Relative growth rate (RGR) of the different tropical species biofertilised with
\r\n\tThe study of populations and plant communities in their different aspects; ecological, structural, functional and dynamic, it is essential to establish a posteriori models of forest and agricultural management.
\r\n\r\n\tFor this, the methodological approaches on the type of sampling are considered essential, since there are differences between the purely ecological and the phytosociological methods, despite the fact that both pursue the same objective.
\r\n\tAlthough the ecological method for the knowledge of the vegetation is widely extended, the phytosociological one is no less so, since in the European Union it has been developed as a consequence of policies on sustainability, through which regulations have been issued, such as the habitats directive.
\r\n\tOn the other hand, research on plant dynamics and knowledge of the landscape in an integral way, have multiplied in the last 30 years, which has favored a deep knowledge of the floristic and phytocenotic wealth, which is fundamental for agricultural management, livestock and forestry.
",isbn:"978-1-83969-386-1",printIsbn:"978-1-83969-385-4",pdfIsbn:"978-1-83969-387-8",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"0abf2a59ee63fc1ba4fb64d77c9b1be7",bookSignature:"Dr. Eusebio Cano Carmona, Dr. Ricardo Quinto Canas, Dr. Ana Cano Ortiz and Dr. Carmelo Maria Musarella",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/9662.jpg",keywords:"Climatic Factors, Bioclimate, Thermotype, Flora, Conservation, Phytocenosis, Plant Dynamics, Landscape, Cartography, Vegetation Series, Crops, Reforestation",numberOfDownloads:55,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 23rd 2020",dateEndSecondStepPublish:"January 25th 2021",dateEndThirdStepPublish:"March 26th 2021",dateEndFourthStepPublish:"June 14th 2021",dateEndFifthStepPublish:"August 13th 2021",remainingDaysToSecondStep:"3 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Cano Carmona and colleagues have directed 12 doctoral theses and more than 200 publications among articles, books, and book chapters. He has participated in national and international congresses with about 250 papers. He has held a number of different academic positions, including Dean of the Faculty of Experimental Sciences at the University of Jaen, Spain, and founder and director of the International Seminar on Management and Conservation of Biodiversity.",coeditorOneBiosketch:"Ricardo Jorge Quinto Canas is currently an Invited Assistant Professor in the Faculty of Sciences and Technology at the University of Algarve – Portugal, and a member of the Centre of Marine Sciences (CCMAR), University of Algarve. His current research projects focus on Botany, Vegetation Science (Geobotany), Biogeography, Plant Ecology, and Biology Conservation, aiming to support Nature Conservation.",coeditorTwoBiosketch:"Ana Cano Ortiz's fundamental line of research is related to botanical bioindicators. She has worked in Spain, Italy, Portugal, and Central America. It presents more than one hundred works published in various national and international journals, as well as books and book chapters; and has presented a hundred papers to national and international congresses.",coeditorThreeBiosketch:"Carmelo Maria Musarella is a biologist, specialized in Plant Biology. He is a member of the permanent scientific committee of the International Seminar on “Biodiversity Conservation and Management” guested by several European universities. He has participated in several international and national congresses, seminars, and workshops and presented oral communications and posters.",coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"87846",title:"Dr.",name:"Eusebio",middleName:null,surname:"Cano Carmona",slug:"eusebio-cano-carmona",fullName:"Eusebio Cano Carmona",profilePictureURL:"https://mts.intechopen.com/storage/users/87846/images/system/87846.png",biography:"Eusebio Cano Carmona obtained a PhD in Sciences from the\nUniversity of Granada, Spain. He is Professor of Botany at the\nUniversity of Jaén, Spain. His focus is flora and vegetation and he\nhas conducted research in Spain, Italy, Portugal, Palestine, the\nCaribbean islands and Mexico. As a result of these investigations,\nDr. Cano Carmona and colleagues have directed 12 doctoral theses\nand more than 200 publications among articles, books and book\nchapters. He has participated in national and international congresses with about\n250 papers/communications. He has held a number of different academic positions,\nincluding Dean of the Faculty of Experimental Sciences at the University of Jaen,\nSpain and founder and director of the International Seminar on Management and\nConservation of Biodiversity, a position he has held for 13 years. He is also a member of the Spanish, Portuguese and Italian societies of Geobotany.",institutionString:"University of Jaén",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"5",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"University of Jaén",institutionURL:null,country:{name:"Spain"}}}],coeditorOne:{id:"216982",title:"Dr.",name:"Ricardo Quinto",middleName:null,surname:"Canas",slug:"ricardo-quinto-canas",fullName:"Ricardo Quinto Canas",profilePictureURL:"https://mts.intechopen.com/storage/users/216982/images/system/216982.JPG",biography:"Ricardo Quinto Canas, Phd in Analysis and Management of Ecosystems, is currently an Invited Assistant Professor in the Faculty\nof Sciences and Technology at the University of Algarve, Portugal, and member of the Centre of Marine Sciences (CCMAR),\nUniversity of Algarve. He is also the Head of Division of Environmental Impact Assessment - Algarve Regional Coordination\nand Development Commission (CCDR - Algarve). His current\nresearch projects focus on Botany, Vegetation Science (Geobotany), Biogeography,\nPlant Ecology and Biology Conservation, aiming to support Nature Conservation.\nDr. Quinto Canas has co-authored many cited journal publication, conference articles and book chapters in above-mentioned topics.",institutionString:"University of Algarve",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorTwo:{id:"203697",title:"Dr.",name:"Ana",middleName:null,surname:"Cano Ortiz",slug:"ana-cano-ortiz",fullName:"Ana Cano Ortiz",profilePictureURL:"https://mts.intechopen.com/storage/users/203697/images/system/203697.png",biography:"Ana Cano Ortiz holds a PhD in Botany from the University of\nJaén, Spain. She has worked in private enterprise, in university\nand in secondary education. She is co-director of four doctoral\ntheses. Her research focus is related to botanical bioindicators.