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dc.creatorFilintas A., Nteskou A., Kourgialas N., Gougoulias N., Hatzichristou E.en
dc.date.accessioned2023-01-31T07:37:58Z
dc.date.available2023-01-31T07:37:58Z
dc.date.issued2022
dc.identifier10.3390/w14172654
dc.identifier.issn20734441
dc.identifier.urihttp://hdl.handle.net/11615/71568
dc.description.abstractThe major global challenge for the coming decades will be increasing crop production with less water consumption. Precision agriculture (PA) and variable deficit irrigation (VDI) are management strategies that help farmers to improve crop production, fertilizer’s efficiency, and water use efficiency (WUE). The effects of irrigation (IR1 = variable deficit irrigation; IR2 = farmers’ irrigation common practices) under three fertilization (Ft1, Ft2, Ft3) treatments were studied on a cotton yield, on various indicators for more efficient water and fertilizer use, and on plant growth characteristics by applying a number of new agrotechnologies (such as TDR sensors; soil moisture (SM); PA; remote-sensing NDVI (Sentinel-2 satellite sensors); soil hydraulic analyses; geostatistical models; and SM root-zone modelling 2D GIS mapping). The reference evapotranspiration was computed based on the F.A.O. Penman–Monteith method. The crop (ETc) and actual (ETa) evapotranspiration were computed using crop coefficients obtained from the remote-sensing NDVI vegetation index (R2 = 0.9327). A daily soil–water–crop–atmosphere (SWCA) balance model and a depletion model were developed using sensor data (climatic parameters’ sensors, as well as soil and satellite sensors) measurements. The two-way ANOVA statistical analysis results revealed that irrigation (IR1 = best) and fertilization treatments (Ft2 = best) significantly affected the cotton yield, the plant height, the plant stem, the boll weight, the above-ground dry matter, nitrogen and fertilizer efficiency, and WUE. VDI, if applied wisely during critical growth stages, could result in a substantial improvement in the yield (up to +28.664%) and water savings (up to 24.941%), thus raising water productivity (+35.715% up to 42.659%), WUE (from farmers’ 0.421–0.496 kg·m−3 up to a VDI of 0.601–0.685 kg·m−3), nitrogen efficiency (+16.888% up to +22.859%), and N-P-K fertilizer productivity (from farmers’ 16.754–23.769 up to a VDI of 20.583–27.957). © 2022 by the authors.en
dc.language.isoenen
dc.sourceWater (Switzerland)en
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85137802912&doi=10.3390%2fw14172654&partnerID=40&md5=71db853b0f5578b47e14e965a7ed7b0e
dc.subjectCottonen
dc.subjectCropsen
dc.subjectCultivationen
dc.subjectEfficiencyen
dc.subjectEvapotranspirationen
dc.subjectGeographic information systemsen
dc.subjectIrrigationen
dc.subjectMappingen
dc.subjectNitrogen fertilizersen
dc.subjectPrecision agricultureen
dc.subjectSoil moistureen
dc.subjectSoil surveysen
dc.subjectWater supplyen
dc.subject2d TDR-GIS soil moisture mappingen
dc.subjectCotton yielden
dc.subjectDeficit irrigationen
dc.subjectGeostatistical modelsen
dc.subjectGIS and NDVIen
dc.subjectHydraulic analysisen
dc.subjectPrecision Agricultureen
dc.subjectPrecision agriculture and remote sensingen
dc.subjectRemote-sensingen
dc.subjectRoot zoneen
dc.subjectSoil hydraulic analyzeen
dc.subjectSoil hydraulicsen
dc.subjectSoil moisture mappingen
dc.subjectStage-based deficit irrigation and fixed partial root-zone variable irrigationen
dc.subjectVariable deficit irrigationen
dc.subjectRemote sensingen
dc.subjectagricultural technologyen
dc.subjectcomparative studyen
dc.subjectcottonen
dc.subjectcrop productionen
dc.subjectcrop yielden
dc.subjectevapotranspirationen
dc.subjectfertilizer applicationen
dc.subjectGISen
dc.subjectgrowthen
dc.subjectirrigationen
dc.subjectmanagement practiceen
dc.subjectNDVIen
dc.subjectprecision agricultureen
dc.subjectsatellite sensoren
dc.subjectsoil moistureen
dc.subjectsoil wateren
dc.subjecttime domain reflectometryen
dc.subjectwater use efficiencyen
dc.subjectMDPIen
dc.titleA Comparison between Variable Deficit Irrigation and Farmers’ Irrigation Practices under Three Fertilization Levels in Cotton Yield (Gossypium hirsutum L.) Using Precision Agriculture, Remote Sensing, Soil Analyses, and Crop Growth Modelingen
dc.typejournalArticleen


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