Development of management zones for site-specific fertilization in potato fields
Bibliographic record
Abstract
Soil variability and the resultant lower potato tuber yield can be mitigated through precision agricultural practices. This study has quantified variability of soil and crop properties, identified significant factors responsible for fluctuations in tuber yield, and delineated management zones (MZs) for site-specific soil fertility characterization of potato fields through proximal sensing of fields. The field experiments were conducted during potato growing seasons of 2017 and 2018 in the region of Souris alongside the La Pierre Lane in Prince Edward Island (PEI), Canada. Grids of 25 m x 25 m were established, and soil was sampled from each grid and analyzed for soil chemical properties. Time Domain Reflectometry (TDR), DualEM-2 sensor, and GreenSeekerTM were used to collect each grid’s moisture content (θ), horizontal coplanar geometry (HCP) of the apparent ground electrical conductivity, and normalized difference vegetation index (NDVI), respectively, at various plant growth stages during the cropping seasons. The soil samples were collected from the same grids to determine soil organic matter content (SOM), pH, Lime Index (LI), phosphorous (P), potassium (K), calcium (Ca), iron (Fe), cation exchange capacity (CEC) and %P/Al (P aluminum ratio) using standard methods. Potato tuber yield was collected manually from 0.91 x 3 m strips at the same grids. Results suggested that most of the parameters had moderate to high variability in both fields. Tuber yield was high in both fields due to high HCP, θ and SOM. Results from semivariograms revealed that the selected soil and crop properties showed a low, moderate, and high spatial dependence within the fields. Tuber yield had highly significant (p < 0.001) correlations with HCP, θ, SOM, and P during first sampling in the beginning of the growing season. However, during the second sampling, the tuber yield was significantly correlated with HCP, θ, NDVI, SOM, P, and K. Stepwise regression (through backward elimination at α = 0.01) excluded the low important variables, namely P and K, leaving HCP, θ, SOM, and NDVI as the most influential variables for tuber yield. Stepwise regression shortlisted the major properties of soil and crop that explained 71 to 86% of within-field variability. The cluster analysis grouped the soil and crop data into three zones, termed as excellent, medium, and poor, at a 40% similarity level. The coefficient of variation and the interpolated maps characterized least to moderate variability of soil fertility parameters except for HCP and K, which were highly variable. The results of multiple means comparison indicated that the tuber yield and HCP were significantly different in all MZs. The significant relationship of HCP and yield suggested that the ground conductivity data can be used to develop MZs for site‐specific fertilization in potato fields like those used in this study. The soil and crop variability data helped establishing MZs that can facilitate site-specific precision nutrient management for improving soil fertility and optimizing potato tuber yield. Moreover, this study suggested that managing the crop inputs based on HCP, θ and NDVI has significant potential to enhance tuber yield. The delineation of MZs has been suggested as a solution to mitigate adverse impacts of soil variability on potato tuber yield.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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 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".