Help for package ALUES

Type:

Package

Title:

Agricultural Land Use Evaluation System

Version:

0.2.1

Date:

2022-01-10

Maintainer:

Al-Ahmadgaid B. Asaad <alahmadgaid@gmail.com>

Author:

Al-Ahmadgaid B. Asaad

[aut, cre], Arnold R. Salvacion

[aut], Bui Tan Yen [aut]

Description:

Evaluates land suitability for different crops production. The package is based on the Food and Agriculture Organization (FAO) and the International Rice Research Institute (IRRI) methodology for land evaluation. Development of ALUES is inspired by similar tool for land evaluation, Land Use Suitability Evaluation Tool (LUSET). The package uses fuzzy logic approach to evaluate land suitability of a particular area based on inputs such as rainfall, temperature, topography, and soil properties. The membership functions used for fuzzy modeling are the following: Triangular, Trapezoidal and Gaussian. The methods for computing the overall suitability of a particular area are also included, and these are the Minimum, Maximum and Average. Finally, ALUES is a highly optimized library with core algorithms written in C++.

License:

MIT + file LICENSE

LazyData:

true

LinkingTo:

Rcpp

Encoding:

UTF-8

Depends:

R (≥ 3.5.0), Rcpp (≥ 0.10.6)

Suggests:

testthat, markdown, knitr, microbenchmark, ggmap, raster, reshape2

RoxygenNote:

7.1.1

VignetteBuilder:

knitr

URL:

https://github.com/alstat/ALUES/

BugReports:

https://github.com/alstat/ALUES/issues/

NeedsCompilation:

yes

Packaged:

2022-01-12 13:31:00 UTC; al-ahmadgaidasaad

Repository:

CRAN

Date/Publication:

2022-01-12 21:12:44 UTC

Agricultural Land Use Evaluation System

Description

Agricultural Land Use Evaluation System (ALUES) is a package that evaluates land suitability for different crop production. The package is based on the Food and Agriculture Organization (FAO) and the International Rice Research Institute (IRRI) methodology for land evaluation. Development of ALUES is inspired by similar tool for land evaluation, Land Use Suitability Evaluation Tool (LUSET). The package uses fuzzy logic approach to evaluate land suitability of a particular area based on inputs such as rainfall, temperature, topography, and soil properties. The membership functions used for fuzzy modeling are the following: Triangular, Trapezoidal, Gaussian, Sigmoidal and custom models with functions that can be defined by the user. The package also aims on complicated methods like considering more than one fuzzy membership function on different suitability class. The methods for computing the overall suitability of a particular area are also included, and these are the Minimum, Maximum, Product, Sum, Average, Exponential and Gamma. Finally, ALUES utilizes the power of Rcpp library for efficient computation.

Author(s)

Al-Ahmadgaid B. Asaad <alahmadgaid@gmail.com> (maintainer)

SlopeD - Slope (degree, 6 classes);
CFragm - Coarse fragment (Vol.%);
SoilDpt - Soil depth (cm);
SoilTe - 12 classes of soil texture (Soil Taxonomy);
CECc - Apparent CEC Clay (cmol (+)/kg clay);
SumBCs - Sum of basic caions (cmol (+)/kg soil);
pHH20 - pH H2O;
BS - Base Saturation (%);
OC - Organic carbon (%);
Flood - Flooding;

Temperature Chracteristics of Lao Cai, Vietnam

Description

A dataset containing the temperature characteristics of the land units in Lao Cai, Vietnam.

Format

A data frame with 2928 rows and 12 columns

Details

Jan - January;
Feb - February;
Mar - March;
Apr - April;
May - May;
Jun - June;
Jul - July;
Aug - August;
Sep - September;
Oct - October;
Nov - November;
Dec - December;

Water Chracteristics of Lao Cai, Vietnam

Description

A dataset containing the water chracteristics of the land units in Lao Cai, Vietnam.

Format

A data frame with 2928 rows and 12 columns

Details

Jan - January;
Feb - February;
Mar - March;
Apr - April;
May - May;
Jun - June;
Jul - July;
Aug - August;
Sep - September;
Oct - October;
Nov - November;
Dec - December;

Maize soil requirement for land evaluation

Description

A dataset containing the soil characteristics of the crop requirements for farming Maize.

