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Agricultural Water-Land-Plant System Engineering
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Agricultural Water-Land-Plant System Engineering

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water, Agriculture and Aquaculture".

Deadline for manuscript submissions: 20 September 2025 | Viewed by 4719

Special Issue Editors

Prof. Dr. Haijun Liu

E-Mail Website
Guest Editor
College of Water Sciences, Beijing Normal University, Beijing, China
Interests: water saving and efficient utilization of water resources in agriculture; sprinkler and surface irrigation technology; fertigation scheduling; SPAC system modeling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Haijun Yan

E-Mail Website
Guest Editor
College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China
Interests: irrigation performance evaluation; irrigation systems management; variable rate irrigation; smart irrigation; fertilization; new technology and equipment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The world is facing the enormous challenge of increasing our food production capacity by 55% by 2050 to meet the needs of an estimated 9.7 billion people. However, under the influence of climate change, water shortage and soil degradation, the vulnerability of agricultural production has increased. In order to promote food production, the agricultural production system of water use, land conditions and crop growth should be taken into account together. Rational regulation and control methods for soil, water, fertilizer, salt, heat and crop systems should be studied and then adopted to achieve efficient food production and the sustainable use of agricultural resources.

The Special Issue focuses on the latest research results in systems engineering for agricultural water–land condition–crop growth, including the regulation mechanism of crop water, fertilizer, salt, heat and microclimate, water-saving irrigation, agronomic technologies and modes, comprehensive observation and simulation technology of farmland ecosystems, and agricultural production management modes to cope with climate change. We welcome original research papers, review articles and short notes.

Prof. Dr. Haijun Liu
Prof. Dr. Haijun Yan
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Water 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

  • plant–water–land nexus
  • agricultural production vulnerability
  • soil–water–salt–nutrient–heat–crop system modelling
  • comprehensive observation means
  • water-saving irrigation
  • climate change
  • smart irrigation
  • irrigation systems management

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Published Papers (5 papers)

