Agricultural and Forest Meteorology最新文献

{"title":"Spring factors controlling interannual CO2 flux variations in a subtropical humid alpine meadow on the southeastern Tibetan Plateau","authors":"Feng Zeng ,&nbsp;Ruowen Yang ,&nbsp;Huizhi Liu ,&nbsp;Qun Du ,&nbsp;Yang Liu ,&nbsp;Huancai Cun","doi":"10.1016/j.agrformet.2025.110603","DOIUrl":"10.1016/j.agrformet.2025.110603","url":null,"abstract":"<div><div>Understanding of the carbon cycling in subtropical humid alpine meadows is still limited due to the scarcity of observations in this area. It is crucial to quantify the temporal dynamics and factors influencing CO₂ fluxes in order to predict ecosystems CO₂ budgets accurately under climate change. This study utilized 11 years (2012–2022) of eddy covariance observation data to investigate CO₂ fluxes and its controlling factors in a subtropical humid alpine meadow on the southeastern Tibetan Plateau. Our findings indicate that this alpine meadow ecosystem consistently acted as a net CO₂ sink, with annual net ecosystem productivity (NEP) ranging from 137 g C <em>m</em>⁻² to 370 g C <em>m</em>⁻². Seasonal variations in CO₂ fluxes were primarily governed by soil water content, soil temperature (Ts), and leaf area index, with notable interactions observed among these variables. On an annual basis, the interannual variability (IAV) of NEP anomalies was predominantly linked to gross primary production (GPP) anomalies during the dry season. IAV of NEP was mainly driven by length of growing season, while that of GPP and ecosystem respiration were significantly affected by summer peak values. Additionally, spring environmental variables, i.e., Ts, vapor pressure deficit, and photosynthetically active radiation, were crucial in influencing the IAV of CO₂ fluxes through the regulation of phenological and physiological metrics. This comprehensive analysis provides valuable insights for future carbon modeling efforts on the Tibetan Plateau.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"370 ","pages":"Article 110603"},"PeriodicalIF":5.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143916311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved fire severity prediction using pre-fire remote sensing and meteorological time series: Application to the French Mediterranean area","authors":"Victor Penot ,&nbsp;Thomas Opitz ,&nbsp;François Pimont ,&nbsp;Olivier Merlin","doi":"10.1016/j.agrformet.2025.110588","DOIUrl":"10.1016/j.agrformet.2025.110588","url":null,"abstract":"<div><div>Fire severity, or how an environment is affected by fire, can be estimated over large areas using remotely sensed indices like the Relative Burnt Ratio (RBR). RBR predictions typically rely on data from a single date just before the fire. However, predicting RBR accurately in both time and space remains challenging. To improve RBR predictability, we developed new models using time series data spanning several months before the fire. These models use fuel proxies derived from optical remote sensing and meteorological data. We applied this approach to fires in the French Mediterranean area during the summers of 2016–2021. We used a Lagged Generalized Additive Model (LGAM) and a Functional Linear Model (FLM) to estimate the influence of variables up to several months before the fire on RBR. A GAM fed with immediate pre-fire predictors served as a benchmark. Training and prediction were conducted at the fire–land-cover spatial scale using a training dataset spatially independent of the test dataset. FLM achieved the best prediction accuracy on test data (R=0.68, RMSE=0.057), outperforming LGAM (R=0.60, RMSE=0.063) and the benchmark (R=0.52, RMSE=0.069). FLM accurately predicted the highest RBR values when the Normalized Difference Vegetation Index decreased faster than the average and when the Duff Moisture Code increased faster than the average over the 65 days before the fire. The 17% decrease in the RMSE of FLM predictions compared to GAM predictions shows that understanding fuel dynamics up to two months before a fire provides valuable information for ranking areas by fire severity.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"371 ","pages":"Article 110588"},"PeriodicalIF":5.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remote sensing assessment of invasive plant species impacts on microclimate and water stress in mediterranean coastal ecosystems","authors":"Giorgi Kozhoridze ,&nbsp;Eyal Ben-Dor ,&nbsp;Vítězslav Moudrý ,&nbsp;Marcelo Sternberg","doi":"10.1016/j.agrformet.2025.110606","DOIUrl":"10.1016/j.agrformet.2025.110606","url":null,"abstract":"<div><div>This study uses multi-source, multi-temporal remote sensing imagery to compare the effects of invasive <em>Heterotheca subaxillaris</em> and <em>Acacia saligna</em> and natural vegetation on microclimate conditions in Israeli coastal plain. The overall accuracy of the classification and mapping of invasive species and other land covers was 85 %, with optimal performance observed using late autumn imagery. Among the natural areas, 45 % were occupied by native vegetation, 30 % by <em>H. subaxillaris</em>, 15 % by <em>A. saligna</em> and 10 % by bare soil/sand.