Pub Date : 2025-01-20DOI: 10.1016/j.agrformet.2025.110407
Rongzhu Qin , Kaiyu Guan , Bin Peng , Feng Zhang , Wang Zhou , Jinyun Tang , Tongxi Hu , Robert Grant , Benjamin R K Runkle , Michele Reba , Xiaocui Wu
<div><div>Cotton (<em>Gossypium hirsutum</em> L.) cultivation contributes to economic development, particularly in the Cotton Belt of the Southern United States (U.S.). As one of the world's largest exporters of cotton, the U.S. cotton industry plays a pivotal role in both the domestic and international markets. Accurate quantification of carbon budgets and their responses to the environment is thus crucial for the sustainable production of cotton, but such quantification at the regional scale remains unclear. Here we use a framework that combines an advanced process-based model, <em>ecosys</em>, and a deep learning-based Model-Data Fusion (MDF) approach to quantify the magnitude and patterns of carbon flux and cotton lint yield under both rainfed and irrigated conditions in the U.S. We first evaluate the performance of the process-based model in simulating carbon budgets of cotton agroecosystems using eddy-covariance (EC) values at production-scale farm sites. We then apply MDF to use satellite-based gross primary production (GPP) and survey-based cotton lint yield data as constraints of the <em>ecosys</em> model to generate the holistic carbon budget of cotton cropland at the county level across the U.S. from 2008 to 2019. Validation at the three EC sites indicates that the <em>ecosys</em> model achieves R<sup>2</sup> values of 0.9 and 0.8 for the simulated versus the EC daily GPP and respiration, respectively, and 0.9 for the simulated versus the experimentally measured leaf area index. The R<sup>2</sup> at county level in our framework is 0.8 for both cotton lint yield and GPP: the simulated versus survey-based cotton lint yield, and the simulated versus satellite-based monthly GPP. The spatio-temporal patterns of the simulated cotton lint yield, GPP, and their responses to climate factors (average temperature, average vapor pressure deficit (VPD), and cumulative precipitation during the growing season) are consistent with the observations, indicating that our framework approach captures the underlying processes relating environmental conditions to cotton growth. Our analysis shows that cotton productivity (lint yield and GPP) decreased with increasing average VPD during the growing season, especially under rainfed conditions. It also shows that the carbon budget terms, including predicted net primary productivity, crop yield, and soil heterotrophic respiration, decreased as the VPD increased. Conversely, the predicted change in soil organic carbon was less influenced by climate, which decreased with increasing initial soil organic carbon content and cation exchange capacity, and increased with increasing soil bulk density. The variable impacts of crop management practices, climatic factors, and soil characteristics on carbon budgets highlight the intricate interactions among these factors that shape carbon dynamics in cotton agroecosystems, and further emphasize the necessity of accurately simulating the carbon budgets of cotton agroecosyste
{"title":"A model-data fusion approach for quantifying the carbon budget in cotton agroecosystems across the United States","authors":"Rongzhu Qin , Kaiyu Guan , Bin Peng , Feng Zhang , Wang Zhou , Jinyun Tang , Tongxi Hu , Robert Grant , Benjamin R K Runkle , Michele Reba , Xiaocui Wu","doi":"10.1016/j.agrformet.2025.110407","DOIUrl":"10.1016/j.agrformet.2025.110407","url":null,"abstract":"<div><div>Cotton (<em>Gossypium hirsutum</em> L.) cultivation contributes to economic development, particularly in the Cotton Belt of the Southern United States (U.S.). As one of the world's largest exporters of cotton, the U.S. cotton industry plays a pivotal role in both the domestic and international markets. Accurate quantification of carbon budgets and their responses to the environment is thus crucial for the sustainable production of cotton, but such quantification at the regional scale remains unclear. Here we use a framework that combines an advanced process-based model, <em>ecosys</em>, and a deep learning-based Model-Data Fusion (MDF) approach to quantify the magnitude and patterns of carbon flux and cotton lint yield under both rainfed and irrigated conditions in the U.S. We first evaluate the performance of the process-based model in simulating carbon budgets of cotton agroecosystems using eddy-covariance (EC) values at production-scale farm sites. We then apply MDF to use satellite-based gross primary production (GPP) and survey-based cotton lint yield data as constraints of the <em>ecosys</em> model to generate the holistic carbon budget of cotton cropland at the county level across the U.S. from 2008 to 2019. Validation at the three EC sites indicates that the <em>ecosys</em> model achieves R<sup>2</sup> values of 0.9 and 0.8 for the simulated versus the EC daily GPP and respiration, respectively, and 0.9 for the simulated versus the experimentally measured leaf area index. The R<sup>2</sup> at county level in our framework is 0.8 for both cotton lint yield and GPP: the simulated versus survey-based cotton lint yield, and the simulated versus satellite-based monthly GPP. The spatio-temporal patterns of the simulated cotton lint yield, GPP, and their responses to climate factors (average temperature, average vapor pressure deficit (VPD), and cumulative precipitation during the growing season) are consistent with the observations, indicating that our framework approach captures the underlying processes relating environmental conditions to cotton growth. Our analysis shows that cotton productivity (lint yield and GPP) decreased with increasing average VPD during the growing season, especially under rainfed conditions. It also shows that the carbon budget terms, including predicted net primary productivity, crop yield, and soil heterotrophic respiration, decreased as the VPD increased. Conversely, the predicted change in soil organic carbon was less influenced by climate, which decreased with increasing initial soil organic carbon content and cation exchange capacity, and increased with increasing soil bulk density. The variable impacts of crop management practices, climatic factors, and soil characteristics on carbon budgets highlight the intricate interactions among these factors that shape carbon dynamics in cotton agroecosystems, and further emphasize the necessity of accurately simulating the carbon budgets of cotton agroecosyste","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"363 ","pages":"Article 110407"},"PeriodicalIF":5.6,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-20DOI: 10.1016/j.agrformet.2025.110398
Yue Yu , Jiaojun Zhu , Tian Gao , Zhihua Liu , Lifang Liu , Fengyuan Yu , Jinxin Zhang
