Dear All,
My friend Naveen who volunteers along with various Farmers Movements and
organisations, has come across a road block while trying to do some
calculations w.r.t Crop Insurance Payout. This year in state of telangana,
the government has not implemented the PMFBY policy and due to heavy rains
many farmers have lost a lot of crops and are in severe losses. So we are
trying to simulate what would have been the payout the farmers would have
received in case PMFBY was in place.
He has written the code in Python for calculating the same.
Below is short description of the issue he needs help with:
"I am analysing daily rainfall data to calculate Insurance payout for
farmers in case of excess rainfall coverage. policy covers if the
"Consecutive 3 day cumulative rainfall" is greater than 50mm between
1st-Oct and 31st-Oct. I was able to write the code in Python to find the
matching criteria. But when it rains continuously then the result has
overlapping dates which is not acceptable payout. Need help in calculating
best payout option in case of overlapping dates."
I have attached the attachments also for further understanding.
He has also summarised the issue in this stack overflow thread.
https://stackoverflow.com/questions/65815331/rainfall-based-crop-insurance-payout-calculation
Request people to share their thoughts and suggestions.
Regards
Sreeharsha
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for dist in data["dcode"].unique():
d_data = data[data["dcode"] == dist]
#print(dist)
for block in d_data["mandal"].unique():
prev_rain = 0
prev_to_date = "01/12/2022"
for each in rain_dev_input:
#[(rain_dev_input["TERM"] == "DEFICIT RAINFALL") &
(rain_dev_input["DIST_CODE"] == 240)]:
#print(each)
distcode = each["DIST_CODE"]
#print(distcode)
term = str(each["TERM"])
if (distcode == dist) & (term == "EXCESS RAINFALL"):
start_date = each["FROM_PERIOD"]
end_date = each["TO_PERIOD"]
s_date = datetime.datetime.strptime(start_date, "%d/%m/%y")
e_date = datetime.datetime.strptime(end_date, "%d/%m/%y")
#s_date = start_date.strftime(start_date, "%Y-%m-%d")
#e_date = end_date.strftime(end_date, "%Y-%m-%d")
#count1 = daterange(s_date, e_date)
#print(s_date, ": ", e_date)
m_data = d_data[d_data["mandal"] == block]
p_data = m_data.loc[start_date:end_date]
for singledate in daterange(s_date, e_date):
#print("Inside Excess Rain")
from_date = datetime.datetime.strftime(singledate,
"%Y-%m-%d")
to_date = datetime.datetime.strftime(
(singledate + timedelta(2)), "%Y-%m-%d"
)
total_rain = p_data.loc[from_date:to_date]["rain"].sum()
#print(total_rain)
range1 = float(each["RANGE1"])
range2 = float(each["RANGE2"])
if (total_rain >= range1) & (total_rain < range2):
#print("inside write to file")
if (from_date <= prev_to_date <= to_date) & (prev_rain
<= total_rain):
temp["Max"] = total_rain;
prev_rain = total_rain
prev_to_date = to_date
temp = dict()
temp["district"] = each["DIST_NAME"]
temp["mandal"] = block
temp[
"category"
] = "excess rainfall for 3 consecutive days, cumulative"
temp["rainfall"] = total_rain
temp["from_date"] = from_date
temp["to_date"] = to_date
temp["phase"] = each["PHASE"]
#temp["insurance"] = insurance_excessrain(each,
total_rain)
temp["insurance"] = (total_rain - range1)*
each["PAYOUT"]
excess_rainfall.append(temp)
# %%
# output excess rainfall rain_dev_list data
excess_rain = pd.DataFrame(excess_rainfall)
excess_rain.to_csv(str(output_folder) + "/excess_rainfall_2020_h2.csv",
index=False)
