A Land Resources Planning Toolbox to Promote Sustainable Land Management
Bibliographic record
Abstract
This paper provides a brief overview of how traditional concepts and approaches of land use planning have evolved into a more widely accepted vision of ‘land resources planning’ and its requirements for participatory processes, involvement of multi-sectoral stakeholders, and multi-thematic information at appropriate scales. Given its comprehensive ambitions, land resource planning (LRP) has a growing demand for a wide-ranging toolset, encompassing different tools in the biophysical, socio-economic, and governance (in a form of guidelines, methods, approaches and support tools). In order to collate knowledge, experiences and lessons from the LRP tools users, the Food and Agriculture Organization of the United Nations (FAO) - Land and Water Division held a consultation process through a survey among a range of stakeholders operating at different levels, sectors and regions. The survey evidenced limited awareness about the current availability of tools for land use planning. To remedy this situation, FAO developed the Land Resources Planning Toolbox (LRPT), a web-based inventory of existing tools. The Toolbox makes distinguishing between the tools in the socio-economic domain, those in the biophysical and the ones combining the two domains. The Toolbox explains the ability and restrictions of the LRP tools and their appropriateness to different regions, stakeholders and levels, and can be searched according to several criteria. It is concluded that, the Toolbox offers a useful mechanism for knowledge sharing and exchange of recent tools to enhance participatory LRP. It also has a great potential to support sustainable land management and landscape restoration. In this way it addresses, indirectly, conflicts and competition over resources.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.013 |
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".