Mineral Nitrogen and Microbial Biomass Dynamics under Different Acid Soil Management Practices for Maize Production
Bibliographic record
Abstract
Field and laboratory incubation studies were conducted to determine the effect of different acid soil managementpractices; liming (L), combined N and P fertilizers (NP), and goat manure (M) application, for maize production on thedynamics of mineral N, microbial biomass nitrogen (MBN) and microbial biomass carbon (MBC). A randomisedcomplete block design with a 23 factorial arrangement replicated thrice was used. The factors, each at two levels, were:NP fertilizers applied as triple superphosphate (0 and 75 kg ha-1), and urea (0 and 50 kg ha-1), L (0 and 2.5 t ha-1) and M(0 and 5 t ha-1) giving a total of eight treatments; L, M, NP, LM, LNP, MNP, LMNP and C (control). Soil samples fordetermination of mineral N, MBC and MBN were collected from the 0-15 and 15-30 cm depths at seedling, tasselling,and maturity stages of maize growth and after 0, 15, 30, 60, 120 and 240 days of laboratory incubation of soils obtainedfrom the same field.The NP treatment had significantly (P< 0.5) higher levels of mineral N in both depths at all stages of maize growth,followed by MNP and LMNP. The net mineralized N (?gN/g dry soil) for the incubated soil followed the order LMNP,MNP, LM, M, L, LNP, C and NP for the two depths. The MNP, LMNP and M treatments had significantly higher MBCand MBN for both field and incubated soils. The correlations between mineral N and MBN were positive butnon-significant at seedling and maturity stages of maize growth in the 0-15 cm depth and at seedling and tassellingstages in the 15-30 cm depth. The correlations between MBN and Mineral N for both depths and sampling periods werepositive and significant for the incubated soils, The maize grain yield increases (%) above control were 43, 36.4, 31.1,25.3, 21.9, 13.7 and 3.0 for LMNP, MNP, NP, M, LNP, LM and L treatments, respectively.Application of LMNP and MNP treatments enhanced mineral N, MBC and MBN and concomitantly soil quality andproductivity as gauged from the improved maize yields in the respective treatments. Combining manure, lime andchemical fertilizers and /or manure and chemical fertilizers is thus a promising alternative to developing a moresustainable acid soil management strategy for increased maize production in Molo district, Kenya.
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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".