Agricultural and Forest Meteorology最新文献

{"title":"Mapping wood area in forests from ground lidar and estimating their light interception using radiative transfer modeling","authors":"Martin Béland","doi":"10.1016/j.agrformet.2025.110883","DOIUrl":"https://doi.org/10.1016/j.agrformet.2025.110883","url":null,"abstract":"Leaves in forests are commonly believed to shade many stems and branches, and thus most of the absorption of photosynthetically active radiation (PAR) within a canopy is done by leaves. Near-infrared radiation (NIR) on the other hand is not used in photosynthesis, and leaf level absorptance of NIR is much lower than it is for PAR. Still, temperate broadleaf canopies absorb about 50–70 % of incoming NIR, and how this absorption is partitioned between leaves and woody structures is unclear. Here, I show that of the NIR absorbed within the canopy space, woody structures contribute about 30–35 %. The results also confirm that leaves account for about 90 % of the absorbed PAR within the canopy space. To establish these figures, I used ground lidar measurements to map leaf area and stem and branch area in 3D in two structurally contrasting broadleaf forests, and from radiative transfer modeling I estimated the fractions of PAR and NIR absorbed by leaves and wood based on illumination conditions measured at each site over multiple years. The findings have implications for the development of land surface models that consider the storage of heat by woody biomass in forests as part of the canopy energy balance.","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"2 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255560","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":"Predicting land-surface specific humidity from radiative temperature and ambient weather for evapotranspiration modelling: Lessons from South Australian field sites","authors":"Jianfeng Gou, Wenjie Liu, Jessica Thompson, Okke Batelaan, Hailong Wang, Karina Gutierrez, Juliette Woods, Huade Guan","doi":"10.1016/j.agrformet.2025.110878","DOIUrl":"https://doi.org/10.1016/j.agrformet.2025.110878","url":null,"abstract":"Land-surface specific humidity is crucial for estimating evapotranspiration (ET) using the Maximum Entropy Production (MEP) method. However, acquiring relevant data, particularly the spatially varying land-surface specific humidity, can be challenging. Here, we show that the deviation of land-surface specific humidity from the ambient specific humidity can be estimated using surface radiative temperature and ambient micrometeorological variables (referred to as the Tr-Weather method). We tested this method at five sites in South Australia with varying vegetation and topography. The results indicate that the Tr-Weather method generally performs the best for early afternoon. The performance varies with seasons, with better results for summer and autumn. Slope and aspect change the timing of optimal predictions, particularly in areas with significant topographic variations. Additionally, this method effectively predicts spatial distribution of the land-surface specific humidity by integrating drone-derived temperature and ambient meteorological data, with an R² value of 0.96. For MEP-based understory ET modelling, the Tr-Weather method outperforms the substituted specific humidity from nearby weather stations, especially under sunny conditions where the MEP ET model using ambient specific humidity tends to underestimate ET. The method is empirical and was developed based on observations in two different environments, further research is required to extend and validate the Tr-Weather approach over other bioclimate zones. Nevertheless, our findings demonstrate the potential of applying the Tr-Weather method, supported by drones and high-resolution satellite data, to advance MEP-based ET modelling across broader landscapes.","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"59 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255354","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":"First evaluation of near-real-time photosynthetically active radiation from Himawari remote sensing observations under different aerosol, precipitable water, and cloud conditions in the tropical environment of Thailand","authors":"Worapan Kanchanachat, Serm Janjai, Rungrat Wattan","doi":"10.1016/j.agrformet.2025.110875","DOIUrl":"https://doi.org/10.1016/j.agrformet.2025.110875","url":null,"abstract":"This study presents the first comprehensive assessment of near-real-time photosynthetically active radiation (PAR) derived from Himawari satellite products (PAR_Himawari) in the complex tropical environment of Thailand, which