Dry Matter Calculator with Steps (Free) | StatsUnlock

A dry matter calculator uses dry matter % = 100 − moisture % and nutrient on a dry matter basis = as-fed % ÷ dry matter % × 100. A canned dog food with 78% moisture and 8% protein has 22% dry matter, so its protein is 8 ÷ 22 × 100 = 36.4% on a dry matter basis, more than many dry kibbles.

Rule of thumb: never compare wet and dry foods on the label numbers. Convert both to a dry matter basis, or better, to grams per 1000 kcal. For livestock, mix as fed but balance on a dry matter basis.

Dry matter is everything in a food or feed except water: protein, fat, carbohydrate, fiber and minerals. Because water content varies so much (about 10% in kibble, 75 to 82% in canned food, 30 to 40% DM in silage), nutrients are compared and requirements are set on a dry matter basis. Nutrient requirement tables for dogs and cats (NRC, 2006) and for beef and dairy cattle (NASEM, 2016; NASEM, 2021) are written this way. A dry matter calculator removes the water so that a wet food and a dry food, or two feeds, can be compared fairly.

A worked one-liner: DM = 100 − 78 = 22%; protein DMB = 8 ÷ 22 × 100 = 36.4%; fat DMB = 5 ÷ 22 × 100 = 22.7%; energy ≈ 10 × (3.5 × 8 + 8.5 × 5 + 3.5 × 5) = 880 kcal/kg as fed = 4000 kcal/kg DM.

Who uses it: pet owners and veterinary teams comparing dog and cat foods, including phosphorus for kidney diets; animal science students; nutritionists and farmers balancing cattle rations; feed and forage labs reporting results; and dairy technologists calculating fat in dry matter for cheese labeling.

Quick answers to the questions people ask most

4.1 What the result means

___

4.2 The formula

___

4.3 The detail that changes the answer

___

4.4 Accuracy and limits

___

4.5 What to do next

___

4.6 Dry matter basis or per 1000 kcal?

A dry matter basis removes water, which is enough to compare most feeds. Pet foods also differ widely in fat and therefore in energy, and an animal eats to meet its energy needs. Expressing a nutrient as grams per 1000 kcal shows how much of it arrives with each day’s calories. AAFCO’s dry matter minimums assume 4000 kcal per kg of dry matter, so for very high-fat foods the per-1000-kcal comparison is the more reliable one. Energy here uses modified Atwater factors (3.5, 8.5 and 3.5 kcal/g for protein, fat and carbohydrate), which predict measured metabolizable energy well on average but can underestimate high-quality diets (Hall et al., 2013; Asaro et al., 2017).

4.7 Why label numbers are not analyses

The guaranteed analysis lists minimum crude protein and fat and maximum fiber and moisture. Measured contents often exceed the protein and fat minimums and can fall short of other claims (Hill et al., 2009; Gosper et al., 2016). Carbohydrate is rarely printed and is calculated by difference, so it carries all the other errors. Ask the manufacturer for typical (average) analyses and calorie content when precision matters (Zicker, 2008).

4.8 Measuring dry matter in feeds

Dry matter is measured by drying to constant mass, but the method matters: fermented feeds such as silage lose volatile acids and alcohols, so oven dry matter can understate true dry matter (Thiex & Richardson, 2003). On farms, the dry matter of wet ingredients varies enough from day to day that frequent checks pay off (St-Pierre & Weiss, 2015).

A nutrient value in a report needs the number, the basis (as fed, dry matter or per 1000 kcal), the source (label guarantee, typical analysis or laboratory analysis) and the dry matter used. The seven write-ups below are filled from the dry matter calculator; they show the headline example until you press Calculate.

Showing the headline example (canned dog food). 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: canned dog food, 78% moisture, 8% protein, 5% fat, 1.5% fiber, 2.5% ash.

