
 The methodology is based on:
– Sampling trees with a procedure similar to the one used for
cork quality assessment (how many plots? How many trees
per plot?)
– the analysis of Xrays of all the shelled pine nuts from each
sampled tree
– Estimate the weight of the nuts classified as healthy through
a regression of weight on some measurements (length and
width) automatically taken from the Xray

100. Main conclusions

101. Main conclusions
 The relevance of the tools presented here (G&Y models
SUBER and PINASTER.pt model) are unquestionnable
 Long term optimisation is useful to support strategic
decisions
 But the landowner has a clear advantage in using an
adaptive management concept based on short term
decision support tools such as WebCorky
 Tools to support negotiation of product price can become
very useful
 Cork oak landowners must replace fixed silvicultural
guidelines by the use of flexible tools that help them to
adapt management to on-going conditions leading to an
optimisation of benefits

102. Thank
you!