Contribution of organic agriculture to global sustainable food security
Bibliographic record
Abstract
Organic agriculture has been proposed as a more sustainable alternative to current conventional agriculture.The debate about organic agriculture is, however, often polarized and ideologically charged and not sufficiently informed by scientific evidence.In order to assess the potential contribution of organic agriculture to sustainable food security we need to systematically evaluate the costs and benefits of this farming system across multiple dimensions.In this thesis I evaluate organic agriculture through several different lenses -from an agronomic, an ecological, a social, and a policy perspective.I start by examining the yield performance of organic agriculture, concluding that organic yields are, on average, significantly lower than conventional yields, but that under certain circumstances they can nearly match those from conventional systems.From an xv ACKNOWLEDGEMENTS Because I could not say it any better, I want to start with the words with which Patrick Heller begins his book about the labour movement in Kerala:"A very wise and famous sociologist once told me that good scholarship was first and foremost about humility.He then went on to say that if you think you have an original idea, it only means you have a lousy memory.This book is the sum total of many ideas that I have absorbed, pilfered, and possibly mangled, from many different people, some of whom I remember, some of whom I don't.My thanks and apologies to all of them."-Patrick Heller, The Labour of development: workers and the transformation of capitalism in Kerala, India.This PhD has been a long and wonderful journey, the most important component of which were the people I met on the way and who have directly or indirectly contributed to this thesis.I will attempt to mention as many as I can, but there are countless others who have contributed to the thinking, the data and the science going into this thesis who have to go unnamed here.First of all I want to thank my supervisor Navin Ramankutty for being the best supervisor I could possibly have imagined.He allowed me to ramble and to explore, to follow my curiosity, and to learn.He was a mentor who not only challenged me intellectually, exposed me to new ideas, who pushed and guided me through the process of writing this thesis, but also a mentor who supported me in my development as a young scientist beyond the thesis.If it was not for him I would not be where I am.Most of all, however, I thank him for teaching me that there are more questions than answers, and that this is good so.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".