Agricultural and Forest Meteorology最新文献

{"title":"Regional patterns of parameter sensitivity in the plant hydraulics scheme of Noah-MP: Insights into plant-water interactions","authors":"Weijing Chen ,&nbsp;Jinliang Hou","doi":"10.1016/j.agrformet.2026.111018","DOIUrl":"10.1016/j.agrformet.2026.111018","url":null,"abstract":"<div><div>A plant hydraulic scheme (PHS) integrated into Noah-MP improves the simulation of hydrological processes, yet its impacts on key variables remain unclear. This study employs a global sensitivity analysis method (the Sobol’ indices) to identify the key parameters in the PHS scheme that affecting evapotranspiration (ET), soil moisture (SMC), gross primary productivity (GPP), and water flux absorbed by the plant roots (Q_root). By combining model simulations, stations measurements and satellite data, this study conducts a systematic analysis of the sensitivity of each parameter to various variables and evaluates the spatial variability of the sensitivity. Additionally, the influence of three climate-related factors—aridity index, precipitation, and vapor pressure deficit—on the regional sensitivity pattern of parameters was investigated. The results indicate that the Sobol’ indices of individual parameters show noticeable differences depending on the data source. Overall, <span><math><mrow><mi>T</mi><mi>L</mi><mi>P</mi></mrow></math></span>(leaf turgor loss water potential) and <span><math><msub><mi>K</mi><mrow><mi>s</mi><mo>,</mo><mi>s</mi><mi>a</mi><mi>t</mi></mrow></msub></math></span>(xylem saturated water conductivity) exert a significant influence on ET and GPP, while the simulation of SMC and Q_root is jointly affected by multiple parameters, with <span><math><msub><mi>r</mi><mi>i</mi></msub></math></span>(root distribution parameter) and <span><math><msub><mi>C</mi><mrow><mi>s</mi><mi>t</mi><mi>e</mi><mi>m</mi></mrow></msub></math></span>(stem volume specific water capacitance) taking the leading roles, respectively. Although the dominant parameters vary across regions, their sensitivity indices show no strong correlation with the three climate factors examined. Furthermore, the model’s simulation accuracy was validated against observation data from both stations and satellite. The evaluation indicates that ET, GPP, and Q_root are simulated with relatively high accuracy, although the performance declines in arid regions. In contrast, the simulation accuracy of SMC exhibits greater spatial variability and performs worse in areas with dense vegetation cover. The findings suggest that identifying region-specific sensitive parameters can provide valuable guidance for model parameter optimization. Targeted parameter optimization or the integration of new schemes can significantly enhance the simulation of specific variables.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"378 ","pages":"Article 111018"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145902916","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":"Evaluating ecosystem water use efficiency and recovery dynamics during flash droughts: insights from observations and model simulations","authors":"Yuefeng Hao ,&nbsp;Jiafu Mao ,&nbsp;Yaoping Wang ,&nbsp;Lianhong Gu ,&nbsp;Jeffrey Wood ,&nbsp;Paul J. Hanson ,&nbsp;Melanie A. Mayes ,&nbsp;Mingzhou Jin ,&nbsp;Peter E. Thornton ,&nbsp;Xiaoying Shi ,&nbsp;Daniel M. Ricciuto","doi":"10.1016/j.agrformet.2025.110982","DOIUrl":"10.1016/j.agrformet.2025.110982","url":null,"abstract":"<div><div>Flash droughts (FD), rapidly emerging in a warming future, disrupt ecosystems, agriculture, and water security. Ecosystem water use efficiency (WUE), the ratio of gross primary production (GPP) to actual evapotranspiration (AET), balances carbon assimilation and water loss. FD rapidly disrupts this balance, making WUE critical for assessing plant stress and recovery. This study investigates the dynamics of landscape-scale WUE, and the components of GPP and AET under FD utilizing both observed data from the Missouri Ozark AmeriFlux site (US-MOz) and version 2 of the U.S. Department of Energy’s Earth, Energy, Exascale System Model (E3SM) Land Model (ELMv2). Observations and simulations reveal GPP as dominant for WUE during earlier FD events (2005, 2007, 2012), shifting to AET in recent events (2014, 2018). This agreement indicates that the ELM can capture the shifting dynamics of GPP and AET in regulating WUE under FD conditions. However, the