Water and nitrogen use efficiency of corn (Zea mays L.) under water table management
Bibliographic record
Abstract
Drainage and water table management are essential for crop production in humid regions.Water table management not only increases crop yield, but also reduces nitrate leaching to water bodies.This study investigated the water and nitrogen use efficiency of corn under two water management conditions and three nitrogen fertilizer levels.The sap flow heat balance method was used to measure the daily water uptake of corn, over an extended period of the growing season.The impacts of climate change on grain corn and biomass yield in eastern Canada under tile drained conditions was also evaluated over a 30 year future period (2040 to 2069).The study was conducted at a field scale in 2008 and 2009 at St. Emmanuel, Quebec.The two water management conditions were: conventional drainage (FD), and controlled drainage with subirrigation (CD-SI).The three nitrogen (N) fertilizer treatments (low, medium, and high N) were applied in a strip across three blocks.The seasonal water balance indicated that the plants in the CD-SI plots had more water than required in the wet periods, despite the system automation, while the FD plots exhibited deficit water conditions.Water could be saved in the wet periods by better regulating water supplied by subirrigation.However, in dry years, the CD-SI system increased yield.The grain corn water use efficiency (WUE) for FD plots was 2.49 and 2.46 kg m -3 , in 2008 and 2009, respectively.In these years, the grain WUE for CDiii SI plots was 2.43 and 2.26 kg m -3 .Water management treatments demonstrated significant difference (p < 0.05) in grain yields in 2009, at low and high nitrogen levels.However, at the medium nitrogen level, water management demonstrated no significant effect (p > 0.05) on grain yields.The two water treatments had no effect on the above-ground dry biomass yields in both years.Mean nitrogen use efficiency (NUE) of grain corn and biomass varied from 27 to 99 kg kg -1 .Highest NUE (99 kg kg -1 ) was observed under low N (~120 kg N ha -1 ) and lowest NUE (41 kg kg -1 ) occurred in the high N (~260 kg N ha -1 ).This might be due to higher nitrogen losses due to leaching, residual nitrogen in the soil, and more denitrification in high N plots.The rate of plant water uptake measured by the sap flow method, varied from 3.55 to 5.11 mm d -1 from silking to full dent stage of corn growth.These rates were consistent with ET c calculated by the FAO-56 Penman-Monteith method (3.70 to 5.93 mm d -1 ) for both years.Although, silking is considered as a critical stage for corn growth, water demand was highest at the milk stage (45.63 to 59.80 mm).Transpiration during this stage constituted 10 to12% of the total water requirement of the corn for the season.The silking to full dent stage accounted for approximately 40% of the total water requirement of the crop.The STICS (JavaStics v1.0) crop model was used to examine the impacts of climate change, under the B1 emissions scenario, on corn yield from 2040-2069.The model was calibrated using 2008 field measured My sincere thanks to thesis supervisor, Dr.
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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 | 0.000 |
| 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 | 0.000 | 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".