Influence of Soil Fertility Management on Nitrogen Mineralization, Urease Activity and Maize Yield
Bibliographic record
Abstract
For studying the effect of soil fertility management practices on N mineralization, urease activity and maize yield, replicated field trials were established in 2015 at Misamfu and Msekera agricultural research stations (ARS) representing two geo-climatic regions of Zambia. The soil at Msekera ARS is a sandy clay loam (SCL) from a Paleustult, while that at Misamfu is a loamy sand (LS) from a Kandiustult. The field trials had three categories of treatments namely legumes, traditional and conventional. The legumes group consisted of researcher-recommended legume-cereal intercrop systems of maize with Cajanus cajan, Crotalaria juncea and Tephrosia vogelii in combination with compound D (10% N, 20% P2O5, 10% K2O) and urea (46% N) at the recommended rate (200 kg ha-1) and half of the recommended rate (100 kg ha-1). Composted cattle manure and Fundikila, a special plant biomass management technique, were the inputs under the traditional category. The conventional category consisted of a treatment to which only chemical fertilizer was applied. Urease activity was determined in surface soil samples (0-20 cm) collected from the field trials after 3 years. For N mineralization, a laboratory incubation study was conducted over 13 weeks. For the laboratory incubation, an additional treatment to which no input was applied was included as control. Application of organic inputs significantly increased the potentially mineralizable N (No) by 127% to 256% on the LS and by 51% to 131% on the SCL in comparison to the control. Similarly, the cumulative N mineralized (Ncum) was twice or thrice higher where organic inputs had been applied in comparison to the control. The No followed the order traditional > legumes > conventional > control, while the mineralization rate constant (k) followed the order legumes > conventional > traditional > control on both soils. The rate of N mineralization was significantly higher on the LS than the SCL. Higher rates of chemical fertilizer resulted in high Ncum and higher maize yield. Maize yield was significantly and positively correlated to Ncum, but inversely correlated to the amount of applied N that was mineralized (%Nmin). Urease activity was stimulated by application of organic inputs and suppressed by higher rates of chemical fertilizers. The type of organic inputs; the rate of chemical fertilizers; and soil texture are factors influencing N mineralization and maize yield. Urease activity was largely influenced by the rate of chemical fertilizer, but not the type of organic inputs or soil texture.
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 | 0.001 | 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 | 0.000 | 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".