Building consensus on water use assessment of livestock production systems and supply chains: Outcome and recommendations from the FAO LEAP Partnership
Bibliographic record
Abstract
The FAO Livestock Environmental Assessment and Performance (LEAP) Partnership organised a Technical Advisory Group (TAG) to develop reference guidelines on water footprinting for livestock production systems and supply chains. The mandate of the TAG was to i) provide recommendations to monitor the environmental performance of feed and livestock supply chains over time so that progress towards improvement targets can be measured, ii) be applicable for feed and water demand of small ruminants, poultry, large ruminants and pig supply chains, iii) build on, and go beyond, the existing FAO LEAP guidelines and iv) pursue alignment with relevant international standards, specifically ISO 14040 (2006)/ISO 14044 (2006), and ISO 14046 (2014). The recommended guidelines on livestock water use address both impact assessment (water scarcity footprint as defined by ISO 14046, 2014) and water productivity (water use efficiency). While most aspects of livestock water use assessment have been proposed or discussed independently elsewhere, the TAG reviewed and connected these concepts and information in relation with each other and made recommendations towards comprehensive assessment of water use in livestock production systems and supply chains. The approaches to assess the quantity of water used for livestock systems are addressed and the specific assessment methods for water productivity and water scarcity are recommended. Water productivity assessment is further advanced by its quantification and reporting with fractions of green and blue water consumed. This allows the assessment of the environmental performance related to water use of a livestock-related system by assessing potential environmental impacts of anthropogenic water consumption (only "blue water"); as well as the assessment of overall water productivity of the system (including "green" and "blue water" consumption). A consistent combination of water productivity and water scarcity footprint metrics provides a complete picture both in terms of potential productivity improvements of the water consumption as well as minimizing potential environmental impacts related to water scarcity. This process resulted for the first time in an international consensus on water use assessment, including both the life-cycle assessment community with the water scarcity footprint and the water management community with water productivity metrics. Despite the main focus on feed and livestock production systems, the outcomes of this LEAP TAG are also applicable to many other agriculture sectors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.160 | 0.092 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.010 | 0.020 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".