Search

Sown biodiverse pastures (SBP) are a pasture system developed in Portugal. Until 2014, farmers were supported in installing and maintaining SBP, but tracking their locations has been lacking. To survey the country, remote sensing tools with machine learning were used. Here, we developed the first algorithm that combines remote sensing data with machine learning algorithms to identify SBP areas. The algorithm combines Landsat-7 and night-light spectral data with terrain and bioclimatic data. Remotely sensed data offer higher spatial resolution compared to bioclimatic data and also cover interannual variability. Gradient-boosted decision trees (XGB) and artificial neural networks (ANN) were the machine learning methods used. The overall classification accuracy, on an independent validation dataset, was 94%, with 82% producer accuracy and 85% user accuracy. The total estimated area of SBP in the Portuguese region of Alentejo region was 1300 km2 in 2013, which is similar to the total known installed area (approximately 1000 km2). The estimated spatial distribution is in accordance with the known distribution at the municipal level. These results are a critical first step towards the future development of remote systems for assessing the state of SBP and for compliance checks of farmer commitments. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]