Response of Agroecosystem Carbon–Water Cycling and Crop Spatial Patterns to Environmental Change: Mechanisms and Adaptive Management

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Innovative Cropping Systems".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 229

Special Issue Editors


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Guest Editor
College of Grassland Agriculture, Northwest A&F University, 712100 Xianyang, China
Interests: vegetation dynamic remote sensing monitoring; assessment of vegetation ecological service function; ecological hydrology and carbon water cycle
Special Issues, Collections and Topics in MDPI journals
College of Grassland Agriculture, Northwest A&F University, 712100 Xianyang, China
Interests: grassland ecosystem restoration effect and mechanism; ecosystem carbon nitrogen water cycle and its coupling process; biodiversity and ecosystem service function
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Global environmental changes, driven by climate variability and anthropogenic activities, are reshaping agroecosystem and crop patterns, with cascading impacts on carbon–water (C-W) cycles. Alterations in temperature, precipitation extremes, and land-use practices disrupt vegetation dynamics, soil carbon sequestration, and hydrological processes, creating feedback loops that threaten ecosystem resilience and agricultural productivity. While advances in remote sensing and modeling have improved our understanding of these interactions, critical gaps persist in quantifying how shifting vegetation–agroecosystem configurations (e.g., crop diversification, afforestation, or land fragmentation) modulate C-W synergies or trade-offs under heterogeneous stressors. Additionally, the nonlinear responses of coupled biogeochemical–hydrological processes to compound drivers (e.g., droughts, CO2 fertilization, and management interventions) remain poorly resolved.

This Special Issue synthesizes interdisciplinary research to unravel mechanisms linking environmental change, landscape reorganization, and C-W cycle dynamics, aiming to inform and support the development of scalable strategies for sustainable intensification, climate adaptation, and nature-based solutions. This Special Issue will offer a comprehensive review of the latest research on the monitoring of agroecosystem structures, agricultural ecological hydrology, and ecosystem carbon–water cycles using machine learning, remote sensing technology, and ecological model simulation. We invite authors to submit review articles, original research articles, or short communications on topics related to spatiotemporal change monitoring and the driving mechanisms of agroecosystem structures and carbon–water processes featuring applications of machine learning or remote sensing technology. As Guest Editors, we look forward to reviewing your contributions to this Special Issue. The specific topics of this Special Issue will include (but are not limited to) the following:

  • remote sensing crop monitoring;
  • ecosystem carbon–water process;
  • agricultural ecological hydrology;
  • driving mechanisms of ecosystem change.

Dr. Yangyang Liu
Dr. Wei Zhang
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • agroecosystem structure
  • ecosystem carbon–water cycle
  • machine learning
  • remote sensing

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Published Papers (1 paper)

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Research

17 pages, 4941 KiB  
Article
Estimating Soil Cd Contamination in Wheat Farmland Using Hyperspectral Data and Interpretable Stacking Ensemble Learning
by Liang Zhong, Meng Ding, Shengjie Yang, Xindan Xu, Jianlong Li and Zhengguo Sun
Agronomy 2025, 15(7), 1574; https://doi.org/10.3390/agronomy15071574 - 27 Jun 2025
Viewed by 116
Abstract
Soil heavy metal pollution threatens agricultural safety and human health, with Cd exceeding standards being the most common problem in contaminated farmland. The development of hyperspectral remote sensing technology has provided a novel methodology of quickly and non-destructively monitoring heavy metal contamination in [...] Read more.
Soil heavy metal pollution threatens agricultural safety and human health, with Cd exceeding standards being the most common problem in contaminated farmland. The development of hyperspectral remote sensing technology has provided a novel methodology of quickly and non-destructively monitoring heavy metal contamination in soil. This study aims to explore the potential of an interpretable Stacking ensemble learning model for the estimation of soil Cd contamination in farmland hyperspectral data. We assume that this method can improve the modeling accuracy. We chose Zhangjiagang City, Jiangsu Province, China, as the study area. We gathered soil samples from wheat fields and analyzed soil spectral data and Cd level in the lab. First, we pre-processed the spectra utilizing fractional-order derivative (FOD) and standard normal variate (SNV) transforms to highlight the spectral features. Second, we applied the competitive adaptive reweighted sampling (CARS) feature selection algorithm to identify the significant wavelengths correlated with soil Cd content. Then, we constructed and compared the estimation accuracy of multiple machine learning models and a Stacking ensemble learning method and utilized the Optuna method for hyperparameter optimization. Ultimately, the SHAP method was used to shed light on the model’s decision-making process. The results show that (1) FOD can further highlight the spectral features, thereby strengthening the correlation between soil Cd content and wavelength; (2) the CARS algorithm extracted 3.4–6.8% of the feature wavelengths from the full spectrum, and most of them were the wavelengths with high correlation with soil Cd; (3) the optimal estimation precision was achieved using the FOD1.5-SNV spectral pre-processing combined with the Stacking model (R2 = 0.77, RMSE = 0.05 mg/kg, RPD = 2.07), and the model effectively quantitatively estimated soil Cd contamination; and (4) SHAP further revealed the contribution of each base model and characteristic wavelengths in the Stacking modeling process. This research confirms the advantages of the interpretable Stacking model in hyperspectral estimation of Cd contamination in farmland wheat soil. Furthermore, it offers a foundational reference for the future implementation of quantitative and non-destructive regional monitoring of heavy metal contamination in farmland soil. Full article
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