A global index to measure crop commodity impacts on nature
Bibliographic record
Abstract
This thesis presents the croP environmentaL sustAiNabiliTy index (PLANTdex), one of the first spatially- explicit (5 arcmin resolution) indexes to measure environmental impacts caused by the production of sixteen crop commodities globally. PLANTdex comprises five globally standardised environmental impact indicators developed from robust spatially-explicit environmental models related to climate change, water stress and terrestrial and aquatic biodiversity loss. \nPLANTdex, indicates spatial variations in production system efficiencies and environmental sensitivities, enabling to locate environmentally sustainable and unsustainable production globally. This thesis found more synergies (e.g., between biodiversity indicators) existed than trade-offs (e.g., between GHG emissions and marine biodiversity loss) existed between indicators. PLANTdex provides significant scientific advancement through spatially analysing multiple environmental impacts rather than resource use intensities attributed to specific crops, particularly biodiversity. Through PLANTDex, the global production vs impact ratio contribution indicator identified regions with greater environmental impacts than the global average impact, these included specific countries such as Indonesia, Turkey & China for crops such as Soybean, Oilpalm, Barley and Wheat. Countries where production for particular crops such as wheat caused impacts less impact than the global average included Ghana, Canada & Kazakhstan. The analysis identified over 32 regions that could collaborate on agricultural management strategies that allows for the exchange of knowledge on sustainable agriculture. PLANTdex has the potential to support initiatives such as the Taskforce on Nature-related Financial Disclosures (TNFD) and ESG strategies at the due diligence stage. \nDue to a lack of indicators to measure biodiversity impacts from fertiliser use, this thesis also focuses on improving current methodologies and estimating aquatic biodiversity impacts from excess fertiliser. The outcome identified large spatial differences between the popularly used Nutrient Use Efficiency indicator and aquatic biodiversity impacts. Significant variability existed at different metric stages (efficiency, loading and impacts), which identified the importance of measuring endpoint impacts rather than efficiencies. Life cycle impact assessments (LCIA) are often used to determine the environmental impacts of fertiliser use. However, the lack of suitable methodologies to estimate the fate and transport of nutrients from soils makes crop and regional impact comparisons challenging. Using a newly devised, spatially-explicit nutrient fate and transport model (fate factor, FF), this thesis estimates the global spatial variability of nutrient loss from fertilisation of crops and their impact on aquatic biodiversity, specifically species richness, relative to their quantity of production. The FF is implemented within a current LCIA methodology framework (LC IMPACT) and enhances it.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".