is characterized by variable aerosol concentration, precipitable water vapor, and cloud movement. Level 2.0 quality-assured PAR_Himawari data were validated against ground-based EKO quantum sensor measurements (PAR_EKO) at three regions (Chiang Mai, Nakhon Pathom, and Songkhla) from 2018 to 2022. Results show strong linear correlations between PAR_Himawari and PAR_EKO (R² > 0.7). However, the magnitude of estimates remains uncertain. The Nash-Sutcliffe equation (NSE) was negative at Chiang Mai (-1.10), indicating poor estimation performance, while moderate NSE values of 0.29 and 0.50 were found at Nakhon Pathom and Songkhla, respectively. The relative root mean square error (rRMSE) ranged from 33.15 % to 64.32 %, and the relative mean bias error (rMBE) ranged from 23.82 % to 55.09 %, suggesting systematic overestimation by the satellite product. Further analysis confirmed that PAR_Himawari residuals increase with aerosol optical depth (AOD) and precipitable water vapor. For cloud optical thickness (COT), residuals median values were positive under clear-sky conditions, approached zero under intermediate sky conditions, and shifted negative under overcast conditions. Overall, PAR_Himawari shows higher errors when providing high PAR values, underscoring the need for improved aerosol and moisture corrections to enhance satellite PAR retrievals in tropical environments for ecological and agricultural applications.","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"11 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255355","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 effect of enhanced rock weathering on soil respiration was modulated by understory removal in a subtropical fir plantation","authors":"Renshan Li, Yankuan Zhang, Yanfeng Bai, Xiongqing Zhang, Fangfang Zhang, Xinkuan Han, Honggang Sun, Weidong Zhang, Qingpeng Yang, Silong Wang","doi":"10.1016/j.agrformet.2025.110876","DOIUrl":"https://doi.org/10.1016/j.agrformet.2025.110876","url":null,"abstract":"Enhanced rock weathering (ERW) involving the modification of soils with crushed silicate rock is proposed an efficient CO₂ removal technology. However, a comprehensive understanding of the influence of silicate rock application on soil respiration (R_s), especially under different silvicultural regimes, is still lacking, limiting our awareness of the potential for ERW to remove CO₂. In this study, a wollastonite addition (WA) experiment was conducted in a Chinese fir plantation with or without understory removal (UR). The R_s and its heterotrophic (R_h) and autotrophic (R_a) components were measured for each treatment over a two-year period. Results showed that WA significantly increased R_s and R_a by 16.1 % and 51.8 %, respectively, but had no effect on R_h. Soil pH value was markedly increased by WA by 12.5 %, from 4.40 to 4.95, on average. The concentrations of available Si and exchangeable Ca and Mg increased but that of exchangeable Al decreased after WA. It seems that WA has provided the plants with higher nutrient availability and lower aluminum toxicity, thereby benefiting root activity. This pattern was evidenced by the positive correlations between R_a and soil pH value, exchangeable Ca and available Si concentration, as well as by the negative correlation between R_a and exchangeable Al concentration. Moreover, the effect of WA on R_s was interactively regulated by UR, leading to a weaker WA effect at understory-free stands. This study highlighted that the increase in soil CO₂ efflux by WA should be taken into consideration while assessing the CO₂ removal potential from ERW.","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"31 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255356","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":"LSTM neural network integrating remote sensing and meteorological data reveals forest fire risk trajectories in China over the past 100 years","authors":"Xiong Yin, Mengqi Bai, Yuwen Peng, Bangqian Chen, Hongyan Lai, Weili Kou, Yue Chen, Jingyi Wang, Mingshi Li","doi":"10.1016/j.agrformet.2025.110884","DOIUrl":"https://doi.org/10.1016/j.agrformet.2025.110884","url":null,"abstract":"Quantifying current forest fire risk is essential to ecological security and sustainable resource management, while reconstructing historical fire risk helps understand the impacts of climate change and human activities on forest fire regimes, enabling effective lessons from past forest management practices. However, the lack of long-term forest fire risk assessment studies (e.g., 1921 to 1960) is due to insufficient reliable spatio-temporally explicit forest fire records, stemming from the