Format

A data frame with 15 rows and 8 columns

Details

The following are the factors for evaluation:

CFragm - Coarse fragment (Vol.%)
SoilDpt - Soil depth (cm)
CaCO3 - CaCO3 (%)
Gyps - Gypsum (%)
CECc - Apparent CEC Clay (cmol (+)/kg clay)
BS - Base Saturation (%)
SumBCs - Sum of basic caions (cmol (+)/kg soil)
pHH2O - pH H2O
OC6 - Organic carbon (%) - Kaolinitic materials
OC7 - Organic carbon (%) - Non Kaolinitic, Non calcareous materials
OC8 - Organic carbon (%) - Calcareous materials
ECedS - ECe (dS/m)
ESP - ESP (%)
OC - Organic carbon (%)
SoilTe - 12 classes of soil texture (Soil Taxonomy)

Maize temp requirement for land evaluation

Description

A dataset containing the temp characteristics of the crop requirements for farming Maize.

Format

A data frame with 2 rows and 8 columns

Details

The following are the factors for evaluation:

TgAv - Mean temperature of the growing cycle (°C)
TgMinAv - Mean min. temp. of growing cycle (°C)

Maize terrain requirement for land evaluation

Description

A dataset containing the terrain characteristics of the crop requirements for farming Maize.

Format

A data frame with 7 rows and 8 columns

Details

The following are the factors for evaluation:

Slope1 - Slope (%) (1. Irrigrated agriculture, basin furrow irrigation)
Slope2 - Slope (%) (2. High level of managemnet with full mechanization. )
Slope3 - Slope (%) (3. Low level of managemnet animal traction or handwork.)
Flood - Flooding
Drainage4 - Drainage (Medium and fine textured soils)
Drainage5 - Drainage (Coarse textured soils - Sandy families)
SlopeD - Slope (degree, 6 classes)

Maize water requirement for land evaluation

Description

A dataset containing the water characteristics of the crop requirements for farming Maize.

Format

A data frame with 9 rows and 8 columns

Details

The following are the factors for evaluation:

WgAv - Precipitation of growing cycle (mm)
WmAv1 - Mean precipitation of first month (mm)
WmAv2 - Mean precipitation of second month (mm)
WmAv3 - Mean precipitation of third month (mm)
WmAv4 - Mean precipitation of fourth month (mm)
WmhAv2 - Relative humidity of devel. Stage (%)
WmhAv3 - Relative humidity of maturation Stage (%)
WmnN2 - n/N develop. Stage (2nd month)
WmnN4 - n/N maturation stage (4th month)

Mango soil requirement for land evaluation

Description

A dataset containing the soil characteristics of the crop requirements for farming Mango.

Format

A data frame with 12 rows and 8 columns

Details

The following are the factors for evaluation:

CFragm - Coarse fragment (Vol.%)
SoilDpt - Soil depth (cm)
CaCO3 - CaCO3 (%)
Gyps - Gypsum (%)
CECc - Apparent CEC Clay (cmol (+)/kg clay)
BS - Base Saturation (%)
SumBCc - Sum of basic cations (cmol (+)/kg of clay)
pHH2O - pH H2O
OC - Organic carbon (%)
ECedS - ECe (dS/m)
ESP - ESP (%)
SoilTe - 12 classes of soil texture (Soil Taxonomy)

Mango temp requirement for land evaluation

Description

A dataset containing the temp characteristics of the crop requirements for farming Mango.

Format

A data frame with 2 rows and 8 columns

Details

The following are the factors for evaluation:

TyAv - Mean annual temperature (°C)
TyMinAv - Mean annual minimum temperature (°C)

Mango terrain requirement for land evaluation

Description

A dataset containing the terrain characteristics of the crop requirements for farming Mango.

Format

A data frame with 6 rows and 8 columns

Details

The following are the factors for evaluation:

Slope1 - Slope (%) (1) Basin furrow irrigation
Slope2 - Slope (%) (2) Mechanized, high management level
Slope3 - Slope (%) (3) Manual, low management level
Flood - Flooding
Drainage - Drainage
SlopeD - 6 classes of slope (Degree)

Mango water requirement for land evaluation

Description

A dataset containing the water characteristics of the crop requirements for farming Mango.

Format

A data frame with 5 rows and 8 columns

Details

The following are the factors for evaluation:

WyAv - Annual precipitation (mm)
WmDryLen - Length dry season (months : P < 1/2 PET)
WmAvDry - Monthly precipitation during dry season (mm)
WyhAv - Mean annual rel. humidity (%)
WmnN4 - n/N maturation stage (4th month)

Millets soil requirement for land evaluation

Description

A dataset containing the soil characteristics of the crop requirements for farming Millets.

