Specialty agriculture & aquaculture kernels
Per-hive, per-tank, per-stock, per-hectare, and per-trap arithmetic for six specialty production sciences -- beekeeping, aquatic farming, wild-fisheries stock assessment, wine-grape growing and cellar
Six modules for production sciences that sit outside row-crop agriculture, each with its own literature and its own unit conventions: managing a honeybee colony (apiculture), raising fish or shrimp in a controlled tank (aquaculture), managing a wild fish stock nobody controls the inputs to (fisheries), growing wine grapes and then turning them into wine (viticulture), managing a forest stand over decades (silviculture), and deciding when a pest population justifies the cost of controlling it (pest_control). None of the six import each other, and none share a status-code range -- every function takes and returns plain Float64 scalars, the same flat-arithmetic convention used throughout this corpus. What ties them together is the shape of the problem: a population or stock changes over time under growth, mortality, and harvest, and the arithmetic here is the per-unit accounting that turns raw counts and measurements into rates, densities, and economic thresholds.
from algenta import MojoRuntime
runtime = MojoRuntime(mode="local")
# 200 kg of honey off 10 hives
runtime.execute("apiculture", "honey_yield_per_hive", [200.0, 10.0])["result"]
# 20.0 kg/hive
# Schaefer surplus production: r=0.5, biomass=5000, carrying capacity K=10000
runtime.execute("fisheries", "schaefer_surplus_production", [0.5, 5000.0, 10000.0])["result"]
# 1250.0 -- the stock is at half its carrying capacity, where logistic growth peaksThe modules
apiculture
Beekeeping and colony management: honey yield per hive, colony population estimate, varroa mite infestation rate, sugar-syrup feeding ratio, foraging range area, nectar-to-honey concentration, brood viability, winter feed requirement, queen laying rate, pollination value to a crop, hive density per hectare, and overwintering survival.
aquaculture
Controlled fish/shrimp farming: stocking density, feed conversion ratio, specific growth rate, dissolved-oxygen consumption and carrying capacity, water exchange rate, ammonia production, projected harvest weight, survival rate, production per cycle, biofilter capacity, and cost per kilogram produced.
fisheries
Wild-stock assessment: catch per unit effort, maximum sustainable yield and biomass at MSY (Schaefer model), the surplus-production curve itself, fishing/natural/total mortality and exploitation rate, annual survival from mortality, von Bertalanffy growth, Fulton's condition factor, gonadosomatic index, and Beverton-Holt stock-recruitment.
viticulture
Wine-grape growing and cellar chemistry: Brix-to-potential-alcohol, chaptalization sugar addition, total acidity, pH buffer capacity, growing-degree-days and the Winkler index, yield per hectare, vine density, the Ravaz pruning-weight index, must volume from crushed grapes, molecular SO2 from free SO2 and pH, and fermentation progress.
silviculture
Forest stand management and timber mensuration: basal area (tree and stand level), site index, Scribner log-rule volume, standing timber yield per hectare, two competing stand-density indices (Curtis RDI and Reineke SDI), mean and current annual increment, and the thinning ratio for a residual-density target.
pest_control
Integrated pest management economics: economic threshold and economic injury level, pest density from a sample, trap catch rate, spray coverage, dilution rate, residual effectiveness decay, insecticide resistance ratio, biocontrol agent release rate, fumigation CT product, bait station density, and population reduction percentage.
apiculture: colony inputs and outputs, worked
honey_yield_per_hive(total_honey_kg, n_hives)
total_honey_kg / n_hives.
colony_strength_estimate(frames_of_bees, bees_per_frame)
frames * bees_per_frame -- a population estimate from a frame count.
varroa_infestation_rate(mites_counted, bees_sampled)
mites / bees * 100, a percentage.
sugar_syrup_ratio(sugar_kg, water_kg)
sugar / (sugar + water), a fraction 0-1.
foraging_range_area(radius_km)
pi * r^2 -- the area a colony forages within a given flight radius.
nectar_to_honey_ratio(nectar_kg)
nectar / 4.0 -- the typical 4:1 evaporation ratio from raw nectar to cured honey.
brood_viability(hatched_cells, total_cells)
hatched / total * 100.
winter_feed_requirement(colony_weight_kg, months, consumption_per_month)
months * consumption - (colony_weight - 15.0).
queen_laying_rate(eggs_per_day)
Identity -- returns the input unchanged (up to ~2000 eggs/day is the docstring's stated realistic ceiling, not an enforced bound).
pollination_value(crop_yield_increase_pct, crop_value)
crop_value * increase_pct / 100 -- the dollar value a colony's pollination adds to a crop.
hive_density_per_hectare(n_hives, area_ha)
n_hives / area_ha.
overwintering_survival_rate(survived, total)
survived / total * 100.
