def test_diet_intermediates(herb_class):
"""Scraps of the function calc_diet_intermediates."""
diet = forage.Diet()
diet.If = 10.
diet.DMDf = 0.64
diet.CPIf = 1.
# copied from calc_diet_intermediates()
supp = forage.Supplement(FreerParam.FreerParamCattle('indicus'), 0, 0, 0,
0, 0, 0)
site = forage.SiteInfo(1, 0)
diet_interm = forage.DietIntermediates()
MEIf = (17.0 * diet.DMDf - 2) * diet.If # eq 31: herbage
MEIs = (13.3 * supp.DMD + 23.4 * supp.EE + 1.32) * diet.Is # eq 32
FMEIs = (13.3 * supp.DMD + 1.32) * diet.Is # eq 32, supp.EE = 0
MEItotal = MEIf + MEIs # assuming no intake of milk
M_per_Dforage = MEIf / diet.If
kl = herb_class.FParam.CK5 + herb_class.FParam.CK6 * M_per_Dforage # eq 34
km = (herb_class.FParam.CK1 + herb_class.FParam.CK2 * M_per_Dforage
) # eq 33 efficiency of energy use for maintenance
Emove = herb_class.FParam.CM16 * herb_class.D * herb_class.W
Egraze = herb_class.FParam.CM6 * herb_class.W * diet.If * \
(herb_class.FParam.CM7 - diet.DMDf) + Emove
Emetab = herb_class.FParam.CM2 * herb_class.W**0.75 * max(
math.exp(-herb_class.FParam.CM3 * herb_class.A), herb_class.FParam.CM4)
# eq 41, energy req for maintenance:
MEm = (Emetab + Egraze) / km + herb_class.FParam.CM1 * MEItotal
if herb_class.sex == 'castrate' or herb_class.sex == 'entire_m':
MEm = MEm * 1.15
if herb_class.sex == 'herd_average':
MEm = MEm * 1.055
if herb_class.sex == 'NA':
MEm = (MEm + MEm * 1.15) / 2
diet_interm.L = (MEItotal / MEm) - 1.
# new calculations to test --- copy any corrections from here
A_foet = 30. # assumed days since conception
RA = A_foet / herb_class.FParam.CP1
BW = (1 - herb_class.FParam.CP4 + herb_class.FParam.CP4 *
herb_class.Z) * herb_class.FParam.CP15 * herb_class.SRW
BC_foet = 1 # assume condition of foetus is 1, ignoring weight
# assume she is pregnant with one foetus
MEc_num1 = (herb_class.FParam.CP8 * (herb_class.FParam.CP5 * BW) * BC_foet)
MEc_num2 = ((herb_class.FParam.CP9 * herb_class.FParam.CP10) /
herb_class.FParam.CP1)
MEc_num3 = math.exp(herb_class.FParam.CP10 * (1 - RA) +
herb_class.FParam.CP9 *
(1 - math.exp(herb_class.FParam.CP10 * (1 - RA))))
MEc = (MEc_num1 * MEc_num2 * MEc_num3) / herb_class.FParam.CK8
Pc_1 = herb_class.FParam.CP11 * (herb_class.FParam.CP5 * BW) * BC_foet
Pc_2 = ((herb_class.FParam.CP12 * herb_class.FParam.CP13) /
herb_class.FParam.CP1)
Pc_3 = herb_class.FParam.CP13 * (1 - RA)
Pc_4 = (herb_class.FParam.CP12 * (1 - math.exp(herb_class.FParam.CP13 *
(1 - RA))))
Pc_5 = math.exp(Pc_3 + Pc_4)
Pc = Pc_1 * Pc_2 * Pc_5
import numpy as np
grass1 = {
'label': 'grass1',
'type': 'C4', # 'C3' or 'C4'
'DMD_green': 0.541,
'DMD_dead': 0.541,
'cprotein_green': 0.023,
'cprotein_dead': 0.023,
'green_gm2': 2000.,
'dead_gm2': 2000.,
'percent_biomass': 1.,
}
grass_list = [grass1]
total_SD = 1.
