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 means between spatial zones

Description

Compares variable means across spatial zones using a spatially-adjusted least significant difference (LSD) approach based on kriging variance.

The function accounts for spatial variability by estimating semivariograms and deriving a spatial variance component, which is then used to assess differences between zone means.

Usage

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

Arguments

Details

When variable is an external sf object, values are interpolated using ordinary kriging before comparison. Otherwise, cross-validation of the variogram model is used to estimate spatial variance.

Pairwise comparisons between zones are evaluated using a spatially-adjusted LSD criterion:

LSD = z_{1-\alpha/2} \times \sigma_{spatial}

where \sigma_{spatial} is derived from kriging variance.

Results are presented using compact letter displays to indicate groups of zones that are not significantly different.

Value

A list with:

differences

list of data frames with mean comparisons per variable

descriptive_stat

data frame with descriptive statistics and spatial variance

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. doi: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")

Spatial data depuration (error removal)

Description

Filters spatial point data by removing erroneous observations based on geometric, statistical, and spatial criteria. The function implements a sequential depuration workflow commonly used in precision agriculture.

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

The depuration process is applied in a fixed sequence:

1. Edge removal ("edges")

2. Global outlier removal ("outlier")

3. Spatial outlier removal ("inlier")

The toremove argument controls which of these steps are applied, but **does not modify the order of execution**.

Available procedures are:

edges

Removes points located within a specified buffer distance from the field boundary. The boundary is computed using a concave hull (concaveman) or a convex hull if the package is not available.

outlier

Removes global outliers based on:

• user-defined limits (ylimitmin, ylimitmax)

• statistical thresholds defined as mean \pm sdout \times sd

inlier

Identifies and removes spatial outliers using:

• Local Moran's I statistic ("LM")

• Moran scatterplot influence ("MP")

Default parameter values are tuned for precision agriculture datasets (e.g., yield maps).

Value

An object of class paar (list) with:

depurated_data

Filtered sf object

condition

Character vector indicating the reason each observation was removed (or NA if retained)

References

Vega, A., Córdoba, M., Castro-Franco, M. et al. (2019). Protocol for automating error removal from yield maps. Precision Agriculture, 20, 1030–1044. doi: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 (non-spatial)

Description

Performs fuzzy k-means clustering on tabular data (non-spatial). This function is a lightweight wrapper around e1071::cmeans, providing a vectorized workflow and clustering quality indices.

It is primarily intended as a fallback method when spatial clustering (e.g., kmspc) cannot be applied, such as when only one variable is available.

Usage

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

Arguments

Details

Missing values are removed prior to clustering. Observations with missing values are reintroduced in the output with NA cluster assignments.

Clustering is performed for each value in number_cluster, and several indices are returned to assist in selecting the optimal number of clusters:

• Xie-Beni index

• Partition coefficient

• Partition entropy

• Summary index

Value

A list with:

cluster

data.frame with cluster assignments for each evaluated number of clusters

indices

data.frame with clustering validity indices

summaryResults

data.frame with clustering metrics

See Also

kmspc

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"])

Spatial PCA-based fuzzy clustering (MULTISPATI-PCA)

Description

Performs clustering of spatial data using a combination of spatial Principal Component Analysis (PCA), and fuzzy k-means clustering.

The workflow consists of:

1. Dimensionality reduction using spatial PCA

2. Selection of components based on explained spatial variance

3. Fuzzy clustering over selected components

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

Details

Spatial relationships are defined using distance-based neighbors (spdep::dnearneigh). These relationships are incorporated into the spatial PCA analysis to extract spatially structured components.

Clustering is performed using fuzzy c-means over selected spatial components. Several indices are computed to help determine the optimal number of clusters:

• Xie-Beni index

• Partition coefficient

• Partition entropy

• Summary index (normalized combination)

Value

A list with the following elements:

cluster

data.frame with cluster assignments for each evaluated number of clusters

indices

data.frame with clustering validity indices

summaryResults

data.frame with clustering metrics (iterations, SSDW)

pca_results

(optional) PCA and/or spatial PCA summaries depending on arguments

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 t-test to assess the correlation between variables while accounting for spatial autocorrelation. This implementation wraps SpatialPack::modified.ttest.

Usage

spatial_t_test(data, variables)

Arguments

Details

The function computes pairwise correlations between the specified variables and adjusts the significance test to account for spatial dependence using coordinates. If data is an sf object, coordinates are extracted automatically. Otherwise, coordinates must be provided as an object with two columns.

Value

A data.frame with the following columns:

Var1

Name of the first variable

Var2

Name of the second variable

corr

Estimated correlation coefficient

p.value

P-value adjusted for spatial autocorrelation

See Also

modified.ttest

Examples

if (requireNamespace("SpatialPack", quietly = TRUE)) {
  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 spatial t test
  t_test_results <-
    spatial_t_test(
      wheat_sf,
      variables = c('CE30', 'CE90')
    )

  # Print the t_test_results
  t_test_results
}

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.