unavailability of spatial information technology for modeling fire occurrences. This study presented a retrospective fire risk modelling approach using deep learning, meteorological station observations, and spatial interpolation to overcome this limitation. By modifying forest fire danger index (MFFDI) and integrating it with a multi-step prediction Long Short-Term Memory model, historical station-wise MFFDI data for 1921–1960 was generated, leading to the creation of a forest fire risk distribution map via spatial interpolation that accounts for terrain and human influences. The results indicated that the model-predicted MFFDI for 1921 to 1960 demonstrated high accuracy, with a mean R² of 0.77 and a mean MSE of 0.24 from an independent validation subset of the 778 meteorological stations across China. For the independent validation of the model-generated forest fire risk maps, 193 of the 265 historical fire events occurred in high or relatively high risk zones, and 72 in moderate risk zones. Spatial analysis revealed that over the past 100 years, high and relatively high risk areas were primarily located in southwest, northwest, and north China, comprising 19 % to 26 % of the total forest fire risk area. After 1980, high and relatively high risk zones gradually concentrated in the central and southwestern regions, while moderate risk zones shifted from the southwest to the southeast. The area of high risk zones was constantly stable, below 8 % over time. This study reconstructs historical fire risk maps, highlighting the century-long dynamics of forest fire risk in China, which is essential for formulating scientific fire control strategies.","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"204 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255555","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":"Surface cooling potential of wetlands across the Prairie Pothole Region of Canada","authors":"Joyson Ahongshangbam, Pascal Badiou, Darian Ng, Zoran Nesic, Aaron Glenn, Sara H. Knox","doi":"10.1016/j.agrformet.2025.110862","DOIUrl":"https://doi.org/10.1016/j.agrformet.2025.110862","url":null,"abstract":"Wetlands provide many ecosystem services such as carbon sequestration, climate regulation, biodiversity and water quality enhancement. Through evaporative cooling, wetland ecosystems also play a significant role in the regulation of local and regional climate by creating microclimates, which benefit local flora and fauna. In this study, the cooling effect of wetlands was evaluated by examining the differences in aerodynamic temperature (T<span><span style=\"\"><math><msub is=\"true\"><mrow is=\"true\"></mrow><mrow is=\"true\"><mi is=\"true\">a</mi><mi is=\"true\">e</mi><mi is=\"true\">r</mi><mi is=\"true\">o</mi></mrow></msub></math></span><span style=\"font-size: 90%; display: inline-block;\" tabindex=\"0\"><svg focusable=\"false\" height=\"1.163ex\" role=\"img\" style=\"vertical-align: -0.582ex;\" viewbox=\"0 -250.4 1466.8 500.8\" width=\"3.407ex\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g fill=\"currentColor\" stroke=\"currentColor\" stroke-width=\"0\" transform=\"matrix(1 0 0 -1 0 0)\"><g is=\"true\"><g is=\"true\"></g><g is=\"true\" transform=\"translate(0,-150)\"><g is=\"true\"><use transform=\"scale(0.707)\" xlink:href=\"#MJMATHI-61\"></use></g><g is=\"true\" transform=\"translate(374,0)\"><use transform=\"scale(0.707)\" xlink:href=\"#MJMATHI-65\"></use></g><g is=\"true\" transform=\"translate(704,0)\"><use transform=\"scale(0.707)\" xlink:href=\"#MJMATHI-72\"></use></g><g is=\"true\" transform=\"translate(1023,0)\"><use transform=\"scale(0.707)\" xlink:href=\"#MJMATHI-6F\"></use></g></g></g></g></svg></span><script type=\"math/mml\"><math><msub is=\"true\"><mrow is=\"true\"></mrow><mrow is=\"true\"><mi is=\"true\">a</mi><mi is=\"true\">e</mi><mi is=\"true\">r</mi><mi is=\"true\">o</mi></mrow></msub></math></script></span>) between wetlands and nearby croplands in the Prairie Pothole Region of Canada. The cooling effect refers to the reduction of air or surface temperature through evapotranspiration and thermal dissipation from the environment. We utilized turbulent flux and meteorological data gathered through eddy covariance measurements over three years (2021–2023) from three distinct wetland sites and two cropland types. Our findings reveal that during the growing season (May to September), wetlands exhibit significantly lower temperatures compared to the croplands, with mean daytime cooling (T<span><span style=\"\"><math><msub is=\"true\"><mrow is=\"true\"></mrow><mrow is=\"true\"><mi is=\"true\">a</mi><mi is=\"true\">e</mi><mi is=\"true\">r</mi><mi