Decoding Soil Constraints in Queensland, Australia: Strategies for Precision Management to Enhance Crop Productivity

Article

Li, Tong, Cui, Lizhen, Wehr, Bernhard, Lai, Yunru, Liu, Hongdou, He, Cong, Tang, Caixian, Filipović, Vilim, Singh, Ranjay K., McLaren, Timothy I., Dalal, Ram C. and Dang, Yash P.. 2025. "Decoding Soil Constraints in Queensland, Australia: Strategies for Precision Management to Enhance Crop Productivity." Land Degradation and Development. https://doi.org/10.1002/ldr.70111

Article Title	Decoding Soil Constraints in Queensland, Australia: Strategies for Precision Management to Enhance Crop Productivity
ERA Journal ID	5264
Article Category	Article
Authors	Li, Tong, Cui, Lizhen, Wehr, Bernhard, Lai, Yunru, Liu, Hongdou, He, Cong, Tang, Caixian, Filipović, Vilim, Singh, Ranjay K., McLaren, Timothy I., Dalal, Ram C. and Dang, Yash P.
Journal Title	Land Degradation and Development
Number of Pages	11
Year	2025
Publisher	John Wiley & Sons
Place of Publication	United Kingdom
ISSN	1085-3278
	1099-145X
Digital Object Identifier (DOI)	https://doi.org/10.1002/ldr.70111
Web Address (URL)	https://onlinelibrary.wiley.com/doi/abs/10.1002/ldr.70111
Abstract	Soil constraints significantly impact crop productivity, yet their direct relationships to yield remain unclear. This limits the development of targeted soil management strategies for improving agricultural output. This study aims to clarify the influence of key soil constraints on crop productivity by examining soil chemical indicators across distinct productivity zones in Queensland. Soil samples were collected from 21 farms across three productivity zones (consistently low, inconsistent, and consistently high) and five soil layers (D1–D5, 0–10, 10–30, 30–60, 60–90, and 90–120 cm). We utilized the Constraint ID tool alongside mixed-effects models, principal component analysis (PCA), and machine learning to evaluate indicators including nitrate (NO3−), electrical conductivity of the saturated soil extract (ECe), pH, chloride (Cl), exchangeable sodium percentage (ESP), and exchangeable cations (Ca, K, and Mg). This integrated approach—among the first in Queensland—enabled depth-specific and spatially explicit analysis of soil constraint impacts. ECe, pH, Cl, and ESP are critical factors influencing soil fertility, particularly in subsoil layers (D3–D5). Low-yielding zones (Zone L) exhibited high pH (up to 8.41), Cl (up to 151 mg/kg), ESP (up to 8.64), and ECe (exceeding 4 dS/m), indicating salinity, alkalinity, and sodicity issues. These subsoil constraints are difficult to remediate, highlighting the need for surface-level strategies that support whole-profile soil health. This study underscores the necessity of site-specific, surface-focused interventions that address constraints across the entire soil profile. The findings offer actionable insights for tailoring soil management and support regional decision-making to optimize crop yields in Queensland's agricultural systems.
Keywords	agroecosystem management; crop yield ; sodicity; soil health; salinity
Contains Sensitive Content	Does not contain sensitive content
ANZSRC Field of Research 2020	300299. Agriculture, land and farm management not elsewhere classified
	410601. Land capability and soil productivity
	300499. Crop and pasture production not elsewhere classified
Public Notes	Files associated with this item cannot be displayed due to copyright restrictions.
Byline Affiliations	University of Queensland
	Chinese Academy of Forestry, China
	State Key Laboratory of Wetland Conservation and Restoration, China
	Centre for Sustainable Agricultural Systems
	Griffith University
	China Agricultural University, China
	La Trobe University
	ICAR-Central Soil Salinity Research Institute, India
	Indian Council of Agricultural Research, India

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