Non-Destructive Assessment of Soil Nutrient Variability in Mbeya Catena Using Dielectric Properties via Low-Cost Antenna Systems

Non-Destructive Assessment of Soil Nutrient Variability in Mbeya Catena Using Dielectric Properties via Low-Cost Antenna Systems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Non-Destructive Assessment of Soil Nutrient Variability in Mbeya Catena Using Dielectric Properties via Low-Cost Antenna Systems Twahir Kazema, Ibrahim L. Kadigi, Stefan Sieber This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6761968/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Soil nutrient depletion and inefficient fertilizer management remain major challenges for sustainable agriculture in Tanzania’s Southern Highlands. This study evaluates a low-cost, non-invasive dielectric sensing approach to monitor soil nutrient and moisture variability using a log-periodic dipole antenna (LPDA) system integrated with a nano vector network analyzer (NanoVNA). Soil samples from the middle and lower catena positions in Mbeya City were treated with varying concentrations (0–12.5%) of UREA and calcium ammonium nitrate (CAN), and dielectric properties permittivity and conductivity were measured under controlled moisture levels (10–40%). The results showed strong positive correlations between UREA concentration, dielectric constant (r = 0.905), and conductivity (r = 0.858), particularly in the lower catena. CAN showed a reliable response in the middle catena (r = 0.913 for εr) but inconsistent trends in the lower catena. Moisture content had a significant non-linear effect on dielectric behavior, with a peak response at 40% moisture. A two-way ANOVA confirmed statistically significant main and interaction effects (p < 0.05) for fertilizer type, concentration, and catena position. These findings validate the LPDA-NanoVNA system as an effective soil nutrient and moisture monitoring tool. The dielectric method is especially promising for supporting site-specific fertilizer applications and precision farming. UREA is more suitable for dielectric-based assessments in variable terrain, while CAN is more effective in stable slope environments. The approach advances low-cost, scalable technologies for climate-smart agriculture and aligns with SDG 15 goals on sustainable land use and soil health. S-parameter Catena Soil Nutrients Fertilizer Dielectric Conductivity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Tanzania’s Southern Highlands, particularly the Mbeya Region (which is located between latitudes 7° and 9° south of the equator and longitudes 32° and 34° east of Greenwich), are known for their fertile volcanic soils and significant agricultural productivity. Agriculture dominates the local economy, with smallholder farmers growing staple crops like maize, rice, and beans and cash crops like coffee, tea, and tobacco. However, declining soil fertility due to continuous cropping, limited following, and mismanagement of fertilizers poses a growing threat to productivity (Kilombele et al., 2023 ). In Mbeya City Catena, nutrient variability and degradation are exacerbated by topographical differences and run-off dynamics (Malinowska & Szumacher, 2013 ). While chemical soil testing remains the gold standard for assessing fertility, it is costly, labor-intensive, and inaccessible to most smallholder farmers (Onyango et al., 2021 ; Nyaga et al., 2021 ). Soil management requires knowledge of the catena (Malinowska & Szumacher, 2013 ). The highland and lowland tend to be fertile compared to the upland areas, which often lack exchangeable bases and phosphorus. The catena concept was introduced to examine the systematic changes in soil types along a slope. Gaining insight into the soils that form a catena can aid in effectively mapping soils within a specific region. In Mbeya, fertilizer is widely applied in farming, but the uncontrolled use has led to soil quality degradation and reduced crop yields. Previously, soil fertility was maintained through fallowing, but growing population pressures have made this practice less feasible in many areas. As a result, a noticeable deterioration of soil pH and a decrease in nutrient levels and organic matter. Research has been done to study the soil quality in Tanzania (De Pauw & Espinosa, 1982 ; Kauzeni et al., 1993 ; Rwehumbiza et al., 1999 ), but they don’t apply the Non-destructive measurements of soil. Mgawe et al. researched nutrient soil management using an antenna to find the relationship between soil nutrients and soil dielectric constant in the Mwanza Region, Tanzania (Mgawe et al., 2021 ). Their research found a linear relationship between dielectric constant, conductivity, and moisture content with the soil nutrients and fertilizer concentration. The relationship between chemical and dielectric properties of soil was also presented in work done by Ahire et al. ( 2013 ; 2015 ) and Dospatliev et al. ( 2014 ). Studies on the relationship between fertilizer concentration and the dielectric properties of soil have been conducted by Rajesh Mohan et al. ( 2015 ), where it was observed that the dielectric properties of soil tend to increase as the fertilizer concentration rises. These investigations provide critical insights into how chemical additives affect the soil’s electromagnetic behavior. The researchers employed various sophisticated measurement methods in controlled laboratory environments, utilizing soil-filled waveguides and dielectric probes in conjunction with a network analyzer to ensure precise and reliable results. However, a notable exception is a study (Mgawe et al., 2021 ), which adopted a different approach by employing a log-periodic antenna, showcasing an alternative methodology for analyzing soil dielectric properties. The techniques discussed are unsuitable for on-farm assessments, which must be contactless and non-invasive. Mgawe et al. ( 2021 ) also proposed the design of a dielectric property determinant robot for detecting soil nutrients suitable for implementation in developing countries (Kazema & Mgawe, 2023 ). These studies focus on soil characteristics that do not apply non-contact, non-destructive techniques. This study is an extension of (Mgawe et al., 2021 ) that now aims to explore the relationship between soil nutrients in Mbeya City and fertilizer concentrations by analyzing the S-parameters of waves reflected from the soil samples using an innovative measurement and instrumentation system that was developed with support from innovators at the Centre for Innovation and Technology Transfer (CITT) of Mbeya University. This system facilitates the measurement of reflected waves from soil samples using a portable network analyzer, a log-periodic dipole antenna centered at 945 MHz, and a control box that ensures measurements are free from human interference, which could compromise the accuracy of the results. The S-parameters the network analyzer detects are used to calculate the soil’s dielectric properties and conductivity (σ). The experiments utilized two commonly used fertilizers in the area, UREA and Calcium Ammonium Nitrate (CAN). Specifically, the study aims to (i) assess the dielectric properties (permittivity and conductivity) of fertilized soils across catena positions in Mbeya using a low-cost LPDA-based sensing system; (ii) evaluate the influence of varying concentrations of UREA and CAN fertilizers on soil dielectric responses and (iii) validate the effectiveness of non-invasive dielectric measurements for potential application in precision soil fertility mapping. Understanding spatial variations in soil nutrients is vital for improving fertilizer application, minimizing environmental impact, and increasing yields. Previous research has applied electromagnetic techniques to estimate soil moisture and texture, yet their application in nutrient assessment remains underexplored in Sub-Saharan Africa (Abdulraheem et al., 2024 ; Zhang et al., 2024 ; Kargas & Soulis et al., 2019). Notably, the study by Mgawe et al. ( 2021 ) demonstrated a potential link between fertilizer concentration and soil dielectric properties using an antenna-based method in Mwanza, Tanzania. However, most prior studies have relied on laboratory-based dielectric probes, which are impractical for field use (Kargas & Soulis, 2019 ; Brovelli & Cassiani, 2011 ; Shruthi & Menon, 2016 ). This study addresses the gap by employing a low-cost, portable, and non-contact sensing system based on log-periodic dipole antennas. It aims to validate the relationship between soil nutrients and electromagnetic parameters, particularly S-parameters (reflection coefficients), using UREA and CAN as representative fertilizers. The broader goal is to develop a scalable real-time soil nutrient monitoring technique to support sustainable land use planning and inform policy interventions. Specifically, the study aims to assess the dielectric properties (permittivity and conductivity) of fertilized soils across catena positions in Mbeya using a low-cost LPDA-based sensing system, evaluate the influence of varying concentrations of UREA and CAN fertilizers on soil dielectric responses and to validate the effectiveness of non-invasive dielectric measurements for potential application in precision soil fertility mapping. The results of this study can empower farmers and agricultural experts to assess the nutrient status of their land using a non-destructive handheld device. With such technology readily accessible, soil management can be significantly improved, contributing to the achievement of the United Nations’ 15th Sustainable Development Goal (SDG 15). 2. Materials and Methods 2.1 Preparation of soil samples Soil samples were collected from the lower and middle catenas in Iyunga ward in the Mbeya Region of Tanzania, as shown in Figure 1. The soil samples were first cleaned of coarser particles; the finer particles were oven-dried at approximately 120 degrees Celsius to eliminate available moisture. Using the moisture sensor Demetra (PAT. 193478), Soil samples with a gravimetric moisture content of ten percent (10%) were meticulously prepared by adding a precisely measured amount of water to ten kilograms (10 kg) of completely dried-out soil to achieve the desired moisture level. Specific concentrations of fertilizers were carefully added to the soil samples to examine the effect of fertilizers on the soil properties. This was achieved by introducing an exact amount of fertilizer into the soil and placing the resulting mixture in a sealed container. The sealing ensured that proper settling, adequate drainage, and thorough mixing occurred over time, creating a consistent and homogeneous soil-fertilizer blend. The experiment utilized two commonly used fertilizers, calcium ammonium nitrate (CAN) and UREA, which were tested for their influence on soil properties. Fertilizer treatments included UREA and CAN applied at six concentration levels (0%, 2.5%, 5%, 7.5%, 10%, and 12.5%). Each treatment was homogenized and incubated to simulate field interaction. This equated to fertilizer quantities ranging from 100 grams to 1000 grams for each sample, ensuring a broad spectrum of concentrations was studied to capture the variations in soil response. 