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Application of machine learning techniques to simulate the evaporative fraction and its relationship with environmental variables in corn crops

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Ecological Processes 2022年第1期11卷 758-771页

作者： Terenzio Zenone Luca Vitale Daniela Famulari vincenzo magliulo National Research Council Institute for Agricultural and Forestry Systems in the MediterraneanP.Le Enrico Fermi 180055 PorticiItaly National Research Council CNR Institute for BioeconomyVia P.Gobetti10140129 BolognaItaly

Background:The evaporative fraction(EF)represents an important biophysical parameter reflecting the distribution of surface available *** this study,we investigated the daily and seasonal patterns of EF in a multi-year corn cultivation located in southern Italy and evaluated the performance of five machine learning(ML)classes of algorithms:the linear regression(LR),regression tree(RT),support vector machine(SVM),ensembles of tree(ETs)and Gaussian process regression(GPR)to predict the EF at daily time *** adopted methodology consisted of three main steps that include:(i)selection of the EF predictors;(ii)comparison of the different classes of ML;(iii)application,cross-validation of the selected ML algorithms and comparison with the observed ***:Our results indicate that SVM and GPR were the best classes of ML at predicting the EF,with a total of four different algorithms:cubic SVM,medium Gaussian SVM,the Matern 5/2 GPR,and the rational quadratic *** com-parison between observed and predicted EF in all four algorithms,during the training phase,were within the 95%confidence interval:the R^(2)value between observed and predicted EF was 0.76(RMSE 0.05)for the medium Gaussian SVM,0.99(RMSE 0.01)for the rational quadratic GPR,0.94(RMSE 0.02)for the Matern 5/2 GPR,and 0.83(RMSE 0.05)for the cubic SVM *** results were obtained during the testing *** results of the cross-validation analysis indicate that the R^(2)values obtained between all iterations for each of the four adopted ML algorithms were basically constant,confirming the ability of ML as a tool to predict ***:ML algorithms represent a valid alternative able to predict the EF especially when remote sensing data are not available,or the sky conditions are not *** application to different geographical areas,or crops,requires further development of the model based on different data sources of soils,climate,and cropping systems.

关键词： Energy flux Evapotranspiration Eddy covariance Artificial intelligence

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Divergent apparent temperature sensitivity of terrestrial ecosystem respiration

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Journal of Plant Ecology 2014年第5期7卷 419-428页

作者： Bing Song Shuli Niu Ruisen Luo Yiqi Luo Jiquan Chen Guirui Yu Janusz Olejnik Georg Wohlfahrt Gerard Kiely Asko Noormets Leonardo Montagnani Alessandro Cescatti vincenzo magliulo Beverly Elizabeth Law Magnus Lund Andrej Varlagin Antonio Raschi Matthias Peichl Mats BNilsson Lutz Merbold Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Yuquan RoadBeijing 100049China Department of Botany and Microbiology University of OklahomaNormanOK 73019USA CGCEO/Geography Michigan State UniversityEast LansingMI 48864USA Meteorology Department Poznan University of Life Sciences(PULS)Poznan 60-667Poland Department of Matter and Energy Fluxes Global Change Research CenterAS CRv.v.i.BrnoCzech Republic Institute of Ecology University of InnsbruckSternwartestr 15Innsbruck 6020Austria Civil and Environmental Engineering Department University College CorkCorkIreland North Carolina State University/USDA Forest Service Southern Global Change ProgramRaleighNC 27606USA Servizi Forestali Agenzia per l’AmbienteProvinciaAutonoma di BolzanoBolzano 39100Italy Faculty of Science and Technology Free University of BolzanoPiazza Università1Bolzano 39100Italy European Commission Joint Research CenterInstitute for Environment and SustainabilityIspraItaly Institute of Forest and Agricultural Mediterranean Ecosystems-CNR ISAFoM Via Patacca85Ercolano(Napoli)Italy College of Forestry Oregon State UniversityCorvallisOR 97331-2209USA Department of Bioscience Aarhus UniversityFrederiksborgvejRoskilde 4000Denmark A.N.Severtsov Institute of Ecology and Evolution Russian Academy of SciencesLenisky pr.33 Moscow119071Russia CNR-Instituto di Biometeorologia(IBIMET) Via Giovanni Caproni 8Firenze 50145Italy Department of Forest Ecology&Management Swedish University of Agricultural SciencesUmeå90183Sweden ETH Zurich Institute for Agricultural Sciences(IAS)Grassland Sciences GroupLFWC55.2Universitätstrasse 2Zurich 8092Switzerland

Aims Recent studies revealed convergent temperature sensitivity of ecosys-tem respiration(Re)within aquatic ecosystems and between terrestrial and aquatic *** do not know yet whether various terres-trial ecosystems have consistent or divergent temperature ***,we synthesized 163 eddy covariance flux sites across the world and examined the global variation of the apparent activation energy(Ea),which characterizes the apparent temperature sensitivity of and its interannual variability(IAV)as well as their controlling *** We used carbon fluxes and meteorological data across FLUXNET sites to calculate mean annual temperature,tempera-ture range,precipitation,global radiation,potential radiation,gross primary productivity and Re by averaging the daily values over the years in each ***,we analyzed the sites with>8 years data to examine the IAV of Ea and calculated the standard deviation of Ea across years at each site to character-ize *** Findings The results showed a widely global variation of Ea,with significantly lower values in the tropical and subtropical areas than in temperate and boreal areas,and significantly higher values in grasslands and wetlands than that in deciduous broadleaf forests and evergreen ***,spatial variations of Ea were explained by changes in temperature and an index of water availability with differing contribution of each explaining variable among climate zones and *** and the corresponding coefficient of variation of Ea decreased with increasing latitude,but increased with radiation and corresponding mean annual *** revealed patterns in the spatial and temporal variations of Ea and its controlling factors indicate divergent temperature sensitivity of Re,which could help to improve our predictive understanding of Re in response to climate change.

关键词： activation energy ecosystem respiration index of water availability interannual variability gross primary productivity

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