1. Benefits of a robotic chamber system for determining evapotranspiration in an erosion-affected, heterogeneous cropland.
- Author
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Dahlmann, Adrian, Hoffmann, Mathias, Verch, Gernot, Schmidt, Marten, Sommer, Michael, Augustin, Jürgen, and Dubbert, Maren
- Subjects
FARMS ,EVAPOTRANSPIRATION ,CLIMATE change ,SOIL management ,SOIL classification ,EROSION - Abstract
In the light of the ongoing global climate crisis and the related increases in extreme hydrological events, it is crucial to assess ecosystem resilience and – in agricultural systems – to ensure sustainable management and food security. For this purpose, a comprehensive understanding of ecosystem water cycle budgets and spatiotemporal dynamics is indispensable. Evapotranspiration (ET) plays a pivotal role in returning up to 90 % of incoming precipitation back to the atmosphere. Here, we studied the impacts of soil types and management on an agroecosystem's seasonal cumulative ET (ET sum) and agronomic water use efficiency (WUE agro , the dry matter per unit of water used by the crop). To do so, a plot experiment with winter rye (17 September 2020 to 30 June 2021) was conducted in an eroded cropland which is located in the hilly and dry ground moraine landscape of the Uckermark region in northeastern Germany. Along the experimental plot (110 m × 16 m), two closed chambers were mounted on a robotic gantry crane system (FluxCrane as part of the AgroFlux platform) and used to determine ET. Three soil types representing the full soil erosion gradient related to the hummocky ground moraine landscape (extremely eroded: Calcaric Regosol; strongly eroded: Nudiargic Luvisol; non-eroded: Calcic Luvisol) and additional topsoil dilution (topsoil removal and subsoil admixture) were investigated (randomized block design, three replicates per treatment). Five different modeling approaches were used and compared in the light of their potential for reliable ET sum over the entire crop cultivation period and to reproduce short-term (e.g., diurnal) water flux dynamics. While machine-learning approaches such as support vector machines (SVMs) and artificial neural networks (with Bayesian regularization; ANN_BR) generally performed well during calibration, SVMs also provided a satisfactory prediction of measured ET during validation (k -fold cross-validation, k=5). We found significant differences in dry biomass (DM) and small trends in ET sum between soil types, resulting in different WUE agro. The extremely eroded Calcaric Regosol showed an up to 46 % lower ET sum and up to 54 % lower WUE agro compared to the non-eroded Calcic Luvisol. The key period contributing to 70 % of ET sum spanned the beginning of stem elongation in April to the harvest in June. However, differences in the ET sum between soil types and topsoil dilution resulted predominantly from small differences between the treatments throughout the cultivation rather than only during this short period of time. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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