Pub Date : 2026-01-13DOI: 10.1016/j.agrformet.2026.111026
Yuanrui Peng , Tao Wang , Ruiying Chang
Determining the optimal measurement timing for soil greenhouse gas (GHG) emissions is essential for improving the accuracy of GHG budgets and deepening understanding of underlying biogeochemical mechanisms. However, due to a lack of high-temporal-resolution and continuous data, the optimal measurement timing of CH₄ and N₂O emissions remains poorly understood—especially compared to the extensively studied CO₂. Based on high-temporal-resolution and continuous in-situ observations, we found clear differences in the optimal measurement windows among CO₂, CH₄, and N₂O. The optimal timing for capturing daily mean CO₂ fluxes was relatively stable across seasons (around 09:00–11:00), whereas no well-defined optimal daily measurement windows could be identified for CH₄ and N₂O. Instead, their fluxes exhibited highly variable and irregular temporal patterns. Importantly, applying the CO₂-based timing to estimate N₂O fluxes resulted in a substantial underestimation (up to 24%), underscoring the risk of using uniform measurement strategies for different gases. This study reveals that the applicability of optimal daily time windows differs strongly among CO₂, CH₄, and N₂O and across seasons, offering key insights for improving flux estimates.
{"title":"Optimal daily time windows for measuring fluxes of soil methane and nitrous oxide in subalpine forests are elusive - unlike for carbon dioxide","authors":"Yuanrui Peng , Tao Wang , Ruiying Chang","doi":"10.1016/j.agrformet.2026.111026","DOIUrl":"10.1016/j.agrformet.2026.111026","url":null,"abstract":"<div><div>Determining the optimal measurement timing for soil greenhouse gas (GHG) emissions is essential for improving the accuracy of GHG budgets and deepening understanding of underlying biogeochemical mechanisms. However, due to a lack of high-temporal-resolution and continuous data, the optimal measurement timing of CH₄ and N₂O emissions remains poorly understood—especially compared to the extensively studied CO₂. Based on high-temporal-resolution and continuous in-situ observations, we found clear differences in the optimal measurement windows among CO₂, CH₄, and N₂O. The optimal timing for capturing daily mean CO₂ fluxes was relatively stable across seasons (around 09:00–11:00), whereas no well-defined optimal daily measurement windows could be identified for CH₄ and N₂O. Instead, their fluxes exhibited highly variable and irregular temporal patterns. Importantly, applying the CO₂-based timing to estimate N₂O fluxes resulted in a substantial underestimation (up to 24%), underscoring the risk of using uniform measurement strategies for different gases. This study reveals that the applicability of optimal daily time windows differs strongly among CO₂, CH₄, and N₂O and across seasons, offering key insights for improving flux estimates.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"378 ","pages":"Article 111026"},"PeriodicalIF":5.7,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956616","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}
Pub Date : 2026-01-12DOI: 10.1016/j.agrformet.2026.111019
Léa Veuillen , Guillaume Simioni , Miquel De Cáceres , Eric Badel , Simon D. Carrière , Hervé Cochard , François Courbet , Claude Doussan , Arsène Druel , Jean Ladier , Bernard Prévosto , Kevyn Raynal , Nicolas Martin-StPaul
Reducing forest stand density through thinning has the potential to improve tree vigor and mitigate hydraulic risk as it reduces competition for water, thereby improving soil water availability at the tree level. However, these positive effects might be compensated over time by the growth of the remaining trees and understory, an aspect that remains understudied. We investigated the long-term effects of thinning on vegetation regrowth, growth resistance to drought and hydraulic risk in a 1968 Cedrus atlantica plantation in southeastern France where contrasting thinning intensities were applied in 1992, resulting in stand densities of 1200 (unthinned control), 800, 600 and 400 trees.ha-1. Field measurements were conducted in 2017, 25 years after thinning, during the most severe drought since the trial’s establishment. To explore underlying mechanisms, they were complemented by a modeling test using SurEau within the cohort-based model MEDFATE.