Rainfall interception loss (I) by forest canopy is a crucial hydrological process in forest ecosystems, and thus its accurate modeling is essential for understanding water balance. The revised Gash model is commonly employed in I modeling; however, its performance is affected by the accuracy of canopy storage capacity (S), which is identified as one of the most sensitive parameters. Consequently, optimizing the estimation of S and then cascading application in the revised Gash model warrants further attention. In this study, we measured gross rainfall, throughfall, and stemflow for the larch (Larix kaempferi) plantation forest (LPF) and the Mongolian oak (Quercus mongolica) forest (MOF) in Northeast China in 2018 and 2019. Terrestrial laser scanning (TLS) was introduced to derive S (Sex). Sex was then compared with values calculated from two commonly regression-based methods (Smean and Smini). Finally, the revised Gash model was run using the three types of S, and the model performances were evaluated. As a result, I of LPF (27.9 %) was higher than that of MOF (20.1 %). For LPF and MOF, S calculated from Sex was the largest (1.45 and 0.51 mm), followed in descending order by Smean (0.98 and 0.32 mm) and Smini (0.29 and 0.13 mm). Compared with models run with Smean and Smini, Sex improved the model performance, regardless of whether the Penman-Monteith equation or a linear regression method was used to calculate the evaporation rate (another sensitive parameter of the revised Gash model). Moreover, the model using Sex particularly enhanced the model's accuracy at middle and heavy rainfall levels. In conclusion, the TLS-derived S improves the model performance in temperate forests in Northeast China. Meanwhile, in contrast to previous studies, which emphasized the contribution of evaporation rate/rainfall intensity (E/R) in modelling larger rainfall events, this study suggests the role of S should not be overlooked.
{"title":"Terrestrial laser scanning-derived canopy storage capacity improves the performance of the revised Gash model in temperate forests","authors":"Yue Yu , Jiaojun Zhu , Tian Gao , Zhihua Liu , Lifang Liu , Fengyuan Yu , Jinxin Zhang","doi":"10.1016/j.agrformet.2025.110398","DOIUrl":"10.1016/j.agrformet.2025.110398","url":null,"abstract":"<div><div>Rainfall interception loss (<em>I</em>) by forest canopy is a crucial hydrological process in forest ecosystems, and thus its accurate modeling is essential for understanding water balance. The revised Gash model is commonly employed in <em>I</em> modeling; however, its performance is affected by the accuracy of canopy storage capacity (<em>S</em>), which is identified as one of the most sensitive parameters. Consequently, optimizing the estimation of <em>S</em> and then cascading application in the revised Gash model warrants further attention. In this study, we measured gross rainfall, throughfall, and stemflow for the larch (<em>Larix kaempferi</em>) plantation forest (LPF) and the Mongolian oak (<em>Quercus mongolica</em>) forest (MOF) in Northeast China in 2018 and 2019. Terrestrial laser scanning (TLS) was introduced to derive <em>S</em> (<em>S<sub>ex</sub></em>). <em>S<sub>ex</sub></em> was then compared with values calculated from two commonly regression-based methods (<em>S<sub>mean</sub></em> and <em>S<sub>mini</sub></em>). Finally, the revised Gash model was run using the three types of <em>S</em>, and the model performances were evaluated. As a result, <em>I</em> of LPF (27.9 %) was higher than that of MOF (20.1 %). For LPF and MOF, <em>S</em> calculated from <em>S<sub>ex</sub></em> was the largest (1.45 and 0.51 mm), followed in descending order by <em>S<sub>mean</sub></em> (0.98 and 0.32 mm) and <em>S<sub>mini</sub></em> (0.29 and 0.13 mm). Compared with models run with <em>S<sub>mean</sub></em> and <em>S<sub>mini</sub>, S<sub>ex</sub></em> improved the model performance, regardless of whether the Penman-Monteith equation or a linear regression method was used to calculate the evaporation rate (another sensitive parameter of the revised Gash model). Moreover, the model using <em>S<sub>ex</sub></em> particularly enhanced the model's accuracy at middle and heavy rainfall levels. In conclusion, the TLS-derived <em>S</em> improves the model performance in temperate forests in Northeast China. Meanwhile, in contrast to previous studies, which emphasized the contribution of evaporation rate/rainfall intensity (<em>E/R</em>) in modelling larger rainfall events, this study suggests the role of <em>S</em> should not be overlooked.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"363 ","pages":"Article 110398"},"PeriodicalIF":5.6,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990774","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 : 2025-01-18DOI: 10.1016/j.agrformet.2025.110394
Kaidi Zhang , Yanyu Lu , Chunfeng Duan , Fangmin Zhang , Xinfeng Ling , Yun Yao , Zhuang Wang , Xintong Chen , Shaowei Yan , Yanfeng Huo , Yuan Gong
Understanding of the crop carbon balance across different time scales and corresponding responses to abiotic and biotic factors is crucial for improving carbon cycle models in the context of future climate change and management practices. In this study, we employed the Random Forest (RF) algorithm, Kolmogorov-Zurbenko filtering method and structural equation modeling (SEM) to quantify the effects of abiotic and biotic factors on CO2 fluxes at various time scales based on 7-years measurements. Our results revealed that O3 primarily manifested indirect effects on NEE and GPP via altering LAI on the daily and monthly scale, and that overall regulatory effect on CO2 fluxes developed greater as the time scale increased. Net radiation (Rn) was the most critical abiotic factor altering net ecosystem exchange (NEE) and gross primary productivity (GPP) at the half-hourly, daily, and monthly scales, with the exception of photosynthetically active radiation (PAR) controlling daily NEE and GPP in the rice system. It was innovatively found that LAI had little control on detrended daily CO2 fluxes, which was much lower than the monthly CO2 fluxes. Air temperature (Ta) was the most important abiotic factor for ecosystem respiration (Reco) at half-hourly and daily scale. For NEE, Reco, and GPP, the maximum explanation of SEM models was 70.10 %, 79.60 % and 76.20 %, respectively. The SEM results indicated that at multiple time scales, Rn exerted significant direct and indirect effects on both NEE and GPP. LAI only showed a strong direct leading effect on NEE and GPP on the monthly scale. The findings we reported have the potential to further develop carbon cycle models of cropland ecosystems under climate change by clarifying the influence path of O3 on CO2 fluxes and highlighting the factors that dominate CO2 fluxes on various time scales.