Format

A data frame with 11 rows and 8 columns

Details

The following are the factors for evaluation:

CFragm - Coarse fragment (Vol.%)
SoilDpt - Soil depth (cm)
CaCO3 - CaCO3 (%)
Gyps - Gypsum (%)
CECc - Apparent CEC Clay (cmol (+)/kg clay)
BS - Base Saturation (%)
SumBCs - Sum of basic caions (cmol (+)/kg soil)
pHH2O - pH H2O
OC6 - Organic carbon (%) - Kaolinitic materials
ECedS - ECe (dS/m)
ESP - ESP (%)

Millets temp requirement for land evaluation

Description

A dataset containing the temp characteristics of the crop requirements for farming Millets.

Format

A data frame with 3 rows and 8 columns

Details

The following are the factors for evaluation:

TgAv - Mean temperature of the growing cycle (°C)
TgMaxAv - Mean max temp. of growing cycle (°C)
TgMinAv - Mean min. temp. of growing cycle (°C)

Millets terrain requirement for land evaluation

Description

A dataset containing the terrain characteristics of the crop requirements for farming Millets.

Format

A data frame with 6 rows and 8 columns

Details

The following are the factors for evaluation:

Slope1 - Slope (%) (1. Irrigrated agriculture, basin furrow irrigation)
Slope2 - Slope (%) (2. High level of managemnet with full mechanization. )
Slope3 - Slope (%) (3. Low level of managemnet animal traction or handwork.)
Flood - Flooding
Drainage4 - Drainage (Medium and fine textured soils)
Drainage5 - Drainage (Coarse textured soils - Sandy families)

Millets water requirement for land evaluation

Description

A dataset containing the water characteristics of the crop requirements for farming Millets.

Format

A data frame with 5 rows and 8 columns

Details

The following are the factors for evaluation:

WgAv - Precipitation of growing cycle (mm)
WmAv1 - Mean precipitation of first month (mm)
WmAv2 - Mean precipitation of second month (mm)
WmAv4 - Mean precipitation of fourth month (mm)
WmhAv4 - Relative humidity at harvest stage (%)

Land and Terrain Chracteristics of Marinduque, Philippines

Description

A dataset containing the land and terrain characteristics of the land units in Marinduque, Philippines.

Format

A data frame with 881 rows and 6 columns

Details

Lat - Latitude of Land Units;
Lon - Longitude of Land Units;
CECc - Apparent CEC Clay (cmol (+)/kg clay);
pHH20 - pH H2O;
CFragm - Coarse fragment (Vol.%);
SoilTe - 12 classes of soil texture (Soil Taxonomy);

Temperature Chracteristics of Marinduque, Philippines

Description

A dataset containing the temperature characteristics of the land units in Marinduque, Philippines.

Format

A data frame with 881 rows and 14 columns

Details

Lat - Latitude of Land Units;
Lon - Longitude of Land Units;
Jan - January;
Feb - February;
Mar - March;
Apr - April;
May - May;
Jun - June;
Jul - July;
Aug - August;
Sep - September;
Oct - October;
Nov - November;
Dec - December;

Water Chracteristics of Marinduque, Philippines

Description

A dataset containing the water characteristics of the land units in Marinduque, Philippines.

Format

A data frame with 881 rows and 14 columns

Details

Lat - Latitude of Land Units;
Lon - Longitude of Land Units;
Jan - January;
Feb - February;
Mar - March;
Apr - April;
May - May;
Jun - June;
Jul - July;
Aug - August;
Sep - September;
Oct - October;
Nov - November;
Dec - December;

Oil Palm soil requirement for land evaluation

Description

A dataset containing the soil characteristics of the crop requirements for farming Oil Palm.

Format

A data frame with 10 rows and 8 columns

Details

The following are the factors for evaluation:

CFragm2 - Coarse fragment in depth (Vol.%)
SoilDpt - Soil depth (cm)
CaCO3 - CaCO3 (%)
Gyps - Gypsum (%)
CECc - Apparent CEC Clay (cmol (+)/kg clay)
BS - Base Saturation (%)
SumBCs - Sum of basic caions (cmol (+)/kg soil)
pHH2O - pH H2O
OC - Organic carbon (%)
ECedS - ECe (dS/m)

Oil Palm temp requirement for land evaluation

Description

A dataset containing the temp characteristics of the crop requirements for farming Oil Palm.

