Calculates cohort-level live weight metrics by combining cohort-level inputs with herd-level biological parameters. The function appends cohort weights (initial, potential final, slaughter), then derives average and final live weights accounting for offtake, and finally computes average daily live weight gain over each cohort stage.
Arguments
- cohort_level_data
A
data.tablein long format with one row per herd \(\times\) cohort. Must include:- herd_id
Character. Unique identifier for the herd, repeated for each cohort belonging to the same herd.
- cohort_short
Character. Sex- and age-specific cohort code describing the production stage of the animals. Supported values include:
FA: adult females (from age at first parturition)FS: sub-adult females (from weaning to age at first parturition)FJ: juvenile females (from birth to weaning)MA: adult males (from age at first breeding)MS: sub-adult males (from weaning to age at first breeding)MJ: juvenile males (from birth to weaning)
- cohort_duration_days
Numeric. Amount of time that each animal spends in a specific cohort (days).
- offtake_rate
Numeric. Annual proportion of animals removed from the herd for each sex-age cohort (fraction).
- herd_level_data
A
data.tablewith one row per herd. Must include:live_weight_female_adultNumeric. Live weight of adult females (kg)live_weight_male_adultNumeric. Live weight of adult males (kg)live_weight_at_birthNumeric. Live weight of the animal at birth (kg).live_weight_at_weaningNumeric. Live weight of the animal at weaning (kg)live_weight_female_at_slaughterNumeric. Slaughter weight of female sub-adult animals (kg)live_weight_male_at_slaughterNumeric. Slaughter weight of male sub-adult animals (kg)
- show_indicator
Logical. Whether to display progress indicators during calculations. Defaults to
TRUE.
Value
A named list with two data.tables:
- cohort_level_results
The input
cohort_level_datawith these additional columns:- live_weight_mature_stage
Numeric. Mature (adult) live weight that the animal can attain under given biological and management conditions (kg).
- live_weight_cohort_initial
Numeric. Live weight at the beginning of the cohort stage (kg).
- live_weight_cohort_potential_final
Numeric. Potential final live weight attainable at the end of the cohort stage in the absence of offtake (kg). (For juveniles: equals weaning weight; For subadults: equals adult live weight; For adults: equals adult live weight)
- live_weight_cohort_at_slaughter
Numeric. Live weight at slaughter for animals removed from the cohort (kg).
- live_weight_cohort_average
Numeric. Average live weight over the cohort stage. Computed by accounting for the share of offtaken animals within the cohort, using their slaughter weight, and the potential final weight of animals that remain in the cohort (kg).
- live_weight_cohort_final
Numeric. Live weight at the end of the cohort stage, accounting for both surviving and offtaken animals. Computed as a weighted average of the potential final weight of surviving animals and the slaughter weight of offtaken animals, based on the offtake rate (kg).
- daily_weight_gain
Numeric. Average live weight gain of the cohort over the cohort stage (kg/head/day).
- herd_level_results
A copy of the input
herd_level_data.
Details
This function represents the intermediate module of the Global Livestock Environmental
Assessment Model (GLEAM) computational pipeline run_gleam() to estimate animals' live
weight and is composed of the following steps:
Cohort-stage weight assignment using
calc_cohort_weights. Herd-level biological parameters are matched to each cohort row byherd_idviadata.tablejoins.Calculation of average and final live weights (accounting for offtake) using
calc_avg_weights.Calculation of average daily live weight gain using
calc_daily_weight_gain.
Examples
# \donttest{
# Load weights inputs (cohort- and herd-level)
weights_chrt_dt <- data.table::fread(system.file(
"extdata/run_modules_examples/weights_input_chrt_data.csv",
package = "gleam"
))
weights_hrd_dt <- data.table::fread(system.file(
"extdata/run_modules_examples/weights_input_hrd_data.csv",
package = "gleam"
))
# Run weight calculations
results <- run_weights_module(
cohort_level_data = weights_chrt_dt,
herd_level_data = weights_hrd_dt
)
#> 🕒 Calculating cohort weights, please wait…
#> ✔ Cohort weights calculation complete.