2.2 Dielectric properties system setup A low-cost dielectric measurement system was developed using a Nano Vector Network Analyzer (NanoVNA) and a log-periodic dipole antenna (LPDA) optimized at 945 MHz. The system, therefore, comprises three main components: NanoVNA, LPDA, and a control circuit. The control circuit regulates the upward and downward movement of the LPDA, ensuring accurate positioning and optimal measurement conditions. The LPDA was vertically positioned 10 cm above the soil surface, and S-parameters were captured and calibrated using standard open-short-load procedures. The NanoVNA, a highly cost-effective and portable vector network analyzer, can operate at frequencies of up to 1.5 GHz, making it suitable for a wide range of applications in soil dielectric property analysis. Paired with this device is a low-cost LPDA (Yang et al., 2017), which serves as the primary measurement antenna. The LPDA, designed for efficiency and reliability, is housed within a compact, lightweight plastic enclosure with dimensions of 40x20x3 cm³. This protective casing not only ensures durability and ease of handling but also minimizes external interference, enhancing the accuracy and repeatability of the measurements performed by the system. Antenna motion was controlled by an Arduino-based motor system for reproducible positioning, following methods validated in Shruthi & Menon (2016). It is important to note that the dimensions of the soil container are large enough to minimize edge diffraction, with the antenna positioned 10 cm above the soil surface. Figure 2 presents the designed automated system, and Figure 3 illustrates the working principle; this work enhances the previous design by incorporating an electronic control system that manages the upward and downward movement of the sensor (antenna). The control system is powered by an alternating current (a.c) source, which provides the necessary electrical energy to drive its operation. This system is integrated with an Arduino microcontroller, meticulously programmed to manage the motor’s rotational movements precisely. The motor is programmed to rotate in clockwise and counterclockwise directions, performing this motion five times to complete a single measurement cycle. When rotating clockwise, the motor lowers the antenna, securely attached via a cable, positioning it exactly 10 cm above the soil surface to accurately measure the soil’s dielectric properties. Conversely, when rotating counterclockwise, the motor raises the antenna to 30 cm from the soil. This elevated position is specifically designed to facilitate the replacement of soil samples, ensuring the process is efficient and does not interfere with subsequent measurements. The Arduino, motor, and antenna coordination ensure seamless and repeatable operation throughout the experimental procedure. Before conducting soil measurements, the NanoVNA was calibrated to eliminate potential errors. The study revealed minimal standard deviation after multiple sample measurements, indicating that the NanoVNA demonstrated reliable stability. Additionally, the equipment was tested, and its results were compared with those obtained from laboratory equipment. The magnitude response closely matched that of the laboratory equipment. However, the phase was observed to be advanced by 90°, as noted by Mgawe et al. (2021). In practice, the NanoVNA results were adjusted by subtracting 90°. The average values were used to calculate the dielectric constant (ϵr) and conductivity (σ) of the soils, as demonstrated in Equations (1) and (2), respectively. The NanoVNA was calibrated using the standard open, short, and load procedures, with a 20 cm coaxial cable serving as the connection to the LPDA. During the calibration process, careful adjustments were made to account for the additional coaxial cable and the LPDA’s feed line to ensure accurate system calibration. Considering these components, the net magnitude and phase of the soil measurements can be accurately determined. This is achieved by subtracting the calibration reference values from the measured values of the soil under examination. Measurement results from the measurements taken when the antenna is pointed at the open air. Dielectric constant and electrical conductivity were calculated from reflection coefficient magnitude and phase (Γ, φ) using the models described in Rhoades et al. (1989) and further supported by Ahire et al. (2015) and can be computed as follows: Additionally, a two-way Analysis of Variance (ANOVA) was conducted to evaluate the effects of fertilizer type (UREA vs. CAN) on εr and σ, the main effect of fertilizer concentration level (six levels: 0%, 2,5%, 7.5%, 10%, 12.5%) on εr and σ, and their interaction on soil dielectric constant and conductivity (εr and σ). The ANOVA tested for main effects and interaction effects, using a significance threshold of using equation 4. Each test was run separately for the middle and lower catenas. This analysis allowed the isolation of individual and combined effects, confirming where the response patterns were consistent or varied, particularly in the lower catena. These inferential tools were crucial in establishing statistically significant relationships between experimental variables and supporting the reliability of the findings. 2.3 Soil physical and chemical properties The distribution of soil particles was thoroughly examined using the hydrometer method, as specified and referenced in (Anderson & Ingram, 1993; Okalebo et al., 2002), including pH, organic carbon, CEC, total nitrogen (Kjeldahl method), and available phosphorus (Bray-1 extraction). This method provides detailed insights into the textural composition of the soil by separating particles based on their size. The total nitrogen content within the soil was determined using the Kjeldahl digestion-distillation method, a widely recognized and reliable procedure for nitrogen analysis. This process adhered to the methodologies outlined in the sources (Bremner & Keeney, 1965; Page, 1982; Keeney, 1982; Hu et al., 1993). Additionally, the available phosphorus content (Bray-1 extraction) was assessed using a standard procedure described by Hu et al. (1993), ensuring accurate quantification of this essential nutrient. The organic carbon content of the soil was quantified through a chemical oxidation method, as meticulously outlined in Webster et al. (1993). To evaluate soil pH, a water suspension was prepared at a ratio of 1:2.5, providing a clear understanding of the soil’s acidity or alkalinity levels. The cation exchange capacity (CEC), a critical indicator of soil fertility, along with the concentrations of exchangeable basic cations, was determined by extracting these elements using a 1.0 M neutral ammonium acetate (NH4AC) solution. This extraction process facilitates precise measurements of cations in the soil. Sodium (Na++) and potassium (K++) levels were analyzed using a flame photometer, a technique known for its accuracy in detecting alkali metals. In contrast, the concentrations of calcium (Ca2+2+) and magnesium (Mg2+), essential nutrients for plant growth, were determined using atomic absorption spectrophotometry (AAS), following the rigorous methodology described in (Anderson & Ingram, 1993). Soil bulk density, a measure of the soil’s compaction and porosity, was measured gravimetrically, adhering to the procedure described in (Webster et al., 1993). The soil’s electrical conductivity (EC), indicative of its salinity and nutrient availability, was determined using a water suspension prepared at the same 1:2.5 ratio. Furthermore, the transitional moisture content (W_t) and the wilting point (WP), which are crucial for understanding the soil’s water retention and availability to plants, were calculated using the method outlined in (Wang & Schmugge, 1980). Dielectric constant and electrical conductivity were calculated from reflection coefficient magnitude and phase (Γ, φ) using the models described in Rhoades et al. (1989) and further supported by Ahire et al. (2015). Tables 1 and 2 summarize the soil’s detailed chemical and physical properties. Table I highlights that those soils sampled across all sites, upper, middle, and lower catena, are predominantly classified as sandy loam. These soils are characterized by their coarse grain structure, high porosity, and well-drained nature, features that influence their agricultural suitability and water retention capabilities. The data presented in Table 3 indicate that the measured moist soil exhibits significantly higher permittivity and conductivity values compared to the dry soil. This difference highlights the influence of moisture content on the soil’s electrical properties, where water molecules substantially enhance the soil’s ability to store and conduct electric charge. Table 1: Soil samples: physical properties Soil Properties Middle catena Lower catena Texture Silt 12 8 Clay 11 9 Sand 79 82 Transition Moisture 0.22 0.17 Textural class Sandy Loam Loam Sandy Wilting Point 0.09 0.05 Table 2: Soil samples: chemical properties Soil Properties Name of the soil sample Middle catena Lower catena AV.P 17.2 18 Ca 2.21 1.90 CEC 5.8 5.1 EC 0.02 0.01 K 0.21 0.18 Mg 0.39 0.34 Na 1.15 0.96 OC 0.37 0.32 pH 6.10 5.81 TN 0.15 0.12 Table 3: Permittivity conductivity of dry soil and soil with 10% moisture content (m.c.) 3. Results Tables 4 and 5 present the summarized experimental results for various UREA and CAN fertilizer concentrations in different catena. Table 4 presents the relative permittivity (εr) values and conductivity (σ) for varying concentrations of UREA fertilizer across the upper, middle, and lower catenas. The dielectric measurements revealed significant trends associated with fertilizer concentration and catena position. A comparative analysis using mean values and standard deviations showed that UREA-treated soils exhibited a nearly linear increase in dielectric constant (εr) and electrical conductivity (σ) with rising concentration across all catena levels. The lower catena exhibits higher ԑr compared to the middle catena, primarily due to run-off accumulation from these higher regions. For example, in the lower catena, εr increased from 20.04 at 0% UREA to 41.09 at 12.5% UREA, while σ increased from 0.757 S/m to 2.027 S/m. Pearson correlation coefficients confirmed strong positive relationships for UREA: εr (Middle, r = 0.905), εr (Lower, r = 0.803), σ (Middle, r = 0.858), and σ (Lower, r = 0.759). These indicate high predictability of dielectric response with increasing UREA concentration. This is also attributed to the chemical interaction between the added fertilizer and existing soil constituents, which enhances the soil’s water-holding capacity. Consequently, the dielectric loss of the soil also increases with higher fertilizer concentrations, as σ reflects the mobility of electric charges that drive conduction. Table 4 Dielectric properties for various fertilizer concentrations of UREA in different catena UREA Fertilizer concentration (%) Dielectric constant (Middle) Dielectric constant (Lower) Conductivity (Middle) (S/m) Conductivity (Lower) (S/m) 0.0 7.64 20.04 0.159 0.757 2.5 9.64 20.31 0.261 0.77 5.0 11.94 25.54 0.333 0.769 7.5 12.76 36.66 0.367 1.743 10.0 13.91 25.29 0.396 0.993 12.5 23.20 41.09 0.90 2.027 Corr (r) 0.90 0.80 0.86 0.76 P < 0.05 < 0.05 < 0.05 < 0.05 < 0.05 Table 5 details the relationship between ε r , σ, and fertilizer concentration for CAN fertilizer. The observed variations in εr and σ for the upper and middle catenas follow trends similar to those for Urea. However, CAN treatments exhibited more varied behavior. In the middle catena, εr increased significantly (r = 0.913), suggesting a strong positive relationship. However, in the lower catena, εr was poorly correlated (r = 0.037), indicating a weak or no consistent relationship with CAN concentration. Conductivity in the middle catena showed a moderate correlation (r = 0.523), while the lower catena showed a negative correlation (r = -0.585), highlighting complex nutrient-soil interactions. This non-linear behavior is likely due to the influence of soil porosity and chemical composition. Conductivity results in the lower catena, reflecting these effects, as run-off from higher catenas contributes to elevated values. These results suggest that while both fertilizers influence dielectric properties, UREA provides a more consistent and reliable response pattern suitable for dielectric-based fertility mapping. A two-way ANOVA confirmed significant effects (p < 0.05) of both fertilizer type and concentration on dielectric properties, with interaction effects observed primarily in the lower catena soils. Moisture content was also found to be a significant covariate, as increasing moisture from 10–40% led to more than a 5-fold increase in εr. Table 5 Dielectric properties for various fertilizer concentrations of CAN in different catena CAN Fertilizer concentration (%) Dielectric constant (Middle) Dielectric constant (Lower) Conductivity (Middle) (S/m) Conductivity (Lower) (S/m) 0.0 7.64 20.04 0.159 0.757 2.5 25.58 18.68 0.813 0.561 5.0 23.28 28.38 0.602 1.021 7.5 28.46 19.63 0.591 0.428 10.0 32.79 17.77 0.744 0.268 12.5 37.21 22.88 0.651 0.464 Corr (r) 0.913 0.037 0.523 -0.585 P < 0.05 < 0.05 < 0.05 < 0.05 < 0.05 Figures 3 a and 3 b graphically illustrate the variations in dielectric constant (εr) with fertilizer concentration for both UREA and CAN across the lower and middle catenas. Figure 3 a presents the dielectric response in the lower catena. UREA-treated soils demonstrate a clear upward trend in this landscape position, with εr rising consistently from approximately 20 at 0% concentration to over 40 at 12.5%. This indicates a strong and predictable positive linear relationship between UREA concentration and dielectric response. The consistent pattern highlights UREA’s suitability for dielectric-based soil assessment in depositional zones. In contrast, CAN-treated soils in the lower catena display a non-linear and inconsistent pattern. Although εr initially increases, it fluctuates erratically at higher concentrations. For example, at 5% concentration, εr shows a noticeable rise, but this trend does not persist uniformly. These irregularities suggest that complex interactions between nutrient dynamics, moisture, and soil texture influence CAN’s impact on dielectric