ELM systematically underestimates both GPP and AET and does so in a manner that does not preserve their ratio. As a result, WUE is also underestimated, suggesting that GPP is more strongly underestimated than AET. Furthermore, the ELM also underestimates the speed of GPP recovery, producing an artificially prolonged GPP recovery time following FD events. Observed environmental drivers such as vapor pressure deficit (VPD), soil moisture (SM), and predawn leaf water potential (PLWP) effectively predict WUE, but ELM primarily highlights SM, underestimating VPD’s role. This study demonstrates that relying solely on soil moisture fails to capture the rapid hydraulic recovery observed in PLWP, underscoring the necessity of integrating plant hydraulics into land surface models to improve flash drought predictability.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"378 ","pages":"Article 110982"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731138","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":"Comprehensive review of detrending methods for crop yields: Approaches, applications, and future directions","authors":"Yanbo He,&nbsp;Xianglong Chen,&nbsp;Haijun Li,&nbsp;Xuan Li,&nbsp;Shaojie Sun,&nbsp;Menxin Wu","doi":"10.1016/j.agrformet.2026.111017","DOIUrl":"10.1016/j.agrformet.2026.111017","url":null,"abstract":"<div><div>Against the backdrop of the severe challenges posed by global climate change and food insecurity, accurate yield forecasting is critically important for agricultural risk management, policymaking, and resource allocation. Crop yields result from the combined effects of meteorological conditions and advances in agricultural technology. A key scientific challenge in yield forecasting is accurately distinguishing short-term yield fluctuations caused by weather variability from long-term trends driven by technological progress. This separation is essential for producing reliable datasets that support the analysis of yield variability and predictive modeling. For decades, the detrending of crop yields has been a central focus in fields such as agricultural meteorology and crop science. This paper systematically reviews the development of detrending techniques in yield forecasting over the past six decades, from traditional linear and polynomial methods to advanced machine learning algorithms and multi-model integration approaches in recent years. It thoroughly examines the theoretical foundations, advantages and limitations, application scenarios, performance variations, and empirical outcomes of different detrending methods. The analysis reveals that the choice of method can significantly influence research outcomes, with important implications for climate change impact assessments, agricultural policymaking, crop yield forecasting, and food security planning. Furthermore, the paper highlights current research hotspots and challenges while outlining future directions and development trends in the field. This paper offers a systematic perspective on understanding the evolving trends in crop yields and proposes that future research should focus on adaptive and dynamic detrending algorithms, uncertainty quantification, integration of external variables, standardization of methods, and the use of big data resources. This comprehensive assessment provides both methodological guidance for researchers and a strategic roadmap for advancing the study of detrending techniques in agricultural yield analysis.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"378 ","pages":"Article 111017"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925478","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":"Northeast Italian viticulture affected by heat and vegetation stress. A satellite-based study from 2000 to 2024","authors":"Vincenzo Baldan,&nbsp;Eugenio Straffelini,&nbsp;Vincenzo D’Agostino,&nbsp;Paolo Tarolli","doi":"10.1016/j.agrformet.2025.110962","DOIUrl":"10.1016/j.agrformet.2025.110962","url":null,"abstract":"<div><div>The impact of extreme temperatures on viticulture in northeastern Italy is emerging as a significant risk for farmers due to the changing climate. Rising temperatures and stronger heatwaves are exacerbating the frequency of heat and the vegetation stress on the phenology of the plants. However, the influence of climate change in extreme surface temperature and the frequency of the vegetation stress over the years in northeastern