Evaluating crop-soil-water dynamics in waterlogged areas using a coupled groundwater-agronomic model

Deng, Chenda; Zhang, Yao; Bailey, Ryan T.

doi:10.1016/j.envsoft.2021.105130

Cited by 12 publications

(6 citation statements)

References 40 publications

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“… Soylu et al. (2014) showed that the 0.8–1.0-m depth was the critical depth for groundwater to produce anaerobic stress, so the growth of winter wheat may be inhibited ( Gou et al., 2020 ; Deng et al., 2021 ). In our study, winter wheat growth attributes and groundwater consumption decreased significantly at 0.6–0.9-m depth with deficient N fertilization ( Supplementary Figures S2A, B ; Figure 8 ), which may be due to anaerobic stress caused by the high water level.…”

Section: Discussionmentioning

confidence: 99%

“…This may be due to too shallow or too deep WTD that is not conducive to crop growth. For too shallow groundwater depth, the hypoxic or anaerobic environment in the root zone is not conducive to nutrient release and would lead to soil nutrient loss (Barrett-Lennard and Shabala, 2013;Najeeb et al, 2015;Ghobadi et al, 2017;Deng et al, 2021;Langan et al, 2022); soil water deficit induces drought stress easily because of too deep groundwater depth, which is also unfavorable to crop growth (Xu et al, 2013;Han et al, 2015;Liu et al, 2017;Zhang et al, 2019). In addition, Zhu et al (2018) found that the contribution rate of groundwater consumption (GC) to winter wheat evapotranspiration (ET a ) in dry (2.72-m depth), normal (1.45-m depth), and wet seasons (1.49-m depth) was 58%, 47%, and 69%, respectively.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Effects of nitrogen application on winter wheat growth, water use, and yield under different shallow groundwater depths

She

Li²,

Qi³

et al. 2023

Front. Plant Sci.

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Shallow groundwater plays a vital role in physiology morphological attributes, water use, and yield production of winter wheat, but little is known of its interaction with nitrogen (N) application. We aimed to explore the effects of N fertilization rate and shallow groundwater table depth (WTD) on winter wheat growth attributes, yield, and water use. Experiments were carried out in micro-lysimeters at WTD of 0.6, 0.9, 1.2, and 1.5 m with 0, 150, 240, and 300 kg/ha N application levels for the winter wheat (Triticum aestivum L.). The results showed that there was an optimum groundwater table depth (Op-wtd), in which the growth attributes, groundwater consumption (GC), yield, and water use efficiency (WUE) under each N application rate were maximum, and the Op-wtd decreased with the increase in N application. The Op-wtd corresponding to the higher velocity of groundwater consumption (Gv) appeared at the late jointing stage, which was significantly higher than other WTD treatments under the same N fertilization. WTD significantly affected the Gv during the seeding to the regreening stage and maturity stage; the interaction of N application, WTD, and N application was significant from the jointing to the filling stage. The GC, leaf area index (LAI), and yield increased with an increase of N application at 0.6–0.9-m depth—for example, the yield and the WUE of the NF300 treatment with 0.6-m depth were significantly higher than those of the NF150–NF240 treatment at 20.51%, and 14.81%, respectively. At 1.2–1.5-m depth, the N application amount exceeding 150–240 kg/ha was not conducive to wheat growth, groundwater use, grain yield, and WUE. The yield and the WUE of 150-kg/ha treatment were 15.02% and 10.67% higher than those of 240–300-kg/ha treatment at 1.2-m depth significantly. The optimum N application rate corresponding to yield indicated a tendency to decrease with the WTD increase. Considering the winter wheat growth attributes, GC, yield, and WUE, application of 150–240 kg/ha N was recommended in our experiment.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effects of nitrogen application on winter wheat growth, water use, and yield under different shallow groundwater depths

She

Li²,

Qi³

et al. 2023

Front. Plant Sci.