Our results show that 25 years after thinning, despite similar stand leaf area index across all thinning treatments, trees in thinned stands exhibited significantly higher growth and reduced hydraulic risk (i.e., higher water potential, wider hydraulic safety margins, lower native embolism) than in the unthinned control. Model simulations suggest that this long-term reduction of hydraulic risk by thinning may result from niche partitioning between the overstory and the understory, either spatially (due to differences in rooting depth) or temporally (due to differences in ecophysiological properties). Interestingly, growth resistance to drought did not differ significantly among thinning treatments. Our results emphasize the potential long-lasting role of thinning in reducing hydraulic risk despite vegetation regrowth. Moreover, this study shows that ecophysiological indicators provide a more accurate understanding of tree drought responses during a specific drought event than the commonly used growth-based indicators.
{"title":"Thinning enhances hydraulic safety but not growth resistance to drought in Atlas cedar on the long-term","authors":"Léa Veuillen , Guillaume Simioni , Miquel De Cáceres , Eric Badel , Simon D. Carrière , Hervé Cochard , François Courbet , Claude Doussan , Arsène Druel , Jean Ladier , Bernard Prévosto , Kevyn Raynal , Nicolas Martin-StPaul","doi":"10.1016/j.agrformet.2026.111019","DOIUrl":"10.1016/j.agrformet.2026.111019","url":null,"abstract":"<div><div>Reducing forest stand density through thinning has the potential to improve tree vigor and mitigate hydraulic risk as it reduces competition for water, thereby improving soil water availability at the tree level. However, these positive effects might be compensated over time by the growth of the remaining trees and understory, an aspect that remains understudied. We investigated the long-term effects of thinning on vegetation regrowth, growth resistance to drought and hydraulic risk in a 1968 <em>Cedrus atlantica</em> plantation in southeastern France where contrasting thinning intensities were applied in 1992, resulting in stand densities of 1200 (unthinned control), 800, 600 and 400 trees.ha<sup>-1</sup>. Field measurements were conducted in 2017, 25 years after thinning, during the most severe drought since the trial’s establishment. To explore underlying mechanisms, they were complemented by a modeling test using SurEau within the cohort-based model MEDFATE.</div><div>Our results show that 25 years after thinning, despite similar stand leaf area index across all thinning treatments, trees in thinned stands exhibited significantly higher growth and reduced hydraulic risk (i.e., higher water potential, wider hydraulic safety margins, lower native embolism) than in the unthinned control. Model simulations suggest that this long-term reduction of hydraulic risk by thinning may result from niche partitioning between the overstory and the understory, either spatially (due to differences in rooting depth) or temporally (due to differences in ecophysiological properties). Interestingly, growth resistance to drought did not differ significantly among thinning treatments. Our results emphasize the potential long-lasting role of thinning in reducing hydraulic risk despite vegetation regrowth. Moreover, this study shows that ecophysiological indicators provide a more accurate understanding of tree drought responses during a specific drought event than the commonly used growth-based indicators.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"378 ","pages":"Article 111019"},"PeriodicalIF":5.7,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956617","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}
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.
{"title":"Comprehensive review of detrending methods for crop yields: Approaches, applications, and future directions","authors":"Yanbo He, Xianglong Chen, Haijun Li, Xuan Li, Shaojie Sun, 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":"2026-01-10","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}
Pub Date : 2026-01-10DOI: 10.1016/j.agrformet.2025.111014
Sebastian Zainali , Silvia Ma Lu , Tomas Landelius , Pietro Elia Campana
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.
