Assessments of the global carbon (C) cycle typically rely on simplified models which consider large areas as homogeneous in terms of the response of soils to land use or consider very broad land classes. For example, “cropland” is typically modelled as an aggregation of distinct practices and individual crops over large regions. Here, we use the process-based Rothamsted soil Carbon Model (RothC model), which has a history of being successfully applied at a global scale, to calculate attainable SOC stocks and C mineralization rates for each of c. 17,000 regions (combination of soil type and texture, climate type, initial land use and country) in the World, under near-past climate conditions. We considered 28 individual crops and, for each, multiple production practices, plus 16 forest types and 1 grassland class (total of 80 classes). We find that conversion to cropland can result in SOC increases, particularly when the soil remains covered with crop residues (an average gain of 12 t C/ha) or using irrigation (4 t C/ha), which are mutually reinforcing effects. Attainable SOC stocks vary significantly depending on the land use class, particularly for cropland. Common aggregations in global modelling of a single agricultural class would be inaccurate representations of these results. Attainable SOC stocks obtained here were compared to long-term experiment data and are well aligned with the literature. Our results provide a regional and detailed understanding of C sequestration that will also enable better greenhouse gas reporting at national level as alternatives to IPCC tier 2 defaults.

The dairy sector is a major contributor to greenhouse gas emissions. Pasture-based dairy production is sometimes credited as environmentally friendlier but is less studied than more intensive production systems. Here we characterize and calculate the carbon footprint (CF), using life cycle assessment, of the “Vacas Felizes” pasture-based milk production system, in the Azores archipelago. Impacts were determined for multiple functional units: mass, energy and nutritional content, farm, area and animal. We performed multivariate analysis to assess the contribution of production parameters to the CF. Finally, we performed a literature review to compare these results with other production systems. Results show that emissions from enteric fermentation, concentrated feed production and (organic and mineral) fertilizer application are the three main sources of impact. Milk yield is a key production feature for the determination of emissions. The average CF is 0.83 kg CO₂/kg raw milk. At each milk yield level, the farms are approximately homogeneous. Compared with other studies, “Vacas Felizes” milk has a lower CF than 80 (out of 84) published CFs and on average it is approximately 32% lower.

The evolution of dryland pasture quality is closely related to the seasonal and inter-annual variability characteristic of the Mediterranean climate. This variability introduces great unpredictability in the dynamic management of animal grazing. The aim of this study is to evaluate the potential of two complementary tools (satellite images, Sentinel-2 and proximal optical sensor, OptRx) for the calculation of the normalized difference vegetation index (NDVI), to monitor in a timely manner indicators of pasture quality (moisture content, crude protein, and neutral detergent fiber). In two consecutive years (2018/2019 and 2019/2020) these tools were evaluated in six fields representative of dryland pastures in the Alentejo region, in Portugal. The results show a significant correlation between pasture quality degradation index (PQDI) and NDVI measured by remote sensing (R² = 0.82) and measured by proximal optical sensor (R² = 0.83). These technological tools can potentially make an important contribution to decision making and to the management of livestock production. The complementarity of these two approaches makes it possible to overcome the limitations of satellite images that result (i) from the interference of clouds (which occurs frequently throughout the pasture vegetative cycle) and (ii) from the interference of tree canopy, an important layer of the Montado ecosystem. This work opens perspectives to explore new solutions in the field of Precision Agriculture technologies based on spectral reflectance to respond to the challenges of economic and environmental sustainability of extensive livestock production systems.

Used in high-tech and everyday products, mercury (Hg), cobalt (Co), and nickel (Ni) are known to be persistent and potentially toxic elements that pose a serious threat to the most vulnerable ecosystems. Despite being on the Priority Hazardous Substances List, existing studies have only assessed the individual toxicity of Co, Ni and Hg in aquatic organisms, with a focus on the latter, ignoring potential synergistic effects that may occur in real-world contamination scenarios. The present study evaluated the responses of the mussel Mytilus galloprovincialis, recognized as a good bioindicator of pollution, after exposure to Hg (25 μg/L), Co (200 μg/L) and Ni (200 μg/L) individually, and to the mixture of the three metals at the same concentration. The exposure lasted 28 days at 17 ± 1 °C, after which metal accumulation and a set of biomarkers related to organisms' metabolic capacity and oxidative status were measured. The results showed that the mussels could accumulate metals in both single- and co-exposure conditions (bioconcentration factors between 115 and 808) and that exposure to metals induced the activation of antioxidant enzymes. Although Hg concentration in organisms in the mixture decreased significantly compared to single exposure (9.4 ± 0.8 vs 21 ± 0.7 mg/kg), the negative effects increased in the mixture of the three elements, resulting in depletion of energy reserves, activation of antioxidants and detoxification enzymes, and cellular damage, with a hormesis response pattern. This study underscores the importance of risk assessment studies that include the effects of the combination of pollutants and demonstrates the limitations of applying models to predict metal mixture toxicity, especially when a hormesis response is given by the organisms.