\nDr. Ortiz has worked in Spain, Italy, Portugal and Central America. She has published more than 100 works in various national\nand international journals, as well as books and book chapters.\nShe has also presented a great number of papers/communications to national and\ninternational congresses.",institutionString:"University of Jaén",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"6",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Jaén",institutionURL:null,country:{name:"Spain"}}},coeditorThree:{id:"276295",title:"Dr.",name:"Carmelo Maria",middleName:null,surname:"Musarella",slug:"carmelo-maria-musarella",fullName:"Carmelo Maria Musarella",profilePictureURL:"https://mts.intechopen.com/storage/users/276295/images/system/276295.jpg",biography:"Carmelo Maria Musarella, PhD (Reggio Calabria, Italy –\n23/01/1975) is a biologist, specializing in plant biology. He\nstudied and worked in several European Universities: Messina,\nCatania, Reggio Calabria, Rome (Italy), Valencia, Jaén, Almeria\n(Spain), and Evora (Portugal). He was the Adjunct Professor\nof Plant Biology at the “Mediterranea” University of Reggio\nCalabria (Italy). His research topics are: floristic, vegetation,\nhabitat, biogeography, taxonomy, ethnobotany, endemisms, alien species, and\nbiodiversity conservation. He has authored many research articles published in\nindexed journals and books. He has been the guest editor for Plant Biosystems and a\nreferee for this same journal and others. He is a member of the permanent scientific\ncommittee of International Seminar on “Biodiversity Conservation and Management”, which includes several European universities. He has participated in several\ninternational and national congresses, seminars, workshops, and presentations of\noral communications and posters.",institutionString:'"Mediterranea" University of Reggio Calabria',position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"6",totalChapterViews:"0",totalEditedBooks:"1",institution:null},coeditorFour:null,coeditorFive:null,topics:[{id:"5",title:"Agricultural and Biological Sciences",slug:"agricultural-and-biological-sciences"}],chapters:[{id:"75595",title:"Assessment of the State of Forest Plant Communities of Scots Pine (Pinus sylvestris L.) in the Conditions of Urban Ecosystems",slug:"assessment-of-the-state-of-forest-plant-communities-of-scots-pine-pinus-sylvestris-l-in-the-conditio",totalDownloads:31,totalCrossrefCites:0,authors:[null]},{id:"76010",title:"Predictive Models for Reforestation and Agricultural Reclamation: A Clearfield County, Pennsylvania Case Study",slug:"predictive-models-for-reforestation-and-agricultural-reclamation-a-clearfield-county-pennsylvania-ca",totalDownloads:24,totalCrossrefCites:0,authors:[null]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"247865",firstName:"Jasna",lastName:"Bozic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/247865/images/7225_n.jpg",email:"jasna.b@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"6893",title:"Endemic Species",subtitle:null,isOpenForSubmission:!1,hash:"3290be83fff5bc015f5bd3d78ae9c6c7",slug:"endemic-species",bookSignature:"Eusebio Cano Carmona, Carmelo Maria Musarella and Ana Cano Ortiz",coverURL:"https://cdn.intechopen.com/books/images_new/6893.jpg",editedByType:"Edited by",editors:[{id:"87846",title:"Dr.",name:"Eusebio",surname:"Cano Carmona",slug:"eusebio-cano-carmona",fullName:"Eusebio Cano Carmona"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6418",title:"Hyperspectral Imaging in Agriculture, Food and Environment",subtitle:null,isOpenForSubmission:!1,hash:"9005c36534a5dc065577a011aea13d4d",slug:"hyperspectral-imaging-in-agriculture-food-and-environment",bookSignature:"Alejandro Isabel Luna Maldonado, Humberto Rodríguez Fuentes and Juan Antonio Vidales Contreras",coverURL:"https://cdn.intechopen.com/books/images_new/6418.jpg",editedByType:"Edited by",editors:[{id:"105774",title:"Prof.",name:"Alejandro Isabel",surname:"Luna Maldonado",slug:"alejandro-isabel-luna-maldonado",fullName:"Alejandro Isabel Luna Maldonado"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"314",title:"Regenerative Medicine and Tissue Engineering",subtitle:"Cells and Biomaterials",isOpenForSubmission:!1,hash:"bb67e80e480c86bb8315458012d65686",slug:"regenerative-medicine-and-tissue-engineering-cells-and-biomaterials",bookSignature:"Daniel Eberli",coverURL:"https://cdn.intechopen.com/books/images_new/314.jpg",editedByType:"Edited by",editors:[{id:"6495",title:"Dr.",name:"Daniel",surname:"Eberli",slug:"daniel-eberli",fullName:"Daniel Eberli"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"17398",title:"Method for Validation of Lagrangian Particle Air Pollution Dispersion Model Based on Experimental Field Data Set from Complex Terrain",doi:"10.5772/17286",slug:"method-for-validation-of-lagrangian-particle-air-pollution-dispersion-model-based-on-experimental-fi",body:'\n\t\tValidation of air pollution dispersion model is very important process. It determines performances and efficiency of model in well defined conditions. Conditions consist of type of terrain orography (flat or complex), size of domain (local, regional, continental, global), number of grid cells in domain, meteorological conditions (strong or weak winds, etc.) and emission types (stacks, traffic, domestic heating). Results of validation give good guidelines how, where and when model can be successfully applied.
\n\t\t\tValidation is especially important when model is used for regulatory purposes. FAIRMODE European guidelines for air pollution modelling explicitly require that modeling tool must be successfully validated in similar environment (FAIRMODE, 2010). Slovenian legislation (Ur.l. RS, št. 31/2007, 2007) that is following European Council Directive of 28th June 1984 on combating air pollution from industrial plants (EUR-Lex 84/360/EEC, 1984) requires that the modeling tool for reconstructions of air pollution around stationary industry sources meet the requirements of complex terrain because most of Slovenian industry is located in the bottom of basins, river canyons and valleys. Complex terrain defines a set of specific atmospheric conditions: low wind speeds, temperature inversions, flow over topography, presence of terrain obstacles or discontinuities (land-sea, urban-rural environment), etc. Lagrangian particle dispersion model is the only air pollution model at the moment that is successfully achieving these requirements (Wilson and Sawford, 1996, Schwere et al., 2002). It has significantly evolved in last years and moved from research usage to usage for operational regulatory purposes (Tinarelli et al., 2000, Graff, 2002).
