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4 pages, 175 KiB

Open AccessEditorial

Soil Sustainability in the Anthropocene

by Long Guo, Xiaodong Song, Abdul M. Mouazen and Fu Peng

Agronomy 2023, 13(5), 1299; https://doi.org/10.3390/agronomy13051299 - 05 May 2023

Viewed by 1114

Abstract

A new geological epoch—the Anthropocene—was voted by the 34-member Anthropocene Working Group (AWG) to mark the profound ways in which humans have altered our planet [...] Full article

(This article belongs to the Special Issue Soil Sustainability in the Anthropocene)

Research

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13 pages, 1693 KiB

Open AccessArticle

Anthropogenic Contribution and Migration of Soil Heavy Metals in the Vicinity of Typical Highways

by Jinling Yang, Yuguo Zhao, Xinling Ruan and Ganlin Zhang

Agronomy 2023, 13(2), 303; https://doi.org/10.3390/agronomy13020303 - 19 Jan 2023

Cited by 4 | Viewed by 1183

Abstract

To reveal the secondary anthropogenic contribution and accumulation rate of heavy metals in highway-side soils, we studied soil heavy metals on three representative highways in Southeast China, with different traffic intensities, service years, land use patterns and distances from roads, with high-resolution sampling [...] Read more.

To reveal the secondary anthropogenic contribution and accumulation rate of heavy metals in highway-side soils, we studied soil heavy metals on three representative highways in Southeast China, with different traffic intensities, service years, land use patterns and distances from roads, with high-resolution sampling of soil profiles. Concentrations of soil Cu, Zn, Pb and Cd were measured. The comparison of concentrations in surface soils with original values and their vertical distributions shows that soils within 150 m of the highway side are contaminated by heavy metals, with surface accumulation and possible movement down the profiles. The transferring depth of heavy metals was 10–30 cm. The contribution ratios of heavy metals were 1.0–30.5% in the surface at 30 cm, with the sequence of Cd >> Cu > Zn > Pb. The accumulation rates were 1.27–2.03 kg Cu ha⁻¹ y⁻¹, 2.44–5.27 kg Zn ha⁻¹ y⁻¹, 0.71–1.40 kg Pb ha⁻¹ y⁻¹ and 0.010–0.018 kg Cd ha⁻¹ y⁻¹ in soils within 50 m range. Furthermore, the accumulation of these metals varied with the traffic intensity, service years and land use patterns. Soils under forest have less heavy metal accumulation, which suggests a protective forest to set beside highways at a distance of at least 50 m to prevent soils from being contaminated. Full article

(This article belongs to the Special Issue Soil Sustainability in the Anthropocene)

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13 pages, 1379 KiB

Open AccessArticle

Effects of Land Use on the Mineralization of Organic Matter in Ultisol

by Peidong Xu, Shihao Ma, Xiongfei Rao, Shipeng Liao, Jun Zhu and Chunlei Yang

Agronomy 2022, 12(12), 2915; https://doi.org/10.3390/agronomy12122915 - 23 Nov 2022

Cited by 3 | Viewed by 1202

Abstract

Soil organic matter mineralization changed by land-use types is still not clearly understood. In this study, soils from typical land-use types including adjacent plantations of bamboo (Bam), camphor (Cam), and tea (Tea) were chosen to systematically investigate the role of organic carbon components [...] Read more.

Soil organic matter mineralization changed by land-use types is still not clearly understood. In this study, soils from typical land-use types including adjacent plantations of bamboo (Bam), camphor (Cam), and tea (Tea) were chosen to systematically investigate the role of organic carbon components and microbial community compositions in the organic matter mineralization in Ultisol. The mineralization of organic matter followed the sequence Bam < Cam < Tea. The higher carbon contents of labile pools were in the Cam and the Tea than that in the Bam. The carbon content of dissolved organic matter (DOM) showed the order Bam < Cam < Tea, whereas the complexity of chemical structure in DOM followed the opposite trend. The land-use types significant shifted the bacterial and fungal communities, and the relative abundances of bacterial or fungal phyla of Actinobacteria, Acidobacteria, Firmicutes, and Basidiomycota were significantly different among the land-use types. The multivariate regression tree results showed that the total organic carbon and/or the C/N ratio were dominant factors in influencing the bacterial and fungal communities. Moreover, the redundancy analysis results demonstrated that the communities of bacteria and fungi in Bam, Cam, and Tea were tightly linked to the C/N ratio, the pH and the labile pool I carbon, and the DOM, respectively. The Pearson’s correlation results revealed that the mineralization of organic matter was significantly correlated with the organic carbon components, but generally not the microbial community compositions, which implied that the organic carbon components were perhaps the major determinant in controlling the organic matter mineralization in Ultisol. Full article

