Introduction

After applying Module 3 constraint to the maximum attainable yield, users can now proceed with the Module 4: Soil Constraints.

The soil constraint module in PyAEZ is implemented based on GAEZ v5 new soil evaluation framework. An algorithm update based on new soil evaluation and AWC calculation is now included in the new version 2.3.

The combination of 7 soil qualities (SQ), which are based on the soil characteristics of each soil unit, and the input level gives us a single yield reduction factor – Soil Rating, which will be applied to the remaining yield. For more details on this calculation, please refer to the GAEZ v4 Model Documentation (Fischer et al., 2021).

• SQ1 : Nutrient Availability
• SQ2 : Nutrient Retention Capacity
• SQ3 : Rooting Conditions
• SQ4 : Oxygen Availability to Roots
• SQ5 : Salinity and Sodicity Conditions
• SQ6 : Calcium Carbonate and Gypsum Conditions
• SQ7 : Workability (constraining field management).

As explained in data preparation, the execution of Module 4 requires three components: Soil map, Soil Characteristics Excel Sheet, and Edaphic Requirement Excel Sheet. Once all preparations are completed, users can start off with Module 4. The overview of Module 4 is as shown in the figure below:

Figure: Overview of Module IV: Soil Constraints

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Mandatory Settings

Initialization of M4 Object Class Creation

Firstly, users requires to import M4 object class named SoilConstraints by executing the following function:

from pyaez import SoilConstraints

soil_con = SoilConstraints.SoilConstraints(rooting_depth)

Input

Arguments	Description	Unit	Data Format
rooting_depth	Crop-specific Rooting Depth Class. Must be one of [1, 2, 3, 4, 5]	Class	Integer

Output

Arguments	Description	Unit	Data Format
None	-	-	-

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Importing Edaphic Rating Excel Sheet

After the object class declaration, the crop/LUT-specific edaphic rating excel sheets for rainfed and irrigated conditions are required to be uploaded into the object class. Both excel sheets' file formats must be in .xlsx format.

soil_con.importSoilReductionSheet(rain_sheet_path, irr_sheet_path)

Input

Arguments	Description	Unit	Data Format
rain_sheet_path	Full file path of LUT/management specific edaphic requirement setting for rainfed condition	None	String (.xlsx format)
irr_sheet_path	Full file path of LUT/management specific edaphic requirement setting for irrigated condition	None	String (.xlsx format)

Output

Arguments	Description	Unit	Data Format
None	-	-	-

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Calculation of Soil Qualities (SQ)

This function calculates seven major soil qualitites for each soil ID based on input soil characteristics.

soil_con.calculateSoilQualities(irr_or_rain, soil_characteristics_path)

Input

Arguments	Description	Unit	Data Format
irr_or_rain	'I' for irrigated, 'R' for rainfed to execute	-	String

Output

Arguments	Description	Unit	Data Format
None	-	-	-

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Calculation of Soil Ratings (SR)

This function calculates input/management-specfic soil ratings (SR) based on existing SQs. Before running this, users are required to execute calculateSoilQualities first.

soil_con.calculateSoilRatings(input_level)

Input

Arguments	Description	Unit	Data Format
input_level	L for low-level input, I for intermediate-level input, H for high-input level	-	String

Output

Arguments	Description	Unit	Data Format
None	-	-	-

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Major Functionalities

Applying Soil Constraints

This function applies all soil-related yield reduction factors. The returned yield map is now soil-adjusted yield map.

yld_out = soil_con.applySoilConstraints(soil_map, yield_in)

Input

Arguments	Description	Unit	Data Format
soil_map	Soil Map containing SMUs which are IDs used to match the soil ratings with the input yield.	-	2D-NumPy Array
yield_in	Input yield map before applying soil reduction factors (irrigated or rainfed)	kg/ha	2D-NumPy Array

Output

Arguments	Description	Unit	Data Format
soil_adj_yld	Soil adjusted yield.	same as yield_in	2D-NumPy Array

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Soil Suitability (Fc4)

This function returns soil suitability map after applying the soil constraints. Soil suitability factor (fc4) ranges from 0 (Not Suitable) to 1 (Very suitable). Note that the soil suitability map reflects all the input/management settings users specified earlier in the soil ratings calculation.

fc4_map = soil_con.getSoilSuitabilityMap()

Input

Arguments	Description	Unit	Data Format
None	-	-	-

Output

Arguments	Description	Unit	Data Format
fc4	LUT/management-specific soil constraint factor (fc4)	-	2D-NumPy Array
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Soil Qualities Output

If users wish to see the each SMU's calculated soil qualities, you can produce soil quality outputs as pandas dataframe by executing the following function. Before running this, users are required to execute calculateSoilQualities first, so that the SQs are produced based on earlier LUT/management setting.

print(soil_con.getSoilQualities())

Input

Arguments	Description	Unit	Data Format
None	---	---	---

Output

Arguments	Description	Unit	Data Format
SQ	LUT/mangement specific SQs for each SMU	-	pd.DataFrame

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Soil Ratings Output

If users wish to see the each SMU's calculated soil ratings, you can produce soil quality outputs as pandas dataframe by executing the following function. Before running this, users are required to execute calculateSoilRatings first, so that the SRs are produced based on earlier LUT/management setting.

print(soil_con.getSoilRatings())

Input

Arguments	Description	Unit	Data Format
None	---	---	---

Output

Arguments	Description	Unit	Data Format
SR	LUT/mangement specific SRs for each SMU	-	pd.DataFrame

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