behavior in the lower catena. Figure 3b displays the dielectric constant patterns in the middle catena. Both fertilizers exhibit increasing εr trends with concentration. However, CAN demonstrates a more pronounced and higher dielectric response at all levels. Specifically, εr for CAN increases sharply from 7.6 at 0% to 37.2 at 12.5%, indicating a robust and linear response. UREA also shows a positive trend, rising from approximately 8 to 23, though at a more moderate rate. The strong Pearson correlation coefficients observed (CAN: r = 0.913; UREA: r = 0.905) reinforce the reliability of these patterns. These results suggest that the middle catena provides a stable and uniform environment for predicting dielectric behavior based on fertilizer input. The comparative trends between UREA and CAN highlight that UREA provides more consistent dielectric responses across varying concentrations, especially in the lower catena. Meanwhile, CAN shows superior responsiveness in the middle catena but with diminished reliability in lower slope positions. These findings suggest that fertilizer selection and topographical context must be considered together when applying dielectric sensing methods for precision agriculture. Figures 4 a and 4 b illustrate the variation in soil electrical conductivity (σ) as influenced by fertilizer concentration in the lower and middle catenas, respectively. Figure 4 a depicts conductivity trends in the lower catena. UREA-treated soils show a clear, nearly linear increase in conductivity with increasing fertilizer concentration. Conductivity rises from approximately 0.76 S/m at 0% to over 2.0 S/m at 12.5%, highlighting a strong and positive relationship. This pattern reflects the consistent ionic contribution of UREA, which enhances charge mobility within the soil matrix. Conversely, CAN-treated soils exhibit a more erratic pattern. While conductivity values begin similarly, peaking at 1.02 S/m at 5% concentration, they then fluctuate irregularly and even decline with higher concentrations. This non-linear behavior suggests that in the lower catena, CAN’s effect on conductivity is influenced by interactions with existing soil chemistry, porosity, and moisture retention capacity. In Fig. 4 b, conductivity trends in the middle catena are more stable for both fertilizers. CAN demonstrate consistently higher conductivity values across the concentration range, increasing steadily from 0.16 S/m to 0.81 S/m. While slightly lower overall, UREA follows a similar increasing trend, rising from 0.159 S/m to 0.900 S/m. The strong positive correlation coefficients for UREA (r = 0.858) and moderate correlation for CAN (r = 0.523) support these visual trends. The smoother behavior of both fertilizers in this zone can be attributed to the middle catena’s more balanced soil structure and water retention properties, which facilitate better ion exchange and mobility. Together, these results emphasize that UREA produces more predictable and linear conductivity changes, particularly in lower slope positions. In contrast, CAN’s variable performance in the lower catena contrasts with its more stable and enhanced response in the middle catena. Such differences are essential for optimizing fertilizer application strategies based on topographic location. Table 6 summarizes the comparison of dielectric constant and Conductivity between UREA and CAN. Table 6 Summary of comparison of dielectric constant and Conductivity between UREA and CAN dielectric constant (εr) Aspect Lower Catena ( Fig. 3 a ) Middle Catena ( Fig. 3 b ) REA Trend Strong, linear increase Steady increase, high reliability CAN Trend Erratic and non-linear Steady and moderate increase Implication UREA is more consistent Both fertilizers are predictive Mapping Suitability UREA is preferred for stability Both UREA and CAN are suitable Conductivity Aspect Lower Catena ( Fig. 4 a ) Middle Catena ( Fig. 4 b ) REA Trend Strong, linear increase Strong, linear increase CAN Trend Erratic and non-linear Erratic and non-linear Implication UREA is more suitable for monitoring UREA is more suitable for monitoring Mapping Suitability UREA preferred for accuracy UREA preferred for accuracy Table 7 provides additional insights into how soil moisture content influences dielectric properties. The dielectric constant and conductivity exhibit non-linear behavior across varying moisture levels. At 10% moisture content, the dielectric constant is relatively low (5.9), and conductivity is also minimal (0.2 S/m), reflecting the limited availability of free water and ionic mobility in the soil matrix. When the moisture content increases to 20%, the dielectric constant rises sharply to 27.7, accompanied by a substantial increase in conductivity to 0.7 S/m. This suggests that the presence of additional water enhances the soil’s ability to polarize and conduct electricity due to increased dielectric relaxation and ion mobility. Interestingly, at 30% moisture content, the dielectric constant decreases to 13.8 while conductivity remains stable at 0.2 S/m. This unexpected drop may reflect structural rearrangements or water redistribution that temporarily reduce the effective dielectric response, possibly due to air entrapment or uneven wetting. At 40% moisture, both dielectric constant and conductivity surge to their highest values, 57.9 and 2.6 S/m, respectively. This indicates a saturation threshold where water fills the soil pores, maximizing electrical conduction and dielectric polarization. These findings highlight that moisture generally enhances dielectric behavior, but the relationship is not strictly linear. Instead, it is governed by complex interactions involving soil texture, porosity, and the spatial distribution of water within the soil matrix. Table 7 Dielectric properties of soil for different moisture content Moisture content (%) Dielectric constant Conductivity(S/m) 10 5.9 0.2 20 27.7 0.7 30 13.8 0.2 40 57.9 2.6 Discussion The findings of this study emphasize the utility of dielectric properties, specifically relative permittivity (εr) and electrical conductivity (σ), as reliable indicators of fertilizer-induced soil changes across different landscape positions. The strong linear correlations between UREA concentration and both εr and σ, particularly in the lower catena, corroborate earlier studies which demonstrate that nitrogen-based fertilizers, such as UREA, significantly enhance the ionic content and polarization capacity of soils (Ahire et al., 2013; Kargas & Soulis, 2019; Brovelli & Cassiani, 2011). The linear response pattern suggests that UREA application leads to increased free ion mobility and enhanced soil conductivity, especially under controlled moisture conditions (Abdulraheem et al., 2024; Zhang et al., 2024). Conversely, the dielectric response to CAN was less predictable in the lower catena, reflecting more complex chemical and physical interactions. This inconsistency aligns with findings from Rhoades et al. (1989) and Malinowska and Szumacher (2013), which indicate that terrain variability and cation exchange dynamics play critical roles in modifying soil electrical properties. The relatively erratic εr and σ values for CAN at higher concentrations suggest that ammonium and calcium ions may interact differently with the colloidal matrix, leading to non-linear electromagnetic responses (Das & Paul, 2015; Akash et al., 2024). The middle catena, however, displayed more consistent behavior across both fertilizer types. CAN demonstrated a strong linear increase in εr and moderate conductivity improvements, supporting previous reports that calcium ammonium nitrate enhances soil structure and moisture retention (Reza et al., 2025; Dospatliev et al., 2014). This topographic zone likely benefits from intermediate run-off and infiltration conditions, leading to more uniform nutrient distribution and electromagnetic behavior (Abdulraheem et al., 2024; Yuzugullu et al., 2020). Moisture content emerged as a significant factor influencing dielectric properties. The sharp rise in both εr and σ between 10% and 20% moisture levels is consistent with the dielectric theory, which postulates that water molecules enhance permittivity through dipole polarization and contribute to increased conductivity via dissociated ions (Kargas & Soulis et al., 2019). However, the dip in dielectric constant at 30%, followed by a sharp increase at 40%, suggests that moisture-soil interactions are not solely dependent on water content but are also governed by pore space configuration and water distribution (Kargas & Soulis, 2019; Brovelli & Cassiani, 2011; Adamchuk et al., 2004). The presence of non-linear trends at higher moisture levels and fertilizer concentrations indicates the onset of saturation and reduced air-filled porosity, which could impact sensor performance and data interpretation in the field. Similar findings have been reported in controlled dielectric studies that associate anomalous readings with partial saturation and the presence of bound water layers (Shruthi & Menon, 2016). Notably, the robustness of the LPDA-NanoVNA sensor system was affirmed by low measurement variability across replicates, which supports its suitability for real-time, non-invasive field assessments. The observed coefficient of variation below 5% aligns with technical performance standards cited in remote sensing applications for soil evaluation (Hendricksen et al., 2025). This study reinforces the applicability of electromagnetic sensing in precision agriculture. The findings show that UREA is more suitable for dielectric mapping in heterogeneous and lower slope soils, while CAN performs optimally in more stable, middle catena positions. These results support the development of adaptive, site-specific fertilizer application strategies and contribute to broader sustainable land management efforts aligned with SDG 15. The experimental findings revealed significant trends in the dielectric response of soils treated with fertilizers. For both UREA and CAN, relative permittivity (εr) and conductivity (σ) generally increased with fertilizer concentration, particularly in the middle and lower catenas. This aligns with the theoretical understanding that ionic content and moisture retention, both affected by fertilizer application, enhance the dielectric behavior of soils (Ahire et al., 2015; Van Dam et al., 2005; Abdulraheem et al., 2024; Rhoades et al., 1989). Notably, UREA-treated soils exhibited a near-linear increase in εr and σ, suggesting a predictable response pattern that could be effectively modeled and mapped. This consistency across catena levels underscores the reliability of UREA as a benchmark fertilizer for dielectric soil mapping (Pathirana et al., 2023; Kargas & Soulis, 2019; Brovelli & Cassiani, 2011). Conversely, CAN showed inconsistent behavior in the lower catena, likely due to its dual nutrient composition (calcium and ammonium) and varying interaction with soil colloids and porosity (Das & Paul, 2015). The variations observed between catena positions reflect the influence of run-off, soil texture, and baseline nutrient levels. The lower catena exhibited higher permittivity and conductivity as a depositional zone, supporting previous findings on landscape-based soil differentiation (Malinowska & Szumacher, 2013; Nyaga et al., 2021; Onyango et al., 2021). These findings validate the LPDA-based system’s potential for in-situ soil nutrient monitoring. The use of S-parameters allows for real-time, non-contact measurements, offering a scalable alternative to traditional lab-based methods (Hendricksen et al., 2025; Reza et al., 2025). This innovation aligns with the push for digital agriculture and supports SDG 15 by promoting sustainable soil management practices. Future work should incorporate machine learning models to predict nutrient classes from dielectric readings and expand the system’s application to other agro-ecological zones (Abdulraheem et al., 2024; Zhang et al., 2024). Integrating GPS and remote sensing data could further enhance the spatial resolution of soil fertility maps, enabling precise and equitable resource allocation for smallholder farmers (Abdulraheem et al., 2024; Yuzugullu et al., 2020; Adamchuk et al., 2004). Conclusion This study confirms that dielectric properties, specifically permittivity and conductivity, can serve as proxies for nutrient status in soils treated with UREA and CAN fertilizers. The findings validate the applicability of LPDA and NanoVNA systems as affordable, non-destructive tools for soil fertility monitoring. The observed trends, especially the linear increase in dielectric properties with UREA concentrations, provide a strong foundation for developing digital soil mapping systems. By capturing real-time nutrient dynamics using contactless