site = forage.SiteInfo(total_SD, 1., -3.5)
prop_legume = 0.0
breed = 'Brahman' # see documentation for allowable breeds; assumed to apply to all animal classes
sex = 'castrate' # right now, we only deal with steers
A = 225. # initial age (days)
W = 161. # initial weight (Rubanza et al 2005)
herd_size = 1
DOY_start = 213
outdir = 'C:\Users\Ginger\Dropbox\NatCap_backup\Forage_model\Forage_model\Verification_calculations\Sensitivity\SRW'
time_step = 'day'
force_supp = False
forage.set_time_step(time_step)
steps = 10
FParam = FreerParam.FreerParam(forage.get_general_breed(breed))
def different_diets(available_forage, herbivore_csv, weight_1):
"""Create different diets for two hypothetical animal types. The weight
ratio describes the ratio of """
time_step = 'month'
forage_u.set_time_step(time_step)
total_SD = 2
prop_legume = 0
DOY = 100
supp_available = 0
site = forage_u.SiteInfo(1., -3.25)
supp_csv = "C:/Users/Ginger/Dropbox/NatCap_backup/Forage_model/Forage_model/model_inputs/Shem_et_al_1995_supp.csv"
supp_list = (pandas.read_csv(supp_csv)).to_dict(orient='records')
supp_info = supp_list[0]
supp = forage_u.Supplement(FreerParam.FreerParamCattle('indicus'),
supp_info['digestibility'],
supp_info['kg_per_day'], supp_info['M_per_d'],
supp_info['ether_extract'],
supp_info['crude_protein'],
supp_info['rumen_degradability'])
herbivore_input = (pandas.read_csv(herbivore_csv)).to_dict(
orient='records')
herbivore_list = []
for h_class in herbivore_input:
herd = forage_u.HerbivoreClass(h_class)
herd.update()
herbivore_list.append(herd)
avail_weights = [weight_1, 0]
qual_weights = [0, weight_1]
diet_dict = {}
for idx in xrange(len(herbivore_list)):
herb_class = herbivore_list[idx]
f_w = avail_weights[idx]
rq_w = qual_weights[idx]
herb_class.calc_distance_walked(total_SD, site.S, available_forage)
max_intake = herb_class.calc_max_intake()
ZF = herb_class.calc_ZF()
HR = forage_u.calc_relative_height(available_forage)
diet = diet_selection_t2(ZF, HR, prop_legume, supp_available, supp,
max_intake, herb_class.FParam,
available_forage, idx, f_w, rq_w)
diet_interm = forage_u.calc_diet_intermediates(diet, supp, herb_class,
site, prop_legume, DOY)
# if herb_class.type != 'hindgut_fermenter':
# reduced_max_intake = forage_u.check_max_intake(diet,
# diet_interm,
# herb_class,
# max_intake)
# if reduced_max_intake < max_intake:
# diet = forage_u.diet_selection_t2(ZF, HR,
# args[u'prop_legume'],
# supp_available, supp,
# reduced_max_intake,
# herb_class.FParam,
# available_forage)
diet_dict[herb_class.label] = diet
return diet_dict
# forage_u.reduce_demand(diet_dict, stocking_density_dict,
# available_forage)
diet_interm = forage_u.calc_diet_intermediates(diet, supp, herb_class,
site, prop_legume, DOY)
delta_W = forage_u.calc_delta_weight(diet_interm, herb_class)
delta_W_step = forage_u.convert_daily_to_step(delta_W)
herb_class.update(delta_weight=delta_W_step,
delta_time=forage_u.find_days_per_step())
def run_test():
SRW_list = np.linspace(160, 600, 9).tolist()
total_SD = 1.
site = forage.SiteInfo(1., -3.25)
prop_legume = 0.0
supp_available = 0
breed = 'Ayrshire'
sex = 'entire_m'
herd_size = 1
DOY_start = 1
outdir = r'C:/Users/Ginger/Dropbox/NatCap_backup/Forage_model/Forage_model/Verification_calculations/Shem_et_al_1995/revisions_10_12/summary_unsupplemented'
if not os.path.exists(outdir):
os.makedirs(outdir)
time_step = 'day'
forage.set_time_step(time_step)
supp_csv = "C:/Users/Ginger/Dropbox/NatCap_backup/Forage_model/Forage_model/model_inputs/Shem_et_al_1995_supp.csv"
herbivore_csv = "C:/Users/Ginger/Dropbox/NatCap_backup/Forage_model/Forage_model/model_inputs/herbivore_Shem_et_al.csv"
grass_csv = "C:/Users/Ginger/Dropbox/NatCap_backup/Forage_model/Forage_model/model_inputs/grasses_Shem_et_al_1995.csv"
