Agricultural and Forest Meteorology最新文献

{"title":"Better practices for inferring ecosystem water use strategy from eddy covariance data","authors":"Brandon P. Sloan ,&nbsp;Xue Feng","doi":"10.1016/j.agrformet.2025.110737","DOIUrl":"10.1016/j.agrformet.2025.110737","url":null,"abstract":"<div><div>Eddy covariance data are critical for inferring ecosystem water use strategies. Yet, such inferences are sensitive to a range of assumptions applied across studies, hindering our understanding of water use strategies within and across eddy covariance sites. A recent analysis across 151 FLUXNET2015 and AmeriFlux-FLUXNET datasets found that poor model performance was the key driver of non-robust inferences of ecosystem water use strategies. Here, we leverage this previous analysis to (i) identify the specific assumptions that improve inference model performance across most sites, (ii) explain the mechanisms behind the performance improvements, and (iii) check whether better performance improves water use inference. We find that the common practice of fitting a model to canopy conductance (<span><math><msub><mrow><mi>G</mi></mrow><mrow><mi>c</mi></mrow></msub></math></span>) derived from the evapotranspiration (ET) observations, rather than to observed ET itself, artificially amplifies data errors and degrades the model performance. Next, accounting for vegetation dynamics by applying a growing season filter or incorporating satellite LAI data improves performance, but the former practice may remove soil water stress periods. Lastly, using the leaf-to-air vapor pressure deficit (<span><math><mrow><mi>V</mi><mi>P</mi><msub><mrow><mi>D</mi></mrow><mrow><mi>l</mi></mrow></msub></mrow></math></span>) derived from ET observations as a model input may artificially inflate performance. Based on these results, we recommend selecting observed ET (rather than derived <span><math><msub><mrow><mi>G</mi></mrow><mrow><mi>c</mi></mrow></msub></math></span>) as the response variable, carefully accounting for vegetation dynamics, and avoiding derived <span><math><mrow><mi>V</mi><mi>P</mi><msub><mrow><mi>D</mi></mrow><mrow><mi>l</mi></mrow></msub></mrow></math></span> as a model input; these best practices improve model performance by c. 20% and robustness by c. 80% across all eddy covariance sites. Nevertheless, the performance improvements do not always correspond to more robust inference of water use strategies, as model parameter selection and surface energy budget closure corrections still strongly influence the ecosystem water use parameter estimation in a site-specific manner.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"373 ","pages":"Article 110737"},"PeriodicalIF":5.7,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829096","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 new digital cover photography dataset and processing tool for SMAPVEX19-22: How siting and sky condition impact plant area index retrievals in continuous measurement set-ups","authors":"Simon Kraatz ,&nbsp;Michael H. Cosh ,&nbsp;V. Kelly ,&nbsp;Laura Bourgeau-Chavez ,&nbsp;Jisung Geba Chang ,&nbsp;Chris Cook ,&nbsp;Victoria A. Walker ,&nbsp;Paul R. Siqueira ,&nbsp;Andreas Colliander","doi":"10.1016/j.agrformet.2025.110767","DOIUrl":"10.1016/j.agrformet.2025.110767","url":null,"abstract":"<div><div>Satellite remote sensing is widely used for Leaf Area Index (LAI) retrievals, but calibration and validation efforts require ground Plant Area Index (PAI) values that may be converted to LAI. Digital Cover Photography (DCP) presents an affordable means for covering large regions (∼2 × 33² km²) and multiple years. The Soil Moisture Active Passive Validation Experiment conducted from 2019–2022 (SMAPVEX19–22) utilized DCP data to study vegetation impacts on soil moisture retrievals in forests. This work reports on the DCP tool “EzPAI”, its outputs for SMAPVEX19–22, and clear sky PAI bias identification and its correction. EzPAI features sky condition tracking, multi-tier data screening, data quality flagging, and two-corner thresholding. We found that PAI is overestimated in clear conditions, and our bias correction approach reduced this by 0.2 on average. Massachusetts (‘MA’) and New York (‘MB’) networks attained comparable results for cloudiness (∼67 %) and poor data quality (21 %). Benchmark comparisons to other approaches (DCP tools, Sentinel-2 LAI, LAI-2200c) showed good agreement and performance. EzPAI showed similar results for in situ and Sentinel-2 LAI comparisons, achieving R∼0.9, and some bias (MD= &lt;0.53). For comparisons to the coveR DCP tool the correlation was 0.73 and bias 0.26. For summer PAI totals, CoveR, coverPy, EzPAI and LAI-220c obtained nearly identical results: 4.08±0.35 4.08±0.33, 4.12±0.33 and 4.16±0.77. Differences may be explained in part due to image quality issues (noted at needleleaf canopies), the LAI-2200c data being noisy (σ_spring=0.43, σ_summer=0.72), the different measurement modalities used (satellite, handheld hemispherical, DCP), and our assumption of a constant extinction coefficient (<em>k</em> = 0.65) across 144 site-years. PAI values ranged from 2.73 to 5.16 (3.92 average) and 2.44 to 4.96 (3.80 average) for MA and MB, respectively. Processing time on a Dell Precision Laptop 7560 was 0.87 per image, which was about 3x speedier than coveR.