Skip to main content
Applied Biological Chemistry
Download PDF

Monitoring of soil EC for the prediction of soil nutrient regime under different soil water and organic matter contents

Applied Biological Chemistry volume 67, Article number: 1 (2024) Cite this article

Abstract

Smart farms and precision agriculture require automatic monitoring and supply of water and nutrients for crops, but sensors to monitor plant available nutrients in soil are not available. Soil electrical conductivity (EC) is related to nutrients in soil solution, which can be affected by soil organic matter, soil texture, temperature, and water content. Therefore, the objective of this study is to evaluate factors influencing soil EC sensor values by monitoring EC under different soil organic matter and water contents. Ten soil samples with various sand and clay contents, EC, pH, and organic matter contents were selected and saturated with water. Volumetric water content and EC of the soil were monitored while drying the soil. Humic acid and manure were added to soils in order to evaluate the effect of organic matter on soil EC. Soil EC values linearly increased with increasing water content at 10–25% which is favorable water content for plant growth. The EC increased when organic matter was added to soils, which was related to ions released from the organic matter. Soil EC calibration factor for soil water content increased when EC of the soil was high and organic matter was added. The sensor EC values in sandy loam and loam soils was related to the ion contents in pore water, and exchangeable ions in soil, respectively. Sensor EC values were highly correlated with organic matter and K contents in soil and can be used as an indicator for plant available nutrients in soil. Therefore, the sensor EC at optimal soil water content for plant growth can be used to monitor changes in plant available nutrients in soil.

Introduction

Precision supply of essential water and nutrients is important for crops because excess nutrients can reduce crop yields and residual nutrients can positively or negatively affect yields in the following year [53]. Importance of effectively managing soil nutrients and water to sustain and optimize agricultural productivity in changing environmental conditions is emerging due to imbalances in nutrients and water caused by recent climate changes [31]. Therefore, soil nutrient and water management are important to maintain the sustainability of agricultural land.

Smart farms reduce costs and improve crop productivity by monitoring and controlling the agricultural environment through integration of automation technologies such as networks and mobile devices [37]. Automatic soil water management system using information and communications technology (ICT) helps in saving water by supplying desired amount of water required by the crops [1]. In addition, the ICT-based water management system contributes to the improvement of crop productivity and quality [16]. Especially, in outdoor smart farms, it is necessary to control the supply of nutrients according to crop growth while monitoring nutrients in real time using sensors [50]. Although there are various sensors for monitoring agricultural environment such as CO2, light intensity, temperature, humidity, and soil water content, sensors for monitoring nutrient availability in soil are still not fully developed [5]. Therefore, it is difficult to supply nutrients while monitoring soil nutrient levels, which is obstacle to precision agriculture in open field.

Soil EC is affected by soil salinity, clay and water content, and adding nutrients to the soil increases the soil EC [17, 26]. Heiniger et al. [27] found that EC measurements of soil extracts could be used to predict soil nutrient content and are highly correlated with soil properties such as water content, cation exchange capacity (CEC), and soluble salts (R2 ranging from 0.51 to 0.75). In addition, nutrients such as N and K directly affect the EC of extracted soil solutions [40]. Therefore, nutrient content can be predicted by monitoring soil EC, but soil EC is highly influenced by soil water, soil texture, and organic matter [29]. In particular, the water content has significant effects on soil EC [22]. Increasing soil water content increased soil EC [14]. When the water content in soil was high, the soil EC increased as a result of the increased solubility of ions in soil [4].

Organic matter is important for plant growth along with nutrients in the soil because it is decomposed into smaller molecules and mineralized elements are absorbed by plants [41]. Organic matter can increase the soil EC due to the nutrients and salts included in organic matter [24]. However, the effect of soil organic matter on EC was not fully understood. To predict soil nutrients through soil EC monitoring using sensor, various soil characteristics including soil texture and organic matter affecting EC should be considered.

In this study, we hypothesized that organic matter increases EC of the soil by releasing nutrients and affects EC by adsorption of ions. Conducting correlation analyses between EC and organic matter can offer insights into setting nutrient supply corresponding to EC values monitored by sensors for soils with different characteristics [46, 51]. Based on the relationship of soil organic matter and EC, the sensor EC values should be calibrated. Calibration of EC based on these correlations can enhance the accuracy of nutrient supply recommendations and improve the efficiency of soil management practices [39, 51]. Therefore, the objectives of this study were to monitor nutrients using EC sensors in soils with different clay and sand composition, water content and organic matter content and evaluate the effect of water content and organic matter on soil EC in different soils.