sensors, this approach offers a scalable pathway toward precision agriculture, especially for smallholder farmers. The study contributes to sustainable soil management and aligns with SDG 15 by promoting resource-efficient fertilizer use. Future research should refine these models through larger field trials and integrate machine learning for predictive soil nutrient classification. Based on the results, the following guidelines for data collection can be proposed: The dielectric constant and conductivity in the middle and lower catenas significantly increase as the concentration of UREA fertilizer rises. This observation highlights the sensitivity of dielectric properties to varying levels of UREA in the soil. Since these properties differ across different geographical locations within the catena, these variations can be systematically mapped to represent the measured dielectric values specifically associated with UREA fertilizer. In the middle catenas, the dielectric constant and conductivity increase with higher UREA concentrations, sufficiently distinct to allow clear differentiation between specific locations. This trend makes identifying patterns and drawing meaningful conclusions from the data easier. In contrast, in the lower catena, the dielectric constant and conductivity measurements for calcium ammonium nitrate (CAN) fertilizer exhibit considerable fluctuations. These inconsistencies result in overlapping values that challenge distinguishing between specific measurement points or fertilizer effects, complicating the mapping and interpretation process. Generally, CAN exhibits higher values for dielectric constant and conductivity than UREA within the middle catena, providing a reliable basis for identifying and distinguishing between these two types of fertilizers in this region. However, in the lower catena, the scenario is more complex. For fertilizer concentrations between 0% and 5%, the dielectric constant and conductivity values for both UREA and CAN are nearly identical, making differentiation between the two fertilizers challenging. Beyond this concentration range, a contrasting trend is observed: UREA values steadily increase while CAN values decrease. This divergence in behavior allows UREA to be more easily identified at concentrations exceeding 5%, providing critical insights for fertilizer analysis and application in these regions. Declarations Declaration of interest statement The authors report there are no competing interests to declare Declaration of funding The authors didn’t receive funding for the research. 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Akash, M., Kumar, P. M., Bhaskar, P., Deepthi, P. R., & Sukhdev, A. (2024). Review of estimation of soil moisture using active microwave remote sensing technique. Remote Sensing Applications: Society and Environment , 33 , 101118. Anderson, J. M., & Ingram, J. S. (1994). Tropical soil biology and fertility: a handbook of methods. Soil Science , 157 (4), 265. Bremmer, J. M., & Mulvaney, C. S. (1996). Nitrogen-total. Methods of soil analysis, Part 2. Soil Science Society of America Book Series , 5 , 1085-1121. Bremner, J. M., & Keeney, D. R. (1965). Steam distillation methods for determination of ammonium, nitrate and nitrite. Analytica chimica acta , 32 , 485-495. Brovelli, A., & Cassiani, G. (2011). Combined estimation of effective electrical conductivity and permittivity for soil monitoring. Water Resources Research , 47 (8). Das, K., & Paul, P. K. (2015). Present status of soil moisture estimation by microwave remote sensing. Cogent Geoscience , 1 (1), 1084669. De Pauw, E. (1982). 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A 12 000 year record of vegetation change and soil development from Wien Lake, central Alaska. Canadian Journal of Botany , 71 (9), 1133-1142. Kargas, G., & Soulis, K. X. (2019). Performance evaluation of a recently developed soil water content, dielectric permittivity, and bulk electrical conductivity electromagnetic sensor. Agricultural Water Management , 213 , 568-579. Kauzeni, A. S., Kikula, I. S., Mohamed, S. A., Lyimo, J. G., & Dalal-Clayton, D. B. (1993). Land use planning and resource assessment in Tanzania: a case study . Dar es Salaam, Tanzania and London, UK: International Institute for Environment and Development. Kazema, T., & Mgawe, B. (2023). Design Concept for Robot-Mounted Dielectric Sensor for On-Field Soil Nutrient Determination. In 2023 First International Conference on the Advancements of Artificial Intelligence in African Context (AAIAC) (pp. 1-4). IEEE. Keeney, D. R. (1982). Nitrogen management for maximum efficiency and minimum pollution. Nitrogen in agricultural soils , 22 , 605-649. Kilombele, H., Feleke, S., Abdoulaye, T., Cole, S., Sekabira, H., & Manyong, V. (2023). Maize productivity and household welfare impacts of mobile money usage in Tanzania. International Journal of Financial Studies , 11 (1), 27. Malinowska, E., & Szumacher, I. (2013). Application of the catena concept in studies of landscape system dynamics. Miscellanea Geographica. Regional Studies on Development , 17 (4), 42-49. Mgawe, B., Kazema, T., Dao, H. N., & Krairiksh, M. (2021, September). Dielectric properties of fertilized soil in a Catena: A case of Mwanza, Tanzania. In 2021 IEEE AFRICON (pp. 1-6). IEEE. Nyaga, J. M., Onyango, C. M., Wetterlind, J., & Söderström, M. (2021). Precision agriculture research in sub-Saharan Africa countries: A systematic map. Precision Agriculture , 22 , 1217-1236. Okalebo, J. R., Gathua, K. W., & Woomer, P. L. (2002). Laboratory methods of soil and water analysis: A working manual. Onyango, C. M., Nyaga, J. M., Wetterlind, J., Söderström, M., & Piikki, K. (2021). Precision agriculture for resource use efficiency in smallholder farming systems in sub-saharan africa: A systematic review. Sustainability , 13 (3), 1158. Oyana, T. J., Kayendeke, E., Bamutaze, Y., & Kisanga, D. (2015). A field assessment of land use systems and soil properties at varied landscape positions in a fragile ecosystem of Mount Elgon, Uganda. African Geographical Review , 34 (1), 83-103. Page, A. L. (Ed.). (1982). Methods of soil analysis. Part 2. Chemical and microbiological properties (pp. 1159-pp). Pathirana, S., Lambot, S., Krishnapillai, M., Cheema, M., Smeaton, C., & Galagedara, L. (2023). Ground-penetrating radar and electromagnetic induction: challenges and opportunities in agriculture. Remote Sensing , 15 (11), 2932. Rajesh Mohan, R. M., Mridula, S., & Mohanan, P. (2015). Study and analysis of dielectric behavior of fertilized soil at microwave frequency. EJAET , 2 , 73-79. Reza, M. N., Lee, K. H., Karim, M. R., Haque, M. A., Bicamumakuba, E., Dey, P. K., ... & Chung, S. O. (2025). Trends of Soil and Solution Nutrient Sensing for Open Field and Hydroponic Cultivation in Facilitated Smart Agriculture. Sensors , 25 (2), 453. Rhoades, J. D., Manteghi, N. A., Shouse, P. J., & Alves, W. J. (1989). Soil electrical conductivity and soil salinity: New formulations and calibrations. Soil Science Society of America Journal , 53 (2), 433-439. Rwehumbiza, F. B. R., Hatibu, N., & Machibya, M. (1999). Land characteristics, run-off and potential for rainwater haversting in semi-arid areas of Tanzania. Shruthi, A., & Menon, S. K. (2016, August). Design and analysis of modified log periodic dipole antenna with enhanced gain. In 2016 Progress In Electromagnetic Research Symposium (PIERS) (pp. 1972-1976). IEEE. Van Dam, R. L., Borchers, B., & Hendrickx, J. M. (2005). Methods for prediction of soil dielectric properties: a review. Detection and remediation technologies for mines and minelike targets X , 5794 , 188-197. Wang, J. R., & Schmugge, T. J. (1980). An empirical model for the complex dielectric permittivity of soils as a function of water content. IEEE Transactions on Geoscience and remote sensing , (4), 288-295. Webster, C. R., May, R. D., Toohey, D. W., Avallone, L. M., Anderson, J. G., Newman, P., ... & Chan, K. R. (1993). Chlorine chemistry on polar stratospheric cloud particles in the Arctic winter. Science , 261 (5125), 1130-1134. Yang, Z., Jingjian, H., Weiwei, W., & Naichang, Y. (2017). A printed LPDA antenna fed by a microstrip line to double sided parallel strip line from backside. International Journal of Antennas and Propagation , 2017 (1), 6259682. Yuzugullu, O., Lorenz, F., Fröhlich, P., & Liebisch, F. (2020). Understanding fields by remote sensing: Soil zoning and property mapping. Remote Sensing , 12 (7), 1116. Zhang, W., Xiong, Y., Zhang, Y., Taiwo, L. B., Farooque, A. A., & Hu, J. (2024). Recent Advances in Dielectric Properties-Based Soil Water Content Measurements. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6761968","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":462691374,"identity":"5fcf6767-6496-4127-815a-cde9a834f9a8","order_by":0,"name":"Twahir Kazema","email":"","orcid":"","institution":"Mbeya University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Twahir","middleName":"","lastName":"Kazema","suffix":""},{"id":462691375,"identity":"5dce303d-6315-4781-8fe7-c371ddbc71e0","order_by":1,"name":"Ibrahim L. Kadigi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYBACCWYGAxDNw8DAfADElyFeCw8DWwKIz0NYCwNEC9AaHph1BIBkO/PGBz933JGxZ+/5/OpGjQUPA/vhoxvwaZFmZis27D3zjIeH5+w265xjQIfxpKXdwKdFjpnHTIK37TAPj0TuNuMcNqAWCR4zQlrMf/4Fa8l5Zpzzjwgt0kBbmCG25DA/zm0jQotkM1uxtCxIy5ljZsy5fRI8bIT8InH+8MaPb9sO27O3Nz/+nPOtTo6f/fAxvFqQAZsEmCRWOQgwfyBF9SgYBaNgFIwcAABRJT5n6r6ChgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-9915-280X","institution":"Mbeya University of Science and Technology (MUST)","correspondingAuthor":true,"prefix":"","firstName":"Ibrahim","middleName":"L.","lastName":"Kadigi","suffix":""},{"id":462691376,"identity":"a4c7af0c-41c6-4474-bf7b-2fe9cc30c677","order_by":2,"name":"Stefan Sieber","email":"","orcid":"","institution":"Leibniz-Zentrum fur Agrarlandschaftsforschung (ZALF e. V.)","correspondingAuthor":false,"prefix":"","firstName":"Stefan","middleName":"","lastName":"Sieber","suffix":""}],"badges":[],"createdAt":"2025-05-27 18:34:43","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6761968/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6761968/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83655545,"identity":"14ee13fc-0bb8-4290-97ee-c97aff4da057","added_by":"auto","created_at":"2025-05-30 08:28:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":274781,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSoil sample collection in the field\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6761968/v1/32dfc5cc4e5e0e6b6c9c3a20.png"},{"id":83655544,"identity":"e14d6ab8-d474-4f5a-9d57-c362244706f2","added_by":"auto","created_at":"2025-05-30 08:28:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41408,"visible":true,"origin":"","legend":"\u003cp\u003eDesigned Automated System\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6761968/v1/8264e41933a277ddbcb7f1e1.png"},{"id":83654782,"identity":"5ec3f329-787e-46a8-8227-76703d57adca","added_by":"auto","created_at":"2025-05-30 08:20:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":12488,"visible":true,"origin":"","legend":"\u003cp\u003eWorking Principle\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6761968/v1/93fa665e9f00f1ace367d918.png"},{"id":83654790,"identity":"8c5d1249-11f2-4f9f-a49b-9692929a0e4e","added_by":"auto","created_at":"2025-05-30 08:20:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":366982,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 3a: Dielectric constant comparison between CAN and UREA fertilizer for Lower Catena\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3b: Dielectric constant comparison between CAN and UREA fertilizer for Middle Catena\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"03.png","url":"https://assets-eu.researchsquare.com/files/rs-6761968/v1/7bc7b12fbaf1f52fa823d81c.png"},{"id":83654792,"identity":"e66fa75b-f083-45c7-b078-a4d0aea41567","added_by":"auto","created_at":"2025-05-30 08:20:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":298039,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 4a: Comparison of conductivity between UREA and CAN fertilizer, Lower Catena\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4b: Comparison of conductivity between UREA and CAN fertilizer, Middle catena\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"04.png","url":"https://assets-eu.researchsquare.com/files/rs-6761968/v1/18b7558e0a04dae04162f600.png"},{"id":83655661,"identity":"a9fc96b3-0a48-42c1-8495-4bd42d4f5376","added_by":"auto","created_at":"2025-05-30 08:36:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2028014,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6761968/v1/78903f30-414a-4cc2-915e-7823fa21db0f.