winter_feed_requirement's subtraction is the whole model: a colony heavier than the 15 kg baseline already has honey stores that offset part of what it needs fed, and a colony lighter than baseline needs more than the raw months * consumption figure. Hand-verified in tests/mojo/test_wave93.mojo (19 assertions): a colony at 25 kg needing 5 months at 3 kg/month consumption gets 5*3 - (25-15) = 15 - 10 = 5 kg of supplemental feed, and the same test confirms foraging_range_area(3.0) is pi * 9 = 28.274333882 km^2 and varroa_infestation_rate(15.0, 300.0) = 5.0 percent -- a count above the commonly cited 1-3% pre-treatment action threshold in real beekeeping practice, though the function itself has no opinion on thresholds, it only computes the rate.
aquaculture: a stocking-to-harvest chain
stocking_density(biomass_kg, volume_m3)
biomass / volume (kg/m^3).
feed_conversion_ratio(feed_kg, weight_gain_kg)
feed / weight_gain -- lower is more efficient.
specific_growth_rate(w_final, w_initial, days)
(ln(Wf) - ln(Wi)) / days * 100 (%/day).
dissolved_oxygen_consumption(biomass_kg, rate_per_kg)
biomass * rate (mg O2/hr).
water_exchange_rate(flow_rate, tank_volume)
flow / volume * 100 (%/hr).
ammonia_production(feed_rate, protein_pct)
feed * protein/100 * 0.03 -- an estimate, not a stoichiometric derivation.
carrying_capacity(do_available, do_per_kg)
do_available / do_per_kg -- the maximum biomass the available oxygen supply can support.
harvest_weight(initial_weight, sgr, days)
initial_weight * exp(sgr * days / 100) -- the exponential-growth counterpart to specific_growth_rate.
survival_rate(final_count, initial_count)
final / initial * 100.
production_per_cycle(harvest_weight_avg, count, survival)
harvest_weight_avg * count * survival / 100 (kg).
biofilter_capacity(surface_area, nitrification_rate)
surface_area * nitrification_rate (g TAN/day).
cost_per_kg_produced(total_cost, production_kg)
total_cost / production_kg.
specific_growth_rate and harvest_weight are exact inverses of each other by construction: growth over 30 days from 10 g to 50 g gives sgr = (ln(50) - ln(10)) / 30 * 100 = 5.364793... %/day (hand-checked in tests/mojo/test_wave97.mojo), and feeding that same full-precision sgr back into harvest_weight(10.0, sgr, 30.0) reproduces 50.0 to floating-point precision -- the two functions round-trip because harvest_weight is just specific_growth_rate's formula solved for w_final. The same test file chains a realistic cycle: dissolved_oxygen_consumption(200.0, 300.0) = 60000 mg/hr feeds directly into carrying_capacity(60000.0, 300.0) = 200 kg -- the biomass level at which that oxygen supply is exactly saturated.
fisheries: the Schaefer surplus-production model, end to end
catch_per_unit_effort(total_catch, total_effort)
C / E; 0.0 if total_effort <= 0.
maximum_sustainable_yield(r, k)
r * K / 4 -- the Schaefer-model peak of the logistic yield curve.
biomass_at_msy(k)
K / 2 -- the stock level at which that peak occurs.
schaefer_surplus_production(r, biomass, k)
r * B * (1 - B/K); 0.0 if k <= 0.
fishing_mortality(catch, biomass)
C / B; 0.0 if biomass <= 0.
total_mortality(fishing_mort, natural_mort)
F + M.
exploitation_rate(fishing_mort, total_mort)
F / Z; 0.0 if total_mort <= 0.
survival_rate(total_mort)
exp(-Z) -- annual survival from total instantaneous mortality.
von_bertalanffy_length(l_inf, k, t, t0)
Linf * (1 - exp(-k*(t - t0))).
condition_factor(weight_g, length_cm)
Fulton's K = 100 * W / L^3; 0.0 if length_cm <= 0.
gonadosomatic_index(gonad_weight, body_weight)
gonad_weight / body_weight * 100; 0.0 if body_weight <= 0.
stock_recruitment_bevholt(alpha, beta, spawners)
Beverton-Holt: alpha * S / (beta + S); 0.0 if |beta + S| < 1e-15.
tests/mojo/test_wave83.mojo hand-verifies the whole model as one chain (24 assertions): at r=0.5, K=10000, maximum_sustainable_yield is 1250 and biomass_at_msy is 5000 -- and plugging that same biomass back into schaefer_surplus_production(0.5, 5000.0, 10000.0) reproduces 1250.0 exactly, confirming MSY really is the surplus-production curve's peak value at its own peak biomass, not just a separately-defined constant. Mortality decomposes the same way: total_mortality(0.2, 0.3) = 0.5, exploitation_rate(0.2, 0.5) = 0.4 (40% of total mortality is fishing, not natural causes), and survival_rate(0.5) = exp(-0.5) = 0.6065.