{"title":"Variations and drivers of CO2 fluxes at multiple temporal scales of subtropical agricultural systems in the Huaihe river Basin","authors":"Kaidi Zhang , Yanyu Lu , Chunfeng Duan , Fangmin Zhang , Xinfeng Ling , Yun Yao , Zhuang Wang , Xintong Chen , Shaowei Yan , Yanfeng Huo , Yuan Gong","doi":"10.1016/j.agrformet.2025.110394","DOIUrl":"10.1016/j.agrformet.2025.110394","url":null,"abstract":"<div><div>Understanding of the crop carbon balance across different time scales and corresponding responses to abiotic and biotic factors is crucial for improving carbon cycle models in the context of future climate change and management practices. In this study, we employed the Random Forest (RF) algorithm, Kolmogorov-Zurbenko filtering method and structural equation modeling (SEM) to quantify the effects of abiotic and biotic factors on CO<sub>2</sub> fluxes at various time scales based on 7-years measurements. Our results revealed that O<sub>3</sub> primarily manifested indirect effects on NEE and GPP via altering LAI on the daily and monthly scale, and that overall regulatory effect on CO<sub>2</sub> fluxes developed greater as the time scale increased. Net radiation (Rn) was the most critical abiotic factor altering net ecosystem exchange (NEE) and gross primary productivity (GPP) at the half-hourly, daily, and monthly scales, with the exception of photosynthetically active radiation (PAR) controlling daily NEE and GPP in the rice system. It was innovatively found that LAI had little control on detrended daily CO<sub>2</sub> fluxes, which was much lower than the monthly CO<sub>2</sub> fluxes. Air temperature (Ta) was the most important abiotic factor for ecosystem respiration (Reco) at half-hourly and daily scale. For NEE, Reco, and GPP, the maximum explanation of SEM models was 70.10 %, 79.60 % and 76.20 %, respectively. The SEM results indicated that at multiple time scales, Rn exerted significant direct and indirect effects on both NEE and GPP. LAI only showed a strong direct leading effect on NEE and GPP on the monthly scale. The findings we reported have the potential to further develop carbon cycle models of cropland ecosystems under climate change by clarifying the influence path of O<sub>3</sub> on CO<sub>2</sub> fluxes and highlighting the factors that dominate CO<sub>2</sub> fluxes on various time scales.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110394"},"PeriodicalIF":5.6,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142989112","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 : 2025-01-17DOI: 10.1016/j.agrformet.2025.110402
Tomas Poblete , Michael S. Watt , Henning Buddenbaum , Pablo J. Zarco-Tejada
<div><div>Radiata pine (<em>Pinus radiata</em> D. Don) is a widely planted tree species. Fertilizers, especially those containing leaf nitrogen (N) and phosphorous (P), are essential for maximizing growth. Nutrient deficiencies and excessive fertilization can limit growth, so monitoring is crucial. Leaf pigments such as chlorophyll <em>a</em>+<em>b</em> (C<sub>a+b</sub>) can be used to assess plant nutrition, specifically leaf N. Remote sensing approaches can be used to monitor forest condition by estimating C<sub>a+b</sub> content as a proxy for leaf N. Conventional methods for C<sub>a+b</sub> estimation are based on empirical relationships using sensitive spectral indices or inversions of Radiative Transfer Models (RTMs). However, the structural complexity of tree crowns composed of multiple layers of clumped leaves/needles and background and shadow effects challenge the use of the indices proposed for both leaf C<sub>a+b</sub> and leaf nitrogen assessment. This study compares the accuracy of methods for C<sub>a+b</sub> estimation in radiata pine using hyperspectral data collected from a greenhouse experiment over the growing season and from a field trial representing a stand with a complex structure. The methods used to predict needle C<sub>a+b</sub> from tree-crown spectra included: 1) empirical relationships between C<sub>a+b</sub> measurements and hyperspectral indices; 2) scaling-up of hyperspectral index-based C<sub>a+b</sub> predictive relationships through RTM simulations; and 3) RTM inversions of C<sub>a+b</sub> content. These methods were tested over two different segmentation strategies, including sunlit-vegetation and full-crown spectra, to assess the effects of the increased structural complexity.</div><div>Predictions of C<sub>a+b</sub> from the greenhouse experiment were generally higher for empirical models that used TCARI/OSAVI (Transformed Chlorophyll Absorption in Reflectance Index normalized by the Optimized Soil-Adjusted Vegetation Index) and CI (Chlorophyll index) hyperspectral indices when looking at full-crown rather than sunlit-vegetation pixels. RMSE measurements for full-crown models based on TCARI/OSAVI and CI across the three seasons ranged between 3.60 and 8.71 µg/cm<sup>2</sup> and between 3.70 and 7.86 µg/cm<sup>2</sup>, respectively. Using the scaling-up methodology, the TCARI-OSAVI-derived models were more stable across different methods of pixel extraction than the CI-derived models were, showing the smallest variations across measurement dates. Predictions of C<sub>a+b</sub> in the field trial showed that PRO4SAIL2, which combines the PROSPECT-D model with the 4SAIL2 model and accounts for clumping and a more complex tree structure, was more accurate than PRO4SAIL, which couples PROSPECT-D with the original 4SAIL model, across both crown segmentation methods. Using PRO4SAIL2, predictions were more accurate for the full-crown spectra (R² = 0.82; RMSE = 3.35 µg/cm²) than for the sunlit-vegetation pixels (R² = 0