</div><div>Quantitative analysis revealed that <em>H. subaxillaris</em>, consistently elevated surface temperatures by 0.6 °C in spring, 1.8 °C in summer and 2.19 °C in autumn compared to native vegetation. This species also increased water vapor and potential evapotranspiration, while reducing soil evaporation and vegetation shading, resulting in both direct and indirect contributions to water stress. In contrast, <em>A. saligna,</em> provided localized cooling due to high vegetation density and shading, yet its high assimilation and transpiration rates led to elevated water vapor, daily total evaporation and PET indirectly amplifying water stress. Native vegeation moderated the local microclimate by decreasing temperature and water vapor, while maintaining stable evapotranspiration and low water stress throughout the dry season. This study highlights the complex interactions between invasive species and microclimate conditions, emphasizing the critical role of remote sensing techniques in monitoring and managing these species. By integrating remote sensing imagery with detailed microclimatic analysis, it provides novel insights into the contrasting ecological impacts of invasive species on temperature regimes, water stress, and evapotranspiration in Mediterranean coastal ecosystems.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"371 ","pages":"Article 110606"},"PeriodicalIF":5.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A decade of lost growth in old trees: aging shapes the impacts of drought and late frost events on European beech","authors":"Álvaro Rubio-Cuadrado ,&nbsp;Isabel Dorado-Liñán ,&nbsp;Rosana López ,&nbsp;J․Julio Camarero","doi":"10.1016/j.agrformet.2025.110601","DOIUrl":"10.1016/j.agrformet.2025.110601","url":null,"abstract":"<div><div>Studying growth declines and the factors that cause them, such as droughts or late spring frosts, is key to understanding their influence on forest productivity. However, most of the currently used methodologies to assess these events have drawbacks that can lead to erroneous conclusions. The increasing frequency and importance of these growth declines is linked to a higher climate variability and thus requires more effort to find suitable approaches to quantify their impacts on long-term tree growth. Furthermore, dendroecology generally focuses its efforts on the study of growth relationships with prevailing climatic conditions, giving little weight to the effect of occasional and discrete climatic events on medium- and long-term growth dynamics. Here, we develop a new methodology that consists in: (I) analyzing the largest growth reductions, (II) characterizing climate in those years, (III) identifying the change points in the tree growth function using Bayesian regression, and (IV) quantifying the impact of climate on short-, medium- and long-term growth trends using relative growth and cumulative growth loss indices. We studied the drops in growth suffered by European beech (<em>Fagus sylvatica</em>), caused by both droughts and late frosts. The study was conducted in stands with contrasting structural features (diameter, age) at the southwestern species distribution limit in the central Iberian Peninsula. Our results indicate that extreme climate events have caused a decade of growth loss in old trees (age ca. 100–330 years), and are the factor responsible for the decline of tree vigor. However, the relationships between prevailing climate conditions and tree growth were not significant, highlighting the importance of occasional and discrete climate events as the main drivers of growth. Tree age, rather than tree diameter, shapes tree growth response to extreme climate events such as droughts and late frosts.