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FarmLCA:A novel approach to assess agroecological innovations in Life Cycle Assessment
No full textContext: Agroecological innovations are seen as solutions to reduce environmental impacts of agriculture but can potentially lead to trade-offs with food production. Appropriate tools are needed to better understand synergies and trade-offs among environmental issues, resource efficiency and food production.Objective: This study presents the FarmLCA tool, which models farms as interconnected crop-livestock systems and assesses environmental impacts from farms and farm-inputs. A mixed beef farm serves as case study to assess synergies and trade-offs of avoiding human edible feed in beef production.Methods: FarmLCA allows the calculation of cradle-to-farm gate life cycle assessments (LCA). Emissions of environmentally harmful substances from crops and livestock are modelled based on the farm management. Upstream impacts from imported inputs (including fertilizer or feed) are accounted for with life cycle inventory data. Yields and nutrient requirements are checked for plausibility, based on management handbooks, while manure availability and composition are calculated based on livestock production. Environmental impacts, nutrient use efficiency and food production for a typical mixed beef farm in Scotland were calculated (baseline) and compared to alternative farm management scenarios: a Feed-no-Food scenario, avoiding concentrate feeds resulting in a smaller herd size and a circular Feed-no-Food scenario, additionally optimizing productivity and synergies between crop and livestock (e.g. more legumes in crop rotation, reduced replacement rate and feed waste).Results and conclusions: In the Feed-no-Food scenario, the beef production was reduced by 25 %, but more calories and protein were produced overall due to cereal and legumes now being available for direct human consumption. However, slower growth of livestock led to increased environmental impact of beef, whilst reduced livestock numbers required more mineral fertilizer for crop production to replace on-farm manure. In the circular Feed-noFood scenario, beef and overall calorie production were slightly reduced compared to the baseline, but 1.5 more high quality protein (expressed by the Digestible Indispensable Amino Acid Score, DIAAS), were produced. Environmental impacts of beef were reduced and nitrogen self-sufficiency improved due to increased legume share in the rotation.Significance: Existing LCA approaches often fail to capture the complex dynamics of integrated crop-livestock systems and agroecological practices. FarmLCA addresses this by modelling both on-farm processes and up-stream inputs, enabling a consistent assessment of environmental impacts, nutrient use efficiency, and food production. It offers a more holistic and systemic view of the consequences of agroecological innovations and enables the identification of synergies and trade-offs between environmental protection, resource efficiency, and food production
PyamilySeq:Exposing the fragility of conventional gene (re)clustering and prokaryotic pangenomic inference methods
No full textPangenomics has become a central framework for exploring microbial diversity and evolution, enabling researchers to distinguish genes that define shared biological function from those that drive adaptation. However, this relies on clustering genes by sequence similarity, a process that is far less deterministic than often assumed. This study introduces PyamilySeq, a transparent and flexible toolkit designed to diagnose and quantify hidden biases within gene clustering and pangenome inference methodologies. Using PyamilySeq, we can see how clustering thresholds (often hard-coded and poorly documented) and paralog handling can substantially alter gene family composition. Surprisingly, even parameters unrelated to clustering, such as decimal precision (0.8 versus 0.80), output selection, and even CPU and memory allocation, can alter gene family assignments, challenging the assumption that identical clustering thresholds yield consistent results. Furthermore, tools often fail to report biologically meaningful or representative sequences for gene families, undermining downstream analyses. These findings reveal systematic fragilities in gene clustering and pangenome construction and highlight that pangenomics is not merely a data-driven task but a methodological one, where transparency, reproducibility, and interpretability are as critical as biological insight. This work calls for a re-evaluation of how pangenomes are constructed and compared, and advocates for methodologies that make their assumptions explicit and their results verifiable.</p
A systems reset for sustainable development
No full textAlthough sustainable development is an agreed vision for all countries, it lacks theoretical grounding. The contemporary market-based economy maximizes flows of material from nature through the economy to society, amplifying trends away from sustainability. We provide an alternative conceptualization of sustainable development, based not only on the flow of contributions from nature to economic actors, but equally of subsequent benefits to society, the effects of indirect drivers from society on economic actors, and direct drivers of economies on nature. This facilitates understanding of the dynamics and limits of the system, impacts on nature, the values influencing current trends away from sustainability, and of potential responses. This more holistic conceptualization enables actors to align their actions, supporting collective action towards sustainability across all scales. It thereby opens up space for inclusive co-habitation of the planet by people with diverse worldviews, enhanced achievement of the Sustainable Development Goals and more holistic framing for a post-2030 agenda for sustainability