Soil Health and Nitrogen Dynamics as Affected by Organic Amendments in Monocropping and Intercropping of Maize and Mung Bean Systems
Bibliographic record
Abstract
ABSTRACT This study, conducted over 2 years (2021 and 2022), evaluated the effects of returning sheep manure (RM) and wheat residues (RR) as organic amendments on nitrogen (N) dynamics and soil properties in maize and mung bean monocultures and their intercropping systems within a semi‐arid environment. Significant variations in soil N levels were observed, with RM enhancing mineral N content and nitrate availability across all cropping systems, particularly benefiting mung bean monocultures and intercropped systems compared to RR treatments. The nitrate/ammonium ratio varied according to crop type and amendment type, influencing N availability. Nitrous oxide emissions were significantly higher in RM, peaking in monocultures of maize and mung bean. The highest grain and straw yields were observed in maize monocultures (6.22 and 8.18 t ha −1 , respectively). Mung bean monoculture exhibited the lowest C/N ratio in grain (15.8) and straw (21.2), which provided a better balance of grain yield and N content. Soil nutrient levels, including essential macronutrients (N, P and K) and micronutrients (Fe, Mn and Zn), were affected by the cropping system and type of organic amendment. Mung bean monoculture showed the highest soil N and P levels (0.062% and 21.5 mg kg −1 dry soil, respectively), while intercropping improved micronutrient availability and soil structure, enhancing water content and aggregate stability. These findings underscore the significance of selecting suitable organic amendments and cropping systems to manage N dynamics, crucial for maintaining soil health and boosting crop productivity in semi‐arid regions. Moreover, the results are vital for agricultural decision‐makers, advocating for sustainable practices that enhance soil fertility, mitigate greenhouse gas emissions and contribute to food security and environmental sustainability.
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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 | 0.000 | 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 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".