Barley grain yield

Description

A dataset containing Barley grain yield using calibrated commercial yield monitors mounted on combines equipped with DGPS.

Usage

barley

Format

A data frame with 7395 rows and 3 variables:

X

X coordinate, in meters

Y

Y coordinate, in meters

Yield

grain yield, in ton per hectare

Details

Coordinate reference system is "WGS 84 / UTM zone 20S", epsg:32720

Bind outlier condition to an object.

Description

Bind outlier condition to an object.

Usage

## S3 method for class 'paar'
cbind(..., deparse.level = 1)

Arguments

Value

cbind called with m.

Compare spatial zone means

Description

Compare spatial zone means

Usage

compare_zone(
  data,
  variable,
  zonesCol,
  alpha = 0.05,
  join = sf::st_nearest_feature,
  returnLSD = FALSE,
  grid_dim
)

Arguments

Value

list with differences and descriptive_stat

References

Paccioretti, P., Córdoba, M., & Balzarini, M. (2020). FastMapping: Software to create field maps and identify management zones in precision agriculture. Computers and Electronics in Agriculture, 175, 105556 https://doi.org/10.1016/j.compag.2020.105556.

Examples

library(sf)
data(wheat, package = "paar")

##Convert to an sf object
wheat <- sf::st_as_sf(wheat,
                      coords = c("x", "y"),
                      crs = 32720)
clusters <- paar::kmspc(
  wheat,
  variables = c('CE30', 'CE90', 'Elev', 'Pe', 'Tg'),
  number_cluster = 3:4
)
data_clusters <- cbind(wheat, clusters$cluster)
compare_zone(data_clusters,
             "Elev",
             "Cluster_3")

Remove errors from spatial data

Description

Data can be filtered by null, edge values, global outliers and spatial outliers or local defective observations. Default values are optimized for precision agricultural data.

Usage

depurate(
  x,
  y,
  toremove = c("edges", "outlier", "inlier"),
  crs = NULL,
  buffer = -10,
  ylimitmax = NA,
  ylimitmin = 0,
  sdout = 3,
  ldist = 0,
  udist = 40,
  criteria = c("LM", "MP"),
  zero.policy = NULL,
  poly_border = NULL
)

Arguments

Details

Possible values for toremove are one or more elements of:

edges

All data points for a distance of buffer m from data edges are deleted.

outlier

Values that are outside the mean±sdout are removed

inlier

Local Moran index of spatial autocorrelation is calculated for each datum as a tool to identify inliers

Value

an object of class paar with two elements:

depurated_data

sf object with the data after the removal process

condition

character vector with the condition of each observation

References

Vega, A., Córdoba, M., Castro-Franco, M. et al. Protocol for automating error removal from yield maps. Precision Agric 20, 1030–1044 (2019). https://doi.org/10.1007/s11119-018-09632-8

Examples

library(sf)
data(barley, package = 'paar')
#Convert to an sf object
barley <- st_as_sf(barley,
                   coords = c("X", "Y"),
                   crs = 32720)
depurated <-
  depurate(barley,
           "Yield")

# Summary of depurated data
summary(depurated)

# Keep only depurate data
depurated_data <- depurated$depurated_data
# Combine the condition for all data
all_data_condition <- cbind(depurated, barley)

Fuzzy k-means clustering

Description

Performs a vectorized fuzzy k-means clustering, this procedure it is not spatial. The function is almost a wrapper of the function cmeans from the package e1071. Is intended to be used when 'KM-sPC' procedure is not possible because data set has only 1 variable.

Usage

fuzzy_k_means(
  data,
  variables,
  number_cluster = 3:5,
  fuzzyness = 1.2,
  distance = "euclidean"
)

Arguments

Value

a list with classification results and indices to select best number of clusters.