\n\t\t\tValidations over complex terrain are still very rare. They are very important for research community and governmental environment agencies. Research community use the results for further developments and improvements of modeling techniques and environment agencies for setting up and implementation of regulatory policies.
\n\t\t\tA study has been made to improve traditional air pollution model validation methodology. It is upgraded to estimate inaccuracy in position and time of the Lagrangian particle air pollution dispersion model. New validation methodology has been demonstrated on a field from a very complex terrain from Šaleška region (Slovenia). For validation Lagrangian particle air pollution dispersion model
Traditional validation methodology for air pollution modeling is based on statistical comparison between measured and reconstructed data about air pollution concentrations in environment. It is well described in model validation framework named “Model evaluation toolkit” that has been established and maintained by Olesen (1996).
\n\t\t\t\tMeasured data are collected from automatic environmental measuring stations located on the area of interest (domain) usually around sources of air pollution. Reconstructed concentrations are obtained from the air pollution modeling simulation.
\n\t\t\t\tThe domain split in 3D grid of cells is presented on the left side where ground layer is colored in green; on the right side only ground layer is presented where the cells where stations are located are highlighted in red color.
In the air pollution model usually area of interest consists of a grid of cells where each cell describes average air pollution situation in certain part of the domain (i.e. in presented study case in next chapter domain is split into 100 x 100 cells in horizontal and in 20 layers in vertical which give 200 000 cells for the domain). For the comparison reconstructed average concentration from the ground cell where measuring station is located is taken. An example is presented on Figure 1.
\n\t\t\t\tStatistical analysis of data is performed for selected time interval where measured and reconstructed data are available. For this time interval a set of data patterns must be prepared. Each data pattern from this set consists of a pair of measured and reconstructed concentraion obtained at time step
Using traditional validation methodology most often three statistical indexes are determined:
\n\t\t\t\tthe correlation coeficient (CR):
the normalized mean square error (NMSE):
the fractional bias (FB):
Definitions of variables and functions for determination of statistical indexes:
\n\t\t\t\t\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
In the model validation framework named “Model evaluation toolkit” maintained by Olesen (1996) difficulties that can arise in model validation are outlined. Differences between measured and reconstructed concentrations are caused by measuring errors, inherent uncertainty, input uncertainty and model formulation error. In the paper by Grašič et al. (2007) it has been determined that inaccuracy in position and time exists in the model. To estimate these inaccuracies enhanced validation methodology is presented. It is based on methodology where additionally reconstructed ground level concentrations in neighboring cells of the cell where station is located are also used in validation. Each measured value is during enhanced validation compared with one reconstructed concentration selected from a set of reconstructed concentrations. Set of this reconstructed concentrations
Definitions of variables for determination of set of neighborhood concentrations
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t
Example of neighboring cells in position
Example of neighboring cells in time
Example of neighboring cells in position and time
Finally in enhanced validation methodology each measured value is compared with one reconstructed concentration selected from a set of neighborhood concentrations
Presented method is demonstrated on a field data set from a complex terrain. In the following sub-chapters field data set from Šaleška region (Slovenia) is described. Field data set from Šaleška region has been chosen for several reasons:
\n\t\t\tThe first reason is the complex terrain of the region where all typical complex terrain meteorological conditions occur (Grašič, 2007, Blumen et al, 1990).
The second reason are high emissions from thermal power plant which were about 100000 tons of sulphur dioxide SO2 and 12400 tons of nitrogen oxides NOx per year (Elisei, 1991) because no desulphurization plant has been installed at that time. These high emissions represented the main air-pollution source in the region where ambient SO2 concentrations higher than 1 mg/m3 were measured at surrounding automatic environmental measuring stations. All other local source of air pollution can be practically neglected for this reason. Experimental campaign had been therefore organized as a tracer experiment.
The third reason is the availability of all measured data from environmental automatic measuring stations and emission station for the whole period of measuring campaign. Complete database is available in final report (Elisei et al., 1991) and also on the internet web page (Šoštanj91 Campaign home page, 2007).
And the fourth reason is that database obtained during the campaign had been used to validate several available air pollution models (Grašič, 2007).
Study case continues with description of air pollution modelling and comparison of validation results using standard and new presented method.
\n\t\t\tAn experimental measuring campaign named had been performed in spring of year 1991 from 15th of March till 5th of April 1991 in surrounding of Thermal power plant Šoštanj (TPPŠ). Main purpose of the campaign was determination of environmental impact of the air pollution from the three stacks of thermal power plant. The emphasis has been on the meteorological conditions that cause severe air pollution episodes.
\n\t\t\t\tTPPŠ is located in the centre Šaleška valley as presented on Figure 5. In the central part of Šaleška valley there is a plain located north of Paka river. Average altitude of the valley is three hundred meters above sea level. Valley is surrounded by hills on the south side and by high mountains Karavanke Alps on the west, north and east side. There are two towns and several small villages in the valley and its surrounding where approximately 36000 people lived in the time when campaign had been performed (Elisei et al., 1991). Map on Figure 6 shows the location of Šaleška valley in the north-eastern part of Slovenia.