Italy’s vineyards is still less explored. This study aims to analyse daytime and nighttime Land Surface Temperature (LST) and the Vegetation Health Index (VHI) from 2000 to 2024. The frequency distribution of extreme temperatures and vegetation stress was analysed using satellite data from the MODIS dataset, also considering three main vineyard classes. The study was conducted using Google Earth Engine platform, followed by a non-parametric trend analysis assessment. Results shows that nighttime LST is increasing significantly across the study area, while the daytime LST shows a significant increasing trend in flat vineyards, which are also more exposed to heat and vegetation stress. In contrast, steep and heroic vineyards are more affected by higher night temperatures. The VHI is getting worse in most of the study area, while the occurrences of the stress level increased in the 2020–2024 period. The findings could be used for structure guidelines for policy makers to design strategies to mitigate the impacts on vineyards. This work aims to stimulate further research into the effects of climate change on land surface temperature and vegetation stress in the Italian viticulture.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"378 ","pages":"Article 110962"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731693","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":"Using point dendrometers to improve forest transpiration estimation accuracy at stand scales","authors":"Ryan M. Bright ,&nbsp;Danielle Creek ,&nbsp;Holger Lange ,&nbsp;Helge Meissner ,&nbsp;Morgane Merlin ,&nbsp;Junbin Zhao","doi":"10.1016/j.agrformet.2025.110986","DOIUrl":"10.1016/j.agrformet.2025.110986","url":null,"abstract":"<div><div>Forest transpiration is often quantified by scaling up stem sap flow measured on a few trees within a stand. This procedure carries uncertainty related to the (ill)representativeness of the sampled trees for the entire stand, often comprising several thousand transpiring trees. Here, we explored the uncertainty reduction potential afforded by increasing the number of sampled trees within the stand – not by costly sap flow monitoring equipment – but by point dendrometers measuring sub-daily fluctuations in stem radii which partially correlate with xylem water movement (i.e., sap flow). Using measurements collected in a forest dominated by even-aged spruce trees over two growing seasons, we built an empirical model for estimating hourly sap flow from individual trees equipped with point dendrometers, then applied it to estimate the daily transpiration of the stand both with and without trees equipped with point dendrometers. We found that the expanded tree sample size reduced the uncertainty of the stand-level estimate by 31–37 %, suggesting that the benefit afforded by increasing the stand representativeness outweighed the cost of introducing modeling error. Given their relative simplicity and affordability, we encourage additional investigations into the use of point dendrometers for studying tree water relations and water consumption patterns of entire forested stands.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"378 ","pages":"Article 110986"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785293","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":"Critical snowpack thresholds and escalating risks for extreme decreases in vegetation productivity across Northern Hemisphere ecosystems","authors":"Hao Liu ,&nbsp;Pengfeng Xiao ,&nbsp;Xueliang Zhang ,&nbsp;Xin Miao ,&nbsp;Bo Tang ,&nbsp;Yantao Liu ,&nbsp;Siyong Chen ,&nbsp;Gareth Rees ,&nbsp;Weimin Ju","doi":"10.1016/j.agrformet.2025.110992","DOIUrl":"10.1016/j.agrformet.2025.110992","url":null,"abstract":"<div><div>Seasonal snowpack is a key driver of vegetation productivity dynamics, but it is unclear at which levels of snowpack changes cause extreme decreases in vegetation productivity (EDVP), increasing the uncertainty in assessing the terrestrial carbon cycle. Here, we investigate the impact of different levels of snowpack changes on EDVP and identify the roles of different ecological processes of snowpack changes in the Northern Hemisphere (NH). The results show that over 30 % of snowpack decrease events are followed by EDVP events in ∼10 % of NH areas (<em>p</em> &lt; 0.05), which is mainly attributed to snowpack’s moisture effect (via altering soil moisture). On average, the response of EDVP