ICAR AICE-JRF/SRF (PhD) — Subject 59 Forestry · India · National Testing Agency for ICAR
Original practice questions written to the published syllabus · not past papersICAR AICE-JRF/SRF (PhD) — Subject 59 Forestry is a PhD, fellowship and lectureship exam run by National Testing Agency for ICAR, for candidates in India. Computer-based, 2 hours, 120 MCQs (Part A 20 general; Part B 50 core group; Part C 50 subject), +4 / −1, maximum 480.
Source: icar.org.in.
Checked against the exam body's published information. Formats and fees change; confirm the current edition there before you book.
Kinds of enumeration, sampling methods (simple random, systematic, stratified, cluster, multistage, multiphase), sample plots and their size and shape, sampling error and sample size, and continuous forest inventory, with short calculations.
Planning 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.
Our 22 ICAR AICE-JRF/SRF questions in this subject: 0 easy, 8 medium, 14 hard, across 9 units.
Choose an answer, then open the working. Each explanation says why the right answer is right and why the others are not.
These 10 were written to ICAR AICE-JRF/SRF and are not shown on any other exam's page.
Precision and accuracy differ because:
C. Precision measures repeatability of estimates, while accuracy also depends on bias
Precision is shown by the standard error; accuracy is closeness to the truth and so includes bias. A large biased sample is precise but inaccurate.
A sample of plots has a mean of 160 m³/ha and a standard deviation of 20 m³/ha. What is the coefficient of variation?
D. 12.5 per cent
CV = SD ÷ mean × 100 = 20 ÷ 160 × 100 = 12.5%. 800% inverts the ratio and multiplies by 100; 1.25% misplaces the decimal; 8.0% is 160 ÷ 20.
In two-stage sampling, primary units of very unequal size are often selected:
A. With probability proportional to their size
Probability proportional to size gives large units a higher chance and makes estimation more efficient when unit totals scale with size. Equal probability is valid but less efficient; the other rules introduce bias.
In metric angle-count sampling with BAF 4 m²/ha, what is the limiting distance for a tree of 50 cm dbh?
B. 12.5 m
Limiting distance (m) = dbh (cm) × 0.5 ÷ √BAF = 50 × 0.5 ÷ 2 = 12.5 m. 25.0 m omits the √BAF; 6.25 m divides by BAF instead of its root; 50.0 m confuses centimetres with metres.
A plot centre falls 5 m inside the forest boundary. What is the correct procedure?
C. Keep the plot where it falls and use a boundary correction such as the mirage method
Moving or discarding edge plots under-samples the edge zone and biases the estimate. The mirage method reflects the outside portion inward and counts trees in the reflected zone twice. Enlarging the radius changes the plot design.
In continuous forest inventory, V1 = 190, V2 = 220, mortality = 10 and cut = 20 m³/ha over the period. What is the gross growth including ingrowth?
A. 60 m³/ha
Gross growth incl. ingrowth = V2 + M + C − V1 = 220 + 10 + 20 − 190 = 60. 30 is the simple difference V2 − V1; 50 is net growth incl. ingrowth (omitting mortality); 0 subtracts M and C.
Why does remeasuring the same permanent plots estimate change more precisely than comparing two independent sets of temporary plots?
D. Positive correlation between successive measurements on the same plot cancels much of the variation
The variance of a difference falls as the correlation between the two measurements rises; on the same plots it is high. Plot size and instruments are not the reason, and permanent plots must be representative, not average.
Why are changes to a national inventory's plot design or definitions made with care and often with an overlap period?
B. Changes can break the time series, so differences may reflect method rather than real change
Comparing estimates made with different methods confuses methodological and real change; overlap periods allow calibration. FAO does not forbid changes, and the other claims are invented.
Adaptive cluster sampling is useful for inventorying a non-timber species that is:
A. Rare and clumped
Adding neighbouring plots wherever the species is found concentrates effort in the rare clumps and improves efficiency. Common, even or planted populations are sampled well by ordinary designs.
An inventory report gives a district volume to the nearest cubic metre but no sampling error. What is the main criticism?
A. Without errors and methods the precision cannot be judged and the figures look more exact than they are
Reports must state design, equations, estimates and their errors; false precision misleads. The other statements are not reporting standards.
This plan follows where our questions actually fall, so the time goes where the marks are likely to be. It is our reading of the syllabus, not the exam body's weighting.
Read the manual chapters for these first and do every worked question you can find on them.
Aim for one sitting per unit, then mixed questions across them.
Learn the definitions, classifications and standard numbers; these units are more often recalled than worked.
Come back to these after a first pass, untimed, and read every explanation, including for the options you rejected.
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