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eNon-Destructive Assessment of Soil Nutrient Variability in Mbeya Catena Using Dielectric Properties via Low-Cost Antenna Systems\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTanzania\u0026rsquo;s Southern Highlands, particularly the Mbeya Region (which is located between latitudes 7\u0026deg; and 9\u0026deg; south of the equator and longitudes 32\u0026deg; and 34\u0026deg; east of Greenwich), are known for their fertile volcanic soils and significant agricultural productivity. Agriculture dominates the local economy, with smallholder farmers growing staple crops like maize, rice, and beans and cash crops like coffee, tea, and tobacco. However, declining soil fertility due to continuous cropping, limited following, and mismanagement of fertilizers poses a growing threat to productivity (Kilombele et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In Mbeya City Catena, nutrient variability and degradation are exacerbated by topographical differences and run-off dynamics (Malinowska \u0026amp; Szumacher, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). While chemical soil testing remains the gold standard for assessing fertility, it is costly, labor-intensive, and inaccessible to most smallholder farmers (Onyango et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nyaga et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSoil management requires knowledge of the catena (Malinowska \u0026amp; Szumacher, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The highland and lowland tend to be fertile compared to the upland areas, which often lack exchangeable bases and phosphorus. The catena concept was introduced to examine the systematic changes in soil types along a slope. Gaining insight into the soils that form a catena can aid in effectively mapping soils within a specific region. In Mbeya, fertilizer is widely applied in farming, but the uncontrolled use has led to soil quality degradation and reduced crop yields. Previously, soil fertility was maintained through fallowing, but growing population pressures have made this practice less feasible in many areas. As a result, a noticeable deterioration of soil pH and a decrease in nutrient levels and organic matter. Research has been done to study the soil quality in Tanzania (De Pauw \u0026amp; Espinosa, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Kauzeni et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Rwehumbiza et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), but they don\u0026rsquo;t apply the Non-destructive measurements of soil. Mgawe et al. researched nutrient soil management using an antenna to find the relationship between soil nutrients and soil dielectric constant in the Mwanza Region, Tanzania (Mgawe et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Their research found a linear relationship between dielectric constant, conductivity, and moisture content with the soil nutrients and fertilizer concentration. The relationship between chemical and dielectric properties of soil was also presented in work done by Ahire et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Dospatliev et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStudies on the relationship between fertilizer concentration and the dielectric properties of soil have been conducted by Rajesh Mohan et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), where it was observed that the dielectric properties of soil tend to increase as the fertilizer concentration rises. These investigations provide critical insights into how chemical additives affect the soil\u0026rsquo;s electromagnetic behavior. The researchers employed various sophisticated measurement methods in controlled laboratory environments, utilizing soil-filled waveguides and dielectric probes in conjunction with a network analyzer to ensure precise and reliable results. However, a notable exception is a study (Mgawe et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which adopted a different approach by employing a log-periodic antenna, showcasing an alternative methodology for analyzing soil dielectric properties. The techniques discussed are unsuitable for on-farm assessments, which must be contactless and non-invasive. Mgawe et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) also proposed the design of a dielectric property determinant robot for detecting soil nutrients suitable for implementation in developing countries (Kazema \u0026amp; Mgawe, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These studies focus on soil characteristics that do not apply non-contact, non-destructive techniques.\u003c/p\u003e \u003cp\u003eThis study is an extension of (Mgawe et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) that now aims to explore the relationship between soil nutrients in Mbeya City and fertilizer concentrations by analyzing the S-parameters of waves reflected from the soil samples using an innovative measurement and instrumentation system that was developed with support from innovators at the Centre for Innovation and Technology Transfer (CITT) of Mbeya University. This system facilitates the measurement of reflected waves from soil samples using a portable network analyzer, a log-periodic dipole antenna centered at 945 MHz, and a control box that ensures measurements are free from human interference, which could compromise the accuracy of the results. The S-parameters the network analyzer detects are used to calculate the soil\u0026rsquo;s dielectric properties and conductivity (σ). The experiments utilized two commonly used fertilizers in the area, UREA and Calcium Ammonium Nitrate (CAN). Specifically, the study aims to (i) assess the dielectric properties (permittivity and conductivity) of fertilized soils across catena positions in Mbeya using a low-cost LPDA-based sensing system; (ii) evaluate the influence of varying concentrations of UREA and CAN fertilizers on soil dielectric responses and (iii) validate the effectiveness of non-invasive dielectric measurements for potential application in precision soil fertility mapping.\u003c/p\u003e \u003cp\u003eUnderstanding spatial variations in soil nutrients is vital for improving fertilizer application, minimizing environmental impact, and increasing yields. Previous research has applied electromagnetic techniques to estimate soil moisture and texture, yet their application in nutrient assessment remains underexplored in Sub-Saharan Africa (Abdulraheem et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kargas \u0026amp; Soulis et al., 2019). Notably, the study by Mgawe et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) demonstrated a potential link between fertilizer concentration and soil dielectric properties using an antenna-based method in Mwanza, Tanzania. However, most prior studies have relied on laboratory-based dielectric probes, which are impractical for field use (Kargas \u0026amp; Soulis, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Brovelli \u0026amp; Cassiani, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Shruthi \u0026amp; Menon, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This study addresses the gap by employing a low-cost, portable, and non-contact sensing system based on log-periodic dipole antennas. It aims to validate the relationship between soil nutrients and electromagnetic parameters, particularly S-parameters (reflection coefficients), using UREA and CAN as representative fertilizers.\u003c/p\u003e \u003cp\u003eThe broader goal is to develop a scalable real-time soil nutrient monitoring technique to support sustainable land use planning and inform policy interventions. Specifically, the study aims to assess the dielectric properties (permittivity and conductivity) of fertilized soils across catena positions in Mbeya using a low-cost LPDA-based sensing system, evaluate the influence of varying concentrations of UREA and CAN fertilizers on soil dielectric responses and to validate the effectiveness of non-invasive dielectric measurements for potential application in precision soil fertility mapping. The results of this study can empower farmers and agricultural experts to assess the nutrient status of their land using a non-destructive handheld device. With such technology readily accessible, soil management can be significantly improved, contributing to the achievement of the United Nations\u0026rsquo; 15th Sustainable Development Goal (SDG 15).\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Preparation of soil samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSoil samples were collected from the lower and middle catenas in Iyunga ward in the Mbeya Region of Tanzania, as shown in Figure 1. The soil samples were first cleaned of coarser particles; the finer particles were oven-dried at approximately 120 degrees Celsius to eliminate available moisture. Using the moisture sensor Demetra (PAT. 193478), Soil samples with a gravimetric moisture content of ten percent (10%) were meticulously prepared by adding a precisely measured amount of water to ten kilograms (10 kg) of completely dried-out soil to achieve the desired moisture level. Specific concentrations of fertilizers were carefully added to the soil samples to examine the effect of fertilizers on the soil properties. This was achieved by introducing an exact amount of fertilizer into the soil and placing the resulting mixture in a sealed container. The sealing ensured that proper settling, adequate drainage, and thorough mixing occurred over time, creating a consistent and homogeneous soil-fertilizer blend.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe experiment utilized two commonly used fertilizers, calcium ammonium nitrate (CAN) and UREA, which were tested for their influence on soil properties. Fertilizer treatments included UREA and CAN applied at six concentration levels (0%, 2.5%, 5%, 7.5%, 10%, and 12.5%). Each treatment was homogenized and incubated to simulate field interaction. This equated to fertilizer quantities ranging from 100 grams to 1000 grams for each sample, ensuring a broad spectrum of concentrations was studied to capture the variations in soil response.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Dielectric properties system setup\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA low-cost dielectric measurement system was developed using a Nano Vector Network Analyzer (NanoVNA) and a log-periodic dipole antenna (LPDA) optimized at 945 MHz. The system, therefore, comprises three main components: NanoVNA, LPDA, and a control circuit. The control circuit regulates the upward and downward movement of the LPDA, ensuring accurate positioning and optimal measurement conditions. The LPDA was vertically positioned 10 cm above the soil surface, and S-parameters were captured and calibrated using standard open-short-load procedures. The NanoVNA, a highly cost-effective and portable vector network analyzer, can operate at frequencies of up to 1.5 GHz, making it suitable for a wide range of applications in soil dielectric property analysis. Paired with this device is a low-cost LPDA (Yang et al., 2017), which serves as the primary measurement antenna.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe LPDA, designed for efficiency and reliability, is housed within a compact, lightweight plastic enclosure with dimensions of 40x20x3 cm\u0026sup3;. This protective casing not only ensures durability and ease of handling but also minimizes external interference, enhancing the accuracy and repeatability of the measurements performed by the system. Antenna motion was controlled by an Arduino-based motor system for reproducible positioning, following methods validated in Shruthi \u0026amp; Menon (2016). It is important to note that the dimensions of the soil container are large enough to minimize edge diffraction, with the antenna positioned 10 cm above the soil surface. Figure 2 presents the designed automated system, and Figure 3 illustrates the working principle; this work enhances the previous design by incorporating an electronic control system that manages the upward and downward movement of the sensor (antenna).\u003c/p\u003e\n\u003cp\u003eThe control system is powered by an alternating current (a.c) source, which provides the necessary electrical energy to drive its operation. This system is integrated with an Arduino microcontroller, meticulously programmed to manage the motor\u0026rsquo;s rotational movements precisely. The motor is programmed to rotate in clockwise and counterclockwise directions, performing this motion five times to complete a single measurement cycle. When rotating clockwise, the motor lowers the antenna, securely attached via a cable, positioning it exactly 10 cm above the soil surface to accurately measure the soil\u0026rsquo;s dielectric properties. Conversely, when rotating counterclockwise, the motor raises the antenna to 30 cm from the soil. This elevated position is specifically designed to facilitate the replacement of soil samples, ensuring the process is efficient and does not interfere with subsequent measurements. The Arduino, motor, and antenna coordination ensure seamless and repeatable operation throughout the experimental procedure. Before conducting soil measurements, the NanoVNA was calibrated to eliminate potential errors. The study revealed minimal standard deviation after multiple sample measurements, indicating that the NanoVNA demonstrated reliable stability.