to snowpack changes increases rapidly when snow water equivalent (SWE) is –0.85 standard deviations (σ) below the mean, peaking at –1.33σ. Moreover, vegetation in warm and dry regions, especially grasslands, is more vulnerable to decreased SWE, and its resistance significantly increases with increasing precipitation. The future risk of EDVP occurrence will significantly increase in more regions owing to decreased snowpack, with ∼8 % of NH areas experiencing EDVP annually after ∼2083 under SSP5-8.5 scenario. Our findings underscore the significance of decreased snowpack in regulating EDVP and provide insights for better projecting and mitigating ecological consequences of snowpack changes.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"378 ","pages":"Article 110992"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785294","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":"Satellite derived product benchmarking and empirical model development for estimating photosynthetically active radiation at high latitudes","authors":"Sebastian Zainali ,&nbsp;Silvia Ma Lu ,&nbsp;Tomas Landelius ,&nbsp;Pietro Elia Campana","doi":"10.1016/j.agrformet.2025.111014","DOIUrl":"10.1016/j.agrformet.2025.111014","url":null,"abstract":"<div><div>Photosynthetically Active Radiation (PAR) is a critical parameter for understanding plant growth and optimising agricultural productivity. Accurate estimation and measurement of PAR are essential for various applications, including the design of agrivoltaic systems, which enable dual use of land for solar energy conversion and crop cultivation. Despite its importance, routine measurements of PAR remain scarce globally, creating a significant gap in comprehensive tracking. This study addresses this gap by comparing PAR estimates derived from satellite sources such as CERES and SARAH-3 and the mesoscale model STRÅNG with weather station measurements. In addition, a multiple linear regression model was developed and calibrated for Sweden using data from the Integrated Carbon Observation System network. Seasonal and hourly variations in the PAR to Global Horizontal Irradiance (GHI) ratio were also analysed to understand their dynamic changes over time. The findings indicate that linear models using GHI as the primary predictor for PAR demonstrated high accuracy, with normalised Mean Absolute Error below 8% at all stations, with values such as 4% at Degerö and 3.2% at Norunda. Seasonal variability in the PAR to GHI ratio was observed, particularly during winter months at higher latitudes, where the ratio fluctuated between 0.39 and 0.42 at Degerö. In contrast, the summer period showed minimal variation, with the PAR/GHI ratio remaining stable across locations. Moreover, the spatial regression model, which combined data from different stations, successfully predicted PAR at new sites such as Norunda, achieving an R² of 0.98 to 0.99. Model residuals were within the typical uncertainty of PAR sensors (±5%), confirming remaining deviations are dominated by measurement error rather than modelling uncertainty. This demonstrates the model’s applicability across Sweden, providing a robust and versatile tool for estimating PAR in areas lacking measurements. The linear model reduces the need for extensive PAR measurement campaigns.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"378 ","pages":"Article 111014"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925377","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":"Corrigendum to “Global optimization of a water-constrained two-leaf light use efficiency model through multi-biome FLUXNET observations” [Agricultural and Forest Meteorology 375 (2025) 1–18/110845]","authors":"Sha Zhang ,&nbsp;Wenchao Wang ,&nbsp;Jinguo Yuan ,&nbsp;Yun Bai","doi":"10.1016/j.agrformet.2025.110997","DOIUrl":"10.1016/j.agrformet.2025.110997","url":null,"abstract":"","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"378 ","pages":"Article 110997"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145895036","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":"Delineating hierarchical agro-ecological zones for crop production environments in Kansas","authors":"Sarah Sexton-Bowser,&nbsp;Kraig Roozeboom,&nbsp;Andres Patrignani","doi":"10.1016/j.agrformet.2025.110999","DOIUrl":"10.1016/j.agrformet.2025.110999","url":null,"abstract":"<div><div>Agro-ecological zones (AZs), defined as areas with relatively homogeneous