{"title":"Satellite derived product benchmarking and empirical model development for estimating photosynthetically active radiation at high latitudes","authors":"Sebastian Zainali , Silvia Ma Lu , Tomas Landelius , 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":"2026-01-10","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}
Pub Date : 2026-01-09DOI: 10.1016/j.agrformet.2025.111011
Xiaojia Yuan , Chen Xu , Jingsong Zhang , Xue Wang , Jinglei Liao , Mingchao Du , Xianliang Zhang
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 Larix principis-rupprechtii trees across six plots in North China to evaluate the combined effects of climate and CI on earlywood and latewood formation.
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.
{"title":"Elevation-dependent responses of xylem lumen traits to competition–climate interactions in temperate forests","authors":"Xiaojia Yuan , Chen Xu , Jingsong Zhang , Xue Wang , Jinglei Liao , Mingchao Du , 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":"2026-01-09","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}
Insect infestation is projected to become more frequent in the Arctic regions under global climate change, posing high socioeconomic and ecological pressure on the local environment and ecosystem. Rapid insect induced defoliation detection is critical for understanding the impact of discernible disturbances on terrestrial ecosystem structures and functions. This paper uses Continuous Change Detection and Classification (CCDC) algorithm and the Harmonized Landsat and Sentinel-2 (HLS) dataset to build a systematic insect defoliation monitoring system in the Abisko region, subarctic Sweden. The Green Chromatic Coordinate (GCC) index was used to link different scale sensors, from close-up and time-lapse imagery to Unmanned Aerial Vehicle (UAV) and HLS observations, and biological processes of biogenic volatile organic compound (BVOC) emissions. The detected defoliation intensity was validated using the ground truth larvae abundance and Greenness Ratio from in situ digital photos. The new insect defoliation detection approach provides a new tool for assessing larvae outbreaks and the associated impact at different scales. The findings underscore the potential of integrating remote sensing data with ground observations from various sensors for effective insect defoliation monitoring and ecological impact assessment, paving the way for future large scale rapid mapping, early warning systems, and BVOC emission estimation.
预计在全球气候变化的背景下，北极地区的虫害将变得更加频繁，给当地环境和生态系统带来巨大的社会经济和生态压力。昆虫引起的落叶快速检测对于理解可识别干扰对陆地生态系统结构和功能的影响至关重要。利用连续变化检测与分类（CCDC）算法和Harmonized Landsat and Sentinel-2 （HLS）数据集，在瑞典亚北极阿比斯库地区建立了系统的昆虫落叶监测系统。绿色色度坐标（GCC）指数用于连接不同尺度的传感器，从特写和延时图像到无人机（UAV）和HLS观测，以及生物源性挥发性有机化合物（BVOC）排放的生物过程。利用现场数码照片的地面真值、幼虫丰度和绿度比对检测到的落叶强度进行了验证。新的昆虫落叶检测方法为评估不同尺度的幼虫爆发及其相关影响提供了新的工具。这些发现强调了将遥感数据与各种传感器的地面观测数据结合起来进行有效的昆虫落叶监测和生态影响评估的潜力，为未来大规模快速制图、早期预警系统和BVOC排放估算铺平了道路。
{"title":"Multi-source remote sensing of insect defoliation events in Abisko from point to regional scales","authors":"Shunan Feng , Simon Nyboe Laursen , Amy Smart , Katrine Stadsholt Sørensen , Monika Lund , Federico Grillini , Jolanta Rieksta , Yi Jiao , Riikka Rinnan , Andreas Westergaard-Nielsen","doi":"10.1016/j.agrformet.2026.111023","DOIUrl":"10.1016/j.agrformet.2026.111023","url":null,"abstract":"<div><div>Insect infestation is projected to become more frequent in the Arctic regions under global climate change, posing high socioeconomic and ecological pressure on the local environment and ecosystem. Rapid insect induced defoliation detection is critical for understanding the impact of discernible disturbances on terrestrial ecosystem structures and functions. This paper uses Continuous Change Detection and Classification (CCDC) algorithm and the Harmonized Landsat and Sentinel-2 (HLS) dataset to build a systematic insect defoliation monitoring system in the Abisko region, subarctic Sweden. The Green Chromatic Coordinate (GCC) index was used to link different scale sensors, from close-up and time-lapse imagery to Unmanned Aerial Vehicle (UAV) and HLS observations, and biological processes of biogenic volatile organic compound (BVOC) emissions. The detected defoliation intensity was validated using the ground truth larvae abundance and Greenness Ratio from <em>in situ</em> digital photos. The new insect defoliation detection approach provides a new tool for assessing larvae outbreaks and the associated impact at different scales. The findings underscore the potential of integrating remote sensing data with ground observations from various sensors for effective insect defoliation monitoring and ecological impact assessment, paving the way for future large scale rapid mapping, early warning systems, and BVOC emission estimation.