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"373 ","pages":"Article 110767"},"PeriodicalIF":5.7,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829704","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":"NASA’s modeled soil moisture data as an index for forage crop insurance and disaster protection programs: The case of Oklahoma","authors":"Ephraim D. Muyombo ,&nbsp;B. Wade Brorsen ,&nbsp;Erik S. Krueger ,&nbsp;Tyson E. Ochsner ,&nbsp;Andrew J. Van Leuven","doi":"10.1016/j.agrformet.2025.110772","DOIUrl":"10.1016/j.agrformet.2025.110772","url":null,"abstract":"<div><div>Due to the lack of measured nationwide forage yield data, U.S. crop insurance and disaster programs for forage producers base payments on an index intended to correlate with forage yields. This study explores the feasibility of using National Aeronautics and Space Administration (NASA) soil moisture data to create an index for drought insurance and disaster programs. Hay yields in Oklahoma were used as the measure of forage yields due to the availability of data. We evaluated the relationship between those yields and soil moisture data from NASA’s Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) and the Oklahoma Mesonet. We also consider forage yield relationships with the United States Drought Monitor (USDM), rainfall, in-situ soil moisture from the Oklahoma Mesonet, and NASA’s North American Land Data Assimilation System (NLDAS). The two main objectives are to (1) to determine the similarities and differences between FLDAS modeled soil moisture measurements and the in-situ soil moisture measurements obtained from the Oklahoma Mesonet, and (2) to quantify the accuracy of soil moisture measures as well as rainfall and the USDM in predicting county hay yields.</div><div>High correlations between FLDAS and Oklahoma Mesonet volumetric water content (VWC) were observed in central and western Oklahoma. Correlations were lower in eastern Oklahoma, urban areas, and irrigated regions.</div><div>Among the datasets considered, FLDAS VWC was consistently the best predictor of Oklahoma hay yields when using June and July drought measures. Linear regression models reveal that FLDAS Fraction of Available Water (FAW) (R² = 0.40), Mesonet FAW (R² = 0.40), 5-cm FLDAS VWC (R² = 0.43), 25-cm FLDAS VWC (R² = 0.43), NLDAS (R² = 0.39), USDM (R² = 0.28), and rainfall (R² = 0.30) were all significant predictors of hay yield anomalies. The ranking of the predictors remained the same when using quadratic or segmented regression models. FLDAS VWC is available in the continental U.S. at a 12 km resolution, making it a feasible alternative indicator for drought relief programs.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"373 ","pages":"Article 110772"},"PeriodicalIF":5.7,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144819907","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":"ENSO impacts on maize production: a case study in Argentina","authors":"Daian FRANCIA LAURENZO ,&nbsp;Adrián CORRENDO ,&nbsp;Carlos Manuel HERNANDEZ ,&nbsp;Ignacio CIAMPITTI ,&nbsp;Octavio CAVIGLIA","doi":"10.1016/j.agrformet.2025.110773","DOIUrl":"10.1016/j.agrformet.2025.110773","url":null,"abstract":"<div><div>El Niño Southern Oscillation (ENSO) significantly influences crop production by affecting crop yield, failure, or land allocation. However, current studies on ENSO impacts on field crops lack the finer resolution needed to implement effective mitigating or boosting strategies. This study presents a comprehensive methodology for conducting finer resolutions assessments of ENSO impacts on maize, exemplified by a case study in the Argentinean Pampas Region. Maize yield, sown, and harvested area (1984–2023) for 122 departments were analyzed using a Generalized Additive Model (GAM) coupled with bootstrapping (<em>n</em> = 1000) resampling to obtain confidence intervals. Two GAMs were developed, analyzing time trends, and time trends and ENSO fixed effects together. ENSO impacts on maize production were computed from yield differences and analyzed probabilistically. ENSO showed distinctive positive effects on yield in El Niño and negative in La Niña, with effects on the size of lost area dependent on the department considered, and with only small to negligible effects on sown area in the year following a given ENSO event. 