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"370 ","pages":"Article 110601"},"PeriodicalIF":5.6,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143912464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"When do clouds and aerosols lead to higher canopy photosynthesis?","authors":"Kevin H.H. van Diepen ,&nbsp;Elias Kaiser ,&nbsp;Oscar K. Hartogensis ,&nbsp;Alexander Graf ,&nbsp;Jordi Vilà-Guerau de Arellano ,&nbsp;Arnold F. Moene","doi":"10.1016/j.agrformet.2025.110597","DOIUrl":"10.1016/j.agrformet.2025.110597","url":null,"abstract":"<div><div>Clouds and aerosols can increase canopy photosynthesis relative to clear-sky values through changes in total and diffuse solar radiation: the diffuse fertilization effect (DFE). DFE varies across observational sites due to (a) inconsistent definitions and quantifications of DFE, (b) unexplored relationships between DFE and cloudiness type, and (c) insufficient knowledge of the effect of site characteristics. We showed that: DFE definitions vary, DFE quantifications do not connect to existing definitions or do not isolate the causal factor, and a systematic protocol to quantify DFE is lacking. A new theoretical framework served to clarify the relation between DFE definitions, and showed how DFE varies with cloudiness types and site characteristics. We proposed guidelines for a systematic DFE quantification across studies, and which aim to isolate the causal factor of DFE.</div><div>Applying our framework to observations of canopy photosynthesis, solar radiation and cloudiness types we quantified DFE at daily and sub-daily time scales. We showed for the first time how DFE varies with cloudiness type, due to the varying trade-off between diffuse radiation and total solar radiation. Using an observation-driven canopy photosynthesis model, we showed that the DFE varies with site characteristics and time of day. The DFE responded strongly to leaf area index, canopy nitrogen distribution, leaf orientation and leaf transmittance, with leaf area index and leaf orientation driving DFE occurrences at our site. Our study emphasizes the importance of quantifying the DFE systematically and accurately across observational sites and highlights the need for information on cloudiness climatology and site characteristics.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"370 ","pages":"Article 110597"},"PeriodicalIF":5.6,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143912622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reversal of the sensitivity of vegetation productivity to precipitation in global terrestrial biomes over the recent decade","authors":"Weirong Zhang ,&nbsp;Zehao Fan ,&nbsp;Chuan Jin ,&nbsp;Yue Jiao ,&nbsp;Kai Di ,&nbsp;Ming Feng ,&nbsp;Yifei Lu ,&nbsp;Kun Zhao ,&nbsp;Hongxian Zhao ,&nbsp;Shaorong Hao ,&nbsp;Zhongmin Hu","doi":"10.1016/j.agrformet.2025.110598","DOIUrl":"10.1016/j.agrformet.2025.110598","url":null,"abstract":"<div><div>The sensitivity of vegetation productivity to precipitation (<em>S</em>_ppt) is crucial for grasping how vegetation responds to changing precipitation and forecasting future shifts in ecosystem function. However, comprehensive assessment of <em>S</em>_ppt globally is limited by specific technical defects or objective limitations, leading to a poor understanding of its spatial distribution and temporal variations. In this study, we examined the spatial patterns and temporal changes o.ff <em>S</em>_ppt across global terrestrial ecosystems from 2001 to 2021 using a change-based method and various satellite observations, including solar-induced fluorescence (SIF), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI). Additionally, we obtained various high-resolution global datasets and applied <em>extreme gradient boosting</em> (<em>XGBoost</em>) along with SHapley Additive Explanations (SHAP) to explain how key climatic, topographic, edaphic, and vegetation variables regulate <em>S</em>_ppt. Spatially, <em>S</em>_ppt exhibited positive values in most regions, particularly in arid areas, while lower values were found in mesic regions. Temporally, <em>S</em>_ppt shifted from a declining to an increasing trend in most regions over the past two decades, with the breakpoint occurring primarily between 2011 and 2015. This shift could be related to the fertilization effect of elevated CO₂, intensified drought caused by increased vapor pressure deficit, and atmospheric nitrogen deposition. In forest ecosystems, radiation, temperature, and soil nutrients were found to be critical in regulating <em>S</em>_ppt, whereas leaf functional traits demonstrated relatively greater importance in grasslands and shrublands. Negative regulatory relationships were shown to exist between land slope and forest age with <em>S</em>_ppt. Overall, this research contributes to a deeper understanding of the mechanisms that drive vegetation productivity in the context of changing precipitation patterns.