Assessment of Nitrogen Leaching of Cropping Pattern by Soil Nitrogen Balance Equation (Case Study: Varamin Irrigation and Drainage Network)
Bibliographic record
Abstract
Nitrogen is often one of the most important limiting factors for biomass production. Usually few soils have proper amount of nitrogen, so it is usually added by fertilizers. In cropping systems, nitrogen fertilization practices can provide a sufficient nitrogen supply for plants to achieve the potential yield. However, to ensure reaching to this potential yield, farmers often apply more nitrogen fertilizers than the required nitrogen for achieving maximum yield. Nitrogen fertilizer should be given to soil according to nitrogen content of soil, water and crop nitrogen demand during the growth periods. Especially in the lands that apply wastewater and groundwater for irrigation and irrigation sources have large quantities of nitrogen, therefore we Should reduce the amount of fertilizer used in agricultural lands, In other words for planning the plant fertilizer demand, nitrogen balance in soil should be considered. Nitrogen overuse increases the risk of nitrogen leaching to groundwater, contaminating groundwater and threatening the human health. In recent years, critical plant nitrogen concentration equation is used to determine crop nitrogen demand during the different growth stages of plants. The purpose of this study is to determine the nitrogen demand of Varamin network’s cropping pattern by using the critical plant nitrogen concentration equation and comparing the result with the amount of nitrogen fertilizer commonly applied in the study area. In this study, monthly biomass production for growing period was determined based on normalized water productivity index and plant canopy development. Coefficient of critical nitrogen concentration equation for each plant was determined by previous researches. The result of this study showed that for barley, the amount of nitrogen applied in Varamin network is equal to nitrogen demand of the cropsand for wheat, maize and tomato are 25%, 61% and 18%, respectively, higher than the amount obtained from critical plant nitrogen concentration equation.also according to the results of soil Nitrogen balance in lands covered by AU canal of Varamin network, 707 ton of nitrogen entered to soil and groundwater by leaching and we should considered appropriate solutions to reduce leaching, such as using high-yielding crops that remove a significant amount of N in the harvested portion, synchronizing fertilizer application with crop demand, conjunctive use of wastewater and groundwater with proper nitrogen concentration and etc.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".