81% of the departments were included in an ENSO-yield-responsive cluster, comprising key production areas. Impacts on maize regional production could result in deviations of +6 million tons (Mt) for El Niño and -5.5 Mt for La Niña, with the responsive cluster accounting for the majority of its effects. This study delineated a framework to assess the effects of ENSO on a crop relevant to food security like maize, providing the tools to conduct future studies in other areas and crops, placing the focus on the finer-resolution scale of analysis and on key variables that determine crop production.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"373 ","pages":"Article 110773"},"PeriodicalIF":5.7,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144819354","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":"Rivers increase drought resistance and resilience of forests in the Northern Hemisphere","authors":"Juan Chen, Xue Xie, Zhiyong Liu","doi":"10.1016/j.agrformet.2025.110769","DOIUrl":"https://doi.org/10.1016/j.agrformet.2025.110769","url":null,"abstract":"Under climate change, increasingly frequent and severe droughts pose a significant threat to forests. Rivers play a dual role, as they not only influence local hydrological cycles but also serve as essential water sources to support forest growth. However, it remains unclear whether there is a distinct variation in drought response between forests growing in areas with rivers and those without. In this study, we investigated the influence of river density on drought resistance and resilience in forests across the Northern Hemisphere. We found that drought resistance and resilience of forests showed a significant increase with the increasing river density. In addition, the river-induced enhancement in drought resistance and resilience was significantly greater in dry regions than in humid regions. We further observed a notable increase in evapotranspiration, precipitation, soil moisture with the increase in river density. These suggest that dense river networks can significantly increase drought resistance and resilience by influencing local hydrological cycles. The observed rivers-induced shifts in drought resistance and resilience are crucial for developing effective ecosystem management strategies in the face of increasing drought frequency and intensity under climate change. Our results highlight the vital role of rivers in sustaining the stability of forest ecosystems in the face of frequent drought events under climate change.","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"7 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144805642","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":"Continuity in phenological monitoring: Assessing the performance of an updated PhenoCam","authors":"Mostafa Javadian, Francisco Salgado-Castillo, Koen Hufkens, Andrew D. Richardson","doi":"10.1016/j.agrformet.2025.110774","DOIUrl":"https://doi.org/10.1016/j.agrformet.2025.110774","url":null,"abstract":"Vegetation phenology plays a crucial role in land-atmosphere interactions and ecosystem productivity, necessitating high-quality, long-term datasets. The PhenoCam Network addresses this need by using digital cameras to capture canopy greenness (GCC, the green chromatic coordinate). Since 2008, the StarDot NetCam SC has been the network's backbone, but its discontinuation, particularly exacerbated by supply chain problems and delays during the COVID-19 pandemic, requires the identification of a successor to ensure continuity. This study evaluates the StarDot NetCam Live 2 camera's performance against the SC model. We visually compared imagery from different seasons, evaluated color accuracy using a ColorChecker, and assessed the similarity of seasonal and diurnal GCC patterns. Results show that the Live 2 provides slightly improved GCC accuracy relative to the ColorChecker. Both cameras effectively capture seasonal changes in canopy greenness for three different vegetation types. High R² values (0.87–0.95) between the cameras 3-day GCC, confirm strong agreement in seasonal GCC time series and phenological transition dates, with an average difference of 4.1 ± 1.6 days. Diurnal GCC patterns also showed consistent agreement, strongest on sunny days (R²=0.71). The results of this study support the integration of the Live 2 camera into the PhenoCam Network, thereby facilitating the continuation of long-term phenological monitoring efforts.","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"15 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144802841","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":"Knowledge-guided machine learning captures key mechanistic pathways for better predicting spatio-temporal patterns of growing season N2O emissions in the U.S. Midwest","authors":"Lexuan Ye, Licheng Liu, Yufeng Yang, Ziyi Li, Wang Zhou, Bin