(This article belongs to the Special Issue Soil Sustainability in the Anthropocene)

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16 pages, 2501 KiB

Open AccessArticle

Mapping of Soil pH Based on SVM-RFE Feature Selection Algorithm

by Jia Guo, Ku Wang and Shaofei Jin

Agronomy 2022, 12(11), 2742; https://doi.org/10.3390/agronomy12112742 - 04 Nov 2022

Cited by 7 | Viewed by 2008

Abstract

The explicit mapping of spatial soil pH is beneficial to evaluate the effects of land-use changes in soil quality. Digital soil mapping methods based on machine learning have been considered one effective way to predict the spatial distribution of soil parameters. However, selecting [...] Read more.

The explicit mapping of spatial soil pH is beneficial to evaluate the effects of land-use changes in soil quality. Digital soil mapping methods based on machine learning have been considered one effective way to predict the spatial distribution of soil parameters. However, selecting optimal environmental variables with an appropriate feature selection method is key work in digital mapping. In this study, we evaluated the performance of the support vector machine recursive feature elimination (SVM-RFE) feature selection methods with four common performance machine learning methods in predicting and mapping the spatial soil pH of one urban area in Fuzhou, China. Thirty environmental variables were collected from the 134 samples that covered the entire study area for the SVM-RFE feature selection. The results identified the five most critical environmental variables for soil pH value: mean annual temperature (MAT), slope, Topographic Wetness Index (TWI), modified soil-adjusted vegetation index (MSAVI), and Band5. Further, the SVM-RFE feature selection algorithm could effectively improve the model accuracy, and the extreme gradient boosting (XGBoost) model after SVM-RFE feature selection had the best prediction results (R² = 0.68, MAE = 0.16, RMSE = 0.26). This paper combines the RFE-SVM feature selection with machine learning models to enable the fast and inexpensive mapping of soil pH, providing new ideas for predicting soil pH at small and medium scales, which will help with soil conservation and management in the region. Full article

(This article belongs to the Special Issue Soil Sustainability in the Anthropocene)

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23 pages, 9794 KiB

Open AccessArticle

Toward Field Soil Surveys: Identifying and Delineating Soil Diagnostic Horizons Based on Deep Learning and RGB Image

by Ruiqing Yang, Jiaying Chen, Junguang Wang and Shuyu Liu

Agronomy 2022, 12(11), 2664; https://doi.org/10.3390/agronomy12112664 - 27 Oct 2022

Cited by 2 | Viewed by 1743

Abstract

The diagnostic horizon in a soil is reflective of the environment in which it developed and the inherent characteristics of the material, therefore quantitative approaches to horizon delineation should focus on the diagnostic horizon. Moreover, it can enable the exchange and transfer of [...] Read more.

The diagnostic horizon in a soil is reflective of the environment in which it developed and the inherent characteristics of the material, therefore quantitative approaches to horizon delineation should focus on the diagnostic horizon. Moreover, it can enable the exchange and transfer of soil information between different taxonomic systems. This study aims to explore the use of deep learning and RGB images to train a soil diagnostic horizon identification model that can help field workers determine soil horizon information quickly, efficiently, easily, and cost-effectively. A total of 331 soil profile images of the main soil categories (five soil orders, including Primosols, Ferrosols, Argosols, Anthrosols, and Cambosols) from Hubei and Jiangxi Provinces were used. Each soil profile image was preprocessed and augmented to 10 images and then inputted into the UNet++ architecture. The mean intersection over union and pixel accuracy of the model were 71.24% and 82.66%, respectively. Results show that the model could accurately identify and delineate the soil diagnostic horizons. Moreover, the model performance varied considerably due to the definition of the horizon and whether the diagnostic conditions applied to a wide range of visual features on RGB images, the number of samples, and the soil characteristics of the study area. Full article

(This article belongs to the Special Issue Soil Sustainability in the Anthropocene)