viticulture: the module covers the vineyard and the cellar
The docstring calls this "Viticulture (Grape Growing & Wine)," and the function list backs that up literally: growing_degree_days_wine, winkler_index_daily, vine_density, and yield_tonnes_per_hectare are field/vineyard measurements, but brix_to_potential_alcohol, chaptalization_sugar, sulfite_molecular_so2, and fermentation_progress are winemaking-cellar chemistry that happens after the grapes are already picked and crushed. A page titled just "viticulture" (the growing of grapes) would undersell half of what this module actually computes -- it is viticulture and enology bundled into one file.
brix_to_potential_alcohol(brix)
brix * 0.55 -- a common rule-of-thumb ABV estimate from must sugar content.
chaptalization_sugar(target_brix, current_brix, volume_l)
(target - current) * volume * 1.7 -- grams of sugar needed to raise must to a target Brix.
total_acidity_as_tartaric(ta_g_per_l)
Identity -- returns the input unchanged; the function exists to name the unit convention (tartaric-acid equivalent), not to convert one.
ph_buffer_capacity(ta, ph)
ta * 0.3 / (ph - 2.5) -- simplified buffering estimate.
growing_degree_days_wine(t_max, t_min, t_base)
max(0, (Tmax + Tmin)/2 - Tbase).
winkler_index_daily(gdd)
Identity -- returns the daily GDD contribution unchanged; the caller sums it April-October to get the seasonal Winkler index.
yield_tonnes_per_hectare(kg_total, area_ha)
kg_total / (1000 * area_ha).
vine_density(vines_per_row, rows, area_ha)
vines_per_row * rows / area_ha.
pruning_weight_ratio(crop_weight, pruning_weight)
The Ravaz index: crop_weight / pruning_weight.
must_volume_from_grapes(grape_kg, extraction_rate)
grape_kg * extraction_rate (typical rate 0.7-0.8).
sulfite_molecular_so2(free_so2, ph)
free_so2 / (1 + 10^(pH - 1.81)).
fermentation_progress(initial_brix, current_brix)
(initial - current) / initial * 100.
tests/mojo/test_wave95.mojo hand-verifies both halves. Field side: brix_to_potential_alcohol(24.0) = 13.2 %ABV, and growing_degree_days_wine(8.0, 4.0, 10.0) = 0.0 -- a day whose average temperature sits below the 10C base contributes nothing, the max(0, ...) guard rather than a negative GDD. Cellar side: sulfite_molecular_so2(30.0, 1.81) = 15.0 -- at exactly pH 1.81 the 10^(pH-1.81) term is 10^0 = 1, so the formula halves the free SO2 exactly, and the same test confirms fermentation_progress(24.0, 0.0) = 100.0 (fully fermented, all sugar consumed) against fermentation_progress(24.0, 24.0) = 0.0 (fermentation hasn't started).
silviculture: forest mensuration and stocking control, not just tending prescriptions
The module's own docstring frames it as "Forest management and timber production calculations," which is broader than the narrow textbook definition of silviculture (the practice of establishing, tending, and harvesting a stand). What's actually here is mostly forest mensuration and growth-and-yield accounting -- basal area, log-volume scaling, stand density indices -- that feeds the silvicultural decisions (like thinning_ratio) rather than being those decisions itself.
basal_area(dbh_cm)
pi * (DBH/2)^2 -- per-tree basal area in cm^2, not m^2; 0.0 if dbh_cm <= 0.
stand_basal_area(mean_dbh_cm, stems_per_ha)
pi * (DBH/200)^2 * stems_per_ha -- stand-level basal area in m^2/ha (the /200 both converts cm to m and halves for radius).
site_index(height_m, age_years, base_age)
height * (base_age / age)^0.5 -- a simplified power-law site index.
volume_scribner(small_end_diameter_in, log_length_ft)
(0.79*D^2 - 2*D - 4) * L / 16 -- the Scribner Decimal C log rule approximation, in board feet.
timber_yield_per_ha(stems_per_ha, mean_vol_m3_per_tree)
stems * mean_vol (m^3/ha).
relative_density_index(stems_per_ha, mean_dbh_cm)
Curtis-style RDI: N / (25.4/DBH)^1.6, algebraically N * (DBH/25.4)^1.6.
reineke_sdi(stems_per_ha, mean_dbh_cm)
Reineke SDI: N * (25.4/DBH)^1.605.
mean_annual_increment(total_volume_m3, age_years)
total_volume / age; 0.0 if age_years <= 0.
current_annual_increment(vol_now, vol_past, years)
(vol_now - vol_past) / years; 0.0 if years <= 0.
