Agricultural and Forest Meteorology最新文献

{"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}

{"title":"Meteorological drivers of air pollution impacts from straw burning in Henan Province, China","authors":"Hui Liu ,&nbsp;Mingyu Wang ,&nbsp;Pengle Cheng ,&nbsp;Xiaodong Liu ,&nbsp;Ying Huang","doi":"10.1016/j.agrformet.2025.110932","DOIUrl":"10.1016/j.agrformet.2025.110932","url":null,"abstract":"<div><div>The increase in crop production has resulted in a significant increase in annual yield of crop straw. To analyze the impact of straw burning on air quality, this study integrates data on the burning of straw obtained from remote sensing satellites with meteorological data provided by meteorological satellites. Statistical analysis has been applied to analyze these data to identify patterns of the influence of straw burning pollutants on air quality. Experimental burnings were conducted to investigate the correlation between the volume of straw burning and the levels of particulate matter (PM). The findings demonstrate that under identical meteorological conditions, the impact of straw burning during the night is less pronounced than that of daytime burning, with pollutants dissipating more rapidly. Furthermore, the subsequent rainfall is found to mitigate the impact of straw burning on air quality. Consequently, the optimal strategy for minimizing the impact on air quality and accelerating the diffusion of pollutants is to conduct straw burning before predicted rainy weather and during nighttime on windy or sunny days.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"378 ","pages":"Article 110932"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145625238","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":"The influence of estimation window configuration on machine learning-based soybean yield estimation across black soil regions","authors":"Shuyuan Huang ,&nbsp;Yujie Liu ,&nbsp;Jiahao Chen ,&nbsp;Ermei Zhang ,&nbsp;Tao Pan","doi":"10.1016/j.agrformet.2025.110957","DOIUrl":"10.1016/j.agrformet.2025.110957","url":null,"abstract":"<div><div>The configuration of phenology-based time windows, which determines how environmental variables are temporally aggregated, plays a pivotal role in crop yield estimation. However, the quantitative effects of different window configurations on model performance and uncertainty require further investigation. This study systematically assesses the effects of four window-configuration strategies (fixed, observation, rule-based, and sliding) on soybean yield estimation across the black soil regions of China and the USA. Multi-source remote sensing and meteorological datasets were integrated with three machine learning algorithms: RF, XGBoost, and LSTM. Results show that dynamic windows (observation, rule-based, and sliding) can better align environmental fluctuations with crop phenological stages, resulting in modest yet consistent improvements in accuracy compared to fixed windows. The LSTM–sliding window combination achieves the largest RMSE decrease (48.4-56.6%), followed by LSTM–rule-based windows (32.9-38.2%) and LSTM–observation windows (11.8-22.0%). A trade-off is identified: while sliding windows (SWs) provide the highest accuracy, they also show greater interannual variability, higher computational cost, and lower interpretability. In comparison, rule-based windows (RBWs) exhibit a moderate decline in accuracy but demonstrate lower inter-group variability, with ΔR² approximately one-third that of SW, offering more stable predictions. RBWs also exhibit better generalizability than observation windows, which rely on limited ground phenology data. Uncertainty decomposition reveals that, although the primary source of variation originates from input features and model structures, the configuration of the estimation window contributes approximately 11.9-13.7% to the total variation, indicating a secondary yet consistent factor influencing estimation stability. This study offers an analytical framework for quantifying the interactions among window design, algorithm type, and feature selection, thereby providing practical insights for future data-driven crop yield modeling.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"378 ","pages":"Article 110957"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685558","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":"Elevation-dependent responses of xylem lumen traits to competition–climate interactions in temperate forests","authors":"Xiaojia Yuan ,&nbsp;Chen Xu ,&nbsp;Jingsong Zhang ,&nbsp;Xue Wang ,&nbsp;Jinglei Liao ,&nbsp;Mingchao Du ,&nbsp;Xianliang Zhang","doi":"10.1016/j.agrformet.2025.111011","DOIUrl":"10.1016/j.agrformet.2025.111011","url":null,"abstract":"<div><div>Lumen traits (area and number) are critical for forest carbon sequestration and hydraulic function, yet their responses to climate and intraspecific competition (CI) along elevational gradients remain unclear. We analyzed lumen and stand inventory data from 39 <em>Larix principis-rupprechtii</em> trees across six plots in North China to evaluate the combined effects of climate and CI on earlywood and latewood formation.