1. Pick what you want to calculate. Choose a mode in Section 2: a dog or cat food label, as fed and dry matter conversion, dry matter content from drying, cattle intake and rations, cheese fat in dry matter, or a comparison of two foods.
2. Load a sample or type your values. Twelve worked problems are in the sample menu. Sample 1 is the headline canned dog food. Nothing is calculated until you press Calculate Dry Matter.
3. Copy the label exactly. Enter moisture, crude protein, crude fat, fiber and ash as printed in the guaranteed analysis. Add phosphorus, calcium or sodium on extra lines.
4. Choose species and life stage. Dog or cat, adult or growth, sets the AAFCO protein and fat minimums used for the check.
5. For feeds, enter dry matter or moisture. The convert mode takes either; add a feed amount in kg to convert masses as well as percentages.
6. For cattle, enter body weight and intake. Use a target intake as % of body weight, or list ration ingredients with kg as fed, % DM, % crude protein and price.
7. Press Calculate Dry Matter. Press the button or Enter. Dry matter basis values, energy, a Verified badge, the formula and every step appear in Section 3.
8. Read the figure and charts. The figure draws your food as fed and on a dry matter basis; four charts show the bases, calories, sensitivity and benchmarks.
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 convert many foods at once from a CSV of moisture and nutrient values in the Batch tab.

One formula per nutrient. The sheet repeats the headline problem (canned dog food, 78% moisture, 8% protein, 5% fat), 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 foods

Put one food per row and fill the formulas down. Sort by the dry matter basis column, not the as-fed column, to rank foods by protein.

9.4 Why Excel may differ from R or Python

9.5 Charting

Select the as-fed and dry matter basis columns for several foods and insert a Clustered Column chart. The wet foods’ bars jump when water is removed.

9.6 Errors you may see

• #DIV/0!: moisture entered as 100.
• A value above 100%: dry matter typed where moisture was needed.
• Numbers 100 times too small: cells formatted as percent.

A complete, copy-and-run script that reproduces the dry matter calculator for the headline problem. It converts a canned dog food label to a dry matter basis with modified Atwater energy and per-1000-kcal values, converts feed between as fed and dry matter, plans a cattle ration, calculates cheese fat in dry matter and saves one 300 dpi figure.

10.1 The complete script

R, base only, saves dry_matter_basis.png

# Dry matter calculator in base R (StatsUnlock)
dmb <- function(asfed, moisture) {               # % as fed -> % of dry matter
  if (any(moisture < 0 | moisture >= 100)) stop("moisture must be 0 to below 100")
  asfed / (100 - moisture) * 100
}
# headline: canned dog food, guaranteed analysis
lab <- c(moisture = 78, protein = 8, fat = 5, fiber = 1.5, ash = 2.5)
if (sum(lab) > 100) stop("label adds to more than 100 %")
nfe <- 100 - sum(lab)
dm <- round(dmb(c(lab[-1], nfe = nfe), lab["moisture"]), 2); print(dm)
me <- 10 * (3.5 * lab["protein"] + 8.5 * lab["fat"] + 3.5 * nfe)   # modified Atwater, kcal/kg
cat(sprintf("ME %.0f kcal/kg as fed (%.0f per kg DM); protein %.1f g/1000 kcal\n",
            me, me / 0.22, lab["protein"] * 10 / (me / 1000)))
# feed: hay at 88 % DM, 16 % CP as fed; silage 35 % DM, 8.5 % CP of DM
cat(sprintf("hay CP %.2f %% of DM; silage CP %.2f %% as fed\n", 16 / 0.88, 8.5 * 0.35))
# ration: kg as fed, % DM, % CP of DM
r <- data.frame(af = c(30, 8, 9), dm = c(35, 88, 89), cp = c(8, 18, 16))
kg_dm <- r$af * r$dm / 100
cat(sprintf("ration: %.2f kg DM, %.1f %% DM, %.1f %% CP of DM\n",
            sum(kg_dm), 100 * sum(kg_dm) / sum(r$af), sum(kg_dm * r$cp) / sum(kg_dm)))
# cheese: fat in dry matter and MFFB
fat <- 33; moist <- 37
cat(sprintf("FDM %.1f %%, MFFB %.1f %%\n", fat / (100 - moist) * 100, moist / (100 - fat) * 100))
png("dry_matter_basis.png", width = 7, height = 4, units = "in", res = 300)
barplot(rbind(c(8, 5, 5), c(dm["protein"], dm["fat"], dm["nfe"])), beside = TRUE,
        names.arg = c("protein", "fat", "carbohydrate"), col = c("grey70", "#9333ea"),
        legend.text = c("as fed", "dry matter basis"), ylab = "%")
invisible(dev.off())

10.2 Output

protein     fat   fiber     ash     nfe 
  36.36   22.73    6.82   11.36   22.73 
ME 880 kcal/kg as fed (4000 per kg DM); protein 90.9 g/1000 kcal
hay CP 18.18 % of DM; silage CP 2.97 % as fed
ration: 25.55 kg DM, 54.4 % DM, 13.3 % CP of DM
FDM 52.4 %, MFFB 55.2 %

10.3 Traps

• dmb() is vectorized: pass a column of moistures and a column of nutrients.
• Stop if the label adds to more than 100%.
• Ration DM must be weighted by kg as fed.

