Stakeholder engagement in the processes of planning local adaptation to climate change faces many challenges. The goal of this work was to explore whether or not the intention of engaging could be understood (Study 1) and promoted (Study 2), by using an extension of the theory of planned behaviour. In Study 1, stakeholders from three European Mediterranean case studies were surveyed: Baixo Vouga Lagunar (Portugal), SCOT Provence Méditerranée (France), and the island of Crete (Greece) (N = 115). Stakeholders' intention of engaging was significantly predicted by subjective norm (which was predicted by injunctive normative beliefs towards policy-makers and stakeholders) and by perceived behavioural control (which was predicted by knowledge of policy and instruments). Study 2 was conducted in the Baixo Vouga Lagunar case study and consisted of a two-workshop intervention where issues on local and regional adaptation, policies, and engagement were presented and discussed. A within-participants comparison of initial survey results with results following the workshops (N = 12, N = 15, N = 12) indicated that these were successful in increasing stakeholders' intention of engaging. This increase was paired with a) an increase in injunctive normative beliefs towards policy-makers and consequently in subjective norm, and to b) a decrease in perceived complexity of planning local adaptation and an increase in knowledge regarding adaptation to climate change.

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.

Sown Biodiverse Pastures (SBP) are the basis of a high-yield grazing system tailored for Mediterranean ecosystems and widely implemented in Southern Portugal. The application of precision farming methods in SBP requires cost-effective monitoring using remote sensing (RS). The main hurdle for the remote monitoring of SBP is the fact that the bulk of the pastures are installed in open Montado agroforestry systems. Sparsely distributed trees cast shadows that hinder the identification of the underlaying pasture using Unmanned Aerial Vehicles (UAV) imagery. Image acquisition in the Spring is made difficult by the presence of flowers that mislead the classification algorithms. Here, we tested multiple procedures for the geographical, object-based image classification (GEOBIA) of SBP, aiming to reduce the effects of tree shadows and flowers in open Montado systems. We used remotely sensed data acquired between November 2017 and May 2018 in three Portuguese farms. We used three machine learning supervised classification algorithms: Random Forests (RF), Support Vector Machine (SVM) and Artificial Neural Networks (ANN). We classified SBP based on: (1) a single-period image for the maximum Normalized Difference Vegetation Index (NDVI) epoch in each of the three farms, and (2) multi-temporal image stacking. RF, SVM and ANN were trained using some visible (red, green and blue bands) and near-infrared (NIR) reflectance bands, plus NDVI and a Digital Surface Model (DSM). We obtained high overall accuracy and kappa index (higher than 79% and 0.60, respectively). The RF algorithm had the highest overall accuracy (more than 92%) for all farms. Multitemporal image classification increased the accuracy of the algorithms. as it helped to correctly identify as SBP the areas covered by tree shadows and flower patches, which would be misclassified using single image classification. This study thus established the first workflow for SBP monitoring based on remotely sensed data, suggesting an operational approach for SBP identification. The workflow can be applied to other types of pastures in agroforestry regions to reduce the effects of shadows and flowering in classification problems.

Grasslands are a crucial resource that supports animal grazing and provides other ecosystem services. We estimated the main properties of Portuguese sown biodiverse permanent pastures rich in legumes (SBP) starting from measured data for soil organic carbon (SOC) and using the Rothamsted Carbon Model. Starting from a dataset of SOC, aboveground production (AGP) and stocking rates (SR) in SBP, we used an inverse approach to estimate root to shoot (RS) ratios, livestock dung (LD), livestock intake (LI) and the ratio between easily decomposable and resistant plant material. Results for the best fit show that AGP and belowground productivity is approximately the same (RS is equal to 0.96). Animals consume 61% of the AGP, which is within the acceptable range of protein and energy intake. Carbon inputs from dung are also within the range found in the literature (1.53 t C/livestock unit). Inputs from litter are equally distributed between decomposable and resistant material. We applied these parameters in RothC for a dataset from different sites that only comprises SOC to calculate AGP and SR. AGP and SR were consistently lower in this case, because these pastures did not receive adequate technical support. These results highlight the mechanisms for carbon sequestration in SBP.

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