grass_list = (pandas.read_csv(grass_csv)).to_dict(orient='records')
out_name = os.path.join(outdir, "summary.csv")
supp_list = (pandas.read_csv(supp_csv)).to_dict(orient='records')
supp_info = supp_list[0]
supp = forage.Supplement(FreerParam.FreerParamCattle('indicus_x_taurus'),
supp_info['digestibility'],
supp_info['kg_per_day'], supp_info['M_per_d'],
supp_info['ether_extract'],
supp_info['crude_protein'],
supp_info['rumen_degradability'])
if supp.DMO > 0.:
supp_available = 1
supp_available = 0 # change to allow supplementation
with open(out_name, 'wb') as out:
writer = csv.writer(out, delimiter=',')
header = ['red_max_intake', 'max_intake', 'intake_forage',
'intake_supp', 'daily_gain', 'grass_label',
'step', 'study', 'SRW']
writer.writerow(header)
for SRW in SRW_list:
modify_SRW(herbivore_csv, SRW)
herbivore_input = (pandas.read_csv(herbivore_csv)).to_dict(orient='records')
for grass in grass_list:
one_grass = [grass]
available_forage = forage.calc_feed_types(one_grass)
herbivore_list = []
for h_class in herbivore_input:
herd = forage.HerbivoreClass(h_class)
herd.update()
herbivore_list.append(herd)
print "beginning weight: " + str(herd.W)
print "beginning BC: " + str(herd.BC)
print "SRW: " + str(herd.SRW)
for step in xrange(60):
DOY = DOY_start + step
row = forage.one_step(site, DOY, herd,
available_forage, prop_legume,
supp_available, supp)
row.append(grass['label'])
row.append(step)
row.append('Schem_et_al')
row.append(SRW)
writer.writerow(row)
def launch_model(herb_csv, grass_list, outdir):
f_args = {
'latitude': 0.02759,
'prop_legume': 0.0,
'steepness': 1.,
'DOY': 1,
'start_year': 2015,
'start_month': 1,
'num_months': 1,
'mgmt_threshold': 0.,
'century_dir':
'C:/Users/Ginger/Dropbox/NatCap_backup/Forage_model/CENTURY4.6/Century46_PC_Jan-2014',
'outdir': outdir,
'template_level': 'GH',
'fix_file': 'drytrpfi.100',
'user_define_protein': 1,
'user_define_digestibility': 0,
'herbivore_csv': herb_csv,
'supp_csv':
"C:/Users/Ginger/Dropbox/NatCap_backup/Forage_model/Forage_model/model_inputs/Rubanza_et_al_2005_supp.csv",
'restart_yearly': 0,
'diet_verbose': 1,
}
now_str = datetime.now().strftime("%Y-%m-%d--%H_%M_%S")
if not os.path.exists(f_args['outdir']):
os.makedirs(f_args['outdir'])
forage.write_inputs_log(f_args, now_str)
forage.set_time_step('month') # current default, enforced by CENTURY
add_event = 1 # TODO should this ever be 0?
steps_per_year = forage.find_steps_per_year()
if f_args['diet_verbose']:
master_diet_dict = {}
diet_segregation_dict = {'step': [], 'segregation': []}
herbivore_list = []
if f_args[u'herbivore_csv'] is not None:
herbivore_input = (pandas.read_csv(
f_args[u'herbivore_csv'])).to_dict(orient='records')
for h_class in herbivore_input:
herd = forage.HerbivoreClass(h_class)
herd.update()
BC = 1 # TODO get optional BC from user
# if BC:
# herd.check_BC(BC)
herbivore_list.append(herd)
results_dict = {'step': [], 'year': [], 'month': []}
for h_class in herbivore_list:
results_dict[h_class.label + '_kg'] = []
results_dict[h_class.label + '_gain_kg'] = []
results_dict[h_class.label + '_intake_forage_per_indiv_kg'] = []
if h_class.sex == 'lac_female':
results_dict['milk_prod_kg'] = []
results_dict['total_offtake'] = []
supp_available = 0
if 'supp_csv' in f_args.keys():
supp_list = (pandas.read_csv(
f_args[u'supp_csv'])).to_dict(orient='records')
assert len(supp_list) == 1, "Only one supplement type is allowed"
supp_info = supp_list[0]
supp = forage.Supplement(FreerParam.FreerParamCattle('indicus'),
supp_info['digestibility'],
supp_info['kg_per_day'], supp_info['M_per_d'],
supp_info['ether_extract'],
supp_info['crude_protein'],
supp_info['rumen_degradability'])
if supp.DMO > 0.:
supp_available = 1
