Current Practice and Future Perspectives for Livestock Production and Industrial Ecology

The year 2016 marked the 10-year anniversary of the publication of the “Livestock’s Long Shadow” FAO report [...

Assessing biodiversity loss due to land use with Life Cycle Assessment: Are we there yet?

Ecosystems are under increasing pressure from human activities, with land use and land use change on the forefront of drivers promoting global and regional biodiversity loss. The challenge of reversing the negative outlook for the coming years starts at measuring loss rates and assigning responsibilities. The effects of land use on biodiversity dynamics are complex, and so pinpointing the main pressures to the state of biodiversity at the global scale is a task for holistic models such as Life Cycle Assessment (LCA), which is the leading method for calculating cradle-to-grave environmental impacts of products and services. LCA is actively promoted by many public policies and is part of environmental information systems in private companies. It already deals with potential biodiversity impacts from land use but there are significant obstacles to overcome before LCA models grasp the full reach of the phenomena involved. We discuss some pressing issues to solve. LCA introduces biodiversity as an endpoint category modeled as a loss in species richness due to transformation and occupation of land extending in time and space. Functional and population effects are mostly absent due to the emphasis on species accumulation with limited geographic and taxonomical reach. Current land use modeling with biodiversity indicators simplifies the real dynamics and complexity of interactions among species and with their habitats. We systematically reviewed all LCA studies on land use with findings in global change and conservation ecology to identify the main areas for improvement. To finalize, we provided indications on how to address some issues raised. If such task is successful, companies will start monitoring the impacts scattered in many locations along increasingly globalized supply chains and take definite steps towards addressing the impacts caused by land use.JRC.H.5-Land Resources Managemen

Development of an algorithm for identification of sown biodiverse pastures in Portugal

ABSTRACT 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

How well does LCA model land use impacts on biodiversity?—A comparison with approaches from ecology and conservation

The modeling of land use impacts on biodiversity is considered a priority in life cycle assessment (LCA). Many diverging approaches have been proposed in an expanding literature on the topic. The UNEP/SETAC Life Cycle Initiative is engaged in building consensus on a shared modeling framework to highlight best-practice and guide model application by practitioners. In this paper, we evaluated the performance of 31 models from both the LCA and the ecology/conservation literature (20 from LCA, 11 from non-LCA fields) according to a set of criteria reflecting (i) model completeness, (ii) biodiversity representation, (iii) impact pathway coverage, (iv) scientific quality, and (v) stakeholder acceptance. We show that LCA models tend to perform worse than those from ecology and conservation (although not significantly), implying room for improvement. We identify seven best-practice recommendations that can be implemented immediately to improve LCA models based on existing approaches in the literature. We further propose building a “consensus model” through weighted averaging of existing information, to complement future development. While our research focuses on conceptual model design, further quantitative comparison of promising models in shared case studies is an essential prerequisite for future informed model choice.JRC.D.1-Bio-econom

Estimating soil organic carbon of sown biodiverse permanent pastures in Portugal using near infrared spectral data and artificial neural networks

Grasslands in Portugal are key managed ecosystems, supporting and providing a diverse number of ecosystem services. Here, we developed a procedure for rapid estimation of soil organic carbon (SOC) in soil samples of sown biodiverse permanent pastures rich in legumes (SBP) in Portugal. We combined laboratory NIR spectral data analysis with artificial neural networks (ANN) to estimate the SOC content of SBP soil samples. To train and test the ANN, we used more than 340 soil samples collected in the 0–20 cm topsoil layer from three farms in 2018 and 2019 and two other farms in 2019 only. The number of bands of the spectra (800–2778 nm) was reduced using two different approaches: (a) aggregation to Sentinel-2 (S2) bands using the average reflectance within each bandwidths; and (b) principal component analysis (PCA). For the S2 approach, we considered the six S2 bands that overlap with the spectral range of the instrument used. For the PCA approach, we considered the five first principal components. Additional covariates were used for prediction, including weather and terrain attributes, e.g. accumulated precipitation, average temperature, elevation, and slope. To test for transferability of the models to different farms, we used an eight-fold leave-one-out cross-validation approach to calculate estimation errors. Each fold is a unique combination of farm and year and is used to assess the model’s performance calibrated from the seven other folds. The ANN was able to estimate both low and high SOC contents without systematic errors and with similar estimation errors for both full and reduced spectral data approaches. The average root mean squared error (RMSE) for the S2 approach was 1.95 g kg 1 (0.45 – 2.33 g kg 1 depending on the hold-out fold) and for the PCA approach was 1.81 g kg 1 (0.74 – 2.42 g kg 1) (compared to the average SOC content of 12 g kg 1). These RMSE values were similar to the RMSE obtained using the full spectra, suggesting that the original spectral resolution could be reduced without losing information. These results suggest the potential for using remotely sensed data to estimate the variation of SOC content for SBP. They are a first step towards developing algorithms that can alleviate the cost and time of soil sampling and chemical SOC laboratory analysis through indirect estimationinfo:eu-repo/semantics/publishedVersio

Evaluation of near infrared spectroscopy (NIRS) for estimating soil organic matter and phosphorous in Mediterranean montado ecosystems

The Montado is an agro-silvo-pastoral ecosystem characteristic of the Mediterranean region. Pasture productivity and, consequently, the possibilities for intensifying livestock production depend on soil fertility. Soil organic matter (SOM) and phosphorus (P2O5) are two indicators of the evolution of soil fertility in this ecosystem. However, their conventional analytical determination by reference laboratory methods is costly, time consuming, and laborious and, thus, does not meet the needs of current production systems. The aim of this study was to evaluate an alternative approach to estimate SOM and soil P2O5 based on near infrared spectroscopy (NIRS) combined with multivariate data analysis. For this purpose, 242 topsoil samples were collected in 2019 in eleven fields. These samples were subjected to reference laboratory analysis and NIRS analysis. For NIRS, 165 samples were used during the calibration phase and 77 samples were used during the external validation phase. The results of this study showed significant correlation between NIRS calibration models and reference methods for quantification of these soil parameters. The coefficient of determination (R2, 0.85 for SOM and 0.76 for P2O5) and the residual predictive deviation (RPD, 2.7 for SOM and 2.2 for P2O5) obtained in external validation indicated the potential of NIRS to estimate SOM and P2O5, which can facilitate farm managers’ decision making in terms of dynamic management of animal grazing and differential fertilizer applicationinfo:eu-repo/semantics/publishedVersio