Affecting Factors of Plant Phyllosphere Microbial Community and Their Responses to Climatic Warming—A Review

Plants, Journal Year: 2023, Volume and Issue: 12(16), P. 2891 - 2891

Published: Aug. 8, 2023

Phyllosphere microorganisms are not only an important part of plants, but also microorganisms. In this review, the function phyllosphere microorganisms, assembly mechanism driving factors microbial community structure, and effects climate warming on structure were reviewed. Generally, have a variety functions (e.g., fixing nitrogen, promoting plant growth). Although selection dispersal processes together regulate phyllospheric communities, which one ecological is dominant how external disturbances alter relative contributions each process remains controversial. Abiotic climatic conditions, geographical location physical chemical properties soil) biological morphological physiological biochemical characteristics, species varieties) can affect structure. However, predominant affecting Moreover, affects its been fully resolved, further relevant studies needed.

Language: Английский

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Evaluating the Reconstructed All-Weather Land Surface Temperature for Urban Heat Island Analysis

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(2), P. 373 - 373

Published: Jan. 17, 2024

With the continuous improvement of urbanization levels in Lhasa area, urban heat island effect (UHI) has seriously affected ecological environment region. However, satellite-based thermal infrared land surface temperature (LST), commonly used for UHI research, is by cloudy weather, resulting a lack spatial and temporal information. In this study, focusing on region, we combine simulated LST data obtained Weather Research Forecasting (WRF) model with remote sensing-based to reconstruct all-weather March, June, September, December 2020 at resolution 0.01° while using Moderate-Resolution Imaging Spectroradiometer (MODIS) as reference (in terms accuracy). Subsequently, based reconstructed LST, an analysis was conducted obtain spatiotemporal distribution region under conditions. The results demonstrate that effectively captures expected characteristics high accuracy, average root mean square error 2.20 K, absolute 1.51 correlation coefficient consistently higher than 0.9. Additionally, primarily observed during spring winter seasons, intensity remaining relatively stable winter. study provide new method reconstruction thereby improving research accuracy from perspective foundational data. it offers theoretical basis governance

Language: Английский

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A Vis/NIR device for detecting moldy apple cores using spectral shape features

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 220, P. 108898 - 108898

Published: April 3, 2024

Language: Английский

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Research on Provincial-Level Soil Moisture Prediction Based on Extreme Gradient Boosting Model

Agriculture, Journal Year: 2023, Volume and Issue: 13(5), P. 927 - 927

Published: April 24, 2023

As one of the physical quantities concerned in agricultural production, soil moisture can effectively guide field irrigation and evaluate distribution water resources for crop growth various regions. However, spatial variability is dramatic, its time series data are highly noisy, nonlinear, nonstationary, thus hard to predict accurately. In this study, taking Jiangsu Province China as an example, 70 meteorological automatic observation stations from 2014 2022 were used establish prediction models 0–10 cm relative humidity (RHs10cm) via extreme gradient boosting (XGBoost) algorithm. Before constructing model, according measured characteristics, divided into three categories: sandy soil, loam clay soil. Based on impacts factors budget balance, 14 predictors chosen among which atmospheric accounted 10 4, respectively. Considering differences characteristics lagged effects environmental impacts, best influence times different types determined through correlation analysis improve rationality model construction. To better importance factors, two sets (Model_soil&atmo Model_atmo) designed by optional put XGBoost model. Meanwhile, contributions results analyzed with Shapley additive explanation (SHAP). Six effect indicators, well a typical drought process that happened 2022, accuracy. The show highest correlations between RHs10cm varied but was similar types. Among these predictors, contribution rates maximum air temperature (Tamax), cumulative precipitation (Psum), (RHa) functioned critical factor affecting variation moisture, relatively high both models. addition, adding could accuracy prediction. certain extent, performed when compared artificial neural networks (ANNs), random forests (RFs), support vector machines (SVMs). values coefficient (R), root mean square error (RMSE), absolute (MAE), (MARE), Nash–Sutcliffe efficiency (NSE), (ACC) Model_soil&atmo 0.69, 11.11, 4.87, 0.12, 0.50, 88%, This study verified applicable at provincial level, it reasonably development processes event.

