Category: Agriculture

The headline problem is this: the Taro Yamane formula that every Nigerian agriculture department expects you to use requires a finite population size, N, and for farmers in a given local government area, no register exists that anyone can hand you on request. Students solve this by writing a round number into Chapter Three and hoping nobody asks. Panels ask.

This piece does two things. First, it names the five sources a Nigerian student can actually cite for a farming population figure, with the vintage of each one stated plainly, because several of the official figures are older than the students using them. Second, it works the calculation through from that figure to a defended, allocated sample.

The five sources you can cite, and how current each one is

Two rules follow from that table, and both of them are what a good examiner is listening for.

Rule one: name the level your figure describes. A national proportion of agricultural households cannot be your N for a study of yam farmers in one LGA. It can support your background in Chapter One, but your population figure must come from the same level as your study area.

Rule two: define your population by what your source actually counted. If your N came from an ADP register, then your population is registered farmers in that LGA, and you must say so in Chapter Three. Writing “the population of the study comprises all farmers in the LGA” when your number came from a register is a claim your own source does not support. Narrowing the population to match the source is not a weakness — it is precision, and it protects you.

Getting an LGA-level figure in practice

The route that works for most agriculture students takes about a week:

1. Ask your supervisor for a letter of introduction addressed to the state ADP or the LGA Department of Agriculture. Departments issue these routinely.
2. Visit the ADP zonal office covering your study area and ask for the number of registered farmers in the block or cell you are studying, broken down by ward if possible.
3. Ask for it on letterhead, signed and dated. This becomes an appendix and it ends any argument about where your N came from.
4. If the ADP cannot help, approach the LGA agriculture desk officer, then the local cooperative union or commodity association.
5. Record the date you obtained the figure. You will cite it as a personal communication with that date.

If every route fails and you genuinely cannot obtain a population figure, you have two honest options. Either switch to a sampling approach that does not require N — a purposive or quota sample, defended on its own terms — or state a documented estimate with its source and limitations, and add the limitation to Chapter Five. What you must not do is invent a number. An examiner who has worked in an ADP will recognise a fabricated register size instantly.

The Taro Yamane calculation, worked

Taro Yamane’s formula is the Nigerian standard for a finite population, far more common in undergraduate projects here than Cochran or Slovin:

n = N ÷ (1 + N(e)²)

where n is the sample size, N is the finite population, and e is the level of precision, conventionally 0.05 for a 95 per cent confidence level.

Suppose the ADP zonal office confirms 4,300 registered farmers across the three wards of your study LGA. Then:

• e² = 0.05 × 0.05 = 0.0025
• N(e)² = 4,300 × 0.0025 = 10.75
• 1 + 10.75 = 11.75
• n = 4,300 ÷ 11.75 = 365.96

Round up, never down: n = 366. Rounding down is one of the small errors that invites a correction, because a sample below the calculated minimum no longer carries the precision you claimed.

Two adjustments are worth making before you go to the field. First, add an attrition allowance. Agriculture fieldwork loses questionnaires to rain, to farmers who are not on their plots, and to incomplete returns. Administering 400 to retrieve 366 is realistic; state the retrieval rate in Chapter Four. Second, if your analysis requires comparing subgroups — irrigated against rain-fed, male against female farmers — check that the smallest subgroup will still hold enough cases to analyse. Thirty per cell is a common working minimum.

Allocating the sample across your wards

A sample size is a number; a sampling procedure is a method. Chapter Three needs both, and this is where agriculture projects most often thin out.

Use proportional allocation, sometimes called Bowley’s proportional allocation formula, so that each ward contributes in proportion to its share of the population:

n_h = (N_h × n) ÷ N

Continuing the worked example with n = 366 and N = 4,300:

Then state how individual farmers were selected within each ward — systematic sampling from the register at a fixed interval, or simple random selection from the list. A multistage description reads well for agriculture: state, then LGA, then wards, then farming households, with the selection method named at each stage.

When Taro Yamane is the wrong tool

Three situations where applying it anyway will cost you marks:

• Your population is genuinely unknown. If no register exists at any level, you cannot compute a finite-population formula. Say so, and use a defensible alternative with a stated justification rather than dressing up a guess.
• Your study is qualitative. In-depth interviews with extension officers or focus groups with farming cooperatives are sized by saturation, not by formula. What panels accept as a defence in that case is set out in the piece on how many respondents a qualitative project needs.
• Your unit of analysis is not a person. Field trials, plot experiments, soil sample points and livestock units are sized by experimental design — replications and treatments — not by Yamane. An agronomy trial with four treatments and three replications needs a randomised complete block design, not a survey formula.

What to write in Chapter Three

A paragraph that survives questioning looks like this:

The population of the study comprised 4,300 farmers registered with the [State] Agricultural Development Programme across the three wards of [LGA], as confirmed by the ADP zonal office on 14 March 2026 (see Appendix C). The sample size was determined using the Taro Yamane formula at a 95 per cent confidence level and 0.05 level of precision, giving a minimum sample of 366 respondents. A multistage sampling procedure was adopted. In the first stage, [LGA] was purposively selected because of its predominance of [crop] production. In the second stage, the three wards were included in full. In the third stage, the sample was allocated proportionally across the wards using Bowley’s formula, yielding 162, 128 and 76 respondents respectively. In the final stage, respondents were selected by systematic random sampling from the ADP register at an interval of every kth farmer. Four hundred questionnaires were administered to allow for attrition.

Every claim in that paragraph is traceable. The population has a source and a date, the formula has a stated precision level, the allocation shows its arithmetic, and the selection method is named at each stage. It slots into the wider structure described in the guide to writing Chapter Three section by section.

Two decisions that follow from your sample size deserve early attention. The instrument you administer to those 366 farmers has to be built and validated, and the sequence for that — expert review, content validity index, pilot, reliability coefficient — is the same one used in the guide to designing and validating a survey instrument. And how you administer it matters more in rural areas than anywhere else, because an online form measures who had network that day rather than who farms; the trade-offs are compared in the piece on collecting project data with Google Forms, KoboToolbox or paper.

Two questions the panel will ask about your N

“Where did this population figure come from?” Answer with the institution, the office, the date and the appendix number. Then stop talking. A precise sourcing answer closes the question.

“Why 0.05 and not 0.01?” Because 0.05 corresponds to the 95 per cent confidence level conventional in social and agricultural survey research, and because 0.01 would have required a sample beyond the resources available for an undergraduate project. Saying the second half out loud is fine — examiners prefer an honest constraint to a pretended one.

Get the chapter written while the figures are fresh

The hardest part of an agriculture project is rarely the arithmetic. It is that fieldwork eats weeks, and by the time you return from the wards with four hundred questionnaires in a bag, the writing has not started and the deadline has not moved.

Tesify drafts your chapters from your own topic, population, sampling procedure and instrument, keeps your citations and terminology consistent as the draft grows, and lets you revise a section after supervisor corrections without rebuilding the chapter. It is a way to write your own project faster, not a way to submit someone else’s.

Start your project on Tesify for free and turn your sampling decisions into a finished Chapter Three.

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