Agricultural and Forest Meteorology最新文献

Deep percolation and soil water dynamics under different sand-fixing vegetation types in two different precipitation regions in semiarid sandy Land, Northern China

IF 5.6 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2025-02-18 DOI: 10.1016/j.agrformet.2025.110455

Liang He , Yiben Cheng , Wenbin Yang , Jianbin Guo , Zhiming Xin , Lin Chen , Wei Xiong , Qianqian Wang , Huaiyuan Liu

Large-scale afforestation has undoubtedly aided in combating desertification but it also exerts negative effects on the hydrological cycle, particularly on deep percolation (DP) and soil water dynamics. This study aims to fill the gap in current research on the effect of different sand-fixing vegetation types on DP and soil water in two different precipitation regions through in-situ tests and direct measurements. The experiment focused on various vegetation types in two sites with different precipitation levels: the Mu Us Sandy Land with four plots (mobile sand [MS], Artemisia ordosica semishrub fixed [AOF], Salix psammophila shrub fixed [SPF], and Pinus sylvestrix var. Mongolica arbor fixed [PSMF] sands) and the Horqin Sandy Land with three plots (mobile sand, Caragana microphylla shrub fixed [CMF] and Populus bolleana Lauche arbor fixed [PBLF] sands). To accurately estimate DP and soil water under various vegetation types, DP was measured using a deep percolation recorder and the relative extractable soil water (RESW) was calculated based on soil water. The rainfall threshold (10 mm) of MS for the occurrence of DP was the same in both sites but the precipitation amount during a rainfall event causing significant increases in DP was different. The canopy interception and root uptake of vegetation significantly reduced DP amount compared with MS at the daily and monthly scales. The DP amount in vegetated plots in the two areas could be ranked as follows: semishrub > shrub > arbor. Compared with MS, the soil profile (0–200 cm) of vegetated plots showed significant decreases in RESW. Within the soil layer of 40–200 cm, RESW was significantly higher in shrub plots than in arbor plots. Arbor plots had an imbalanced water budget, consuming more deep soil water (120–200 cm). Our findings provide a scientific foundation for ecological restoration and water resource management.

{"title":"Deep percolation and soil water dynamics under different sand-fixing vegetation types in two different precipitation regions in semiarid sandy Land, Northern China","authors":"Liang He , Yiben Cheng , Wenbin Yang , Jianbin Guo , Zhiming Xin , Lin Chen , Wei Xiong , Qianqian Wang , Huaiyuan Liu","doi":"10.1016/j.agrformet.2025.110455","DOIUrl":"10.1016/j.agrformet.2025.110455","url":null,"abstract":"<div><div>Large-scale afforestation has undoubtedly aided in combating desertification but it also exerts negative effects on the hydrological cycle, particularly on deep percolation (DP) and soil water dynamics. This study aims to fill the gap in current research on the effect of different sand-fixing vegetation types on DP and soil water in two different precipitation regions through in-situ tests and direct measurements. The experiment focused on various vegetation types in two sites with different precipitation levels: the Mu Us Sandy Land with four plots (mobile sand [MS], <em>Artemisia ordosica</em> semishrub fixed [AOF], <em>Salix psammophila</em> shrub fixed [SPF], and <em>Pinus sylvestrix</em> var. <em>Mongolica</em> arbor fixed [PSMF] sands) and the Horqin Sandy Land with three plots (mobile sand, <em>Caragana microphylla</em> shrub fixed [CMF] and <em>Populus bolleana Lauche</em> arbor fixed [PBLF] sands). To accurately estimate DP and soil water under various vegetation types, DP was measured using a deep percolation recorder and the relative extractable soil water (RESW) was calculated based on soil water. The rainfall threshold (10 mm) of MS for the occurrence of DP was the same in both sites but the precipitation amount during a rainfall event causing significant increases in DP was different. The canopy interception and root uptake of vegetation significantly reduced DP amount compared with MS at the daily and monthly scales. The DP amount in vegetated plots in the two areas could be ranked as follows: semishrub > shrub > arbor. Compared with MS, the soil profile (0–200 cm) of vegetated plots showed significant decreases in RESW. Within the soil layer of 40–200 cm, RESW was significantly higher in shrub plots than in arbor plots. Arbor plots had an imbalanced water budget, consuming more deep soil water (120–200 cm). Our findings provide a scientific foundation for ecological restoration and water resource management.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"364 ","pages":"Article 110455"},"PeriodicalIF":5.6,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430247","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}

