The Ouarzazate Group in the Anti-Atlas Belt of southern Morocco, part of the West African Craton (WAC), is a significant Proterozoic lithostratigraphic unit formed during the late Ediacaran period. It includes extensive volcanic rocks associated with the early stages of Iapetus Ocean opening. Zircon U-Pb dating and geochemical analyses of the Oued Dar’a Caldera (ODC) volcanic succession in the Saghro Massif reveal two major eruptive cycles corresponding to the lower and upper Ouarzazate Group. The 1st cycle (588–563 Ma) includes pre- and syn-caldera volcanic succession characterized by basaltic andesite to rhyolitic rocks, formed in a volcanic arc setting through lithospheric mantle-derived mafic magmatism and crustal melting. A major caldera-forming eruption occurred approximately 571–562 Ma, with associated rhyolitic dyke swarms indicating a larger caldera extent than previously known. The 2nd cycle (561–543 Ma) features post-caldera bimodal volcanism, with tholeiitic basalts and intraplate felsic magmas, signaling a shift to continental flood basalts and silicic volcanic systems. The entire volcanic activity spans approximately 23–40 million years. This succession is linked to late Ediacaran intracontinental super-eruptions tied to orogenic collapse and continental extension, likely in association with the Central Iapetus Magmatic Province (CIMP), marking a significant transition in the geodynamic evolution of the WAC.

Enhanced weathering is a promising approach for removing carbon dioxide from the atmosphere at scale while improving agricultural yields. However, accurately quantifying carbon dioxide removal in the field is critical for this approach to scale, particularly given that nearly all of the current deployment activity caters to the voluntary carbon market. Here, we present an updated framework and a signal-to-noise analysis for using soil-based mass balance approaches to quantify rock powder dissolution from field-scale data of soil composition. With additional assumptions, the quantification of rock powder dissolution can be used to estimate carbon dioxide removal potential of EW deployments. The framework we present explicitly accounts for the enrichment of immobile elements in topsoil due to feedstock mass loss and demonstrates that omission of this process systematically overestimates feedstock dissolution. We suggest that the framework should only be used when average post-weathering sample compositions fall within the parameter space representing physically meaningful results (i.e., set out by the mixing relationships between soil, feedstock, and a hypothetical weathered feedstock residue endmember). Building from this, we provide support for the idea that feedstock dissolution should be quantified using the sample population mean rather than individual samples. Given the potential for signal-to-noise issues with this framework, it is critical that it is utilized only when signals are statistically robust. To illustrate this, we present a signal-to-noise analysis based on a new dataset of soil cation heterogeneity from high-density spatial sampling of 5 fields (0.6-19.2 samples ha-1, 7.1-39.6 pooled cores ha-1). The analysis is based on simulated geolocated sample pairs and suggests that detecting rock powder dissolution via soil mass balance should be feasible when application rates, dissolution fractions, and sampling frequencies are above certain threshold values. When planning deployments, signal emergence can be optimized through careful selection of feedstock composition, strategic feedstock application, and improved sampling protocols.

Enhanced weathering is a promising approach for removing carbon dioxide from the atmosphere at scale while improving agricultural yields. However, accurately quantifying carbon dioxide removal in the field is critical for this approach to scale, particularly given that nearly all of the current deployment activity caters to the voluntary carbon market. Here, we present an updated framework and a signal-to-noise analysis for using soil-based mass balance approaches to quantify rock powder dissolution from field-scale data of soil composition. With additional assumptions, the quantification of rock powder dissolution can be used to estimate carbon dioxide removal potential of EW deployments. The framework we present explicitly accounts for the enrichment of immobile elements in topsoil due to feedstock mass loss and demonstrates that omission of this process systematically overestimates feedstock dissolution. We suggest that the framework should only be used when average post-weathering sample compositions fall within the parameter space representing physically meaningful results (i.e., set out by the mixing relationships between soil, feedstock, and a hypothetical weathered feedstock residue endmember). Building from this, we provide support for the idea that feedstock dissolution should be quantified using the sample population mean rather than individual samples. Given the potential for signal-to-noise issues with this framework, it is critical that it is utilized only when signals are statistically robust. To illustrate this, we present a signal-to-noise analysis based on a new dataset of soil cation heterogeneity from high-density spatial sampling of 5 fields (0.6-19.2 samples ha-1, 7.1-39.6 pooled cores ha-1). The analysis is based on simulated geolocated sample pairs and suggests that detecting rock powder dissolution via soil mass balance should be feasible when application rates, dissolution fractions, and sampling frequencies are above certain threshold values. When planning deployments, signal emergence can be optimized through careful selection of feedstock composition, strategic feedstock application, and improved sampling protocols.

Agriculture is the primary anthropogenic source of nitrous oxide (N2O), a potent greenhouse gas, and mitigating soil emissions without compromising food security is critical for climate targets. We assess the role of copper (Cu), as a micronutrient, in regulating nitrogen cycling enzymes. Using mesocosm experiments and long-term field trials, we demonstrate that Cu addition, coupled with pH modification, significantly reduces N2O fluxes. Metagenomic and metatranscriptomic analyses reveal that Cu availability regulates the expression of moxA (a Cu-containing enzyme) and its coupling with nitrous oxide reductase (nosZ). These findings suggest that targeted Cu amendments—via direct application or silicate-enhanced weathering—offer a scalable strategy to decrease agricultural N2O emissions while maintaining soil productivity.