Introduction

Economical Suitability Analysis Module is the most recent addition to AEZ framework (Figure 12). This module converts AEZ's final crop suitability (a result of the previous 5 modules) into an economic suitability. Additionally, all crops of interest are compared to the umbrella crop (crop with the highest economical potential) in order to indicate and map out its comparative advantage in terms of an attainable net revenue relative to the best available option. For more detailed calculations, refer to Module 6 chapter in National Agro-Economic Zoning for Major Crops in Thailand (NAEZ) report (FAO, 2017a).

Figure: Overview of Module VI: Economic Suitability

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Mandatory Functions

Initialization of M6 Object Class Creation

Firstly, we have to import M6 object class named EconomicSuitability to initiate the mandatory process.

from pyaez import EconomicSuitability
eco = EconomicSuitability.EconomicSuitability()

Input

Arguments	Description	Unit	Data Format
None	-	-	-

Output

Arguments	Description	Unit	Data Format
None	-	-	-

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Crop Economic Data Input

This function allows usres to set up crop-specific parameters for an economic analysis/ The key inputs for Module 5 are the crop yield information which passed through from Module 2 to Module 5 in PyAEZ, market price and the cost of the crop.

This mandatory function can be called multiple times if users want to import multiple crop-specific information. However, at least one crop data should be provided. If users want to analyze comparative economic analysis at least two crop data should be provided.

eco.addACrop(crop_name, crop_cost, crop_yield, farm_price, yield_map)

Input

Arguments	Description	Unit	Data Format
crop_name	Unique ID of a crop. This name will be used later in other function arguments.	-	String
crop_cost	Cost of production for each yield value in crop_yield variable.	Currency-based	1D-NumPy Array
crop_yield	Yield of the crop.	tonnes per hectare.	1D-NumPy Array
farm_price	Historical crop price that farmers sell	currency unit per tonne	1D-NumPy Array
yield_map	Yield map of the crop	tonne per hectare	2D-NumPy Array

Output

Arguments	Description	Unit	Data Format
None	-	-	-

Additional Information

• Value of crop_cost and crop_yield must be corresponding to each other for all elements, i.e., the length of crop_cost and crop_yield must be the same.

• Both arrays of crop-cost and crop_yield must be arranged in ascending order from left to right.

• Unit of crop_cost must be currency per hectare.

• In M6, the currency can be flexible. However, users are warned that for all variables which applies currency must have the same currency names. In case the crop economic data have different currencies, they must be converted into a single currency.

• In farm_price, the array is used to calculate distribution (mean) of prices. Unit is currency unit per tonne.

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Major Functionalities

Net Revenue

This function returns net revenue from the crop identified with crop_name.

crop_rev = eco.getNetRevenue(crop_name)

Input

Arguments	Description	Unit	Data Format
crop_name	Unique crop ID from earlier setting.	-	String

Output

Arguments	Description	Unit	Data Format
crop_revenue	Net revenue of input crop	currency per hectare	2D-NumPy Array

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Classified Net Revenue

This function returns classified net revenue for the crop crop_name. This classified scheme for crop net revenue is provided in the table below.

This classification is based on identification of minimum and maximum value of net revenue in a given area of interest. The net revenue percentage is delineated between zero percent (minimum net revenue) and 100 (maximum net revenue), and seven classes are divided based on percentage ranges.

crop_rev_class = eco.getClassifiedNetRevenue(crop_name)

Input

Arguments	Description	Unit	Data Format
crop_name	Unique crop ID from earlier setting.	-	String

Output

Arguments	Description	Unit	Data Format
crop_rev_class	Classified net revenue of input crop	-	2D-NumPy Array

Table: Net Revenue Classification

Pixel Value	Net Revenue Class	Description
0	Not suitable	Net revenue less than 0%
1	Very marginal	Net revenue between 0% and 10%
2	Marginal	Net revenue between 10% and 20%
3	Moderate	Net revenue between 20% and 35%
4	Medium	Net revenue between 35% and 50%
5	Good	Net revenue between 50% and 63%
6	High	Net revenue between 63% and 75%
7	Very high	Net revenue greater than 75%

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Normalized Net Revenue

This function returns the normalized net revenue for the crop crop_name, which requires at least two crops to be provided in the crop economic data input. This classification checks the net revenues for all crops and select the maximum net revenue value of the crop (known as umbrella crop) and is compared with user-specified crop net revenue to generate normalized net revenue. After normalization, the normalized values are classified to six major classes, providing the comparative performance information of the economic crops.

crop_rev_norm = eco.getNormalizedNetRevenue(crop_name)

Input

Arguments	Description	Unit	Data Format
crop_name	Unique crop ID from earlier setting.	-	String

Output

Arguments	Description	Unit	Data Format
crop_rev_norm	Classified net revenue of input crop	-	2D-NumPy Array

Table: Normalized Net Revenue Classification

Pixel Value	Normalized Net Revenue Class	Description
0	Normalized value less than 0	Not Suitable
1	Normalized value between 0 and 0.2	Marginal
2	Normalized value between 0.2 and 0.4	Moderate
3	Normalized value between 0.4 and 0.6	Fair
4	Normalized value between 0.6 and 0.8	Good
5	Normalized value greater than 0.8	Very Good

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