Unleashing the sequestration potential of soil organic carbon under climate and land use change scenarios in Danish agroecosystems.

Future global climate changes are expected to increase soil organic carbon (SOC) decomposition. However, the combined effect of C inputs, land use changes, and climate on SOC turnover is still unclear. Exploring this SOC-climate-land use interaction allows us to understand the SOC stabilization mechanisms and examine whether the soil can act as a source or a sink for CO2. The current study estimates the SOC sequestration potential in the topsoil layer of Danish agricultural lands by 2038, considering the effect of land use change and future climate scenarios using the Rothamsted Carbon (RothC) model. Additionally, we quantified the loss vulnerability of existing and projected SOC based on the soil capacity to stabilize OC. We used the quantile random forest model to estimate the initial SOC stock by 2018, and we simulated the SOC sequestration potential with RothC for a business-as-usual (BAU) scenario and a crop rotation change (LUC) scenario under climate change conditions by 2038. We compared the projected SOC stocks with the carbon saturation deficit. The initial SOC stock ranged from 10 to 181 Mg C ha-1 in different parts of the country. The projections showed a SOC loss of 8.1 Mg C ha-1 for the BAU scenario and 6 Mg C ha-1 after the LUC adoption. This SOC loss was strongly influenced by warmer temperatures and clay content. The proposed crop rotation became a mitigation measure against the negative effect of climate change on SOC accumulation, especially in sandy soils with a high livestock density. A high C accumulation in C-saturated soils suggests an increase in non-complexed SOC, which is vulnerable to being lost into the atmosphere as CO2. With these results, we provide information to prioritize areas where different soil management practices can be adopted to enhance SOC sequestration in stable forms and preserve the labile-existing SOC stocks.

Unveiling spatial variability in herbicide soil sorption using Bayesian digital mapping.

Regional mapping herbicide sorption to soil is essential for risk assessment. However, conducting analytical quantification of adsorption coefficient (Kd ) in large-scale studies is too costly; therefore, a research question arises on goodness of Kd spatial prediction from sampling. The application of a spatial Bayesian regression (BR) is a newer technique in agricultural and natural resources sciences that allows converting spatially discrete samples into maps covering continuous spatial domains. The objective of this work was to unveil herbicide sorption to soil at a landscape scale by developing a predictive BR model. We integrated a large set of ancillary soil and climate covariables from sites with Kd measurements into a spatial mixed model including site random effects. The models were fitted using glyphosate and atrazine Kd s, determined in 80 and 120 sites, respectively, from central Argentina. For model assessment, measurements of global and point-wise prediction errors were obtained by cross-validation; residual variability was estimated by bootstrap to compare BR with regression kriging. Results showed that the BR spatial predictions outperformed regression kriging. The glyphosate Kd model (root mean square prediction error, 13% of the mean) included aluminum oxides, pH, and clay content, whereas the atrazine Kd model strongly depended on soil organic carbon and clay and on climatic variables related to water availability (root mean square prediction error, 27%). Spatial modeling of a complex edaphic process as herbicide sorption to soils enhanced environmental interpretations. An efficient approach for spatial mapping provides a modern perspective on the study of herbicide sorption to soil.

Site-specific data on herbicide soil retention and ancillary environmental variables.

This article presents original geospatial data on soil adsorption coefficient (Kd) for two widely used herbicides in agriculture, glyphosate and atrazine. Besides Kds, the dataset includes site-specific soil data: pH, total nitrogen, total organic carbon, Na, K, Ca, Mg, Zn, Mn, Cu, cation exchange capacity, percentage of sand, silt and clay, water holding capacity, aluminum and iron oxides, as well as climatic and topographic variables. The quantification of herbicides soil retention was made on a sample of soils selected by Conditionated Latin Hypercube method to capture the underlying edaphoclimatic variability in Cordoba, Argentina. The glyphosate data presented here has been used to evaluate statistical methods for model-based digital mapping (F. Giannini Kurina, S. Hang, R. Macchiavelli, M. Balzarini, 2019) [1]. The dataset is made publicly available to enable future analyzes on processes that leads the dynamics of both herbicides in soil.