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Agriculture
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Feature Papers in Ecosystem, Environment and Climate Change in Agriculture
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Feature Papers in Ecosystem, Environment and Climate Change in Agriculture

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Ecosystem, Environment and Climate Change in Agriculture".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1497

Special Issue Editor

Prof. Dr. Jianlin Shen

E-Mail Website
Guest Editor
Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China
Interests: carbon and nitrogen cycling; carbon sequestration; greenhouse gas emission; non-point source pollution; nitrogen deposition; biochar
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nutrients cycling in farmlands has significant effects on global warming, water eutrophication as well as food security. Unreasonable fertilizer application, inefficient irrigation, and soil erosion in farmlands have driven carbon, nitrogen and phosphorus in soil leaching into water systems, causing eutrophication, groundwater contamination, and greenhouse gas emissions. It is important to clarify the nutrients cycling processes in farmlands, and to manage the nutrients for enhancing use efficiency, reducing losses to the environment, and improve soil health.

This Special Issue, entitled "Feature Papers in Ecosystem, Environment and Climate Change in Agriculture", aims to publish high-quality papers that advance understanding of nutrient migration processes, mechanisms, their environmental impacts, and mitigation strategies for green production in farmlands. It focuses on the new mechanisms of nutrient cycling, responses of nutrient cycling to climate change, the measurements of nutrient migration fluxes under air–soil–water interface, prediction of nutrient losses using machine learning, and new techniques for managing nutrients cycling to reduce losses and improve soil health in farmlands. The Special Issue also welcomes critical reviews and syntheses of the current state of affairs and emerging themes in ecosystem, environment and climate change in agriculture.

Prof. Dr. Jianlin Shen
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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. Agriculture is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • ecological process
  • nutrient cycling
  • greenhouse gases emissions
  • non-point source pollution
  • climate change
  • soil health
  • green agriculture
  • pollution mitigation
  • environmental impact
  • machine learning
  • isotope tracing

