Special Issue "Soil Sustainability in the Anthropocene"

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Soil and Plant Nutrition".

Deadline for manuscript submissions: 20 October 2022 | Viewed by 4810

Special Issue Editors

Dr. Long Guo
E-Mail Website
Guest Editor
College of Resources and Environment & The Research Center of Territorial Spatial Governance and Green Development, Huazhong Agricultural University, Wuhan 430070, China
Interests: digital soil mapping; hyperspectral remote sensing images; soil modeling; soil and life
Dr. Xiaodong Song
E-Mail Website
Guest Editor
Institute of Soil Science, Chinese Academy of Science, Nanjing 210008, China
Interests: digital soil mapping; Earth's Critical Zone; soil modeling and lift
Dr. Peng Fu
E-Mail Website1 Website2
Guest Editor
Center for Environment, Energy, and Economy, Harrisburg University, Harrisburg, PA 17101, USA
Interests: remote sensing; plant physiology; urban climate; soil science; machine learning; digital agriculture; ecology
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Special Issue Information

Dear Colleagues,

A new geologic epoch—the Anthropocene—was voted by the 34-member Anthropocene Working Group (AWG) to mark the profound ways in which humans have altered the planet. In the past 200 years of the Anthropocene, human activities have become an important driving force for the important changes to the Earth’s environment. The pedosphere, as the foundation and central junction of the Earth’s Critical Zone, dominates the biogeochemical and hydro-pedological coupling processes and provides necessary ecological functions that sustain terrestrial life. However, unreasonable anthropogenic activities, such as those associated with intensive agricultural management and rapid urbanization, have caused a series of issues to soils, such as soil acidification, salinization, pollution, and erosion. To help to address these challenging issues, many new technologies have been used in soil science, such as digital soil mapping, soil remote sensing inversion, proximal soil sensing, geostatistics, spatial analysis, and machine learning. 

Therefore, this Special Issue will collect new developments and methodologies, best practices, and applications in soil science. We welcome submissions that provide the community with the most recent advancements in all aspects of soil and life, including but not limited to the following: 

  • Data processing, machine learning, and geostatistical and spatial analysis in soil science;
  • Spatial and temporal changes in soil organic carbon, nitrogen, phosphorus, heavy metals, salinity, and others in representative areas;
  • The global cycle of soil carbon, nitrogen, and water;
  • Digital soil mapping;
  • The relationships between soil properties and human activities;
  • Inversion of soil properties from single and/or multisource sensor-based data (e.g., multispectral, hyperspectral, thermal, LiDAR, SAR, gas, radioactivity sensors);
  • Climate modeling of soil systems;
  • Soils for sustainable agriculture;
  • Emerging approaches to characterize soil carbon and greenhouse gas emissions;
  • Soil biodiversity.

Dr. Long Guo
Dr. Xiaodong Song
Prof. Dr. Abdul Mouazen
Dr. Peng Fu
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 2000 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

  • soil sustainability
  • anthropocene
  • critical zone
  • soil health
  • geostatistics
  • remote/proximal sensing
  • carbon cycle
  • urbanization
  • climate change

Published Papers (6 papers)