{"title":"Chlorophyll content estimation in radiata pine using hyperspectral imagery: A comparison between empirical models, scaling-up algorithms, and radiative transfer inversions","authors":"Tomas Poblete , Michael S. Watt , Henning Buddenbaum , Pablo J. Zarco-Tejada","doi":"10.1016/j.agrformet.2025.110402","DOIUrl":"10.1016/j.agrformet.2025.110402","url":null,"abstract":"<div><div>Radiata pine (<em>Pinus radiata</em> D. Don) is a widely planted tree species. Fertilizers, especially those containing leaf nitrogen (N) and phosphorous (P), are essential for maximizing growth. Nutrient deficiencies and excessive fertilization can limit growth, so monitoring is crucial. Leaf pigments such as chlorophyll <em>a</em>+<em>b</em> (C<sub>a+b</sub>) can be used to assess plant nutrition, specifically leaf N. Remote sensing approaches can be used to monitor forest condition by estimating C<sub>a+b</sub> content as a proxy for leaf N. Conventional methods for C<sub>a+b</sub> estimation are based on empirical relationships using sensitive spectral indices or inversions of Radiative Transfer Models (RTMs). However, the structural complexity of tree crowns composed of multiple layers of clumped leaves/needles and background and shadow effects challenge the use of the indices proposed for both leaf C<sub>a+b</sub> and leaf nitrogen assessment. This study compares the accuracy of methods for C<sub>a+b</sub> estimation in radiata pine using hyperspectral data collected from a greenhouse experiment over the growing season and from a field trial representing a stand with a complex structure. The methods used to predict needle C<sub>a+b</sub> from tree-crown spectra included: 1) empirical relationships between C<sub>a+b</sub> measurements and hyperspectral indices; 2) scaling-up of hyperspectral index-based C<sub>a+b</sub> predictive relationships through RTM simulations; and 3) RTM inversions of C<sub>a+b</sub> content. These methods were tested over two different segmentation strategies, including sunlit-vegetation and full-crown spectra, to assess the effects of the increased structural complexity.</div><div>Predictions of C<sub>a+b</sub> from the greenhouse experiment were generally higher for empirical models that used TCARI/OSAVI (Transformed Chlorophyll Absorption in Reflectance Index normalized by the Optimized Soil-Adjusted Vegetation Index) and CI (Chlorophyll index) hyperspectral indices when looking at full-crown rather than sunlit-vegetation pixels. RMSE measurements for full-crown models based on TCARI/OSAVI and CI across the three seasons ranged between 3.60 and 8.71 µg/cm<sup>2</sup> and between 3.70 and 7.86 µg/cm<sup>2</sup>, respectively. Using the scaling-up methodology, the TCARI-OSAVI-derived models were more stable across different methods of pixel extraction than the CI-derived models were, showing the smallest variations across measurement dates. Predictions of C<sub>a+b</sub> in the field trial showed that PRO4SAIL2, which combines the PROSPECT-D model with the 4SAIL2 model and accounts for clumping and a more complex tree structure, was more accurate than PRO4SAIL, which couples PROSPECT-D with the original 4SAIL model, across both crown segmentation methods. Using PRO4SAIL2, predictions were more accurate for the full-crown spectra (R² = 0.82; RMSE = 3.35 µg/cm²) than for the sunlit-vegetation pixels (R² = 0","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110402"},"PeriodicalIF":5.6,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142989064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-17DOI: 10.1016/j.agrformet.2025.110397
Wenqiang Zhang , Geping Luo , Rafiq Hamdi , Xiumei Ma , Piet Termonia , Philippe De Maeyer , Anping Chen
The scarcity and uneven global distribution of eddy covariance (EC) towers are the key factors that contribute to significant uncertainties in carbon cycle studies of terrestrial ecosystems. To address this limitation of EC towers, Zhang et al. (2023b) developed a meteorological station-based net ecosystem exchange (NEE) dataset. This dataset includes 4674 global meteorological stations, representing a 22-fold increase compared to the 212 existing EC towers and covering a broader range of ecosystem types. Here, we propose a systematic framework for the comprehensive assessment of spatio-temporal representativeness and global uncertainty of the meteorological station-based carbon flux dataset. Meteorological stations effectively enhance the spatial representativeness of the EC towers and reduce the latitudinal variability of the spatial representativeness. In most regions, the temporal trends of carbon flux data from meteorological stations did not significantly differ from those observed by EC towers (p < 0.001). The global uncertainty of carbon fluxes from meteorological station is 0.37, followed by the VISIT and FLUXCOM products with uncertainties of 0.44 and 0.45, respectively. Overall, the carbon fluxes from meteorological stations exhibit higher spatial representativeness and better temporal representativeness compared to the EC tower observations and possess lower global uncertainties than the existing carbon flux gridded products. Consequently, the carbon flux data derived from meteorological stations is a trade-off dataset that addresses the low spatial representativeness of the EC towers and the high uncertainty of the gridded products. It effectively complements the existing EC tower data while ensuring accuracy. The development of this dataset will play an important role in reducing the uncertainty of global carbon sink-related studies.
