Agricultural and Forest Meteorology最新文献

{"title":"Observed increasing light-use efficiency of terrestrial gross primary productivity","authors":"","doi":"10.1016/j.agrformet.2024.110269","DOIUrl":"10.1016/j.agrformet.2024.110269","url":null,"abstract":"<div><div>Widespread global greening driven by CO₂ fertilization implies a denser canopy structure, and more leaves could be used to collect light from the atmosphere for plant photosynthesis. Whether this increase in leaf quantity could enhance the capacity of vegetation to convert absorbed light to photosynthate remains unclear. In this study, we investigate the spatial-temporal variations of canopy light-use efficiency (LUE), an indicator of leaf photosynthesis capacity, with FLUXNET recordings of 540 site-years and seven satellite-derived proxies. We find that flux tower measurements identify an increasing trend of LUE, and the temporal variations of cross-site LUE are mainly caused by nitrogen fertilization (18.09 %), temperature (17.06 %), and CO₂ fertilization (16.59 %). Globally, satellite-derived datasets also show widespread increasing LUE over the past two decades, most attributed to the nitrogen deposition and CO₂ fertilization effects, especially in evergreen broadleaf forests. Future projections of terrestrial LUE by CMIP6 Earth system models further suggest an overall increasing trend of LUE to the end of the 21st century. Our findings highlight the importance of vegetation physiology such as LUE in understanding of enhancement on terrestrial plant photosynthesis and carbon sink under climate change.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Greening of a boreal rich fen driven by CO2 fertilisation","authors":"","doi":"10.1016/j.agrformet.2024.110261","DOIUrl":"10.1016/j.agrformet.2024.110261","url":null,"abstract":"<div><div>Boreal peatlands store vast amounts of soil organic carbon (C) owing to the imbalance between productivity and decay rates. In the recent decades, this carbon stock has been exposed to a warming climate. During the past decade alone, the Arctic has warmed by ∼ 0.75°C which is almost twice the rate of the global average. Although, a wide range of studies have assessed peatlands’ C cycling, our understanding of the factors governing source / sink dynamics of peatland C stock under a warming climate remains a critical uncertainty at site, regional, and global scales. Here our focus was on answering two key questions: (1) What drives the interannual variability of carbon dioxide (CO₂) fluxes at the Bonanza Creek rich fen in Alaska, and (2) What are the internal carbon allocation patterns during the study years? We addressed these knowledge-gaps using an intermediate complexity terrestrial ecosystem model calibrated by a Bayesian model-data fusion framework at a weekly timestep with publicly available eddy covariance, satellite-based earth observation, and in-situ datasets for 2014 to 2020. We found that the greening trend (a relative increase of leaf area index ∼0.12 m² m^-2 by 2020) in the fen ecosystem is forced by a CO₂ fertilisation effect which in combination resulted in increased gross primary production (GPP). Relative to 2014, GPP increased by ∼75 gC m^-2 year^-1 (by 2020; 95% confidence interval (CI): -41.35 gC m^-2 year^-1 to 213.55 gC m^-2 year^-1) while heterotrophic respiration stayed constant. Consistent with the observed greening, our analysis indicates that the ecosystem allocated more C to foliage (∼50%) over the structural (A carbon pool consisting of branches, stems and coarse roots; ∼30%) and fine root C pools (∼20%).</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142449985","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}

{"title":"Tracking the impact of typhoons on maize growth and recovery using Sentinel-1 and Sentinel-2 data: A case study of Northeast China","authors":"","doi":"10.1016/j.agrformet.2024.110266","DOIUrl":"10.1016/j.agrformet.2024.110266","url":null,"abstract":"<div><div>The increasing frequency of typhoon events, attributed to global climate change, has significantly affected agricultural production, predominantly resulting in substantial negative consequences. Accurate and timely assessment of crop damage is crucial for understanding economic implications, devising effective agricultural strategies, and enhancing resilience amid mounting climate uncertainties. This study investigates the utility of Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 MultiSpectral Instrument (MSI) data in tracking maize damage severity following typhoon events. By employing continuous field sampling techniques and conducting visual interpretation of high-resolution remote sensing imagery, sample sets representing a spectrum of maize damage severity were systematically established. The application of time series analysis on Sentinel data enables a comprehensive exploration of spectral and polarization responses, providing insights into the correlation with maize damage severity. Segmentation of the maize damage timeline into pre-disaster, disaster, and recovery periods, coupled with optimization of relevant feature parameters, was undertaken to bolster monitoring precision. Leveraging the Google Earth Engine (GEE) cloud platform, a