Evidence map›Paper›PMID 36936670›Full record

ArticleComputational intelligence and neuroscience2023

Prediction of Soil Water-Soluble Organic Matter by Continuous Use of Corn Biochar Using Three-Dimensional Fluorescence Spectra and Deep Learning.

Liang Jin, Dan Wei, Dawei Yin, Guoyuan Zou, Yan Li, Yitao Zhang, JianLi Ding, Lei Wang, Lina Liang, Lei Sun and 4 more

Open access · hybridFull text read
In one paragraph

Article in Computational intelligence and neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.5field-weighted citation impact, top 30% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors at 5 institutions in 1 country.

Liang JinPlant Nutrition and Resources Institute, Beijing Academy of Agriculture and Forestry Sciences, Beijing100097, China.
Dan WeiPlant Nutrition and Resources Institute, Beijing Academy of Agriculture and Forestry Sciences, Beijing100097, China.ORCID https://orcid.org/0000-0003-1408-7613
Dawei YinCollege of Agricultural Science and Technology, Heilongjiang Bayi Agricultural University, Daqing 163319, China.
Guoyuan ZouPlant Nutrition and Resources Institute, Beijing Academy of Agriculture and Forestry Sciences, Beijing100097, China.
Yan LiPlant Nutrition and Resources Institute, Beijing Academy of Agriculture and Forestry Sciences, Beijing100097, China.
Yitao ZhangInstitute of Geographic Sciences and Natural Resources Research, Beijing 100101, China.
JianLi DingPlant Nutrition and Resources Institute, Beijing Academy of Agriculture and Forestry Sciences, Beijing100097, China.
Lei WangPlant Nutrition and Resources Institute, Beijing Academy of Agriculture and Forestry Sciences, Beijing100097, China.
Lina LiangPlant Nutrition and Resources Institute, Beijing Academy of Agriculture and Forestry Sciences, Beijing100097, China.
Lei SunHeilongjiang Institute of Black Soil Protection and Utilization, Harbin 150086, China.
Wei WangHeilongjiang Institute of Black Soil Protection and Utilization, Harbin 150086, China.
Huibo ShenQiqihar Branch of Heilongjiang Academy of Agricultural Sciences, Qiqihar 161006, China.
Yuxian WangQiqihar Branch of Heilongjiang Academy of Agricultural Sciences, Qiqihar 161006, China.
Junsheng XuQingdao Reserved Materials Management Station, Qingdao 266000, China.
Beijing Academy of Agricultural and Forestry Sciences · CNHeilongjiang Provincial Academy of Agricultural Sciences · CNHeilongjiang Bayi Agricultural University · CNInstitute of Geographic Sciences and Natural Resources Research · CNQingdao Center of Resource Chemistry and New Materials · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The purpose is to study the soil's water-soluble organic matter and improve the utilization rate of the soil layer. This exploration is based on the theories of three-dimensional fluorescence spectroscopy, deep learning, and biochar. Chernozem in Harbin City, Heilongjiang Province, is taken as the research object. Three-dimensional fluorescence spectra and a deep learning model are used to analyze the content of water-soluble organic matter in the soil layer after continuous application of corn biochar for six years and to calculate different fluorescence indexes in the whole soil depth. Among them, the three-dimensional fluorescence spectrum theory provides the detection standard for the application effect detection of biochar, the deep learning theory provides the technical support for this exploration, and the biochar theory provides the specific research direction. The results show that the application of corn biochar for six consecutive years significantly reduces the average content of water-soluble organic matter in different soil layers. Among them, the highest average content of soil water-soluble organic matter is "nitrogen, potassium, phosphorous" (NPK) and the lowest is "boron, carbon" (BC). Comparing the soil with BC alone, in the topsoil, the second section (330-380 nm/200-250 nm) with BC + NPK increases by 13.3%, the third section (380-550 nm/220-250 nm) increases by 8.4%, and the fourth section (250-380 nm/250-600 nm) increases by 50.1%. The combination of nitrogen (N) + BC has a positive effect of 20.7%, 12.2%, and 28.4% on sections I, II, and IV, respectively. In addition, in the topsoil, the combination of NPK + BC significantly increases the content of acid-like substances compared with the application of BC alone. In the black soil, with or without fertilizer NPK, there is no significant difference in the level of fulvic acid-like components. The prediction of soil water-soluble organic matter after continuous application of corn biochar based on three-dimensional fluorescence spectra and deep learning is carried out, which has reference significance for the rapid identification and early prediction of subsequent soil activity.

Indexed as

Deep LearningSoilCarbonCharcoalFluorescenceNitrogenWaterZea maysbiocharCarbonCharcoalNitrogenSoilWater

Identifiers

PMID36936670
PMCPMC10017222
OpenAlexW4323537731

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read39
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.