{"title":"Bridging the gap in carbon cycle studies: Meteorological station-based carbon flux dataset as a complement to EC towers","authors":"Wenqiang Zhang , Geping Luo , Rafiq Hamdi , Xiumei Ma , Piet Termonia , Philippe De Maeyer , Anping Chen","doi":"10.1016/j.agrformet.2025.110397","DOIUrl":"10.1016/j.agrformet.2025.110397","url":null,"abstract":"<div><div>The scarcity and uneven global distribution of eddy covariance (EC) towers are the key factors that contribute to significant uncertainties in carbon cycle studies of terrestrial ecosystems. To address this limitation of EC towers, Zhang et al. (2023b) developed a meteorological station-based net ecosystem exchange (NEE) dataset. This dataset includes 4674 global meteorological stations, representing a 22-fold increase compared to the 212 existing EC towers and covering a broader range of ecosystem types. Here, we propose a systematic framework for the comprehensive assessment of spatio-temporal representativeness and global uncertainty of the meteorological station-based carbon flux dataset. Meteorological stations effectively enhance the spatial representativeness of the EC towers and reduce the latitudinal variability of the spatial representativeness. In most regions, the temporal trends of carbon flux data from meteorological stations did not significantly differ from those observed by EC towers (p < 0.001). The global uncertainty of carbon fluxes from meteorological station is 0.37, followed by the VISIT and FLUXCOM products with uncertainties of 0.44 and 0.45, respectively. Overall, the carbon fluxes from meteorological stations exhibit higher spatial representativeness and better temporal representativeness compared to the EC tower observations and possess lower global uncertainties than the existing carbon flux gridded products. Consequently, the carbon flux data derived from meteorological stations is a trade-off dataset that addresses the low spatial representativeness of the EC towers and the high uncertainty of the gridded products. It effectively complements the existing EC tower data while ensuring accuracy. The development of this dataset will play an important role in reducing the uncertainty of global carbon sink-related studies.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110397"},"PeriodicalIF":5.6,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142989111","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 : 2025-01-16DOI: 10.1016/j.agrformet.2025.110393
Paulina A. Asante , Eric Rahn , Niels P.R. Anten , Pieter A. Zuidema , Alejandro Morales , Danaё M.A. Rozendaal
Climate change is expected to negatively impact cocoa production in West and Central Africa, where over 70 % of cocoa is grown. However, effects of temperature, precipitation and atmospheric carbon dioxide concentration [CO2] on cocoa tree physiology and productivity are poorly understood. Consequently, climate-change implications have not been adequately considered. The objective was to improve understanding of potential cocoa productivity responses to climate change by mid-century (2060).
Using a crop model, we simulated potential water-limited cocoa yields (Yw) to evaluate effects of warming and precipitation changes based on five plausible general circulation models (GCMs) climate-change scenarios, with and without elevated CO2. We examined how variation in Yw was associated with that of climate using mixed-effects models and estimated total cocoa production on current plantation area under current low-input and high-input scenarios.
With notable exceptions, by mid-century, Yw and suitable area were projected to increase, particularly when assuming full elevated [CO2] effects and under wetter climate-change scenarios. We identified a (south) east - west gradient with higher yield increases (∼39–60 %) in Cameroon and Nigeria compared to Ghana and Côte d'Ivoire (∼30–45 %). Larger yield reductions (∼12 %) were identified in Côte d'Ivoire and Ghana than in Nigeria (∼10 %) and Cameroon (∼2 %). Additionally, gains in suitable area were projected for Nigeria (∼17–20 Mha), Cameroon (∼11–12 Mha), and Ghana (∼2 Mha) while Côte d'Ivoire could lose ∼6–11 Mha (i.e., ∼27–50 % of current suitable area). Inter-annual yield variability was higher in areas with low yields. Based on the mid climate-change scenario, country-level production on current plantation area in Côte d'Ivoire and Ghana could be maintained. Projected increases and shorter length in dry season precipitation strongly determined increases in Yw and reductions in Yw variability, respectively. Thus, despite projected warming and precipitation changes, many current cocoa-growing areas may maintain or increase their productivity, particularly if full effects of elevated [CO2] are assumed.
