Agricultural and Forest Meteorology最新文献_第4页

Optimizing biochar application rate and predicting of climate change impacts on net greenhouse gas emissions in paddy systems using DNDC-BC model

IF 5.6 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2025-02-21 DOI: 10.1016/j.agrformet.2025.110461

Zewei Jiang , Shihong Yang , Qingqing Pang , Mohamed Abdalla , Suting Qi , Jiazhen Hu , Haonan Qiu , Pete Smith

The effects of biochar application and controlled irrigation (CI, a water-saving irrigation technique) on greenhouse gas (GHG) emissions, soil organic carbon (SOC), and rice yield from paddy fields under future climate change have not been thoroughly investigated. The purpose of this study was to optimize biochar application rates to minimize net greenhouse gas emissions (NGHGE), sequestered carbon, and increase rice yield. After testing the performance of Denitrification-Decomposition-Biochar-CI (DNDC-BC), based on a two-year field experiment, the biochar application rate was optimized based on the DNDC-BC model and CMIP5 future data after Bayesian Model Averaging. The scenarios under 6 biochar amounts (C0-C50 represent 0 t ha⁻¹ to 50 t ha⁻¹) were simulated in the next 17 years (2024–2040), and the NGHGE of each were also assessed. Under the four future climate scenarios, compared with C0, the average non-CO₂ greenhouse gas emissions of C40 decreased by 48.41 %-62.63 % over the next 17 years, while the average SOC and rice yield increased by 14.58 %-21.02 % and 7.58 %-8.76 %, respectively. 40 t ha⁻¹ is the optimal biochar application rate for CI paddy fields in decreasing NGHGE in the Lake Taihu region of China. The biochar amount has a strong positive correlation with SOC and rice yield. This is the first study to optimize the biochar application for mitigating GHG, sequestrating SOC and boosting rice yield in CI paddy fields under future climate change. This study evaluates GHG, SOC, rice yield, and economic benefits of the rice system at the same time, and can be extended to other systems and regional scales.

{"title":"Optimizing biochar application rate and predicting of climate change impacts on net greenhouse gas emissions in paddy systems using DNDC-BC model","authors":"Zewei Jiang , Shihong Yang , Qingqing Pang , Mohamed Abdalla , Suting Qi , Jiazhen Hu , Haonan Qiu , Pete Smith","doi":"10.1016/j.agrformet.2025.110461","DOIUrl":"10.1016/j.agrformet.2025.110461","url":null,"abstract":"<div><div>The effects of biochar application and controlled irrigation (CI, a water-saving irrigation technique) on greenhouse gas (GHG) emissions, soil organic carbon (SOC), and rice yield from paddy fields under future climate change have not been thoroughly investigated. The purpose of this study was to optimize biochar application rates to minimize net greenhouse gas emissions (NGHGE), sequestered carbon, and increase rice yield. After testing the performance of Denitrification-Decomposition-Biochar-CI (DNDC-BC), based on a two-year field experiment, the biochar application rate was optimized based on the DNDC-BC model and CMIP5 future data after Bayesian Model Averaging. The scenarios under 6 biochar amounts (C0-C50 represent 0 t ha<sup>−1</sup> to 50 t ha<sup>−1</sup>) were simulated in the next 17 years (2024–2040), and the NGHGE of each were also assessed. Under the four future climate scenarios, compared with C0, the average non-CO<sub>2</sub> greenhouse gas emissions of C40 decreased by 48.41 %-62.63 % over the next 17 years, while the average SOC and rice yield increased by 14.58 %-21.02 % and 7.58 %-8.76 %, respectively. 40 t ha<sup>−1</sup> is the optimal biochar application rate for CI paddy fields in decreasing NGHGE in the Lake Taihu region of China. The biochar amount has a strong positive correlation with SOC and rice yield. This is the first study to optimize the biochar application for mitigating GHG, sequestrating SOC and boosting rice yield in CI paddy fields under future climate change. This study evaluates GHG, SOC, rice yield, and economic benefits of the rice system at the same time, and can be extended to other systems and regional scales.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"364 ","pages":"Article 110461"},"PeriodicalIF":5.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454399","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

Corrigendum to “Shifted trend in drought sensitivity of vegetation productivity from 1982 to 2020” [Agricultural and Forest Meteorology 362 (2024) 1-10/110388]

IF 5.6 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2025-02-21 DOI: 10.1016/j.agrformet.2025.110457

Jiwang Tang , Ben Niu , Gang Fu , Jinlong Peng , Zhigang Hu , Xianzhou Zhang

引用次数: 0

Responses of methane emissions to global wetland restoration and influencing factors

IF 5.6 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2025-02-21 DOI: 10.1016/j.agrformet.2025.110459