10.4 What each part does

10.5 Adapting it

Change the label values for your food, or pass whole columns: dmb() is vectorized and works on a data frame of many foods at once.

A complete, copy-and-run script that reproduces the dry matter calculator for the headline problem. It converts a canned dog food label to a dry matter basis with modified Atwater energy and per-1000-kcal values, converts feed between as fed and dry matter, plans a cattle ration, calculates cheese fat in dry matter and saves one 300 dpi figure.

Install once: pip install numpy matplotlib

11.1 The complete script

Python with NumPy and Matplotlib, saves dry_matter_basis.png

# Dry matter calculator in Python (StatsUnlock)
import numpy as np
import matplotlib.pyplot as plt

def dmb(asfed, moisture):
    """Nutrient % as fed -> % of dry matter."""
    asfed, moisture = np.asarray(asfed, float), np.asarray(moisture, float)
    if ((moisture < 0) | (moisture >= 100)).any():
        raise ValueError("moisture must be 0 to below 100")
    return asfed / (100 - moisture) * 100

lab = {"moisture": 78, "protein": 8, "fat": 5, "fiber": 1.5, "ash": 2.5}   # canned dog food
if sum(lab.values()) > 100:
    raise ValueError("label adds to more than 100 %")
nfe = 100 - sum(lab.values())
for k in ("protein", "fat", "fiber", "ash"):
    print(f"{k}: {dmb(lab[k], lab['moisture']):.2f} % DM")
print(f"carbohydrate (NFE): {dmb(nfe, lab['moisture']):.2f} % DM")

me = 10 * (3.5 * lab["protein"] + 8.5 * lab["fat"] + 3.5 * nfe)        # modified Atwater
print(f"ME {me:.0f} kcal/kg as fed ({me / 0.22:.0f} per kg DM); protein {lab['protein'] * 10 / (me / 1000):.1f} g/1000 kcal")

print(f"hay CP {16 / 0.88:.2f} % of DM; silage CP {8.5 * 0.35:.2f} % as fed")

af, dm, cp = np.array([30, 8, 9]), np.array([35, 88, 89]), np.array([8, 18, 16])   # ration
kg_dm = af * dm / 100
print(f"ration: {kg_dm.sum():.2f} kg DM, {100 * kg_dm.sum() / af.sum():.1f} % DM, {(kg_dm * cp).sum() / kg_dm.sum():.1f} % CP of DM")

fat, moist = 33, 37                                                       # cheese
print(f"FDM {fat / (100 - moist) * 100:.1f} %, MFFB {moist / (100 - fat) * 100:.1f} %")

x = np.arange(3); w = 0.38
fig, ax = plt.subplots(figsize=(7, 4))
ax.bar(x - w / 2, [8, 5, nfe], w, label="as fed", color="grey")
ax.bar(x + w / 2, dmb([8, 5, nfe], 78), w, label="dry matter basis", color="#9333ea")
ax.set_xticks(x, ["protein", "fat", "carbohydrate"]); ax.set_ylabel("%"); ax.legend()
fig.tight_layout(); fig.savefig("dry_matter_basis.png", dpi=300)

11.2 Output

protein: 36.36 % DM
fat: 22.73 % DM
fiber: 6.82 % DM
ash: 11.36 % DM
carbohydrate (NFE): 22.73 % DM
ME 880 kcal/kg as fed (4000 per kg DM); protein 90.9 g/1000 kcal
hay CP 18.18 % of DM; silage CP 2.97 % as fed
ration: 25.55 kg DM, 54.4 % DM, 13.3 % CP of DM
FDM 52.4 %, MFFB 55.2 %

11.3 Traps

• dmb() accepts arrays or pandas columns.
• Moisture of 100 would divide by zero; it is rejected.
• Keep percentages as numbers, not fractions, throughout.

11.4 What each part does

11.5 Adapting it

Change the label values for your food, or pass whole columns: dmb() is vectorized and works on a data frame of many foods at once.

12.1 As fed to dry matter multiplier

DMB = as fed × 100 ÷ (100 − moisture). Exact. Last checked: October 2026.

