LC Ag Science common mistakes
LC Ag Science · Understand the traps and how to avoid them.
When asked to evaluate a fertiliser or feed trial, the candidate describes what happened in the data but never states a conclusion or recommendation tied back to the original hypothesis, so the answer stops at description.
Candidates treat 'analyse' and 'evaluate' as synonyms for 'describe the results'. School science writing rewards reporting observations, and the command word is skimmed rather than decoded, so the higher-order step is never attempted.
What to do instead
Before writing, underline the command word and build a two-part scaffold: (1) what the data show, with a figure or trend quoted; (2) what that means for the hypothesis and for the farmer. Practise rewriting past descriptive answers by adding a forced final sentence beginning 'Therefore, the recommendation is...' that cites a specific number from the data.
The candidate designs a field or glasshouse experiment with no replication, no control treatment, or several variables changed at once, then cannot separate treatment effect from soil or positional variation.
Candidates confuse a demonstration (one treated plot vs one untreated plot) with an experiment. They also underestimate how much soil heterogeneity, shading and edge effects vary within a paddock, so they assume single plots are representative.
What to do instead
Apply a fixed design checklist to every investigation: independent variable, dependent variable, at least three replicates, a negative control, all other variables held constant, and randomisation or blocking of replicate positions. State explicitly how blocking handles a known gradient such as slope or shade, and justify the number of replicates in terms of variability.
The candidate classifies a soil from a description of its profile and parent material using only one characteristic (often colour or texture) and omits the diagnostic horizon, drainage or climate context needed to reach the named soil order.
Colour is the most visually memorable feature and textbook photos anchor it, so candidates jump from 'red' to a conclusion. They also memorise soil names as a list rather than as a key based on diagnostic criteria.
What to do instead
Work through a written key in a fixed order: parent material, dominant texture, presence and depth of diagnostic horizons, drainage and aeration status, pH and carbonate reaction, then climate. Write a one-line justification citing at least two diagnostic features for every classification, and practise classifying unfamiliar profiles from text descriptions rather than images.
When explaining cation exchange capacity or nutrient availability, the candidate treats CEC as a direct measure of fertility and ignores pH, base saturation, anion behaviour and leaching losses.
CEC is taught as a single headline number, so candidates equate 'high CEC' with 'fertile' and forget that a high-CEC clay can still be acidic, sodic or poorly drained. Anion nutrients such as nitrate and phosphate behave oppositely to cations and are dropped from the reasoning.
What to do instead
Always analyse fertility as a set of interacting properties: CEC plus base saturation, pH, organic matter, texture and drainage. For each nutrient discussed, state whether it is held on exchange sites or leached as an anion, and link the recommendation (lime, gypsum, split applications) to the specific property being corrected.
The candidate explains a plant or animal process by naming the hormone or enzyme alone, without describing the sequence of events, the target tissue or the feedback that regulates it.
Revision concentrates on vocabulary lists, and mark schemes often reward the named term, so candidates stop there. They have not practised writing causal chains, so the mechanism collapses into a label.
What to do instead
Write every physiological explanation as a numbered causal chain: stimulus, sensor or receptor, signal (with source and target), response, and feedback loop. For example, trace water stress from soil water potential through root sensing, ABA transport, stomatal closure and reduced transpiration, and name the tissue at each step.
The candidate recommends a pesticide or a fertilizer rate without identifying the target pest, the economic threshold, the application timing, or the resistance and off-target risks, and gives a single blanket rate for a whole farm.
Candidates treat recommendations as recall of a standard product or rate rather than as a decision made from pest pressure, crop stage, soil test and cost. They also omit integrated management because it is harder to state than a product name.
What to do instead
Structure every recommendation as: identify the problem with evidence (threshold counts, soil test, diagnosis), list non-chemical options first, then chemical options with mode of action group, rate basis, timing and resistance rotation. State how the result will be monitored and what would trigger a change of plan.
The candidate calculates a feed ration or fertilizer requirement using a remembered formula but mixes units (kg versus g, dry matter versus fresh weight, per head versus per hectare) and reports an answer with no check on whether it is biologically plausible.
Unit conversion is treated as trivial bookkeeping, and candidates copy numbers from the question without labelling them. They also do not carry out a sanity check because they have never been taught what a realistic intake or application rate looks like.
What to do instead
Label every quantity with its unit and basis as you write it, convert to a single consistent basis (usually dry matter or per hectare) before calculating, and finish with a plausibility check against known benchmarks such as dry matter intake as a percentage of body weight. Show the check explicitly in the answer.
The candidate discusses animal welfare or environmental impact only in general terms ('it is better for the animals', 'it reduces pollution') without linking to a specific measure, indicator or management practice.
These topics attract moral language in class discussion, so candidates reproduce opinions rather than evidence. They have not learned the concrete indicators, such as lameness scores, body condition score, stocking density, leaching figures or greenhouse gas per unit of product.
What to do instead
Convert every claim into a measurable indicator plus a practice: name the welfare or environmental measure (for example locomotion score, somatic cell count, nitrate concentration in drainage water), the practice that changes it, and the trade-off involved. Use a cause-effect-trade-off sentence structure for each point.
In the farm business question, the candidate calculates gross margin but omits fixed costs, depreciation or opportunity cost, then presents the result as profit, and ignores the sensitivity of the result to price and yield.
Gross margin is the most practised calculation and is mistaken for profit. Candidates also assume single-point values for price and yield, so they never test how robust the decision is.
What to do instead
Distinguish gross margin (output minus variable costs) from profit (minus fixed and overhead costs) explicitly in every answer. Add a short sensitivity statement: recompute the key figure at a lower price or yield and say whether the decision changes. Name the break-even price or yield as the decision threshold.
For the practical investigation write-up, the candidate reports a method as a narrative of what was done but omits risk assessment, calibration, repeat readings, and a treatment of anomalous results or sources of error in the discussion.
Candidates write up practicals as a diary of actions because that is what they did in the laboratory, and error analysis is left until the end, when it is reduced to 'human error'. They have not been taught to distinguish random from systematic error.
What to do instead
Use a fixed write-up template: aim, hypothesis with a predicted trend, variables table, risk assessment with control measures, calibration and equipment precision, results with repeats and means, then an error section separating random from systematic error with a suggested improvement for each. Ban the phrase 'human error' and require a specific cause and remedy.