{"title":"Climate change impacts on cocoa production in the major producing countries of West and Central Africa by mid-century","authors":"Paulina A. Asante , Eric Rahn , Niels P.R. Anten , Pieter A. Zuidema , Alejandro Morales , Danaё M.A. Rozendaal","doi":"10.1016/j.agrformet.2025.110393","DOIUrl":"10.1016/j.agrformet.2025.110393","url":null,"abstract":"<div><div>Climate change is expected to negatively impact cocoa production in West and Central Africa, where over 70 % of cocoa is grown. However, effects of temperature, precipitation and atmospheric carbon dioxide concentration [CO<sub>2</sub>] on cocoa tree physiology and productivity are poorly understood. Consequently, climate-change implications have not been adequately considered. The objective was to improve understanding of potential cocoa productivity responses to climate change by mid-century (2060).</div><div>Using a crop model, we simulated potential water-limited cocoa yields (Yw) to evaluate effects of warming and precipitation changes based on five plausible general circulation models (GCMs) climate-change scenarios, with and without elevated CO<sub>2</sub>. We examined how variation in Yw was associated with that of climate using mixed-effects models and estimated total cocoa production on current plantation area under current low-input and high-input scenarios.</div><div>With notable exceptions, by mid-century, Yw and suitable area were projected to increase, particularly when assuming full elevated [CO<sub>2</sub>] effects and under wetter climate-change scenarios. We identified a (south) east - west gradient with higher yield increases (∼39–60 %) in Cameroon and Nigeria compared to Ghana and Côte d'Ivoire (∼30–45 %). Larger yield reductions (∼12 %) were identified in Côte d'Ivoire and Ghana than in Nigeria (∼10 %) and Cameroon (∼2 %). Additionally, gains in suitable area were projected for Nigeria (∼17–20 Mha), Cameroon (∼11–12 Mha), and Ghana (∼2 Mha) while Côte d'Ivoire could lose ∼6–11 Mha (i.e., ∼27–50 % of current suitable area). Inter-annual yield variability was higher in areas with low yields. Based on the mid climate-change scenario, country-level production on current plantation area in Côte d'Ivoire and Ghana could be maintained. Projected increases and shorter length in dry season precipitation strongly determined increases in Yw and reductions in Yw variability, respectively. Thus, despite projected warming and precipitation changes, many current cocoa-growing areas may maintain or increase their productivity, particularly if full effects of elevated [CO<sub>2</sub>] are assumed.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110393"},"PeriodicalIF":5.6,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142986890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-16DOI: 10.1016/j.agrformet.2025.110396
Junliang Zou , Yun Zhang , Brian Tobin , Matthew Saunders , Erica Cacciotti , Giuseppi Benanti , Bruce Osborne
Climate change is expected to increase the frequency and intensity of water deficits and extreme rainfall events in temperate regions, with significant effects on greenhouse gas (GHG) emissions. In this study, we investigated the impact of water deficits and drying and rewetting events on GHG fluxes in two Irish sites with adjacent forest and grassland ecosystems. We deployed rain-out shelters to simulate drought and applied water to mimic the extreme precipitation events. The effects of warming on these events were also examined using soil cores collected from the field. Water deficits increased carbon dioxide (CO2) emissions at the evergreen coniferous forest site but decreased it at the broadleaf deciduous forest site, likely due to differences in the prevailing soil moisture contents and the availability of oxygen for microbial activity. Rewetting triggered pulses of CO2 (1.1 – 7.2 fold), methane (CH4) (> 20 fold), and nitrous oxide (N2O) (3.3 – 71.7 fold) emissions in both ecosystems. Warming amplified the effects of water additions, leading to a 1.9 – 3.4-fold increase in CO2 and N2O fluxes, compared to the pre-wetting levels and a 1.2 – 1.5-fold increase compared to the controls. Cumulative CO2 emissions over 24 hours showed a negative response to increasing soil moisture and a positive response to the changes in soil moisture (difference between the initial value before water addition and the final soil moisture after water addition). CH4 fluxes exhibited an opposite trend. Multiple linear regression revealed that at higher soil carbon concentrations CO2 emissions were reduced but CH4 emissions increased, for the same change in soil moisture. Given that future climate scenarios predict an increase in extreme rainfall events a better understanding of the influence of soil drying-rewetting events on GHG emissions is required that accounts for multiple influencing factors, including differences in regional and site characteristics.
{"title":"Contrasting effects of water deficits and rewetting on greenhouse gas emissions in two grassland and forest ecosystems","authors":"Junliang Zou , Yun Zhang , Brian Tobin , Matthew Saunders , Erica Cacciotti , Giuseppi Benanti , Bruce Osborne","doi":"10.1016/j.agrformet.2025.110396","DOIUrl":"10.1016/j.agrformet.2025.110396","url":null,"abstract":"<div><div>Climate change is expected to increase the frequency and intensity of water deficits and extreme rainfall events in temperate regions, with significant effects on greenhouse gas (GHG) emissions. In this study, we investigated the impact of water deficits and drying and rewetting events on GHG fluxes in two Irish sites with adjacent forest and grassland ecosystems. We deployed rain-out shelters to simulate drought and applied water to mimic the extreme precipitation events. The effects of warming on these events were also examined using soil cores collected from the field. Water deficits increased carbon dioxide (CO<sub>2</sub>) emissions at the evergreen coniferous forest site but decreased it at the broadleaf deciduous forest site, likely due to differences in the prevailing soil moisture contents and the availability of oxygen for microbial activity. Rewetting triggered pulses of CO<sub>2</sub> (1.1 – 7.2 fold), methane (CH<sub>4</sub>) (> 20 fold), and nitrous oxide (N<sub>2</sub>O) (3.3 – 71.7 fold) emissions in both ecosystems. Warming amplified the effects of water additions, leading to a 1.9 – 3.4-fold increase in CO<sub>2</sub> and N<sub>2</sub>O fluxes, compared to the pre-wetting levels and a 1.2 – 1.5-fold increase compared to the controls. Cumulative CO<sub>2</sub> emissions over 24 hours showed a negative response to increasing soil moisture and a positive response to the changes in soil moisture (difference between the initial value before water addition and the final soil moisture after water addition). CH<sub>4</sub> fluxes exhibited an opposite trend. Multiple linear regression revealed that at higher soil carbon concentrations CO<sub>2</sub> emissions were reduced but CH<sub>4</sub> emissions increased, for the same change in soil moisture. Given that future climate scenarios predict an increase in extreme rainfall events a better understanding of the influence of soil drying-rewetting events on GHG emissions is required that accounts for multiple influencing factors, including differences in regional and site characteristics.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110396"},"PeriodicalIF":5.6,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142986889","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 : 2025-01-15DOI: 10.1016/j.agrformet.2025.110389