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"378 ","pages":"Article 111023"},"PeriodicalIF":5.7,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145920255","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}
Pub Date : 2026-01-09DOI: 10.1016/j.agrformet.2025.111012
Chenghao Ding , Runpeng Cai , Lei Zhou , Zhenzhen Zhang , Xiaowei Zhang , Enxiang Xu , Yonggang Chi
Vegetation phenology and photosynthetic physiology provided a new perspective for in-depth understanding of gross primary productivity (GPP). However, it is unclear whether different phenological stages and photosynthetic physiology affect the variability of GPP. Here, seasonal dynamics of green chromatic coordinate (GCC), solar-induced chlorophyll fluorescence (SIF) and GPP were measured synchronously throughout the growing season in subtropical rice paddies in Zhejiang Province, China. Phenological metrics in the growing season were extracted by GCC and SIF. Our study found that nitrogen fertilization significantly advanced phenological timing during reproductive stage while prolonging its duration (57 ± 0.58 days). GPPtotal of the growing season was significantly positively correlated with length of reproductive stage but negatively correlated with length of vegetative stage. Reproductive stage length and GPPmax jointly explain 92 % of the growing season GPPtotal variability. These findings highlight the critical role of reproductive stage in crop growth process and provide a new insight into understanding the variability of GPP from the perspective of vegetation phenology.
{"title":"Nitrogen fertilization enhances gross primary productivity by prolonging reproductive stage in the subtropical rice paddies","authors":"Chenghao Ding , Runpeng Cai , Lei Zhou , Zhenzhen Zhang , Xiaowei Zhang , Enxiang Xu , Yonggang Chi","doi":"10.1016/j.agrformet.2025.111012","DOIUrl":"10.1016/j.agrformet.2025.111012","url":null,"abstract":"<div><div>Vegetation phenology and photosynthetic physiology provided a new perspective for in-depth understanding of gross primary productivity (GPP). However, it is unclear whether different phenological stages and photosynthetic physiology affect the variability of GPP. Here, seasonal dynamics of green chromatic coordinate (GCC), solar-induced chlorophyll fluorescence (SIF) and GPP were measured synchronously throughout the growing season in subtropical rice paddies in Zhejiang Province, China. Phenological metrics in the growing season were extracted by GCC and SIF. Our study found that nitrogen fertilization significantly advanced phenological timing during reproductive stage while prolonging its duration (57 ± 0.58 days). GPP<sub>total</sub> of the growing season was significantly positively correlated with length of reproductive stage but negatively correlated with length of vegetative stage. Reproductive stage length and GPP<sub>max</sub> jointly explain 92 % of the growing season GPP<sub>total</sub> variability. These findings highlight the critical role of reproductive stage in crop growth process and provide a new insight into understanding the variability of GPP from the perspective of vegetation phenology.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"378 ","pages":"Article 111012"},"PeriodicalIF":5.7,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925476","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}
Pub Date : 2026-01-09DOI: 10.1016/j.agrformet.2025.111015
Kai Wang , Xiaohua Gou , Takeshi Nakatsuka , Yiran Zhang , Tao Wang , Linlin Gao , Yang Deng , Zhen Li , Kaixuan Yang , Xuan Li , Chongshan Wang , Zibo Wang