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“…Indeed, crop models predominantly have a high level of precision temporally, but only simulate growth at a single point in space. It is however worth noting that recent work by [32] is an exception to this.…”

Section: Introductionmentioning

confidence: 87%

“…The application of these stress factors in plant growth models requires knowledge of critical thresholds in soil air content for different crops and soils. In APSIM, vertical root growth in soybean is inhibited when volumetric soil moisture approaches 3% below saturation [17,32]. AquaCrop considers these thresholds as a percentage below soil saturation based on a crop's tolerance to waterlogging.…”

Section: Aeration Stressmentioning

confidence: 99%

Modelling Waterlogging Impacts on Crop Growth: A Review of Aeration Stress Definition in Crop Models and Sensitivity Analysis of APSIM

Githui

Beverly

Aiad

et al. 2022

IJPB

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Currently, crop physiological responses to waterlogging are considered only in a few crop models and in a limited way. Here, we examine the process bases of seven contemporary models developed to model crop growth in waterlogged conditions. The representation of plant recovery in these models is over-simplified, while plant adaptation or phenotypic plasticity due to waterlogging is often not considered. Aeration stress conceptualisation varies from the use of simple multipliers in equations describing transpiration and biomass to complex linkages of aeration-deficit factors with root growth, transpiration and nitrogen fixation. We recommend further studies investigating more holistic impacts and multiple stresses caused by plant behaviours driven by soils and climate. A sensitivity analysis using one model (a developer version of APSIM) with default parameters showed that waterlogging has the greatest impact on photosynthesis, followed by phenology and leaf expansion, suggesting a need for improved equations linking waterlogging to carbon assimilation. Future studies should compare the ability of multiple models to simulate real and in situ effects of waterlogging stress on crop growth using consistent experimental data for initialisation, calibration and validation. We conclude that future experimental and modelling studies must focus on improving the extent to which soil porosity, texture, organic carbon and nitrogen and plant-available water affect waterlogging stress, physiological plasticity and the ensuing temporal impacts on phenology, growth and yield.

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“…The boundaries of sloping cropland erosion areas are relatively clear and appear elongated, whereas erosion risk areas exhibit a planar spreading form in comparison. The present research on the identification of waterlogged areas predominantly centers on ecological investigations of vast waterlogged regions, with the objective of appraising and analyzing the effects of waterlogging events on crop development [3,4]. The presence of seasonal waterlogging disasters severely impacts regional food security [5,6].…”

Section: Introductionmentioning

confidence: 99%

Waterlogged Area Identification Models Based on Object-Oriented Image Analysis and Deep Learning Methods in Sloping Croplands of Northeast China

Xie,

Wang,

Wang

et al. 2024

Sustainability

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Drainage difficulties in the waterlogged areas of sloping cropland not only impede crop development but also facilitate the formation of erosion gullies, resulting in significant soil and water loss. Investigating the distribution of these waterlogged areas is crucial for comprehending the erosion patterns of sloping cropland and preserving black soil resource. In this study, we built varied models based on two stages (one using only deep learning methods and the other combining object-based image analysis (OBIA) with deep learning methods) to identify waterlogged areas using high-resolution remote sensing data. The results showed that the five deep learning models using original remote sensing imagery achieved precision rates varying from 54.6% to 60.9%. Among these models, the DeepLabV3+-Xception model achieved the highest accuracy, as indicated by an F1-score of 53.4%. The identified imagery demonstrated a significant distinction in the two categories of waterlogged areas: sloping cropland erosion zones and erosion risk areas. The former had obvious borders and fewer misclassifications, exceeding the latter in terms of identification accuracy. Furthermore, the accuracy of the deep learning models was significantly improved when combined with object-oriented image analysis. The DeepLabV3+-MobileNetV2 model achieved the maximum accuracy, with an F1-score of 59%, which was 6% higher than that of the model using only original imagery. Moreover, this advancement mitigated issues related to boundary blurriness and image noise in the identification process. These results will provide scientific assistance in managing and reducing the impact in these places.

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Evaluating crop-soil-water dynamics in waterlogged areas using a coupled groundwater-agronomic model

Cited by 12 publications

References 40 publications

Effects of nitrogen application on winter wheat growth, water use, and yield under different shallow groundwater depths

Effects of nitrogen application on winter wheat growth, water use, and yield under different shallow groundwater depths

Modelling Waterlogging Impacts on Crop Growth: A Review of Aeration Stress Definition in Crop Models and Sensitivity Analysis of APSIM

Waterlogged Area Identification Models Based on Object-Oriented Image Analysis and Deep Learning Methods in Sloping Croplands of Northeast China

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