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Research

Article
Characteristics of a Benchmark Loess–Paleosol Profile in Northeast China
Agronomy 2022, 12(6), 1376; https://doi.org/10.3390/agronomy12061376 - 07 Jun 2022
Viewed by 351
Abstract
The Chaoyang profile represents a rare multi-period, continuous and complete sequence of aeolian paleo-deposits with a stable sedimentary origin and multi-stage paleoclimatic cycles. Benchmark profiles including soil types at different pedogenic stages can be used for the recognition and classification of paleosols and [...] Read more.
The Chaoyang profile represents a rare multi-period, continuous and complete sequence of aeolian paleo-deposits with a stable sedimentary origin and multi-stage paleoclimatic cycles. Benchmark profiles including soil types at different pedogenic stages can be used for the recognition and classification of paleosols and paleoclimate reconstruction. The loess–paleosol sequence benchmark profile (LBP) is also helpful in comparing the results of paleoenvironment reconstruction from different ecological regions. In this study, a loess–paleosol profile derived from thick loess in Chaoyang city of Liaoning province, Northeast China, was investigated as a well-preserved LBP that included various paleosol types. To determine the nature and origin of the Chaoyang profile, the geographic, stratigraphic and morphological characteristics were described in the field. Bulk samples from 42 horizons were collected for chemical and physical analysis, and sub-sampling of 946 samples at 2 cm intervals from the surface to the bottom were taken to measure grain size distributions and magnetic susceptibility. Results showed that the 19.85 m thick loess–paleosol profile had been continuously deposited since 423 ka BP. The upper part (0–195 cm), or UPP, was predominantly of aeolian loess deposition origin but was mixed with water-reworked materials from a nearby secondary loess source. The middle part (195–228 cm), or MIP, was also indirectly affected by the water-reworking process through the leaching of materials from the overlying UPP. The lower part (228–1985 cm), or LOP, was characterized by four reddish stratigraphic layers interbedded with five yellowish ones, indicating several types of paleosols developed under different ecological environments. The multi-stage paleoclimatic cycles as evidenced by morphological and physical characteristics as well as age dating and magnetic susceptibility correlated well with the Lingtai section and LR04 benthic δ18O. Because of these attributes, the Chaoyang profile can be deemed as a benchmark loess–paleosol profile for the recognition and classification of paleosols and paleoclimate reconstruction in Northeast China. The differences in morphological and physical properties between paleosols and loess suggest different soil fertility and agronomic properties and need further studies to assess their functionality with climate fluctuation. Full article
(This article belongs to the Special Issue Soil Sustainability in the Anthropocene)
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Article
Spatial Variation and Influencing Factors of Trace Elements in Farmland in a Lateritic Red Soil Region of China
Agronomy 2022, 12(2), 478; https://doi.org/10.3390/agronomy12020478 - 14 Feb 2022
Viewed by 561
Abstract
Trace elements in farmland soil are important indicators of soil quality and farmland health, and also maintain the nutrient balance and promote the healthy growth of plants. In this study, taking Conghua District of Guangzhou city as the study area, the effects of [...] Read more.
Trace elements in farmland soil are important indicators of soil quality and farmland health, and also maintain the nutrient balance and promote the healthy growth of plants. In this study, taking Conghua District of Guangzhou city as the study area, the effects of topography, soil, land use, and other factors on trace elements in soil were investigated, and the spatial variability of boron (B), manganese (Mn), molybdenum (Mo), copper (Cu), and zinc (Zn) in farmland soil in a typical red soil region were mapped using a geographically weighted regression (GWR) method. The pH and land economic index (LEI) were important factors affecting the changes in trace element concentrations in the five soils, and the Cu and Zn concentrations were clearly affected by human factors. In the study area, 86.99% of B measurements were classified as low and very low levels, 50.61% and 49.20% of Mo measurements were also low and very low, 71.79% of Mn measurements were classified as moderate, while 91.02% of Cu and 52.95% of Zn measurements were classified as high. After a cross validation, the GWR Kriging (GWRK) model results of each element were relatively stable, and the order of the fitting coefficient (R2) was Cu > Zn > B> Mn > Mo. This study clarifies the spatial distribution and influencing factors of soil microelements in the studied region. This information can be used to improve the nutrient imbalance, further guide agricultural production, strengthen the management of farmland, and improve the healthy productivity of cultivated land. Full article
(This article belongs to the Special Issue Soil Sustainability in the Anthropocene)
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Article
An Integrated Yield-Based Methodology for Improving Soil Nutrient Management at a Regional Scale
Agronomy 2022, 12(2), 298; https://doi.org/10.3390/agronomy12020298 - 25 Jan 2022
Viewed by 790
Abstract
The relationships between crop yield and its selected related impact factors has often been explored using ordinary least squares regression (OLSR). However, this model is non-spatial and non-robust. This study first used stepwise regression to identify the main factors affecting winter wheat yield [...] Read more.
The relationships between crop yield and its selected related impact factors has often been explored using ordinary least squares regression (OLSR). However, this model is non-spatial and non-robust. This study first used stepwise regression to identify the main factors affecting winter wheat yield from twelve potential related factors in Yucheng County, China. Next, robust geographically weighted regression (RGWR) was used to explore the spatially non-stationary relationships between wheat yield and its main impact factors. Then, its modeling effect was compared with that of GWR and OLSR. Last, robust geostatistical analysis was conducted for spatial soil management measures in low-yield areas. Results showed that: (i) three main impact factors on wheat yield were identified by stepwise regression, namely soil organic matter, soil total phosphorus, and pH; (ii) the spatially non-stationary effects of the main impact factors on wheat yield were revealed by RGWR but were ignored by OLSR; (iii) RGWR obtained the best modeling effect (RI = 52.31%); (iv) robust geostatistics obtains a better spatial prediction effect and the low-yield areas are mainly located in the northeast and the middle east of the study area. Therefore, the integrated yield-based methodology effectively improves soil nutrient management at a regional scale. Full article
(This article belongs to the Special Issue Soil Sustainability in the Anthropocene)