thinning_ratio(initial_stems, target_stems)
(initial - target) / initial, floored at 0.0 if the target is above the initial density.
basal_area returns cm^2 per tree while stand_basal_area returns m^2/ha -- both correct per their own docstrings, but a caller multiplying basal_area's tree-level output by a stem count to approximate stand basal area would be off by a factor of 10,000 (cm^2 to m^2) unless they convert first. Worked examples, hand-computed against mojo/engine/silviculture.mojo and independently checked in Python (no hand-verified wave test exists for this module -- see Verification below): basal_area(30.0) is 706.858 cm^2 per tree; stand_basal_area(30.0, 400.0) is 28.274 m^2/ha, a realistic figure for a moderately stocked stand; volume_scribner(16.0, 16.0) (a 16-inch, 16-foot log) is 166.24 board feet; and thinning_ratio(1200.0, 700.0) is 0.41667 -- 41.7% of stems must be removed to bring 1,200 stems/ha down to a 700 stems/ha target.
pest_control: the economics of when to spray
economic_threshold(control_cost, crop_value, damage_per_pest)
cost / (value * damage_per_pest) -- the pest density that justifies control.
economic_injury_level(et, gain_threshold)
et * gain_threshold.
pest_density(count, sample_area)
count / area.
trap_catch_rate(caught, traps, days)
caught / (traps * days).
spray_coverage(volume_applied, area)
volume / area (L/ha).
dilution_rate(concentrate_ml, water_ml)
concentrate / (concentrate + water).
residual_effectiveness(initial_kill_pct, half_life_days, days_elapsed)
initial * exp(-0.693 * days / half_life) -- 0.693 ~ ln(2), an exponential decay to the stated half-life.
resistance_ratio(ld50_resistant, ld50_susceptible)
LD50_R / LD50_S; >1 indicates resistance.
biocontrol_release_rate(target_density, predator_prey_ratio, area)
target_density * area / predator_prey_ratio.
fumigation_ct(concentration, time_hours)
C * T (g·h/m^3), the fumigation exposure-dose product.
bait_station_density(stations, area_m2)
stations / area_m2 * 10000 -- per-hectare density from an area given in m^2.
population_reduction(before, after)
(before - after) / before * 100.
tests/mojo/test_wave98.mojo hand-verifies the whole chain (its assertions run alongside geomorphology and watchmaking in the same wave file). economic_threshold(50.0, 5000.0, 0.01) = 1.0: at $50 control cost, $5,000 crop value per unit area, and 1% damage per pest, the breakeven pest density is exactly 1 per unit area. residual_effectiveness is verified at three points on its decay curve: days_elapsed=0 returns the initial kill percentage unchanged (residual_effectiveness(90.0, 10.0, 0.0) = 90.0), and one full half-life halves it almost exactly (residual_effectiveness(100.0, 10.0, 10.0) = 100 * exp(-0.693) = 50.0074, not the exact 50.000 a true half-life implies) -- 0.693 is a three-decimal approximation of ln(2) = 0.693147..., so the "half-life" is off by about 0.015%, a small, deliberate rounding built into the constant rather than a bug, since the test file itself (test_wave98.mojo) asserts against 100.0 * exp(-0.693), the same approximate constant, not against a recomputed ln(2)). population_reduction(200.0, 0.0) = 100.0 confirms complete elimination reads as a clean 100%, not a divide-by-zero on the "after" side (there is no guard needed since after never appears in the denominator).
Verification
Five of the six modules have real, hand-verified numeric test suites living in tests/mojo/ alongside their auto-generated smoke tests: apiculture in test_wave93.mojo (19 assertions, shared with welding and color_science), aquaculture in test_wave97.mojo (shared with adhesives and traffic_signal), fisheries in test_wave83.mojo (24 assertions, shared with museum_science), viticulture in test_wave95.mojo (shared with semiconductors and hydraulics), and pest_control in test_wave98.mojo (shared with geomorphology and watchmaking) -- each asserts specific literal expected values with a relative-tolerance helper, not just a finite-value check. silviculture has no such wave test anywhere in the corpus (a targeted grep across every .mojo file under this worktree turns up only its own mojo/tests/libs/test_silviculture.mojo, which -- like all six of these modules' test_libs files -- is the auto-generated smoke suite that calls every function once with literal 0.5 arguments and checks only that the result is finite). The silviculture worked examples and the reineke_sdi finding above were hand-computed from the real formulas in mojo/engine/silviculture.mojo and independently checked with standalone Python arithmetic rather than against any pinned test in this codebase.
Next: see Hydrology & water systems kernels for the water-quality and flow arithmetic aquaculture's tank calculations sit beside, or browse the Runtime library catalog for every module and function.
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