climate and soil conditions, are important for agricultural management and planning, yet previous studies have often relied on single-step clustering, excluded edaphic attributes, or lacked validation with independent datasets. The objectives of this study were to: 1) delineate hierarchical AZs for Kansas using climate and edaphic variables, and 2) validate AZs using external datasets of environmental causes of crop yield loss and land cover. AZs were delineated using the <em>k</em>-means clustering with macro-AZs derived from long-term climatic features and nested micro-AZs based on soil physical attributes. Climate features included annual precipitation, annual reference evapotranspiration, and mean annual temperature. Soil physical attributes included plant available water capacity, soil organic matter, and effective soil depth. The validation consisted of a multinomial regression model with datasets of environmental causes of crop yield loss and land cover, which reflect the influence of climate and soils on crop performance. The analysis resulted in three macro-AZs partitioning the state into northwest, southwest, and east regions, each with two nested micro-AZs based on soil attributes. The multinomial model resulted in a validation accuracy of 88% for the macro-AZs and 67% for nested micro-AZs. Our study provides a scalable framework for hierarchical AZs that capture spatial variability in climate and soil conditions and can support regional agricultural planning, guide the design of area crop performance trials, facilitate scaling of point-level crop model simulations to broader regions, and inform the placement of future environmental monitoring stations.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"378 ","pages":"Article 110999"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883951","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":"Synergistic importance of memory and spatial neighbourhood effects in modelling net ecosystem productivity","authors":"Jian Liu ,&nbsp;Tao Zhou ,&nbsp;Jingyu Zeng ,&nbsp;Jingzhou Zhang ,&nbsp;Xuemei Wu ,&nbsp;Yajie Zhang ,&nbsp;Qi Zhang ,&nbsp;Yancheng Qu ,&nbsp;Peixia Liu ,&nbsp;Wenjuan Zhang ,&nbsp;E Tan ,&nbsp;Ying Yu ,&nbsp;Li Cao","doi":"10.1016/j.agrformet.2025.110985","DOIUrl":"10.1016/j.agrformet.2025.110985","url":null,"abstract":"<div><div>Net ecosystem productivity (NEP) is a key indicator of terrestrial carbon balance, and elucidating its spatiotemporal patterns is essential for understanding global carbon cycle processes and response mechanisms under climate change. However, substantial uncertainty persists owing to lag, legacy, and spatial neighbourhood effects, necessitating the joint consideration of memory and neighbourhood information for accurate NEP simulation. We developed a convolutional long short-term memory (ConvLSTM) deep-learning model using global flux observations, multisource remote sensing, and environmental driver data to capture spatiotemporal dependencies in simulating global terrestrial NEP and quantitatively compared it with models that consider only memory (LSTM), only neighbourhoods (CNN), or neither (ANN) to assess the synergistic importance of memory and neighbourhood effects in NEP simulations. The results demonstrated that (1) ConvLSTM performed best (mean site-level validation R² = 0.72; RMSE = 0.86 g C m^-2 d^-1), with closer agreement to observations in long-term trends and interannual variation. LSTM (R² = 0.66; RMSE = 0.94 g C m^-2 d^-1) and CNN (R² = 0.67; RMSE = 0.93 g C m^-2 d^-1) results were intermediate, and the ANN was the worst (R² = 0.56; RMSE = 1.06 g C m^-2 d^-1). (2) The results from ConvLSTM indicate that global terrestrial NEP increased from 2001 to 2023 (0.052 Pg C yr^-2), with marked regional differences: parts of the Amazon and Congo showed declining sink capacity, whereas eastern China and India strengthened. Ignoring both memory and neighbourhood effects overestimated sink strength and growth in highly productive regions, and memory effects dominated neighbourhood effects in shaping NEP spatial patterns. (3) Jointly modelling memory and neighbourhood information also improved the detection of drought legacy effects and NEP responses to ENSO events.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"378 ","pages":"Article 110985"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731181","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}