stocking_density_dict = forage.populate_sd_dict(herbivore_list)
total_SD = forage.calc_total_stocking_density(herbivore_list)
site = forage.SiteInfo(f_args[u'steepness'], f_args[u'latitude'])
threshold_exceeded = 0
try:
for step in xrange(f_args[u'num_months']):
step_month = f_args[u'start_month'] + step
if step_month > 12:
mod = step_month % 12
if mod == 0:
month = 12
else:
month = mod
else:
month = step_month
year = (step / 12) + f_args[u'start_year']
if month == 1 and f_args['restart_yearly'] and \
f_args[u'herbivore_csv'] is not None:
threshold_exceeded = 0
herbivore_list = []
for h_class in herbivore_input:
herd = forage.HerbivoreClass(h_class)
herd.update()
herbivore_list.append(herd)
# get biomass and crude protein for each grass type
available_forage = forage.calc_feed_types(grass_list)
results_dict['step'].append(step)
results_dict['year'].append(year)
results_dict['month'].append(month)
if not f_args[u'user_define_digestibility']:
for feed_type in available_forage:
feed_type.calc_digestibility_from_protein()
total_biomass = forage.calc_total_biomass(available_forage)
if step == 0:
# threshold biomass, amount of biomass required to be left
# standing (kg per ha)
threshold_biomass = total_biomass * float(
f_args[u'mgmt_threshold'])
diet_dict = {}
for herb_class in herbivore_list:
herb_class.calc_distance_walked(total_SD, site.S,
available_forage)
max_intake = herb_class.calc_max_intake()
ZF = herb_class.calc_ZF()
HR = forage.calc_relative_height(available_forage)
diet = forage.diet_selection_t2(ZF, HR, f_args[u'prop_legume'],
supp_available, supp,
max_intake, herb_class.FParam,
available_forage,
herb_class.f_w, herb_class.q_w)
diet_interm = forage.calc_diet_intermediates(
diet, supp, herb_class, site, f_args[u'prop_legume'],
f_args[u'DOY'])
if herb_class.type != 'hindgut_fermenter':
reduced_max_intake = forage.check_max_intake(
diet, diet_interm, herb_class, max_intake)
if reduced_max_intake < max_intake:
diet = forage.diet_selection_t2(
ZF, HR, f_args[u'prop_legume'], supp_available,
supp, reduced_max_intake, herb_class.FParam,
available_forage)
diet_dict[herb_class.label] = diet
forage.reduce_demand(diet_dict, stocking_density_dict,
available_forage)
if f_args['diet_verbose']:
# save diet_dict across steps to be written out later
master_diet_dict[step] = diet_dict
# diet_segregation = forage.calc_diet_segregation(diet_dict)
# diet_segregation_dict['step'].append(step)
# diet_segregation_dict['segregation'].append(diet_segregation)
total_intake_step = forage.calc_total_intake(
diet_dict, stocking_density_dict)
if (total_biomass - total_intake_step) < threshold_biomass:
print "Forage consumed violates management threshold"
threshold_exceeded = 1
total_intake_step = 0
for herb_class in herbivore_list:
if threshold_exceeded:
diet_dict[herb_class.label] = forage.Diet()
diet = diet_dict[herb_class.label]
# if herb_class.type != 'hindgut_fermenter':
diet_interm = forage.calc_diet_intermediates(
diet, supp, herb_class, site, f_args[u'prop_legume'],
f_args[u'DOY'])
if herb_class.sex == 'lac_female':
milk_production = forage.check_milk_production(
herb_class.FParam, diet_interm)
milk_kg_day = herb_class.calc_milk_yield(milk_production)
if threshold_exceeded:
delta_W = -(forage.convert_step_to_daily(herb_class.W))
else:
delta_W = forage.calc_delta_weight(diet_interm, herb_class)
delta_W_step = forage.convert_daily_to_step(delta_W)
herb_class.update(delta_weight=delta_W_step,
delta_time=forage.find_days_per_step())
results_dict[herb_class.label + '_kg'].append(herb_class.W)
results_dict[herb_class.label +
'_gain_kg'].append(delta_W_step)
results_dict[herb_class.label +
'_intake_forage_per_indiv_kg'].append(
forage.convert_daily_to_step(diet.If))
if herb_class.sex == 'lac_female':
