The Effects on Greenhouse Gas Emissions of Ecological Intensification of Meat Production with Rainfed Sown Biodiverse Pastures

Are genetic drift and stem cell adherence in laboratory culture issues for cultivated meat production?

Mesenchymal stem cell-based cultivated meat is a promising solution to the ecological and ethical problems posed by traditional meat production, since it exhibits a protein content and composition that is more comparable to original meat proteins than any other source of cultivated meat products, including plants, bacteria, and fungi. Nonetheless, the nature and laboratory behavior of mesenchymal stem cells pose two significant challenges for large-scale production: genetic drift and adherent growth in culture. Culture conditions used in the laboratory expose the cells to a selective pressure that causes genetic drift, which may give rise to oncogene activation and the loss of "stemness." This is why genetic and functional analysis of the cells during culture is required to determine the maximum number of passages within the laboratory where no significant mutations or loss of function are detected. Moreover, the adherent growth of mesenchymal stem cells can be an obstacle for their large-scale production since volume to surface ratio is limited for high volume containers. Multi-tray systems, roller bottles, and microcarriers have been proposed as potential solutions to scale-up the production of adherent cells required for cultivated meat. The most promising solutions for the safety problems and large-scale obstacles for cultivated meat production are the determination of a limit number of passages based on a genetic analysis and the use of microcarriers from edible materials to maximize the volume to surface proportion and decrease the downstream operations needed for cultivated meat production.

Data-driven agent-based modelling of incentives for carbon sequestration: The case of sown biodiverse pastures in Portugal

Sown biodiverse permanent pastures rich in legumes (SBP) offset animal farming emissions due to their potential to sequester carbon. From 2009 to 2014 Portugal implemented a programme that provided payments to incentivize the adoption of SBP. However, no proper evaluation of its outcome was conducted. To address this gap, we develop an agent-based model (ABM) at the municipality level to study the adoption of SBP in Portugal and assess the outcome of the programme. We applied the first pure data-driven approach in agricultural land-use ABM, which relies on machine learning algorithms to define the agents' behavioural rules and capture their interaction with biophysical conditions. The ABM confirms that the program effectively expanded the adoption of SBP. However, our estimates indicate that the adoption rate in the absence of payments would have been higher than originally predicted. Furthermore, the existence of the program decreased the adoption rate after its conclusion. These findings underscore the importance of using reliable models and considering residual effects to properly design land use policies. The ABM developed in this study provides a basis for future research aimed at supporting the development of new policies to further promote the adoption of SBP.

Effect of different botanically-diverse diets on the fatty acid profile, tocopherol content and oxidative stability of beef

BACKGROUND

Beef from pasture-fed animals is viewed as a healthier and more welfare-friendly alternative to concentrate-fed beef. Botanically-diverse pastures consisting of numerous plant species may alter the fatty acid (FA) profile and the tocopherol content of beef, as well as the oxidative stability of the meat. In the present study, steers were assigned to one of three botanically-diverse diets: perennial ryegrass (PRG), perennial ryegrass + white clover (PRG + WC) or multi-species (MS), all with a finishing diet of the respective botanically-diverse silages plus a cereal-based concentrate, consistent with production systems in Ireland. The FA profile, tocopherol content, oxidative stability and colour of meat during storage were measured.

RESULTS

Compared to the other diets, the MS diet resulted in higher proportions of linolenic acid (C18:3n-3c), linoleic acid (C18:2n-6c) and total polyunsaturated fatty acids (PUFA), with higher PUFA:saturated fatty acid and n-6:n-3 ratios in the meat. α-Tocopherol concentrations were lowest in the meat of animals from the MS diet. In uncooked meat, lipid oxidation and colour values were affected by storage time across all diets, whereas the MS diet led to higher hue values only on day 14 of storage. When cooked, meat from animals on PRG + WC and MS diets had higher lipid oxidation on days 1 and 2 of storage than meat from animals on the PRG diet.

CONCLUSION

Feeding steers on a botanically-diverse diet consisting of six plant species can improve the n-3 FA and PUFA concentration of beef, affecting the susceptibility of cooked, but not uncooked, beef to oxidation. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Object-Based Classification Approaches for Multitemporal Identification and Monitoring of Pastures in Agroforestry Regions using Multispectral Unmanned Aerial Vehicle Products

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.

“BalSim”: A Carbon, Nitrogen and Greenhouse Gas Mass Balance Model for Pastures

Animal production systems are increasingly required to co-produce meat products and other ecosystem services. Sown biodiverse pastures (SBP) were developed in Portugal as an improvement over semi-natural pastures (SNP). SBP increase yields and animal intake during grazing, are substantial carbon sinks, and the abundance of legumes in the mixtures provides plants with a biological source of nitrogen. However, the data available and the data demands of most models make integrated modelling of these effects difficult. Here, we developed “BalSim”, a mass balance approach for the estimation of carbon and nitrogen flows and the direct greenhouse gas (GHG) balance of the two production systems. Results show that, on average, the on-farm GHG balance is −2.6 and 0.8 t CO2e/ha.yr for SBP and SNP, respectively. Ignoring the effects of carbon sequestration, and taking into account only non-CO2 emissions, the systems are responsible for 17.0 and 16.3 kg CO2e/kg live weight.yr. The annual analysis showed that non-CO2 emissions were highest in a drought year due to decreased yield and stocking rate. We also showed through scenario analysis that matching the grazing level to the yield is crucial to minimize emissions and ensure reduced feed supplementation while maintaining high soil carbon stocks.

Characterizing Livestock Production in Portuguese Sown Rainfed Grasslands: Applying the Inverse Approach to a Process-Based Model

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.

A Practical Comparison of Regionalized Land Use and Biodiversity Life Cycle Impact Assessment Models Using Livestock Production as a Case Study

Land use is increasingly important for impact assessment in life cycle assessment (LCA). Its impacts on biodiversity and provision of ecosystem services are crucial to depict the environmental performance of products. Life cycle impact assessment (LCIA) models are commonly selected by consensus through processes frequently misinformed by the absence of practical application studies. Here, we performed an assessment of all free and peer-reviewed LCIA models for land use. We started with spatial correlation analysis at the country scale. Models that use the same indicators are strongly correlated, suggesting that regionalization is no longer a decisive issue in model selection. We applied these models in a case study for cattle production where feeds are replaced by sown biodiverse pastures (SBP). We tested (1) a non-regionalized inventory from an LCA database and, (2) a regionalized inventory that explicit considered the locations of land occupation and transformation. We found the same qualitative result: the installation of SBP avoids impacts due to feed substitution. Each hectare of SBP installed avoids the occupation of 0.5 hectares per year for feed ingredient production. Adding inventory regionalization for 70% of land use flows leads to a change of 15% in results, suggesting limited spatial differentiation between country-level characterization factors.

Carbon Footprint of Milk from Pasture-Based Dairy Farms in Azores, Portugal

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.