Every small-scale science project meets the same question early on: spend little and start now, or spend more and trust the results. For Project 2 of MSASpaces, the answer shapes everything that follows. The aim is not only to explore a garden’s soil, but to generate data that is comparable with other sites, accurate enough to rely on, and clear enough to act upon.
This post sets out the first purchases for Project 2: a soil sieve, a pH meter with its calibration buffers, distilled water and coffee filters. They support three experiments, soil texture, soil pH and water holding capacity, which together begin the basic mapping of a site’s land. Here is the reasoning behind each choice, what the first results showed, and what the research suggests about where investment matters most.
Read if you want to understand the decision-making behind spending on scientific equipment to begin basic land mapping and soil experiments.
What we bought, and why
| Item | Used for | GEL | EUR | AUD | The decision |
|---|---|---|---|---|---|
| Universal pH strips, pH 1–14 | First pH screening | 15.00 | 5.16 | 8.32 | Bought to save money; later found too coarse for decisions |
| Certified pH pen (Milwaukee pH600) | Soil pH | 120.00 | 41.30 | 66.56 | Chosen over a 60-lari pen for its inspection certificate and visible quality control |
| pH 4.01 and 7.01 buffers | Calibrating and checking the pen | 16.00 | 5.51 | 8.88 | Without them, the pen’s accuracy can’t be shown |
| 2 mm stainless sieve | Texture and water holding capacity | 100.00 | 34.42 | 55.47 | Defines “soil” as the fraction finer than 2 mm, so results match published methods |
| Distilled water | pH mixes | 1.20 | 0.41 | 0.67 | The cheapest item, and one that protects every pH reading |
| Coffee filters | Water holding capacity | [add] | Same brand every time, as the published method allows | ||
| Total recorded | 252.20 | 86.81 | 139.90 |
The pen was the hardest decision. At 60 lari, the basic model looked like a sensible saving. But the 120-lari pen came with inspection certificates and was made in Romania, with its quality control on show. For data meant to be compared across gardens, knowing how an instrument was checked is part of what is being bought.
Three experiments, and what they showed first
Soil texture. Sieved soil is moistened, worked into a ball and pressed into a ribbon; the ribbon’s length gives an estimate of clay content, using the Australian field texture grades (National Committee on Soil and Terrain, 2021). Clay content matters because it shapes how soil holds water and how strongly it binds lead.
Soil pH. Before testing any soil, the strips were checked against known liquids. Grape vinegar read 3–4 (about 3 expected) and baking soda solution read 8–9 (about 8.3 expected), so the strips worked, but only to the nearest whole unit. They could not separate pH 6.5 from 7, the range where lead is held most tightly in soil. Tap water alone read 7–8, enough to shift an acidic reading, which is why distilled water is now used for every mix. The pen reads to 0.1 pH, provided it is calibrated at pH 7 and checked against the pH 4.01 buffer each session.
Water holding capacity. Following the funnel method of Nelson and colleagues (2024), 9.9 g of sieved, air-dried soil was wetted in a coffee filter, drained for 8.5 hours and weighed against a blank filter. It held 0.56 g of water per gram of air-dried soil, in the middle of the range the authors report. One funnel is a first reading rather than a result: the next round uses three funnels, and a second sampling point away from the mulched apple-tree margin where this sample was taken.
The first calculation also held a lesson. Leaving the soil filter’s own mass in the equation gave 0.67 instead of 0.56, an error of almost a fifth. Good equipment can’t protect against a slip in the arithmetic, so every calculation is now written out in full and checked.
What the research adds
Looking at the purchases through the lens of comparable, accurate and actionable data, five findings stand out.
1. The biggest uncertainty is often in the ground, not the instrument. In geochemical surveys, the uncertainty from where and how samples are taken has been found to reach up to 55%, against about 5% from laboratory analysis (Ramsey, 1997). For a small project, a better instrument narrows the smaller error. More sampling points, duplicate samples and a consistent method narrow the larger one, often for little extra money.
