Site‐specific weed management on organic grain farms using variable rate seeding and data driven simulation
Bibliographic record
Abstract
Abstract Integrated weed management is integral to organic farming, with increased crop seeding rates as one effective weed suppression tactic. Precision agriculture, which uses guidance and sensor technologies to direct site‐specific management, could allow for targeted weed management using variable seeding rates. On farm precision experimentation (OFPE) can be used to predict site‐specific sub‐field weed biomass response to a range of varied crop and cover crop seed rates across whole fields to provide decision support directly to farmers. We used OFPE to compare five simulated precision seeding strategies to either maximize net return or minimize weed biomass. Five site‐years of OFPE data were collected from organic farms in Manitoba, Canada and Montana, USA. Seeding rate, weed biomass, crop yield, topographic variables and other remotely sensed data were collected on a 10‐m grid to model yield and weed response to seeding rates for each field. Simulated site‐specific variable seeding rate net returns improved upon farmer chosen uniform seeding rate net returns on average by $115 ha −1 . When variable seeding rates were optimized to minimize weed biomass, simulated weeds were reduced on average by 10 kg ha −1 relative to the farmer chosen uniform seed rates. A combined variable rate approach which balanced net return and weed minimization improved both net return and weed suppression compared to farmer‐chosen seeding rates in every site‐year. The various seeding rate strategies represent different methods from which farmers can choose to implement OFPE to optimize sub‐field‐specific planting rates and to increase their field‐scale ecological knowledge.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".