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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".