The intra-annual distribution of precipitation has a significant impact on vegetation growth in arid and semi-arid regions. Intra-annual variations in tree-ring cellulose oxygen isotope ratios (δ18Otree) can capture seasonal climate signals. In this study, we collected tree-ring cores of Picea crassifolia from three sampling sites in the northeastern Tibetan Plateau and established both interannual and intra-annual δ18Otree series spanning approximately the past 30 years. We found that all three sites exhibited a consistent pattern of intra-annual variation, with δ18Otree values gradually decreasing from earlywood to latewood reflecting the relative humidity of the corresponding growth periods. Further analysis revealed that the amplitude of intra-annual variations in δ18Otree can indicate the intra-annual distribution of precipitation, specifically the difference in precipitation amounts between the late and early growing season. Additionally, when examining the relationship between annual-resolution and intra-annual-resolution δ18Otree series, we found that annual-resolution δ18Otree primarily reflect the isotopic signals corresponding to the periods of fastest tree growth within the year. Our findings provide valuable insights into the interpretation of annual-resolution δ18Otree signals and the investigation of seasonal moisture variations in arid and semi-arid regions under the context of climate change.
{"title":"The intra-annual tree-ring δ18O records from the northeastern Tibetan Plateau can reflect seasonal variations of relative humidity and the intra-annual distribution of precipitation","authors":"Kai Wang , Xiaohua Gou , Takeshi Nakatsuka , Yiran Zhang , Tao Wang , Linlin Gao , Yang Deng , Zhen Li , Kaixuan Yang , Xuan Li , Chongshan Wang , Zibo Wang","doi":"10.1016/j.agrformet.2025.111015","DOIUrl":"10.1016/j.agrformet.2025.111015","url":null,"abstract":"<div><div>The intra-annual distribution of precipitation has a significant impact on vegetation growth in arid and semi-arid regions. Intra-annual variations in tree-ring cellulose oxygen isotope ratios (δ<sup>18</sup>O<sub>tree</sub>) can capture seasonal climate signals. In this study, we collected tree-ring cores of <em>Picea crassifolia</em> from three sampling sites in the northeastern Tibetan Plateau and established both interannual and intra-annual δ<sup>18</sup>O<sub>tree</sub> series spanning approximately the past 30 years. We found that all three sites exhibited a consistent pattern of intra-annual variation, with δ<sup>18</sup>O<sub>tree</sub> values gradually decreasing from earlywood to latewood reflecting the relative humidity of the corresponding growth periods. Further analysis revealed that the amplitude of intra-annual variations in δ<sup>18</sup>O<sub>tree</sub> can indicate the intra-annual distribution of precipitation, specifically the difference in precipitation amounts between the late and early growing season. Additionally, when examining the relationship between annual-resolution and intra-annual-resolution δ<sup>18</sup>O<sub>tree</sub> series, we found that annual-resolution δ<sup>18</sup>O<sub>tree</sub> primarily reflect the isotopic signals corresponding to the periods of fastest tree growth within the year. Our findings provide valuable insights into the interpretation of annual-resolution δ<sup>18</sup>O<sub>tree</sub> signals and the investigation of seasonal moisture variations in arid and semi-arid regions under the context of climate change.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"378 ","pages":"Article 111015"},"PeriodicalIF":5.7,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145920257","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}
Pub Date : 2026-01-09DOI: 10.1016/j.agrformet.2025.110998
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
{"title":"Retraction notice to “Knowledge-guided machine learning captures key mechanistic pathways for better predicting spatio-temporal patterns of growing season N2O emissions in the U.S. Midwest” [Agricultural and Forest Meteorology 373 (2025) 110750]","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.2025.110998","DOIUrl":"10.1016/j.agrformet.2025.110998","url":null,"abstract":"","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"378 ","pages":"Article 110998"},"PeriodicalIF":5.7,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961777","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}
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