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Research

26 pages, 10825 KiB  
Article
Analysis of Carbon Density Distribution Characteristics in Urban Wetland Ecosystems: A Case Study of Shanghai Fish and Dishui Lake
by Jin Wang, Jingren Yu, Manjuan Shen and Shengquan Che
Water 2025, 17(5), 650; https://doi.org/10.3390/w17050650 - 23 Feb 2025
Viewed by 340
Abstract
This paper examines two major artificial wetlands in Shanghai—Shanghai Fish and Dishui Lake—as case studies to explore the biomass, carbon content, carbon density, and carbon sequestration functions of wetland plants in urban ecosystems. Through field sampling and elemental analysis of 20 common wetland [...] Read more.
This paper examines two major artificial wetlands in Shanghai—Shanghai Fish and Dishui Lake—as case studies to explore the biomass, carbon content, carbon density, and carbon sequestration functions of wetland plants in urban ecosystems. Through field sampling and elemental analysis of 20 common wetland plant species, this study investigated the differences in aboveground and underground biomass and carbon storage capacity across different plant types. The results indicated that emergent plants have the highest carbon storage capacities, with species such as Cyperus involucratus, Arundo donax, Phragmites australis, and Nelumbo sp. exhibiting higher carbon densities, while floating plants demonstrated relatively weaker carbon storage capacity. The carbon content varied significantly between different parts and species of plants, while soil carbon density was much higher than that of the plant portions, highlighting the crucial role of soil in wetland carbon sequestration. Additionally, an inversion model for wetland plant carbon density was established, and remote sensing data were used to assess the vegetation distribution characteristics and carbon density variations in the two artificial wetlands. This distribution pattern reflects the influence of wetland vegetation and water level (which affect water availability and nutrient distribution) on carbon density. The results showed a significant increase in carbon density from 2018 to 2023, particularly in lakeshore areas, suggesting that wetland ecological restoration and management measures have achieved positive outcomes, including a measurable increase in carbon density and enhanced vegetation coverage. The findings are significant for understanding and enhancing the carbon sequestration potential of artificial wetlands in urban ecosystems. Full article
(This article belongs to the Special Issue Agricultural Water-Land-Plant System Engineering)
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21 pages, 8016 KiB  
Article
Revealing Climate-Induced Patterns in Crop Yields and the Water-Energy-Food-Carbon Nexus: Insights from the Pearl River Basin
by Changxin Ye, Ze Yuan, Xiaohong Chen, Ruida Zhong and Lie Huang
Water 2024, 16(24), 3693; https://doi.org/10.3390/w16243693 - 21 Dec 2024
Viewed by 844
Abstract
In the context of growing concerns over food security and climate change, research on sustainable agricultural development increasingly emphasizes the interconnections within agricultural systems. This study developed a regionally integrated optimization and prediction agricultural model to systematically analyze the impacts of climate change [...] Read more.
In the context of growing concerns over food security and climate change, research on sustainable agricultural development increasingly emphasizes the interconnections within agricultural systems. This study developed a regionally integrated optimization and prediction agricultural model to systematically analyze the impacts of climate change on agricultural systems and their feedback mechanisms from a water-energy-food-carbon (WEFC) nexus perspective. Applied to the Pearl River Basin, the model evaluates future trends in grain yield, water use, energy consumption, and carbon emissions under various climate scenarios throughout this century. The results indicate that rising temperatures significantly reduce crop yields, particularly in the western basin, increasing the environmental footprint per unit of grain produced. However, the CO2 fertilization effect substantially offsets these negative impacts. Under the SSP585 scenario, CO2 concentrations rising from 599.77 ppm to 1135.21 ppm by the century’s end led to a shift in crop yield trends from negative (Z = −7.03) to positive (Z = 11.01). This also reduces water, energy, and carbon footprints by 12.82%, 10.62%, and 10.59%, respectively. These findings highlight the critical importance of adaptive management strategies, including precision irrigation, optimized fertilizer use, and climate-resilient practices, to ensure sustainable agricultural production. Despite these insights, the model has limitations. Future research should incorporate uncertainty analysis, diverse adaptation pathways, and advanced technologies such as machine learning and remote sensing to improve predictive accuracy and applicability. This study offers valuable guidance for mitigating the adverse impacts of climate change on the WEFC nexus, supporting sustainable agricultural practices and science-based policy development. Full article
(This article belongs to the Special Issue Agricultural Water-Land-Plant System Engineering)
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16 pages, 6401 KiB  
Article
Estimation of Water Interception of Winter Wheat Canopy Under Sprinkler Irrigation Using UAV Image Data
by Xueqing Zhou, Haijun Liu and Lun Li
Water 2024, 16(24), 3609; https://doi.org/10.3390/w16243609 - 15 Dec 2024
Viewed by 649
Abstract
Canopy water interception is a key parameter to study the hydrological cycle, water utilization efficiency, and energy balance in terrestrial ecosystems. Especially in sprinkler-irrigated farmlands, the canopy interception further influences field energy distribution and microclimate, then plant transpiration and photosynthesis, and finally crop [...] Read more.
Canopy water interception is a key parameter to study the hydrological cycle, water utilization efficiency, and energy balance in terrestrial ecosystems. Especially in sprinkler-irrigated farmlands, the canopy interception further influences field energy distribution and microclimate, then plant transpiration and photosynthesis, and finally crop yield and water productivity. To reduce the field damage and increase measurement accuracy under traditional canopy water interception measurement, UAVs equipped with multispectral cameras were used to extract in situ crop canopy information. Based on the correlation coefficient (r), vegetative indices that are sensitive to canopy interception were screened out and then used to develop canopy interception models using linear regression (LR), random forest (RF), and back propagation neural network (BPNN) methods, and lastly these models were evaluated by root mean square error (RMSE) and mean relative error (MRE). Results show the canopy water interception is first closely related to relative normalized difference vegetation index (R△NDVI) with r of 0.76. The first seven indices with r from