**1. Introduction**

Soils are essential to human survival, and they provide a wide diversity of ecosystem services. Among them, soils are at the base of food production, carbon storage and cycles to atmosphere, water availability, turning biomass into valuable nutrients and degrading toxic elements, and supporting biodiversity [1]. Soil surveys are a relevant source of soil information and environmental data for many uses in agriculture and, otherwise, are fundamental for evaluating land capability and preventing degradation [2]. Ramalho Filho and Pereira [3] highlight the importance of assessing regional conditions and soil properties to recommend adequate landuse management.

have been interpreted to accentuate carbonate radicals, ferrous iron, and hydroxyl

*Pedometric Tools Applied to Zoning Management of Areas in Brazilian Semiarid Region*

The identification of homogeneous zones of management consists in grouping areas whose environmental characteristics, as relief and soil, are similar. The design of homogeneous zones must be done according to the soil management, considering as well the risk of degradation, and it contributes to the sustainable land-use planning. Sánchez and Silva [26] pointed out that management areas present similar response when applied similar agronomic practices and they are subject to the same

The cluster analysis deals with segmentation of a set of *N* objects into clusters (groups) in a fashion that the same type of datasets falls in a cluster that is different from those with dissimilar datasets [27]. The results of cluster analysis reveal internal data structure and improve understanding of data. There are many cluster algorithms that are available for data partitioning into. This analysis was used to delineate management zone methods [28], and the authors prefer to apply fuzzy cluster algorithms, and they are commonly used to define management zones for precision agriculture. To develop management zones [29], the cluster analysis was conducted by Management Zone Analyst 1.0.1 (Agricultural Research Services, University of Missouri, Columbia), with a combination of variables (elevation-Electrical

Conductivity-EC, ECah-%Na; elevation-ECah-%Na, elevation-pH 1:1-%Na).

The study was conducted in an area located between 9°53<sup>0</sup>

Juazeiro in Bahia State, northeast region of Brazil (**Figure 1**).

The hypothesis of this study is that the use of soil legacy data allied with modern tools that are able to analyze large datasets can assist the definition of soil potential and limitations to agricultural practices and thus assist the land-use planning and zoning. The work was developed from a legacy dataset of soil surveys made by the *Companhia de Desenvolvimento do Vale do São Francisco* (CODEVASF) [30] in the

The specific goals of this study are (i) to identify soil key properties for definition of different management zones based on area databased and literature; (ii) to harmonize the dataset according predefined properties; (iii) to produce soil key property maps through digital soil mapping; and (iv) to create a map zoning the area according the potential to agricultural uses. This approach was used to establish the main trends from the profile collection by means of soil-depth functions and to select soil key properties adequate for the area (flatland landscape and semiarid climate), in this way, separating the areas according to their agriculture

<sup>30</sup>″W (with 34,437.82 hectares), in the municipality of

The biome in the region studied was the caatinga [31], an ecoregion characterized by xeromorphic vegetation with broadleaf thorny shrubs and trees that shed their leaves seasonally, cactus plants, and sparse arid-adapted grasses [32]. The climate is characterized as semiarid with dry winter and rainy summer, and the coldest month mean temperature remains above 18°C (BSwh' in the Köppen classification). The average annual rainfall is 400 mm, with the rainy season extending from November to April (highest precipitation in March); and average annual temperature is around 26°C. The xerothermic indexes are between 200 and 150,

<sup>0</sup>″ and 9°36<sup>0</sup>

<sup>30</sup>″ S and

radicals, respectively, in exposed soil and geologic materials [25].

risks and limitations of agricultural use.

*DOI: http://dx.doi.org/10.5772/intechopen.88526*

Brazilian semiarid region.

**2. Materials and methods**

**2.1 Study area and dataset**

<sup>30</sup>″ and 40°23<sup>0</sup>

comprising of 7 to 8 dry months.

potentials.

40°34<sup>0</sup>

**41**

However, in Brazil, even though this is well-known, the soil class information is not commonly taken into account in planning or land-use zoning. One of the problems is the small scale of available maps from systematic soil surveys (1:250,000–1:1,000,000) in the country; this small scale is not suitable for recommendations regarding soil management at the farm level [4]. This limits the implementation of land-use zoning and soil sustainable management practices and policies.

The northeast region of Brazil presents complex and heterogeneous natural features in relation to rainfall, soil, and vegetation. It represents a huge challenge for the use and management of soil and water in sustainable agricultural systems. The soil degradation observed in some areas of this region is due to irregular rainfall, soil fertility conditions, and population pressures in a typically fragile environment [5].