引用次数: 0

Global vegetation vulnerability to drought is underestimated due to the lagged effect

IF 5.6 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2025-02-17 DOI: 10.1016/j.agrformet.2025.110451

Mijia Yin , Yunhe Yin , Xuezheng Zong , Haoyu Deng

Quantifying vegetation vulnerability plays a critical role in the field of impacts and risks of extreme weather and climate. However, vegetation vulnerability assessments remain facing challenges due to complexity, nonlinearity and spatiotemporal heterogeneity of the lagged effect. In this study, we develop a Drought Vulnerability Index (DVI) based dynamic of vegetation response during the lagged period using the Standardized Precipitation Evapotranspiration Index and the Normalized Differential Vegetation Index. We examine spatiotemporal pattern of vulnerability of global terrestrial vegetation to drought and explore related driving factors. Our findings reveal that 68.22 % of terrestrial vegetation exhibits a lagged effect, primarily at 1–3 lagged months. Vegetation vulnerability of terrestrial vegetation is underestimated without considering the lagged effect. The underestimation is particularly higher in regions with 1–3 lagged months. Vegetation has higher vulnerability to more severe drought. Approximately 56.15 % of global terrestrial vegetation experiences an elevated vulnerability to drought from 1982 to 2022. Our study introduces a new perspective for a systematic scientific assessment of drought impacts, aiding in the formulation of proactive adaptation measures to mitigate drought risks.

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引用次数: 0

Evaluating the sensitivity of vegetation indices to leaf area index variability at individual tree level using multispectral drone acquisitions

IF 5.6 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2025-02-17 DOI: 10.1016/j.agrformet.2025.110441

Xianchao Tian , Xingyu Jia , Yizhuo Da , Jingyi Liu , Wenyan Ge

Vegetation indices (VIs) are widely applied to estimate leaf area index (LAI) for monitoring vegetation vigor and growth dynamics. However, the saturation issues in VIs caused by crown closure during the growing season pose significant challenges to the application of VIs in LAI estimation, particularly at the individual tree level. To address this, the feasibility of common VIs for LAI estimation at the individual tree level throughout the growing season was analyzed using data from digital hemispherical photography (DHP) and Unmanned Aerial Vehicle (UAV) acquisition. Additionally, the physical mechanisms underlying a generic VI-based estimation model were explored using the PROSAIL model and Global Sensitivity Analysis (GSA). Furthermore, the relationships between observed LAI derived from DHP and UAV-based VIs across different phenological development phases throughout the growing season were analyzed. The results suggested that the normalized difference vegetation index (NDVI) and its faster substitute infrared percentage vegetation index (IPVI) exhibited the best capabilities for LAI estimation (R² = 0.55 and RMSE = 0.77 for both) across the entire growing season. The LAI-VI relationship varied seasonally due to the saturation issues on VIs, with R² values increasing from the leaf budburst to the growing stage, decreasing during maturation, and rising again in the senescence stage. This indicated that seasonal effects induced by phenological changes should be considered when estimating LAI using VIs. Additionally, the saturation of VIs was influenced by soil background, leaf properties (especially leaf chlorophyll content [C_ab] and dry matter content [C_m]), and canopy structures (especially average leaf inclination angle, ALA). Compared to satellites, UAV-based sensors were more effective at mitigating spectral saturation at fine-scale due to their finer spatial resolution and narrower bandwidth. The drone-based VIs used in this study provided reliable estimates and effectively described temporal variability in LAI, contributing to a better understanding of VI saturation effects.

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引用次数: 0

Rice yield stability and its determinants across different rice-cropping systems in China

IF 5.6 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2025-02-16 DOI: 10.1016/j.agrformet.2025.110452

Siyuan Wang , Yijiang Liu , Senthold Asseng , Matthew Tom Harrison , Liang Tang , Bing Liu , Ke Liu , Zhongkui Luo , Enli Wang , Jinfeng Chang , Xiaolei Qiu , Leilei Liu , Xiaohu Zhang , Weixing Cao , Yan Zhu , Liujun Xiao