</div><div>At high elevations, earlywood lumen area represented 50–51 % of total lumen area and nearly 85 % of annual ring area. These earlywood lumens showed strong negative correlations with minimum temperature (Tmin), precipitation (PRE), and the Palmer Drought Severity Index (PDSI), indicating that their formation is constrained by both temperature and drought stress. At low elevations, the proportion of earlywood lumens declined to 48–49 %, and their climatic sensitivities weakened, with positive effects of maximum temperature (Tmax) primarily expressed in latewood traits. Increasing competition at high elevations reduced earlywood area in response to Tmax, while at low elevations it strengthened correlations of PDSI, PRE, Tmax, and mean temperature (Tmean) with latewood traits, and enhanced Tmin effects on earlywood structure. Extreme lumen traits exhibited clear climate–competition interactions: at high elevations, Tmin and Tmean promoted large earlywood lumens under stronger competition; at low elevations, competition amplified positive responses of small earlywood lumens to PDSI, PRE, Tmean, and Tmax, and increased Tmin sensitivity of large latewood lumens. Overall, earlywood formation is temperature-limited at high elevations, whereas latewood growth at low elevations is jointly regulated by temperature, drought, and competition. These findings clarify the regulatory role of climate–competition interactions in shaping xylem traits, thereby improving our understanding of forest adaptation under climate change.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"378 ","pages":"Article 111011"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925477","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 tale of two towers: comparing NEON and AmeriFlux data streams at Bartlett Experimental Forest","authors":"Yujie Liu ,&nbsp;Paul Stoy ,&nbsp;Housen Chu ,&nbsp;Dave Y. Hollinger ,&nbsp;Scott V. Ollinger ,&nbsp;Andrew P. Ouimette ,&nbsp;David J. Durden ,&nbsp;Cove Sturtevant ,&nbsp;Ben Lucas ,&nbsp;Andrew D. Richardson","doi":"10.1016/j.agrformet.2025.110939","DOIUrl":"10.1016/j.agrformet.2025.110939","url":null,"abstract":"<div><div>Long-term ecological data are essential for detecting impacts of climate change and other global change factors, and for making informed predictions about future change. However, long-term measurements are rarely replicated at the site level, which raises questions about their representativeness. We used a multiscale approach to evaluate the agreement of parallel observations from AmeriFlux and NEON (National Ecological Observatory Network) towers at Bartlett Experimental Forest, New Hampshire, USA. The two towers are separated by a horizontal distance of 93 m. We focused our analysis on standard meteorological variables; fluxes of CO₂, sensible heat, and latent heat measured by eddy covariance; and phenology derived from PhenoCam imagery. Results suggest excellent agreement between AmeriFlux and NEON in meteorology and phenology, and good agreement in fluxes at the half-hourly scale. However, large disagreements in CO₂ and latent heat fluxes occurred at the annual scale, with implications especially for the forest carbon balance. The AmeriFlux tower measurements indicate a site that is close to carbon-neutral (-8 ± 65 g C m^-2 y^-1, mean ± 1 SD), whereas the NEON tower measurements indicate a forest that is a carbon sink (-137 ± 10 g C m^-2 y^-1). Causes of this disagreement may include measurement height (26 m vs. 35 m), which resulted in different flux footprints being measured by the two towers, and differences in the flux measurement systems. Our results suggest the need for caution when attempting to merge long-term flux data from two different measurement platforms, and when using measurements from any one measurement platform to inform decision-making on issues related to carbon accounting or natural climate solutions.