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"370 ","pages":"Article 110598"},"PeriodicalIF":5.6,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143906557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In vitro plant spectral response reveals dust stress","authors":"Ali Darvishi Boloorani ,&nbsp;Saham Mirzaei ,&nbsp;Hossein Ali Bahrami ,&nbsp;Masoud Soleimani ,&nbsp;Najmeh Neysani Samany ,&nbsp;Ramin Papi ,&nbsp;Maryam Mahmoudi ,&nbsp;Mohsen Bakhtiari ,&nbsp;Alfredo Huete","doi":"10.1016/j.agrformet.2025.110599","DOIUrl":"10.1016/j.agrformet.2025.110599","url":null,"abstract":"<div><div>Early-stage plant stress detection is a key measure for sustainable agriculture management. Mineral dust as an abiotic stressor affects the physical, chemical, and physiological characteristics of plants, which are linked to the plant's visible and near-infrared (VNIR) reflectance. However, considering the intensity of plant exposure to dust and associated spectral feedback remain unclear. This study investigates the effects of dust particles on the spectral properties of 11 plant species over the growing season by conducting an in-vitro experiment based on VNIR spectroscopy. The capabilities of machine learning algorithms based on VNIR data, including partial least-squares regression (PLSR) and support vector machine (SVM), were also evaluated for dust stress detection. Analyses show that increases in dust concentration lead to (<em>i</em>) reduction of leaf chlorophyll and water contents; (<em>ii</em>) increase of spectral reflectance at 450–490, 640–660, 1370–1450, and 1820–1940 nm; (<em>iii</em>) decrease of spectral reflectance at 530–590, 740–1200 nm; (<em>iv</em>) decrease the slope and height of the red edge; (<em>v</em>) red absorption feature (AF) became smaller and shifted towards shorter wavelength; (<em>vi</em>) reduction of area, width, and depth of AFs at 400–740, 1350–1450, and 1800–1900 nm; and (<em>vii</em>) shift of AF position at 400–740 nm towards shorter wavelength. The results show that, PLSR estimates dust concentration with an R² ranging from 0.83 to 0.95. Additionally, the SVM successfully distinguishes between dust-exposed and non-dust-exposed samples, achieving an overall accuracy of 80–96 %. The research reveals how mineral dust affects the spectral behavior of plants, providing a basis for early-stage dust stress detection through the combination of VNIR spectroscopy and machine learning. Leveraging the research findings, transition from laboratory spectroscopy to hyperspectral remote sensing imagery enables cost-effective and extensive spatiotemporal monitoring, facilitating timely protective measures to mitigate dust-induced damage to plants.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"370 ","pages":"Article 110599"},"PeriodicalIF":5.6,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143906454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temperature and moisture both control net methane uptake in a temperate forest soil","authors":"Yuqi Liu ,&nbsp;Jesper Riis Christiansen ,&nbsp;Kai Huang ,&nbsp;Dongwei Liu ,&nbsp;Yihang Duan ,&nbsp;Gang Liu ,&nbsp;Geshere Abdisa Gurmesa ,&nbsp;Xiaoming Fang ,&nbsp;Shushi Peng ,&nbsp;Yunting Fang","doi":"10.1016/j.agrformet.2025.110574","DOIUrl":"10.1016/j.agrformet.2025.110574","url":null,"abstract":"<div><div>The role of well-aerated forest soils as sinks for atmospheric methane (CH₄) and their impact on mitigating climate warming have gained attention recently. However, there is a lack of continuous time series data on net soil CH₄ flux in these forest soils, making annual budget estimates uncertain. In this study, we investigated the spatiotemporal variations and driving factors of soil CH₄ uptake in a temperate forest ecosystem over 4 years using continuous automatic in-situ chamber measurements. Our results showed that the soil consistently acted as a CH₄ sink, averaging 5.24 kg CH₄-C ha⁻¹ yr⁻¹, with a peak uptake rate of 243.98 µg C m⁻² h⁻¹ in summer and minimum uptake rates of 0.82 µg C m⁻² h⁻¹ in winter. Soil CH₄ uptake was mainly influenced by soil temperature and moisture, with methanotroph abundance and soil organic carbon content also playing roles. A simple linear regression model indicated that soil temperature and moisture explained 36 % and 56 % of the variance in CH₄ uptake, respectively. Moreover, the Temp-WFPS model and diffusion-reaction equation model explained 86 % and 53 % of the annual CH₄ uptake variance, respectively. Through the provision of comprehensive measurements detailing daily, seasonal, and annual CH₄ uptake, along with their environmental determinants, our data aids in the advancement of more precise biogeochemical models, thereby enhancing the estimation of global CH₄ budgets.