Shangqi Xu , Meng Na , Yuqing Miao , Chunjie Tian , Jihai Zhou , Xia Liu

Wetlands are among the largest sources of methane (CH₄) on Earth. To restore degraded ecological functions, wetland restoration has been implemented worldwide, but its impact on CH₄ emissions remains poorly understood. This study conducted a comprehensive global meta-analysis, integrating data from 59 field studies, to assess CH₄ emission responses to wetland restoration and identify key influencing factors through subgroup analyses and structural equation modeling (SEM). Our results indicated that CH₄ emissions increased by 122.3 % after wetland restoration compared to disturbed wetlands but remained 21.17 % lower than those of natural wetlands. Across all subgroup analyses, CH₄ emissions of restored wetlands were neither significantly lower than those of disturbed wetlands nor significantly higher than those of natural wetlands. The total greenhouse gas emissions of restored wetlands were not higher than those of disturbed wetlands but were significantly lower than those of natural wetlands (−22.92 %), primarily influenced by CH₄ and CO₂ emissions. CH₄ emissions were particularly sensitive to disturbance and restoration in freshwater wetlands, peatlands, and wetlands with seasonal flooding, high nutrient contents, or colder climates. In contrast, saline wetlands exhibited no significant changes in CH₄ emissions post-restoration. The SEM analysis identified restored hydrology, salinity, soil pH, restored type, and restored years as the dominant factors influencing CH₄ emissions, with climate exerting only indirect effects. These findings underscore the importance of rewetting (to water table depths of at least −15 cm), sustained restoration efforts (lasting at least 5 years), and the recovery of natural vegetation (rather than selective species planting) for effective wetland recovery. CH₄ emissions without these measures were significantly lower than those of natural wetlands. This study offers in-depth insights into CH₄ emissions following wetland restoration, providing a scientific foundation for wetland management strategies and CH₄ emission assessments under global change scenarios.

{"title":"Responses of methane emissions to global wetland restoration and influencing factors","authors":"Shangqi Xu , Meng Na , Yuqing Miao , Chunjie Tian , Jihai Zhou , Xia Liu","doi":"10.1016/j.agrformet.2025.110459","DOIUrl":"10.1016/j.agrformet.2025.110459","url":null,"abstract":"<div><div>Wetlands are among the largest sources of methane (CH<sub>4</sub>) on Earth. To restore degraded ecological functions, wetland restoration has been implemented worldwide, but its impact on CH<sub>4</sub> emissions remains poorly understood. This study conducted a comprehensive global meta-analysis, integrating data from 59 field studies, to assess CH<sub>4</sub> emission responses to wetland restoration and identify key influencing factors through subgroup analyses and structural equation modeling (SEM). Our results indicated that CH<sub>4</sub> emissions increased by 122.3 % after wetland restoration compared to disturbed wetlands but remained 21.17 % lower than those of natural wetlands. Across all subgroup analyses, CH<sub>4</sub> emissions of restored wetlands were neither significantly lower than those of disturbed wetlands nor significantly higher than those of natural wetlands. The total greenhouse gas emissions of restored wetlands were not higher than those of disturbed wetlands but were significantly lower than those of natural wetlands (−22.92 %), primarily influenced by CH<sub>4</sub> and CO<sub>2</sub> emissions. CH<sub>4</sub> emissions were particularly sensitive to disturbance and restoration in freshwater wetlands, peatlands, and wetlands with seasonal flooding, high nutrient contents, or colder climates. In contrast, saline wetlands exhibited no significant changes in CH<sub>4</sub> emissions post-restoration. The SEM analysis identified restored hydrology, salinity, soil pH, restored type, and restored years as the dominant factors influencing CH<sub>4</sub> emissions, with climate exerting only indirect effects. These findings underscore the importance of rewetting (to water table depths of at least −15 cm), sustained restoration efforts (lasting at least 5 years), and the recovery of natural vegetation (rather than selective species planting) for effective wetland recovery. CH<sub>4</sub> emissions without these measures were significantly lower than those of natural wetlands. This study offers in-depth insights into CH<sub>4</sub> emissions following wetland restoration, providing a scientific foundation for wetland management strategies and CH<sub>4</sub> emission assessments under global change scenarios.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"364 ","pages":"Article 110459"},"PeriodicalIF":5.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465398","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

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.

{"title":"Evaluating the sensitivity of vegetation indices to leaf area index variability at individual tree level using multispectral drone acquisitions","authors":"Xianchao Tian , Xingyu Jia , Yizhuo Da , Jingyi Liu , Wenyan Ge","doi":"10.1016/j.agrformet.2025.110441","DOIUrl":"10.1016/j.agrformet.2025.110441","url":null,"abstract":"<div><div>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 (R<sup>2</sup> = 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 R<sup>2</sup> 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<sub>ab</sub>] and dry matter content [C<sub>m</sub>]), 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.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"364 ","pages":"Article 110441"},"PeriodicalIF":5.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143418912","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

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.

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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