Introduction

In this module, various yield reduction factors will be applied to the maximum attainable yield estimated from Module 2 to consider the constraining effects which are difficult to simulate during the crop cycle simulation. For example, climatic effects can be pests, diseases, and poor workability due to excess soil moisture. These effects, in turn, depend on the different levels of inputs, LGP and LGP Equivalent.

All of the reduction factors used in Modules 3, 4, and 5 are located in 2 parameter files corresponding to irrigated and rain-fed conditions. These files MUST be edited with the reduction factors values corresponding to each crop and input level. Users are strongly encouraged to advise to use specific reduction factors based on national research for national-level analysis. This module considers four types of agro-climatic constraints:

• Pests, diseases, and weeds damages on plant growth (‘b’ group).
• Pests, diseases, and weeds damages on produce’s quality (‘c’ group).
• Climatic factors affecting the efficiency of farming operations (‘d’ group).
• Frost hazards (‘e’ group).

Note: The existing ‘a’ group corresponding with yield reduction due to rainfall variability (by GAEZ v4 documentation) is not considered in PyAEZ. See the GAEZ v4 Model Documentation for further details on the climate constraints (Fischer et al., 2021).

In PyAEZ v2.3, we have included two different types of object classes: ClimaticConstraints and ProcessExcelWrapper. ClimaticConstraints object class handls the calculation of agro-climatic constraints factor for yield reduction while ProcessExcelWrapper, a newly introduced object class, contains an all-round wrapper function to extract user-specified crop/LUT's agro-climatic factors from GAEZ Appendices excel sheet automatically instead of manual value retrieval from the data sheet.

To apply yield reduction due to agro-climatic constraints, firstly an object class named ClimaticConstraints is required to call as shown in figure below.

Figure: Overview of Crop Simulation Routine

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Initialization of M3 ClimaticConstraints Object Class Creation

We need to import an object class ClimaticConstraints to initiate for agro-climatic constraints by the following way of setting.

from pyaez import ClimaticConstraints

clim_con = ClimaticConstraints.ClimaticConstraints(lat_min, lat_max, elevation, mask, no_mask_value = 0)

Input

Arguments	Description	Unit	Data Format
lat_min	minimum latitude extent	Decimal Degrees	float
lat_max	maximum latitude extent	Decimal Degrees	float
elevation	Elevation	meters	2D NumPy Array
mask	Mask Layer/Region of Interest	Binary(0,1)/Categorical(1,2,...)	2D NumPy Array
no_mask_value	Value of mask layer to skip calculation	Mask value from the layer. Default to 0	Integer

Output

Arguments	Description	Data Type
None	-	-

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Setting the Climate Data

Next, we import the necessary climate data required for agro-climatic constraints. The function can handle both monthly and daily and will adjust the mandatory calculation accordingly. Please make sure the input climate data has 12 layers for monthly or 365 or 366 layers for daily data.

clim_con.setClimateData(min_temp, max_temp, wind_speed, short_rad, rel_humidity, precip)

Input

Arguments	Description	Unit	Data Format	Time
min_temp	Minimum temperature	°C	3D-NumPy Array	Daily (365, 366)/Monthly (12)
max_temp	Maximum temperature	°C	3D-NumPy Array	Daily (365, 366)/Monthly (12)
wind_speed	Wind speed measured at 2-meter height	ms-1	3D-NumPy Array	Daily (365, 366)/Monthly (12)
short_rad	Shortwave radiation	Wm-2	3D-NumPy Array	Daily (365, 366)/Monthly (12)
rel_humidity	Relative humidity	Decimal (0-1)	3D-NumPy Array	Daily (365, 366)/Monthly (12)
precip	Total precipitation	mmday-1	3D-NumPy Array	Daily (365, 366)/Monthly (12)

Output

Arguments	Description	Data Type
None	-	-
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Setting the Agro-climatic Reduction Factors

Starting from v2.2, agro-climatic constraints will be provided using the excel sheet. For Module III, users must prepare two excel sheets of agro-climatic reduction factors; a single excel file must contain three separate sheets for three look-up tables:

1. Mean>20 (for temperature greater than 20℃)
2. Mean<10 (for temperature less than 10℃)
3. Lgpt10 (for frost-free periods)

The first and second look-up tables required ‘b’, ‘c’ and ‘d’ constraints factors (represented along row) for 14 wetness days (LGP_agc) class intervals. All agro-climatic reduction factors range from 0 (Most Suitable) to 100 (Not Suitable). An example excel sheet setting for mean>20 and mean<10 is provided below in Table 13 and Table 14. Notice that there are two categories for LGP_agc of 365 days, namely 365+ and 365-, depending on the number months where monthly precipitation is greater than monthly reference evapotranspiration (ETo). For locations with monthly precipitation greater than monthly ETo for all months, 365+ column will be used, otherwise, 365- column will be used.

For ‘e’ constraints factor settings, users will need to set up in lgpt10 spreadsheet and set up reduction factors for 13 lgpt10 interval classes. An example excel sheet setting for lgpt10 is provided in the table below . If the considered LUT/crops are not frost-sensitive, reduction factor for all day intervals should set up zero to cancel out ‘e’ constraint consideration.

