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000916953 037__ $$aFZJ-2023-00219
000916953 041__ $$aEnglish
000916953 1001_ $$0P:(DE-Juel1)180553$$aLärm, Lena$$b0$$eCorresponding author
000916953 1112_ $$aEGU General Assembly 2022$$cVienna$$d2022-05-23 - 2022-05-27$$gEGU22$$wAustria
000916953 245__ $$aEstimating the effect of maize crops on time-lapse horizontal crosshole GPR data
000916953 260__ $$c2022
000916953 3367_ $$033$$2EndNote$$aConference Paper
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000916953 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1673265784_6371$$xAfter Call
000916953 520__ $$aInvestigating soil, roots and their interaction is important to optimize agricultural practices like irrigation and fertilization and therefore increase the sustainability and productivity of crop production. In this study, we are combining two methods to examine non-invasively, characterize and monitor the soil-root zone throughout crop growing seasons: crosshole ground penetrating radar (GPR) and root-images within horizontal mini-rhizotrons. Over three maize crop growing seasons, we acquired in-situ time-lapse crosshole ground penetrating radar data and time-lapse root images, at two mini-rhizotron facilities in Selhausen, Germany. These facilities allow to horizontally measure data at six different depths, ranging between 0.1 m - 1.2 m and below three different plots with varying agricultural treatments, such as irrigation, sowing density, sowing date and cultivars. The GPR measurements result in the dielectric permittivity slices by applying standard ray-based analysis to zero-offset measurements along a pair of rhizotubes. Such horizontal permittivity slices can be linked to soil water content using petro physical relationships. Additionally, the root images provide a root fraction per image, which is derived by using a workflow combining state-of-the-art software tools, deep neural networks and automated feature extraction. The dielectric permittivity slices suggest a permittivity variation along the horizontal and vertical axes, depending on atmospheric conditions, soil properties, and root architecture. To quantify the influence of the roots on the spatial and temporal distribution of dielectric permittivity, we used statistical methods to reduce the impacting factors like soil heterogeneity, tube deviations and changing atmospheric conditions, which results in the spatial and temporal variability. For verification these permittivity variabilities are compared to the root fraction values. In general, using the spatial and temporal permittivity variations, we can detect the presence of roots and additionally recognize a varying influence of the roots over the duration of the crop growing season. Using these first results, we demonstrate that GPR can be applied to improve the characterization of the root-soil system related to maize plants. This could be the first step towards developing proxies e.g. for irrigation and fertilization applications using this non-invasive method.
000916953 536__ $$0G:(DE-HGF)POF4-2173$$a2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217)$$cPOF4-217$$fPOF IV$$x0
000916953 536__ $$0G:(BMBF)390732324$$aEXC 2070:  PhenoRob - Robotics and Phenotyping for Sustainable Crop Production (390732324)$$c390732324$$x1
000916953 7001_ $$0P:(DE-Juel1)186730$$aBauer, Felix$$b1
000916953 7001_ $$0P:(DE-Juel1)129561$$avan der Kruk, Jan$$b2
000916953 7001_ $$0P:(DE-Juel1)129548$$aVanderborght, Jan$$b3
000916953 7001_ $$0P:(DE-Juel1)129549$$aVereecken, Harry$$b4
000916953 7001_ $$0P:(DE-Juel1)157922$$aSchnepf, Andrea$$b5
000916953 7001_ $$0P:(DE-Juel1)129483$$aKlotzsche, Anja$$b6
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000916953 9141_ $$y2022
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