Random Forest algorithm was used to develop a model for monitoring maize damage severity across different post-typhoon periods, yielding maps delineating the distribution and magnitude of maize damage in Northeast China. Results indicate that integrating spectral indices from the pre-disaster phase with backscatter variations of polarization bands during various post-typhoon periods enhances maize damage assessment. Maize damage severity is notably elevated during the disaster period, achieving an overall accuracy of 87.22 %. While mitigated during the recovery phase, localized exacerbation occurs in severely affected regions, yielding an overall accuracy of 88.54 %. Analysis incorporating terrain and meteorological data reveals that post-typhoon maize disasters predominantly occur in low-lying and flat areas, with meteorological factors, particularly maximum wind speed and daily cumulative precipitation, exerting significant influence on damage severity. This study underscores the critical role of SAR and optical data fusion in elucidating typhoon-induced crop damage dynamics, thereby providing essential insights for proactive mitigation strategies against future agricultural losses.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142449986","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}

{"title":"Simulating the land carbon sink: Progresses and challenges of terrestrial ecosystem models","authors":"","doi":"10.1016/j.agrformet.2024.110264","DOIUrl":"10.1016/j.agrformet.2024.110264","url":null,"abstract":"<div><div>Terrestrial ecosystems play an important role in regulating the balance of global carbon cycle by sequestrating CO₂ of atmosphere. Terrestrial ecosystem models are a critical tool for quantifying the magnitude, interannual variability and long-term trends of the land carbon sink across various spatial and temporal scales; however, despite extensive research, large uncertainties and challenges still persist. This review first summarizes decades of history in ecosystem model development in terms of model theory and methods. We then identify model uncertainties, including those arising from model algorithms, parameterization and forcing data. Finally, we propose new opportunities to improve ecosystem models for accurately simulating the land carbon sink, including emerging process-based knowledge from observations and big data, as well as model-data fusion methods.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445831","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}

{"title":"High resolution (1-km) surface soil moisture generation from SMAP SSM by considering its difference between freezing and thawing periods in the source region of the Yellow River","authors":"","doi":"10.1016/j.agrformet.2024.110263","DOIUrl":"10.1016/j.agrformet.2024.110263","url":null,"abstract":"<div><div>Soil moisture (SM) is a critical component of the land surface hydrological cycle, significantly impacting various sectors such as hydrology, meteorology, and agriculture. Accurate, high-resolution SM data are essential for effective flood forecasting, water resource management, and understanding the soil freeze-thaw processes in cold regions. This study aims to generate 1 km resolution liquid surface SM (SSM) data with a twice-daily update frequency by downscaling SMAP Level-4 SSM data using random forest (RF) and multiple linear regression (MLR) in the source region of the Yellow River (SRYR), by considering the differences in SM changes between freezing and thawing periods. To obtain the SSM data, 16 downscaling schemes of both RF and MLR were designed for each of the three scenarios. In each downscaling process, both land surface temperature (LST) and normalized difference vegetation index (NDVI) were utilized in MLR and RF models, alongside various combinations of additional variables such as albedo, elevation, leaf area index (LAI), soil texture. Results showed that during the freezing period, RF produced superior SSM estimates when supplemented with NDVI, LST, albedo, elevation, LAI, and soil texture. MLR was more effective during the thawing period when paired with NDVI, LST, elevation, LAI, and soil texture. During the freezing period, the downscaled SMAP SSM exhibited average <em>R</em>, RMSE, ubRMSE of 0.76, 0.029 m³·m^-3, and 0.023 m³·m^-3, respectively, when compared with in-situ measurements. During the thawing period, the average <em>R</em>, MAE, RMSE, and ubRMSE between the downscaled SMAP SSM and in-situ measurements were 0.52, 0.057 m³·m^-3, 0.067 m³·m^-3, and 0.054 m³·m^-3, respectively, compared to 0.45, 0.070 m³·m^-3, 0.083 m³·m^-3, and 0.060 m³·m^-3 for the original SMAP SSM. Thus, the research significantly enhances both the accuracy and spatial resolution of SMAP SSM estimations, underscoring its vital role in advancing hydrological studies within the SRYR.