Peng, Shaoming Xu, Vipin Kumar, Wendy H. Yang, Jinyun Tang, Zhenong Jin, Kaiyu Guan","doi":"10.1016/j.agrformet.2025.110750","DOIUrl":"https://doi.org/10.1016/j.agrformet.2025.110750","url":null,"abstract":"Accurately predicting agricultural N₂O emission hot moments has long been a key focus of N₂O models, which is challenging given the complex mechanisms involved and the high spatio-temporal heterogeneity of controlling factors. In this study, we improve a knowledge-guided machine learning model for agricultural N₂O flux prediction (KGML-ag-N₂O) by (i) incorporating a physical module to explicitly represent fertilization, (ii) pre-training KGML-ag-N₂O with synthetic data from a process-based (PB) model <em>ecosys</em> under diverse fertilization strategies and environmental conditions, and (iii) fine-tuning KGML-ag-N₂O with field observations of daily N₂O flux and key controlling factors from 29 sites across the U.S. Midwest. We then assess whether it can outperform the PB model and the pure machine learning model over different spatio-temporal scales. Through integrating knowledge from both the PB model and field observations, KGML-ag-N₂O shows the best performance in capturing site-level daily N₂O emission hot moments (<em>r</em> = 0.63, RMSE = 3.765 mgN m^-2 d^-1), mainly due to the strengthened triggering effect of soil water content increase on N₂O emissions in KGML-ag-N₂O. The improved causality representations between key controlling factors and N₂O emissions further lead to KGML-ag-N₂O outperformance in capturing N₂O emission patterns over larger spatial (e.g., regional) and temporal (e.g., inter-monthly) scales than both conventional approaches. By validating and interpreting the improvements in KGML-ag-N₂O performance, our study illustrates its potential to quantify agricultural N₂O emissions in the U.S. Midwest, and provides important insights into identifying and reducing PB model structure uncertainties using KGML techniques.","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"33 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144802947","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":"Mediterranean pine forests: Comparison of fluxes and tree rings of Pinus pinaster Aiton and Pinus pinea L","authors":"Nicola Arriga, Matteo Campioli, Mara Bernardi, Andrea Cerasa, Josep Peñuelas, Michele Brunetti, Claudia Cocozza, Alessandro Dell’Acqua, Ernest N. Koffi, Ignacio Goded, Giovanni Manca, Marco Matteucci, Michela Nocetti, Andrea Scartazza, Alessio Giovannelli","doi":"10.1016/j.agrformet.2025.110761","DOIUrl":"https://doi.org/10.1016/j.agrformet.2025.110761","url":null,"abstract":"Flux-monitoring networks nowadays integrate several stations, with more than 20 years of records. However, many questions are still open, and this work focused on the following two: (i) what is known about carbon and energy exchanges and efficiencies of evergreen conifer forests in the hot and dry Mediterranean climate zone, and (ii) what can be learned by combining information derived from flux and tree ring measurements in this context? These issues are addressed in this study using micrometeorological and dendrochronological measurements from a Mediterranean pine forest, San Rossore, in central Italy. The forest’s flux time series exceeds 20 years, merging two consecutive (not overlapping) time series of flux measurements above two evergreen conifer species: <em>Pinus pinaster</em> Aiton, 1789 (maritime pine) and <em>Pinus pinea</em> L., 1753 (stone pine). Despite both conifer species having shown high rates of photosynthesis, substantial differences have been found between them in terms of annual productivity, hydrological balance and efficiencies of carbon and water use. The more drought-tolerant <em>Pinus pinea</em> L. has been less productive than the drought-avoiding <em>Pinus pinaster</em> Aiton: interannual net ecosystem productivity averages and standard errors have been estimated to be 441 ± 46 gC m^–2 y^–1 and 224 ± 35 gC m^–2 y^–1 for <em>Pinus pinaster</em> Aiton and <em>Pinus pinea</em> L., respectively, and the latter has been found to consume more water as a consequence of the sustained transpiration process during prolonged summer droughts. Annual carbon accumulation, based on eddy covariance flux measurements, and annual wood increments, based on tree ring widths, for the two species are in overall alignment in terms of absolute values; however, a weak correlation suggests the need for more detailed analyses to gain more information about the carbon dynamics of the ecosystem, because the two approaches do not quantify exactly the same processes.","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"16 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144797246","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":"Variability of evapotranspiration fluxes affected