Materials and methods

Soil characterization

Soils with different characteristics were collected from different agricultural lands in Korea, dried at room temperature, and sieved to a particle size of less than 2 mm using a stainless-steel sieve. The sand, silt and clay content of the soil was determined by Stoke’s law [23]. Soil pH and EC were measured using pH and EC meter after extracting 5 g of soil with 25 mL of deionized water for 30 min. The Walkley–Black method was used to determine the amount of soil organic matter [52]. Two grams of soil and 20 mL of 1 M NH4CH3COOH solution at pH 7 were shaken for 30 min and filtered through 0.45 μm syringe filter. Filtered solution was acidified and elements were analyzed using inductively coupled plasma optical emission spectroscopy (ICP-OES, Perkin Elmer, Avio 500). The soil CEC was calculated with Ca, Mg, K, and Na [47]. Ammonium (NH4+) and nitrate (NO3−) in the soil were measured using the indophenol-blue method and the vanadium (III) reduction method, respectively, after extracting 5 g soil with 25 mL of 2 M KCl solution [19, 20]. For NH4+ analysis, 0.125 mL EDTA, 2 mL phenol/nitroprusside (PNP), and 1 mL NaOH/hypochlorite were mixed with 1 mL of the extract. The mixture was incubated at 37 °C for 20 min and absorbance of the solution was measured at 630 nm using a UV–VIS spectrometer (Orion AquaMate 7000, Thermo-Fisher Scientific, USA) [20]. For NO3− analysis, 20 µL of soil extract was mixed with 1 mL of the reagent prepared by dissolving 0.4 g of VCl3 in 50 mL of 1 M HCl and mixing with 0.2 g of sulfanilamide and 0.01 g of N-(1-Naphthyl)-ethylendiamine-dihydrochloride (NEDD) in 400 mL of de-ionized water. Absorbance of the solution was measured at 540 nm using a UV–VIS spectrometer after 18 h at 25 °C to determine soil NO3− concentration [19]. Table 1 shows the physicochemical properties of soils used.

Table 1 Physicochemical properties of soil samples used for EC monitoring
Full size table

Soil EC and water content monitoring

Air dried soil (1.5 L) was packed in a 2.2 L square container. For organic matter treatment, 40 g of compost manure were added to silty clay loam soil, 30 g of humic acid were added to loam soil, and 50 g of compost manure were added to sandy loam soil. Soils after adding organic matter were also characterized to evaluate effect of organic matter on sensor EC (Table 1). To monitor EC and water content, an EC sensor (Teros 12, METER Group, USA) was placed in soil to a depth of about 10 cm in an incubator at 20 °C. The Teros 12 sensor measures EC, volumetric water content, and temperature in soil at the same time. Water (900 mL) was added to the soil for saturation and the EC and water content were monitored until the soil water content evaporated to be less than 10% (v/v). Soil EC sensors were connected to ZL6 logger (METER Group, USA) and soil bulk EC (mS/cm), temperature (°C) and volumetric soil water content (m3/m3) were recorded every 15 min. Soils with added organic matter (manure and humic acid) were also monitored for soil EC and water content in the same manner.

Analysis of ion contents in soil pore water

Rhizon sampler (Rhizosphere Research Products, Netherlands) was inserted into the soil to a depth of about 10 cm and pressure was applied to collect pore water in the soil at 25% water content. The pH and EC of the pore water were measured and element contents in the pore water were analyzed using ICP-OES after filtration using 0.45 μm syringe filter. Ammonium and NO3− contents of the pore water were also analyzed using the indophenol-blue method and the vanadium (III) reduction method, respectively. Soil samples were taken after monitoring EC, dried, and mixed with 1 M NH4CH3COOH solution (pH 7) at a soil to solution ratio of 1:10 for 30 min. The solution was filtered through 0.45 μm syringe filter and exchangeable ion concentrations in the soil were analyzed using ICP-OES. Soluble NH4+ and NO3− in the soil before drying were extracted with de-ionized water and analyzed using a UV–vis spectrometer following the indophenol-blue method and the vanadium (III) reduction method, respectively.