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13 pages, 3140 KiB

Open AccessArticle

Spatial Contribution of Environmental Factors to Soil Aggregate Stability in a Small Catchment of the Loess Plateau, China

by Luping Ye, Lingling Ji, Hongfeng Chen, Xueye Chen and Wenfeng Tan

Agronomy 2022, 12(10), 2557; https://doi.org/10.3390/agronomy12102557 - 19 Oct 2022

Cited by 5 | Viewed by 1171

Abstract

Soil aggregate stability and erodibility are the influential factors governing soil resistance to water erosion. The interactions among aggregate stability, erodibility, and their influencing factors have not been fully explored. We collected soil samples from 0–10 cm and 10–20 cm layers in the [...] Read more.

Soil aggregate stability and erodibility are the influential factors governing soil resistance to water erosion. The interactions among aggregate stability, erodibility, and their influencing factors have not been fully explored. We collected soil samples from 0–10 cm and 10–20 cm layers in the Zhifanggou watershed. Then, the major contributors to aggregate stability and erodibility and how soil properties, environmental factors and land use contributed to them were explored by using partial least-squares regression and path analysis, respectively. The results showed that the major contributors included the slope, soil organic carbon (SOC), elevation, the percentage of landscape area of farmland (PLAND_F) and grassland (PLAND_G), the land surface temperature difference between seasons (ΔLST), topographic wetness index (TWI), pH, amorphous iron (poorly ordered forms of iron, Fe_o), and calcium carbonate (CaCO₃). In which, the slope, SOC, and elevation were the most important contributors to the mean weight diameter (MWD) and the percentage of water-stable aggregates greater than 0.25 mm (WSA_>0.25) and had a direct contribution to MWD, WSA_>0.25, and K factors. The PLAND_F and PLAND_G had a significant and indirect contribution to those three indices by affecting slope. Meanwhile, the effects of pH, Fe_o, and CaCO₃ on WSA_>0.25 should also not be underestimated. For MWD and WSA_>0.25, there was a significantly higher effect of the land use types and composition than hydrothermal conditions. For K factors, PLAND’s contribution was still higher than ΔLST and TWI, but they were all significant. The other soil properties, including pH, CaCO₃, and Fe_o, indirectly affected them by influencing SOC. However, the direct contributions of soil properties increased as the soil layer deepened. Full article

(This article belongs to the Special Issue Soil Sustainability in the Anthropocene)

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13 pages, 2599 KiB

Open AccessEditor’s ChoiceArticle

Effects of Long-Term Straw Return and Environmental Factors on the Spatiotemporal Variability of Soil Organic Matter in the Black Soil Region: A Case Study

by Yang Yan, Wenjun Ji, Baoguo Li, Guiman Wang, Bifeng Hu, Chao Zhang and Abdul Mounem Mouazen

Agronomy 2022, 12(10), 2532; https://doi.org/10.3390/agronomy12102532 - 17 Oct 2022

Cited by 9 | Viewed by 2127

Abstract

Exploring the effects of straw return and environmental factors on the spatiotemporal variation of soil organic matter (SOM) in black soil regions is essential for soil carbon sequestration research. However, studies seldom quantified the effects of long-term straw return on a long-term SOM [...] Read more.

Exploring the effects of straw return and environmental factors on the spatiotemporal variation of soil organic matter (SOM) in black soil regions is essential for soil carbon sequestration research. However, studies seldom quantified the effects of long-term straw return on a long-term SOM variation at a regional scale in typical black soil areas. The case was conducted in one of the three major black soil regions in the Northern Hemisphere, where the straw return policy has been implemented for a long time. The study obtained the SOM spatial distribution in 2007, 2009, 2012, 2015, and 2018 with approximately 9000 samples and analyzed the effects of soil types, texture, elevation, and human management on the spatiotemporal variation. The results indicated that from the 1980s to 2007, before the straw return policy implementation, the mean SOM content decreased from 24.38 g kg⁻¹ to 18.94 g kg⁻¹. In contrast, the mean SOM content gradually increased from 2007 to 2018 after implementing straw return practices. In addition, the area of SOM within 20–30 g kg⁻¹ increased gradually, with 32.2%, 40.5%, 50.2%, 49.4%, and 60.5% in 2007, 2009, 2012, 2015, and 2018, respectively. Surprisingly, the SOM within 30–40 g kg⁻¹ emerged in 2018. The results indicated that returning straw to the field might promote SOM accumulation. However, the SOM contents in Phaezems (19.25–21.82 g kg⁻¹) were lower than that in natural Phaezems (40–60 g kg⁻¹), indicating severe degradation. The clay content positively correlated to SOM and was a major explanatory variable for the response of SOM to straw return. Straw return practices are promising measures in the black soil region and are worth exploring more effective approaches to allow straw return to play a better role. Full article

