Moisture Content Calculator with Steps (Free) | StatsUnlock

A moisture content calculator uses moisture content = (wet mass − dry mass) ÷ dry mass × 100 on a dry basis, used for soil, sand and wood, or ÷ wet mass on a wet basis, used for food, grain and sludge. A tin of 15.20 g holding 65.80 g of wet soil and 57.40 g after oven drying lost 8.40 g of water from 42.20 g of dry soil: 19.91% moisture content (dry basis), or 16.60% wet basis.

Rule of thumb: subtract the container first, always say which basis you used, and remember that dry-basis moisture can exceed 100% (peat, green wood, sludge) while wet-basis moisture never can.

Moisture content is the amount of water in a material, expressed as a percentage of either its dry mass (dry basis) or its wet mass (wet basis). It is measured most simply by weighing a sample, drying it in an oven to constant mass and weighing it again: the loss is the water. The gravimetric method is still the reference for soil (Reynolds, 1970) against which sensors are calibrated (Bittelli, 2011). A moisture content calculator applies the right basis, converts between them, carries the balance uncertainty, and extends the same idea to soil volume, air, wood, grain and compaction.

A worked one-liner: water = 65.80 − 57.40 = 8.40 g; dry soil = 57.40 − 15.20 = 42.20 g; w = 8.40 ÷ 42.20 × 100 = 19.91%; wet basis = 8.40 ÷ 50.60 × 100 = 16.60%.

Who uses it: geotechnical and soil science students running ASTM D2216; agronomists and hydrologists converting to volumetric water; woodworkers and kiln operators checking lumber and EMC; food technologists reporting moisture and total solids; grain handlers calculating shrink; HVAC engineers finding the moisture content of air; concrete technicians correcting sand moisture; and wastewater operators measuring sludge solids.

Quick answers to the questions people ask most

4.1 What the result means

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4.2 The formula

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4.3 The detail that changes the answer

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4.4 Accuracy and limits

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4.5 What to do next

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4.6 Dry basis or wet basis?

Both describe the same water. Dry basis divides by the dry solids, which do not change as the sample dries, so it is the natural choice in soil mechanics, agronomy and wood science; it can exceed 100%. Wet basis divides by the whole wet sample, which is how food labels, grain contracts and sludge reports express it; it is always below 100%. Convert with wet = dry ÷ (1 + dry) and dry = wet ÷ (1 − wet), working in fractions: 25% dry basis is 0.25 ÷ 1.25 = 20% wet basis.

4.7 Gravimetric, volumetric and sensor readings

Oven drying gives gravimetric moisture by mass. Hydrology, irrigation and soil sensors use volumetric water content θv, the volume of water per volume of soil, which equals gravimetric moisture times dry bulk density. Time-domain reflectometry and capacitance probes read θv from the soil’s dielectric constant (Topp et al., 1980) and are calibrated against gravimetric samples (Dobriyal et al., 2012). Soil moisture varies strongly in space and time, so several samples are needed to characterize a field (Vereecken et al., 2014).

4.8 Equilibrium and humidity

Hygroscopic materials such as wood and grain exchange water with the air until they reach an equilibrium moisture content set mainly by relative humidity and slightly by temperature. Wood’s sorption behavior is reviewed by Engelund et al. (2013) and Thybring et al. (2022); grain isotherms are compared by Chen and Morey (1989).

A moisture content in a report needs the number, the basis (dry or wet), the method (oven drying and temperature, or the sensor or equation) and, for measured values, the uncertainty or the replicate spread. The seven write-ups below are filled from the moisture content calculator; they show the headline example until you press Calculate.

Showing the headline example (soil in a tin). Press Calculate above to fill all seven write-ups with your own result.

5.1 Style rules

5.2 Seven ready-to-copy write-ups

The same worked problem as Sections 0, 9, 10, 11 and 14: tin 15.20 g, tin + wet soil 65.80 g, tin + dry soil 57.40 g.

1. Pick what you want to calculate. Choose a mode in Section 2: moisture from wet and dry mass, volumetric soil moisture, moisture content of air, equilibrium moisture content, Proctor optimum moisture, or drying and blending.
2. Load a sample or type your data. Thirteen worked problems are in the sample menu. Sample 1 is the headline soil test. Nothing is calculated until you press Calculate Moisture Content.
3. Choose the material. Soil, aggregate and wood report a dry basis; food, grain and sludge a wet basis. Both are always shown.
4. Enter the masses with the container. Type the container, container plus wet sample and container plus oven-dry sample. Leave the container blank if you weighed the sample alone.
5. Add the balance resolution. Optional: the balance tolerance gives an expanded uncertainty and a budget showing which weighing matters most.
6. For other modes, fill the extra fields. Bulk density for volumetric moisture; temperature with RH or dew point for air; temperature and RH for EMC; moisture and density pairs for Proctor; masses and targets for drying.
7. Press Calculate Moisture Content. Press the button or Enter. The result, a Verified badge, the formula and every step appear in Section 3.
8. Read the figure and charts. The figure draws your sample; four charts compare the two bases, show the parts of the sample, and how the result changes with the inputs.
9. Copy the write-up. Section 5 fills seven ready texts for a report, a log, code, an exam, chat, a results paragraph and a table.
10. Download or run a batch. Download Doc or PDF, or calculate many samples at once from a CSV of wet mass, dry mass and container in the Batch tab.