12.2 AAFCO minimum protein and fat

AAFCO dog and cat food nutrient profiles (2016 revision for dogs), stated on a dry matter basis assuming 4000 kcal ME/kg DM; g/1000 kcal = % DM × 2.5. Last checked: October 2026.

12.3 Codex cheese classes

Codex General Standard for Cheese (CXS 283-1978); the MFFB ranges overlap as published. Last checked: October 2026.

12.4 Dry matter intake and as-fed feed (3% of body weight)

Illustrative; actual intake depends on class of animal, production and diet (NASEM, 2016; NASEM, 2021). Last checked: October 2026.

12.5 Modified Atwater energy

Factors used for pet food calorie statements; prediction accuracy reviewed by Hall et al. (2013). Last checked: October 2026.

Eight worked problems that cover what a dry matter calculator meets: a kibble, a renal cat food, a hay test, an oven dry matter, a steer, a cheese, food cost and the classic ration mistake. Each number was checked in Python.

The written method, calculator keys and a drying procedure, for the headline problem: canned dog food, 78% moisture, 8% protein, 5% fat, 1.5% fiber, 2.5% ash.

14.1 The written method

1. Dry matter. 100 − 78 = 22%.
2. Protein. 8 ÷ 22 × 100 = 36.4%.
3. Fat. 5 ÷ 22 × 100 = 22.7%.
4. Carbohydrate. 100 − 78 − 8 − 5 − 1.5 − 2.5 = 5% as fed = 22.7% DM.
5. Energy. 10 × (28 + 42.5 + 17.5) = 880 kcal/kg.
6. Check. 36.4% × 0.22 = 8.0% ✓

14.2 On a calculator

14.3 Lab SOP: dry matter of a feed or food by oven drying

14.4 Practice problems

14.5 Mistakes that lose marks

• Dividing by moisture instead of dry matter.
• Comparing wet and dry foods as fed.
• Converting masses the same way as percentages.
• Averaging ration DM without weights.
• Leaving out the basis in the answer.
• Forgetting ash in carbohydrate by difference.

14.6 Check-yourself and lab questions

Almost every wrong dry matter result comes from mixing bases or misreading the label. The dry matter calculator guards against each one below.

This calculator converts and compares; it does not formulate a complete diet. For pets with disease, work with a veterinarian. Cats on wet food take in more water, which matters for urinary health (Buckley et al., 2011).

Your result

The dry matter calculator gives 36.4% protein and 22.7% fat on a dry matter basis for this dog food (22% dry matter).

What the dry matter calculator does

Water hides what is in a food. This dry matter calculator removes it: pet food labels to a dry matter basis with energy and per-1000-kcal values, any nutrient between as fed and dry matter, dry matter content from drying, cattle intake and rations, fat in dry matter for cheese, and side-by-side food comparison, with every step shown.

Watch out for

• Compare wet and dry foods only on DM or per 1000 kcal.
• Enter ash, or treat carbohydrate as an upper bound.
• Labels are guarantees, not analyses.
• Re-measure wet feed dry matter often.
• Weight ration DM by amount fed.

Next steps

For the percentages behind dry matter, use the percent solution calculator; to prepare standards for feed analysis, the molarity calculator and dilution calculator help. Whatever the food, start and finish with the dry matter calculator.

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

Peer-reviewed sources behind this dry matter calculator: nutrient requirements of dogs, cats and cattle, pet food labels and their accuracy, metabolizable energy prediction, feed moisture and ash methods, feed variation on farms, dry matter intake, cat protein and kidney diets, water intake, and measurement uncertainty. Standards (AAFCO nutrient profiles, Codex CXS 283-1978, JCGM 100:2008) are named where they are used. Every DOI checked October 2026.

