ALUES comes with two datasets for input land units. These are the land units of Marinduque, Philippines; and, Lao Cai, Vietnam. However, users will likely have their own region of interest. As such, this article will illustrate how to prepare the data.

For any region of interest, users must specify the properties of the land units into three categories:

- terrain and soil
- water
- temperature

Note that both terrain and soil factors must be specified as one dataframe, not separate. Suppose there are 5 units under study, the three characteristics can be specified as follows:

## Terrain and soil

Suppose for terrain, we want to target the following factors: Flood and SlopeD; and, suppose for soil we have CFragm and SoilDpt. The dataframe can be prepared as follows:

    terrain_input <- data.frame(
      Flood = c(1, 2, 2, 2, 3),
      SlopeD = c(3, 4, 5, 1, 2),
      CFragm = c(10, 30, 50, 60, 40),
      SoilDpt = c(45, 60, 90, 70, 30)
    )

Note: the column names must be the same with the naming convention used by the crop requirements datasets.

So that, if this input is assessed for avocado, then the suitability score for these land units are computed as follows:

    library(ALUES)
    AVOCADOTerrain

    ##       code s3_a s2_a s1_a s1_b s2_b s3_b  wts
    ## 1   Slope1  6.0  4.0    2 <NA> <NA> <NA> <NA>
    ## 2   Slope2 16.0  8.0    4 <NA> <NA> <NA> <NA>
    ## 3   Slope3 30.0 16.0    8 <NA> <NA> <NA> <NA>
    ## 4    Flood  1.5  1.2    1 <NA> <NA> <NA> <NA>
    ## 5 Drainage  3.0  2.0    1 <NA> <NA> <NA> <NA>

    AVOCADOSoil

    ##       code s3_a s2_a  s1_a s1_b s2_b s3_b  wts
    ## 1   CFragm 55.0 33.0  15.0 <NA> <NA> <NA> <NA>
    ## 2  SoilDpt 50.0 75.0 100.0 <NA> <NA> <NA> <NA>
    ## 3    CaCO3 10.0  4.0   1.0 <NA> <NA> <NA> <NA>
    ## 4     CECc 16.0 16.0  16.0 <NA> <NA> <NA> <NA>
    ## 5       BS 19.9 20.0  35.0 <NA> <NA> <NA> <NA>
    ## 6   SumBCs  1.5  1.6   2.8 <NA> <NA> <NA> <NA>
    ## 7    pHH2O  4.3  4.6   5.0  6.5  7.5  8.3 <NA>
    ## 8       OC  0.7  0.8   1.2 <NA> <NA> <NA> <NA>
    ## 9    ECedS  5.0  4.0   3.0 <NA> <NA> <NA> <NA>
    ## 10     ESP 20.0 15.0  10.0 <NA> <NA> <NA> <NA>

    avocado_suit <- suit("avocado", terrain=terrain_input)
    head(avocado_suit[["terrain"]][["Suitability Score"]])

    ##       Flood
    ## 1 0.4285714
    ## 2 0.0000000
    ## 3 0.0000000
    ## 4 0.0000000
    ## 5 0.0000000

    head(avocado_suit[["terrain"]][["Suitability Class"]])

    ##   Flood
    ## 1    S3
    ## 2     N
    ## 3     N
    ## 4     N
    ## 5     N

    head(avocado_suit[["soil"]][["Suitability Score"]])

    ##      CFragm SoilDpt
    ## 1 0.8666667    0.36
    ## 2 0.6000000    0.48
    ## 3 0.3333333    0.72
    ## 4 0.2000000    0.56
    ## 5 0.4666667    0.24

    head(avocado_suit[["soil"]][["Suitability Class"]])

    ##   CFragm SoilDpt
    ## 1     S1      S3
    ## 2     S2      S3
    ## 3     S3      S2
    ## 4      N      S2
    ## 5     S3       N

## Water

For water characteristics, suppose the average rainfall for 3 land units were recorded for four months with the following data:

    water_input <- data.frame(
      Apr = c(150, 140, 120),
      May = c(70, 90, 100),
      Jun = c(85, 90, 105)
    )
    water_input

    ##   Apr May Jun
    ## 1 150  70  85
    ## 2 140  90  90
    ## 3 120 100 105

Note that when specifying the factors for water chacteristics, the month must be specified in three characters (correct case) only, that is, it shouldn't be specified as January, February, etc.