results_dict['milk_prod_kg'].append(
forage.convert_daily_to_step(milk_kg_day))
results_dict['total_offtake'].append(total_intake_step)
finally:
if f_args['diet_verbose']:
# df = pandas.DataFrame(diet_segregation_dict)
# save_as = os.path.join(f_args['outdir'], 'diet_segregation.csv')
# df.to_csv(save_as, index=False)
for h_label in master_diet_dict[0].keys():
new_dict = {}
new_dict['step'] = master_diet_dict.keys()
new_dict['DMDf'] = [
master_diet_dict[step][h_label].DMDf
for step in master_diet_dict.keys()
]
new_dict['CPIf'] = [
master_diet_dict[step][h_label].CPIf
for step in master_diet_dict.keys()
]
grass_labels = master_diet_dict[0][h_label].intake.keys()
for g_label in grass_labels:
new_dict['intake_' + g_label] = \
[master_diet_dict[step][h_label].intake[g_label] for
step in master_diet_dict.keys()]
df = pandas.DataFrame(new_dict)
save_as = os.path.join(f_args['outdir'], h_label + '_diet.csv')
df.to_csv(save_as, index=False)
filled_dict = forage.fill_dict(results_dict, 'NA')
df = pandas.DataFrame(filled_dict)
df.to_csv(os.path.join(f_args['outdir'], 'summary_results.csv'))
def run_simulations():
century_dir = 'C:/Users/Ginger/Dropbox/NatCap_backup/Forage_model/CENTURY4.6/Century46_PC_Jan-2014'
fix_file = 'drytrpfi.100'
graz_file = os.path.join(century_dir, "graz.100")
site_list = ['Research', 'Loidien'] #, 'Rongai', 'Kamok']
input_dir = "C:/Users/Ginger/Dropbox/NatCap_backup/Forage_model/CENTURY4.6/Kenya/input"
outer_dir = "C:/Users/Ginger/Dropbox/NatCap_backup/Forage_model/CENTURY4.6/Output/Stocking_density_test"
prop_legume = 0
template_level = 'GL'
herb_class_weights = "C:/Users/Ginger/Dropbox/NatCap_backup/Forage_model/CENTURY4.6/Kenya/Boran_weights.csv"
sd_dir = 'C:/Users/Ginger/Dropbox/NatCap_backup/Forage_model/CENTURY4.6/Kenya/OPC_stocking_density'
breed = 'Boran'
steepness = 1.
latitude = 0
supp_available = 0
FParam = FreerParam.FreerParam(forage.get_general_breed(breed))
supp = forage.Supplement(FParam, 0, 0, 0, 0, 0, 0)
forage.set_time_step('month')
add_event = 1
grass_file = "C:/Users/ginge/Dropbox/NatCap_backup/Forage_model/Forage_model/model_inputs/grass.csv"
grass = (pandas.read_csv(grass_file)).to_dict(orient='records')[0]
grass['DMD_green'] = 0.64
grass['DMD_dead'] = 0.64
grass['cprotein_green'] = 0.1
grass['cprotein_dead'] = 0.1
for site in site_list:
spin_up_outputs = [site + '_hist_log.txt', site + '_hist.lis']
century_outputs = [site + '_log.txt', site + '.lis', site + '.bin']
filename = 'average_animals_%s_2km_per_ha.csv' % site
stocking_density_file = os.path.join(sd_dir, filename)
sd_df = pandas.read_table(stocking_density_file, sep=',')
outdir = os.path.join(outer_dir, site)
if not os.path.exists(outdir):
os.makedirs(outdir)
# write CENTURY bat for spin-up simulation
hist_bat = os.path.join(input_dir, (site + '_hist.bat'))
hist_schedule = site + '_hist.sch'
hist_output = site + '_hist'
cent.write_century_bat(input_dir, hist_bat, hist_schedule, hist_output,
fix_file, 'outvars.txt')
# write CENTURY bat for extend simulation
extend_bat = os.path.join(input_dir, site + '.bat')
schedule = site + '.sch'
output = site
extend = site + '_hist'
cent.write_century_bat(century_dir, extend_bat, schedule, output,
fix_file, 'outvars.txt', extend)
# move CENTURY run files to CENTURY dir
site_file = os.path.join(input_dir, site + '.100')
weather_file = os.path.join(input_dir, site + '.wth')
e_schedule = os.path.join(input_dir, site + '.sch')
h_schedule = os.path.join(input_dir, site + '_hist.sch')
file_list = [
hist_bat, extend_bat, e_schedule, h_schedule, site_file,
weather_file
]
for file in file_list:
shutil.copyfile(file,
os.path.join(century_dir, os.path.basename(file)))
# make a copy of the original graz params and schedule file
shutil.copyfile(graz_file, os.path.join(century_dir, 'graz_orig.100'))
