Indian Forest Service Examination Growth & Yield Modelling: practice questions

1 · Growth curves and growth functions · easy

In the Chapman-Richards function Y = A(1 − e^(−kt))^c, the parameter A represents:

1. The asymptote, the maximum size approached at great age
2. The rate at which the curve approaches its maximum value over time
3. The shape of the curve and the position of its inflection point
4. The age at which the current annual increment reaches its peak

2 · Growth curves and growth functions · hard

A growth model and a yield model are called compatible when:

1. They were both fitted to data from the same set of temporary sample plots
2. Integrating the growth equation over a period gives the change in yield
3. They use the same site-index curves but different volume equations
4. The growth model predicts a larger volume than the yield model

3 · Self-thinning rule and density management diagrams · medium

Along Reineke's self-thinning line, if quadratic mean diameter doubles, stems per hectare fall by a factor of about:

1. 2.0
2. 3.0
3. 1.6
4. 4.0

4 · Self-thinning rule and density management diagrams · medium

A density management diagram usually plots which pair of axes?

1. Stand age against mean annual increment, plotted on linear axes
2. Site index against rotation age for each species
3. Log stems per hectare against log mean tree volume or Dq
4. Dominant height against crown ratio for each tree

5 · Individual-tree distance-independent and distance-dependent models · easy

Distance-dependent individual-tree models require which data that distance-independent models do not?

1. Stem maps giving the position of each tree
2. Diameter of each tree at breast height, measured over bark
3. Site index of the stand
4. Species of each tree

6 · Individual-tree distance-independent and distance-dependent models · hard

A common finding when distance-dependent and distance-independent models are compared for stand totals is that:

1. Spatial models are only slightly more accurate, despite costly stem maps
2. Spatial models are always several times more accurate for stand totals
3. Distance-independent models cannot predict stand totals at all
4. Spatial models cannot be used in any planted forest

7 · Individual-tree distance-independent and distance-dependent models · medium

Why do individual-tree models suit mixed, uneven-aged forests better than whole-stand models?

1. They need no field data and run from climate data alone
2. They assume all trees are the same age and size
3. They ignore mortality, which is rare in mixed forests
4. They represent any mix of species, sizes and treatments tree by tree

8 · Process-based and hybrid models · medium

A principal advantage claimed for process-based models is that they:

1. Can in principle predict growth under conditions outside past data, such as climate change
2. Are always more accurate than empirical models under current conditions, as they may use more data
3. Need far fewer parameters than empirical models
4. Require no weather or soil data at all

9 · Model fitting, validation and projection · hard

A growth model is unbiased over all validation plots but over-predicts on young stands and under-predicts on old ones. What does this show?

1. The model is valid everywhere because its overall bias is zero
2. The validation data must contain measurement errors only
3. A zero overall bias hides systematic errors; check residuals by age, site and density
4. RMSE alone is enough to judge a model's validity

10 · Using growth models in harvest scheduling · medium

If a growth model used for harvest scheduling overestimates growth, the likely consequence is:

1. Scheduled harvests are too low and growing stock rises
2. No effect at all, because harvest schedules are always revised every single year
3. Timber prices rise because volumes are underestimated
4. Scheduled harvests exceed sustainable levels and growing stock declines