Modeling Effects of Genetic Improvement in Loblolly Pine Plantations

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Modeling Effects of Genetic Improvement in
Loblolly Pine Plantations

• Barry D. Shiver
• Stephen Logan

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Modeling Genetic Effects

• Plantation Management Research Cooperative (PMRC)
  established a study in 1986 in the Southeastern
  USA to evaluate effects of improved genetics on
  yields from block rather than row plantings
• The level of genetic improvement at the time was
  first generation improvement
• Seedlings were planted in January 1987

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Study Design

• 16 Locations in Piedmont
• 15 Locations in Coastal Plain
• Six top ranked families in each region chosen to
  represent single family genetic material
• Unimproved seed obtained from region encompassed
  by study
• Bulk lot improved stock obtained by mixing equal
  amounts of seed from the six selected families in
  each region

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Study Design

• Eight 0.4 ac. treatment plots were included at
  each study installation
• For this analysis only treatments one and three
  are considered (no veg control and no single
  family)

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Unimproved, No Veg Cntl-Age 13
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Bulk Lot, No Veg Cntl - Age 13
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Measurements

• Measurements made at ages 3, 6, 9, 12, 15, and 18
  years
• All typical measurements made to estimate yields
  (dbh, total height, etc.)
• Overall analysis results show that genetic
  improvement and vegetation control significantly
  improve yields and that the effects of the two
  treatments are largely additive
• Genetic improvement reduces fusiform rust
  incidence by about half
• Tree form and percentage of trees qualifying for
  solid wood are significantly higher for
  genetically improved plots

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Adjusting for Silvicultural Treatments

• A common method used by practicing foresters is
  to adjust the exhibited SI value in an existing
  growth and yield model
• For most silvicultural practices (weed control,
  fertilization, etc.) this method does not work
  well because the response is not anamorphic (a
  proportional (constant ) increase across ages)

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Silvicultural Treatment Response

• 4 type of silvicultural responses
• Type A growth gains on treated areas continue
  to increase throughout the rotation.
• Type B - growth gains achieved early in rotation
  are maintained but do not continue to increase
  after an initial response period.
• Type C early growth gains are subsequently
  lost.
• Type D - growth gains on treated areas fall below
  levels observed on nontreated areas.

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Silvicultural Treatment ResponseTypes of Response
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Silvicultural Treatment Response Pienaars
Modified Adjustment Type B

• Treatment Age, Rmax, and Yst until 90 of max
  response occurs must be provided by users, so
  that

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Cultural Treatment Response

• Use Pienaar and Rheney (1995) adjustment
  function to create C response

R growth response associated with the cultural
treatment of interest Yst years since cultural
treatment was applied c 1/(years to expected
maximum response) b (Maximum response)cexp(1)
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Type A Response

• Creates a response where the gain gets wider as
  the stand gets older possibly even anamorphic
  (stays the same amount larger proportionately as
  age increases)
• Same effect on height as increasing the site
  index
• Would get this with fertilization with P on a P
  deficient site
• Do we get this type response with genetic
  improvement?

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Adjusting for Genetic Improvement

• Unlike the majority of silvicultural treatment
  responses, there is some evidence through age 18
  that the genetic treatment response is anamorphic
• There is also some evidence that it is primarily
  effected through a height response
• A disturbing finding is the amount of variability
  in the data in some cases genetic improvement
  is negative rather than positive

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Dom Ht by Genetic Improvement
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Structure of our GY Models

• Models are actually a system of models
• H f (Age, Site)
• N f (Age, Site)
• BA f (Age, H, N)
• Y f (age, H, N, BA)
• With our intensive silviculture plots we have
  found that if we have the basal area per acre and
  the height correct we can accurately predict the
  yield (weight/ac)

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Basal Area Prediction

• The actual observed heights and observed trees
  per acre were used to estimate basal area per
  acre using the PMRC basal area prediction
  equation
• Residuals were calculated and graphed
• Lack of much of a trend in residuals is an
  indication that the only factor affecting basal
  area is change in height

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Residual BA using Actual Height on Genetically
Improved Plots
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Height Residuals for Improved using 10 Adjusted
SI for Unimproved at Age 15
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Genetically Improved Prediction

• Works reasonably well on average
• But, lots of variability on average the
  improved are higher, but some are even lower
• A real problem when trying to predict yields for
  specific stands
• If we have data, we can use projection from
  existing inventory data in the existing stand

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Projection

• To evaluate projection, we took the dominant
  height from the improved plot at age 12
• The projection was done by projecting the
  dominant height from age 12 to age 18 using the
  existing equation with no adjustment
• The basal area at age 18 was projected from the
  age 12 existing basal area using the projected
  dominant height at age 18

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Height Projection Residuals
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Basal Area Projection Residuals
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Green Weight Residuals
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Conclusions

• Adjusting the site index is a reasonable way to
  adjust yield models for genetic effects on
  average
• The response does not fit our other response
  models well except for perhaps a response where
  the maximum response does not occur until after
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• There is much variability in such adjustments and
  the variability increases with age, but appears
  to be well behaved across the range of site
  indices
• Using actual height data from genetically
  improved plots at some inventory age and then
  projecting to an older age shows promise for
  reducing variability by about half for green
  weight

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Conclusions

• In this case each stand has its own adjustment
  depending on what the dominant height is on the
  plot at the inventory age there is no
  adjustment in the model itself
• The residuals found in this study point out just
  how variable the stands are with very similar
  inputs (age, site index, tpa, ba)