Examples

library(sf)
data(wheat, package = 'paar')

# Transform the data.frame into a sf object
wheat_sf <- st_as_sf(wheat,
                     coords = c('x', 'y'),
                     crs = 32720)

# Run the fuzzy_k_means function
fuzzy_k_means_results <- fuzzy_k_means(wheat_sf,
                               variables = 'Tg',
                               number_cluster = 2:4)

# Print the summaryResults
fuzzy_k_means_results$summaryResults

# Print the indices
fuzzy_k_means_results$indices

# Print the cluster
head(fuzzy_k_means_results$cluster, 5)

# Combine the results in a single object
wheat_clustered <- cbind(wheat_sf, fuzzy_k_means_results$cluster)

# Plot the results
plot(wheat_clustered[, "Cluster_2"])

MULTISPATI-PCA clustering

Description

MULTISPATI-PCA clustering

Usage

kmspc(
  data,
  variables,
  number_cluster = 3:5,
  explainedVariance = 70,
  ldist = 0,
  udist = 40,
  center = TRUE,
  fuzzyness = 1.2,
  distance = "euclidean",
  zero.policy = FALSE,
  only_spca_results = TRUE,
  all_results = FALSE
)

Arguments

Value

a list with classification results and indices to select best number of clusters.

Examples

library(sf)
data(wheat, package = 'paar')

# Transform the data.frame into a sf object
wheat_sf <- st_as_sf(wheat,
                     coords = c('x', 'y'),
                     crs = 32720)

# Run the kmspc function
kmspc_results <- kmspc(wheat_sf,
                       number_cluster = 2:4)

# Print the summaryResults
kmspc_results$summaryResults

# Print the indices
kmspc_results$indices

# Print the cluster
head(kmspc_results$cluster, 5)

# Combine the results in a single object
wheat_clustered <- cbind(wheat_sf, kmspc_results$cluster)

# Plot the results
plot(wheat_clustered[, "Cluster_2"])

Print paar objects

Description

Print paar objects

Usage

## S3 method for class 'paar'
print(x, n = 3, ...)

Arguments

Value

invisible object x

Print summarized paar object

Description

Print summarized paar object

Usage

## S3 method for class 'summary.paar'
print(x, digits, ...)

Arguments

Value

A data.frame with the summarized condition of the object.

Modified t test

Description

Performs a modified version of the t test to assess the correlation between spatial processes. See SpatialPack::modified.ttest for details.

Usage

spatial_t_test(data, variables)

Arguments

Value

a data.frame with the correlation and p-value for each pair of variables

Summarizing paar objects

Description

Summarizing paar objects

Usage

## S3 method for class 'paar'
summary(object, ...)

Arguments

Value

An object of class summary.paar (data.frame) with the following columns:

• condition a character vector with the final condition.

• n a numeric vector with the number of rows for each condition.

• percentage a numeric vector with the percentage of rows for each condition.

Database from a production field under continuous agriculture

Description

A database from a wheat (Triticum aestivum L.) production field (60 ha) under continuous agriculture, located in south-eastern Pampas, Argentina.

Usage

wheat

Format

A data frame with 5982 rows and 7 variables:

x

X coordinate, in meters

y

Y coordinate, in meters

CE30

apparent electrical conductivity taken at 0–30 cm

CE90

apparent electrical conductivity taken at 0–90 cm

Elev

elevation, in meters

Pe

soil depth, in centimeters

Tg

wheat grain yield

Details

Coordinate reference system is "WGS 84 / UTM zone 20S", epsg:32720 Wheat grain yield was recorded in 2009 using calibrated commercial yield monitors mounted on combines equipped with DGPS. Soil ECa measurements were taken using Veris 3100 (VERIS technologies enr., Salina, KS, USA). Soil depth was measured using a hydraulic penetrometer on a 30 × 30 m regular grid (Peralta et al., 2015). Re-gridding was performed to obtain values of all variables at each intersection point of a 10 × 10 m grid.

References

N.R. Peralta, J.L. Costa, M. Balzarini, M. Castro Franco, M. Córdoba, D. Bullock Delineation of management zones to improve nitrogen management of wheat Comput. Electron. Agric., 110 (2015), pp. 103-113, 10.1016/j.compag.2014.10.017

Paccioretti, P., Córdoba, M., & Balzarini, M. (2020). FastMapping: Software to create field maps and identify management zones in precision agriculture. Computers and Electronics in Agriculture, 175, 105556.