Despite of the environmental variability, the northeast region of Brazil has a unique biome with a particular vegetation type (named "caatinga"). The majority of soils present shallow superficial horizons and profiles and very low organic carbon contents [6–8]. The rainfall deficit together with an inappropriate soil management for agricultural use can lead to desertification processes and to increase soil salinity in many areas, further degrading the land. Also common is the presence of rocks on the soil mass and/or surface layers and soils with high clay activity, both implying strong limitations for tillage. Thus, knowing the distribution of soil classes and properties is a requirement for land-use zoning.

The manipulation and analysis of a large soil profile collection, in the existing datasets, can be difficult due to the wide variability of horizon depths and thickness, as well as the variability among and between soil classes and orders. Addressing this issue, studies about the variability of soil properties along with depth were developed by [9–15]. Beaudette et al. [16] introduced the algorithm for quantitative pedology (AQP) package, which gathers tools to produce standard profile sketches highlighting differences in soil properties between horizons. It allows to standardize profile sketches, according to Munsell color chart, and dataset harmonization by applying the slice-wise algorithm, among other useful tools to express variations inside a given soil profile collection.

Quantitative studies have been developed in soil science through numerical modeling relationships between environmental variables and soil, which are applied to a geographic dataset to create a preliminary or predictive map [17, 18]. All these techniques are known as pedometrics [17], which represents an interface between statics and soil science, supporting predictive analysis related with soil classes and attributes [19, 20]. Based in pedometric concepts, the digital soil mapping (DSM) allows to study the relationships between soil and forming factors represented by spectral bands from remote sensing data, surface numerical models, and thematic maps [17]. Remote sensing data deserves an important place in digital soil mapping, particularly in flat landscapes, where morphometric covariates have lesser influence in soil formation [21].

Landsat spectral bands, particularly in the short-wave infrared range (SWIR), are commonly used to represent the environmental covariates of parent material and/or soil. Different mineral assemblages will have different spectral reflectances, which may be separable by analyzing bands 1–5 and 7 [22, 23]. The normalized difference vegetation index (NDVI) values range between 1 and +1. High positive values usually indicate the occurrence of dense green vegetation, pointing to an appropriate supply of water and nutrients. Low values express limited photosynthetic activity, and negative ones correspond to sparse lacking ground coverage [24]. The soil enhancement ratios of Landsat spectral band ratios 3:2, 3:7, and 5:7

#### *Pedometric Tools Applied to Zoning Management of Areas in Brazilian Semiarid Region DOI: http://dx.doi.org/10.5772/intechopen.88526*

have been interpreted to accentuate carbonate radicals, ferrous iron, and hydroxyl radicals, respectively, in exposed soil and geologic materials [25].

The identification of homogeneous zones of management consists in grouping areas whose environmental characteristics, as relief and soil, are similar. The design of homogeneous zones must be done according to the soil management, considering as well the risk of degradation, and it contributes to the sustainable land-use planning. Sánchez and Silva [26] pointed out that management areas present similar response when applied similar agronomic practices and they are subject to the same risks and limitations of agricultural use.

The cluster analysis deals with segmentation of a set of *N* objects into clusters (groups) in a fashion that the same type of datasets falls in a cluster that is different from those with dissimilar datasets [27]. The results of cluster analysis reveal internal data structure and improve understanding of data. There are many cluster algorithms that are available for data partitioning into. This analysis was used to delineate management zone methods [28], and the authors prefer to apply fuzzy cluster algorithms, and they are commonly used to define management zones for precision agriculture. To develop management zones [29], the cluster analysis was conducted by Management Zone Analyst 1.0.1 (Agricultural Research Services, University of Missouri, Columbia), with a combination of variables (elevation-Electrical Conductivity-EC, ECah-%Na; elevation-ECah-%Na, elevation-pH 1:1-%Na).

The hypothesis of this study is that the use of soil legacy data allied with modern tools that are able to analyze large datasets can assist the definition of soil potential and limitations to agricultural practices and thus assist the land-use planning and zoning. The work was developed from a legacy dataset of soil surveys made by the *Companhia de Desenvolvimento do Vale do São Francisco* (CODEVASF) [30] in the Brazilian semiarid region.

The specific goals of this study are (i) to identify soil key properties for definition of different management zones based on area databased and literature; (ii) to harmonize the dataset according predefined properties; (iii) to produce soil key property maps through digital soil mapping; and (iv) to create a map zoning the area according the potential to agricultural uses. This approach was used to establish the main trends from the profile collection by means of soil-depth functions and to select soil key properties adequate for the area (flatland landscape and semiarid climate), in this way, separating the areas according to their agriculture potentials.

