There are two categories of datasets available in ALUES, land units and crop requirements.

## Input Land Units

The land units datasets are input land units with properties meant for evaluation for crop production. Two regions are available for these datasets: Marinduque, Philippines; and, Lao Cai, Vietnam. Further, these datasets are encoded into three characteristics: land or soil and terrain (LT), water and temp (for temperature). So that, for Marinduque, the following are the datasets:

- `MarinduqueLT`
- `MarinduqueWater`
- `MarinduqueTemp`

And for Lao Cai, Vietnam, the following are the datasets:

- `LaoCaiLT`
- `LaoCaiWater`
- `LaoCaiTemp`

Sample head of the datasets are as follows:

    library(ALUES)
    head(MarinduqueLT)

    ##        Lat      Lon CECc pHH2O CFragm SoilTe
    ## 1 121.8792 13.52084   12    53     11     12
    ## 2 121.8875 13.52084   12    52      9     12
    ## 3 121.8958 13.52084   12    53     10     12
    ## 4 121.9375 13.52084   12    52     10     12
    ## 5 121.9458 13.52084   12    54     12     12
    ## 6 121.9542 13.52084   13    54     11     12

    head(LaoCaiLT)

    ##   SlopeD CFragm SoilDpt SoilTe CECc SumBCs pHH2O    BS   OC Flood
    ## 1      1      0      45      9 18.6   3.30   5.4 50.00 1.40     3
    ## 2      1      0      45      9 18.6   3.30   5.4 50.00 1.40     3
    ## 3      1      0      45      9 18.6   3.30   5.4 50.00 1.40     3
    ## 4      1      0      45      9 18.6   3.30   5.4 50.00 1.40     3
    ## 5      1      0      45      9 18.6   3.30   5.4 50.00 1.40     3
    ## 6      1      5      65      3 19.8   3.13   5.3 34.72 1.21     2

The columns of the datasets correspond to the factors or parameters measured from the land units. These parameters are used to compare to the standard values required for the target crop. The score of the comparison is referred to as the suitability score.

## Crop Requirements

There are 56 crops available in ALUES, each of which encodes standard properties of the target crop. These crop datasets are further categorized into four characteristics: terrain, soil, water and temp. So that, for avocado, the datasets are encoded as `AVOCADOTerrain`, `AVOCADOSoil`, `AVOCADOWater` and `AVOCADOTemp`. The list of crop datasets can be extracted as follows:

    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

    ##  [1] "ALFALFA"    "AVOCADO"    "BAMBOO"     "BANANA"     "BARLEY"
    ##  [6] "BEANCA"     "BEANS"      "CABBAGE"    "CARROTS"    "CASHEW"
    ## [11] "CASSAVA"    "CHICKPEA"   "CINNAMON"   "CITRUS"     "COCOA"
    ## [16] "COCONUT"    "COFFEEAR"   "COFFEERO"   "COTTON"     "COWPEA"
    ## [21] "CUCUMBER"   "GROUNDNUTS" "GUAVA"      "LONGAN"     "MAIZE"
    ## [26] "MANGO"      "MILLETS"    "OILPALM"    "OLIVES"     "ONION"
    ## [31] "PAPAYA"     "PEACH"      "PEAR"       "PEA"        "PEPPERGR"
    ## [36] "PERSIMMON"  "PINEAPPLE"  "PLUM"       "POTATOSW"   "POTATO"
    ## [41] "RICEBR"     "RICEIW"     "RICENF"     "RICEUR"     "RUBBER"
    ## [46] "SAFFLOWER"  "SESAME"     "SORGHUM"    "SOYA"       "SUGARCANE"
    ## [51] "SUNFLOWER"  "TEA"        "TOBACCO"    "TOMATO"     "WATERMELON"
    ## [56] "WHEAT"

Sample crop requirement datasets are given below:

    GUAVASoil

    ##      code s3_a s2_a  s1_a s1_b s2_b s3_b  wts
    ## 1  CFragm 55.0 35.0  15.0 <NA> <NA> <NA> <NA>
    ## 2 SoilDpt 30.0 50.0 100.0 <NA> <NA> <NA> <NA>
    ## 3    CECc 16.0 16.0  16.0 <NA> <NA> <NA> <NA>
    ## 4      BS 19.9 20.0  35.0 <NA> <NA> <NA> <NA>
    ## 5  SumBCs  1.5  1.6   2.8 <NA> <NA> <NA> <NA>
    ## 6   pHH2O  4.5  5.2   5.5  7.8    8  8.2 <NA>
    ## 7      OC  0.7  0.8   1.2 <NA> <NA> <NA> <NA>
    ## 8   ECedS  4.0  3.0   2.0 <NA> <NA> <NA> <NA>
    ## 9     ESP 25.0 20.0  15.0 <NA> <NA> <NA> <NA>

    GUAVATemp

    ##   code s3_a s2_a s1_a s1_b s2_b s3_b  wts
    ## 1 TyAv   10   15   20   28   34 34.1 <NA>

    CINNAMONTerrain

    ##     code s3_a s2_a s1_a s1_b s2_b s3_b wts
    ## 1 SlopeD  6.0  4.0    2 <NA> <NA> <NA>   2
    ## 2  Flood  1.5  1.2    1 <NA> <NA> <NA>   1

    CINNAMONWater

    ##    code s3_a s2_a s1_a s1_b s2_b s3_b  wts
    ## 1  WgAv <NA> <NA> 1000 2000 <NA> <NA> <NA>
    ## 2 WghAv <NA> <NA>   85   90 <NA> <NA> <NA>

Each datasets are well documented, so make sure to check it for details and descriptions of the parameters used.