\u003c/p\u003e\n\u003cp\u003eAdditionally, the equipment was tested, and its results were compared with those obtained from laboratory equipment. The magnitude response closely matched that of the laboratory equipment. However, the phase was observed to be advanced by 90\u0026deg;, as noted by Mgawe et al. (2021).\u003c/p\u003e\n\u003cp\u003eIn practice, the NanoVNA results were adjusted by subtracting 90\u0026deg;. The average values were used to calculate the dielectric constant (ϵr) and conductivity (\u0026sigma;) of the soils, as demonstrated in Equations (1) and (2), respectively. The NanoVNA was calibrated using the standard open, short, and load procedures, with a 20 cm coaxial cable serving as the connection to the LPDA. During the calibration process, careful adjustments were made to account for the additional coaxial cable and the LPDA\u0026rsquo;s feed line to ensure accurate system calibration. Considering these components, the net magnitude and phase of the soil measurements can be accurately determined. This is achieved by subtracting the calibration reference values from the measured values of the soil under examination. Measurement results from the measurements taken when the antenna is pointed at the open air. Dielectric constant and electrical conductivity were calculated from reflection coefficient magnitude and phase (\u0026Gamma;, \u0026phi;) using the models described in Rhoades et al. (1989) and further supported by Ahire et al. (2015) and can be computed as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Additionally, a two-way Analysis of Variance (ANOVA) was conducted to evaluate the effects of fertilizer type (UREA vs. CAN) on \u0026epsilon;r and \u0026sigma;, the main effect of fertilizer concentration level (six levels: 0%, 2,5%, 7.5%, 10%, 12.5%) on \u0026epsilon;r and \u0026sigma;, and their interaction on soil dielectric constant and conductivity (\u0026epsilon;r and \u0026sigma;). The ANOVA tested for main effects and interaction effects, using a significance threshold of \u0026nbsp; using equation 4. Each test was run separately for the middle and lower catenas.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eThis analysis allowed the isolation of individual and combined effects, confirming where the response patterns were consistent or varied, particularly in the lower catena. These inferential tools were crucial in establishing statistically significant relationships between experimental variables and supporting the reliability of the findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Soil physical and chemical properties\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe distribution of soil particles was thoroughly examined using the hydrometer method, as specified and referenced in (Anderson \u0026amp; Ingram, 1993; Okalebo et al., 2002), including pH, organic carbon, CEC, total nitrogen (Kjeldahl method), and available phosphorus (Bray-1 extraction). This method provides detailed insights into the textural composition of the soil by separating particles based on their size. The total nitrogen content within the soil was determined using the Kjeldahl digestion-distillation method, a widely recognized and reliable procedure for nitrogen analysis. This process adhered to the methodologies outlined in the sources (Bremner \u0026amp; Keeney, 1965; Page, 1982; Keeney, 1982; Hu et al., 1993). Additionally, the available phosphorus content (Bray-1 extraction) was assessed using a standard procedure described by Hu et al. (1993), ensuring accurate quantification of this essential nutrient. The organic carbon content of the soil was quantified through a chemical oxidation method, as meticulously outlined in Webster et al. (1993). To evaluate soil pH, a water suspension was prepared at a ratio of 1:2.5, providing a clear understanding of the soil\u0026rsquo;s acidity or alkalinity levels. The cation exchange capacity (CEC), a critical indicator of soil fertility, along with the concentrations of exchangeable basic cations, was determined by extracting these elements using a 1.0 M neutral ammonium acetate (NH4AC) solution. This extraction process facilitates precise measurements of cations in the soil. Sodium (Na++) and potassium (K++) levels were analyzed using a flame photometer, a technique known for its accuracy in detecting alkali metals.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast, the concentrations of calcium (Ca2+2+) and magnesium (Mg2+), essential nutrients for plant growth, were determined using atomic absorption spectrophotometry (AAS), following the rigorous methodology described in (Anderson \u0026amp; Ingram, 1993). Soil bulk density, a measure of the soil\u0026rsquo;s compaction and porosity, was measured gravimetrically, adhering to the procedure described in (Webster et al., 1993). The soil\u0026rsquo;s electrical conductivity (EC), indicative of its salinity and nutrient availability, was determined using a water suspension prepared at the same 1:2.5 ratio. Furthermore, the transitional moisture content (W_t) and the wilting point (WP), which are crucial for understanding the soil\u0026rsquo;s water retention and availability to plants, were calculated using the method outlined in (Wang \u0026amp; Schmugge, 1980). Dielectric constant and electrical conductivity were calculated from reflection coefficient magnitude and phase (\u0026Gamma;, \u0026phi;) using the models described in Rhoades et al. (1989) and further supported by Ahire et al. (2015).\u003c/p\u003e\n\u003cp\u003eTables 1 and 2 summarize the soil\u0026rsquo;s detailed chemical and physical properties. Table I highlights that those soils sampled across all sites, upper, middle, and lower catena, are predominantly classified as sandy loam. These soils are characterized by their coarse grain structure, high porosity, and well-drained nature, features that influence their agricultural suitability and water retention capabilities. The data presented in Table 3 indicate that the measured moist soil exhibits significantly higher permittivity and conductivity values compared to the dry soil. This difference highlights the influence of moisture content on the soil\u0026rsquo;s electrical properties, where water molecules substantially enhance the soil\u0026rsquo;s ability to store and conduct electric charge. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Soil samples: physical properties\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil Properties\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiddle catena\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower catena\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTexture\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eSilt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eClay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eSand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eTransition Moisture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eTextural class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eSandy Loam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003eLoam Sandy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eWilting Point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Soil samples: chemical properties\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"552\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSoil Properties\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 408px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eName of the soil sample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiddle catena\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower catena\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eAV.P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e17.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eCa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eCEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eMg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eNa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eOC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e6.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e5.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eTN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: \u0026nbsp;Permittivity conductivity of dry soil and soil with 10% moisture content (m.c.)\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eTables \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e present the summarized experimental results for various UREA and CAN fertilizer concentrations in different catena. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e presents the relative permittivity (\u0026epsilon;r) values and conductivity (\u0026sigma;) for varying concentrations of UREA fertilizer across the upper, middle, and lower catenas. The dielectric measurements revealed significant trends associated with fertilizer concentration and catena position. A comparative analysis using mean values and standard deviations showed that UREA-treated soils exhibited a nearly linear increase in dielectric constant (\u0026epsilon;r) and electrical conductivity (\u0026sigma;) with rising concentration across all catena levels. The lower catena exhibits higher ԑr compared to the middle catena, primarily due to run-off accumulation from these higher regions. For example, in the lower catena, \u0026epsilon;r increased from 20.04 at 0% UREA to 41.09 at 12.5% UREA, while \u0026sigma; increased from 0.757 S/m to 2.027 S/m. Pearson correlation coefficients confirmed strong positive relationships for UREA: \u0026epsilon;r (Middle, r\u0026thinsp;=\u0026thinsp;0.905), \u0026epsilon;r (Lower, r\u0026thinsp;=\u0026thinsp;0.803), \u0026sigma; (Middle, r\u0026thinsp;=\u0026thinsp;0.858), and \u0026sigma; (Lower, r\u0026thinsp;=\u0026thinsp;0.759). These indicate high predictability of dielectric response with increasing UREA concentration. This is also attributed to the chemical interaction between the added fertilizer and existing soil constituents, which enhances the soil\u0026rsquo;s water-holding capacity. Consequently, the dielectric loss of the soil also increases with higher fertilizer concentrations, as \u0026sigma; reflects the mobility of electric charges that drive conduction.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDielectric properties for various fertilizer concentrations of UREA in different catena\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUREA\u003c/p\u003e\n \u003cp\u003eFertilizer concentration (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDielectric constant (Middle)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDielectric constant (Lower)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConductivity (Middle) (S/m)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConductivity (Lower) (S/m)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.757\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.769\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.743\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorr (r)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.80\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.86\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.76\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e details the relationship between \u0026epsilon;\u003csub\u003er\u003c/sub\u003e, \u0026sigma;, and fertilizer concentration for CAN fertilizer. The observed variations in \u0026epsilon;r and \u0026sigma; for the upper and middle catenas follow trends similar to those for Urea. However, CAN treatments exhibited more varied behavior. In the middle catena, \u0026epsilon;r increased significantly (r\u0026thinsp;=\u0026thinsp;0.913), suggesting a strong positive relationship. However, in the lower catena, \u0026epsilon;r was poorly correlated (r\u0026thinsp;=\u0026thinsp;0.037), indicating a weak or no consistent relationship with CAN concentration. Conductivity in the middle catena showed a moderate correlation (r\u0026thinsp;=\u0026thinsp;0.523), while the lower catena showed a negative correlation (r = -0.585), highlighting complex nutrient-soil interactions. This non-linear behavior is likely due to the influence of soil porosity and chemical composition. Conductivity results in the lower catena, reflecting these effects, as run-off from higher catenas contributes to elevated values. These results suggest that while both fertilizers influence dielectric properties, UREA provides a more consistent and reliable response pattern suitable for dielectric-based fertility mapping.