Excel needs one formula per quantity. The sheet repeats the headline problem (tin 15.20 g, wet 65.80 g, dry 57.40 g), so the numbers match Sections 0, 7, 10, 11 and 14.

9.1 The formulas you need

9.2 Step by step

9.3 Many samples

Paste the weighings for every sample into columns A to C, select D2:F2 and fill down. Use =AVERAGE(F2:F20) and =STDEV.S(F2:F20) for replicate statistics.

9.4 Why Excel may differ from R or Python

9.5 Charting

For a Proctor test, select the moisture and dry density columns, insert a Scatter chart and add a polynomial trendline of order 2 with “Display equation”. The peak of that curve is the optimum moisture content.

9.6 Errors you may see

• #DIV/0!: the dry mass equals the tin mass.
• A negative moisture: wet and dry columns swapped.
• A value about 25% too low: divided by C2 instead of C2−A2.

A complete, copy-and-run script that reproduces the moisture content calculator for the headline problem. It calculates soil moisture content on a dry and wet basis from three weighings with its uncertainty, converts to volumetric moisture, finds the moisture content of air and the equilibrium moisture content of wood, fits a Proctor curve and saves one 300 dpi figure.

10.1 The complete script

R, base only, saves compaction_curve.png

# Moisture content calculator in base R (StatsUnlock)
moisture <- function(tin, wet, dry) {           # three weighings, g
  if (any(dry > wet) || any(dry <= tin)) stop("check the masses")
  water <- wet - dry; solids <- dry - tin
  data.frame(water = water, dry_basis = 100 * water / solids, wet_basis = 100 * water / (wet - tin))
}
# headline: tin 15.20 g, tin + wet soil 65.80 g, tin + dry soil 57.40 g
m <- moisture(15.20, 65.80, 57.40); print(round(m, 3))
# uncertainty: balance +/- 0.01 g, rectangular, three weighings
u <- 0.01 / sqrt(3); s <- 57.40 - 15.20
cf <- c(tin = 100 * 8.40 / s^2, wet = 100 / s, dry = -100 * 50.60 / s^2)
cat(sprintf("w = %.2f +/- %.3f %% (U, k = 2)\n", m$dry_basis, 2 * sqrt(sum((cf * u)^2))))
# volumetric: w = 20 %, bulk density 1.40 g/cm3, 30 cm layer
theta <- 0.20 * 1.40; cat(sprintf("theta_v = %.2f; water depth = %.0f mm\n", theta, theta * 300))
# moisture content of air: 25 C, 60 % RH, 101.325 kPa (Magnus)
es <- function(t) 0.61094 * exp(17.625 * t / (t + 243.04))
e <- 0.60 * es(25); W <- 621.945 * e / (101.325 - e)
cat(sprintf("air: e = %.3f kPa, W = %.2f g/kg\n", e, W))
# wood EMC (Hailwood-Horrobin, Simpson coefficients, T in F)
emc_wood <- function(TF, h) {
  W <- 330 + 0.452 * TF + 0.00415 * TF^2; K <- 0.791 + 4.63e-4 * TF - 8.44e-7 * TF^2
  K1 <- 6.34 + 7.75e-4 * TF - 9.35e-5 * TF^2; K2 <- 1.09 + 2.84e-2 * TF - 9.04e-5 * TF^2
  kh <- K * h; 1800 / W * (kh / (1 - kh) + (K1 * kh + 2 * K1 * K2 * kh^2) / (1 + K1 * kh + K1 * K2 * kh^2))
}
cat(sprintf("wood EMC at 70 F, 50 %% RH = %.2f %%\n", emc_wood(70, 0.50)))
# Proctor: quadratic fit, optimum moisture content
w <- c(10, 12, 14, 16, 18); rd <- c(1.72, 1.80, 1.84, 1.81, 1.74)
f <- lm(rd ~ w + I(w^2)); b <- coef(f)
omc <- -b[2] / (2 * b[3]); mdd <- predict(f, data.frame(w = omc))
cat(sprintf("OMC = %.2f %%, MDD = %.3f g/cm3\n", omc, mdd))
png("compaction_curve.png", width = 7, height = 4, units = "in", res = 300)
ww <- seq(8, 20, 0.1)
plot(w, rd, pch = 19, xlab = "moisture content, %", ylab = "dry density, g/cm3", ylim = c(1.65, 2.05))
lines(ww, predict(f, data.frame(w = ww))); lines(ww, 2.65 / (1 + ww / 100 * 2.65), lty = 2)
abline(v = omc, lty = 3)
invisible(dev.off())