The suitability scores for rainfed bunded rice water requirement are computed as follows:

    RICEBRWater

    ##     code  s3_a  s2_a  s1_a s1_b s2_b s3_b  wts
    ## 1  WmAv1 100.0 125.0 175.0  500  650  750 <NA>
    ## 2  WmAv2 100.0 125.0 175.0  500  650  750 <NA>
    ## 3  WmAv3 100.0 125.0 175.0  500  650  750 <NA>
    ## 4  WmAv4  29.0  30.0  50.0  300  500  600 <NA>
    ## 5 WmhAv2  30.0  40.0  50.0   90 <NA> <NA> <NA>
    ## 6 WmhAv4  29.9  30.0  33.0   80 <NA> <NA> <NA>
    ## 7   WynN  0.44  0.45  0.65 <NA> <NA> <NA> <NA>

    water_suit <- suit("ricebr", water=water_input, sow_month=1)
    water_suit

    ## $water
    ## $`Factors Evaluated`
    ## [1] "Apr"
    ##
    ## $`Suitability Score`
    ##         Apr
    ## 1 0.8571429
    ## 2 0.8000000
    ## 3 0.6857143
    ##
    ## $`Suitability Class`
    ##   Apr
    ## 1  S1
    ## 2  S1
    ## 3  S2
    ##
    ## $`Factors' Minimum Values`
    ## Apr
    ##   0
    ##
    ## $`Factors' Maximum Values`
    ##   Apr
    ## 714.2
    ##
    ## $`Factors' Weights`
    ## [1] NA
    ##
    ## $`Crop Evaluated`
    ## [1] "RICEBRWater"
    ##
    ## attr(,"class")
    ## [1] "suitability"

Setting the `sow_month=1` indicates that the factors for `RICEBRWater`'s `WmAv1` correspond to January, `WmAv2` to February, `WmAv3` to March, and `WmAv4` to April. Thus, the only factors that were targetted by the `water_input` is April. So that, setting the `sow_month=3`, would make `WmAv1` of `RICEBRWater` as March, `WmAv2` as April, etc. This in turn targets the months April to Jun.

    water_suit <- suit("ricebr", water=water_input, sow_month=3)
    water_suit

    ## $water
    ## $`Factors Evaluated`
    ## [1] "Apr" "May" "Jun"
    ##
    ## $`Suitability Score`
    ##         Apr       May       Jun
    ## 1 0.4444444 0.2074074 0.4857143
    ## 2 0.4148148 0.2666667 0.5142857
    ## 3 0.3555556 0.2962963 0.6000000
    ##
    ## $`Suitability Class`
    ##   Apr May Jun
    ## 1  S3   N  S3
    ## 2  S3  S3  S2
    ## 3  S3  S3  S2
    ##
    ## $`Factors' Minimum Values`
    ## Apr May Jun
    ##   0   0   0
    ##
    ## $`Factors' Maximum Values`
    ##   Apr   May   Jun
    ## 880.0 880.0 714.2
    ##
    ## $`Factors' Weights`
    ## [1] NA NA NA
    ##
    ## $`Crop Evaluated`
    ## [1] "RICEBRWater"
    ##
    ## attr(,"class")
    ## [1] "suitability"

## Temperature

Another characteristics that can be targetted is the temperature.

    temp_input <- data.frame(
      Sep = c(34.2, 35.5, 33.4),
      Oct = c(32.5, 34.2, 32.0),
      Nov = c(30.3, 32.2, 31.1)
    )
    RICEBRTemp

    ##       code s3_a s2_a s1_a s1_b s2_b s3_b  wts
    ## 1     TgAv   10   18   24   36 36.1 36.1    1
    ## 2  TmMaxXm   21   26   30   40 45.0 50.0 <NA>
    ## 3    TmAv2   10   18   24   36 42.0 45.0    2
    ## 4 TmMinAv4    7   10   14   25 28.0 30.0 <NA>

    temp_suit <- suit("ricebr", temp=temp_input, sow_month=9)

The `RICEBRTemp` crop requirement has factor `TmAv2`, which is the mean temperature for the 2nd month. Thus, setting the sowing month to 9 suggest that the sowing month started at September, and thus sets `TmAv2` to October. Hence, the factor that was targetted by the input land units is the October as seen below:

    temp_suit

    ## $temp
    ## $`Factors Evaluated`
    ## [1] "Oct"
    ##
    ## $`Suitability Score`
    ##         Oct
    ## 1 0.8863636
    ## 2 0.8090909
    ## 3 0.9090909
    ##
    ## $`Suitability Class`
    ##   Oct
    ## 1  S1
    ## 2  S1
    ## 3  S1
    ##
    ## $`Factors' Minimum Values`
    ## Oct
    ##   0
    ##
    ## $`Factors' Maximum Values`
    ## Oct
    ##  52
    ##
    ## $`Factors' Weights`
    ## [1] 2
    ##
    ## $`Crop Evaluated`
    ## [1] "RICEBRTemp"
    ##
    ## attr(,"class")
    ## [1] "suitability"