Comparative Evaluation Methods of Comprehensive Soil Fertility in Jiangsu’s Coastal Saline–Alkali Land

Zhiwang Wang, Shihang Wang, Lingying Xu (), Qiankun Guo, Yuqi Chen, Weiwen Qiu and Jiabei Sun
Additional contact information
Zhiwang Wang: School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China
Shihang Wang: School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China
Lingying Xu: Changshu National Agro-Ecosystem Observation and Research Station, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 211135, China
Qiankun Guo: Jiangsu Cultivated Land Quality and Agro-Environment Protection Station, Nanjing 210029, China
Yuqi Chen: Changshu National Agro-Ecosystem Observation and Research Station, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 211135, China
Weiwen Qiu: The New Zealand Institute for Plant and Food Research Limited, Private Bag 3230, Hamilton 3240, New Zealand
Jiabei Sun: School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China

Land, 2025, vol. 14, issue 3, 1-14

Abstract: In coastal saline–alkali regions, the intrusion of saline water exacerbates the nutrient depletion in the plow layer, posing a significant challenge to agricultural productivity. Given the limited understanding of soil fertility in these areas and the inconsistent results among different assessment methods, this study aims to develop a more accurate and reliable soil fertility evaluation system. To achieve this objective, 108 topsoil samples were systematically collected from saline–alkali lands in Jiangsu Province. Several key soil fertility indicators, including soil pH, total salinity (TS), soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), total potassium (TK), alkaline-hydrolyzable nitrogen (AN), available phosphorus (AP), and available potassium (AK), were comprehensively evaluated. Four advanced methods, namely principal component analysis indexing–linear scoring (SQI PCAL ), principal component analysis indexing–nonlinear scoring (SQI PCANL ), modified Nemerow–linear scoring (SQI NemeroL ), and modified Nemerow indexing–nonlinear scoring (SQI NemeroNL ), were employed to conduct a multi-dimensional examination of soil fertility. Additionally, principal component analysis (PCA) was utilized to establish a minimum data set (MDS), which was then compared with the total data set (TDS) for a more precise assessment of soil fertility. Linear scoring methods (SQI PCAL and SQI NemeroL ) had higher semi-variogram R 2 values compared to nonlinear methods. Moreover, under the SQI PCAL and SQI NemeroL evaluation methods, a strong correlation was observed between the TDS and MDS, with R 2 values reaching 0.63 and 0.65, respectively. Based on these findings, the SQI NemeroL method, integrated with MDS, is recommended as an effective approach for soil fertility assessments in coastal saline–alkali regions in Jiangsu Province. This research not only enriches the theoretical understanding of soil fertility in such regions but also provides practical insights for sustainable agricultural management.

Keywords: coastal saline–alkali lands; soil fertility evaluation methods; principal component analysis; modified Nemero index method; minimum data set (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/14/3/469/pdf (application/pdf)
https://www.mdpi.com/2073-445X/14/3/469/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:14:y:2025:i:3:p:469-:d:1598071

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().