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Agronomy
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Geostatistics and Machine Learning in the Mapping of Agricultural Soils:...
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Geostatistics and Machine Learning in the Mapping of Agricultural Soils: State-of-the-Art and Perspectives

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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 8772

Special Issue Editors

Dr. László Pásztor

E-Mail Website
Guest Editor
Department of Soil Mapping and Environmental Informatics, Institute for Soil Sciences, Centre for Agricultural Research, 1022 Budapest, Hungary
Interests: GIS; spatial modelling; digital soil mapping; agri-environmental modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Gábor Szatmári

E-Mail Website
Guest Editor
Department of Soil Mapping and Environmental Informatics, Institute for Soil Sciences, Centre for Agricultural Research, 1022 Budapest, Hungary
Interests: geostatistics; geomathematics; digital soil and environmental mapping

Special Issue Information

Dear Colleagues,

Agronomy is one of the applications demanding the most spatial information on the state, functions, potential and processes of soils. Very recently, predictive soil maps, in the form of digital soil maps, are considered as the most effective representation of specific features of the soil mantle. The evolution of digital soil mapping is strongly related to the availability of spatially exhaustive, relatively low-cost data (available in the form of space-borne imagery and digital elevation models) as well as geostatistical and data mining methods suitable for the identification of hidden relationships between soil features and environmental factors, which then can be used for building predictive models. Recent advantages in proximal sensing increased the interest to apply and exploit the products serviced by these instruments for digital soil mapping at local and farm scale to support the spatial assessment of land and soil features (properties, functions, processes and services). Research papers presenting innovative approaches for the high resolution spatial assessment and mapping of various soil characteristics are welcomed in the present Special Issue.

Dr. László Pásztor
Dr. Gábor Szatmári
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

  • digital soil mapping
  • geostatistics
  • machine learning
  • delineation of management zones
  • plant nutrition advising
  • precision farming
  • predictive mapping
  • proximal soil sensing
  • spatial assessment
  • spatial uncertainty

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Published Papers (3 papers)