label = os.path.basename(e_schedule)[:-4]
copy_name = label + '_orig.sch'
shutil.copyfile(e_schedule, os.path.join(input_dir, copy_name))
# run CENTURY for spin-up up to start_year and start_month
hist_bat = os.path.join(century_dir, site + '_hist.bat')
century_bat = os.path.join(century_dir, site + '.bat')
p = Popen(["cmd.exe", "/c " + hist_bat], cwd=century_dir)
stdout, stderr = p.communicate()
p = Popen(["cmd.exe", "/c " + century_bat], cwd=century_dir)
stdout, stderr = p.communicate()
# save copies of CENTURY outputs, but remove from CENTURY dir
intermediate_dir = os.path.join(outdir, 'CENTURY_outputs_spin_up')
if not os.path.exists(intermediate_dir):
os.makedirs(intermediate_dir)
to_move = century_outputs + spin_up_outputs
for file in to_move:
shutil.copyfile(os.path.join(century_dir, file),
os.path.join(intermediate_dir, file))
os.remove(os.path.join(century_dir, file))
grass_list = [grass]
results_dict = {'year': [], 'month': []}
herbivore_input = (pandas.read_csv(herb_class_weights).to_dict(
orient='records'))
herbivore_list = []
for h_class in herbivore_input:
results_dict[h_class['label'] + '_gain_kg'] = []
results_dict[h_class['label'] + '_offtake'] = []
results_dict['milk_prod_kg'] = []
for grass in grass_list:
results_dict[grass['label'] + '_green_kgha'] = []
results_dict[grass['label'] + '_dead_kgha'] = []
results_dict['total_offtake'] = []
results_dict['stocking_density'] = []
try:
for row in xrange(len(sd_df)):
herbivore_list = []
for h_class in herbivore_input:
herd = forage.HerbivoreClass(FParam,
breed,
h_class['weight'],
h_class['sex'],
h_class['age'],
h_class['stocking_density'],
h_class['label'],
Wbirth=24)
herd.update(FParam, 0, 0)
herbivore_list.append(herd)
for h_class in herbivore_list:
h_class.stocking_density = sd_df.iloc[row][h_class.label]
total_SD = forage.calc_total_stocking_density(herbivore_list)
results_dict['stocking_density'].append(total_SD)
siteinfo = forage.SiteInfo(total_SD, steepness, latitude)
month = sd_df.iloc[row].month
year = sd_df.iloc[row].year
suf = '%d-%d' % (month, year)
DOY = month * 30
# get biomass and crude protein for each grass type from CENTURY
output_file = os.path.join(intermediate_dir, site + '.lis')
outputs = cent.read_CENTURY_outputs(output_file, year,
year + 2)
target_month = cent.find_prev_month(year, month)
grass['prev_g_gm2'] = grass['green_gm2']
grass['prev_d_gm2'] = grass['dead_gm2']
grass['green_gm2'] = outputs.loc[target_month, 'aglivc']
grass['dead_gm2'] = outputs.loc[target_month, 'stdedc']
grass['cprotein_green'] = (
outputs.loc[target_month, 'aglive1'] /
outputs.loc[target_month, 'aglivc'])
grass['cprotein_dead'] = (
outputs.loc[target_month, 'stdede1'] /
outputs.loc[target_month, 'stdedc'])
if row == 0:
available_forage = forage.calc_feed_types(grass_list)
else:
available_forage = forage.update_feed_types(
grass_list, available_forage)
results_dict['year'].append(year)
results_dict['month'].append(month)
for feed_type in available_forage:
results_dict[feed_type.label + '_' +
feed_type.green_or_dead + '_kgha'].append(
feed_type.biomass)
siteinfo.calc_distance_walked(FParam, available_forage)
for feed_type in available_forage:
feed_type.calc_digestibility_from_protein()
total_biomass = forage.calc_total_biomass(available_forage)
# Initialize containers to track forage consumed across herbivore
# classes
total_intake_step = 0.
total_consumed = {}
for feed_type in available_forage:
label_string = ';'.join(
[feed_type.label, feed_type.green_or_dead])
total_consumed[label_string] = 0.
for herb_class in herbivore_list:
max_intake = herb_class.calc_max_intake(FParam)
if herb_class.Z < FParam.CR7:
ZF = 1. + (FParam.CR7 - herb_class.Z)
else:
ZF = 1.