Mengliang Ma , Qiang Li , Yaping Wang , Jin Liang , Jiangyao Wang , Jinliang Liu , Mingfang Zhang
Rainfall canopy interception plays a crucial role in rainfall redistribution and hydrological processes in forests. While previous studies have often focused on monthly or yearly time scales, the responses of forest canopy interception to different rainfall magnitudes, frequencies and intensities, particularly under changing climate conditions have been less explored. In addition, the performance of canopy interception models that capture the dynamics of rainfall interception under changing climate remains largely unknown. In this study, we conducted field observations across various tree species and used the Revised Gash model to evaluate the canopy interception under different rainfall intensities. Our findings revealed that the observed interception loss of gross precipitation were 26.1 %, 42.1 %, and 41.6 % for Pinus tabuliformis (PT), Quercus wutaishanica (QW), and Betula platyphylla (BP), respectively. The Revised Gash model accurately estimated canopy interception, with percentage errors of 0.4 %, 5.6 %, and 22.3 % for PT, QW, and BP, respectively. Interestingly, the model performed better for PT, especially under light to moderate rain, while its applicability for QW and BP were diminished under moderate to heavy rain. Overall, the Revised Gash model underestimated interception loss across different rainfall intensities, with more pronounced underestimations observed at higher rainfall intensities. Evaporation during and after rainfall contributed significantly to over 85.3 % of interception loss across three tree species. Sensitivity analysis highlighted that parameters including mean rainfall intensity, mean wet canopy evaporation rate, and canopy storage capacity were critical in influencing canopy interception simulation. These findings highlight the influence of rainfall intensity on the model's reliability in simulating interception loss and provide insights for forest hydrology research in semi-arid regions.
{"title":"Rainfall intensities determine accuracy of canopy interception simulation using the Revised Gash model","authors":"Mengliang Ma , Qiang Li , Yaping Wang , Jin Liang , Jiangyao Wang , Jinliang Liu , Mingfang Zhang","doi":"10.1016/j.agrformet.2025.110389","DOIUrl":"10.1016/j.agrformet.2025.110389","url":null,"abstract":"<div><div>Rainfall canopy interception plays a crucial role in rainfall redistribution and hydrological processes in forests. While previous studies have often focused on monthly or yearly time scales, the responses of forest canopy interception to different rainfall magnitudes, frequencies and intensities, particularly under changing climate conditions have been less explored. In addition, the performance of canopy interception models that capture the dynamics of rainfall interception under changing climate remains largely unknown. In this study, we conducted field observations across various tree species and used the Revised Gash model to evaluate the canopy interception under different rainfall intensities. Our findings revealed that the observed interception loss of gross precipitation were 26.1 %, 42.1 %, and 41.6 % for <em>Pinus tabuliformis</em> (<em>PT</em>), <em>Quercus wutaishanica</em> (<em>QW</em>), and <em>Betula platyphylla</em> (<em>BP</em>), respectively. The Revised Gash model accurately estimated canopy interception, with percentage errors of 0.4 %, 5.6 %, and 22.3 % for <em>PT, QW</em>, and <em>BP</em>, respectively. Interestingly, the model performed better for <em>PT</em>, especially under light to moderate rain, while its applicability for <em>QW</em> and <em>BP</em> were diminished under moderate to heavy rain. Overall, the Revised Gash model underestimated interception loss across different rainfall intensities, with more pronounced underestimations observed at higher rainfall intensities. Evaporation during and after rainfall contributed significantly to over 85.3 % of interception loss across three tree species. Sensitivity analysis highlighted that parameters including mean rainfall intensity, mean wet canopy evaporation rate, and canopy storage capacity were critical in influencing canopy interception simulation. These findings highlight the influence of rainfall intensity on the model's reliability in simulating interception loss and provide insights for forest hydrology research in semi-arid regions.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110389"},"PeriodicalIF":5.6,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142981536","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 : 2025-01-13DOI: 10.1016/j.agrformet.2024.110378
Jasper Schreiber , Václav Pouska , Petr Macek , Dominik Thom , Claus Bässler
Deadwood is a crucial component of forest ecosystems, supporting numerous forest-dwelling species and ecosystem functions, such as water and nutrient cycling. Temperature is a major driver of processes, affecting, inter alia, metabolic rates within deadwood. Deadwood temperature is determined by factors at both the forest stand-scale and individual deadwood object-scale. Yet, the contribution of individual factors within the complex hierarchy of scales that drive temperature in deadwood remains poorly understood. We conducted a real-world experiment to analyze the effects of forest stand canopy cover (open vs. closed canopies), surrounding deadwood amount (high vs. low), deadwood tree species (beech vs. fir), position (soil contact vs. uplifted) and diameter (range: 19-47 cm) of coarse woody debris on within-deadwood daily mean, minimum and maximum temperature at monthly and seasonal level. Stand-scale factors were more important than object-scale factors for explaining the variance in temperature. Canopy cover exhibited the strongest relationship with temperature. Daily mean and maximum temperature were higher and daily minimum temperature was lower in open than in closed canopies during the growing season (May-October). Further, daily minimum was lower in open canopies during winter (November-April). Annual daily mean and maximum temperature were about 1 °C and 5 °C warmer, respectively, and minimum temperature about 2 °C colder in open compared to closed canopies. Effects of deadwood amount, object diameter, position, and tree species on temperature were less important and statistically significant in only a few months. We conclude that canopy cover is more important than deadwood characteristics in determining internal deadwood temperature. An increase of canopy disturbance will hence elevate the temperature in deadwood, which might have important consequences on deadwood-dwelling species and ecological processes, such as heterotrophic respiration. To diversify habitat conditions for multiple species, we recommend enriching deadwood under various canopy conditions.