(This article belongs to the Special Issue Soil Sustainability in the Anthropocene)

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21 pages, 2291 KiB

Open AccessArticle

How Well Can Reflectance Spectroscopy Allocate Samples to Soil Fertility Classes?

by Rong Zeng, David G. Rossiter, Jiapeng Zhang, Kai Cai, Weichang Gao, Wenjie Pan, Yuntao Zeng, Chaoying Jiang and Decheng Li

Agronomy 2022, 12(8), 1964; https://doi.org/10.3390/agronomy12081964 - 20 Aug 2022

Cited by 2 | Viewed by 1549

Abstract

Fertilization decisions depend on the measurement of a large set of soil fertility indicators, usually through laboratory determination, which is costly and time-consuming. Visible and near-infrared (vis-NIR) spectroscopy combined with machine learning can simultaneously predict various soil fertility indicators. Spectroscopy is inherently less [...] Read more.

Fertilization decisions depend on the measurement of a large set of soil fertility indicators, usually through laboratory determination, which is costly and time-consuming. Visible and near-infrared (vis-NIR) spectroscopy combined with machine learning can simultaneously predict various soil fertility indicators. Spectroscopy is inherently less accurate than direct laboratory determination. However, in many fertilization recommendation contexts, farmers mainly fertilize according to classified fertility indicators, rather than by continuous soil property values. These classes have defined limits of property values. We hypothesized that the additional inaccuracy from spectroscopy may not be important for properties grouped into classes. This study compared the indirect and direct prediction of soil fertility classes. Indirectly, by (1) using vis-NIR spectra with machine learning to predict 20 soil fertility indicators (pH, soil organic matter (SOM), cation exchange capacity (CEC), total nitrogen (TN), total phosphorus (TP), total potassium (TK), alkali-hydrolyzable nitrogen (AN), available phosphorus (AP), available potassium (AK), calcium (Ca), magnesium (Mg), silicon (Si), sulfur (S), boron (B), iron (Fe), manganese (Mn), copper (Cu), Zinc (Zn), molybdenum (Mo) and chlorine (Cl)) and (2) allocating the indicators to soil fertility classes. Directly, by predicting soil fertility classes directly from vis-NIR spectra using machine learning. The prediction accuracy of these two methods were compared and the accuracies needed for the acceptable class allocation of the fertility indicators were determined. The example dataset is a soil spectral library from the Guizhou Province, southwest China. The model performance was evaluated by the overall allocation accuracy and tau index, which accounts for class imbalance. For direct allocation based on three fertility classes (low, medium and high), the overall allocation accuracy of eight properties (CEC, Cu, Si, Zn, S, Mn, Ca and Mg), nine properties (B, AN, TK, AK, SOM, TN, TP, Fe and Mo) and three properties (Cl, AP and pH) were within the range of 0.80–1.0, 0.60–0.80 and 0.40–0.60, respectively. For indirect allocation based on the same classes, the allocation accuracy of nine properties (TN, CEC, Cu, S, Zn, Si, Mn, Ca and Mg), nine properties (B, TK, pH, TP, AK, AN, Fe, Mo and SOM) and two properties (Cl and AP) were within the range of 0.80–1.0, 0.60–0.80 and 0.40–0.60, respectively. We conclude that vis-NIR spectroscopy was fairly successful for soil fertility class allocation for most of the soil properties, using either direct or indirect models. The advantage of indirect models is that both specific property values and soil fertility classes can be obtained at no increase in cost, while direct models are suggested when only soil fertility class information are available. Full article

(This article belongs to the Special Issue Soil Sustainability in the Anthropocene)

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13 pages, 3679 KiB

Open AccessArticle

Multi-Risk Assessment to Evaluate the Environmental Impact of Outdoor Pig Production Areas: A Case Study

by Carmo Horta, Natália Roque, Marta Batista and António Canatário Duarte

Agronomy 2022, 12(8), 1898; https://doi.org/10.3390/agronomy12081898 - 12 Aug 2022

Cited by 1 | Viewed by 1402

Abstract

Outdoor pig production (OPP) can be considered an intensive system in many areas of the Mediterranean region. The concentration of the rainfall in the winter season, the OPP’s topographic and soil properties, together with the continuous input of food and pigs’ excreta, contribute [...] Read more.