# Access results
print(results$cohort_level_results)
#> herd_id cohort_short cohort_duration_days offtake_rate
#> <int> <char> <int> <num>
#> 1: 1 FA 989 0.000
#> 2: 1 FJ 60 0.247
#> 3: 1 FS 710 0.247
#> 4: 1 MA 823 0.000
#> 5: 1 MJ 60 0.949
#> 6: 1 MS 710 0.949
#> 7: 2 FA 5000 0.000
#> 8: 2 FJ 60 0.661
#> 9: 2 FS 1400 0.661
#> 10: 2 MA 1820 0.000
#> 11: 2 MJ 60 0.783
#> 12: 2 MS 1400 0.783
#> 13: 3 FA 1110 0.000
#> 14: 3 FJ 60 0.453
#> 15: 3 FS 448 0.453
#> 16: 3 MA 2090 0.000
#> 17: 3 MJ 60 0.913
#> 18: 3 MS 448 0.913
#> 19: 4 FA 2430 0.000
#> 20: 4 FJ 60 0.543
#> 21: 4 FS 669 0.543
#> 22: 4 MA 2680 0.000
#> 23: 4 MJ 60 0.836
#> 24: 4 MS 669 0.836
#> 25: 5 FA 2230 0.000
#> 26: 5 FJ 60 0.737
#> 27: 5 FS 415 0.737
#> 28: 5 MA 1540 0.000
#> 29: 5 MJ 60 0.974
#> 30: 5 MS 415 0.974
#> 31: 6 FA 2110 0.000
#> 32: 6 FJ 60 0.359
#> 33: 6 FS 564 0.359
#> 34: 6 MA 2600 0.000
#> 35: 6 MJ 60 0.644
#> 36: 6 MS 600 0.644
#> 37: 7 FA 3650 0.000
#> 38: 7 FJ 60 0.664
#> 39: 7 FS 974 0.664
#> 40: 7 MA 1170 0.000
#> 41: 7 MJ 60 0.906
#> 42: 7 MS 974 0.906
#> 43: 8 FA 1830 0.000
#> 44: 8 FJ 60 0.134
#> 45: 8 FS 1400 0.134
#> 46: 8 MA 2430 0.000
#> 47: 8 MJ 60 0.862
#> 48: 8 MS 1400 0.862
#> 49: 9 FA 890 0.000
#> 50: 9 FJ 27 0.948
#> 51: 9 FS 359 0.948
#> 52: 9 MA 890 0.000
#> 53: 9 MJ 27 0.973
#> 54: 9 MS 359 0.973
#> 55: 10 FA 3650 0.000
#> 56: 10 FJ 90 0.952
#> 57: 10 FS 340 0.952
#> 58: 10 MA 3650 0.000
#> 59: 10 MJ 90 0.974
#> 60: 10 MS 340 0.974
#> 61: 11 FA 5000 0.000
#> 62: 11 FJ 370 0.000
#> 63: 11 FS 2190 0.000
#> 64: 11 MA 5000 0.004
#> 65: 11 MJ 370 0.000
#> 66: 11 MS 2190 0.496
#> 67: 12 FA 5000 0.000
#> 68: 12 FJ 365 0.000
#> 69: 12 FS 1280 0.000
#> 70: 12 MA 5000 0.004
#> 71: 12 MJ 365 0.000
#> 72: 12 MS 1280 0.500
#> herd_id cohort_short cohort_duration_days offtake_rate
#> <int> <char> <int> <num>
#> live_weight_mature_stage live_weight_cohort_initial
#> <num> <num>
#> 1: 680.0 680.00
#> 2: 680.0 41.00
#> 3: 680.0 250.00
#> 4: 916.0 916.00
#> 5: 916.0 41.00
#> 6: 916.0 250.00
#> 7: 350.0 350.00
#> 8: 350.0 14.00
#> 9: 350.0 220.00
#> 10: 450.0 450.00
#> 11: 450.0 14.00
#> 12: 450.0 220.00
#> 13: 51.0 51.00
#> 14: 51.0 4.19
#> 15: 51.0 30.00
#> 16: 59.0 59.00
#> 17: 59.0 4.19
#> 18: 59.0 30.00
#> 19: 60.1 60.10
#> 20: 60.1 5.21
#> 21: 60.1 35.00
#> 22: 70.3 70.30
#> 23: 70.3 5.21
#> 24: 70.3 35.00
#> 25: 70.0 70.00
#> 26: 70.0 3.50
#> 27: 70.0 15.00
#> 28: 110.0 110.00
#> 29: 110.0 3.50
#> 30: 110.0 15.00
#> 31: 51.0 51.00
#> 32: 51.0 3.30
#> 33: 51.0 14.00
#> 34: 75.2 75.20
#> 35: 75.2 3.30
#> 36: 75.2 14.00
#> 37: 600.0 600.00
#> 38: 600.0 38.00
#> 39: 600.0 130.00
#> 40: 800.0 800.00
#> 41: 800.0 38.00
#> 42: 800.0 130.00
#> 43: 478.0 478.00
#> 44: 478.0 32.60
#> 45: 478.0 110.00
#> 46: 500.0 500.00
#> 47: 500.0 32.60
#> 48: 500.0 110.00
#> 49: 225.0 225.00