Rice production faces increasing challenges from climate change and soil degradation. The conversion from double to single-cropping rice over the past decades has further threatened rice self-sufficiency in China. Understanding the spatial and temporal variations of rice yield across different rice-cropping systems is crucial for creating adaptation strategies. Here we used a process-based modelling approach combined with a nationwide field dataset from 1981 to 2020 to evaluate rice yield gaps and temporal yield variabilities for single and double rice-cropping systems, and further assessed their underlying determinants in China. We showed that single rice had the largest yield gap and the greatest temporal variability in yield, followed by late rice and early rice. The coefficient of variation (CV) for actual yield ranged from 6 % to 64 %, 4 % to 36 %, and 5 % to 28 % for single rice, late rice, and early rice, respectively. Regions with unstable yields were primarily located in southwestern (for single rice) and southern China (for late rice), and determinants of yield stability varied across subregions. Overall, the combined effects of climate and soil factors generally reduce yield stability. Improved management, such as appropriate sowing dates, precise fertilization, and cultivars with favorable traits, significantly enhanced the stability. Socio-economic factors including sufficient labor and advanced agricultural mechanization also contributed to closing yield gaps and stabilizing yield. This study provides spatial insights for developing region-specific strategies to ensure a sufficient and stable rice supply.

水稻生产面临着气候变化和土壤退化带来的日益严峻的挑战。过去几十年来，中国水稻从双季稻种植转变为单季稻种植，这进一步威胁到中国水稻的自给自足。了解不同水稻种植制度下水稻产量的时空变化对于制定适应战略至关重要。在此，我们采用基于过程的建模方法，结合 1981 年至 2020 年的全国田间数据集，评估了中国单季稻和双季稻种植制度的水稻产量差距和产量时空变异，并进一步评估了其基本决定因素。结果表明，单季稻的产量差距最大，产量的时间变异性也最大，其次是晚稻和早稻。单季稻、晚稻和早稻实际产量的变异系数（CV）分别为 6 % 至 64 %、4 % 至 36 % 和 5 % 至 28 %。产量不稳定的地区主要分布在西南地区（单季稻）和华南地区（晚稻），而产量稳定的决定因素在各次区域之间存在差异。总体而言，气候和土壤因素的综合影响普遍降低了产量稳定性。改进管理，如适当的播种期、精确施肥和具有良好性状的栽培品种，可显著提高产量的稳定性。社会经济因素，包括充足的劳动力和先进的农业机械化，也有助于缩小产量差距和稳定产量。这项研究为制定针对具体地区的战略以确保充足稳定的稻米供应提供了空间见解。

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引用次数: 0

Robust filling of extra-long gaps in eddy covariance CO2 flux measurements from a temperate deciduous forest using eXtreme Gradient Boosting

IF 5.6 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2025-02-16 DOI: 10.1016/j.agrformet.2025.110438

Yujie Liu , Benjamin Lucas , Darby D. Bergl , Andrew D. Richardson

Eddy Covariance measurements are often subject to missing values, or gaps in the data record. Methods to fill short gaps are well-established, but robustly filling gaps longer than a few weeks remains a challenge. Marginal Distribution Sampling (MDS) is a standard gap-filling method, but its effectiveness for long gaps (> 30 days) is limited. We compared the performance of a machine learning algorithm, eXtreme Gradient Boosting (XGB) against MDS, using various artificial scenarios of gap lengths and locations. We gapfilled half hourly CO₂ flux from a temperate deciduous forest, Bartlett Experimental Forest, from 2010 to 2022. Whereas the standard implementation of MDS uses a narrowly-prescribed set of predictor variables, with XGB we were able to include additional variables. The Green Chromatic Coordinate (GCC), derived from PhenoCam imagery, and diffuse photosynthetic photon flux density, emerged as two of the three most important predictor variables. Compared to MDS, the root mean square error (RMSE) of XGB decreased by 9.5 %, and the R² increased by 2.7 % in a randomized 10-fold cross validation test. XGB outperformed MDS for both day and night times across different seasons. But annual NEE integrals varied across methods, with weaker annual net carbon uptake, by -110 ± 74 g C m^-2 y^-1 for XGB compared to MDS (214 ± 11 g C m^-2 yr^-1). In artificial gap experiments, when trained using the 13-year data record, XGB reliably filled gaps, showing little change in RMSE for gaps up to 240 days. In contrast, the performance of MDS steadily decreased as gap lengths increased. MDS was unable to fill gaps longer than 2 months. In summary, XGB demonstrates excellent performance as an alternative method to MDS, providing reliable predictions for temperate deciduous forest carbon fluxes under different gap lengths and location scenarios. Implementation of XGB is facilitated by easy-to-use packages.