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Research

19 pages, 2721 KiB  
Article
High-Resolution Mapping and Assessment of Salt-Affectedness on Arable Lands by the Combination of Ensemble Learning and Multivariate Geostatistics
by Fatemeh Hateffard, Kitti Balog, Tibor Tóth, János Mészáros, Mátyás Árvai, Zsófia Adrienn Kovács, Nóra Szűcs-Vásárhelyi, Sándor Koós, Péter László, Tibor József Novák, László Pásztor and Gábor Szatmári
Agronomy 2022, 12(8), 1858; https://doi.org/10.3390/agronomy12081858 - 6 Aug 2022
Cited by 11 | Viewed by 3051
Abstract
Soil salinization is one of the main threats to soils worldwide, which has serious impacts on soil functions. Our objective was to map and assess salt-affectedness on arable land (0.85 km2) in Hungary, with high spatial resolution, using a combination of [...] Read more.
Soil salinization is one of the main threats to soils worldwide, which has serious impacts on soil functions. Our objective was to map and assess salt-affectedness on arable land (0.85 km2) in Hungary, with high spatial resolution, using a combination of ensemble machine learning and multivariate geostatistics on three salt-affected soil indicators (i.e., alkalinity, electrical conductivity, and sodium adsorption ratio (n = 85 soil samples)). Ensemble modelling with five base learners (i.e., random forest, extreme gradient boosting, support vector machine, neural network, and generalized linear model) was carried out and the results showed that ensemble modelling outperformed the base learners for alkalinity and sodium adsorption ratio with R2 values of 0.43 and 0.96, respectively, while only the random forest prediction was acceptable for electrical conductivity. Multivariate geostatistics was conducted on the stochastic residuals derived from machine learning modelling, as we could reasonably assume that there is spatial interdependence between the selected salt-affected soil indicators. We used 10-fold cross-validation to check the performance of the spatial predictions and uncertainty quantifications, which provided acceptable results for each selected salt-affected soil indicator (for pH value, electrical conductivity, and sodium adsorption ratio, the root mean square error values were 0.11, 0.86, and 0.22, respectively). Our results showed that the methodology applied in this study is efficient in mapping and assessing salt-affectedness on arable lands with high spatial resolution. A probability map for sodium adsorption ratio represents sodic soils exceeding a threshold value of 13, where they are more likely to have soil structure deterioration and water infiltration problems. This map can help the land user to select the appropriate agrotechnical operation for improving soil quality and yield. Full article
(This article belongs to the Special Issue Geostatistics and Machine Learning in the Mapping of Agricultural Soils: State-of-the-Art and Perspectives)
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22 pages, 5949 KiB  
Article
Choosing Feature Selection Methods for Spatial Modeling of Soil Fertility Properties at the Field Scale
by Caner Ferhatoglu and Bradley A. Miller
Agronomy 2022, 12(8), 1786; https://doi.org/10.3390/agronomy12081786 - 29 Jul 2022
Cited by 7 | Viewed by 2500
Abstract
With the growing availability of environmental covariates, feature selection (FS) is becoming an essential task for applying machine learning (ML) in digital soil mapping (DSM). In this study, the effectiveness of six types of FS methods from four categories (filter, wrapper, embedded, and [...] Read more.
With the growing availability of environmental covariates, feature selection (FS) is becoming an essential task for applying machine learning (ML) in digital soil mapping (DSM). In this study, the effectiveness of six types of FS methods from four categories (filter, wrapper, embedded, and hybrid) were compared. These FS algorithms chose relevant covariates from an exhaustive set of 1049 environmental covariates for predicting five soil fertility properties in ten fields, in combination with ten different ML algorithms. Resulting model performance was compared by three different metrics (R2 of 10-fold cross validation (CV), robustness ratio (RR; developed in this study), and independent validation with Lin’s concordance correlation coefficient (IV-CCC)). FS improved CV, RR, and IV-CCC compared to the models built without FS for most fields and soil properties. Wrapper (BorutaShap) and embedded (Lasso-FS, Random forest-FS) methods usually led to the optimal models. The filter-based ANOVA-FS method mostly led to overfit models, especially for fields with smaller sample quantities. Decision-tree based models were usually part of the optimal combination of FS and ML. Considering RR helped identify optimal combinations of FS and ML that can improve the performance of DSM compared to models produced from full covariate stacks. Full article
(This article belongs to the Special Issue Geostatistics and Machine Learning in the Mapping of Agricultural Soils: State-of-the-Art and Perspectives)
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27 pages, 5390 KiB  
Article
Machine Learning Strategy for Improved Prediction of Micronutrient Concentrations in Soils of Taif Rose Farms Based on EDXRF Spectra
by Hala M. Abdelmigid, Mohammed A. Baz, Mohammed A. AlZain, Jehad F. Al-Amri, Hatim Ghazi Zaini, Maissa M. Morsi, Matokah Abualnaja and Elham A. Althagafi
Agronomy 2022, 12(4), 895; https://doi.org/10.3390/agronomy12040895 - 7 Apr 2022
Cited by 2 | Viewed by 2208
Abstract
This study attempts to utilize newly developed machine learning techniques in order to develop a general prediction algorithm for agricultural soils in Saudi Arabia, specifically in the Taif region. Energy dispersive X-ray fluorescence (EDXRF) measurements were used to develop national predictive models that [...] Read more.
This study attempts to utilize newly developed machine learning techniques in order to develop a general prediction algorithm for agricultural soils in Saudi Arabia, specifically in the Taif region. Energy dispersive X-ray fluorescence (EDXRF) measurements were used to develop national predictive models that predict the concentrations of 14 micronutrients in soils of Taif rose farms, for providing high-quality data comparable to conventional methods. Machine learning algorithms used in this study included the simple linear model, the multivariate linear regression (MLR); and two nonlinear models, the random forest (RF) and multivariate adaptive regression splines (MARS). Our study proposes a machine learning (ML) strategy for predicting fertility parameters more accurately in agricultural soils using 10 farms of the Taif rose (Rosa damascena) in Taif, Saudi Arabia as a case study. Results demonstrated that MARS provides higher prediction performance when the number of explanatory variables is small, while RF is superior when the number of variables is large. On the other hand, the MLR is recommended as a moderate method for predicting multivariate variables. The study showed that multivariate models can be used to overwhelm the drawbacks of the EDXRF device, such as high detection limits and an element that cannot be directly measured. Full article
(This article belongs to the Special Issue Geostatistics and Machine Learning in the Mapping of Agricultural Soils: State-of-the-Art and Perspectives)
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