## 3 Technologies of Monitoring in Montado
### Precision Agriculture
#### 3.2.2 Soil Monitoring  
As previously mentioned, soil laboratory analysis is time-consuming and expensive in determining its physicochemical characteristics. Thus, there is a growing need to use expeditious and fast methods for this characterization through sensors, intending to complement and/or replace traditional sampling methods [102]. The soils have significant spatial and temporal variability, conditioning the productivity of the established crops. This variability can be monitored through several sensors, not having one that can, by itself, completely characterize the complexity of the soil [6]. However, if the sensor incorporates a GPS, field maps can be obtained, identifying low- and high-productivity areas [56].  
Proximal sensors for measuring soil characteristics provide data quickly, with low associated costs, and also allow for the understanding of the spatial and temporal variability of the soil in a given plot ([PERSON] et al., 2012) cited by [6]. However, it should be noted that the acquisition cost of these sensors is high. Soil Apparent Electrical Conductivity (ECa) has been described as the primary variable for characterizing the soil and defining differentiated Management Zones (MZ) [103]. Since the soil is not uniform, the term is ECa, the electrical conductivity of uniform soil that gives the same reading [104]. The ECa expresses the concentration of soluble salts in the soil [105]. The ECa of the soil is a function of the humidity, salinity, temperature, apparent density, and percentage of clay [6,104]. ECa can be used as an estimate of these characteristics if the contributions of other soil properties that affect electrical conductivity are known or can be estimated ([PERSON], 2004). Soil ECa, according to [PERSON] and [PERSON] [79], is negatively correlated with altitude (r\({}^{2}=-0.91\)), where the values of salt content, pH, sodium, and cation exchange capacity are high. According to this study, ECa is also negatively correlated with soil OM (r\({}^{2}=-0.72\)).  
The ECa measurement can be performed by electromagnetic sensors, which measure the variations in soil moisture, clay percentage, texture, depth, and ion content [79]. The soil sensors most used in pasture monitoring are electromagnetic induction sensors, which are used in agriculture to monitor salinity and identify soils affected by sodium [106]. Interest in this technology has grown in response to the high spatial resolution, possibly with GPS being used to determine the spatial variability of soil properties [106]. Electromagnetic sensors are a non-invasive, non-contact method for characterizing soil spatial variability based on Faraday's law and have been used for about 20 years to characterize agricultural soils [106]. These mobile sensors are a fast and inexpensive method that allow you to assess soil variability over large areas more quickly [6]. However, according to [PERSON] et al. (2012), cited by [6], electromagnetic sensors are limited to quantifying the soil parameters mentioned above, although they are a fast method. Direct ground contact sensors are generally only used on arable land. However, the Veris conductivity meters are capable of physically penetrating pasture soil [107], being widely used in Montado plots in Alentejo. In a study, [PERSON] et al. [108] aimed to show interest in measuring ECa with Veris to define different MZs in undercover Montado pastures in the Evora region and found significant and positive correlations between ECa and soil moisture (r\({}^{2}\) = 0.7088). In another study on pastures in Alentejo, [PERSON] et al. [109] obtained significant correlations between ECa, measured with the Veris sensor in 2012 and 2013, and soil moisture, clay, silt, sand, OM, pH, and P. In this study, with an electromagnetic sensor, the authors only mentioned significant correlations in 2013 for the following parameters: soil moisture, silt, pH, P, and N.