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Signs of frost drought in stem diameter variations","authors":"","doi":"10.1016/j.agrformet.2024.110247","DOIUrl":"10.1016/j.agrformet.2024.110247","url":null,"abstract":"<div><div>Frost drought refers to the chronic or acute desiccation of trees exposed to high evaporative pressures while being rooted in cold or frozen soils. This phenomenon has been known for more than a century but is still poorly characterized. Summer desiccation manifests itself as long-term stem contractions. Similar contractions have been reported in winter. In this study, we investigated the causes of total winter stem contraction (WSC) using 14 years of dendrometer data from evergreen (<em>P.abies</em>) and deciduous (<em>L.decidua</em>) mature trees growing along an elevational transect (from 800 to 2200 m asl) in the Swiss Alps. Results indicated that WSC varied between <span><math><mrow><mn>30</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span> and <span><math><mrow><mn>1478</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span> and were strongly dependent on species, elevation, and tree height. Moreover, the magnitude of contractions was strongly associated with stem contractions subsequent to freeze–thaw events (<span><math><mi>Δ</mi></math></span>F). We suggest that both <span><math><mi>Δ</mi></math></span>F and WSC are the consequences of water losses due to ice blockage associated frost drought, occurring when the distal parts of the tree are thawed and transpiring, while the larger basal parts remain frozen, thus inhibiting water uptake and creating a hydraulic imbalance.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142440456","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}

{"title":"Current and future cropland suitability for cereal production across the rainfed agricultural landscapes of Ethiopia","authors":"","doi":"10.1016/j.agrformet.2024.110262","DOIUrl":"10.1016/j.agrformet.2024.110262","url":null,"abstract":"<div><div>One of the major challenges posed by climate change in agriculture is the alteration in cropland suitability. This alteration has serious consequences for food security and economic stability at global, regional, and local scales, especially in smallholder and rainfed agricultural systems like in Ethiopia. A comprehensive understanding of the current state of croplands and future changes under warming temperatures and increasing rainfall uncertainty is critical for national climate adaptation planning. Here, we evaluated cropland suitability (CLS) for four major cereal crops (teff, maize, sorghum, and wheat), under both current and future climates across the rainfed agriculture (RFA) landscapes of Ethiopia. We utilized a novel suitability modelling approach that establishes functional relationships between crop yield, and climatic factors (rainfall, temperature, and solar radiation) and soil factors (texture, pH, and organic carbon). Furthermore, we analyzed the relative influences of the growing season rainfall and temperature on the changes in CLS. The results show that 54 % of the RFA area has a suitability index of 0.6 or higher (moderately to highly suitable) for teff and that 51 %, 63 %, and 29 % of the grid cells are suitable for maize, sorghum, and wheat crops, respectively. The suitable agroecologies of the four crops will likely undergo altitudinal shifts and areal contraction, with magnitudes of the changes depending on the emission scenarios. Under the SSP2–4.5, the suitable areas are projected to decrease by 25 % for teff, 7 % for maize, 10 % for sorghum, and 16 % for wheat in the 2080s. In semi-arid and hyper-humid climates, CLS is sensitive to changes in the growing season rainfall, whereas in low and high elevation regions, it is temperature-sensitive. In light of our results, we argue that adaptation actions tailored to agroecological conditions and topographic locations are vitally necessary to mitigate the long-term impacts of climate change on Ethiopia's rainfed agriculture.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142440457","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}

{"title":"Distribution of evapotranspiration components along vertical layers and their controls in dry days of larch plantation in the Liupan Mountains of northwest China","authors":"","doi":"10.1016/j.agrformet.2024.110265","DOIUrl":"10.1016/j.agrformet.2024.110265","url":null,"abstract":"<div><div>Precise quantification of forest evapotranspiration (ET_f) including transpiration from tree (T_t), shrub (T_s), and herb (T_h) layers, as well as evaporation from litter (E_l) and soil (E_s) layers, and elucidating their responses to environmental conditions and stand structures are crucial for forest water management in water-limited forests. In this study, we observed the T_t, T_s, T_h, E_l, and E_s, reference evapotranspiration (ET_o), soil volumetric water content (SWC), litter water content (LWC), leaf area index (LAI) in tree, shrub, and herb layers, and canopy shade (K_sc) from tree layer of the <em>Larix principis-rupprechtii</em> plantation during the dry days from May to October of 2021 and 2022 to elucidate the distribution of evapotranspiration along vertical layers and their environmental and structural determinants. The results indicated that the contributions of T_t, T_s, T_h, E_l, and E_s to ET_f during the dry days in 2021 (2022) were 42.1 % (44.7 %), 9.2 % (8.1 %), 8.2 % (8.6 %), 15.0 % (13.1 %), and 25.5 % (25.5 %), respectively. Although T_t and T_s demonstrated quadratic relationships with ET_o, T_h, E_l, and E_s