Browsing by Subject "Soil monitoring"

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Description and prediction of copper contents in soils using different modeling approaches - results of long‐term monitoring of soils of northern Germany
(2022) Ludwig, Bernard; Klüver, Karen; Filipinski, Marek; Greenberg, Isabel; Piepho, Hans‐Peter; Cordsen, Eckhard
Background: Different regression approaches may be useful to predict dynamics of copper (Cu), an essential element for plants and microorganisms that becomes toxic at increased contents, in soils. Aim: Our objective was to explore the usefulness of mixed-effects modeling and rule-based models for a description and prediction of Cu contents in aqua regia (CuAR) in surface soils using site, pH, soil organic carbon (SOC), and the cation exchange capacity (CEC) as predictors. Methods: Three sites in northern Germany were intensively monitored with respect to CuAR and SOC contents, pH, and CEC. Data analysis consisted of calibrations using the entire data set and of calibration/validation approaches with and without spiking. Results: There was no consistent temporal trend, so data could be combined for the subsequent regressions. Calibration using the entire data set and calibration/validation after random splitting (i.e., pseudo-independent validation) were successful for mixed-effects and cubist models, with Spearman's rank correlation coefficients rs ranging from 0.83 to 0.91 and low root mean squared errors (RMSEs). Both algorithms included SOC, CEC, and pH as essential predictors, whereas site was important only in the mixed-effects models. Three-fold partitioning of the data according to site to create independent validations was again successful for the respective calibrations, but validation results were variable, with rs ranging from 0.04 to 0.76 and generally high RMSEs. Spiking the calibration samples resulted in generally marked improvements of the validations, with rs ranging from 0.45 to 0.67 and lower RMSEs. Conclusions: Overall, the information provided by SOC, pH, and CEC is beneficial for predicting CuAR contents in a closed population of sites using either mixed-effects or cubist models. However, for a prediction of CuAR dynamics at new sites in the region, spiking is required.
Monitoring soil carbon in smallholder carbon projects: insights from Kenya
(2024) Okoli, Adaugo O.; Birkenberg, Athena
Voluntary carbon market schemes facilitate funding for projects promoting sustainable land management practices to sequester carbon in natural sinks such as biomass and soil, while also supporting agricultural production. The effectiveness of VCM schemes relies on accurate measurement mechanisms that can directly attribute carbon accumulation to project activities. However, measuring carbon sequestration in soils has proven to be difficult and costly, especially in fragmented smallholdings predominant in global agriculture. The cost and accuracy limitations of current methods to monitor soil organic carbon (SOC) limit the participation of smallholder farmers in global carbon markets, where they could potentially be compensated for adopting sustainable farming practices that provide ecosystem benefits. This study evaluates nine different approaches for SOC accounting in smallholder agricultural projects. The approaches involve the use of proximal and remote sensing, along with process models. Our evaluation centres on stakeholder requirements for the Measurement, Reporting, and Verification system, using the criteria of accuracy, level of standardisation, costs, adoptability, and the advancement of community benefits. By analysing these criteria, we highlight opportunities and challenges associated with each approach, presenting suggestions to enhance their applicability for smallholder SOC accounting. The contextual foundation of the research is a case study on the Western Kenya Soil Carbon Project. Remote sensing shows promise in reducing costs for direct and modelling-based carbon measurement. While it is already being used in certain carbon market applications, transparency is vital for broader integration. This demands collaborative work and investment in infrastructure like spectral libraries and user-friendly tools. Balancing community benefits against the detached nature of remote techniques is essential. Enhancing information access aids farmers, boosting income through improved soil and crop productivity, even with remote monitoring. Handheld sensors can involve smallholders, given consistent protocols. Engaging the community in monitoring can cut project costs, enhance agricultural capabilities, and generate extra income.