\n\t\t\t\tThe experimental campaign had been performed by researchers from three research institutions: ENEL-CRAM and CISE, Milano, Italy and Jozef Stefan Institute, Ljubljana, Slovenia. Data obtained during the campaign had been used to validate several available air pollution models: standard and advanced Gaussian models, Gaussian puff model and Lagrangian particle dispersion model (Brusasca et al., 1992, Božnar et al., 1993, Božnar et al., 1994). Final results of this studies proved that the Lagrangian particle dispersion model is the most effective tool for air pollution modelling in very complex terrain. Campaign was described in details in a final report (Elisei et al., 1991) where also all measured data is available. Database consists of measurements from different measuring systems: automatic measuring stations of Environmental Information System (EIS) maintained by TPPŠ, automatic mobile laboratory, one mobile Doppler SODAR and DIAL. Pictures of some of equipment are presented on Figure 7.\n\t\t\t\t
\n\t\t\t\tMap of Šaleška region with locations of automatic environmental stations and location of the Thermal power plant Šoštanj in the centre (left picture) and the topography of the region (right picture)
Location of Saleška region in the north-eastern part of Slovenia
Pictures of some of equipment used in campaign in spring 1991: environmental automatic measuring station (left), mobile SODAR (right - upper) and DIAL (right -lower)
Environmental Information System of TPPŠ consisted of six stationary automatic measuring stations and one mobile station. Locations of the stations are presented on Figure 5. Environmental parameters measured on stations are presented in Table 1.\n\t\t\t\t
\n\t\t\t\tList of parameters measured at automatic environmental stations (x denotes that parameter is measured at certain station)
TPPŠ had during the campaign three operating stacks of different heights: 100m, 150 m and 230 m. Neither of the stacks had installed desulphurization plant during the experimental campaign. Measured emissions are presented in Table 2 where static and dynamic parameters are given. Emissions from generators Block 1, Block 2 and Block 3 are emitted from one stack named Block 1,2,3. Picture of TPP Šoštanj is presented on Figure 7.\n\t\t\t\t
\n\t\t\t\tList of emission parameters for all TPP Šoštanj stacks that were operating during experimental campaign in spring 1991, upper table presents static and lower dynamic parameters.
Thermal power plant Šoštanj
In this study case the Lagrangian particle air pollution dispersion model has been chosen for validation. The name of the model is SPRAY and its detailed description is given in papers by its authors (Brusasca et al, 1992, Tinarelli et al., 2000).
\n\t\t\t\tModel has been chosen for validation due to several reasons:
\n\t\t\t\tFirst version of the model has already been validated on the “Soštanj91” field data set (Brusasca et al., 1992, Božnar et al., 1993, Božnar et al., 1994). Model has significantly evolved in last years. It has moved from research usage to usage for operational regulatory purposes (Tinarelli et al., 2000).
Model follows new Slovenian legislation where for complex terrain it is required to use Lagrangian particle dispersion model. Model is coupled with corresponding meteorological pre-processor module which is able to reconstruct three dimensional diagnostic non-divergent wind fields.
Most of Slovenian industry is located in the complex terrain at the bottom of valleys, river canyons or basins. The results of validation can therefore be applied also on the other similar cases in Slovenia or anywhere else where complex terrain is present.
Latest version has also been recently validated on “Soštanj91” field data set (Grašič, 2007). The validation results in this study are extended and focused on the validation method rather than on the validation of the model.
Detailed description of model parameters and settings for this study are described in paper about latest validation of the model (Grašič, 2007). For input into meteorological pre-processor measured data mobile Doppler SODAR and from automatic environmental measuring stations as described in Table 1 has been used. All measured data are available in half-hour intervals. Mobile Doppler SODAR has been located in the centre of the domain. Meteorological fields have been reconstructed at 150 m horizontal resolution. The same resolution has also been used to describe the complex topography (i.e. orography, Corine land use, etc.). Given all this topography and local meteorological data three dimensional mass consistent wind fields have been generated and used in Lagrangian particle dispersion model for air pollution reconstruction.
\n\t\t\t\tLagrangian particle dispersion model has been generating half-hour average ground concentration fields at the same resolution 150 m as meteorological pre-processor. It has been using Thomson’s 1987 scheme with Gaussian random forcing (Thomson, 1987). The number of emitted virtual particles has been set in order to assure minimum resolution for ground level concentrations less than 1 μg/m3. Anfossi’s formulation (Anfossi, 1993) has been used for plume rise of hot stack plumes where horizontal and vertical variations of both mean wind and atmospheric stability had been taken into consideration.
\n\t\t\t\tAir pollution reconstruction has been made for the full duration of the experimental campaign: from 15th of March till 5th of April 1991. Results from simulation are available in half-hour intervals. Each half-hour result represents average air pollution situation over complete domain for one half-hour interval. This result is a three-dimensional (3D) concentration field describing concentrations for each cell of the domain. Domain consists of 100x100 grid cells in horizontal and of 20 layers in vertical that is 200000 grid cells in one 3D concentration field. For validation of the model only two-dimensional (2D) concentration field is relevant representing ground level concentrations. This ground-level concentration field consists of 100x100 cells from ground layer where each cell size is 150 m x 150 m in horizontal and 10 m in vertical.
\n\t\t\t\tFor demonstration of new validation method only one very complex air pollution situation has been selected. It is a typical complex terrain situation, very difficult for reconstruction and still represents greatest challenge to all available air pollution dispersion models. The situation is described in details in paper by Grašič et al. (2007). It lasted from 1st of April 1991 at 20:00 until 2nd of April 1991 at 20:00.
\n\t\t\t\tSpreading of reconstructed plume in three-dimensional domain is presented on Figure 9 where it is shown that plume has been spreading in all directions over domain during a relatively short period of time. This is also seen from the Doppler SODAR measurements presented on Figure 10. This graph represents measurements from SODAR for each half-hour time interval at different heights. Each arrow on the graph represents direction of horizontal wind component at certain height. The length of the arrow represents the magnitude of horizontal wind speed component.
\n\t\t\t\tAir pollution spreading in all directions is also proven by measurements of half-hour average SO2 concentrations at four environmental stations at different directions from TPPŠ as presented on Figures 11, 12, 13, 14.