10.2 Output

  water dry_basis wet_basis
1   8.4    19.905    16.601
w = 19.91 +/- 0.043 % (U, k = 2)
theta_v = 0.28; water depth = 84 mm
air: e = 1.897 kPa, W = 11.87 g/kg
wood EMC at 70 F, 50 % RH = 9.25 %
OMC = 14.19 %, MDD = 1.835 g/cm3

10.3 Traps

• moisture() is vectorized: pass columns of a data frame.
• lm(rd ~ w + I(w^2)) needs I() so ^ means a power, not an interaction.
• Keep moisture in percent for the fit; ZAV uses w/100.

10.4 What each part does

10.5 Adapting it

Change the three masses for your sample, or pass whole columns: moisture() is vectorized and works on a data frame of many samples at once.

A complete, copy-and-run script that reproduces the moisture content calculator for the headline problem. It calculates soil moisture content on a dry and wet basis from three weighings with its uncertainty, converts to volumetric moisture, finds the moisture content of air and the equilibrium moisture content of wood, fits a Proctor curve and saves one 300 dpi figure.

Install once: pip install numpy matplotlib

11.1 The complete script

Python with NumPy and Matplotlib, saves compaction_curve.png

# Moisture content calculator in Python (StatsUnlock)
import numpy as np
import matplotlib.pyplot as plt

def moisture(tin, wet, dry):
    """Dry- and wet-basis moisture content (%) from three weighings (g)."""
    tin, wet, dry = map(np.asarray, (tin, wet, dry))
    if (dry > wet).any() or (dry <= tin).any():
        raise ValueError("check the masses")
    water, solids = wet - dry, dry - tin
    return 100 * water / solids, 100 * water / (wet - tin)

w_db, w_wb = moisture(15.20, 65.80, 57.40)            # headline soil sample
print(f"dry basis {w_db:.2f} %, wet basis {w_wb:.2f} %")

u, s = 0.01 / np.sqrt(3), 57.40 - 15.20               # balance +/- 0.01 g
c = np.array([100 * 8.40 / s**2, 100 / s, -100 * 50.60 / s**2])
print(f"U (k = 2) = {2 * np.sqrt(np.sum((c * u) ** 2)):.3f} percentage points")

theta = 0.20 * 1.40                                    # volumetric soil water
print(f"theta_v = {theta:.2f}; {theta * 300:.0f} mm in 30 cm")

es = lambda t: 0.61094 * np.exp(17.625 * t / (t + 243.04))   # Magnus, kPa
e = 0.60 * es(25)
print(f"air at 25 C, 60 % RH: W = {621.945 * e / (101.325 - e):.2f} g/kg")

def emc_wood(TF, h):                                   # Simpson (1973), T in F
    W = 330 + 0.452 * TF + 0.00415 * TF**2
    K = 0.791 + 4.63e-4 * TF - 8.44e-7 * TF**2
    K1 = 6.34 + 7.75e-4 * TF - 9.35e-5 * TF**2
    K2 = 1.09 + 2.84e-2 * TF - 9.04e-5 * TF**2
    kh = K * h
    return 1800 / W * (kh / (1 - kh) + (K1 * kh + 2 * K1 * K2 * kh**2) / (1 + K1 * kh + K1 * K2 * kh**2))
print(f"wood EMC at 70 F, 50 % RH = {emc_wood(70, 0.50):.2f} %")

w = np.array([10, 12, 14, 16, 18]); rd = np.array([1.72, 1.80, 1.84, 1.81, 1.74])
a, b, c0 = np.polyfit(w, rd, 2)
omc = -b / (2 * a); mdd = np.polyval([a, b, c0], omc)
print(f"OMC = {omc:.2f} %, MDD = {mdd:.3f} g/cm3")

ww = np.linspace(8, 20, 121)
fig, ax = plt.subplots(figsize=(7, 4))
ax.plot(w, rd, "o", ww, np.polyval([a, b, c0], ww), "-", ww, 2.65 / (1 + ww / 100 * 2.65), "--")
ax.axvline(omc, ls=":"); ax.set_xlabel("moisture content, %"); ax.set_ylabel("dry density, g/cm3")
fig.tight_layout(); fig.savefig("compaction_curve.png", dpi=300)

11.2 Output

dry basis 19.91 %, wet basis 16.60 %
U (k = 2) = 0.043 percentage points
theta_v = 0.28; 84 mm in 30 cm
air at 25 C, 60 % RH: W = 11.87 g/kg
wood EMC at 70 F, 50 % RH = 9.25 %
OMC = 14.19 %, MDD = 1.835 g/cm3

11.3 Traps

• moisture() accepts arrays or pandas columns.
• np.polyfit returns the highest power first: a, b, c.
• Raise an error when dry mass exceeds wet mass instead of returning a negative.

