Frontiers in Soil Science

Soil nitrogen and phosphorus are directly related to soil quality and vegetation growth and are, therefore, a common research topic in studies on global climate change, material cycling, and information exchange in terrestrial ecosystems. However, collecting soil hyperspectral data under in situ conditions and predicting soil properties, which can effectively save time, manpower, material resources, and financial costs, have been generally undervalued. Recent optimization techniques have, however, addressed several of the limitations previously restricting this technique. In this study, hyperspectral data were taken from surface soils under different vegetation types in the wetlands of the Shaanxi Yellow River Wetland Provincial Nature Reserve. Through in situ original and first-order differential transformation spectral data, three prediction models for soil carbon, nitrogen, and phosphorus contents were established: partial least squares (PLSR), random forest (RF), and Gaussian process regression (GPR). The <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="im1"><mml:mrow><mml:msup><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:math> and RMSR of the constructed models were then compared to select the optimal model for evaluating soil content. The soil organic carbon, total nitrogen, and total phosphorus content models established based on the first-order differential had a higher accuracy when modeling and during model validation than those of other models. Moreover, the PLSR model based on the original spectrum and the Gaussian process regression model had a superior inversion performance. These results provide solid theoretical and technical support for developing the optimal model for the quantitative inversion of wetland surface soil carbon, nitrogen, and phosphorus based on in situ hyperspectral technology.

Leichao Nie

Keying Qu

Lijuan Cui

Xiajie Zhai

Xinsheng Zhao

Yinru Lei

Jing Li

Jinzhi Wang

Rumiao Wang

Wei Li

Inversion of soil carbon, nitrogen, and phosphorus in the Yellow River Wetland of Shaanxi Province using field in situ hyperspectroscopy

利用现场高光谱技术反演陕西黄河湿地土壤碳、氮和磷

Triven Koganti

Philippe De Smedt

Mohammad Farzamian

Maria Knadel

John Triantafilis

Anders Vest Christiansen

Mogens H. Greve

Editorial: Digital soil mapping using electromagnetic sensors

编者按：利用电磁传感器进行数字土壤制图

Regression models were developed to estimate the electrical conductivity of saturated paste extract (ECe) from the electrical conductivity of soil-water ratio (EC 1:2.5 ) for different soil textural classes. ECe is a crucial parameter used to indicate the presence, type, and distribution of salinity in soils. However, determining ECe is demanding, time-consuming, requires considerable skill to accurately identify the correct soil saturation point, and is not routinely performed by soil testing laboratories. Many laboratories, instead, commonly measure the electrical conductivity of soil-water extracts at various dilutions, such as EC 1:1 , EC 1:2.5 , or EC 1:5 . In this study, 706 soil samples were collected from depths of 0 - 30 cm across three rice irrigation schemes to determine EC 1:2.5 , with 50% analyzed for ECe. ECe values were grouped based on soil textural classes. The results showed a strong linear relationship between EC 1:2.5 and ECe values, with a high coefficient of determination (R² &amp;gt; 0.95). The Root Mean Square Error values were low (1.4 &amp;lt; RMSE), and the Mean Absolute Error values were similarly low (0.85 &amp;lt; MAE). Therefore, the regression models developed provide a practical means of estimating ECe for various soil textural classes, thereby enhancing soil salinity assessment and management strategies.

Moh’d M. Omar

Mawazo J. Shitindi

Boniface H. J. Massawe

Ole Pedersen

Joel L. Meliyo

Kadeghe G. Fue

Modeling the electrical conductivity relationship between saturated paste extract and 1:2.5 dilution in different soil textural classes