Format

A data frame with 3 rows and 8 columns

Details

The following are the factors for evaluation:

TyMaxAv - Mean annual maximum temperature (°C)
TdMinXm - Mean daily minimum temperature of coldest month (°C)
TyAv - Mean annual temperature (°C)

Oil Palm terrain requirement for land evaluation

Description

A dataset containing the terrain characteristics of the crop requirements for farming Oil Palm.

Format

A data frame with 4 rows and 8 columns

Details

The following are the factors for evaluation:

Slope - Slope
Flood - Flooding
Drainage4 - Drainage (4) Medium & fine textured soils
Drainage5 - Drainage (5) Coarse textured soils

Oil Palm water requirement for land evaluation

Description

A dataset containing the water characteristics of the crop requirements for farming Oil Palm.

Format

A data frame with 3 rows and 8 columns

Details

The following are the factors for evaluation:

WyAv - Annual precipitation (mm)
WmDryLen - Length dry season (months : P < 1/2 PET)
WynN - Mean annual n/N

Olives soil requirement for land evaluation

Description

A dataset containing the soil characteristics of the crop requirements for farming Olives.

Format

A data frame with 10 rows and 8 columns

Details

The following are the factors for evaluation:

CFragm - Coarse fragment (Vol.%)
SoilDpt - Soil depth (cm)
Gyps - Gypsum (%)
CECc - Apparent CEC Clay (cmol (+)/kg clay)
BS - Base Saturation (%)
SumBCs - Sum of basic caions (cmol (+)/kg soil)
pHH2O - pH H2O
OC - Organic carbon (%)
ECedS - ECe (dS/m)
ESP - ESP (%)

Olives temp requirement for land evaluation

Description

A dataset containing the temp characteristics of the crop requirements for farming Olives.

Format

A data frame with 2 rows and 8 columns

Details

The following are the factors for evaluation:

TyAv - Mean annual temperature (°C)
TmMinXm - Avarage minimum temperature of coldest month ( C )

Olives terrain requirement for land evaluation

Description

A dataset containing the terrain characteristics of the crop requirements for farming Olives.

Format

A data frame with 6 rows and 8 columns

Details

The following are the factors for evaluation:

Slope1 - Slope (%) (1. Irrigrated agriculture, basin furrow irrigation)
Slope2 - Slope (%) (2. High level of managemnet with full mechanization. )
Slope3 - Slope (%) (3. Low level of managemnet animal traction or handwork.)
Flood - Flooding
Drainage - Drainage
SlopeD - Slope (degree, 6 classes)

Olives water requirement for land evaluation

Description

A dataset containing the water characteristics of the crop requirements for farming Olives.

Format

A data frame with 3 rows and 8 columns

Details

The following are the factors for evaluation:

WyAv - Annual precipitation (mm)
WmSpecial1 - Monthly rainfall during the sclerification of stone (mm) - August (N hem) February (S hem.)
WmSpecial2 - Monthly rainfall during the sclerification of stone (mm) - September (N hem) March (S hem.)

Onion soil requirement for land evaluation

Description

A dataset containing the soil characteristics of the crop requirements for farming Onion.

Format

A data frame with 11 rows and 8 columns

Details

The following are the factors for evaluation:

CFragm - Coarse fragment (Vol.%)
SoilDpt - Soil depth (cm)
CaCO3 - CaCO3 (%)
Gyps - Gypsum (%)
CECc - Apparent CEC Clay (cmol (+)/kg clay)
BS - Base Saturation (%)
SumBCs - Sum of basic caions (cmol (+)/kg soil)
pHH2O - pH H2O
OC - Organic carbon (%)
ECedS - ECe (dS/m)
ESP - ESP (%)

Onion temp requirement for land evaluation

Description

A dataset containing the temp characteristics of the crop requirements for farming Onion.

Format

A data frame with 2 rows and 8 columns

Details

The following are the factors for evaluation:

TgAv - Mean temperature of the growing cycle (°C)
TmAv0 - Mean temp. at germination (°C) (1st month)

Onion terrain requirement for land evaluation

Description

A dataset containing the terrain characteristics of the crop requirements for farming Onion.

Format

A data frame with 6 rows and 8 columns

Details

The following are the factors for evaluation:

Slope1 - Slope (%) (1. Irrigrated agriculture, basin furrow irrigation)
Slope2 - Slope (%) (2. High level of managemnet with full mechanization. )
Slope3 - Slope (%) (3. Low level of managemnet animal traction or handwork.)
Flood - Flooding
Drainage4 - Drainage (Medium and fine textured soils)
Drainage5 - Drainage (Coarse textured soils - Sandy families)

Onion water requirement for land evaluation

Description

A dataset containing the water characteristics of the crop requirements for farming Onion.