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"378 ","pages":"Article 110939"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884074","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":"Projecting shifts in drought-induced thresholds for wheat yield loss under climate change in southeastern Australia","authors":"Keyu Xiang ,&nbsp;Bin Wang ,&nbsp;De Li Liu ,&nbsp;Chao Chen ,&nbsp;Fei Ji ,&nbsp;Fangzheng Chen ,&nbsp;Shijin Yao ,&nbsp;Siyi Li ,&nbsp;Alfredo Huete ,&nbsp;Yi Li ,&nbsp;Qiang Yu","doi":"10.1016/j.agrformet.2025.111003","DOIUrl":"10.1016/j.agrformet.2025.111003","url":null,"abstract":"<div><div>Drought is a principal determinant of yield variability in rain-fed wheat systems, with climate change expected to exacerbate both the frequency and severity of water deficits. However, knowledge gaps remain in quantifying (i) yield loss probability across different drought indices and (ii) the dynamic thresholds at which drought induces yield losses under divergent climate scenarios. A systematic quantification of these relationships is essential to improve the empirical foundation for risk assessment and adaptive strategies in water-limited agricultural systems. This study analyses future wheat yield loss probability and dynamic drought thresholds in southeastern Australia using the APSIM model and copula functions, comparing a soil water index (SPAWI) against a precipitation index (SPI). We found a higher future wheat yield loss probability for SPAWI-based drought (5–20% greater than for SPI), underscoring the limitation of rainfall-only indices by neglecting soil buffer effects during drought. Drought thresholds were higher for SPAWI than SPI, due to soil moisture buffering, and lower in wetter regions. Including CO₂ fertilization increases yields and partially offsets drought impacts, lowering both loss probabilities and thresholds, while climate-model choice remains the dominant source of projected threshold shifts. Our analysis demonstrates that drought index selection influences yield-loss risk projections, and the quantified shifts in drought yield thresholds under climate change reveal key soil moisture buffering effects and CO₂ mitigation potential. These findings provide evidence-based drought thresholds to guide adaptive management in dryland wheat cropping systems under climate change.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"378 ","pages":"Article 111003"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884075","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":"Carbon dioxide dynamics across three stages of tropical peatland conversion to oil palm plantations","authors":"Frankie Kiew ,&nbsp;Ryuichi Hirata ,&nbsp;Takashi Hirano ,&nbsp;Guan Xhuan Wong ,&nbsp;Joseph Wenceslaus Waili ,&nbsp;Kim San Lo ,&nbsp;Kaido Soosaar ,&nbsp;Kuno Kasak ,&nbsp;Ülo Mander ,&nbsp;Lulie Melling","doi":"10.1016/j.agrformet.2025.110956","DOIUrl":"10.1016/j.agrformet.2025.110956","url":null,"abstract":"<div><div>This study represents the first long-term investigation spanning from a tropical peat swamp forest (PSF) to its conversion into an oil palm plantation (OPP), offering valuable data for assessing carbon dioxide (CO₂) dynamics across different conversion stages. The conversion of tropical peat swamp forests to oil palm plantations has significant implications for CO₂ dynamics. However, ecosystem-scale studies investigating CO₂ dynamics across different stages of land conversion are lacking. This study used the eddy covariance (EC) technique to measure the net ecosystem exchange (NEE) of CO₂ above a tropical peat swamp forest in Sarawak, Malaysia, from 2011 until it was cleared in 2017 and ultimately converted into an OPP in 2018. Our study found that the removal of forest biomass during land preparation led to a substantial increase in annual NEE from 25 ± 179 (2011 to 2016) to 2732 ± 655 g C m⁻² year⁻¹ (2017 to 2019). This increase was attributed to an 83 % reduction in gross primary productivity (GPP) and a 14 % reduction in ecosystem respiration (<em>R</em>_eco). The near-ground environmental conditions also significantly changed across the conversion stages, inducing drier conditions compared to the forest. These changes were found to affect the controlling factors of nighttime NEE during conversion, resulting in a negative relationship with both air temperature and vapor pressure deficit above canopy, in contrast to the typical relationship with groundwater level observed before conversion. The conversion is also found to cause significant reduction in overall ecosystem photosynthetic activity as evidenced by the reduction in maximum gross photosynthetic rate (<em>P</em>_max), photosynthetic photon flux density (PPFD), quantum yeild (α), and dark respiration (<em>RE</em>_d). Although ecosystem-scale assessments of CO₂ dynamics provide insights into how ecosystems respond to changes in relation to land conversion, it is crucial to assess other respiration components, such as soil respiration and aboveground woody debris, for a more comprehensive analysis.