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Article
Incorporating Auxiliary Data of Different Spatial Scales for Spatial Prediction of Soil Nitrogen Using Robust Residual Cokriging (RRCoK)
Agronomy 2021, 11(12), 2516; https://doi.org/10.3390/agronomy11122516 - 10 Dec 2021
Viewed by 562
Abstract
Auxiliary data has usually been incorporated into geostatistics for high-accuracy spatial prediction. Due to the different spatial scales, category and point auxiliary data have rarely been incorporated into prediction models together. Moreover, traditionally used geostatistical models are usually sensitive to outliers. This study [...] Read more.
Auxiliary data has usually been incorporated into geostatistics for high-accuracy spatial prediction. Due to the different spatial scales, category and point auxiliary data have rarely been incorporated into prediction models together. Moreover, traditionally used geostatistical models are usually sensitive to outliers. This study first quantified the land-use type (LUT) effect on soil total nitrogen (TN) in Hanchuan County, China. Next, the relationship between soil TN and the auxiliary soil organic matter (SOM) was explored. Then, robust residual cokriging (RRCoK) with LUTs was proposed for the spatial prediction of soil TN. Finally, its spatial prediction accuracy was compared with that of ordinary kriging (OK), robust cokriging (RCoK), and robust residual kriging (RRK). Results show that: (i) both LUT and SOM are closely related to soil TN; (ii) by incorporating SOM, the relative improvement accuracy of RCoK over OK was 29.41%; (iii) by incorporating LUTs, the relative improvement accuracy of RRK over OK was 33.33%; (iv) RRCoK obtained the highest spatial prediction accuracy (RI = 43.14%). It is concluded that the recommended method, RRCoK, can effectively incorporate category and point auxiliary data together for the high-accuracy spatial prediction of soil properties. Full article
(This article belongs to the Special Issue Soil Sustainability in the Anthropocene)
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Article
Impacts of Farming Layer Constructions on Cultivated Land Quality under the Cultivated Land Balance Policy
Agronomy 2021, 11(12), 2403; https://doi.org/10.3390/agronomy11122403 - 25 Nov 2021
Viewed by 535
Abstract
Cultivated Land Balance Policy (CLBP) has led to the “better land occupied and worse land supplemented” program. At the same time, the current field-scale cultivated land quality (CLQ) evaluation cannot meet the work requirements of the CLBP. To this end, this study selected [...] Read more.
Cultivated Land Balance Policy (CLBP) has led to the “better land occupied and worse land supplemented” program. At the same time, the current field-scale cultivated land quality (CLQ) evaluation cannot meet the work requirements of the CLBP. To this end, this study selected 24 newly added farmland in Fuping County and performed eight different high quality farming layer construction experiments to improve the CLQ. A new comprehensive model was constructed on a field scale to evaluate the CLQ using different tests from multi-dimensional perspectives of soil fertility, engineering, environment, and ecology, and to determine the best test mode. The results showed that after the test, around 62% of the cultivated land improved by one level, and the average cultivated land quality level and quality index of the test area increased by 0.63 and 30.63, respectively. The treatment of “woody peat + rotten crop straw + biostimulation regulator II + conventional fertilization” had the best effect on the improvement of organic matter, soil aggregates, and soil microbial activity, and was the best treatment method. In general, application of soil amendments, such as woody peat when constructing high quality farmland, could quickly improve CLQ, and field-scale CLQ evaluation model constructed from a multi-dimensional perspective could accurately assess the true quality of farmland and allow managers to improve and manage arable land resources under CLBP. Full article
(This article belongs to the Special Issue Soil Sustainability in the Anthropocene)
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Article
Changes of Soil Organic Carbon after Wildfire in a Boreal Forest, Northeast CHINA
Agronomy 2021, 11(10), 1925; https://doi.org/10.3390/agronomy11101925 - 25 Sep 2021
Cited by 1 | Viewed by 743
Abstract
Boreal forests with high carbon sequestration capacity play a crucial role in mitigating global climate change. Addressing dynamic changes of soil organic carbon (SOC) after wildfire helps in understanding carbon cycling. The objective of this study is to investigate changes in soil organic [...] Read more.
Boreal forests with high carbon sequestration capacity play a crucial role in mitigating global climate change. Addressing dynamic changes of soil organic carbon (SOC) after wildfire helps in understanding carbon cycling. The objective of this study is to investigate changes in soil organic carbon after wildfires in a boreal forest. The post-fire soil chronosequence after 3 months, 17 years, and 25 years within a boreal forest was used to examine dynamic and stable SOC after wildfire at the decadal scale. Soils in genetic horizons were sampled and analyzed for dynamic and stable SOC, including water stable aggregates (WSA), WSA associated organic carbon (WSA-SOC), soil heavy fractions (HF) associated organic carbon (HF-SOC), and soil total organic carbon (TOC). The TOC and WSA-SOC content of the A horizon was the greatest in the control site. There was no significant difference for TOC between burned and unburned deep BC horizons. The TOC for the A and B horizons at the 17-year-old site was significantly lower compared to the other sites. TOC did not recover to the pre-fire levels (control site) in any of the burned areas. The lowest WSA was found in the A and B horizons of the 3-month-old site. The WSA at the 25-year-old site was higher compared to the 17-year-old site. WSA increased with time following fire, but the recovery rate differed among different sites. The lowest concentration of WSA-SOC for the A horizon occurred at the 17-year-old site, and no significant difference was observed between B and BC horizons. The HF content for the A horizon was the greatest at the 3-month-old site. There was no significant difference in HF-SOC between B and BC horizons in all sites. TOC and stable SOC (HF and WSA) increased over time in species-dominance relay stand areas, while self-replacement stands areas showed the opposite. The results indicate that overall, the ability of soil to sequester carbon decreased after wildfire disturbances. Stable SOC accumulated more in areas where species-dominance relay succession occurred compared to the self-replacement stands. These disturbances were more pronounced for surface soil horizons. This study provides a quantitative assessment of SOC changes after wildfires that are useful for forest management and modeling forecasts of SOC stocks, especially in boreal forests. Full article
(This article belongs to the Special Issue Soil Sustainability in the Anthropocene)
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