In alstat/ALUES: Agricultural Land Use Evaluation System

knitr::opts_chunk$set(echo = TRUE)

Introduction

The suit function is used for computing the suitability score and class of the land units for a given target crop. The function has the following input arguments:

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

Check the R documentation for details of the arguments. This article will focus on how to use the said function. To evaluate the suitability score of Marinduque land units for terrain, soil, water and temperature characteristics, simply run the suit function for each of these characteristics. That is,

library(ALUES)
banana_suit <- suit("banana", terrain=MarinduqueLT)
names(banana_suit)

The warning above simply tells the user that one of the factor, CECc, in the target crop requirement, has parameter intervals for all suitability classes equal to 16, and the package used this value as the maximum constant for computing the suitability scores. For more, please refer to the Article 2: Methodology used in ALUES of the documentation.

The suit function returns a list of output of target characteristics, in this case "terrain" and "soil". To access the output, simply run the following:

banana_suit[["terrain"]]
banana_suit[["soil"]]

Each of these are lists, with the following names:

names(banana_suit[["soil"]])

So that, to access the factors evaluated, simply run the following:

banana_suit[["soil"]][["Factors Evaluated"]]

Targetting Crop

There are 56 crops available in ALUES, and what we've illustrated above is for banana only. Other crops are listed below:

d <- utils::data(package = "ALUES")
alues_data <- d$results[, "Item"]
crop_data <- regmatches(alues_data, gregexpr(paste0("^[A-Z]{2,}", collapse = "|"), alues_data))
crop_data <- unique(unlist(lapply(crop_data, function(x) substr(x, 1, nchar(x)-1))))
crop_data

These are the names for the input string for the suit function. For example, to target sweet potato the input string is not "sweet potato" but rather potatosw. That is,

potato_suit1 <- suit("sweet potato", terrain=MarinduqueLT)
potato_suit2 <- suit("potatosw", terrain=MarinduqueLT)

Targetting Crop Factors

The idea of evaluating a land unit is to match the quality of the land against the standard value of the target factor. Therefore, if the crop does not include the factor you are targeting, then there won't be any matching to be done. For example, the land units evaluated above are those in Marindque, which has the following soil and terrain characteristics:

head(MarinduqueLT)

The crop that we are trying to target is banana. The suit function simply require the user to input a string name for the target crop, and the function will look for the corresponding crop datasets. For example, for banana these are the crop requirements datasets for the four characteristics:

BANANATerrain
BANANASoil
BANANAWater
BANANATemp

These datasets are used by the suit function depending on the targetted characteristics of the input land units (specified by the user) on the said function. So for banana_suit object above, the target crop datasets were BANANATerrain and BANANASoil since the input land unit specified is terrain=MarinduqueLT. Further, the input land unit only targetted the soil factors and not the terrain factors, since none of the factors in MarinduqueLT matched with the factors in BANANATerrain. That is why, accessing the output for the terrain characteristics for the banana_suit object will return the following:

banana_suit[["terrain"]]

Targetting Multiple Characteristics

The example above only targetted the terrain and soil characteristics, but the suit function allows user to also target water and temp simultaneously. For examples, we can evaluate the land units of Lao Cai, Vietnam for all three characteristics as follows for irrigated rice (`riceiw``):

riceiw_multi <- suit("riceiw", terrain=LaoCaiLT, water=LaoCaiWater, temp=LaoCaiTemp, sow_month=10)
names(riceiw_multi)

It is necessary to specify the sowing month when specifying the water and temperature characteristics of the input land units. In this case, we are saying that the first sowing month for both water and temperature characteristics correspond to October (See Article 6 for more on this). No factors were targetted by input land unit for banana for terrain, water and temperature characteristics.

lapply(riceiw_multi[["terrain"]], function(x) head(x))
lapply(riceiw_multi[["soil"]], function(x) head(x))
lapply(riceiw_multi[["water"]], function(x) head(x))
lapply(riceiw_multi[["temp"]], function(x) head(x))

Only the head (first six) of the output of the items are shown.

Membership Function

There are three membership functions (MFs) available in the suit function, namely triangular, trapezoidal and Gaussian. For example, the following computes the suitability scores and classes using trapezoidal MF.

banana_suit <- suit("banana", terrain=MarinduqueLT, mf="trapezoidal")
head(banana_suit[["soil"]][["Suitability Score"]])
head(banana_suit[["soil"]][["Suitability Class"]])

Intervals

Another option available in the suit function is the interval. By default, ALUES uses an equally spaced suitability class intervals for deriving the suitability class. That is, for N [0, 0.25), S3 [0.25, 0.50), S2 [0.50, 0.75), and S1 [0.75, 1].

Custom Intervals

Users can modify the default equally spaced intervals, for example:

banana_suit <- suit("banana", terrain=MarinduqueLT, mf="trapezoidal", interval=c(0, 0.3, 0.6, 0.9, 1))
head(banana_suit[["soil"]][["Suitability Score"]])
head(banana_suit[["soil"]][["Suitability Class"]])

The above code sets the new suitability class intervals into: N [0, 0.3), S3 [0.3, 0.6), S2 [0.6, 0.9), and S1 [0.9, 1].

Unbias Intervals

The problem with the fixed interval is that the said intervals does not take into account the shape of the membership function and the spacing of the parameter interval limits (See Article 2 for parameter intervals). Custom intervals might be able to capture this if the user computed the interval limits manually, but ALUES provides an option just for this, by setting interval="unbias". That is,

banana_suit <- suit("banana", terrain=MarinduqueLT, mf="trapezoidal", interval="unbias")
head(banana_suit[["soil"]][["Suitability Score"]])
head(banana_suit[["soil"]][["Suitability Class"]])

By setting the interval="unbias", the suit function will generate a different likely unequally spaced suitability class intervals, but the interval limits are mathematically correct, in terms of the mapping of the parameter interval limits to suitability class limits via the membership function.

Maximum and Minimum

Another parameter that can be set for suit are the minimum and maximum. These are the constants used by the membership function for computing the suitability score.

banana_suit <- suit("banana", terrain=MarinduqueLT, mf="trapezoidal", interval="unbias")
banana_suit[["soil"]][["Factors Evaluated"]]

From the above result, there are four factors targetted by the input land unit, these are CFragm, CECc, pHH2O and SoilTe. Suppose we know the maximum value that these factors can take, say 60 for CFragm, 20 for CECc, 9 for pHH2O, and 10 for SoilTe. We can specify these as follows:

banana_suit <- suit("banana", terrain=MarinduqueLT, mf="trapezoidal", interval="unbias", maximum=c(60, 20, 9, 10))
banana_suit

The result gave us an error. We understand the error for terrain characteristics, but for soil it says that the argument maximum must be equal in length with the target factors specified in the input land unit datasets. We know that there should be 4 factors, but upon checking, we see that the MarinduqueLT also have Lon and Lat columns, which ALUES assumes to be a target factor as well. Indeed, we need to exclude these columns (those that are not the target factors, rather spatial variables) when specifying minimum or maximum constants. Thus, it should be:

MarinduqueLT2 <- MarinduqueLT[, 3:ncol(MarinduqueLT)]
banana_suit <- suit("banana", terrain=MarinduqueLT2, mf="trapezoidal", interval="unbias", maximum=c(60, 20, 9, 10))
head(banana_suit[["soil"]][["Suitability Score"]])
head(banana_suit[["soil"]][["Suitability Class"]])