2. Volunteer data can match professional data, by design. A review of citizen science projects found volunteer accuracy can equal or surpass that of professionals when projects use standardised and calibrated equipment, training, recorded metadata and expert checks; professionals also disagree among themselves (Kosmala and colleagues, 2016). The certificate on the pen helps, but so does recording which pen, which buffer batch and which person took each reading.
3. Some skills can’t be bought. In a comparison of texture-by-feel estimates against laboratory results, professional soil scientists matched the exact class 66% of the time (91% within a neighbouring class), while seasonal field technicians managed 27–41% (71–78% within a neighbouring class). Most novice errors came from judging ribbon length (Salley and colleagues, 2018). For texture, the investment that pays is practice and a reference soil with known laboratory results, not a new tool.
4. Not every component deserves the premium. The funnel method’s own sensitivity tests found that swapping laboratory filter paper for supermarket coffee filters changed results by only about 1%, while a hard cover over the funnel lowered a clay loam’s result by about 12% (Nelson and colleagues, 2024). Saving on filters is safe; skipping the protocol is not.
5. Open, shared tools change the arithmetic. A review of open-source scientific hardware found average savings of 87% compared with proprietary equivalents (Pearce, 2020), and an earlier case study estimated returns of several hundred to several thousand percent when a design is shared openly (Pearce, 2015). Where a validated open design exists, building it and sharing the results can stretch a small budget much further.
Taken together, these findings refine the original lesson. Investing in a certified instrument was right where a decision depends on precision. But the larger gains for a small project come from investing in method, sampling and skill, the parts of science that no price tag guarantees.
A decision guide: where to invest, where to save
| Where | Invest or save? | Why |
|---|---|---|
| pH meter and buffers | Invest | Decisions near pH 6.5–7 need 0.1-unit precision, and a calibration record makes readings comparable |
| Scales | Invest | A 10 g sample holds only about 5 g of water; scales reading to 0.01 g keep the error small |
| Sieve | Invest once, share | One certified sieve can check every low-cost sieve in the network |
| Filters, jars, funnels | Save | Brand barely changes the result, as long as it stays the same |
| Distilled water | Save, but never skip | It costs almost nothing and protects every pH reading |
| Sampling points and duplicates | Invest time | Sampling can carry far more uncertainty than the instrument |
| Training and reference soils | Invest time | Texture by feel improves with practice against known results |
| Records and metadata | Invest time | Device, batch, method and person make data comparable later |
The question to ask of any purchase is not “is it cheap?” but “will it change a decision, and can someone else compare my result with theirs?” If you would like to run these experiments in your own garden, or compare results, write to info@msaspaces.org.
Sources
- Kosmala, M., Wiggins, A., Swanson, A. and Simmons, B. (2016). Assessing data quality in citizen science. Frontiers in Ecology and the Environment, 14(10), 551–560. doi:10.1002/fee.1436
- National Committee on Soil and Terrain (2021). Australian Soil and Land Survey Field Handbook, 4th ed. CSIRO Publishing. Field texture grades.
- Nelson, J.T., et al. (2024). A simple, affordable, do-it-yourself method for measuring soil maximum water holding capacity. Communications in Soil Science and Plant Analysis, 55(8), 1190–1204. doi:10.1080/00103624.2023.2296988
- Pearce, J.M. (2015). Return on investment for open source scientific hardware development. Science and Public Policy, 43(2), 192–195.
- Pearce, J.M. (2020). Economic savings for scientific free and open source technology: a review. HardwareX.
- Ramsey, M.H. (1997). Measurement uncertainty arising from sampling: implications for the objectives of geoanalysis. The Analyst, 122(11).
- Salley, S.W., et al. (2018). A comparison of soil texture-by-feel estimates: implications for the citizen soil scientist. Soil Science Society of America Journal, 82(6). doi:10.2136/sssaj2018.04.0137
- Milwaukee Instruments. pH600 user manual.
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