Format

A data frame with 2 rows and 8 columns

Details

The following are the factors for evaluation:

WgAv - Precipitation of growing cycle (mm)
TmAvDlen3 - Daylength (h) during yield form. Period

Papaya soil requirement for land evaluation

Description

A dataset containing the soil characteristics of the crop requirements for farming Papaya.

Format

A data frame with 9 rows and 8 columns

Details

The following are the factors for evaluation:

CFragm - Coarse fragment (Vol.%)
SoilDpt - Soil depth (cm)
CECc - Apparent CEC Clay (cmol (+)/kg clay)
BS - Base Saturation (%)
SumBCs - Sum of basic caions (cmol (+)/kg soil)
pHH2O - pH H2O
OC - Organic carbon (%)
ECedS - ECe (dS/m)
ESP - ESP (%)

Papaya temp requirement for land evaluation

Description

A dataset containing the temp characteristics of the crop requirements for farming Papaya.

Format

A data frame with 2 rows and 8 columns

Details

The following are the factors for evaluation:

TyAv - Mean annual temperature (°C)
TyMinAv - Mean annual minimum temperature (°C)

Papaya terrain requirement for land evaluation

Description

A dataset containing the terrain characteristics of the crop requirements for farming Papaya.

Format

A data frame with 0 rows and 8 columns

Details

WgAv - Precipitation of growing cycle (mm)
WmAv2 - Mean precipitation of second month (mm)
WmAv3 - Mean precipitation of third month (mm)
WmAv4 - Mean precipitation of fourth month (mm)

Overall Suitability Scores/Class of the Land Units

Description

This function computes the overall suitability scores and class of the land units.

Usage

overall_suit(suit, method = NULL, interval = NULL)

Arguments

suit

an object of class suitability.

method

a character for the method for computing the overall suitability, choices are: "minimum", "maximum", and "average". If NULL, method is set to "minimum".

interval

if NULL, the interval of the suitability class are the following: 0% - 25% (Not suitable, N), 25% - 50% (Marginally Suitable, S3), 50% - 75% (Moderately Suitable, S2), and 75% - 100% (Highly Suitable, S1). But users can assign custom intervals by specifying the values of the end points of the intervals. Say for intervals: 0% - 20% (Not suitable, N), 20% - 50% (Marginally Suitable, S3), 50% - 80% (Moderately Suitable, S2), and 80% - 100% (Highly Suitable, S1), is equivalent to interval = c(0, 0.2, 0.5, 0.8, 1).

Value

A data frame with columns:

Score - the overall suitability scores
Class - the overall suitability classes

Examples

library(ALUES)
out <- suit("ricebr", terrain=MarinduqueLT, water=MarinduqueWater, temp=MarinduqueTemp, sow_month=1)
out[["terrain"]]

# Soil Overall Suitability
head(overall_suit(out[["soil"]]))
head(overall_suit(out[["soil"]], "average"))
head(overall_suit(out[["soil"]], "maximum"))
head(overall_suit(out[["soil"]], "average", c(0, 0.3, 0.35, 0.6, 1.0)))

# Water Overall Suitability
head(overall_suit(out[["water"]], "average"))
head(overall_suit(out[["water"]], "maximum"))
head(overall_suit(out[["water"]], "average", c(0, 0.3, 0.35, 0.6, 1.0)))

# Temperature Overall Suitability
head(overall_suit(out[["temp"]], "average"))
head(overall_suit(out[["temp"]], "maximum"))
head(overall_suit(out[["temp"]], "average", c(0, 0.3, 0.35, 0.6, 1.0)))

Suitability Scores/Class of the Land Units

Description

This function calculates the suitability scores and class of the land units.

Usage

suit(
  crop,
  terrain = NULL,
  water = NULL,
  temp = NULL,
  mf = "triangular",
  sow_month = NULL,
  minimum = NULL,
  maximum = "average",
  interval = NULL,
  sigma = NULL
)

Arguments

crop

a string for the name of the crop;

terrain

a data frame for the terrain characteristics of the input land units;

water

a data frame for the water characteristics of the input land units;

temp

a data frame for the temperature characteristics of the input land units;

mf

membership function with default assigned to "triangular" fuzzy model. Other fuzzy models included are "trapezoidal" and "gaussian".

sow_month

sowing month of the crop. Takes integers from 1 to 12 (inclusive), representing the twelve months of the year. So if sets to 1, the function assumes sowing month to be January.