is=\"true\">o</mi></mrow></msub></math></span><span style=\"font-size: 90%; display: inline-block;\" tabindex=\"0\"><svg focusable=\"false\" height=\"1.163ex\" role=\"img\" style=\"vertical-align: -0.582ex;\" viewbox=\"0 -250.4 1466.8 500.8\" width=\"3.407ex\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g fill=\"currentColor\" stroke=\"currentColor\" stroke-width=\"0\" transform=\"matrix(1 0 0 -1 0 0)\"><g is=\"true\"><g is=\"true\"></g><g is=\"true\" transform=\"translate(0,-150)\"><g is=\"true\"><use transform=\"scale(0.707)\" xlink:href=\"#MJMATHI-61\"></use></g><g is=\"true\" transform=\"translate(374,0)\"><use transform=\"scale(0.","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"57 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145241774","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 variability of microclimate in the Amazon Rainforest","authors":"Zhimin Ma, Darlene Gris, Paulo de Jesus Feitosa Paes do Nascimento, Carolina Volkmer de Castilho, Sabina Cerruto Ribeiro, Raphael Tapajós, Wilderclay Machado, Miércio Alves Júnior, José Luís Camargo, Samuel de Padua Chaves e Carvalho, Leena Kalliovirta, Ilya M.D. Maclean, Eduardo Eiji Maeda","doi":"10.1016/j.agrformet.2025.110866","DOIUrl":"https://doi.org/10.1016/j.agrformet.2025.110866","url":null,"abstract":"The Amazon rainforest represents one of the most biodiverse places on Earth. However, the temperature experienced by organisms inside Amazonian forests, and the biophysical factors regulating microclimate, remain understudied. Microclimate patterns influence the nuances of ecological processes such as nutrient cycling, species dynamics, and tree regeneration, which cannot be captured by coarse resolution free-air temperature datasets. This study presents an unprecedented effort to measure microclimate across different biogeographical sites using standardized in-situ data collection and methodology. We analyzed temperature data from 145 sensors across eight distinct areas within the Amazon basin. From 2016 to 2023, nearly four million temperature readings revealed that Amazonian understory temperatures remained consistently lower than those of the surrounding macroclimate. The central Amazon exhibited the greatest difference between microclimate and macroclimate temperatures. All monitored sites exhibit higher buffering capacity against macroclimate during the rainy season than in the dry season. Higher precipitation, Leaf Area Index, and canopy height enhance microclimatic buffering capacity, while increased terrain slope exert negative effects. Our study provides new insights into the spatial and temporal patterns of microclimate across the Amazon basin, thus advancing our understanding of the impacts of climate change on the Amazonian Forest ecosystem.","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"83 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247301","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":"Precipitation regimes influence differently the biogeochemical cycles of the tropical dry forest","authors":"Isela Jasso-Flores, T. Luke Smallman, Rogelio O. Corona-Núñez","doi":"10.1016/j.agrformet.2025.110880","DOIUrl":"https://doi.org/10.1016/j.agrformet.2025.110880","url":null,"abstract":"Tropical dry forests (TDFs), comprising ∼40 % of all tropical forests, are crucial for global biogeochemical cycling and a leading driver of global interannual variation in net carbon dioxide exchange. Their sensitivity to precipitation variability affects microbial activity, nutrient mineralization, and organic matter decomposition, yet their responses to environmental change remain insufficiently understood. This study evaluates how different precipitation regimes—annual precipitation, seasonal precipitation, and El Niño events—on the biogeochemical cycling of carbon (C), nitrogen (N), and phosphorus (P) in TDFs. Using long-term datasets on biogeochemistry and plant diversity (both taxonomic and functional), we isolated precipitation effects through a multi-ensemble approach. Our results highlight the role of plant biodiversity–soil interactions in shaping population, community, and ecosystem level responses. Annual precipitation emerged as a key driver of functional trait expression and C cycling. This resulted in a negative trend for most C and N metrics, while the ratio of soil basal respiration to microbial C showed a positive trend. Seasonal precipitation explained much of the variation in N cycling, with wet seasons linked to elevated N metrics and dry seasons associated with higher dissolved organic C and microbial biomass. Post-El Niño recovery of soil C and N required at least two years. While P cycling was influenced