\n\t\t\t\tIn the paper by Grašič et al. (2007) it is also reported that during this selected period the phenomenon of air pollution accumulation occurred. Very stable meteorological situation was main cause for very slow mixing of plume with air. Pollution plume was moving very slowly according to measured average wind speed and direction. At the beginning of this situation the air pollution from the point of view of a measuring station came from the direction of the source. But when the main wind changed its direction to opposite direction, also the air pollution cloud changed its direction. From now on from the point of view of measuring station it appeared that the air pollution cloud is coming from the virtual emission source located on the other side. In our case selected domain was not wide enough to capture this phenomenon by Lagrangian particle dispersion model. Part of the air pollution cloud has been lost out of domain which should be taken into account when model is being validated. Lagrangian particle model could reproduce this phenomenon correctly if the domain would be widened but in this case we would have to decrease the final resolution of the domain due to computational limits of the model. Decrease of the resolution (i.e. from 150 m grid cell to 500 m grid cell) would result in more coarse results and also some local complex terrain effects could be lost.
\n\t\t\t\treconstruction of plume spreading in all directions during selected air pollution situation
SODAR measurements during selected air pollution situation from 1st of April 1991 at 20:00 until 2nd of April 1991 at 20:00 when air pollution accumulation occurred; direction of arrows presents horizontal wind direction at certain height; length and color of arrow presents horizontal wind speed at certain height
Validation of modelling results is performed at four stations located in different directions from the point of view of thermal power plant Šoštanj. Four locations are selected according to positions of four environmental automatic measuring stations as presented on Figure 5: Graška Gora, Šoštanj, Veliki Vrh and Zavodnje. From all these stations measurements of half-hour average SO2 concentrations are available for selected air pollution situation from 1st of April 1991 at 20:00 till 2nd of April 1991 at 20:00.
\n\t\t\t\tAs presented on Figure 11 measured SO2 concentration was increased due to wind change at the beginning of selected air pollution situation. Wind at approximate height 250 m changed its direction from north-west to south-east. Next wind change was toward the south which caused an increase of SO2 concentrations at Šoštanj (Figure 12) and Veliki Vrh (Figure 13) stations. Figure 14 presents measured SO2 concentrations at Zavodnje station which is the most distant station from the TPPŠ. This result is interesting especially because of the measured SO2 concentration peak at the ending of air pollution situation. This peak was caused by air pollution accumulation phenomenon as describe in previous sub-chapter about air pollution modelling. Because the station is located near the border of domain (Figure 5) it is expected that the model results will be underestimated in this case.
\n\t\t\t\tIn the following sub-chapters a comparison between measured and reconstructed SO2 concentrations at the locations of presented four stations is made using traditional and enhanced validation methods. Within validation using traditional validation methodology modelling problems will be described that cause under or over estimations of reconstructed concentrations. And the sub-chapter using enhanced validation methodology is presenting different comparison results which can be used as a good estimation of model’s inaccuracy of position and time.
\n\t\t\t\t\n\t\t\t\t\t\tFigure 11 shows comparison between measured and reconstructed SO2 concentrations at station Graška Gora. Reconstructed concentrations agree very well with measured ones. Also comparison using traditional statistical indexes for complete duration of experimental measuring campaign from 15th of March till 5th of April 1991presented in Table 3 shows good correlation where correlation reaches value higher than 0.3.
\n\t\t\t\t\tSame comparison of SO2 concentrations at station Šoštanj is presented on Figure 12. Comparison on the graph shows underestimation of reconstructed concentration values. The first reconstructed peak at 11:30 hour is underestimated due to model’s inaccuracy of position. In the paper by Grašič et al. (2007) it is shown that correct peak has been reconstructed just two cells away from the station. The second underestimated concentration peak is caused by short distance between station and stacks (approximately 500 m). There are two effects that are not well captured due to this short distance. First is the stack tip down-wash effect. And the second is the combination of low-wind speed in direction towards the station and convective turbulences (Grašič et al., 2007). Comparison using traditional statistical indexes presented in Table 3 shows almost no correlation and medium underestimation of reconstructed concentrations
\n\t\t\t\t\tThe comparison of SO2 concentrations at Veliki Vrh station are presented on Figure 13. During the air pollution situation two concentration peaks have been reconstructed (from 00:00 till 04:00 and from 06:00 till 12:00). Both peaks are not correctly reconstructed due to inaccuracy of the model in position. Such peaks can appear in real situation just few meters from the measuring station without being detected (Grašič et al., 2007). Comparison presented in Table 3 shows poor correlation between measured and reconstructed concentrations.
\n\t\t\t\t\tEven more obvious phenomenon of model’s inaccuracy in position is presented on Figure 14 where comparison between measured and reconstructed SO2 concentrations at the location of Zavodnje station is made. The phenomenon is more expressed because of the long distance between the station and thermal power plant. It generated first reconstructed peak in time interval from 00:00 till 04:00 hour. The second measured concentration peak has been underestimated due to air pollution accumulation that has been lost because the domain was not wide enough to capture the phenomenon. This event occurred at the end of air pollution situation when the wind changed direction from south back to north-west direction (Grašič et al., 2007). Comparison presented in Table 3 shows none correlation between measured and reconstructed concentrations and very high normalized mean square error.
\n\t\t\t\t\tComparison of measured and reconstructed ambient SO2 concentrations at Graška Gora station
Comparison of measured and reconstructed ambient SO2 concentrations at Šoštanj station
Comparison of measured and reconstructed ambient SO2 concentrations at Veliki Vrh station
Comparison of measured and reconstructed ambient SO2 concentrations at Zavodnje station
\n\t\t\t\t\t\tTable 3 presents results of comparison between measured and reconstructed SO2 concentrations using statistical indexes for complete duration of experimental measuring campaign in spring of year 1991 from 15th of March till 5th of April 1991. In this case traditional point-to-point comparison has been made. From the results seems that only the reconstructed concentrations at Graška Gora are satisfactory comparing to results of many authors in published papers (Ferrero et al., 1993, Rizza et al., 1996, Kaasik, 2005) which were also participating in model validation framework named “Model evaluation toolkit” that has been established and maintained by Olesen (1996). Within this research Olesen aslo outlined difficulties that can arise in model validation: differences between measured and reconstructed concentrations are caused by measuring errors, inherent uncertainty, input uncertainty and model formulation error.