Statistical analysis

Analyses were conducted in triplicate and presented as mean and standard deviation. The correlation among sensor EC, soil organic matter (SOM), EC calibration factor for water content, soil clay and sand contents, pH, and exchangeable elements was analyzed using Xlstat (Addinsoft). Principal component analysis (PCA) was conducted to evaluate relationship among the parameters including sensor EC values and SOM contents (Xlstat, Addinsoft). For PCA, soil sensor EC values were selected at water content of 30.9%, 22.5% and 17.0% v/v for silt clay loam, loam and sandy loam soils, respectively.

Results and discussion

Response of soil EC sensor in relation to soil water content

Soil EC showed a linear correlation with water content at water content of 10–25% for loam and sandy loam soils and at water content of 10–40% for silty clay loam soil (Table 2). The water content range having linear relationship with EC was suitable water content for plant growth [11]. The linear correlation between soil water content and EC in the range of soil water content appropriate for plant growth indicates that soil EC can be used for nutrient monitoring regardless of soil moisture content by calibrating EC values for soil water content. Soil EC can be calibrated for desired water content because it is linearly proportional to water content. Other studies also reported that soil EC linearly increased with soil water content [14, 25, 54]. Sensor EC value decreased with decreasing water content in soil because the mobile fractions of ions decreased [18, 55]. In addition, water containing ions is a conductor of electricity and water content was closely related to EC [33]. Sudduth et al. [48] also reported that EC could be explained by water content and bulk density of soil. However, at higher water content, the relationship between EC and water content was not linear, which was also reported by Rusydi [44]. When soil water is more than optimum water content, the relationship becomes nonlinear, wherein further increase of water content does not linearly raise EC due to saturation and mobility constraints [44].

Table 2 Calibration factor of soil EC for soil water content without and with added organic matter
Full size table

Soil EC is related to soil texture, water content, organic matter, etc., and varies with soil properties [30]. Therefore, it is necessary to calculate the calibration factor considering the soil properties and apply it to calibrate the EC sensor value against soil water content. Sensor EC values were plotted against soil water contents and the slope of linear regression was defined as calibration factor. Higher calibration factor indicates that EC changes more with water content variation. Calibration factor derived from the slope of the linear relationship between soil EC and water content serves as a valuable metric [39]. This coefficient aids in predicting soil EC based on water content variations, which in turn can offer insights into nutrient contents and other essential soil properties [21]. Calibration factor for each soil was calculated in the range of available water content of each soil and the correlation coefficient was to be higher than 0.97 (Table 2). In comparison to the soil without organic matter, the calibration factor was higher when organic matter was added to the soil (Table 2). The increased calibration factor is because of adding organic matter to the soil and increases in exchangeable ions and nutrients (Table 3). Soil organic matter increased nutrient contents and finally increased soil EC [2, 28, 49]. When the ion contents of the soil are high, more dispersal occurs with the higher water content, which might be attributed to higher calibration factor with organic matter [42]. In addition, since sensitivity of the EC sensor increases as the water content increases, the calibration factor can be higher when organic matter and water are added to the soil [12]. Therefore, higher the soil nutrient content, the greater increase in soil EC by water content.

Table 3 Exchangeable ion contents in soil without and with added organic matter
Full size table

Changes in soil ion contents by addition of organic matter

The concentration of exchangeable ions such as K, and Ca in the soil increased when organic matter was added in the soil because of dissolution of minerals containing K and exchangeable Ca by organic acids (Table 3). Initially manure compost was added as organic matter, but it increased EC and humic acid was added as organic matter for loam soil to avoid effect of salts released from added organic matter. However, both organic matters increased available nutrient concentrations increasing soil EC. Adding organic matter to the soil increased the content of N, C and S etc., which were included in manure compost and humic acid [3, 15, 38]. In addition, Bader et al. [7] reported that K concentration was significantly increased by the amount of organic matter. This is because some soil minerals that contain K are dissolved by organic acids (humic and fulvic) as a result of the decomposition of organic fertilizer [6]. Exchangeable Ca content was also related to soil organic carbon because Ca protects soil organic carbon from microbial degradation [45]. As a result, while monitoring soil EC with sensors, it is important to consider organic matter which influence the ion contents in the soil.

The sensor EC value of sandy loam soil was highly correlated with the ion content in pore water, and the sensor EC value of loam soil was highly correlated with exchangeable ions (Fig. 1). Generally, loam soil has a higher adsorption capacity than sandy soil because it has a higher clay content than sandy loam [35]. Since sandy loam has a low water and ion holding capacity, the sensor EC value was highly correlated with ion content of the pore water (Fig. 1a, [10]). In addition, loam soil has a higher ion holding capacity than sandy loam, so the sensor EC value showed significant correlation with NO3− and Na which are soluble in water and with exchangeable cations that are relatively less soluble in water (Fig. 1b, [56]).

Fig. 1
figure 1

Correlation of sensor EC with soil properties and ion contents of sandy loam (a) and loam (b) (PW. pore water, W. water soluble ion, Ex. exchangeable ion)

Full size image

The pH of the pore water and the sensor EC value were negatively correlated (Fig. 1a). Luce et al. [36] explained that the negative correlation between soil EC and pH was associated with microbial mineralization and nitrification processes in the soil.

Fig. 2
figure 2

Biplots of PC1 and PC2 for sensor EC monitoring values by soil characteristics and ion content and organic matter treatment (OM. organic matter added soil, PW. pore water, Ex. exchangeable ion)

Full size image

Relationship between sensor EC values and soil properties

PCA was performed to evaluate the relationship between sensor EC values and soil properties with soil samples before and after adding organic matter. PCA is used to identify the variables that are related to associations among samples [13]. The PC1, PC2, PC3 and PC4 accounted for 28.3%, 23.2%, 15.5% and 11.3% of the total variation, respectively (Table 4). The four PCs explained more than 78% of cumulative variance. The first PC showed positive correlations with sand content, pore water pH, and exchangeable Ca and P. The second PC was correlated with sensor EC, soil EC, CEC, pore water Ca, K, and P, and exchangeable Na, P and S, which were parameters contributing EC. The third PC was correlated with clay, CEC and exchangeable Mg, which were related to adsorption of cations.

Table 4 Factor loadings based on correlations matrix and total variance explained by the principal components (OM.: added organic matter, PW.: pore water, Ex.: exchangeable ion)
Full size table

The sensor EC monitoring values showed a high correlation with the contents of NO3−, K, S and Ca in the pore water (Fig. 2). Since pore water exists between soil particles, ion content in pore water reflects plant available nutrients which increases EC [9, 43]. Nitrate is water soluble and increasing the mobility of ions increased the EC [8]. In addition, increased mobility of adsorbed K increased EC [34]. Since soil nutrients such as N and K are closely related to EC, which can be monitored using soil EC sensor.

The close correlation between sensor EC and SOM was found because increased organic matter in the soil increased the ion contents and finally increased the soil EC (Fig. 2). In particular, samples with additional organic matter were clearly separated from samples without added organic matter. Samples with added organic matter were distributed along soluble and exchangeable nutrients on PC1 and PC2 (Fig. 2). Soils with different texture were also well differentiated indicating that texture and organic matter affect available nutrients and sensor EC. Since the soil sensor EC values are related to the soluble ion contents in sandy loam and the exchangeable ion contents in loam soil, soil texture should be considered in the interpretation of sensor EC for the prediction of nutrient levels in soil. In addition, soil sensor EC values are highly correlated with N, K and SOM, which are important for plant growth. Therefore, when monitoring nutrient levels in soil using EC sensor, organic matter and soil texture need to be considered. If organic matter and water content are considered, the sensor EC can be used as an indicator of the nutrients available to plants in the soil. Conclusively, at optimal soil water content for plant growth, EC can be used to monitor changes in plant-available nutrients in the soil.

Availability of data and materials

The data that support the findings of this study are available from the corresponding author, [J.H. Park], upon reasonable request.

References

  1. Abd Rahman MKI, Abidin MSZ, Buyamin S, Mahmud MSA (2018) Enhanced fertigation control system towards higher water saving irrigation. Indonesian J Electr Eng Comp Sci 10(3):859–866

    Article  Google Scholar 

  2. Adak T, Singha A, Kumar K, Shukla SK, Singh A, Kumar Singh V (2014) Soil organic carbon, dehydrogenase activity, nutrient availability and leaf nutrient content as affected by organic and inorganic source of nutrient in mango orchard soil. J Soil Sci Plant Nutr 14(2):394–406

    Google Scholar 

  3. Adegoke TO, Moon TI, Ku HH (2022) Ammonia emission from sandy loam soil amended with manure compost and urea. Appl Biol Chem 65(1):83

    Article  CAS  Google Scholar 

  4. Adviento-Borbe MAA, Doran JW, Drijber RA, Dobermann A (2006) Soil electrical conductivity and water content affect nitrous oxide and carbon dioxide emissions in intensively managed soils. J Environ Qual 35(6):1999–2010

    Article  PubMed  CAS  Google Scholar 

  5. Ali MA, Dong L, Dhau J, Khosla A, Kaushik A (2020) Perspective—electrochemical sensors for soil quality assessment. J Electrochem Soc 167(3):037550

    Article  CAS  Google Scholar 

  6. Al-Jabori JSJ, Al-Obaed BSO, Al-Amiri AHF (2011) Effect of soil gypsum content and kind of organic matter on status and behavior of potassium. Tikrit J Agric Sci 11(4):299–310

    Google Scholar 

  7. Bader BR, Taban SK, Fahmi AH, Abood MA, Hamdi GJ (2021) Potassium availability in soil amended with organic matter and phosphorous fertiliser under water stress during maize (Zea mays L) growth. J Saudi Soc Agric Sci 20(6):390–394

    Google Scholar 

  8. Baldi E, Quartieri M, Muzzi E, Noferini M, Toselli M (2020) Use of in situ soil solution electric conductivity to evaluate mineral N in commercial orchards: preliminary results. Horticulturae 6(3):39

    Article  Google Scholar 

  9. Baligar VC, Fageria NK, He ZL (2001) Nutrient use efficiency in plants. Commun Soil Sci Plant Anal 32(7–8):921–950

    Article  CAS  Google Scholar 

  10. Banedjschafie S, Durner W (2015) Water retention properties of a sandy soil with superabsorbent polymers as affected by aging and water quality. J Plant Nutr Soil Sci 178(5):798–806

    Article  CAS  Google Scholar 

  11. Bessembinder JJE, Leffelaar PA, Dhindwal AS, Ponsioen TC (2005) Which crop and which drop, and the scope for improvement of water productivity. Agric Water Manag 73(2):113–130

    Article  Google Scholar 

  12. Bogena HR, Huisman JA, Oberdörster C, Vereecken H (2007) Evaluation of a low-cost soil water content sensor for wireless network applications. J Hydrol 344(1–2):32–42

    Article  Google Scholar 

  13. Brereton RG (2015) Pattern recognition in chemometrics. Chemom Intell Lab Syst 149:90–96

    Article  CAS  Google Scholar 

  14. Brevik EC, Fenton TE, Lazari A (2006) Soil electrical conductivity as a function of soil water content and implications for soil mapping. Precision Agric 7:393–404

    Article  Google Scholar 

  15. Britannica, The Editors of Encyclopaedia. “humic acid”. Encyclopedia Britannica, 10 Sep. 2020, https://www.britannica.com/science/humic-acid. Accessed 19 Dec 2022

  16. Bunyamin Z, Andayani NN, Fahdiana T, Azrai M (2020) Application of ICT tools in food crops monitoring in Indonesia. IOP Conf Series Earth Environ Sci 484(1):012060

    Article  Google Scholar 

  17. Cheng H, Zhang D, Huang B, Song Z, Ren L, Hao B, Cao A (2020) Organic fertilizer improves soil fertility and restores the bacterial community after 1, 3-dichloropropene fumigation. Sci Total Environ 738:140345

    Article  PubMed  CAS  Google Scholar 

  18. Chowdhury N, Marschner P, Burns RG (2011) Soil microbial activity and community composition: impact of changes in matric and osmotic potential. Soil Biol Biochem 43(6):1229–1236

    Article  CAS  Google Scholar 

  19. Doane TA, Horwáth WR (2003) Spectrophotometric determination of nitrate with a single reagent. Anal Lett 36(12):2713–2722

    Article  CAS  Google Scholar 

  20. Dorich RA, Nelson DW (1983) Direct colorimetric measurement of ammonium in potassium chloride extracts of soils. Soil Sci Soc Am J 47(4):833–836

    Article  CAS  Google Scholar 

  21. Dwevedi A, Kumar P, Kumar P, Kumar Y, Sharma YK, Kayastha AM. (2017). Soil sensors: detailed insight into research updates, significance, and future prospects. In New pesticides and soil sensors (pp. 561–594). Academic press

  22. Friedman SP (2005) Soil properties influencing apparent electrical conductivity: a review. Comput Electron Agric 46(1–3):45–70

    Article  Google Scholar 

  23. Gee GW, Or D. (2002). 2.4 Particle‐size analysis. Methods of soil analysis: Part 4 physical methods, 5, 255–293

  24. Gondek M, Weindorf DC, Thiel C, Kleinheinz G (2020) Soluble salts in compost and their effects on soil and plants: a review. Compost Sci Utiliz 28(2):59–75

    Article  CAS  Google Scholar 

  25. Hanson BR, Kaita K (1997) Response of electromagnetic conductivity meter to soil salinity and soil-water content. J Irrig Drain Eng 123(2):141–143

    Article  Google Scholar 

  26. Hardie M, Doyle R (2012) Measuring soil salinity. Plant salt tolerance. Humana Press, Totowa, NJ, pp 415–425

    Book  Google Scholar 

  27. Heiniger RW, McBride RG, Clay DE (2003) Using soil electrical conductivity to improve nutrient management. Agron J 95(3):508–519

    Article  CAS  Google Scholar 

  28. Hossain MZ, Bahar MM, Sarkar B, Donne SW, Ok YS, Palansooriya KN, Bolan N (2020) Biochar and its importance on nutrient dynamics in soil and plant. Biochar 2(4):379–420

    Article  Google Scholar 

  29. Juhos K, Czigány S, Madarász B, Ladányi M (2019) Interpretation of soil quality indicators for land suitability assessment–A multivariate approach for Central European arable soils. Ecol Ind 99:261–272

    Article  CAS  Google Scholar 

  30. Kekane SS, Chavan RP, Shinde DN, Patil CL, Sagar SS (2015) A review on physico-chemical properties of soil. Int J Chem Stud 3(4):29–32

    Google Scholar 

  31. Kim HN, Park JH (2021) Research trends using soil sensors for precise nutrient and water management in soil for smart farm. Korean J Soil Sci Fert 54(3):366–382

    Article  CAS  Google Scholar 

  32. Kim HN, Seok YJ, Park GM, Vyavahare G, Park JH (2023) Monitoring of plant-induced electrical signal of pepper plants (Capsicum annuum L) according to urea fertilizer application. Scient Rep 13(1):291

    Article  CAS  Google Scholar 

  33. Korsaeth A (2005) Soil apparent electrical conductivity (EC a) as a means of monitoring changesin soil inorganic N on heterogeneous morainic soils in SE Norway during two growing seasons. Nutr Cycl Agroecosyst 72:213–227

    Article  CAS  Google Scholar 

  34. Kundu MC, Hazra GC, Biswas PK, Mondal S, Ghosh GK (2014) Forms and distribution of potassium in some soils of Hooghly district of West Bengal. Crucible 2(4):4

    Google Scholar 

  35. Libohova Z, Seybold C, Wysocki D, Wills S, Schoeneberger P, Williams C, Owens PR (2018) Reevaluating the effects of soil organic matter and other properties on available water-holding capacity using the National Cooperative Soil Survey Characterization Database. J Soil Water Conserv 73(4):411–421

    Article  Google Scholar 

  36. Luce MS, Whalen JK, Ziadi N, Zebarth BJ (2011) Nitrogen dynamics and indices to predict soil nitrogen supply in humid temperate soils. Adv Agron 112:55–102

    Article  Google Scholar 

  37. Muangprathub J, Boonnam N, Kajornkasirat S, Lekbangpong N, Wanichsombat A, Nillaor P (2019) IoT and agriculture data analysis for smart farm. Comput Electron Agric 156:467–474

    Article  Google Scholar 

  38. Możdżer E, Jałoszyński S (2019) Effect of fertilizer granulates on ionic and weight relations among macronutrients in spring rape seeds. J Ecol Eng. https://doi.org/10.12911/22998993/105330

    Article  Google Scholar 

  39. Nawar S, Corstanje R, Halcro G, Mulla D, Mouazen AM (2017) Delineation of soil management zones for variable-rate fertilization: a review. Adv Agron 143:175–245

    Article  Google Scholar 

  40. Ouyang D, MacKenzie AF, Fan M (1998) Phytotoxicity of banded urea amended with triple superphosphate and potassium chloride. Agron J 90(6):734–739

    Article  Google Scholar 

  41. Ozlu E, Kumar S (2018) Response of soil organic carbon, pH, electrical conductivity, and water stable aggregates to long-term annual manure and inorganic fertilizer. Soil Sci Soc Am J 82(5):1243–1251

    Article  CAS  Google Scholar 

  42. Robinson DA, Jones SB, Wraith JM, Or D, Friedman SP (2003) A review of advances in dielectric and electrical conductivity measurement in soils using time domain reflectometry. Vadose zone journal 2(4):444–475