## **2. Materials and methods**

#### **2.1 Study area and dataset**

The study was conducted in an area located between 9°53<sup>0</sup> <sup>0</sup>″ and 9°36<sup>0</sup> <sup>30</sup>″ S and 40°34<sup>0</sup> <sup>30</sup>″ and 40°23<sup>0</sup> <sup>30</sup>″W (with 34,437.82 hectares), in the municipality of Juazeiro in Bahia State, northeast region of Brazil (**Figure 1**).

The biome in the region studied was the caatinga [31], an ecoregion characterized by xeromorphic vegetation with broadleaf thorny shrubs and trees that shed their leaves seasonally, cactus plants, and sparse arid-adapted grasses [32]. The climate is characterized as semiarid with dry winter and rainy summer, and the coldest month mean temperature remains above 18°C (BSwh' in the Köppen classification). The average annual rainfall is 400 mm, with the rainy season extending from November to April (highest precipitation in March); and average annual temperature is around 26°C. The xerothermic indexes are between 200 and 150, comprising of 7 to 8 dry months.

of assessing regional conditions and soil properties to recommend adequate land-

(1:250,000–1:1,000,000) in the country; this small scale is not suitable for recommendations regarding soil management at the farm level [4]. This limits the implementation of land-use zoning and soil sustainable management practices and

The northeast region of Brazil presents complex and heterogeneous natural features in relation to rainfall, soil, and vegetation. It represents a huge challenge for the use and management of soil and water in sustainable agricultural systems. The soil degradation observed in some areas of this region is due to irregular rainfall, soil fertility conditions, and population pressures in a typically fragile environment [5]. Despite of the environmental variability, the northeast region of Brazil has a unique biome with a particular vegetation type (named "caatinga"). The majority of soils present shallow superficial horizons and profiles and very low organic carbon contents [6–8]. The rainfall deficit together with an inappropriate soil management for agricultural use can lead to desertification processes and to increase soil salinity in many areas, further degrading the land. Also common is the presence of rocks on the soil mass and/or surface layers and soils with high clay activity, both implying strong limitations for tillage. Thus, knowing the distribution of soil classes and

The manipulation and analysis of a large soil profile collection, in the existing datasets, can be difficult due to the wide variability of horizon depths and thickness, as well as the variability among and between soil classes and orders. Addressing this issue, studies about the variability of soil properties along with depth were developed by [9–15]. Beaudette et al. [16] introduced the algorithm for quantitative pedology (AQP) package, which gathers tools to produce standard profile sketches highlighting differences in soil properties between horizons. It allows to standardize profile sketches, according to Munsell color chart, and dataset harmonization by applying the slice-wise algorithm, among other useful tools to express variations

Quantitative studies have been developed in soil science through numerical modeling relationships between environmental variables and soil, which are applied to a geographic dataset to create a preliminary or predictive map [17, 18]. All these techniques are known as pedometrics [17], which represents an interface between statics and soil science, supporting predictive analysis related with soil classes and attributes [19, 20]. Based in pedometric concepts, the digital soil mapping (DSM) allows to study the relationships between soil and forming factors represented by spectral bands from remote sensing data, surface numerical models, and thematic maps [17]. Remote sensing data deserves an important place in digital soil mapping, particularly in flat landscapes, where morphometric covariates have lesser influence

Landsat spectral bands, particularly in the short-wave infrared range (SWIR), are commonly used to represent the environmental covariates of parent material and/or soil. Different mineral assemblages will have different spectral reflectances, which may be separable by analyzing bands 1–5 and 7 [22, 23]. The normalized difference vegetation index (NDVI) values range between 1 and +1. High positive values usually indicate the occurrence of dense green vegetation, pointing to an appropriate supply of water and nutrients. Low values express limited photosynthetic activity, and negative ones correspond to sparse lacking ground coverage [24]. The soil enhancement ratios of Landsat spectral band ratios 3:2, 3:7, and 5:7

properties is a requirement for land-use zoning.

inside a given soil profile collection.

in soil formation [21].

**40**

not commonly taken into account in planning or land-use zoning. One of the problems is the small scale of available maps from systematic soil surveys

*Multifunctionality and Impacts of Organic and Conventional Agriculture*

However, in Brazil, even though this is well-known, the soil class information is

use management.

policies.