{"title":"Effects of canopy-mediated microclimate and object characteristics on deadwood temperature","authors":"Jasper Schreiber , Václav Pouska , Petr Macek , Dominik Thom , Claus Bässler","doi":"10.1016/j.agrformet.2024.110378","DOIUrl":"10.1016/j.agrformet.2024.110378","url":null,"abstract":"<div><div>Deadwood is a crucial component of forest ecosystems, supporting numerous forest-dwelling species and ecosystem functions, such as water and nutrient cycling. Temperature is a major driver of processes, affecting, <em>inter alia</em>, metabolic rates within deadwood. Deadwood temperature is determined by factors at both the forest stand-scale and individual deadwood object-scale. Yet, the contribution of individual factors within the complex hierarchy of scales that drive temperature in deadwood remains poorly understood. We conducted a real-world experiment to analyze the effects of forest stand canopy cover (open vs. closed canopies), surrounding deadwood amount (high vs. low), deadwood tree species (beech vs. fir), position (soil contact vs. uplifted) and diameter (range: 19-47 cm) of coarse woody debris on within-deadwood daily mean, minimum and maximum temperature at monthly and seasonal level. Stand-scale factors were more important than object-scale factors for explaining the variance in temperature. Canopy cover exhibited the strongest relationship with temperature. Daily mean and maximum temperature were higher and daily minimum temperature was lower in open than in closed canopies during the growing season (May-October). Further, daily minimum was lower in open canopies during winter (November-April). Annual daily mean and maximum temperature were about 1 °C and 5 °C warmer, respectively, and minimum temperature about 2 °C colder in open compared to closed canopies. Effects of deadwood amount, object diameter, position, and tree species on temperature were less important and statistically significant in only a few months. We conclude that canopy cover is more important than deadwood characteristics in determining internal deadwood temperature. An increase of canopy disturbance will hence elevate the temperature in deadwood, which might have important consequences on deadwood-dwelling species and ecological processes, such as heterotrophic respiration. To diversify habitat conditions for multiple species, we recommend enriching deadwood under various canopy conditions.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110378"},"PeriodicalIF":5.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-13DOI: 10.1016/j.agrformet.2025.110386
Huijun Qin , Yuanshang Guo , Chengyi Li , Chunming Xin , Rui Hu , Mingzhu He
Climate change has significantly altered precipitation patterns worldwide, resulting in more frequent and intense droughts and heavy rainstorms, particularly in vulnerable ecosystems such as arid deserts. This study investigated how dominant desert shrubs, the C3 plant Kalidium gracile and the C4 plant Salsola passerina, respond to varying precipitation regimes. A six-year controlled experiment (2016–2021) employing a five-level precipitation gradient, ranging from extreme drought to increased water availability, was conducted to elucidate changes in leaves carbon content and its components under these conditions. Results indicated a substantial increase in starch (ST) content in S. passerina under heightened rainfall conditions (P < 0.05), whereas K. gracile showed a propensity tendency to accumulate ST content under moderate drought condition. These findings indicated distinct adaptive strategies between the two species in response to water availability. Additionally, both shrubs maintained a relatively stable ratio of non-structural carbohydrates (NSC) to structural carbohydrates (SC) (P > 0.05), suggesting an active regulation of carbon balance within plant structures, independent of precipitation changes. Notably, S. passerina demonstrated greater responsiveness to precipitation alterations compared to K. gracile, highlighting species-specific differences in carbon allocation strategies. This study provides mechanistic insights into plant carbon dynamics in response to precipitation changes in desert ecosystems, contributing to a deeper understanding of carbon cycling processes and ecosystem functioning in arid landscapes.
{"title":"Carbohydrate allocation strategies in leaves of dominant desert shrubs in response to precipitation variability","authors":"Huijun Qin , Yuanshang Guo , Chengyi Li , Chunming Xin , Rui Hu , Mingzhu He","doi":"10.1016/j.agrformet.2025.110386","DOIUrl":"10.1016/j.agrformet.2025.110386","url":null,"abstract":"<div><div>Climate change has significantly altered precipitation patterns worldwide, resulting in more frequent and intense droughts and heavy rainstorms, particularly in vulnerable ecosystems such as arid deserts. This study investigated how dominant desert shrubs, the C<sub>3</sub> plant <em>Kalidium gracile</em> and the C<sub>4</sub> plant <em>Salsola passerina</em>, respond to varying precipitation regimes. A six-year controlled experiment (2016–2021) employing a five-level precipitation gradient, ranging from extreme drought to increased water availability, was conducted to elucidate changes in leaves carbon content and its components under these conditions. Results indicated a substantial increase in starch (ST) content in <em>S. passerina</em> under heightened rainfall conditions (<em>P</em> < 0.05), whereas <em>K. gracile</em> showed a propensity tendency to accumulate ST content under moderate drought condition. These findings indicated distinct adaptive strategies between the two species in response to water availability. Additionally, both shrubs maintained a relatively stable ratio of non-structural carbohydrates (NSC) to structural carbohydrates (SC) (<em>P</em> > 0.05), suggesting an active regulation of carbon balance within plant structures, independent of precipitation changes. Notably, <em>S. passerina</em> demonstrated greater responsiveness to precipitation alterations compared to <em>K. gracile</em>, highlighting species-specific differences in carbon allocation strategies. This study provides mechanistic insights into plant carbon dynamics in response to precipitation changes in desert ecosystems, contributing to a deeper understanding of carbon cycling processes and ecosystem functioning in arid landscapes.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110386"},"PeriodicalIF":5.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968310","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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