Language: Английский

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Ground-Based Hyperspectral Estimation of Maize Leaf Chlorophyll Content Considering Phenological Characteristics

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(12), P. 2133 - 2133

Published: June 13, 2024

Accurately measuring leaf chlorophyll content (LCC) is crucial for monitoring maize growth. This study aims to rapidly and non-destructively estimate the LCC during four critical growth stages investigate ability of phenological parameters (PPs) LCC. First, spectra were obtained by spectral denoising followed transformation. Next, sensitive bands (Rλ), indices (SIs), PPs extracted from all at each stage. Then, univariate models constructed determine their potential independent estimation. The multivariate regression (LCC-MR) built based on SIs, SIs + Rλ, Rλ after feature variable selection. results indicate that our machine-learning-based LCC-MR demonstrated high overall accuracy. Notably, 83.33% 58.33% these showed improved accuracy when successively introduced SIs. Additionally, model accuracies milk-ripe tasseling outperformed those flare–opening jointing under identical conditions. optimal was created using XGBoost, incorporating SI, PP variables R3 These findings will provide guidance support management.

Language: Английский

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Assessing feature importance for forecasting soil moisture in subarctic regions using gridded historical and forecasted climate data

Geoderma, Journal Year: 2025, Volume and Issue: 458, P. 117304 - 117304

Published: April 27, 2025

Language: Английский

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Can meteorological data and normalized difference vegetation index be used to quantify soil pH in grasslands?

Frontiers in Ecology and Evolution, Journal Year: 2023, Volume and Issue: 11

Published: July 12, 2023

Quantifying soil pH at manifold spatio-temporal scales is critical for examining the impacts of global change on quality. It still unclear whether meteorological data and normalized difference vegetation index (NDVI) can be used to quantify in grasslands. Here, nine methods (i.e., RF: random-forest, GLR: generalized-linear-regression, GBR: generalized-boosted-regression, MLR: multiple-linear-regression, ANN: artificial-neural-network, CIT: conditional-inference-tree, SVM: support-vector-machine, eXGB: eXtreme-gradient-boosting, RRT: recursive-regression-tree) were applied pH. Three independent variables AP: annual precipitation, AT: temperature, ARad: radiation) potential (pH p ), four AP, AT, ARad NDVI max : maximum during growing season) actual a ). Overall, developed eXGB models performed worst (linear regression slope < 0.60; R 2 = 0.99; relative deviation ≤ –43.54%; RMSE ≥ 3.14), but RF best slope: 0.99–1.01; 1.00; deviation: from –1.26% 0.65%; 0.28). The linear slope, , absolute value between modelled measured 0.96–1.03, 0.99–1.00, 3.87% 0.88 other seven methods, respectively. Accordingly, except approach, eight have greater accuracies quantifying However, had uppermost quantification accuracy Whether or not was dependent chosen models. by this study may conducive scientific studies related quality degradation (e.g., acidification salinization) spatial-temporal under future globe change.

Language: Английский

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Can normalized difference vegetation index and climate data be used to estimate soil carbon, nitrogen, and phosphorus and their ratios in the Xizang grasslands?