#### *Multifunctionality and Impacts of Organic and Conventional Agriculture*

As covariates, Landsat 5 TM spectral bands were used in this study (**Table 1**), from September 1989, available in http://www.dgi.inpe.br/CDSR/. The NDVI values range between 1 and + 1. In the study area, most of the NDVI values were

*Pedometric Tools Applied to Zoning Management of Areas in Brazilian Semiarid Region*

Quantitative soil properties, such as clay and sand content, can be estimated along the depth of soil profile through slice-wise algorithm [16] implemented in R software through algorithm for quantitative pedology (AQP) package [46]. The functions calculate an estimative of the main trend of values, represented by median and the variation interval by quartiles (25th and 75th of data distribution). The values shown in a column corresponds to percentage of soil profiles contribut-

Initially, Pearson's linear correlation analysis was used to measure the linear association between variables, to determine among the soil properties defined by AQP, which correlated with environmental covariates (**Table 1**). This analysis was implemented in R [46], through the function cor.test, according to [42, 44, 47]. In Pearson's correlation the p-value defines whether or not two variables are statistically correlated, and for this study, it was defined that values lower than 0.02

The modeling procedure to execute the random forest (RF) prediction was performed in R software, through randomForest (RF). RF is a nonparametric technique developed by Breiman [48] as an extension of classification and regression tree (CART) systems, to improve the performance of the predictors. To implement the RF models, three parameters are necessary: number of trees in the forest (ntree); minimum amount of data in each terminal node (nodesize); and number of covariates used in each tree (mtry) [49]. The ntree value was set to system default (500), although more stable results can be achieved with a larger number [50]. The

**(m)**

Band 1 30 0.450–0.515 [23, 39–42]

NDVI (Band 4 - Band 3)/(Band 4 + Band 3) 30 — [11, 23, <sup>41</sup>–44] Band 3/Band 2 <sup>30</sup> —

Band 2 30 0.525–0.605 Band 3 30 0.630–0.690 Band 4 30 0.755–0.900 Band 5 30 1.550–1.750 Band 7 30 2.090–2.350

Band 3/Band 7 30 — Band 5/Band 7 30 —

GSI (Band 3 - Band 1)/(Band 3 + Band

*Covariates used in spatial prediction of soil properties.*

2 + Band 1)

*\*Landsat 5 satellite*

**Table 1.**

**43**

**Spectral range (μm)\***

30 — [45]

**References**

below 0.1, indicating little vegetation cover in the study area [39].

**2.3 Modeling procedures**

ing with the function at each depth.

*DOI: http://dx.doi.org/10.5772/intechopen.88526*

indicate that the correlation is significant.

**Covariates Spatial resolution**

At the study site, a portion of the original vegetation has been removed, and signs of land degradation such as bare soils are common. The landscape is mainly compounded by flat surfaces, with maximum slopes of 8%, and plateaus are common in the region. Geology comprises of limestone rocks from the caatinga formation, gneiss-granite rocks of the Caraiba-Paramirim complex, and recent colluvium/ alluvium sediments [33].

The soils types in the northeast region of Brazil show large variation, according to parent material and relief, from shallow and with high content of basic cations (Ca, Mg, K, and Na) to deep and leached profiles [34]. Dominant soil classes according to the World Reference Base for Soil Resources [35] are Vertisols, Cambisols, and Planosols, with smaller extension of Regosols, Acrisols, and Luvisols.

#### **2.2 Soil input data and covariates**

The soil properties used in this study were sand, clay, and cation exchange capacity (CEC), determined according to [36]. The selection of soil properties was based on two main conditions: (i) presenting variability according to depth and ii) having importance to agricultural management (e.g., clay or sodium (Na) content).

From the original dataset, with 523 profiles, the ones with complete morphological description and analytical data were selected, performed by *Companhia de Desenvolvimento do Vale do São Francisco* (CODEVASF) in 1989 [29]. So, the input dataset comprised of the topsoil (0–20 cm) and subsoil (20–60 cm) layers for 290 soil profiles from a soil survey (legacy data). The sampling was performed according to the requirements of national soil survey service, correspondent to the detailed soil survey level [37, 38].

### *Pedometric Tools Applied to Zoning Management of Areas in Brazilian Semiarid Region DOI: http://dx.doi.org/10.5772/intechopen.88526*

As covariates, Landsat 5 TM spectral bands were used in this study (**Table 1**), from September 1989, available in http://www.dgi.inpe.br/CDSR/. The NDVI values range between 1 and + 1. In the study area, most of the NDVI values were below 0.1, indicating little vegetation cover in the study area [39].