minimum

factor's minimum value. If NULL (default), minimum is set to 0. But if numeric of length one, say 0.5, then minimum is set to 0.5, for all factors. To set multiple minimums for multiple factors, simply concatenate these into a numeric vector, the length of this vector should be equal to the number of factors in input land units parameters. However, it can also be set to "average", please refer to the online documentation for more, link in the "See Also" section below.

maximum

maximum value for factors. To set multiple maximums for multiple factors, simply concatenate these into a numeric vector, the length of this vector should be equal to the number of factors in input land units parameters. However, it can also be set to "average", please refer to the online documentation for more, link in the "See Also" section below.

interval

domains for every suitability class (S1, S2, S3). If fixed (NULL), the interval would be 0 to 25% for N (Not Suitable), 25% to 50% for S3 (Marginally Suitable), 50% to 75% for S2 (Moderately Suitable), and 75% to 100% for (Highly Suitable). If "unbias", the package will take into account the shape of the membership function, and provide the appropriate suitability class intervals. However, it can also be customized by specifying the limits of the suitability classes. Please refer to the online documentation for more, link in the "See Also" section below.

sigma

If mf = "gaussian", then sigma represents the constant sigma in the Gaussian formula.

Value

A list of outputs of target characteristics, with the following components:

"terrain" - a list of outputs for terrain characteristics
"soil" - a list of outputs for soil characteristics
"water" - a list of outputs for water characteristics
"temp" - a list of outputs for temperature characteristics

These components are only available when specified as the target characteristics in either of the arguments above, that is, if terrain argument is specified above, then the "terrain" and "soil" components will be available in the output list. This is also true if water and temp are specified in the arguments above.

Each of the components returned above contains a list of outputs as well with the following components:

"Factors Evaluated" - a character of factors that matched between the input land units factor and the targetted crop requirement factor
"Suitability Score" - a data frame of suitability scores for each of the matched factors
"Suitability Class" - a data frame of suitability classes for each of the matched factors
"Factors' Minimum Values" - a numeric of minimum values used in the membership function for computing the suitability scores
"Factors' Minimum Values" - a numeric of maximum values used in the membership function for computing the suitability scores
"Factors' Weights" - a numeric of weights of the factors specified in the input crop requirements
"Crop Evaluated" - a character of the name of the targetted crop requirement dataset

Examples

library(ALUES)

rice_suit <- suit("ricebr", water=MarinduqueWater, temp=MarinduqueTemp, sow_month = 1)
lapply(rice_suit[["water"]], function(x) head(x)) # access results for water suitability
lapply(rice_suit[["temp"]], function(x) head(x)) # access results for temperature suitability
rice_suit <- suit("ricebr", terrain=MarinduqueLT)
lapply(rice_suit[["terrain"]], function(x) head(x))
lapply(rice_suit[["soil"]], function(x) head(x))

Suitability Scores/Class of the Land Units

Description

This function calculates the suitability scores and class of the land units.

Usage

suitability(
  x,
  y,
  mf = "triangular",
  sow_month = NULL,
  minimum = NULL,
  maximum = "average",
  interval = NULL,
  sigma = NULL
)

Arguments

x

a data frame consisting the properties of the land units;

y

a data frame consisting the requirements of a given characteristics (terrain, soil, water and temperature) for a given crop (e.g. coconut, cassava, etc.);

mf

membership function with default assigned to "triangular" fuzzy model. Other fuzzy models included are "trapezoidal" and "gaussian".

sow_month

sowing month of the crop. Takes integers from 1 to 12 (inclusive), representing the twelve months of the year. So if sets to 1, the function assumes sowing month to be January.

minimum

maximum

interval

sigma

If mf = "gaussian", then sigma represents the constant sigma in the Gaussian formula.

Value

A list with the following components:

"Factors Evaluated" - a character of factors that matched between the input land units factor and the targetted crop requirement factor
"Suitability Score" - a data frame of suitability scores for each of the matched factors
"Suitability Class" - a data frame of suitability classes for each of the matched factors
"Factors' Minimum Values" - a numeric of minimum values used in the membership function for computing the suitability scores
"Factors' Minimum Values" - a numeric of maximum values used in the membership function for computing the suitability scores
"Factors' Weights" - a numeric of weights of the factors specified in the input crop requirements
"Crop Evaluated" - a character of the name of the targetted crop requirement dataset

#' @seealso https://alstat.github.io/ALUES/