11.4 What each part does

11.5 Adapting it

Change the three masses for your sample, or pass whole columns: moisture() is vectorized and works on a data frame of many samples at once.

12.1 Dry basis to wet basis

wet = dry ÷ (100 + dry) × 100. Exact. Last checked: October 2026.

12.2 Moisture content of saturated air (101.325 kPa)

Magnus form over water (Alduchov & Eskridge, 1996); W = 0.621945 e ÷ (P − e). Below 0 °C values are over supercooled water. Last checked: October 2026.

12.3 Equilibrium moisture content of wood (%)

Hailwood-Horrobin equation with Simpson’s coefficients (Hailwood & Horrobin, 1946), the model behind the Wood Handbook EMC table. Last checked: October 2026.

12.4 Oven-drying methods by material

Food methods summarized by Bradley (2010). Last checked: October 2026.

12.5 Volumetric water content θv (cm³/cm³)

θv = w × ρb ÷ ρw with ρb in g/cm³. Values above the soil’s porosity are not physically possible. Last checked: October 2026.

12.6 Water shrink when drying to 14% (wet basis)

Water shrink only: M_f = M_i(1 − MC_i) ÷ (1 − 0.14). Handling losses are added separately in trade. Last checked: October 2026.

Eight worked problems that cover what a moisture content calculator meets: a textbook soil can, a wood section, cheese, concrete sand, sludge, air, a Proctor test and the classic mistake. Each number was checked in Python.

The written method, calculator keys and the oven-drying procedure, for the headline problem: tin 15.20 g, tin + wet soil 65.80 g, tin + dry soil 57.40 g.

14.1 The written method

1. Water. 65.80 − 57.40 = 8.40 g.
2. Dry soil. 57.40 − 15.20 = 42.20 g.
3. Dry basis. 8.40 ÷ 42.20 × 100 = 19.91%.
4. Wet basis. 8.40 ÷ 50.60 × 100 = 16.60%.
5. Uncertainty. ± 0.01 g balance: U = ± 0.04 points.
6. Check. 0.1991 ÷ 1.1991 = 0.1660 ✓

14.2 On a calculator

14.3 Lab SOP: water content of soil by oven drying (ASTM D2216)

14.4 Practice problems

14.5 Mistakes that lose marks

• Not subtracting the container.
• Not stating the basis.
• Dividing by wet mass for soil.
• Treating points of moisture as percent of mass.
• Averaging moisture percentages of unequal lots.
• Using °C in the wood EMC equation, which needs °F.

14.6 Check-yourself and lab questions

Almost every wrong moisture value comes from the basis, the container or incomplete drying. The moisture content calculator guards against each one below.

Soil moisture also drives climate feedbacks and drought (Seneviratne et al., 2010); a single sample describes only its spot and moment.

Your result

The moisture content calculator gives 19.91% (dry basis) and 16.60% (wet basis) for this soil sample. Moisture content = water ÷ dry solids × 100.

What the moisture content calculator does

Moisture content is one ratio, water to material, applied in many ways. This moisture content calculator handles oven-dry tests on both bases with uncertainty, volumetric soil water, the moisture content of air from RH or dew point, equilibrium moisture of wood and grain, Proctor optimum moisture, drying targets and blended averages, with every step shown.

Watch out for

• State the basis: dry or wet.
• Subtract the container.
• Dry to constant mass.
• Points of moisture are not percent of mass.
• Weight averages by mass.

Next steps

For density of soil solids, use the specific gravity calculator; for oven and air temperatures, the temperature converter; and for w/w percentages, the percent solution calculator. Whatever the material, start and finish with the moisture content calculator.

More lab, chemistry and measurement tools from StatsUnlock, with worked steps, code and references.

Peer-reviewed sources behind this moisture content calculator: the gravimetric method, soil moisture measurement and sensors, soil moisture variability and climate, vapor pressure, dew point and wet-bulb equations, wood sorption and equilibrium moisture, grain isotherms, food moisture analysis, and measurement uncertainty. Standards (ASTM D2216, C566, D4442, D698, D1557 and JCGM 100:2008) are named where they are used. Every DOI checked October 2026.

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2. Bittelli, M. (2011). Measuring soil water content: A review. HortTechnology, 21(3), 293–300. https://doi.org/10.21273/HORTTECH.21.3.293
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