State Forest Service — Assistant Conservator of Forests (state PSC exams) · India · State Public Service Commissions (e.g. TNPSC, MPPSC, UPPSC, HPPSC, KPSC, MPSC)
Original practice questions written to the published syllabus · not past papersPlanning and running forest inventories: kinds of enumeration, sampling designs and plot types, sample size, estimation and error, from a timber cruise to a national forest inventory.
In the exams: ICAR AICE-JRF 59 Unit 4 (forest inventory, kinds of enumeration, sampling methods, sample plots); ICAR AIEEA-PG Group 09 (forest inventory); IFoS Forestry Paper II Section A; SAF Certified Forester; Indonesia timber-cruiser certification
State Forest Service and Range Forest Officer examinations
Total and partial enumeration, strip and line-plot cruising for working plans, plot laying, enumeration registers and stock maps.
How our questions for State Forest Service spread across the units of Forest Inventory & Sampling Design. Difficulty: 8 easy, 12 medium, 3 hard.
| Objectives and kinds of enumeration: complete and partial | 3 | |
| Sample plots: fixed-area, variable-radius, permanent | 3 | |
| National forest inventories and continuous forest inventory | 3 | |
| Inventory of non-timber resources and biodiversity | 3 | |
| Inventory data processing and reporting | 3 | |
| Stratified sampling | 2 | |
| Cluster, multistage and multiphase sampling | 2 | |
| Sample size and sampling error | 2 | |
| Simple random and systematic sampling | 1 | |
| Point, line and strip cruising | 1 |
Choose an answer, then open the working. Each explanation says why the right answer is right and why the others are not.
A volume equation built for trees up to 60 cm diameter is applied to an inventory where many trees exceed 90 cm. Which kind of error does this most likely add?
D. Prediction or model error, and possible bias
Extrapolating an equation beyond its data range causes prediction error and often systematic bias. More plots reduce sampling error but not this; edge effects and the finite population correction are unrelated.
Further reading: Kershaw et al., Forest Mensuration (5th ed., 2016); Shiver & Borders, Sampling Techniques for Forest Resource Inventory (1996); de Vries, Sampling Theory for Forest Inventory (1986)
Why do most forest inventories use systematic grids despite their statistical weakness?
D. Grids are easy to lay out, spread plots evenly over the forest and give a map of variation
Practicality and even coverage are the reasons. No unbiased error formula exists for a single systematic sample, they do not always need fewer plots, and plots on a grid are still measured individually.
Further reading: Kershaw et al., Forest Mensuration (5th ed., 2016); Shiver & Borders, Sampling Techniques for Forest Resource Inventory (1996); de Vries, Sampling Theory for Forest Inventory (1986)
In Neyman (optimum) allocation, the number of plots in a stratum is proportional to:
A. Stratum area multiplied by stratum standard deviation
Neyman allocation gives more plots to large and variable strata: nh ∝ Ah × sh. Area alone is proportional allocation; mean volume and species count are not the basis.
Further reading: Kershaw et al., Forest Mensuration (5th ed., 2016); Shiver & Borders, Sampling Techniques for Forest Resource Inventory (1996); de Vries, Sampling Theory for Forest Inventory (1986)
In double sampling for stratification, what does each phase estimate?
A. Photo points estimate stratum proportions; ground plots on a subsample estimate stratum means
The large photo sample classifies points into strata to estimate their proportions; the ground subsample gives stratum means. This was the classic design of many national inventories.
Further reading: Kershaw et al., Forest Mensuration (5th ed., 2016); Shiver & Borders, Sampling Techniques for Forest Resource Inventory (1996); de Vries, Sampling Theory for Forest Inventory (1986)
A 0.05 ha plot contains 18 trees above the minimum diameter. How many trees per hectare does this represent?
D. 360 trees/ha
Expansion factor = 1 ÷ 0.05 = 20; 18 × 20 = 360. 900 uses a factor of 50; 90 multiplies by 5; 36 by 2.
Further reading: Kershaw et al., Forest Mensuration (5th ed., 2016); Shiver & Borders, Sampling Techniques for Forest Resource Inventory (1996); de Vries, Sampling Theory for Forest Inventory (1986)
Two independent strata totals have standard errors of 30,000 m³ and 40,000 m³. What is the standard error of the forest total?
B. 50,000 m³
Independent variances add: √(30,000² + 40,000²) = 50,000 m³. Adding the SEs (70,000) overstates; averaging (35,000) or subtracting (10,000) are wrong.
Further reading: Kershaw et al., Forest Mensuration (5th ed., 2016); Shiver & Borders, Sampling Techniques for Forest Resource Inventory (1996); de Vries, Sampling Theory for Forest Inventory (1986)
Which is an advantage of line-plot cruising over strip cruising?
B. Fewer borderline trees and clearly defined sample units for calculating error
Plots have shorter perimeters than long strips and each plot is a unit for statistics. Line-plot cruising still needs compass and distance, is a sample, and works in natural forest.
Further reading: Kershaw et al., Forest Mensuration (5th ed., 2016); Shiver & Borders, Sampling Techniques for Forest Resource Inventory (1996); de Vries, Sampling Theory for Forest Inventory (1986)
In phytosociological studies the importance value index (IVI) of a species is:
C. Relative density + relative frequency + relative dominance, summing to 300 over all species
IVI adds three relative measures in per cent, so all species together total 300. Multiplication, density alone and basal area ÷ height are not the IVI.
Further reading: Kershaw et al., Forest Mensuration (5th ed., 2016); Shiver & Borders, Sampling Techniques for Forest Resource Inventory (1996); de Vries, Sampling Theory for Forest Inventory (1986)
Which carbon pools are recognised in IPCC guidance for forest land?
C. Above-ground biomass, below-ground biomass, deadwood, litter and soil organic carbon
IPCC defines five forest carbon pools: above-ground biomass, below-ground biomass, deadwood, litter and soil organic matter. The other lists are incomplete or not pools.
Further reading: Kershaw et al., Forest Mensuration (5th ed., 2016); Shiver & Borders, Sampling Techniques for Forest Resource Inventory (1996); de Vries, Sampling Theory for Forest Inventory (1986)
Before analysis, field data are screened mainly for:
A. Missing values, impossible values, wrong species codes and duplicated plots
Checks target errors in the data. Removing all above-average plots or rainy-day plots would bias the results; handwriting is irrelevant if values are correct.
Further reading: Kershaw et al., Forest Mensuration (5th ed., 2016); Shiver & Borders, Sampling Techniques for Forest Resource Inventory (1996); de Vries, Sampling Theory for Forest Inventory (1986)
Ten here, 23 in the app. The Laisha app has every practice question we have written to State Forest Service in this subject, marked as you go, with timed papers, a jump-to-unanswered list and a certificate when you pass at 70 per cent.
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