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"370 ","pages":"Article 110574"},"PeriodicalIF":5.6,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of rice yield using multi-source remote sensing data combined with crop growth model and deep learning algorithm","authors":"Jian Lu ,&nbsp;Jian Li ,&nbsp;Hongkun Fu ,&nbsp;Wenlong Zou ,&nbsp;Junrui Kang ,&nbsp;Haiwei Yu ,&nbsp;Xinglei Lin","doi":"10.1016/j.agrformet.2025.110600","DOIUrl":"10.1016/j.agrformet.2025.110600","url":null,"abstract":"<div><div>Accurate rice yield estimation is vital for agricultural planning and food security, especially in Northeast China, a key rice-producing region. This study presents an integrated framework combining multi-source remote sensing data, crop growth modeling, and deep learning techniques to enhance rice yield prediction accuracy. We utilized Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 satellite data to capture both temporal and spatial crop dynamics. High-resolution Leaf Area Index (LAI) data from Sentinel-2 were assimilated into the World Food Studies (WOFOST) crop growth model using the Ensemble Kalman Filter (EnKF), improving the model’s simulation precision. To further refine yield estimates, we developed the Bayesian-optimized Convolutional Long Short-Term Memory with Attention (BCLA) model, which integrates Residual Convolutional Neural Networks (ResNet-CNN), Long Short-Term Memory (LSTM) networks, and Multi-Head Attention mechanisms, optimized through Bayesian optimization. The proposed hybrid framework was applied to rice growing seasons from 2019 to 2021, demonstrating significant improvements in prediction accuracy compared to traditional models such as Random Forest and XGBoost. The BCLA model achieved higher R² and lower Root Mean Square Error (RMSE) values, indicating its superior ability to capture complex spatial and temporal patterns. SHapley Additive exPlanations (SHAP)-based feature importance analysis identified key factors influencing yield predictions, including LAI, Net Photosynthesis (PsnNet), and Kernel Noramlized Difference Vegetation Index (kNDVI). Regional yield maps validated against statistical data showcased the model’s robustness, although some regional discrepancies highlighted areas for further refinement. This comprehensive approach offers a scalable and accurate solution for high-resolution rice yield estimation, supporting precision agriculture and sustainable food security initiatives.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"370 ","pages":"Article 110600"},"PeriodicalIF":5.6,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143901856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparison of chamber-based methods for measuring N2O emissions from arable soils","authors":"Meng Kong ,&nbsp;Farhana Ferdous Mitu ,&nbsp;Søren O. Petersen ,&nbsp;Poul Erik Lærke ,&nbsp;Diego Abalos ,&nbsp;Peter Sørensen ,&nbsp;Andreas Brændholt ,&nbsp;Sander Bruun ,&nbsp;Jørgen Eriksen ,&nbsp;Christian Dold","doi":"10.1016/j.agrformet.2025.110591","DOIUrl":"10.1016/j.agrformet.2025.110591","url":null,"abstract":"<div><div>Static chamber-based flux measurements with gas chromatography are commonly used to estimate nitrous oxide (N₂O) emissions from arable soils. The LI-COR 7820 N₂O/H₂O (LI-7820) enables higher-frequency in situ measurements, but side-by-side comparisons with traditional methods are limited. To address this gap, we compared non-steady-state chamber methods including non-flow-through (NFT) and flow-through (FT) chamber methods under field and laboratory conditions with plant cover or bare soil. The LI-7820 was used with the LI-8200S smart chamber (FT-1: ⌀ 20 cm) and a self-built chamber (FT-2: 60 × 60 cm), and compared to differently sized NFTs (1–4: 75 × 75, 27 × 37, 60 × 60, and ⌀ 20 cm) with manual sampling with gas chromatography. Field experiments showed high RMSE for daily N₂O fluxes within 20 days after fertilizer application between FT-1 and NFTs, particularly for maize and spring barley (183 and 214 μg N₂O-N m⁻² h⁻¹), which dropped sharply after 20 days (47 and 54 μg N₂O-N m⁻² h⁻¹, respectively). FT-2 and NFT-3 for pastures had lower RMSE and MAE, both below 40 μg N₂O-N m⁻² h⁻¹. In the incubation experiment, bare soil showed smaller error values, remaining below 26 μg N₂O-N m⁻² h⁻¹. Significant differences were observed between the cumulative N₂O emissions measured with NFTs and FT-1, while differences were not significant between NFT-3 and FT-2. Several factors may explain these differences. The smaller chamber dimensions of FT-1 may influence water and nitrogen distribution and constrain the capture of spatial heterogeneity, while NFTs could be affected by prolonged deployment times and in-chamber pressure changes. Furthermore, the lack of water-vapor correction in NFTs, unlike the LI-7820, contributed to discrepancies between methods. Understanding these nuances including the impact of the chamber design, is essential for enhancing the comparability of N₂O emissions and getting closer to achieving unbiased measurements of the true flux.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"370 ","pages":"Article 110591"},"PeriodicalIF":5.6,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143898856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}