In alstat/ALUES: Agricultural Land Use Evaluation System

knitr::opts_chunk$set(echo = TRUE)

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. This article will illustrate how to prepare such 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 land 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
AVOCADOSoil
avocado_suit <- suit("avocado", terrain=terrain_input)
head(avocado_suit[["terrain"]][["Suitability Score"]])
head(avocado_suit[["terrain"]][["Suitability Class"]])
head(avocado_suit[["soil"]][["Suitability Score"]])
head(avocado_suit[["soil"]][["Suitability Class"]])

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

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

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

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
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: