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Agronomy
Special Issues
Recent Advances in Soil Monitoring and Mapping in Agriculture Systems
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Recent Advances in Soil Monitoring and Mapping in Agriculture Systems

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (15 April 2022) | Viewed by 15921

Special Issue Editors

Prof. Dr. Thomas Scholten

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Guest Editor
Eberhard Karls University Tübingen, Soil Science and Geomorphology, Rümelinstraße 19-23, D-72070 Tübingen, Germany
Interests: soil science; environment; geomorphology; geoecology; soil erosion; machine learning in soil science
Special Issues, Collections and Topics in MDPI journals
Dr. Ruhollah Taghizadeh-Mehrjardi

E-Mail Website
Guest Editor
Soil Science and Geomorphology, University of Tübingen, Tübingen, Germany
Interests: digital soil mapping; machine learning; pedology; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Kabindra Adhikari

E-Mail Website
Guest Editor
Grassland, Soil & Water Research Laboratory, United States Department of Agriculture, Temple, TX 76502, USA
Interests: digital soil mapping; precision agriculture; soil–landscape modelling; soil health; sustainable soil management
Special Issues, Collections and Topics in MDPI journals
Dr. Amelie Beucher

E-Mail Website
Guest Editor
Department of Agroecology, Aarhus University, Aarhus, Denmark
Interests: machine learning in soil science; digital soil mapping; agronomy; pedology

Special Issue Information

Dear Colleagues,

Rapid population growth means that more food will be required to meet the demands of growing populations in an era of increasing climate uncertainty and environmental degradation. In this fast-changing world, and given the urgent need to ensure food security and nutrition, understanding and attaining sustainable soil management has never been more important. In this regard, soil mapping and monitoring provide the necessary information to sustain food safety and security. 

Conventional methods for mapping and monitoring soil properties involve regular field visits to collect soil samples followed by laboratory analyses and are labor intensive, time-consuming, and expensive. In recent decades, soil mapping and monitoring practices have largely transitioned toward the use of digital soil mapping approaches as a means to reduce the cost and time required to develop soil maps and leverage advances in modern technologies related to computing, remote sensing, UAVs, geostatistics, machine learning, and geographical information systems.

Novel methods, new applications, comparative analyses of models, case studies, and state-of-the-art review papers on topics pertaining to advances in soil monitoring and mapping in agriculture systems of agronomy are particularly welcomed.

Prof. Dr. Thomas Scholten
Dr. Ruhollah Taghizadeh-Mehrjardi
Dr. Kabindra Adhikari
Dr. Amelie Beucher
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • remote sensing
  • digital soil mapping
  • spatial prediction
  • deep learning
  • soil properties
  • digital agriculture
  • precision agriculture

Published Papers (5 papers)

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Research

15 pages, 3697 KiB  
Article
Recalcitrant C Source Mapping Utilizing Solely Terrain-Related Attributes and Data Mining Techniques
by Arezou Siami, Nasser Aliasgharzad, Leili Aghebati Maleki, Nosratollah Najafi, Farzin Shahbazi and Asim Biswas
Agronomy 2022, 12(7), 1653; https://doi.org/10.3390/agronomy12071653 - 11 Jul 2022
Cited by 4 | Viewed by 1312
Abstract
Agricultural practices affect arbuscular mycorrhizal fungal (AMF) hyphae growth and glomalin production, which is a recalcitrant carbon (C) source in soil. Since the spatial distribution of glomalin is an interesting issue for agronomists in terms of carbon sequestration, digital maps are a cost-free [...] Read more.
Agricultural practices affect arbuscular mycorrhizal fungal (AMF) hyphae growth and glomalin production, which is a recalcitrant carbon (C) source in soil. Since the spatial distribution of glomalin is an interesting issue for agronomists in terms of carbon sequestration, digital maps are a cost-free and useful approach. For this study, a set of 120 soil samples was collected from an experimental area of 310 km2 from the Sarab region of Iran. Soil total glomalin (TG) and easily extractable glomalin (EEG) were determined via ELISA using the monoclonal antibody 32B11. Soil organic carbon (OC) was also measured. The ratios of TG/OC and EEG/OC as the glomalin–C quotes of OC were calculated. A total of 17 terrain-related attributes were also derived from the digital elevation model (DEM) and used as static environmental covariates in digital soil mapping (DSM) using three predictive models, including multiple linear regression (MLR), random forests (RF), and Cubist (CU). The major findings were as follows: (a) DSM facilitated the interpretation of recalcitrant C source variation; (b) RF outperformed MLR and CU as models in predicting and mapping the spatial distribution of glomalin using available covariates; (c) the best accuracy in predictions was for EEG, followed by EEG/OC, TG, and TG/OC. Full article
(This article belongs to the Special Issue Recent Advances in Soil Monitoring and Mapping in Agriculture Systems)
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24 pages, 4699 KiB  
Article
Digital Mapping of Soil Organic Matter and Cation Exchange Capacity in a Low Relief Landscape Using LiDAR Data
by Shams R. Rahmani, Jason P. Ackerson, Darrell Schulze, Kabindra Adhikari and Zamir Libohova
Agronomy 2022, 12(6), 1338; https://doi.org/10.3390/agronomy12061338 - 31 May 2022
Cited by 11 | Viewed by 3867
Abstract
Soil organic matter content (SOM) and cation exchange capacity (CEC) are important agronomic soil properties. Accurate, high-resolution spatial information of SOM and CEC are needed for precision farm management. The objectives of this study were to: (1) map SOM and CEC in a [...] Read more.
Soil organic matter content (SOM) and cation exchange capacity (CEC) are important agronomic soil properties. Accurate, high-resolution spatial information of SOM and CEC are needed for precision farm management. The objectives of this study were to: (1) map SOM and CEC in a low relief area using only lidar elevation-based terrain attributes, and (2) compare the prediction accuracy of SOM and CEC maps created by universal kriging, Cubist, and random forest with Soil Survey Geographic (SSURGO) database. For this study, 174 soil samples were collected from a depth from 0 to 10 cm. The topographic wetness index, topographic position index, multi resolution valley bottom flatness, and multi resolution ridge top flatness indices generated from the lidar data were used as covariates in model predictions. No major differences were found in the prediction performance of all selected models. For SOM, the predictive models provided results with coefficient of determination (R2) (0.44–0.45), root mean square error (RMSE) (0.8–0.83%), bias (0–0.22%), and concordance correlation coefficient (ρc) (0.56–0.58). For CEC, the R2 ranged from 0.39 to 0.44, RMSE ranged from 3.62 to 3.74 cmolc kg−1, bias ranged from 0–0.17 cmolc kg−1, and ρc ranged from 0.55 to 0.57. We also compared the results to the USDA Soil Survey Geographic (SSURGO) data. For both SOM and CEC, SSURGO was comparable with our predictive models, except for few map units where both SOM and CEC were either under or over predicted. Full article
(This article belongs to the Special Issue Recent Advances in Soil Monitoring and Mapping in Agriculture Systems)
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16 pages, 2887 KiB  
Article
Mapping Within-Field Soil Health Variations Using Apparent Electrical Conductivity, Topography, and Machine Learning
by Kabindra Adhikari, Douglas R. Smith, Harold Collins, Chad Hajda, Bharat Sharma Acharya and Phillip R. Owens
Agronomy 2022, 12(5), 1019; https://doi.org/10.3390/agronomy12051019 - 24 Apr 2022
Cited by 4 | Viewed by 3609
Abstract
High-resolution maps of soil health measurements could help farmers finetune input resources and management practices for profit maximization. Within-field soil heath variations can be mapped using local topography and apparent electrical conductivity (ECa) as predictors. To address these issues, a study was conducted [...] Read more.
High-resolution maps of soil health measurements could help farmers finetune input resources and management practices for profit maximization. Within-field soil heath variations can be mapped using local topography and apparent electrical conductivity (ECa) as predictors. To address these issues, a study was conducted in Texas Blackland Prairie soils with the following objectives: (i) to assess and map within-field soil health variations using machine learning; (ii) to evaluate the usefulness of topography and ECa as soil health predictors; and (iii) to quantify the relationship between ECa and soil health index and use ECa to estimate soil health spatial distribution. We collected 218 topsoil (0–15 cm) samples following a 35 m × 35 m grid design and analyzed for one-day CO2, organic C, organic N, and soil health index (SHI) based on the Haney Soil Health Tool. A random forest model was applied to predict and map those properties on a 5 m × 5 m grid where ECa, and terrain attributes were used as predictors. Furthermore, the empirical relationship between SHI and ECa was established and mapped across the field. Results showed that the study area was variable in terms of one-day CO2, organic C, organic N, SHI, and ECa distribution. The ECa, wetness index, multiresolution valley bottom flatness, and topographic position index were among the top predictors of soil health measurements. The model was sufficiently robust to predict one day CO2, organic C, organic N (R2 between 0.24–0.90), and SHI (R2 between 0.47–0.90). Overall, we observed a moderate to strong spatial dependency of soil health measurements which could impact within-field yield variability. The study confirmed the applicability of easy to obtain ECa as a good predictor of SHI, and the predicted maps at high resolution which could be useful in site-specific management decisions within these types of soils. Full article
(This article belongs to the Special Issue Recent Advances in Soil Monitoring and Mapping in Agriculture Systems)
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16 pages, 3361 KiB  
Article
Combining Variable Selection and Multiple Linear Regression for Soil Organic Matter and Total Nitrogen Estimation by DRIFT-MIR Spectroscopy
by Hong Li, Junwei Wang, Jixiong Zhang, Tongqing Liu, Gifty E. Acquah and Huimin Yuan
Agronomy 2022, 12(3), 638; https://doi.org/10.3390/agronomy12030638 - 05 Mar 2022
Cited by 5 | Viewed by 2157
Abstract
The successful estimation of soil organic matter (SOM) and soil total nitrogen (TN) contents with mid-infrared (MIR) reflectance spectroscopy depends on selecting appropriate variable selection techniques and multivariate methods for regression analysis. This study aimed to explore the potential of combining a multivariate [...] Read more.
The successful estimation of soil organic matter (SOM) and soil total nitrogen (TN) contents with mid-infrared (MIR) reflectance spectroscopy depends on selecting appropriate variable selection techniques and multivariate methods for regression analysis. This study aimed to explore the potential of combining a multivariate method and spectral variable selection for soil SOM and TN estimation using MIR spectroscopy. Five hundred and ten topsoil samples were collected from Quzhou County, Hebei Province, China, and their SOM and TN contents and reflectance spectra were measured using DRIFT-MIR spectroscopy (diffuse reflectance infrared Fourier transform in the mid-infrared range, MIR, wavenumber: 4000–400 cm−1; wavelength: 2500–25,000 nm). Two multivariate methods (partial least-squares regression, PLSR; multiple linear regression, MLR) combined with two variable selection techniques (stability competitive adaptive reweighted sampling, sCARS; bootstrapping soft shrinkage approach, BOSS) were used for model calibration. The MLR model combined with the sCARS method yielded the most accurate estimation result for both SOM (Rp2 = 0.72 and RPD = 1.89) and TN (Rp2 = 0.84 and RPD = 2.50). Out of the 2382 wavenumbers in a full spectrum, sCARS determined that only 31 variables were important for SOM estimation (accounting for 1.30% of all variables) and 27 variables were important for TN estimation (accounting for 1.13% of all variables). The results demonstrated that sCARS was a highly efficient approach for extracting information on wavenumbers and mitigating redundant wavenumbers. In addition, the current study indicated that MLR, which is simpler than PLSR, when combined with spectral variable selection, can achieve high-precision prediction of SOM and TN content. As such, DRIFT-MIR spectroscopy coupled with MLR and sCARS is a good alternative for estimating the SOM and TN of soils. Full article
(This article belongs to the Special Issue Recent Advances in Soil Monitoring and Mapping in Agriculture Systems)
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13 pages, 4776 KiB  
Article
Spatiotemporal Assessment of Soil Organic Carbon Change Using Machine-Learning in Arid Regions
by Hassan Fathizad, Ruhollah Taghizadeh-Mehrjardi, Mohammad Ali Hakimzadeh Ardakani, Mojtaba Zeraatpisheh, Brandon Heung and Thomas Scholten
Agronomy 2022, 12(3), 628; https://doi.org/10.3390/agronomy12030628 - 04 Mar 2022
Cited by 12 | Viewed by 3071
Abstract
Soil organic carbon (SOC) is an essential property of soil, and understanding its spatial patterns is critical to understanding vegetation management, soil degradation, and environmental issues. This study applies a framework using remote sensing data and digital soil mapping techniques to examine the [...] Read more.
Soil organic carbon (SOC) is an essential property of soil, and understanding its spatial patterns is critical to understanding vegetation management, soil degradation, and environmental issues. This study applies a framework using remote sensing data and digital soil mapping techniques to examine the spatiotemporal dynamics of SOC for the Yazd-Ardakan Plain, Iran, from 1986 to 2016. Here, a conditioned Latin hypercube sampling method was used to select 201 sampling sites. A set of 37 environmental predictors were obtained from Landsat imagery taken in 1986, 1999, 2010 and 2016. Here, SOC was modeled for 2016 using the Random Forest (RF), support vector regression (SVR), and artificial neural networks (ANN) machine-learners by correlating environmental predictors with soil data. The results showed that RF yielded the highest accuracy (R2 = 0.53), compared to the other two learners. By performing a variable importance analysis of the RF model, normalized difference vegetation index, modified vegetation index, and ground-adjusted vegetation index were determined to be the most important environmental predictors. By applying the model calibrated from 2016 data to 1986, 1999 and 2010, the results showed a substantial decrease in SOC; these decreases in SOC were mainly attributed to land use changes and agricultural activities. Full article
(This article belongs to the Special Issue Recent Advances in Soil Monitoring and Mapping in Agriculture Systems)
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