During excel sheet preparation, users are advised to set up the column names and row names as strings in excel software before importing into the object class. Note also that the module calculates for a single water supply management. For instance, if users run Module III for rainfed condition, you will need to rerun with different yield and excel sheets for irrigated condition.

The calling of importing the excel sheet of agro-climatic constraints are as follows:

clim_con.setReductionFactors(file_path)

Input

Arguments	Description	Unit	Data Format
file_path	The full filepath where the excel sheet for agro-climatic constraints for rainfed/irrigated conditions.	None	String

Output

Arguments	Description	Unit	Data Format
None	-	-	-

Table: Agro-climatic Constraint Settings for Temperature Greater than 20 °C (Example for Irrigated Maize)

type	0,29	30,59	60,89	90,119	120,149	150,179	180,209	210,239	240,269	270,299	300,329	330,364	365-	365+
b	0	0	0	0	0	0	0	0	25	25	25	25	25	50
c	50	50	50	25	0	0	0	0	0	0	0	25	25	50
d	0	0	0	0	0	0	0	10	30	30	30	30	30	30

Table: Agro-climatic Constraint Settings for Temperature Less than 10°C (Example for Irrigated Maize)

type	0,29	30,59	60,89	90,119	120,149	150,179	180,209	210,239	240,269	270,299	300,329	330,364	365-	365+
b	0	0	0	0	0	0	0	0	10	10	10	10	10	30
c	30	30	30	10	0	0	0	0	0	0	0	10	10	30
d	0	0	0	0	0	0	0	10	30	30	30	30	30	30

Table: Agro-climatic Constraint Settings Frost-Free Periods (Example for Irrigated Maize)

type	0,29	30,59	60,89	90,119	120,149	150,179	180,209	210,239	240,269	270,299	300,329	330,364	365
lgpt10	0	0	0	0	0	0	0	0	0	0	0	0	0

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Main Functionalities

Applying Climatic Constraints

Once all the mandatory functions are all executed, the yield reduction due to agro-climatic constraints (fc3) can now be applied by using the following function.

clim_con.applyClimaticConstraints(yield_input, lgp, lgp_equv, lgpt10, omit_yld_0 = True)

Input

Arguments	Description	Unit	Data Format
yield_input	Input Yield	kg/ha	2D-NumPy Array
lgp	Length of Growing Period	Days	2D-NumPy Array
lgp_equv	Equivalent Length of Growing Period	Days	2D-NumPy Array
lgpt10	Frost-Free Periods	Days	2D-NumPy Array
omit_yld_0	1 = Any pixels with zero yield will not calculate fc3, 0 = Calculate fc3 regardless of input yield value	Binary (1,0)	Integer

Output

Arguments	Description	Unit	Data Format
None	-	-	-

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Climate Adjusted Yield

This function returns the adjusted yield map with agro-climatic constraints applied based on the settings above.

clim_adj_yld = clim_con.getClimateAdjustedYield()

Input

Arguments	Description	Unit	Data Format
None	-	-	-

Output

Arguments	Description	Unit	Data Format
clim_adj_yld	Climate adjusted yield (rainfed/irrigated)	kg/ha	2D-NumPy Array

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Agro-climatic Suitability Factor (Fc3)

This function returns yield reduction factor due to agro-climatic constraints for a specific management level. The value ranges from 0 (most limiting condition) to 1 (the most suitable condition).

fc3_map = clim_con.getClimateReductionFactor()

Input

Arguments	Description	Unit	Data Format
None	-	-	-

Output

Arguments	Description	Unit	Data Format
fc3	Yield reduction factor due to agro-climatic constraints (rainfed/irrigated)	None	2D-NumPy Array

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Initialization of M3 ProcessExcelWrapper Object Class

In order to automatically extract the crop/management-specific agro-climatic factors tables as excel sheet, an object class ProcessExcelWrapper is called.

from pyaez.ClimaticConstraints import ProcessExcelWrapper

Extraction of Agro-climatic Factor Table

Users need to identify the crop and managments, cycle length, water supply and data sheet from GAEZ Data Sheet to execute the following function.

# example setting for user to provide
filename_input = r"D:\Swun_code\GAEZ4_Appendices.xlsx"
crop_name = 'Wheat (subtropical cultivars)'
crop_cycle_length = 105
input_management = 'High'
condition = 'ir'
output_path = r'D:\Swun_code'

ex_writer = ProcessExcelWrapper(filename_input, crop_name, crop_cycle_length, input_management, condition, output_path)

Input

Arguments	Description	Unit	Data Format
filename_input	GAEZ Data Appendix excel sheet.	-	xlsx format
crop_name	Name of the crop. Must refer to the filename_input	-	String
crop_cycle_length	Reference cycle length	Days	Integer
input_management	Rainfed ['rainfed','rain-fed','Rainfed','RainFed', 'RAINFED','r','R', 'rf','RF'], Irrigated ['irrigated','Irrigated', 'IRRIGATED', 'I','ir']	-	String
output_path	Full file path to save user-specified excel sheet.	-	.xlsx format

Output

Arguments	Description	Unit	Data Format
None	-	-	-

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Execution Run

Once the setting is completed, users can now execute the extraction process.

ex_writer.run()

Input

Arguments	Description	Unit	Data Format
None	-	-	-

Output

The output will be saved at the output_path directory as xlsx file format.

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