by dry and wet year transitions in beech, pine and mixed stands in the lowland of northeast Germany","authors":"Yeye Liu, Marco Natkhin, Doris Duethmann","doi":"10.1016/j.agrformet.2025.110771","DOIUrl":"https://doi.org/10.1016/j.agrformet.2025.110771","url":null,"abstract":"Increasing climate variability, especially prolonged droughts, shapes forest water consumption and constrains vegetation growth. However, the effects of drought-induced changes on evapotranspiration (ET) fluxes vary due to species-specific differences and drought characteristics. Here, we analyzed the seasonal variations in ET in pine, beech, and mixed forest stands in northeast Germany (2012–2021) and explored the ability of a process-based ecohydrological model (EcH₂O) in reproducing the water balance components observed at three forested lysimeters. To better understand how individual climate variables control ET fluxes, we performed simulation experiments with detrended climate inputs. Multi-variable calibration showed that the model reproduced well in-situ soil moisture, seepage, and interception (EI) in the three stands. Precipitation (P) was the main driver of ET anomalies, with above-average ET in wet years and below-average ET in dry years. However, only small reductions in ET were observed during the dry year 2018. This could be attributed to high P in the previous year, i.e., P legacy effects, which led to only small reductions or even positive anomalies in ET. The beech stand, with a seasonal leaf cycle, had lower ET and interception losses compared to the pine and mixed stands, which maintain year-round foliage. This resulted in greater percolation to deeper soil layers in beech forests. These findings suggest that broadleaf species such as beech by allowing greater water transfer to groundwater, offer a distinct hydrological advantage in terms of promoting deep percolation. Our results therefore provide a process-based rationale for the strategic selection of broadleaf species in forest management to enhance groundwater recharge and promote sustainable water management. Additionally, model testing at such data-rich sites will be valuable for improving the process-consistency and reliability of other hydrological models, particularly in studies aimed at investigating the effects of different vegetation cover.","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"15 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144797184","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":"Weekly carbon and oxygen isotope dynamics in black spruce: A case study in the northeastern boreal forest of Quebec, Canada","authors":"Sepideh Namvar, Étienne Boucher, Annie Deslauriers, Fabio Gennaretti, Hubert Morin","doi":"10.1016/j.agrformet.2025.110768","DOIUrl":"https://doi.org/10.1016/j.agrformet.2025.110768","url":null,"abstract":"The stable isotopic composition of carbon (δ¹³C) and oxygen (δ¹⁸O) in tree rings is widely used to explore tree eco-physiological dynamics across various time scales. However, interpreting these isotopic signals is challenging due to multiple interacting factors, including gas exchange at the leaf level, stored carbohydrate reserves, and xylem water, whose timing and interactions during the growing season remain poorly understood. In this study, weekly δ¹³C and δ¹⁸O signals were tracked within the cambial region and forming xylem of black spruce (<em>Picea mariana</em> (Mill.) BSP.) in boreal forests of Quebec, Canada. The study covered three consecutive growing seasons (2019–2021) at two forest sites with differing temperature and soil water content. Weekly isotopic profiles were developed for the cambial region (δ¹³C_cam and δ¹⁸O_cam) and developing xylem cellulose (δ¹³C_xc and δ¹⁸O_xc). Strong positive correlations were observed between δ¹³C_cam and δ¹⁸O_cam, with an increasing trend along the growing season. Conversely, negative relationships were observed between δ¹³C_xc and δ¹⁸O_xc, characterized by an increasing trend in δ¹³C_xc and a decreasing trend in δ¹⁸O_xc. The results illustrated that stomatal conductance is the dominant physiological factor controlling seasonal fractionation of δ¹³C_cam and δ¹⁸O_cam. Increasing proportional exchanges between xylem water and sugars at the sites of cellulose synthesis (i.e., <em>P</em>_ex effect) are thought to be strong enough to completely blur the observed trends in δ¹⁸O_cam during the growing season. This suggests that δ¹⁸O_xc signals differ from those originating in the earlier cambium sink. These findings highlight the need to carefully consider the processes influencing isotopic signals to avoid misinterpretations in dendroclimatological studies.","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"27 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144802867","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}