{"title":"Robust filling of extra-long gaps in eddy covariance CO2 flux measurements from a temperate deciduous forest using eXtreme Gradient Boosting","authors":"Yujie Liu , Benjamin Lucas , Darby D. Bergl , Andrew D. Richardson","doi":"10.1016/j.agrformet.2025.110438","DOIUrl":"10.1016/j.agrformet.2025.110438","url":null,"abstract":"<div><div>Eddy Covariance measurements are often subject to missing values, or gaps in the data record. Methods to fill short gaps are well-established, but robustly filling gaps longer than a few weeks remains a challenge. Marginal Distribution Sampling (MDS) is a standard gap-filling method, but its effectiveness for long gaps (> 30 days) is limited. We compared the performance of a machine learning algorithm, eXtreme Gradient Boosting (XGB) against MDS, using various artificial scenarios of gap lengths and locations. We gapfilled half hourly CO<sub>2</sub> flux from a temperate deciduous forest, Bartlett Experimental Forest, from 2010 to 2022. Whereas the standard implementation of MDS uses a narrowly-prescribed set of predictor variables, with XGB we were able to include additional variables. The Green Chromatic Coordinate (GCC), derived from PhenoCam imagery, and diffuse photosynthetic photon flux density, emerged as two of the three most important predictor variables. Compared to MDS, the root mean square error (RMSE) of XGB decreased by 9.5 %, and the R<sup>2</sup> increased by 2.7 % in a randomized 10-fold cross validation test. XGB outperformed MDS for both day and night times across different seasons. But annual NEE integrals varied across methods, with weaker annual net carbon uptake, by -110 ± 74 g C m<sup>-2</sup> y<sup>-1</sup> for XGB compared to MDS (214 ± 11 g C m<sup>-2</sup> yr<sup>-1</sup>). In artificial gap experiments, when trained using the 13-year data record, XGB reliably filled gaps, showing little change in RMSE for gaps up to 240 days. In contrast, the performance of MDS steadily decreased as gap lengths increased. MDS was unable to fill gaps longer than 2 months. In summary, XGB demonstrates excellent performance as an alternative method to MDS, providing reliable predictions for temperate deciduous forest carbon fluxes under different gap lengths and location scenarios. Implementation of XGB is facilitated by easy-to-use packages.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"364 ","pages":"Article 110438"},"PeriodicalIF":5.6,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143418073","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}

引用次数: 0

Quantifying the effects of aerosols and cloud radiative effect on rice growth and yield

IF 5.6 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2025-02-16 DOI: 10.1016/j.agrformet.2025.110453

Yuting Liu , Lunche Wang , Xinxin Chen , Zigeng Niu , Ming Zhang , Jia Sun , Junfang Zhao

Radiative Effect (RE) caused by aerosols and clouds significantly impacts crop growth by altering both the spectral distribution and the amount of diffuse light reaching the crop canopy. Traditional crop models often fail to account for these variations in photosynthetically active radiation (PAR), leading to biases in crop growth simulations. To address this, we modified the ORYZA2000 crop model to improve the accuracy of radiation assessment. Using the RTM LibRadtran, we evaluated the effects of aerosol and cloud RE on rice growth and yield, utilizing data from Jiangxi Province, China (2011–2016). The results indicated that aerosols scatter PAR more effectively, with a scatter intensity 1.42 times greater than that of Shortwave Radiation (SW). Clouds increased the ratio of PAR in SW (FPAR) from 0.437 ± 0.01 to 0.448 ± 0.01. Ignoring PAR assessment in the crop model led to a 25.55 % overestimation of rice yield. When accounting for the diffuse fraction of PAR (PARDF) and FPAR, aerosol direct radiative effect (ADRE) increased rice yield by 18.58 %, cloud radiative effect (CRE) decreased yields by 16.13 %, and combined aerosol and cloud radiative effect (ACRE) resulted in a 31.71 % decrease in yields. Although aerosols alone increased yield by enhancing diffuse PAR, the combined effect of clouds and aerosols resulted in a lower overall PAR and caused a greater reduction in yield than clouds alone. Early rice exhibited more sensitivity to RE, allocating more biomass to the panicle in the early stages, while late rice increased leaf biomass during late stages under RE. This study underscores the importance of accurate PAR estimation in crop modeling and highlights the need to integrate the diverse impacts of RE into future crop yield predictions.