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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22 pages, 4615 KB  
Article
Selection of Candidate Bacteria for Microbial Enrichment of Soil Amendments to Manage Contaminants of Emerging Concern in Agricultural Soils
by Rossana Sidari, Maria Teresa Rodinò, Giulio Scarpino, Stefano Mocali, Sara Del Duca, Elisabetta Loffredo and Antonio Gelsomino
Agriculture 2025, 15(23), 2507; https://doi.org/10.3390/agriculture15232507 - 2 Dec 2025
Viewed by 276
Abstract
Recycled bio-wastes such as compost and vermicompost, and bioenergy byproducts such as digestate and biochar are widely acknowledged for their role as soil conditioners capable of preserving soil fertility, maintaining soil health, and acting as a bio-adsorbent of organic soil pollutants (BIOSORs). Moreover, [...] Read more.
Recycled bio-wastes such as compost and vermicompost, and bioenergy byproducts such as digestate and biochar are widely acknowledged for their role as soil conditioners capable of preserving soil fertility, maintaining soil health, and acting as a bio-adsorbent of organic soil pollutants (BIOSORs). Moreover, they are attracting increasing attention for use as effective carriers of microbial consortia into arable soils. This study aims to combine selection of bacteria tolerating contaminants of emerging concern (CECs) and their use to fortify BIOSORs. Seventeen bacterial strains isolated from commercial bio-stimulant formulations were studied together with three strains previously isolated and identified as Bacillus subtilis, Bacillus licheniformis, and Serratia plymuthica. All the strains were tested in vitro for their ability to grow under increasing concentrations (0, 0.2, 0.5 and 1 mg L−1) of CECs: bisphenol A, 4-nonylphenol, penconazole, and S-metolachlor. Results highlighted a variability in the tolerance of the bacteria to the tested CECs. The B. subtilis, B. licheniformis, and S. plymuthica were the most promising strains, individually or as consortium, to tolerate individual CECs and their mix. Moreover, they exhibited metabolic activity when inoculated in the BIOSORs. Nevertheless, additional investigations such as quantitative assessment of CECs are needed to validate the methodology. This work contributes to investigate the feasibility of stable and functionally active microbially enriched bio-sorbents (Me-BIOSORs) and provides preliminary evidence supporting the potential to be used in soil–plant systems at the field scale. Full article
(This article belongs to the Special Issue Feature Papers in Ecosystem, Environment and Climate Change in Agriculture)
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21 pages, 13381 KB  
Article
Research on Grassland Classification Method in Water Conservation Areas of the Qinghai–Tibet Plateau Based on Multi-Source Data Fusion
by Kexin Yan, Yueming Hu, Lu Wang, Xiaoyan Huang, Runyan Zou, Liangjun Zhao, Fan Yang and Taibin Wen
Agriculture 2025, 15(23), 2503; https://doi.org/10.3390/agriculture15232503 - 1 Dec 2025
Viewed by 259
Abstract
The Qinghai–Tibet Plateau is a crucial ecological security barrier in China and Asia. Its grassland ecosystem has high ecological service value. Scientific assessments and classifications of grasslands are crucial for determining the value of grassland resources and implementing refined management. Traditional grassland classification [...] Read more.
The Qinghai–Tibet Plateau is a crucial ecological security barrier in China and Asia. Its grassland ecosystem has high ecological service value. Scientific assessments and classifications of grasslands are crucial for determining the value of grassland resources and implementing refined management. Traditional grassland classification methods have used expert knowledge and linear models, which are subjective and cannot describe complex nonlinear relationships. We conducted a case study in Hongyuan County, Sichuan Province, in the water conservation area of the Qinghai–Tibet Plateau, using multi-source data including Landsat 8 (15 m/30 m), MOD15A2 (500 m), ALOS imagery (12.5 m), and 435 field survey samples, combined with machine learning models such as convolutional neural network (CNN), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), histogram gradient boosting (HistGradientBoosting), and random forest (RF). The objective was to develop a novel grassland classification method that integrates multi-source remote sensing data with machine learning algorithms. Based on the evaluation metrics of SHAP values, mean annual precipitation (MAP, 0.675), >0 °C Accumulated Temperature (AT, 0.591), and aspect (ASPECT, 0.548) were the most critical factors influencing alpine grasslands, revealing a driving mechanism characterized by climate dominance, topographic regulation, soil support, and vegetation response. The XGBoost model demonstrated the best performance (with an accuracy of 0.829, Precision of 0.818, Recall of 0.829, weighted F1-score of 0.820, and an AUC value of 0.870). The pixel-by-pixel absolute difference calculation between the model-predicted and the actual classification results showed that regions with no discrepancy (absolute value = 0) accounted for 75.82%, those with a minor discrepancy (absolute value = 1) accounted for 23.63%, and regions with a major discrepancy (absolute value = 2) accounted for only 0.54%. This study has established a replicable paradigm for the precise management and conservation of alpine grassland resources. Through the synergistic application of deep learning and machine learning, it generated superior baseline data, quantitatively uncovered a grassland differentiation mechanism dominated by hydrothermal factors and fine-tuned by topography in the complex Qinghai–Tibet Plateau, and delivered high-precision spatial distribution maps of grassland classes. Full article
(This article belongs to the Special Issue Feature Papers in Ecosystem, Environment and Climate Change in Agriculture)
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26 pages, 5190 KB  
Article
Analyzing the Driving Mechanism of Drought Using the Ecological Aridity Index Considering the Evapotranspiration Deficit—A Case Study in Xinjiang, China
by Hao Tang, Qiao Li, Hongfei Tao, Pingan Jiang, Congcang Tang and Xiangzhi Kong
Agriculture 2025, 15(19), 2016; https://doi.org/10.3390/agriculture15192016 - 26 Sep 2025
Viewed by 615
Abstract
With global warming, the increasing frequency of drought events threatens the stability of ecosystems, so the development of a rational ecological drought monitoring and assessment model is urgently needed. In this study, an evapotranspiration deficit (ED) was added for the first time into [...] Read more.
With global warming, the increasing frequency of drought events threatens the stability of ecosystems, so the development of a rational ecological drought monitoring and assessment model is urgently needed. In this study, an evapotranspiration deficit (ED) was added for the first time into the construction of an ecological drought index. Considering atmospheric water deficit (WD), soil moisture (SM) and runoff (RF), both the Copula method and a nonparametric method were used to construct a multivariate comprehensive drought index (MCDI) to monitor ecological drought. The MCDI was evaluated using Pearson, actual drought validation, Theil–Sen, Mann–Kendall and ExtraTrees+SHAP methods, in order to assess differences between construction methods, analyze the drivers and sensitivities of ecological drought in Xinjiang, China, and specifically explore the role of ED in ecological drought. The results showed that (1) ED based on the ratio form is more suitable for capturing SM changes; (2) the performance of the composite drought index was improved in all aspects when cumulative effects were considered, and the ecological drought index based on the nonparametric method was superior to the index using the Copula method; (3) soil moisture was identified as the main contributor to ecological drought in Xinjiang, with the strongest synergistic effect occurring between SM and ED; and (4) the sensitivity of ecological drought to soil moisture within the arid regions increased nonlinearly along the decreasing SM gradient. In addition, the sensitivity to all drivers increased over time, with the largest increase observed for RF, followed by SM and then ED. The findings of this paper provide a useful reference for constructing a comprehensive drought index at the global scale, since the nonparametric method requires considerably fewer computational resources compared with the Copula method. In addition, the identified synergistic effect of ED and SM offers a new theoretical basis for ecological drought prevention and management in arid regions. Full article
(This article belongs to the Special Issue Feature Papers in Ecosystem, Environment and Climate Change in Agriculture)
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