\n\t\t\t\t\tDetailed analysis of selected air pollution situation (Grašič et al., 2007) determined that inaccuracy in position and time of reconstructed concentrations have been caused mostly by model’s sensitivity to wind speeds and directions measured at different stations and by SODAR. Model’s sensitivity strongly depends on the complexity of the terrain which is in our case highly complex.
\n\t\t\t\t\tStatistical indexes of comparison using traditional methodology for complete duration of experimental measuring campaign from 15th of March till 5th of April 1991
\n\t\t\t\t\t\tFigure 15, 16, 17 and 18 shows comparison between measured and reconstructed SO2 concentrations at stations Graška Gora, Šoštanj, Velikih Vrh and Zavodnje. There are three types of reconstructed concentrations with different size of neighborhood as described in section
Recon. (ΔH=0,ΔT=0) - size of neighborhood is 0, only 1 cell where station is located is used for comparison, results are identical to traditional validation method
Recon. (ΔH=1,ΔT=1) - size of neighborhood is 27 cells (9 cells in horizontal scale and 3 cells in time scale)
Recon. (ΔH=2,ΔT=2) - size of neighborhood is 125 cells (9 cells in horizontal scale and 3 cells in time scale)
Agreement between measured and reconstructed concentrations is significantly improving when neighborhood is expanding. Similar result is obtained within comparison using traditional statistical indexes for complete duration of experimental measuring campaign from 15th of March till 5th of April 1991 presented in Tables 4 and 5. Comparison of results presented in Tables 3, 4 and 5 show significant improvement of all statistical indexes.
\n\t\t\t\t\tComparison of measured and reconstructed ambient SO2 concentrations at Graška Gora station using different sizes of neighborhood
Comparison of measured and reconstructed ambient SO2 concentrations at Šoštjan station using different sizes of neighborhood
Comparison of measured and reconstructed ambient SO2 concentrations at Veliki Vrh station using different sizes of neighborhood
Comparison of measured and reconstructed ambient SO2 concentrations at Zavodnje station using different sizes of neighborhood
Statistical indexes of comparison using enhanced validation methodology for complete duration of experimental measuring campaign from 15th of March till 5th of April 1991 where size of neighborhood consists of 27 cells (
Statistical indexes of comparison using enhanced validation methodology for complete duration of experimental measuring campaign from 15th of March till 5th of April 1991 where size of neighborhood consists of 125 cells (
In the paper by Grašič et al. (2007) validation has been performed using enhanced validation methodology as explained in chapter
This comparison shows slightly better results for recent study than for the previous study. This is also apparent from statistical indexes presented in Table 6. Main difference between previous and recent study is in removing of used reconstructed concentrations for further comparison in the old method. Main idea of enhanced validation methodology is to assign each measured concentration one reconstructed concentration from the neighborhood. Focusing on the time scale this means that one the same reconstructed concentration can be assigned to three measured concentrations when size of neighborhood is one time interval
Statistical indexes of comparison using enhanced validation methodology for complete duration of experimental measuring campaign from 15th of March till 5th of April 1991 where size of neighborhood consists of 27 cells (
Comparison of measured and reconstructed ambient SO2 concentrations at Graška Gora station where for first comparison unique reconstructed concentrations are used
Comparison of measured and reconstructed ambient SO2 concentrations at Šoštanj station where for first comparison unique reconstructed concentrations are used
Comparison of measured and reconstructed ambient SO2 concentrations at Veliki Vrh station where for first comparison unique reconstructed concentrations are used
Comparison of measured and reconstructed ambient SO2 concentrations at Zavodnje station where for first comparison unique reconstructed concentrations are used
Traditional air pollution model validation methodology has been extended in this paper. It is based on statistical comparison between measured and reconstructed air pollution concentrations in the environment where different statistical indexes are determined. The method been upgraded to estimate inaccuracy in position and time of the Lagrangian particle air pollution dispersion model. To obtain these inaccuracies additional reconstructed air pollution concentrations from the neighborhood are used. Neighborhood is defined in spatial and time scale.
\n\t\t\tEnhanced validation methodology has been demonstrated on a field data set »Šoštanj91« from a very complex terrain from Šaleška region (Slovenia). Field data set is described in details and it has been selected mainly due to high emissions during experimental campaign where SO2 air pollution situation behaved as tracer experiment.
\n\t\t\tAir pollution modeling has been made using Lagrangian particle air pollution dispersion model
For validation of the model only one very complex air pollution situation has been selected. It is a typical complex terrain situation, very difficult for reconstruction and includes phenomenon of air pollution accumulation and convective mixing afterwards. Validation using standard statistical indexes has been made at four locations in different directions from the point of view of air pollution source.
\n\t\t\tValidation begins using traditional validation methodology. Comparison between measured and reconstructed SO2 concentrations gives relatively poor results. Only reconstructed concentrations at one station are satisfactory. It has been determined that these results are caused by model’s sensitivity to measured wind speeds and directions.
\n\t\t\tTo “measure” this model’s inaccuracies in position and time enhanced validation methodology is demonstrated. It gives more satisfactory results at location of all stations and it also estimates inaccuracies. It has been estimated that model’s inaccuracy in position is about +-300 m and in time +-1 hour which is indeed excellent result for such a complex terrain. These results give good information for future improvement of air pollution dispersion model.