1. Allen, M. S. (2000). Effects of diet on short-term regulation of feed intake by lactating dairy cattle. Journal of Dairy Science, 83(7), 1598–1624. https://doi.org/10.3168/jds.S0022-0302(00)75030-2
2. Asaro, N. J., Guevara, M., Berendt, K., Zijlstra, R. T., & Shoveller, A. K. (2017). Digestibility is similar between commercial diets that provide ingredients with different perceived glycemic responses and the inaccuracy of using the modified Atwater calculation to calculate metabolizable energy. Veterinary Sciences, 4(4), 54. https://doi.org/10.3390/vetsci4040054
3. Buckley, C. M. F., Hawthorne, A. J., Colyer, A., & Stevenson, A. E. (2011). Effect of dietary water intake on urinary output, specific gravity and relative supersaturation for calcium oxalate and struvite in the cat. British Journal of Nutrition, 106(S1), S128–S130. https://doi.org/10.1017/S0007114511001875
4. Cole, T. J. (2015). Too many digits: The presentation of numerical data. Archives of Disease in Childhood, 100(7), 608–609. https://doi.org/10.1136/archdischild-2014-307149
5. Goldberg, D. (1991). What every computer scientist should know about floating-point arithmetic. ACM Computing Surveys, 23(1), 5–48. https://doi.org/10.1145/103162.103163
6. Gosper, E. C., Raubenheimer, D., Machovsky-Capuska, G. E., & Chaves, A. V. (2016). Discrepancy between the composition of some commercial cat foods and their package labelling and suitability for meeting nutritional requirements. Australian Veterinary Journal, 94(1–2), 12–17. https://doi.org/10.1111/avj.12397
7. Hall, J. A., Melendez, L. D., & Jewell, D. E. (2013). Using gross energy improves metabolizable energy predictive equations for pet foods whereas undigested protein and fiber content predict stool quality. PLoS ONE, 8(1), e54405. https://doi.org/10.1371/journal.pone.0054405
8. Hill, R. C., Choate, C. J., Scott, K. C., & Molenberghs, G. (2009). Comparison of the guaranteed analysis with the measured nutrient composition of commercial pet foods. Journal of the American Veterinary Medical Association, 234(3), 347–351. https://doi.org/10.2460/javma.234.3.347
9. Kragten, J. (1994). Calculating standard deviations and confidence intervals with a universally applicable spreadsheet technique. Analyst, 119(10), 2161–2165. https://doi.org/10.1039/AN9941902161
10. Laflamme, D. P., & Hannah, S. S. (2013). Discrepancy between use of lean body mass or nitrogen balance to determine protein requirements for adult cats. Journal of Feline Medicine and Surgery, 15(8), 691–697. https://doi.org/10.1177/1098612X12474448
11. National Academies of Sciences, Engineering, and Medicine. (2016). Nutrient requirements of beef cattle (8th rev. ed.). The National Academies Press. https://doi.org/10.17226/19014
12. National Academies of Sciences, Engineering, and Medicine. (2021). Nutrient requirements of dairy cattle (8th rev. ed.). The National Academies Press. https://doi.org/10.17226/25806
13. National Research Council. (2006). Nutrient requirements of dogs and cats. The National Academies Press. https://doi.org/10.17226/10668
14. Ross, S. J., Osborne, C. A., Kirk, C. A., Lowry, S. R., Koehler, L. A., & Polzin, D. J. (2006). Clinical evaluation of dietary modification for treatment of spontaneous chronic kidney disease in cats. Journal of the American Veterinary Medical Association, 229(6), 949–957. https://doi.org/10.2460/javma.229.6.949
15. St-Pierre, N. R., & Weiss, W. P. (2015). Partitioning variation in nutrient composition data of common feeds and mixed diets on commercial dairy farms. Journal of Dairy Science, 98(7), 5004–5015. https://doi.org/10.3168/jds.2015-9431
16. Thiex, N., & Richardson, C. R. (2003). Challenges in measuring moisture content of feeds. Journal of Animal Science, 81(12), 3255–3266. https://doi.org/10.2527/2003.81123255x
17. Thiex, N., Novotny, L., & Crawford, A. (2012). Determination of ash in animal feed: AOAC Official Method 942.05 revisited. Journal of AOAC International, 95(5), 1392–1397. https://doi.org/10.5740/jaoacint.12-129
18. Thompson, A. (2008). Ingredients: Where pet food starts. Topics in Companion Animal Medicine, 23(3), 127–132. https://doi.org/10.1053/j.tcam.2008.04.004
19. Weiss, W. P., Conrad, H. R., & St. Pierre, N. R. (1992). A theoretically-based model for predicting total digestible nutrient values of forages and concentrates. Animal Feed Science and Technology, 39(1–2), 95–110. https://doi.org/10.1016/0377-8401(92)90034-4
20. Zicker, S. C. (2008). Evaluating pet foods: How confident are you when you recommend a commercial pet food? Topics in Companion Animal Medicine, 23(3), 121–126. https://doi.org/10.1053/j.tcam.2008.04.003