high to low are R△NDVI, reflectance values of the blue band (Blue), reflectance values of the near-infrared band (Nir), three-band gradient difference vegetation index (TGDVI), difference vegetation index (DVI), normalized difference red edge index (NDRE), and soil-adjusted vegetation index (SAVI) were chosen to develop canopy interception models. All the developed linear regression models based on three indices (R△NDVI, Blue, and NDRE), the RF model, and the BPNN model performed well in canopy water interception estimation (r: 0.53–0.76, RMSE: 0.18–0.27 mm, MRE: 21–27%) when the interception is less than 1.4 mm. The three methods underestimate the canopy interception by 18–32% when interception is higher than 1.4 mm, which could be due to the saturation of NDVI when leaf area index is higher than 4.0. Because linear regression is easy to perform, then the linear regression method with NDVI is recommended for canopy interception estimation of sprinkler-irrigated winter wheat. The proposed linear regression method and the R△NDVI index can further be used to estimate the canopy water interception of other plants as well as forest canopy. Full article
(This article belongs to the Special Issue Agricultural Water-Land-Plant System Engineering)
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17 pages, 2917 KiB  
Article
Sensitivity and Uncertainty Analysis of the GeeSEBAL Model Using High-Resolution Remote-Sensing Data and Global Flux Site Data
by Shunjun Hu, Changyan Tian and Ping Jiao
Water 2024, 16(20), 2978; https://doi.org/10.3390/w16202978 - 18 Oct 2024
Viewed by 828
Abstract
Actual evapotranspiration (ETa) is an important component of the surface water cycle. The geeSEBAL model is increasingly being used to estimate ETa using high-resolution remote-sensing data (Landsat 4/5/7/8). However, due to surface heterogeneity, there is significant uncertainty. By optimizing [...] Read more.
Actual evapotranspiration (ETa) is an important component of the surface water cycle. The geeSEBAL model is increasingly being used to estimate ETa using high-resolution remote-sensing data (Landsat 4/5/7/8). However, due to surface heterogeneity, there is significant uncertainty. By optimizing the quantile values of the reverse-modelling automatic calibration algorithm (CIMEC) endpoint-component selection algorithm under extreme conditions through 212 global flux sites, we obtained the optimized quantile values of 11 vegetation types of cold- and hot-pixel endpoint components (Ts and NDVI). Based on the observation data of the global FLUXNET tower, the sensitivity of 20 parameters in the improved geeSEBAL model was determined through Sobol’s sensitivity analysis. Among them, the parameters dT and SAVI,hot were confirmed as the most sensitive parameters of the algorithm. Subsequently, we used the differential evolution Markov chain (DE-MC) method to analyse the uncertainty of the parameters in the geeSEBAL model used the posterior distribution of the parameters to modify the sensitive parameter values or ranges in the improved geeSEBAL model and to simulate the daily ETa. The results indicate that by analysing the end element components of the geeSEBAL model (Ts and NDVI), quantile numerical optimization and parameter optimization can be performed. Compared with the original algorithm, the improved geeSEBAL model has significantly improved simulation performance, as shown by higher R2 values, higher NSE values, smaller bias values, and lower RMSE values. The most suitable values of the predefined parameter Zoh were determined, and the reanalysis of meteorological data inputs (relative humidity (RH), temperature (T), wind speed (WS), and net radiation (Rn)) was also found to be an important source of uncertainty for the accurate estimation of ETa. This study indicates that optimizing the quantiles and key parameters of the model end component has certain potential for further improving the accuracy of the geeSEBAL model based on high-resolution remote-sensing data in estimating the ETa for various vegetation types. Full article
(This article belongs to the Special Issue Agricultural Water-Land-Plant System Engineering)
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23 pages, 1613 KiB  
Article
Enhancing Soil Conditions and Maize Yield Efficiency through Rational Conservation Tillage in Aeolian Semi-Arid Regions: A TOPSIS Analysis
by Zijian Cong, Jian Gu, Chunqian Li, Fei Li and Fengming Li
Water 2024, 16(16), 2228; https://doi.org/10.3390/w16162228 - 7 Aug 2024
Viewed by 1234
Abstract
Conservation tillage technology possesses substantial potential to enhance agricultural production efficiency and tackle issues such as wind erosion and land degradation in semi-arid regions. The integration of no-tillage and straw mulching technologies in the conventional aeolian semi-arid agricultural zones of western Liaoning, China, [...] Read more.
Conservation tillage technology possesses substantial potential to enhance agricultural production efficiency and tackle issues such as wind erosion and land degradation in semi-arid regions. The integration of no-tillage and straw mulching technologies in the conventional aeolian semi-arid agricultural zones of western Liaoning, China, has led to notable improvements in crop yield and soil quality. However, a comprehensive assessment of the mechanisms and kinetics involved in soil nutrient variations is yet to be conducted. During a two-year study period, we assessed four tillage systems in the aeolian semi-arid regions of Northern China: no-tillage with full straw mulching (NTFS), no-tillage with half straw mulching (NTHS), no-tillage without straw mulching (NT), and conventional tillage (CT). The investigation focused on examining nutrient conditions, enhancing photosynthetic activity, and increasing maize yield while improving water use efficiency (WUE). Our findings emphasize the beneficial impact of combining no-tillage and straw mulching on enhancing soil water retention, resulting in a notable rise in soil moisture levels during the crucial growth phases of maize. This approach also positively influenced soil nutrient levels, particularly in the 0–20 cm layer, fostering an environment conducive to maize cultivation. In terms of ecological benefits, no-tillage with straw mulching curtailed soil sediment transport and wind erosion, notably at 30–40 cm heights, aiding in the ecological protection of the region. The yield and WUE were substantially higher under NTFS and NTHS than under CT, with NTHS demonstrating the most significant gains in yield (14.5% to 16.6%) and WUE (18.3% to 21.7%) throughout the study period. A TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) analysis confirmed NTHS as the optimal treatment, achieving the highest scores for soil water, nutrient availability, wind erosion control, maize photosynthesis, yield, and WUE, thus emerging as the most effective conservation tillage strategy for sustainable agriculture in aeolian semi-arid regions. Full article
(This article belongs to the Special Issue Agricultural Water-Land-Plant System Engineering)
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