Outdoor pig production (OPP) can be considered an intensive system in many areas of the Mediterranean region. The concentration of the rainfall in the winter season, the OPP’s topographic and soil properties, together with the continuous input of food and pigs’ excreta, contribute to a profound increase in the nutrients leaching and soil erosion. This work aimed to evaluate the accuracy of the DRASTIC-LU index and the Revised Universal Soil Loss Equation (RUSLE) to provide early information to improve the planning of this type of pig production through more adequate location and sustainable management practices. The two models were applied to an OPP with 2.24 ha, with a heavy animal charge (one adult per 1.120 m²). The results showed that 85% of the OPP area has a moderate risk to the vulnerability index to groundwater pollution and 15% high risk. The risk of soil erosion ranged from very severe to extremely severe in 96% of the area. The DRASTIC-LU indexes and the RUSLE model produce a multi-risk assessment that agreed with the observed field data. These two models showed accuracy to be used for early assessment when choosing the best location and improving management practices for OPP systems. Full article

(This article belongs to the Special Issue Soil Sustainability in the Anthropocene)

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14 pages, 4955 KiB

Open AccessArticle

Characteristics of a Benchmark Loess–Paleosol Profile in Northeast China

by Zhong-Xiu Sun, Ying-Ying Jiang, Qiu-Bing Wang, Zhuo-Dong Jiang, Zamir Libohova and Phillip R. Owens

Agronomy 2022, 12(6), 1376; https://doi.org/10.3390/agronomy12061376 - 07 Jun 2022

Cited by 2 | Viewed by 1493

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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12 pages, 2204 KiB

Open AccessArticle

Spatial Variation and Influencing Factors of Trace Elements in Farmland in a Lateritic Red Soil Region of China

by Runyan Zou, Yuting Zhang, Yueming Hu, Lu Wang, Yingkai Xie, Luo Liu, Hao Yang and Jie Liao

Agronomy 2022, 12(2), 478; https://doi.org/10.3390/agronomy12020478 - 14 Feb 2022

Cited by 3 | Viewed by 2212

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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13 pages, 2398 KiB

Open AccessArticle

An Integrated Yield-Based Methodology for Improving Soil Nutrient Management at a Regional Scale

by Mingkai Qu, Xu Guang, Jinfen Li, Hongbo Liu, Yongcun Zhao and Biao Huang

Agronomy 2022, 12(2), 298; https://doi.org/10.3390/agronomy12020298 - 25 Jan 2022

Cited by 2 | Viewed by 2247

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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10 pages, 2068 KiB

Open AccessArticle

Incorporating Auxiliary Data of Different Spatial Scales for Spatial Prediction of Soil Nitrogen Using Robust Residual Cokriging (RRCoK)

by Mingkai Qu, Xu Guang, Hongbo Liu, Yongcun Zhao and Biao Huang

Agronomy 2021, 11(12), 2516; https://doi.org/10.3390/agronomy11122516 - 10 Dec 2021

Cited by 1 | Viewed by 1775

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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19 pages, 3035 KiB

Open AccessArticle

Impacts of Farming Layer Constructions on Cultivated Land Quality under the Cultivated Land Balance Policy

by Long Kang, Rui Zhao, Kening Wu, Qin Huang and Sicheng Zhang

Agronomy 2021, 11(12), 2403; https://doi.org/10.3390/agronomy11122403 - 25 Nov 2021

Cited by 7 | Viewed by 2029

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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14 pages, 1699 KiB

Open AccessArticle

Changes of Soil Organic Carbon after Wildfire in a Boreal Forest, Northeast CHINA

by Chun-Lan Han, Zhong-Xiu Sun, Shuai Shao, Qiu-Bing Wang, Zamir Libohova and Phillip Ray Owens

Agronomy 2021, 11(10), 1925; https://doi.org/10.3390/agronomy11101925 - 25 Sep 2021

Cited by 10 | Viewed by 2499

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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