气溶胶和云层造成的辐射效应（RE）会改变到达作物冠层的光谱分布和漫射光量，从而对作物生长产生重大影响。传统的作物模型通常无法考虑光合有效辐射（PAR）的这些变化，从而导致作物生长模拟的偏差。为此，我们修改了 ORYZA2000 农作物模型，以提高辐射评估的准确性。利用 RTM LibRadtran，我们利用中国江西省（2011-2016 年）的数据评估了气溶胶和云 RE 对水稻生长和产量的影响。结果表明，气溶胶能更有效地散射 PAR，其散射强度是短波辐射（SW）的 1.42 倍。云使 PAR 与 SW 的比率（FPAR）从 0.437 ± 0.01 增加到 0.448 ± 0.01。在作物模型中忽略 PAR 评估会导致水稻产量高估 25.55%。当考虑 PAR 的漫射部分（PARDF）和 FPAR 时，气溶胶直接辐射效应（ADRE）使水稻增产 18.58%，云辐射效应（CRE）使水稻减产 16.13%，气溶胶和云辐射效应（ACRE）导致水稻减产 31.71%。虽然气溶胶本身通过提高漫射PAR增加了产量，但云层和气溶胶的综合效应导致总体PAR降低，造成的减产幅度大于云层本身。早稻对可再生能源更敏感，在早期阶段将更多生物量分配给圆锥花序，而晚稻则在可再生能源的后期阶段增加叶片生物量。这项研究强调了在作物建模中准确估计PAR的重要性，并突出了将RE的各种影响纳入未来作物产量预测的必要性。

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引用次数: 0

Building spring development indices for woody species in the conterminous United States 建立美国大陆地区木本植物春季生长指数

IF 5.6 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2025-02-15 DOI: 10.1016/j.agrformet.2025.110443

Joshua J. Hatzis , Mark D. Schwartz , Toby R. Ault , Alison Donnelly , Amanda Gallinat , Xiaolu Li , Theresa M. Crimmins

Phenological indices are an effective approach for assessing spatial and temporal patterns and variability in plant development. The Spring Indices (SI-x), two widely adopted phenological indices, have been used in recent decades to predict development of woody plants, and document changes in spring growth timing, especially in North America. However, these two indices (Leaf and Bloom) capture only two “moments” in the continuum of spring when quantities of thermal or photo/thermal energy, associated with seasonal events in plants, are accumulated, limiting their utility to characterize the remainder of the spring season. Further, the Spring Indices do not account for intraspecific variation, limiting their ability to reflect non-cloned plant development. To address these shortcomings, we developed a novel suite of phenological indices that encompass a broader span of the spring season. These indices were constructed using observations contributed to the USA National Phenology Network's Nature's Notebook platform across many non-cloned tree and shrub species’ ranges, thereby incorporating differing regional responses within species due to genetic variations.

Individual species model predictions of leaf or bloom timing exhibited an average mean absolute error of 8.55 days; most were improved by the inclusion of site-specific latitude, elevation, or 30-year average temperature. Leaf and bloom model outputs for individual species across the spring season were temporally aggregated into four leaf and bloom groups to produce a suite of Spring Development Indices (SDI). Accuracy of the SDI predictions was 0.89 days lower, on average, than the species models, but 2.65 days better than SI-x. Generally, all SDIs were highly correlated. The SDIs exhibiting the most difference from the others were Early leaf, Very Early bloom, and Late bloom. As such, these SDIs provide novel insights, beyond SI-x, into the relative timing of spring-season “moments” across species in space and time.