#> 50: 225.0 1.20
#> 51: 225.0 7.00
#> 52: 265.0 265.00
#> 53: 265.0 1.20
#> 54: 265.0 7.00
#> 55: 64.0 64.00
#> 56: 64.0 1.00
#> 57: 64.0 6.00
#> 58: 71.0 71.00
#> 59: 71.0 1.00
#> 60: 71.0 6.00
#> 61: 352.0 352.00
#> 62: 352.0 28.70
#> 63: 352.0 120.00
#> 64: 382.0 382.00
#> 65: 382.0 28.70
#> 66: 382.0 120.00
#> 67: 537.0 537.00
#> 68: 537.0 32.70
#> 69: 537.0 150.00
#> 70: 572.0 572.00
#> 71: 572.0 32.70
#> 72: 572.0 150.00
#> live_weight_mature_stage live_weight_cohort_initial
#> <num> <num>
#> live_weight_cohort_potential_final live_weight_cohort_at_slaughter
#> <num> <num>
#> 1: 680.0 680.0
#> 2: 250.0 250.0
#> 3: 680.0 557.0
#> 4: 916.0 916.0
#> 5: 250.0 250.0
#> 6: 916.0 605.0
#> 7: 350.0 350.0
#> 8: 220.0 220.0
#> 9: 350.0 250.0
#> 10: 450.0 450.0
#> 11: 220.0 220.0
#> 12: 450.0 250.0
#> 13: 51.0 51.0
#> 14: 30.0 30.0
#> 15: 51.0 35.0
#> 16: 59.0 59.0
#> 17: 30.0 30.0
#> 18: 59.0 35.0
#> 19: 60.1 60.1
#> 20: 35.0 35.0
#> 21: 60.1 51.8
#> 22: 70.3 70.3
#> 23: 35.0 35.0
#> 24: 70.3 55.9
#> 25: 70.0 70.0
#> 26: 15.0 15.0
#> 27: 70.0 25.0
#> 28: 110.0 110.0
#> 29: 15.0 15.0
#> 30: 110.0 25.0
#> 31: 51.0 51.0
#> 32: 14.0 14.0
#> 33: 51.0 29.0
#> 34: 75.2 75.2
#> 35: 14.0 14.0
#> 36: 75.2 29.0
#> 37: 600.0 600.0
#> 38: 130.0 130.0
#> 39: 600.0 420.0
#> 40: 800.0 800.0
#> 41: 130.0 130.0
#> 42: 800.0 420.0
#> 43: 478.0 478.0
#> 44: 110.0 110.0
#> 45: 478.0 110.0
#> 46: 500.0 500.0
#> 47: 110.0 110.0
#> 48: 500.0 110.0
#> 49: 225.0 225.0
#> 50: 7.0 7.0
#> 51: 225.0 122.0
#> 52: 265.0 265.0
#> 53: 7.0 7.0
#> 54: 265.0 122.0
#> 55: 64.0 64.0
#> 56: 6.0 6.0
#> 57: 64.0 60.0
#> 58: 71.0 71.0
#> 59: 6.0 6.0
#> 60: 71.0 60.0
#> 61: 352.0 352.0
#> 62: 120.0 120.0
#> 63: 352.0 352.0
#> 64: 382.0 382.0
#> 65: 120.0 120.0
#> 66: 382.0 382.0
#> 67: 537.0 537.0
#> 68: 150.0 150.0
#> 69: 537.0 537.0
#> 70: 572.0 572.0
#> 71: 150.0 150.0
#> 72: 572.0 572.0
#> live_weight_cohort_potential_final live_weight_cohort_at_slaughter
#> <num> <num>
#> live_weight_cohort_average live_weight_cohort_final daily_weight_gain
#> <num> <num> <num>
#> 1: 680.00000 680.0000 0.00000000
#> 2: 145.50000 250.0000 3.48333333
#> 3: 449.80950 649.6190 0.60563380
#> 4: 916.00000 916.0000 0.00000000
#> 5: 145.50000 250.0000 3.48333333
#> 6: 435.43050 620.8610 0.93802817
#> 7: 350.00000 350.0000 0.00000000
#> 8: 117.00000 220.0000 3.43333333
#> 9: 251.95000 283.9000 0.09285714
#> 10: 450.00000 450.0000 0.00000000
#> 11: 117.00000 220.0000 3.43333333
#> 12: 256.70000 293.4000 0.16428571
#> 13: 51.00000 51.0000 0.00000000
#> 14: 17.09500 30.0000 0.43016667
#> 15: 36.87600 43.7520 0.04687500
#> 16: 59.00000 59.0000 0.00000000
#> 17: 17.09500 30.0000 0.43016667
#> 18: 33.54400 37.0880 0.06473214
#> 19: 60.10000 60.1000 0.00000000
#> 20: 20.10500 35.0000 0.49650000
#> 21: 45.29655 55.5931 0.03751868