    Article  CAS  Google Scholar 

  43. Roose T, Fowler AC, Darrah PR (2001) A mathematical model of plant nutrient uptake. J Math Biol 42(4):347–360

    Article  PubMed  CAS  Google Scholar 

  44. Rusydi AF (2018) Correlation between conductivity and total dissolved solid in various type of water: a review. In IOP Conf Series Earth Environ Sci 118(1):012019

    Article  Google Scholar 

  45. Saby NPA, Arrouays D, Antoni V, Lemercier B, Follain S, Walter C, Schvartz C (2008) Changes in soil organic carbon in a mountainous French region, 1990–2004. Soil Use Manag 24(3):254–262

    Article  Google Scholar 

  46. Saxton KE, Rawls WJ (2006) Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil Sci Soc Am J 70(5):1569–1578

    Article  CAS  Google Scholar 

  47. Skinner MF, Zabowski D, Harrison R, Lowe A, Xue D (2001) Measuring the cation exchange capacity of forest soils. Commun Soil Sci Plant Anal 32(11–12):1751–1764

    Article  CAS  Google Scholar 

  48. Sudduth KA, Drummond ST, Kitchen NR (2001) Accuracy issues in electromagnetic induction sensing of soil electrical conductivity for precision agriculture. Computers Electr Agric 31(3):239–264

    Article  Google Scholar 

  49. Sung J, Kim W, Oh TK, So YS (2023) Nitrogen (N) use efficiency and yield in rice under varying types and rates of N source: chemical fertilizer, livestock manure compost and food waste-livestock manure compost. Appl Biol Chem 66(1):4

    Article  CAS  Google Scholar 

  50. Vadalia D, Vaity M, Tawate K, Kapse D, Sem SV (2017) Real Time soil fertility analyzer and crop prediction. Int Res J Eng Technol 4(3):3–5

    Google Scholar 

  51. Vyavahare G, Lee Y, Seok YJ, Kim H, Sung J, Park JH. (2023). Monitoring of soil nutrient levels by an EC sensor during spring onion (Allium fistulosum) cultivation under different fertilizer treatment

  52. Walkley A, Black IA (1934) An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Sci 37(1):29–38

    Article  CAS  Google Scholar 

  53. Wang D, Xu Z, Zhao J, Wang Y, Yu Z (2011) Excessive nitrogen application decreases grain yield and increases nitrogen loss in a wheat–soil system. Acta Agric Scandinavica Sect B-Soil Plant Sci 61(8):681–692

    Google Scholar 

  54. Wyseure GCL, Mojid MA, Malik MA (1997) Measurement of volumetric water content by TDR in saline soils. Eur J Soil Sci 48(2):347–354

    Article  Google Scholar 

  55. Tanji KK (2002) Salinity in the soil environment. Salinity: environment-plants-molecules. Springer, Dordrecht, pp 21–51

    Google Scholar 

  56. Xing X, Ma X (2017) Differences in loam water retention and shrinkage behavior: effects of various types and concentrations of salt ions. Soil Tillage Res 167:61–72

    Article  Google Scholar 

Download references

Acknowledgements

This work was carried out with the support of "Cooperative Research Program for Agriculture Science & Technology Development (Project No. PJ015635)" Rural Development Administration, Republic of Korea.

Funding

This work was carried out with the support of "Cooperative Research Program for Agriculture Science & Technology Development (Project No. PJ015635)" Rural Development Administration, Republic of Korea.

Author information

Authors and Affiliations

  1. Department of Environmental and Biological Chemistry, Chungbuk National University, 1 Chungdae-Ro, Seowon-Gu, Cheongju, 28644, Chungbuk, Republic of Korea

    Han Na Kim & Jin Hee Park

Authors
  1. Han Na Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jin Hee Park
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JHP contributed to the study conception and design. Material preparation, data collection and analysis were performed by HNK and JHP. The first draft of the manuscript was written by HNK and JHP commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jin Hee Park.

Ethics declarations

Ethics approval and consent to participate

The manuscript does not have potential conflicts of interest. The research does not involve human participants or animals.

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, H.N., Park, J.H. Monitoring of soil EC for the prediction of soil nutrient regime under different soil water and organic matter contents. Appl Biol Chem 67, 1 (2024). https://doi.org/10.1186/s13765-023-00849-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13765-023-00849-4

Keywords