\n\t\t\tOn the other hand also inaccuracies of measurements should be taken into account during the validation process. It is very important to be aware that the measurements are made at certain location. In certain meteorological conditions these measurements are not representative for the nearest neighborhood. This effect is even more obvious for the complex terrain where the air pollution plume can be present in the nearest neighborhood of the station but it is not detected due to certain local phenomenon.
\n\t\tThe study was partially financed by the Slovenian Research Agency, Project No. L1-2082.
\n\t\tForest ecosystems are affected by various environmental and anthropogenic factors such as drought and the establishment of annual crops; these actions have caused their degradation. The above situation has generated several reforestation programs in Mexico through the massive production of forest species in nurseries, however, when planted in the field, the survival of plants is less than 50%, due in part to the low quality of the plants produced in the nursery and the intermittent drought they face when taken to the field [1]. In such procedures, the strengthening of the radical development of the host plant through biofertilisation with microorganisms has not been considered. Under these conditions, it is possible that microorganisms help plants survive in adverse environmental conditions [2].
Some bacteria and fungi that live in the rhizosphere interact with species present in agroforestry systems and may or may not manifest themselves in some morphological or physiological attribute of anthropocentric interest of the host plant, especially in sustainable or low input production systems, but their effective incidence depends on the microorganism and environmental and soil conditions [3]. The interaction of the plant with endomycorrhizal fungi stimulates its growth [4], even in adverse environmental conditions [3, 5], such as drought [6], presence of nematodes [7] and also activate defence mechanisms against various pathogens [8, 9].
Endomycorrhizal fungi interact symbiotically with more than 80% of terrestrial plants [10]. It is the most common symbiosis on earth [11] and important part of the development, maintenance and stability of ecosystems and represents an important mechanism for plant and reproductive development [12]. They can be found in all terrestrial ecosystems and their universality implies vast taxonomic diversity [13]. Native populations of endomycorrhizal fungi have favoured the sustainability of agricultural systems, while introduced ones may not be adapted to the environment and may have ecological specificity [14] or host preference [15]. In the rhizosphere, in addition to microorganisms, there are abundant root exudates that have a selective effect on soil microbiota [16].
When endomycorrhizal fungi are introduced in the seed or in the soil to colonise the root, the establishment of symbiosis is facilitated and the benefits are expressed in early stages [17], as an increase in growth and in the case of nursery plants, a decrease of time to be taken to the field. Symbiosis improves the supply, availability and physical access of nutrients to the plant [18].
Currently, endomycorrhizal fungi are considered essential organisms for the sustainable management of agriculture. In general, when new species are introduced to a region and adapt quickly to the new environment, the possibility that the species has the capacity in its root system to establish symbiosis with the microorganisms is considered. They are capable of linking to the development of the subway community [19].
Radical colonisation by endomycorrhizal fungi initiates with the exchange of carbon from the host plant to the fungus, and in turn, establishes by exploring the soil through mycelium the transport of nutrients to the root [20], such as phosphorus [2, 21, 22], water [20] and other nutrients to the plant. If Phosphorus (P) is not available for the initial development of the plant, it becomes limiting [23] and being a low mobility ion, hyphae can be the bridge for phosphorus supplementation [20] and by supplying it, growth is significantly influenced [2]. In addition, it improves the physical state of the soil by producing glomalin [20], to form more stable soil aggregates [24].
The beneficial effect of the application of endomycorrhizal fungi has been demonstrated in the increase of biomass in perennial crops such as
Understanding growth, as the irreversible increase in the size of an individual almost always associated with an increase in its complexity, helps us to identify the effect of endomycorrhizal fungi on symbiosis with the host plant. The analysis of plant growth represents the first step in the analysis of primary production [34] in its different components, which are those that regulate the final production. In this way, the yield understood as the phenotypic expression of anthropocentric interest, is the final result of the physiological processes that are reflected in the plant’s morphology [35].
The assignment of dry matter to the different structures of the plant, such as the root system, the stem and the foliar lamina are modified when endomycorrhizal fungi are included, either in soil or in substrates with the addition of other components, such as bovine manure, from the poultry industry, or agro-industrial wastes, such as sugarcane bagasse, coffee husk, cocoa shell, among others. This symbiotic association between fungus and plant generates the enlargement of the root system and acts as a root complement [36]. Endomycorrhizal fungi, together with the rest of microorganisms, are fundamental in the cycle of nutrients, even more, when considering that the availability of nutrients is heterogeneous in soils.
Mycelium is the means of transporting nutrients and water to the plant and is elemental in soil exploration. Especially in conditions of exploitation of monocultures that generate through time, the “depletion zone” of nutrients near its root system. Also, the tillage exercised in these production systems breaks the mycelium of the fungus and diminishes the beneficial effect it has on the structure of the soil, affecting the diversity and productivity of the communities [37, 38]. In addition, the applications of agrochemicals adversely affect the diversity and abundance of endomycorrhizal fungi in agroecosystems [39], causing the decrease or loss in the functioning of the plant community [40, 41].
In these conditions, the mycelium of the fungus can explore spaces in the soil where roots do not reach, that is, explore areas beyond the known “depletion zone” of the root and increase the absorption surface by exploring greater volume of soil compared to non-mycorrhised roots. This fact is more relevant when considering that they have the capacity to transport nutrients that are not very mobile, such as phosphorus [42].
The preference of endomycorrhizal fungi to transport phosphorus has been documented [21, 43] and this nutrient is fundamental for plant growth, even more so when considering that it has low availability in tropical soils [44]. Its supply by the fungus favours plant growth, but the symbiosis can be reduced or inhibited if the P level in the soil is high and the plant root can absorb it by itself [45]. [46] On the other hand, they cite that the efficiency in P absorption by the mycorrhised roots is mainly due to an acceleration of the dissociation of insoluble phosphate and it is extracted by the mycorrhizae according to the needs of the host plant. In general, it has been established that mycorrhised plants favour the absorption and transport of P, Zn, Ca, S, Cu, and Mg and their effects are more noticeable in low fertility soils [47].