\u003c/p\u003e\n\u003cp\u003eA two-way ANOVA confirmed significant effects (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) of both fertilizer type and concentration on dielectric properties, with interaction effects observed primarily in the lower catena soils. Moisture content was also found to be a significant covariate, as increasing moisture from 10\u0026ndash;40% led to more than a 5-fold increase in \u0026epsilon;r.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDielectric properties for various fertilizer concentrations of CAN in different catena\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCAN\u003c/p\u003e\n \u003cp\u003eFertilizer concentration (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDielectric constant (Middle)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDielectric constant (Lower)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConductivity (Middle) (S/m)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConductivity (Lower) (S/m)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.757\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorr (r)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.913\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.037\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.523\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.585\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFigures \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb graphically illustrate the variations in dielectric constant (\u0026epsilon;r) with fertilizer concentration for both UREA and CAN across the lower and middle catenas. Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea presents the dielectric response in the lower catena. UREA-treated soils demonstrate a clear upward trend in this landscape position, with \u0026epsilon;r rising consistently from approximately 20 at 0% concentration to over 40 at 12.5%. This indicates a strong and predictable positive linear relationship between UREA concentration and dielectric response. The consistent pattern highlights UREA\u0026rsquo;s suitability for dielectric-based soil assessment in depositional zones. In contrast, CAN-treated soils in the lower catena display a non-linear and inconsistent pattern. Although \u0026epsilon;r initially increases, it fluctuates erratically at higher concentrations. For example, at 5% concentration, \u0026epsilon;r shows a noticeable rise, but this trend does not persist uniformly. These irregularities suggest that complex interactions between nutrient dynamics, moisture, and soil texture influence CAN\u0026rsquo;s impact on dielectric behavior in the lower catena.\u003c/p\u003e\n\u003cp\u003eFigure 3b displays the dielectric constant patterns in the middle catena. Both fertilizers exhibit increasing \u0026epsilon;r trends with concentration. However, CAN demonstrates a more pronounced and higher dielectric response at all levels. Specifically, \u0026epsilon;r for CAN increases sharply from 7.6 at 0% to 37.2 at 12.5%, indicating a robust and linear response. UREA also shows a positive trend, rising from approximately 8 to 23, though at a more moderate rate. The strong Pearson correlation coefficients observed (CAN: r = 0.913; UREA: r = 0.905) reinforce the reliability of these patterns. These results suggest that the middle catena provides a stable and uniform environment for predicting dielectric behavior based on fertilizer input. The comparative trends between UREA and CAN highlight that UREA provides more consistent dielectric responses across varying concentrations, especially in the lower catena. Meanwhile, CAN shows superior responsiveness in the middle catena but with diminished reliability in lower slope positions. These findings suggest that fertilizer selection and topographical context must be considered together when applying dielectric sensing methods for precision agriculture.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigures \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb illustrate the variation in soil electrical conductivity (\u0026sigma;) as influenced by fertilizer concentration in the lower and middle catenas, respectively. Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea depicts conductivity trends in the lower catena. UREA-treated soils show a clear, nearly linear increase in conductivity with increasing fertilizer concentration. Conductivity rises from approximately 0.76 S/m at 0% to over 2.0 S/m at 12.5%, highlighting a strong and positive relationship. This pattern reflects the consistent ionic contribution of UREA, which enhances charge mobility within the soil matrix. Conversely, CAN-treated soils exhibit a more erratic pattern. While conductivity values begin similarly, peaking at 1.02 S/m at 5% concentration, they then fluctuate irregularly and even decline with higher concentrations. This non-linear behavior suggests that in the lower catena, CAN\u0026rsquo;s effect on conductivity is influenced by interactions with existing soil chemistry, porosity, and moisture retention capacity.\u003c/p\u003e\n\u003cp\u003eIn Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb, conductivity trends in the middle catena are more stable for both fertilizers. CAN demonstrate consistently higher conductivity values across the concentration range, increasing steadily from 0.16 S/m to 0.81 S/m. While slightly lower overall, UREA follows a similar increasing trend, rising from 0.159 S/m to 0.900 S/m. The strong positive correlation coefficients for UREA (r\u0026thinsp;=\u0026thinsp;0.858) and moderate correlation for CAN (r\u0026thinsp;=\u0026thinsp;0.523) support these visual trends. The smoother behavior of both fertilizers in this zone can be attributed to the middle catena\u0026rsquo;s more balanced soil structure and water retention properties, which facilitate better ion exchange and mobility. Together, these results emphasize that UREA produces more predictable and linear conductivity changes, particularly in lower slope positions. In contrast, CAN\u0026rsquo;s variable performance in the lower catena contrasts with its more stable and enhanced response in the middle catena. Such differences are essential for optimizing fertilizer application strategies based on topographic location. Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e summarizes the comparison of dielectric constant and Conductivity between UREA and CAN.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of comparison of dielectric constant and Conductivity between UREA and CAN\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003edielectric constant (\u0026epsilon;r)\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAspect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower Catena (\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiddle Catena (\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREA Trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStrong, linear increase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSteady increase, high reliability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAN Trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eErratic and non-linear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSteady and moderate increase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImplication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUREA is more consistent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBoth fertilizers are predictive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMapping Suitability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUREA is preferred for stability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBoth UREA and CAN are suitable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eConductivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAspect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower Catena (\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiddle Catena (\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREA Trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStrong, linear increase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStrong, linear increase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAN Trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eErratic and non-linear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eErratic and non-linear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImplication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUREA is more suitable for monitoring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUREA is more suitable for monitoring\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMapping Suitability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUREA preferred for accuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUREA preferred for accuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e provides additional insights into how soil moisture content influences dielectric properties. The dielectric constant and conductivity exhibit non-linear behavior across varying moisture levels. At 10% moisture content, the dielectric constant is relatively low (5.9), and conductivity is also minimal (0.2 S/m), reflecting the limited availability of free water and ionic mobility in the soil matrix. When the moisture content increases to 20%, the dielectric constant rises sharply to 27.7, accompanied by a substantial increase in conductivity to 0.7 S/m. This suggests that the presence of additional water enhances the soil\u0026rsquo;s ability to polarize and conduct electricity due to increased dielectric relaxation and ion mobility.\u003c/p\u003e\n\u003cp\u003eInterestingly, at 30% moisture content, the dielectric constant decreases to 13.8 while conductivity remains stable at 0.2 S/m. This unexpected drop may reflect structural rearrangements or water redistribution that temporarily reduce the effective dielectric response, possibly due to air entrapment or uneven wetting. At 40% moisture, both dielectric constant and conductivity surge to their highest values, 57.9 and 2.6 S/m, respectively. This indicates a saturation threshold where water fills the soil pores, maximizing electrical conduction and dielectric polarization.\u003c/p\u003e\n\u003cp\u003eThese findings highlight that moisture generally enhances dielectric behavior, but the relationship is not strictly linear. Instead, it is governed by complex interactions involving soil texture, porosity, and the spatial distribution of water within the soil matrix.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDielectric properties of soil for different moisture content\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMoisture content (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDielectric constant\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConductivity(S/m)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings of this study emphasize the utility of dielectric properties, specifically relative permittivity (\u0026epsilon;r) and electrical conductivity (\u0026sigma;), as reliable indicators of fertilizer-induced soil changes across different landscape positions. The strong linear correlations between UREA concentration and both \u0026epsilon;r and \u0026sigma;, particularly in the lower catena, corroborate earlier studies which demonstrate that nitrogen-based fertilizers, such as UREA, significantly enhance the ionic content and polarization capacity of soils (Ahire et al., 2013; Kargas \u0026amp; Soulis, 2019; Brovelli \u0026amp; Cassiani, 2011). The linear response pattern suggests that UREA application leads to increased free ion mobility and enhanced soil conductivity, especially under controlled moisture conditions (Abdulraheem et al., 2024; Zhang et al., 2024). Conversely, the dielectric response to CAN was less predictable in the lower catena, reflecting more complex chemical and physical interactions. This inconsistency aligns with findings from Rhoades et al. (1989) and Malinowska and Szumacher (2013), which indicate that terrain variability and cation exchange dynamics play critical roles in modifying soil electrical properties. The relatively erratic \u0026epsilon;r and \u0026sigma; values for CAN at higher concentrations suggest that ammonium and calcium ions may interact differently with the colloidal matrix, leading to non-linear electromagnetic responses (Das \u0026amp; Paul, 2015; Akash et al., 2024).