by annual precipitation, the effect was not significant. These findings advance our understanding of TDF vulnerability under climate change, emphasizing how changes in rainfall regimes may reshape biogeochemical processes. Notably, our results suggest that TDFs may shift from C sinks to sources under scenarios of intensified drought and incomplete ecosystem recovery after stronger and more frequent drought events.","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"20 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145241453","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 hybrid model based on machine learning and improved Jayaweera-Mikkelsen model to simulate NH3 volatilization in paddy field water","authors":"Xuerong Lang, Xiang Gao, Housheng Wang, Wei Jiang, Shuai Shen, Xian Hu, Linkai Wen, Qinchun Xu, Yan Zhang, Jinyang Wang, Yanfeng Ding, Yue Mu, Yang Ou, Xiaosan Jiang, Jianwen Zou","doi":"10.1016/j.agrformet.2025.110877","DOIUrl":"https://doi.org/10.1016/j.agrformet.2025.110877","url":null,"abstract":"NH₃ volatilization is one of the primary pathways of nitrogen loss in paddy field ecosystems, and accurately quantifying its flux remains a significant challenge, particularly under flooded conditions. To address this issue, we developed a hybrid model named NAU-_PNH₃. Based on the original Jayaweera-Mikkelsen (JM) model, this new model incorporates improvements in the NH_3(aq) concentration and key volatilization functions (K_gN and K_IN), accounting for the effects of solution activity coefficients, crop growth, and rainfall events. In addition, the model integrates machine learning (ML) algorithms to efficiently simulate the NH⁺₄-N concentrations (A_N) and pH in paddy field water, thereby enhancing overall model performance. We conducted a comprehensive evaluation of the original and improved models using field observations from four representative sites across major rice-producing regions in China. The results demonstrated that the NAU-_PNH₃ model outperforms the original JM model in simulating NH₃ volatilization fluxes from paddy fields, significantly reducing uncertainty and improving adaptability. Based on the NAU-_PNH₃ model, we developed the NAU-_PNH₃ Tool, an online platform for simulating daily NH₃ volatilization flux in paddy fields. This model offers a novel process-based approach for simulating NH₃ volatilization in paddy fields and provides an effective tool for understanding the mechanisms and dynamics of ammonia loss under varying environmental conditions.","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"7 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145241445","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":"Comparing statistical methods for detecting weather cues of mast seeding in European beech (Fagus sylvatica) across Europe","authors":"Valentin Journé, Emily G. Simmonds, Maciej K. Barczyk, Michał Bogdziewicz","doi":"10.1016/j.agrformet.2025.110857","DOIUrl":"https://doi.org/10.1016/j.agrformet.2025.110857","url":null,"abstract":"Understanding the drivers of mast seeding is important for predicting reproductive dynamics in perennial plants. Here, we evaluate the performance of four statistical methods for identifying weather-associated drivers of annual seed production, i.e, weather cues: climate sensitivity profile, P-spline regression, sliding window analysis, and peak signal detection. Using long-term seed production data from 50 European beech (<em>Fagus sylvatica</em>) populations and temperature records, we assessed each method’s ability to detect a benchmark window around the summer solstice. All methods successfully identified biologically meaningful windows, but their performance varied with data quality, signal strength, and sample size. Sliding window and climate sensitivity profile methods showed the best balance of accuracy and robustness, while peak signal detection had lower consistency. Cue identification was more reliable with at least 20 years of data, and predictive accuracy was highest when models were based on seed trap data. A simulation study showed method-specific sensitivity to signal strength, with the sliding window performing best. This simulation further validated the methods by testing their ability to detect a predefined cue window under varying signal strengths. Our findings provide a means to improve masting forecasts through a practical guide for selecting appropriate cue identification methods under varying data constraints.","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"105 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145241222","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}