exhibited linear relationships. All ET_cs demonstrated a saturated exponential relationship with either SWC or LWC. Furthermore, T_t, T_s, and T_h showed a saturated exponential relationship with their respective LAI, whereas E_l and E_s exhibited a cubic relationship with LAI in tree layers. All understory ET_cs (T_s, T_h, E_l, and E_s) decreased exponentially with the K_sc. The multi-factor models of evapotranspiration component (ET_c) from different vertical layers, which coupled the impacts of environmental conditions and vertical structure, were developed, and provided superior accuracy (R² = 0.78–0.88, NSE = 0.78–0.86, RSME = 0.03–0.16). Such insights deepened the understanding of vertical structural distribution and multi-factor responses of ET_cs in forest ecosystems and hold the potential to inform and optimise forest water management strategies.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142440455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing nitrogen fertilizer application in Chinese rice production under current and warming climatic scenarios","authors":"","doi":"10.1016/j.agrformet.2024.110252","DOIUrl":"10.1016/j.agrformet.2024.110252","url":null,"abstract":"<div><div>Optimizing the nitrogen (N) fertilizer use is the key to facilitating the sustainable development of agricultural systems. In this study, a DeNitrification–DeComposition model was used to analyze the effects of N fertilization on yield, profit, and reactive N losses in single-season rice production of China. The comprehensive optimum N application rate (CONR) considering the trade-off between economy and environment was calculated for each rice production county. The results showed that the high CONR values were mainly concentrated in middle and south China, like Yunnan-guizhou plateau, and Guangxi and Guangdong Provinces, where the rice yield potential is high. In addition to the Qinghai-tibet rice cultivation zone (RZ), the mean values of CONR in each RZ were mainly in the range of 150 kg N ha^-1 to 200 kg N ha^-1. Moreover, the 1.5 °C warming scenario with CO₂ concentration up to 423 ppm increased the CONR in most RZs of China. A generalized additive model (GAM) was used to develop quick prediction models of CONR using soil, terrain, climate, and crop data as predictors. The outcomes revealed that the GAM model could well predict the CONR across China's single-season rice production counties under both current (R² = 0.58, RRMSE = 18.3 %) and warming (R² = 0.61, RRMSE = 18.3 %) climatic scenarios. The rice yield potential, soil clay fraction, and soil organic carbon were the most important factors affecting CONR among different RZs. The cost-effectiveness analysis showed that CONR may save 9.7 % – 9.8 % more N fertilizer use and reduce 13 % – 14.5 % more N pollutant emissions in Chinese single-season rice production than the economic optimum N rate under current and warming climatic scenarios, and the net profit is only reduced by 1.1 % – 1.3 %. This research would offer a novel strategy for rice N fertilization management across China by optimizing the economic and environmental consequences at the same time.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142431834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A global meta-analysis of forest harvesting effects on soil respiration, its components, and temperature sensitivity","authors":"Lu Yang, Huiru Zhang, Jianghuan Qin, Xianzhao Liu, Mathias Mayer","doi":"10.1016/j.agrformet.2024.110259","DOIUrl":"https://doi.org/10.1016/j.agrformet.2024.110259","url":null,"abstract":"Understanding the effects of timber harvesting on soil respiration, including its autotrophic and heterotrophic components and their temperature sensitivity, is crucial for predicting how forest management affects the carbon cycle. Here, we conducted a meta-analysis to assess these effects on a global scale, synthesizing data from 1656 paired observations from 143 studies worldwide. On average, harvesting increased soil respiration by 6.0 %, most significantly in coniferous forests and subtropical regions. The response of total soil respiration was more closely coupled to changes in its heterotrophic than in its autotrophic component. The positive effects of harvesting on both respiration components decreased with increasing harvest intensity and were positively correlated with changes in soil nitrogen, root biomass, and microbial biomass carbon. Harvesting reduced the temperature sensitivity of soil respiration by 6.4 %, particularly in coniferous forests and temperate regions. The temperature sensitivity of soil autotrophic respiration increased in the first years after harvesting compared to the control but was significantly lower in later stages (<em>c</em>. &gt; 6 years) after harvesting. Furthermore, the effects of harvesting on soil respiration, its components and temperature sensitivity varied greatly between post-harvest treatments and seasons of measurement. The results of our synthesis provide a basis for refining ecosystem models to better predict soil carbon dynamics in harvested forests on a global scale.","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142431289","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}