\u003c/p\u003e\n\u003cp\u003eThe middle catena, however, displayed more consistent behavior across both fertilizer types. CAN demonstrated a strong linear increase in \u0026epsilon;r and moderate conductivity improvements, supporting previous reports that calcium ammonium nitrate enhances soil structure and moisture retention (Reza et al., 2025; Dospatliev et al., 2014). This topographic zone likely benefits from intermediate run-off and infiltration conditions, leading to more uniform nutrient distribution and electromagnetic behavior (Abdulraheem et al., 2024; Yuzugullu et al., 2020). Moisture content emerged as a significant factor influencing dielectric properties. The sharp rise in both \u0026epsilon;r and \u0026sigma; between 10% and 20% moisture levels is consistent with the dielectric theory, which postulates that water molecules enhance permittivity through dipole polarization and contribute to increased conductivity via dissociated ions (Kargas \u0026amp; Soulis et al., 2019). However, the dip in dielectric constant at 30%, followed by a sharp increase at 40%, suggests that moisture-soil interactions are not solely dependent on water content but are also governed by pore space configuration and water distribution (Kargas \u0026amp; Soulis, 2019; Brovelli \u0026amp; Cassiani, 2011; Adamchuk et al., 2004).\u003c/p\u003e\n\u003cp\u003eThe presence of non-linear trends at higher moisture levels and fertilizer concentrations indicates the onset of saturation and reduced air-filled porosity, which could impact sensor performance and data interpretation in the field. Similar findings have been reported in controlled dielectric studies that associate anomalous readings with partial saturation and the presence of bound water layers (Shruthi \u0026amp; Menon, 2016). Notably, the robustness of the LPDA-NanoVNA sensor system was affirmed by low measurement variability across replicates, which supports its suitability for real-time, non-invasive field assessments. The observed coefficient of variation below 5% aligns with technical performance standards cited in remote sensing applications for soil evaluation (Hendricksen et al., 2025).\u003c/p\u003e\n\u003cp\u003eThis study reinforces the applicability of electromagnetic sensing in precision agriculture. The findings show that UREA is more suitable for dielectric mapping in heterogeneous and lower slope soils, while CAN performs optimally in more stable, middle catena positions. These results support the development of adaptive, site-specific fertilizer application strategies and contribute to broader sustainable land management efforts aligned with SDG 15.\u003c/p\u003e\n\u003cp\u003eThe experimental findings revealed significant trends in the dielectric response of soils treated with fertilizers. For both UREA and CAN, relative permittivity (\u0026epsilon;r) and conductivity (\u0026sigma;) generally increased with fertilizer concentration, particularly in the middle and lower catenas. This aligns with the theoretical understanding that ionic content and moisture retention, both affected by fertilizer application, enhance the dielectric behavior of soils (Ahire et al., 2015; Van Dam et al., 2005; Abdulraheem et al., 2024; Rhoades et al., 1989). Notably, UREA-treated soils exhibited a near-linear increase in \u0026epsilon;r and \u0026sigma;, suggesting a predictable response pattern that could be effectively modeled and mapped. This consistency across catena levels underscores the reliability of UREA as a benchmark fertilizer for dielectric soil mapping (Pathirana et al., 2023; Kargas \u0026amp; Soulis, 2019; Brovelli \u0026amp; Cassiani, 2011). Conversely, CAN showed inconsistent behavior in the lower catena, likely due to its dual nutrient composition (calcium and ammonium) and varying interaction with soil colloids and porosity (Das \u0026amp; Paul, 2015).\u003c/p\u003e\n\u003cp\u003eThe variations observed between catena positions reflect the influence of run-off, soil texture, and baseline nutrient levels. The lower catena exhibited higher permittivity and conductivity as a depositional zone, supporting previous findings on landscape-based soil differentiation (Malinowska \u0026amp; Szumacher, 2013; Nyaga et al., 2021; Onyango et al., 2021). These findings validate the LPDA-based system\u0026rsquo;s potential for in-situ soil nutrient monitoring. The use of S-parameters allows for real-time, non-contact measurements, offering a scalable alternative to traditional lab-based methods (Hendricksen et al., 2025; Reza et al., 2025). This innovation aligns with the push for digital agriculture and supports SDG 15 by promoting sustainable soil management practices.\u003c/p\u003e\n\u003cp\u003eFuture work should incorporate machine learning models to predict nutrient classes from dielectric readings and expand the system\u0026rsquo;s application to other agro-ecological zones (Abdulraheem et al., 2024; Zhang et al., 2024). Integrating GPS and remote sensing data could further enhance the spatial resolution of soil fertility maps, enabling precise and equitable resource allocation for smallholder farmers (Abdulraheem et al., 2024; Yuzugullu et al., 2020; Adamchuk et al., 2004).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study confirms that dielectric properties, specifically permittivity and conductivity, can serve as proxies for nutrient status in soils treated with UREA and CAN fertilizers. The findings validate the applicability of LPDA and NanoVNA systems as affordable, non-destructive tools for soil fertility monitoring. The observed trends, especially the linear increase in dielectric properties with UREA concentrations, provide a strong foundation for developing digital soil mapping systems. By capturing real-time nutrient dynamics using contactless sensors, this approach offers a scalable pathway toward precision agriculture, especially for smallholder farmers. The study contributes to sustainable soil management and aligns with SDG 15 by promoting resource-efficient fertilizer use. Future research should refine these models through larger field trials and integrate machine learning for predictive soil nutrient classification.\u003c/p\u003e\n\u003cp\u003eBased on the results, the following guidelines for data collection can be proposed:\u0026nbsp;\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eThe dielectric constant and conductivity in the middle and lower catenas significantly increase as the concentration of UREA fertilizer rises. This observation highlights the sensitivity of dielectric properties to varying levels of UREA in the soil. Since these properties differ across different geographical locations within the catena, these variations can be systematically mapped to represent the measured dielectric values specifically associated with UREA fertilizer.\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"2\"\u003e\n \u003cli\u003eIn the middle catenas, the dielectric constant and conductivity increase with higher UREA concentrations, sufficiently distinct to allow clear differentiation between specific locations. This trend makes identifying patterns and drawing meaningful conclusions from the data easier. In contrast, in the lower catena, the dielectric constant and conductivity measurements for calcium ammonium nitrate (CAN) fertilizer exhibit considerable fluctuations. These inconsistencies result in overlapping values that challenge distinguishing between specific measurement points or fertilizer effects, complicating the mapping and interpretation process.\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"3\"\u003e\n \u003cli\u003eGenerally, CAN exhibits higher values for dielectric constant and conductivity than UREA within the middle catena, providing a reliable basis for identifying and distinguishing between these two types of fertilizers in this region. However, in the lower catena, the scenario is more complex. For fertilizer concentrations between 0% and 5%, the dielectric constant and conductivity values for both UREA and CAN are nearly identical, making differentiation between the two fertilizers challenging. Beyond this concentration range, a contrasting trend is observed: UREA values steadily increase while CAN values decrease. This divergence in behavior allows UREA to be more easily identified at concentrations exceeding 5%, providing critical insights for fertilizer analysis and application in these regions.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of interest statement \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report there are no competing interests to declare\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors didn’t receive funding for the research. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Authors of this article agree to share the data upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbdulraheem, M. I., Chen, H., Li, L., Moshood, A. Y., Zhang, W., Xiong, Y., ... \u0026amp; Hu, J. (2024). Recent advances in dielectric properties-based soil water content measurements. \u003cem\u003eRemote Sensing\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(8), 1328.\u003c/li\u003e\n \u003cli\u003eAdamchuk, V. I., Hummel, J. W., Morgan, M. T., \u0026amp; Upadhyaya, S. K. (2004). On-the-go soil sensors for precision agriculture. \u003cem\u003eComputers and electronics in agriculture\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(1), 71-91.\u003c/li\u003e\n \u003cli\u003eAhire, D. V., Chaudhari, P. R., Ahire, V. D., \u0026amp; Patil, A. A. (2013). Correlations of electrical conductivity and dielectric constant with physico-chemical properties of black soils. \u003cem\u003eInternational Journal of Scientific and Research Publications\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(2), 1-16.\u003c/li\u003e\n \u003cli\u003eAhire, V., Ahire, D. V., \u0026amp; Chaudhari, P. R. (2015). 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Recent Advances in Dielectric Properties-Based Soil Water Content Measurements.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"S-parameter, Catena, Soil Nutrients, Fertilizer, Dielectric, Conductivity ","lastPublishedDoi":"10.21203/rs.3.rs-6761968/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6761968/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSoil nutrient depletion and inefficient fertilizer management remain major challenges for sustainable agriculture in Tanzania’s Southern Highlands. This study evaluates a low-cost, non-invasive dielectric sensing approach to monitor soil nutrient and moisture variability using a log-periodic dipole antenna (LPDA) system integrated with a nano vector network analyzer (NanoVNA). Soil samples from the middle and lower catena positions in Mbeya City were treated with varying concentrations (0–12.5%) of UREA and calcium ammonium nitrate (CAN), and dielectric properties permittivity and conductivity were measured under controlled moisture levels (10–40%). The results showed strong positive correlations between UREA concentration, dielectric constant (r = 0.905), and conductivity (r = 0.858), particularly in the lower catena. CAN showed a reliable response in the middle catena (r = 0.913 for εr) but inconsistent trends in the lower catena. Moisture content had a significant non-linear effect on dielectric behavior, with a peak response at 40% moisture. A two-way ANOVA confirmed statistically significant main and interaction effects (p \u0026lt; 0.05) for fertilizer type, concentration, and catena position. These findings validate the LPDA-NanoVNA system as an effective soil nutrient and moisture monitoring tool. The dielectric method is especially promising for supporting site-specific fertilizer applications and precision farming. UREA is more suitable for dielectric-based assessments in variable terrain, while CAN is more effective in stable slope environments. The approach advances low-cost, scalable technologies for climate-smart agriculture and aligns with SDG 15 goals on sustainable land use and soil health.\u003c/p\u003e","manuscriptTitle":"Non-Destructive Assessment of Soil Nutrient Variability in Mbeya Catena Using Dielectric Properties via Low-Cost Antenna Systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-30 08:20:01","doi":"10.21203/rs.3.rs-6761968/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0146b457-fb4d-4afe-817b-ae58e261d8d7","owner":[],"postedDate":"May 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-30T08:20:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-30 08:20:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6761968","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6761968","identity":"rs-6761968","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}