if herb_class.stocking_density > 0:
adj_forage = forage.calc_adj_availability(
available_forage, herb_class.stocking_density)
else:
adj_forage = list(available_forage)
diet = forage.diet_selection_t2(ZF, prop_legume,
supp_available, supp,
max_intake, FParam,
adj_forage)
diet_interm = forage.calc_diet_intermediates(
FParam, diet, supp, herb_class, siteinfo, prop_legume,
DOY)
reduced_max_intake = forage.check_max_intake(
FParam, diet, diet_interm, herb_class, max_intake)
if reduced_max_intake < max_intake:
diet = forage.diet_selection_t2(
ZF, prop_legume, supp_available, supp,
reduced_max_intake, FParam, adj_forage)
diet_interm = forage.calc_diet_intermediates(
FParam, diet, supp, herb_class, siteinfo,
prop_legume, DOY)
total_intake_step += (
forage.convert_daily_to_step(diet.If) *
herb_class.stocking_density)
if herb_class.sex == 'lac_female':
milk_production = forage.check_milk_production(
FParam, diet_interm)
milk_kg_day = forage.calc_milk_yield(
FParam, milk_production)
delta_W = forage.calc_delta_weight(FParam, diet,
diet_interm, supp,
herb_class)
delta_W_step = forage.convert_daily_to_step(delta_W)
herb_class.update(FParam, delta_W_step,
forage.find_days_per_step())
if herb_class.stocking_density > 0:
results_dict[herb_class.label +
'_gain_kg'].append(delta_W_step)
results_dict[herb_class.label + '_offtake'].append(
diet.If)
else:
results_dict[herb_class.label +
'_gain_kg'].append('NA')
results_dict[herb_class.label +
'_offtake'].append('NA')
if herb_class.sex == 'lac_female':
results_dict['milk_prod_kg'].append(milk_kg_day * 30.)
# after have performed max intake check, we have the final diet
# selected
# calculate percent live and dead removed for each grass type
consumed_by_class = forage.calc_percent_consumed(
available_forage, diet, herb_class.stocking_density)
forage.sum_percent_consumed(total_consumed,
consumed_by_class)
results_dict['total_offtake'].append(total_intake_step)
# send to CENTURY for this month's scheduled grazing event
date = year + float('%.2f' % (month / 12.))
schedule = os.path.join(century_dir, site + '.sch')
target_dict = cent.find_target_month(add_event, schedule, date,
1)
if target_dict == 0:
er = "Error: no opportunities exist to add grazing event"
raise Exception(er)
new_code = cent.add_new_graz_level(grass, total_consumed,
graz_file, template_level,
outdir, suf)
cent.modify_schedule(schedule, add_event, target_dict,
new_code, outdir, suf)
# call CENTURY from the batch file
century_bat = os.path.join(century_dir, site + '.bat')
p = Popen(["cmd.exe", "/c " + century_bat], cwd=century_dir)
stdout, stderr = p.communicate()
# save copies of CENTURY outputs, but remove from CENTURY dir
intermediate_dir = os.path.join(
outdir, 'CENTURY_outputs_m%d_y%d' % (month, year))
if not os.path.exists(intermediate_dir):
os.makedirs(intermediate_dir)
for file in century_outputs:
shutil.copyfile(os.path.join(century_dir, file),
os.path.join(intermediate_dir, file))
os.remove(os.path.join(century_dir, file))
# remove files from CENTURY directory
finally:
# replace graz params used by CENTURY with original file
os.remove(graz_file)
shutil.copyfile(os.path.join(century_dir, 'graz_orig.100'),
graz_file)
os.remove(os.path.join(century_dir, 'graz_orig.100'))
files_to_remove = [
os.path.join(century_dir, os.path.basename(f))
for f in file_list
]
for file in files_to_remove:
os.remove(file)
os.remove(os.path.join(century_dir, site + '_hist.bin'))
filled_dict = forage.fill_dict(results_dict, 'NA')
df = pandas.DataFrame(filled_dict)
df.to_csv(os.path.join(outdir, 'summary_results.csv'))
import csv
grass1 = {
'label': 'grass1',
'type': 'C4', # 'C3' or 'C4'
'DMD_green': 0.541,
'DMD_dead': 0.541,
'cprotein_green': 0.023,
'cprotein_dead': 0.023,
'green_gm2': 2000.,
'dead_gm2': 2000.,
'percent_biomass': 1.,
}
grass_list = [grass1]
total_SD = 1.
site = forage.SiteInfo(1., -3.5)
prop_legume = 0.0
breed = 'Brahman' # see documentation for allowable breeds; assumed to apply to all animal classes
sex = 'castrate' # right now, we only deal with steers
A = 225. # initial age (days)
W = 161. # initial weight (Rubanza et al 2005)
herd_size = 1
Wbirth = 34.7
SRW = 297
DOY_start = 213
outdir = r'C:\Users\Ginger\Dropbox\NatCap_backup\Forage_model\Forage_model\Verification_calculations\Rubanza_et_al_2005'
time_step = 'day'
force_supp = False
forage.set_time_step(time_step)
available_forage = forage.calc_feed_types(grass_list)
def calc_equivalent_intake():
"""Calculate the approximately equivalent amount of one feed type that
would need to be eaten to replace another. For example, how much hay would
be nutritionally equivalent to a given amount of forage, in terms of
liveweight gain?"""
