Towards Incorporating Within-Field Variation into Spatial Agronomic Decision Processes
Bibliographic record
Abstract
In this memoire I will present my journey and the knowledge that I have gained within the Precision Agriculture domain over the past two decades. The first section broadly introduces variation in cropping systems and serves as a very general introduction to the research domain. Section 2 provides a more detailed overview of PA and develops some of the main issues and challenges that have been historically faced by PA practitioners. This includes clearly defining the domain and a recognition of the diversity and multi-disciplinary nature of PA. The role and importance of variability in production systems is then introduced along with the challenges in correctly and properly defining production variance. The final three parts of Section 2 turn the focus to how information, particularly multiple layers and multiple types of information, start to be translated into decisions. This shifts PA from a technical question on measuring and managing variation to a socio-technical innovation question on how technology is perceived and adopted and the potential disconnect between what scientists consider important and what commercial users want. Section 3 further develops two key areas – the disconnect between academic research and commercial application (innovation) and the disconnect in the data that we have and what we want (in terms of data type and the quality). There is a deliberate focus on how PA technologies and methodologies are effectively translated into successful commercial services and what I have learnt about this from my various placements and from my attempts to define industry-facing protocols at various stages of my research career. Section 4 provides an overview of how my research activities and publications that have (I hope) helped to address the issues raised in Sections 2 and 3. A focus is given to a large body of work that provides a descriptive reference base to observed spatial variation in yield and crop quality parameters in various annual and perennial systems. This is followed by the role and the evolution in zonage approaches over the past 2 decades and how these zones or management units have been used as a basis for more advantaged analysis to improve crop production knowledge and ultimately crop management decisions. The latter parts of Section 4 shift from natural, agronomic applications to more recent research that has started to interrogate and to build models to understand socio-technical aspects of PA. It develops ideas on how PA translation and adoption can be enhanced by better considering barriers and drivers of adoption. The last section, Section 5, outlines my own vision for the evolution of Precision Agriculture over the next decade. It outlines how Precision Agriculture needs to develop so that producers have a more ‘personalised’ agricultural decision system. It highlights key areas of research that I would like to pursue in my future career, and how advances in digital technology will help to achieve this research and translation. There are some concluding remarks. Throughout the memoire, I have deliberately avoided the incorporation of mathematical notation. I have preferred to keep the discussion more general and not too specific for a broader audience. The cited references contain more specific details for those wishing to understand the derivation of the geostatistical approaches and models discussed. By convention, I have indicated in bold the references that are self-citations within the document. These are all listed in the Bibliography. A more detailed publication list is appended with my full publication list. The publication list contains work that is not cited in the document, particularly research that I have performed in soil science, rather than in PA, and teaching and industry-oriented publications.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".