不同土壤质地类别中饱和浸提液与1:2.5稀释液电导率关系建模

This study evaluated the relationships and sensitivity of denitrifying microbial gene abundance, as well as the activities of soil enzymes β-glucosidase (GLU) and arylsulfatase (ARYL), to assess the quality of sugarcane soils managed with vinasse (V), filter cake (FC), and mineral fertilizer (MF). Composite soil samples were collected using a systematic sampling approach that included two soil classes (Ferralsol and Acrisol), two textures (clayey and sandy), three management systems (V, V+FC, and MF), two sampling seasons (rainy and dry), and three replicates, totaling 72 samples. Analysis of soil organic carbon (SOC), and macro- and micronutrients differentiated the Ferralsol and Acrisol samples into distinct groups based on agricultural management (Global R = 0.554) and showed some overlap based on soil texture (Global R = 0.369). The number of nir K, nir S, and nos Z I gene copies per gram of soil, determined by Real-Time Quantitative PCR (qPCR) based on genomic DNA isolated from the 72 soil samples, was higher in the rainy season compared to the dry season ( P &amp;lt;0.05). None of the genes evaluated revealed a consistent response to different sugarcane soil managements, showing specific response patterns for each soil class and texture. In the Ferralsol, the activities of GLU and ARYL increased in the following order: V &amp;lt; MF &amp;lt; V+FC, regardless of soil texture (sandy or clayey) and sampling season. The average activity of the two enzymes in both V+FC and MF treatments was 1.8 times higher in sandy soil and 3.9 times higher in clayey soil compared to soil managed with vinasse. In the Acrisol, no significant differences among the treatments were observed. Statistical analyses revealed negative correlations ( P &amp;lt;0.05) between the number of copies of the nir K and nos Z I genes and GLU and ARYL activities in the soil during both seasonal periods analyzed. The number of copies of these two microbial genes was also negatively correlated with the soil organic matter in the rainy season. Thus, the indications of sugarcane soil quality based on enzymatic analyses were corroborated by the lower abundance of genes associated with denitrification process. The findings of this study open the possibilities to infer about the potential for N 2 O emission from these sugarcane soils based on GLU and ARYL activities.

Luciano Ricardo de Oliveira

Dora Inés Kozusny-Andreani

Gabriel Gustavo Tavares Nunes Monteiro

Ieda de Carvalho Mendes

Raffaella Rossetto

Luiz Sergio Vanzela

Gisele Herbst Vazquez

Acacio Aparecido Navarrete

Denitrifying microbial genes quantification attests inference for potential N2O emissions in sugarcane soils by enzymatic bioanalysis

反硝化微生物基因定量通过酶学生物分析验证甘蔗土壤潜在N2O排放的推断

Approximately 20 years ago (June 11 th , 2004), a highly reputable scientific journal, on a front cover much celebrated by soil scientists, printed an eye-catching phrase: “Soils: The Final Frontier”. In the introduction to that special issue, it was mentioned that “in many ways the ground beneath our feet is as alien as a distant planet”, to then state that, owing to the booming interest in soil research at that time, together with the development of advanced techniques (e.g., next-generation sequencing), subterraneana would be made “seem far less of an alien experience”. In this perspective article, using as illustrations, by way of example only, two topics of great interest nowadays – soil carbon sequestration and soil biodiversity –, it is claimed that, despite all the efforts devoted in the last 20 years to the understanding of the soil ecosystem, the “final frontier” appears to have receded, as we are discovering increasing levels of complexity that are slowing down our trip to the far reaches of the edaphic universe. Due to, among other features, its high structural and functional complexity, biodiversity, heterogeneity, opacity, and processual and dynamic nature, the soil still remains a great unknown (“a black box”). The ultimate goal of this perspective article is to draw attention to the need for further long-term investment in research into this highly complex and fascinating ecosystem.

Abbreviation	Front. Soil Sci.
Journal Impact	1.94
Quartiles(Global)	SOIL SCIENCE(Q3)

Publication Information	Publisher: Frontiers Media SA，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2021，Proportion of original research papers: ，Self Citation Rate:， Gold OA Rate:
Journal Fees	$2125Details
Average review cycle	13 weeks

Frontiers in Soil Science

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Inversion of soil carbon, nitrogen, and phosphorus in the Yellow River Wetland of Shaanxi Province using field in situ hyperspectroscopy

Editorial: Digital soil mapping using electromagnetic sensors

Modeling the electrical conductivity relationship between saturated paste extract and 1:2.5 dilution in different soil textural classes

Denitrifying microbial genes quantification attests inference for potential N2O emissions in sugarcane soils by enzymatic bioanalysis

Soils: the final frontier recedes

Editorial: Digital soil mapping using electromagnetic sensors

Denitrifying microbial genes quantification attests inference for potential N2O emissions in sugarcane soils by enzymatic bioanalysis

Modeling the electrical conductivity relationship between saturated paste extract and 1:2.5 dilution in different soil textural classes

Soils: the final frontier recedes

Editorial: Cover crops and green manures: providing services to agroecosystems

ISSN	2673-8619
h-index	8