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Modeliranje prostorske in časovne variabilnosti vsebnosti vode v tleh z različnimi lastnostmi, povzročenimi z obdelavo tal
ID Pečan, Urša (Author), ID Pintar, Marina (Mentor) More about this mentor... This link opens in a new window, ID Kastelec, Damijana (Comentor)

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Abstract
Za točne meritve vsebnosti vode v tleh z merilniki, ki temeljijo na meritvah navidezne dielektričnosti tal, smo ocenili merilne napake ob uporabi privzete tovarniške umeritvene funkcije v tleh z različnimi lastnostmi. Visoka vsebnost gline in organske snovi ter velika gostota tal so imele največji vpliv na napake. Na površini z različno obdelavo tal smo na treh globinah ovrednotili fizikalne lastnosti tal v prostoru in dve rastni dobi zvezno merili stanje vode v tleh. Prvo rastno dobo s pogostejšimi padavinami je na površini rastla soja (Glycine max (L.) Merr.) in drugo koruza (Zea mays L.). Izdelali smo terenske krivulje vodozadrževalnih lastnosti tal ter ovrednotili njihovo prostorsko in časovno variabilnost. Potek krivulj smo opisali s parametri modela van Genuchten. Ugotovili smo, da so terenske krivulje različne glede na lokacijo ter da se spreminjajo znotraj rastne dobe in med rastnima dobama. Na podlagi zveznih meritev vsebnosti vode v tleh v prostoru smo ovrednotili napake meritev ob zmanjševanju prostorske in časovne ločljivosti meritev. Za vsako ploskev (obdelava in globina) je napaka v oceni prostorskega povprečja pod ± 0,02 m3 m⠒3, dosežena ob štirih merilnih mestih v prostoru. V primeru dveh merilnih mest v prostoru so bile največje napake v ocenjenem prostorskem povprečju do ± 0,08 m3 m⠒3. Največje razlike v dnevnih vsebnostih vode v tleh so ustrezno zajete vse do časovne ločljivosti štirih ur. Na podlagi modela prostorsko časovne povezanosti vsebnosti vode v tleh na 20 cm med rastno dobo 2021 smo modelirali ta prostorski proces, napovedali vrednosti na ne merjenih lokacijah in ovrednotili negotovosti napovedi.

Language:Slovenian
Keywords:vsebnost vode v tleh, umerjanje merilnikov, frekvenca meritev, krivulje vodozadrževalnih lastnosti tal, prostorska variabilnost, časovna variabilnost, obdelava tal
Work type:Doctoral dissertation
Typology:2.08 - Doctoral Dissertation
Organization:BF - Biotechnical Faculty
Year:2023
PID:20.500.12556/RUL-151742 This link opens in a new window
COBISS.SI-ID:169031939 This link opens in a new window
Publication date in RUL:18.10.2023
Views:980
Downloads:98
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Secondary language

Language:English
Title:Modeling spatial and temporal variations of water content in soil with different properties caused by tillage
Abstract:
For accurate measurements of soil water content with sensors that measure the apparent dielectric permittivity of the soil, we estimated the measurement errors using the manufacturer's default calibration function for soils with different properties. A high clay and organic matter content and a high soil bulk density had the greatest influence on the errors. Soil physical properties were evaluated at three depths on a plot with different tillage systems and soil water status was measured continuously during two growing seasons. Soybean (Glycine max (L.) Merr.) was grown in the first growing season with more frequent rainfall, and maize (Zea mays L.) in the second. Field soil water retention curves were determined and their spatial and temporal variability was evaluated. The shape of the curves was described using the parameters of the van Genuchten model. The field retention curves varied spatially, within and between growing seasons. Based on continuous measurements of soil water content in space, we evaluated the measurement errors with decreasing spatial and temporal resolution of the measurements. For each plot (tillage system and depth), an error in estimating the spatial mean of less than ± 0.02 m3 m⠒3 is achieved when using four measurement locations. When using only two spatial locations, the maximum error in the estimation of the spatial mean was up to ± 0.08 m3 m⠒3. The maximum differences in daily soil water content are adequately captured up to a temporal resolution of 4 hours. Based on a model of the spatio-temporal correlation structure of soil water content at a depth of 20 cm during the 2021 growing season, we used a spatio-temporal model to predict values at unmeasured loactions and to evaluate the uncertainty of the prediction.

Keywords:soil water content, sensor calibration, soil water retention curves, measurement frequency, spatial variability, temporal variability, soil tillage intensity

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