物候指数是评估植物发育的时空模式和变异性的有效方法。春季指数（SI-x）是两个被广泛采用的物候指数，近几十年来一直被用于预测木本植物的生长发育，并记录春季生长时间的变化，尤其是在北美洲。然而，这两个指数（叶片指数和开花指数）只捕捉到春季连续过程中的两个 "时刻"，即与植物季节性事件相关的热能或光/热能积累的时刻，这限制了它们在描述春季剩余时间特征方面的作用。此外，"春季指数 "没有考虑种内差异，限制了其反映非克隆植物发育的能力。为了解决这些缺陷，我们开发了一套新的物候指数，涵盖了春季更广泛的范围。这些指数是利用在美国国家物候网络的自然笔记本平台上对许多非克隆乔木和灌木物种分布区的观测数据构建的，从而纳入了物种内部因遗传变异而产生的不同区域反应。单个物种模型预测的叶期或花期平均绝对误差为 8.55 天；纳入特定地点的纬度、海拔或 30 年平均气温后，大多数指数都得到了改善。单个物种在整个春季的发叶和开花模型输出被按时间汇总为四个发叶和开花组，以产生一套春季发育指数（SDI）。春季发育指数的预测精度平均比物种模型低 0.89 天，但比 SI-x 高 2.65 天。一般来说，所有 SDI 都高度相关。差异最大的 SDI 是早期叶片、极早期开花和晚期开花。因此，除了 SI-x 外，这些 SDI 还提供了关于不同物种在空间和时间上的春季季节 "时刻 "的相对时间的新见解。

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引用次数: 0

Vapor pressure deficit dominates dryness stress on forest biomass carbon in China under global warming

IF 5.6 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2025-02-12 DOI: 10.1016/j.agrformet.2025.110440

Yunfeng Cen , Mei Tang , Qingyuan Wang , Guanfang Sun , Zhiming Han , Yonghong Li , Zhaoliang Gao

Soil moisture (SM) and vapor pressure deficit (VPD) are key factors affecting forest carbon stock. However, their effects on forest biomass carbon under hotter and drier climate trends are unclear. These knowledge gaps limit forest management practices and the implementation of climate change mitigation programs. In this study, satellite observations and meteorological data were combined to analyze the asymmetric response of forest biomass carbon to wet and dry changes in China from 2002 to 2020 and identify the relative contributions and influence pathways of SM and VPD on forest biomass carbon under global warming. The results showed that drought did not lead to a decrease in forest biomass carbon but slowed its rate of increase. Excluding the interaction effects of SM and VPD with temperature (Tmp), the dominant effects of SM and VPD on forest biomass carbon differed between dry and wet regions, but the effects of VPD on forest biomass carbon were broader and larger. Notably, the interaction of Tmp and VPD not only amplifies the positive effects of warming on humid regions but also amplifies the negative effects of warming on semi-arid regions, and to some extent offsets the positive effects of warming on sub-humid regions. Additionally, in warming environments, VPD exerts the greatest stress on forest biomass carbon in areas where precipitation (Pre) is 400–700 mm yr⁻¹ and potential evapotranspiration (Pet) is 650–900 mm yr⁻¹. Our results reconcile the contradictions regarding the relative importance of SM and VPD on forest carbon storage and the direction of the influence of VPD on forest carbon sequestration, thereby enhancing our understanding of forest ecosystem carbon cycling in response to climate change.

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引用次数: 0

A lightweight SIF-based crop yield estimation model: A case study of Australian wheat

IF 5.6 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2025-02-11 DOI: 10.1016/j.agrformet.2025.110439

Jinru Xue , Alfredo Huete , Zhunqiao Liu , Sicong Gao , Xiaoliang Lu

As Australia's primary staple and export crop, wheat necessitates reliable yield mapping to ensure timely alerts about food insecurity. Conventional crop yields are estimated using either process-based or statistical models, but both face challenges in large-scale application due to the extensive data required. Recent studies have shown that the gross primary production (GPP) of plants can be mechanistically estimated from the fraction of open PSII reaction centers (q_L), solar-induced chlorophyll fluorescence (SIF), and readily accessible meteorological datasets including air temperature (T_air), dew-point temperature, and soil water content. q_L can be modeled as a function of SIF and T_air. Along with these theoretical advances, the resolution of satellite SIF has greatly improved, boosting the potential for accurate large-scale crop yield estimation. In this study, we develop a SIF-based lightweight crop model which uses q_L and SIF to track crop GPP. This approach allows for a direct mechanistic estimation of GPP without the need to explicitly account for numerous complex agro-climatic processes. We apply this model to estimate Australian wheat yields from 2019 to 2022. The model exhibits strong predictive power, explaining 86 % of wheat production variance at the regional level (RMSE: 91 kilotons, rRMSE: 7.24 %) and 91 % at the state level (RMSE: 1509 kilotons, rRMSE: 14.13 %). Australian wheat yields exhibit a positive correlation with soil water content and vapor pressure deficit (VPD) when VPD remains below 0.80 kPa. However, the correlation turns negative once VPD exceeds this threshold. We also identify the main sources of error in estimating wheat production as: (1) inaccuracies in estimating the harvested area of wheat, and (2) the relatively low spatial resolution of current satellite SIF data. Our model, with its lightweight design and its ability to mechanistically estimate crop photosynthetic CO₂ assimilation, offers a promising, novel framework for practical, large-scale crop yield mapping.