When endomycorrhizal fungi are introduced into plants, the response can be diverse, influenced either by plant metabolism or by root architecture [48] and the interaction is considered non-specific, because any species of endomycorrhizal can colonise a plant [49], however, in different crops of the same species, the induction of growth is differential, according to the endomycorrhizal fungus introduced [31] or according to the tillage. In some forest species the root volume increases [50], in others, it decreases [51], or it is also expressed in an increase in the thickness of the stem needed to be taken to the field in less time compared to non-biofertilised plants. Also, it decreases mortality after transplantation [17] and improves its survival capacity in adverse conditions [52].
The growth of biofertilised plants with endomycorrhizal fungi presents changes in their aerial and root structure since the beginning of their evaluation. Generally, the dry weight of the aerial part of the plants is greater than the dry weight of the root system (Figure 1).
Shoot and root growth of different tropical plant species with and without
The root system development of biofertilised plants shows little difference in growth in
In
Shoot and root growth of different tropical plant species with and without
In
The response of microorganisms in plant development reflects differential growth in time and between their organs. The greater or lesser allocation of biomass to some plant organs, seems to be influenced by the biofertilisation applied to its radical system and in almost all cases, after the ample development of an organ in a period of time, it tends to diminish in the following period, but with important increase in another plant organ.
The relative growth rate or the amount of dry matter produced per unit of time induced by the symbiosis with endomycorrhizal fungi presents the highest growth rate during the first 56 days after planting (days) (Table 1). In the period between 112 and 40 das, all species show a decrease in growth rate. This can be mainly related to the higher proportion of cells that do not divide in relation to those that do [56], whereas, in annual crops, the highest growth occurs around 30 days after biofertilisation with an increase in the mean relative growth rate [57] and leaf area [58].
Plant species | Time (days after sowing) | |||
---|---|---|---|---|
28–56 | 56–84 | 84–112 | 112–140 | |
Control | 0.126* | 0.042 | 0.054 | 0.021 |
0.109 | 0.047 | 0.054 | 0.012 | |
56–84 | 84–112 | 112–140 | 140–168 | |
Control | 0.065 | 0.022 | 0.019 | 0.012 |
0.064 | 0.021 | 0.033 | 0.017 | |
0.073 | 0.022 | 0.023 | 0.014 | |
0.063 | 0.022 | 0.027 | 0.015 | |
30–60 | 60–90 | 90–120 | 120–150 | |
Control | 0.0070 | 0.0087 | 0.0072 | 0.0084 |
0.0097 | 0.0076 | 0.0057 | 0.0085 | |
28–56 | 56–84 | 84–112 | 112–140 | |
Control | 0.033 | 0.039 | 0.024 | 0.017 |
0.037 | 0.036 | 0.030 | 0.044 |
Relative growth rate (RGR) of the different tropical species biofertilised with
Dry weight (g.g−1.day−1).
Values are the average of four replications by sampling and treatment.
In
In
On the other hand, in
In all tropical forest species biofertilised with the endomycorrhizal fungus, a higher content of phosphorus was found in their plant tissue even though a large part of the available phosphorus in the soil is fixed in the andosol soils (Table 2). Many studies have shown that mycorrhised plants have benefits in their association with endomycorrhizal fungi under phosphorus-deficient conditions, especially in the acid soils of the tropics [62, 63, 64].
Plant species | Time (days)* | Nutrient (%) | |
---|---|---|---|
N | P | ||
Control | 140 | 2.50 | 0.11 |
140 | 3.11 | 0.19 | |
Control | 140 (shoot) | 0.74 | 0.08 |
140 (root) | 0.76 | 0.07 | |
140 (shoot) | 0.73 | 0.12 | |
140 (root) | 0.94 | 0.08 | |
Control | 28 | 1.95 | 0.27 |
56 | 1.77 | 0.10 | |
140 | 1.70 | 0.10 | |
28 | 2.62 | 0.33 | |
56 | 1.99 | 0.21 | |
140 | 1.83 | 0.11 | |
Control | 56 | 1.98 | 0.13 |
140 | 2.62 | 0.14 | |
56 | 1.88 | 0.14 | |
140 | 2.84 | 0.19 | |
Control | 140 | 3.75 | 0.07 |
140 | 3.55 | 0.131 |
N, P and Ca content of different tropical species biofertilised with
Days after sowing.
Values are the average of four replications by sampling and treatment.
P is captured by the external mycelium and subsequently transported through the hyphae or intraradical structures in the form of polyphosphate granules and finally the process of transfer by the bush to the host cells [65].
In
In general, it has been established that mycorrhised plants favour the absorption and transport not only of P but also of Zn, Ca, S, Cu, and Mg. The mycorrhizae are more active in soils of low fertility, especially when there is a deficiency of phosphorus [47].
The N content in the tissue of the biofertilised plants at 56 das was higher in the control with
In the case of
The benefits of transport of other nutrients and water, in addition to phosphorus to the plant, by mycorrhiza have been reported by several authors [68, 69].
Nowadays, the knowledge of microorganisms and their interaction with the rhizosphere has demonstrated the importance of symbiosis in the soil-plant system.
Plants with
The radical colonisation in the controls confirms the presence of other endomycorrhizal fungi in the soils used, as part of the regional microbiota, but with less capacity to stimulate growth. Even though it has been indicated that the symbiosis lacks taxonomic specificity [70], there is a certain functional compatibility with the host plant, the substrate and the introduced microorganisms.
On the other hand, in biofertilised species, the speed of colonisation of
At the end of the evaluation (140 days) the percentage of colonisation in the control was 44% and in the treatment with
The biofertilisation of tropical plants with
There are different periods in the vegetative growth of plants. At the beginning of the 28 to 56 days, the nutritional benefits are expressed by means of the increase in growth followed by a period of diminution and to continue in the following ones with the increase in biomass accumulation.
The content of nutrients such as phosphorus was always higher in the biofertilised plants.
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