Geostatistical Analysis of Yield Monitor Data for Precision Agriculture
Bibliographic record
Abstract
It is long known that yield and other crop and soil characteristics vary across a farm field with measurements in the neighborhood being more similar than those far apart. However, such in-field spatial variability has been generally ignored because uniformity is required as a convenient means of operating modern farm equipment for most farming practices such as crop inputs and harvest. Moreover, until recently, the ability to detect and assess the in-field spatial variability has been limited. The situation is now changing with the recent advent of geomatic technologies such as yield monitors equipped with GPS on combine harvesters. The objective of this research was the geostatistical analysis of data from one such technology (yield monitor data). The focus was investigating the utility of multi-year yield monitor data from the same farm field located in southern Alberta for identifying patterns and stability of spatial variability. In this 125 ha field, three crops were grown in four years: wheat (Triticum aestivum L.) in 2008, canola (Brassica napus L.) in 2009, wheat in 2010 and barley (Hordeum vulgare L.) in 2011. Yield readings were cleaned using Yield Editor version 2.0 and normalized to remove scaling effect over different crops and years. The cleaned and normalized data were analyzed to fit three variogram models (exponential, Gaussian, and spherical) that are commonly used in geostatistical applications. The model fitting indicated that the similarity between yield readings were best described by an exponential function of the distance separating the readings, but with the similarity disappearing at different distances in all four crop years, ranging from 39.6 m (2008) to 99.6 m (2009). The spatial stability of yield patterns over the years was measured by Pearson’s correlations using interpolated yields mapped to a common grid. The apparent lack of spatial stability over the years suggests that recommended inputs or farm-level decisions such as variable rate applications cannot be based just on ‘eyeballing’ yield/soil maps from raw data at one farm in one year. Instead, these recommendations or decisions should be based on the maps or information derived from predicted data at multiple farms/locations over multiple years under tested, statistically sound spatial models for precise and profitable management of farm fields.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".