Fertilizer Effects on Soil Moisture Changes during Crop Growing Seasons of Dryland Agriculture in Northwestern Alberta, Canada
Bibliographic record
Abstract
Efficient use of limited soil moisture resources is important for crop production in dryland agriculture in the study area. Understanding the changes in soil moisture during crop growing seasons can improve crop production. The objectives of the current study were to assess the effects of fertilizer application on soil moisture content (SMC) and its depletion patterns during the growing season. Changes in SMC in the 0-10, 10-20, 20-30, and 30-40 cm depths soil were monitored during the 2013-2015 growing seasons under canola (Brassica napus L.) and barley (Hordeum vulgare L.) crops with 0 and 100% rates of commercial chemical fertilizers. The crops were grown using direct seeding (DS) on a clay loam soil in the southeast Peace Region (legal: NW7-77-20W5; GPS: 55o39′38.43″ N, 117o6′10.64″ W) of Alberta, Canada. Fertilizer application reduced the SMC at all the soil depths during considerable crop growing seasons. Depletion of SMC started earlier in fertilized pots in 2013 and 2014, but not in the drier early season of 2015. Rapid depletion during the early and middle of growing seasons was followed by slower or no soil moisture depletion by crops near the end. The SMC tended to be somewhat lower under 100 than 0% fertilizer by the end of the growing seasons, with few exceptions. The start of SMC depletion and appearance of fertilizer rates effect after seeding was also influenced by the amount of SMC at seeding in spring, i.e. earlier in dry year of 2015 than in other years with higher SMC in spring and more rain. The results demonstrated that applying fertilizer increased soil water use by plants regardless of the crop type or growing season. They also indicated that if more soil moisture was available, the differences between fertilizer treatments might have continued for extended periods, and yields of fertilized crops may have benefitted more.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".