def calc_liveweight(site, herb_class, available_forage):
supp_available = 0
DOY = 150
prop_legume = 0
herb_class.calc_distance_walked(site.S, herb_class.stocking_density,
available_forage)
max_intake = herb_class.calc_max_intake()
ZF = herb_class.calc_ZF()
HR = forage_u.calc_relative_height(available_forage)
diet = forage_u.diet_selection_t2(ZF, HR, prop_legume, supp_available,
max_intake, herb_class.FParam,
available_forage)
diet_interm = forage_u.calc_diet_intermediates(diet, herb_class,
prop_legume, DOY, site)
reduced_max_intake = forage_u.check_max_intake(diet, diet_interm,
herb_class, max_intake)
if reduced_max_intake < max_intake:
diet = forage_u.diet_selection_t2(ZF, HR, prop_legume,
supp_available,
reduced_max_intake,
herb_class.FParam,
available_forage)
diet_interm = forage_u.calc_diet_intermediates(
diet, herb_class, prop_legume, DOY, site)
delta_W = forage_u.calc_delta_weight(diet_interm, herb_class)
return delta_W
grass_gm2 = 87. # does it matter?
forage_args = default_forage_args()
site = forage_u.SiteInfo(1., 44.6)
grass_csv = r"C:\Users\ginge\Dropbox\NatCap_backup\WitW\model_inputs\Ucross\grass_high_quality_only.csv"
hay_csv = r"C:\Users\ginge\Dropbox\NatCap_backup\WitW\model_inputs\Ucross\Merlin_hay.csv"
herb_csv = forage_args['herbivore_csv']
h_class = (pd.read_csv(
forage_args[u'herbivore_csv'])).to_dict(orient='records')[0]
herd = forage_u.HerbivoreClass(h_class)
herd.update()
# get liveweight from grass
grass_list = (pd.read_csv(grass_csv)).to_dict(orient='records')
for grass in grass_list:
grass['green_gm2'] = grass_gm2
grass['dead_gm2'] = grass_gm2 * 0.5
available_forage = forage_u.calc_feed_types(grass_list)
for feed_type in available_forage:
feed_type.calc_digestibility_from_protein(
forage_args['digestibility_flag'])
grass_liveweight = calc_liveweight(site, herd, available_forage)
# try n times to match liveweight with hay, given a tolerance, and changing
# the amount of hay supplied
hay_list = (pd.read_csv(hay_csv)).to_dict(orient='records')
tolerance = 10.
hay_liveweight = 0
upper_bound = grass_gm2 * 2.
lower_bound = grass_gm2 / 2.
n = 1
while n < 15:
for hay in hay_list:
hay['green_gm2'] = upper_bound
hay['dead_gm2'] = upper_bound * 0.5
available_forage = forage_u.calc_feed_types(hay_list)
for feed_type in available_forage:
feed_type.calc_digestibility_from_protein(
forage_args['digestibility_flag'])
hay_upper_lw = calc_liveweight(site, herd, available_forage)
if (grass_liveweight - hay_upper_lw) > tolerance:
raise Exception, "upper bound hay gm2 is too low"
for hay in hay_list:
hay['green_gm2'] = lower_bound
hay['dead_gm2'] = lower_bound * 0.5
available_forage = forage_u.calc_feed_types(hay_list)
for feed_type in available_forage:
feed_type.calc_digestibility_from_protein(
forage_args['digestibility_flag'])
hay_lower_lw = calc_liveweight(site, herd, available_forage)
if (hay_lower_lw - grass_liveweight) > tolerance:
raise Exception, "lower bound hay gm2 is too high"
if (hay_upper_lw - grass_liveweight) > (grass_liveweight -
hay_lower_lw):
upper_bound = lower_bound + ((upper_bound - lower_bound) / 2.)
else:
lower_bound = lower_bound + ((upper_bound - lower_bound) / 2.)
n = n + 1
print "Hay biomass: between {} and {}".format(lower_bound, upper_bound)
print """liveweight gain: {0:.3f} (grass), {0:.3f} (hay upper bound),
{0:.3f} (hay lower bound)""".format(grass_liveweight, hay_upper_lw,
hay_lower_lw)