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"378 ","pages":"Article 110956"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145657549","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":"Drivers and thresholds of carbon and water flux dynamics in a semi-humid urban forest ecosystem","authors":"Tuqiang Chen ,&nbsp;Han Li ,&nbsp;Jinhui Jeanne Huang","doi":"10.1016/j.agrformet.2025.110933","DOIUrl":"10.1016/j.agrformet.2025.110933","url":null,"abstract":"<div><div>Urban forests play a critical role in regulating microclimates, sequestering carbon, and providing various ecosystem services. Due to urban land use changes and human activities, urban water and thermal environments have been significantly altered. This has made urban ecosystems more complex than natural ecosystems. However, our understanding of the magnitude, driving factors, and environmental response thresholds of carbon and water fluxes in urban forests remains limited. This study measured carbon and water fluxes in a semi-humid urban forest in China using eddy covariance technology over four consecutive growing seasons (May–October) from 2020 to 2023. Multi-year averages of gross primary production (GPP), evapotranspiration (ET), and water use efficiency (WUE) during the growing seasons were 1177.1 <em>g</em> C m⁻² yr⁻¹, 520.5 mm yr⁻¹, and 2.2 <em>g</em> C kg⁻¹ H₂O, respectively. Under non-drought conditions, canopy conductance (Gc), GPP, and ET were significantly (<em>p</em> &lt; 0.05) higher than under drought conditions. Higher soil water content (SWC) partially alleviated the negative effects of high net radiation (Rn), air temperature (Ta), and vapor pressure deficit (VPD) on GPP and ET during droughts, although it was not the primary driver of their variability. Structural equation modeling revealed that under drought conditions, GPP was primarily regulated by atmospheric demand (e.g., VPD), whereas ET was primarily controlled by energy availability (e.g., Rn and Ta), with SWC exerting a positive influence on both GPP and ET. In contrast, under non-drought conditions, energy availability dominated the regulation of GPP and ET. Threshold analyses further revealed that GPP and ET responded nonlinearly to environmental drivers, initially increasing with Rn, Ta, and VPD but declining after reaching specific thresholds. These findings enhance our understanding of the mechanisms underlying carbon and water flux dynamics in urban forest ecosystems, particularly in the context of drying and warming conditions.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"378 ","pages":"Article 110933"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145658147","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":"Carbon fluxes and partitioning in Eucalyptus and Pinus plantations across a climatic gradient in Brazil","authors":"Fernanda Leite Cunha ,&nbsp;Otávio Camargo Campoe ,&nbsp;Cléber Rodrigo de Souza ,&nbsp;Isaira Leite & Lopes ,&nbsp;Yann Nouvellon ,&nbsp;Timothy Albaugh ,&nbsp;David R. Carter ,&nbsp;Rafael Rubilar ,&nbsp;Isabel Deliberari ,&nbsp;Rachel Cook ,&nbsp;Joannès Guillemot ,&nbsp;Guerric Le Maire ,&nbsp;Jean-Paul Laclau ,&nbsp;Jose Luiz Stape ,&nbsp;Clayton Alcarde Alvares","doi":"10.1016/j.agrformet.2025.110977","DOIUrl":"10.1016/j.agrformet.2025.110977","url":null,"abstract":"<div><div>Brazilian <em>Eucalyptus</em> and <em>Pinus</em> forests are the most productive forests worldwide. The growth rates of these intensively managed plantations depend strongly on environmental conditions and matching genotypes to local environments. Changing climates underscore the value of understanding the intricacies of how these plantations can fix high amounts of carbon (C) and grow so much wood. We measured the full C budgets of <em>Eucalyptus</em> and <em>Pinus</em> forests across climate gradients in Brazil, focusing on the rates of C uptake, the allocation of C to belowground roots and mycorrhizae, and stem growth. We found that gross primary production (GPP) varied more than sixfold across the climate conditions in Brazil. Maximum temperature was the main climatic driver of productivity, where extreme temperatures reduced fluxes to stem production while increasing fluxes to root production. Net ecosystem production varied with management and age across the sites. The ecophysiological investigation presented in this work is fundamental for understanding C partitioning behavior under extreme temperature conditions. In this way, our results provide tools for forest managers to support their decision-making processes as well as starting points for strategies to be implemented in projects aimed at mitigating the effects of climate change.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"378 ","pages":"Article 110977"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145753299","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}