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- ItemProximal Soil Sensing - A Contribution for Species Habitat Distribution Modelling of Earthworms in Agricultural Soils?(San Francisco, California, US : PLOS, 2016) Schirrmann, Michael; Joschko, Monika; Gebbers, Robin; Kramer, Eckart; Zörner, Mirjam; Barkusky, Dietmar; Timmer, JensBackground: Earthworms are important for maintaining soil ecosystem functioning and serve as indicators of soil fertility. However, detection of earthworms is time-consuming, which hinders the assessment of earthworm abundances with high sampling density over entire fields. Recent developments of mobile terrestrial sensor platforms for proximal soil sensing (PSS) provided new tools for collecting dense spatial information of soils using various sensing principles. Yet, the potential of PSS for assessing earthworm habitats is largely unexplored. This study investigates whether PSS data contribute to the spatial prediction of earthworm abundances in species distribution models of agricultural soils. Methodology/Principal Findings: Proximal soil sensing data, e.g., soil electrical conductivity (EC), pH, and near infrared absorbance (NIR), were collected in real-time in a field with two management strategies (reduced tillage / conventional tillage) and sandy to loam soils. PSS was related to observations from a long-term (11 years) earthworm observation study conducted at 42 plots. Earthworms were sampled from 0.5 x 0.5 x 0.2 m³ soil blocks and identified to species level. Sensor data were highly correlated with earthworm abundances observed in reduced tillage but less correlated with earthworm abundances observed in conventional tillage. This may indicate that management influences the sensor-earthworm relationship. Generalized additive models and state-space models showed that modelling based on data fusion from EC, pH, and NIR sensors produced better results than modelling without sensor data or data from just a single sensor. Regarding the individual earthworm species, particular sensor combinations were more appropriate than others due to the different habitat requirements of the earthworms. Earthworm species with soil-specific habitat preferences were spatially predicted with higher accuracy by PSS than more ubiquitous species. Conclusions/Significance: Our findings suggest that PSS contributes to the spatial modelling of earthworm abundances at field scale and that it will support species distribution modelling in the attempt to understand the soil-earthworm relationships in agroecosystems.
- ItemAssessing the contribution of soil NOx emissions to European atmospheric pollution(Bristol : IOP Publ., 2021) Skiba, Ute; Medinets, Sergiy; Cardenas, Laura M.; Carnell, Edward John; Hutchings, Nick; Amon, BarbaraAtmospheric NOx concentrations are declining steadily due to successful abatement strategies predominantly targeting combustion sources. On the European continent, total NOx emissions fell by 55% between 1990 and 2017, but only modest reductions were achieved from the agricultural sector; with 7.8% from 20 Eastern European countries and 19.1% from 22 Western European countries. Consequently, the share of agricultural NOx emissions for these 42 European countries have increased from 3.6% to 7.2%. These values are highly uncertain due to serious lack of studies from agricultural soils and manure management. The emission factor (EFNO 1.33%), currently used for calculating soil NOx emissions from European agricultural categories ‘N applied to soils’ and ‘manure management’ was evaluated here by including recently published data from temperate climate zones. The newly calculated EFNO (average 0.60%, 0.0625th%/0.5475th%, n = 65 studies) is not notably different from the current value, given the large uncertainties associated with the small pool of studies, and therefore continued use of EFNO (1.33%) is recommended until more data become available. An assessment of the contribution of agricultural and non-agricultural NOx sources found that of the 42 European countries, the 8 most populated countries achieved considerable reductions (1990–2017) from categories ‘non-agricultural sources’ (55%), ‘N applied to soils’ (43%) and ‘manure management’ (1.2%), compared to small reductions from the remaining 34 countries. Forests are also large sources of soil NOx. On average, emissions from Eastern European forests were 4 times larger than from ‘N applied agricultural soil’, whereas Western European NOx emissions from ‘N applied agricultural soil’ were two times larger than from forest soils. Given that non-agricultural sources of NOx continue to decline, soil related emissions from agriculture, forests and manure management become more important, and require rigorous investigation in order to improve atmospheric pollution forecasts.
- ItemSoil Nutrient Detection for Precision Agriculture Using Handheld Laser-Induced Breakdown Spectroscopy (LIBS) and Multivariate Regression Methods (PLSR, Lasso and GPR)(Basel : MDPI, 2020) Erler, Alexander; Riebe, Daniel; Beitz, Toralf; Löhmannsröben, Hans-Gerd; Gebbers, RobinPrecision agriculture (PA) strongly relies on spatially differentiated sensor information. Handheld instruments based on laser-induced breakdown spectroscopy (LIBS) are a promising sensor technique for the in-field determination of various soil parameters. In this work, the potential of handheld LIBS for the determination of the total mass fractions of the major nutrients Ca, K, Mg, N, P and the trace nutrients Mn, Fe was evaluated. Additionally, other soil parameters, such as humus content, soil pH value and plant available P content, were determined. Since the quantification of nutrients by LIBS depends strongly on the soil matrix, various multivariate regression methods were used for calibration and prediction. These include partial least squares regression (PLSR), least absolute shrinkage and selection operator regression (Lasso), and Gaussian process regression (GPR). The best prediction results were obtained for Ca, K, Mg and Fe. The coefficients of determination obtained for other nutrients were smaller. This is due to much lower concentrations in the case of Mn, while the low number of lines and very weak intensities are the reason for the deviation of N and P. Soil parameters that are not directly related to one element, such as pH, could also be predicted. Lasso and GPR yielded slightly better results than PLSR. Additionally, several methods of data pretreatment were investigated.