#> 22: 70.30000 70.3000 0.00000000
#> 23: 20.10500 35.0000 0.49650000
#> 24: 46.63080 58.2616 0.05276532
#> 25: 70.00000 70.0000 0.00000000
#> 26: 9.25000 15.0000 0.19166667
#> 27: 25.91750 36.8350 0.13253012
#> 28: 110.00000 110.0000 0.00000000
#> 29: 9.25000 15.0000 0.19166667
#> 30: 21.10500 27.2100 0.22891566
#> 31: 51.00000 51.0000 0.00000000
#> 32: 8.65000 14.0000 0.17833333
#> 33: 28.55100 43.1020 0.06560284
#> 34: 75.20000 75.2000 0.00000000
#> 35: 8.65000 14.0000 0.17833333
#> 36: 29.72360 45.4472 0.10200000
#> 37: 600.00000 600.0000 0.00000000
#> 38: 84.00000 130.0000 1.53333333
#> 39: 305.24000 480.4800 0.48254620
#> 40: 800.00000 800.0000 0.00000000
#> 41: 84.00000 130.0000 1.53333333
#> 42: 292.86000 455.7200 0.68788501
#> 43: 478.00000 478.0000 0.00000000
#> 44: 71.30000 110.0000 1.29000000
#> 45: 269.34400 428.6880 0.26285714
#> 46: 500.00000 500.0000 0.00000000
#> 47: 71.30000 110.0000 1.29000000
#> 48: 136.91000 163.8200 0.27857143
#> 49: 225.00000 225.0000 0.00000000
#> 50: 4.10000 7.0000 0.21481481
#> 51: 67.17800 127.3560 0.60724234
#> 52: 265.00000 265.0000 0.00000000
#> 53: 4.10000 7.0000 0.21481481
#> 54: 66.43050 125.8610 0.71866295
#> 55: 64.00000 64.0000 0.00000000
#> 56: 3.50000 6.0000 0.05555556
#> 57: 33.09600 60.1920 0.17058824
#> 58: 71.00000 71.0000 0.00000000
#> 59: 3.50000 6.0000 0.05555556
#> 60: 33.14300 60.2860 0.19117647
#> 61: 352.00000 352.0000 0.00000000
#> 62: 74.35000 120.0000 0.24675676
#> 63: 236.00000 352.0000 0.10593607
#> 64: 382.00000 382.0000 0.00000000
#> 65: 74.35000 120.0000 0.24675676
#> 66: 251.00000 382.0000 0.11963470
#> 67: 537.00000 537.0000 0.00000000
#> 68: 91.35000 150.0000 0.32136986
#> 69: 343.50000 537.0000 0.30234375
#> 70: 572.00000 572.0000 0.00000000
#> 71: 91.35000 150.0000 0.32136986
#> 72: 361.00000 572.0000 0.32968750
#> live_weight_cohort_average live_weight_cohort_final daily_weight_gain
#> <num> <num> <num>
print(results$herd_level_results)
#> herd_id live_weight_female_adult live_weight_male_adult
#> <int> <num> <num>
#> 1: 1 680.0 916.0
#> 2: 2 350.0 450.0
#> 3: 3 51.0 59.0
#> 4: 4 60.1 70.3
#> 5: 5 70.0 110.0
#> 6: 6 51.0 75.2
#> 7: 7 600.0 800.0
#> 8: 8 478.0 500.0
#> 9: 9 225.0 265.0
#> 10: 10 64.0 71.0
#> 11: 11 352.0 382.0
#> 12: 12 537.0 572.0
#> live_weight_at_birth live_weight_female_at_slaughter
#> <num> <num>
#> 1: 41.00 557.0
#> 2: 14.00 250.0
#> 3: 4.19 35.0
#> 4: 5.21 51.8
#> 5: 3.50 25.0
#> 6: 3.30 29.0
#> 7: 38.00 420.0
#> 8: 32.60 110.0
#> 9: 1.20 122.0
#> 10: 1.00 60.0
#> 11: 28.70 352.0
#> 12: 32.70 537.0
#> live_weight_male_at_slaughter live_weight_at_weaning
#> <num> <int>
#> 1: 605.0 250
#> 2: 250.0 220
#> 3: 35.0 30
#> 4: 55.9 35
#> 5: 25.0 15
#> 6: 29.0 14
#> 7: 420.0 130
#> 8: 110.0 110
#> 9: 122.0 7
#> 10: 60.0 6
#> 11: 382.0 120
#> 12: 572.0 150
# }