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引用次数: 0

Drying-rewetting cycles decrease temperature sensitivity of soil organic matter decomposition

IF 5.6 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2025-02-11 DOI: 10.1016/j.agrformet.2025.110442

Junjie Lin , Wenling Zhang , Amit Kumar , Dafeng Hui , Changai Zhang , Shengdao Shan , Zhiguo Yu , Biao Zhu , Yakov Kuzyakov

Soil organic carbon (SOC) decomposition is crucial in the global carbon cycle. Its sensitivity to warming significantly impacts climate change. However, the effect of soil drying-rewetting, a consequence of climate change-induced water cycling shifts, on SOC decomposition sensitivity remains poorly understood. This study investigated how drying-rewetting cycles affect the temperature sensitivity (Q₁₀) of SOC decomposition and its underlying mechanisms. We collected soils from two farmlands with 23- and 33-year C₃C₄ vegetation switches The soils were incubated at 20 °C or 30 °C for 180 days under alternate drying-rewetting cycles (100 %−20 % water holding capacity, WHC) or constant moisture (60 % WHC). Using ¹³C natural abundance, we differentiated CO₂ sources from recent SOC (C₄, <23 or <33 years) and old SOC (C₃, >23 or >33 years). Results showed that warming and drying-rewetting enhanced total SOC decomposition. Across moisture conditions, the Q₁₀ of old SOC was 0.25−0.40 units higher than that of recent SOC. Six drying-rewetting cycles decreased the Q₁₀ of total, recent, and old SOC by 0.30−0.44 units compared to constant moisture, as warming became less dominant during the drying-rewetting process. This indicates that the commonly used Q₁₀ might be overestimated under constant moisture, suggesting that the feedback of SOC pools to climate warming might be weaker than previously expected under real soil moisture fluctuations.

{"title":"Drying-rewetting cycles decrease temperature sensitivity of soil organic matter decomposition","authors":"Junjie Lin , Wenling Zhang , Amit Kumar , Dafeng Hui , Changai Zhang , Shengdao Shan , Zhiguo Yu , Biao Zhu , Yakov Kuzyakov","doi":"10.1016/j.agrformet.2025.110442","DOIUrl":"10.1016/j.agrformet.2025.110442","url":null,"abstract":"<div><div>Soil organic carbon (SOC) decomposition is crucial in the global carbon cycle. Its sensitivity to warming significantly impacts climate change. However, the effect of soil drying-rewetting, a consequence of climate change-induced water cycling shifts, on SOC decomposition sensitivity remains poorly understood. This study investigated how drying-rewetting cycles affect the temperature sensitivity (Q<sub>10</sub>) of SOC decomposition and its underlying mechanisms. We collected soils from two farmlands with 23- and 33-year C<sub>3</sub><img>C<sub>4</sub> vegetation switches The soils were incubated at 20 °C or 30 °C for 180 days under alternate drying-rewetting cycles (100 %−20 % water holding capacity, WHC) or constant moisture (60 % WHC). Using <sup>13</sup>C natural abundance, we differentiated CO<sub>2</sub> sources from recent SOC (C<sub>4</sub>, <23 or <33 years) and old SOC (C<sub>3</sub>, >23 or >33 years). Results showed that warming and drying-rewetting enhanced total SOC decomposition. Across moisture conditions, the Q<sub>10</sub> of old SOC was 0.25−0.40 units higher than that of recent SOC. Six drying-rewetting cycles decreased the Q<sub>10</sub> of total, recent, and old SOC by 0.30−0.44 units compared to constant moisture, as warming became less dominant during the drying-rewetting process. This indicates that the commonly used Q<sub>10</sub> might be overestimated under constant moisture, suggesting that the feedback of SOC pools to climate warming might be weaker than previously expected under real soil moisture fluctuations.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"364 ","pages":"Article 110442"},"PeriodicalIF":5.6,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143385616","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}

引用次数: 0