Frontiers in Earth Science, Journal Year: 2024, Volume and Issue: 11

Published: Feb. 2, 2024

Accurately quantifying the relative effects of climate change and human activities on soil carbon, nitrogen, phosphorus in alpine grasslands their feedback is an important aspect global change, high-precision models are key to solving this scientific problem with high quality. Therefore, nine models, random forest model (RFM), generalized boosted regression (GBRM), multiple linear (MLRM), support vector machine (SVMM), recursive tree (RRTM), artificial neural network (ANNM), (GLMR), conditional inference (CITM), eXtreme gradient boosting (eXGBM), were used for modeling organic carbon (SOC), total nitrogen (TN), (TP), ratio SOC TN (C:N), TP (C:P), (N:P) at depths 0–10, 10–20, 20–30 cm under non-grazing free-grazing scenarios Xizang grasslands. Annual radiation (ARad), annual precipitation (AP), temperature (AT) as independent variables scenarios, whereas ARad, AP, AT, growing season maximum normalized difference vegetation index (NDVI max ) scenarios. Overall, RFM GBRM more accurate than other seven models. However, numbers much larger those RFM, indicating that may have a greater complexity lower running speed. had best performance among SOC, TN, TP, C:N, C:P, N:P The established study can not only help scientists save time money massive sampling analysis, but also be construct database ratios, further research related ecological environmental issues (e.g., examining whether systems intensified warming over past few decades by exploring altered carbon) Plateau.

Language: Английский

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Modelling Soil Ammonium Nitrogen, Nitrate Nitrogen and Available Phosphorus Using Normalized Difference Vegetation Index and Climate Data in Xizang’s Grasslands

Sustainability, Journal Year: 2024, Volume and Issue: 16(11), P. 4695 - 4695

Published: May 31, 2024

There is still a lack of high-precision and large-scale soil ammonium nitrogen (NH4+-N), nitrate (NO3−-N) available phosphorus (AP) in alpine grasslands at least on the Qinghai–Xizang Plateau, which may limit our understanding sustainability grassland ecosystems (e.g., changes NH4+-N, NO3−-N AP can affect productivity, turn alter livestock development), given that are important limiting factors regions. The construction big data mining models key to solving problem mentioned above. Therefore, observed 0–10 cm 10–20 cm, climate (air temperature, precipitation radiation) and/or normalized vegetation index (NDVI) were used model Xizang under fencing grazing conditions. Nine algorithms, including random forest algorithm (RFA), generalized boosted regression (GBRA), multiple linear (MLRA), support vector machine (SVMA), recursive tree (RRTA), artificial neural network (ANNA), (GLMA), conditional inference (CITA), eXtreme gradient boosting (eXGBA), used. RFA had best performance among nine algorithms. Climate based explain 78–92% variation NDVI together 83–93% conditions RFA. absolute values relative bias, slopes, R2 RMSE between simulated ≤8.65%, ≥0.90, ≥0.91 ≤3.37 mg kg−1, respectively. be difference Xizang’s grasslands. constructed this study obtain long-term 2000–2020) raster dataset whole Qinghai–Tibet Plateau. productivity from perspective constraints across Tibetan grasslands, provide an basis for sustainable development ecosystem itself animal husbandry

Language: Английский

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Modelling Fresh and Dry Weight of Aboveground Biomass of Plant Community and Taxonomic Group Using Normalized Difference Vegetation Index and Climate Data in Xizang’s Grasslands

Agronomy, Journal Year: 2024, Volume and Issue: 14(7), P. 1515 - 1515

Published: July 12, 2024

In grassland ecosystems, aboveground biomass (AGB) is critical for energy flow, biodiversity maintenance, carbon storage, climate regulation, and livestock husbandry. Particularly on the climate-sensitive Tibetan Plateau, accurate AGB monitoring crucial assessing large-scale capacity. Previous studies focused predicting mainly at plant community level from perspective of dry weight (AGBd). This study aims to predict in Xizang both taxonomic group (sedge, graminoid, forb) levels, an AGBd a fresh (AGBf) perspective. Three four independent variables (growing mean temperature, total precipitation, radiation NDVImax, maximum normalized difference vegetation index) were used prediction using nine models grasslands. The random forest model (RFM) showed greatest potential simulating (training R2 ≥ 0.62, validation 0.87). could be due nonlinear relationships between AGB, meteorological factors, NDVImax. RFM exhibited robustness against outliers zero values resulting groups that absent quadrats. accuracies different weight, among three groups. RFM’s use fewer can reduce complexity costs compared previous studies. Therefore, emerged as optimal models, offering investigations into especially analyzing spatiotemporal patterns

Language: Английский

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