Long-Term Effects of Animal Manures on Nutrient Recovery and Soil Quality in Acid Typic Hapludalf under No-Till Conditions
Bibliographic record
Abstract
No-till farming and manure application are means of storing carbon in soil and increasing soil quality. However, the organic fertilization of no-till soils may sustain subtropical agroecosystems for only a limited period until the soil quality declines. The C, N, P, and other nutrient cycles are perturbed due to the nutrient imbalance in manures and their application on the soil surface, which poses a risk for nutrient dispersion into the environment. Long-term N-based manure application also impacts the apparent nutrient recovery (ANR) by crops and, hence, the crop quality. Our objective was to monitor changes in soil quality and to measure nutrient recovery from N-based organic fertilization in a Brazilian agroecosystem. A long-term experiment was established in May 2004 on Typic Hapludalf soil in Southern Brazil. The experiment comprised two periods: 2004–2009 and 2009–2020. The treatments were unfertilized (control), pig slurry, pig deep litter (swine manure and rice husk), cattle slurry, and mineral fertilizer (urea, triple superphosphate, and K chloride). Grain and cover crops were grown. The maize grain yield and oat biomass were used as indicators of productivity. The application of cattle slurry, pig deep litter, and pig slurry increased the grain production by 20% on average compared to mineral fertilization. Organic manuring increased the soil pH by up to one unit, while urea acidified the soil. The highest crop yields were obtained with pig deep litter, which increased the levels of P, K, Ca, Mg, and C in the soil test. Pig deep litter showed the highest N-ANR for the aboveground part of the plants. The ANRs for P, K, Ca, and Mg were also highest when pig slurry had been applied. The ANR values for the micronutrients varied widely among the organic sources but indicated large marginal offtake gains compared to mineral fertilization. The N-based fertilization with animal manures increased the rate of nutrient uptake by plants but led to an accumulation of nutrients in the soil, threatening the sustainability of the system. Manure applications must be monitored regularly to avoid the accumulation of nutrients that could impair the soil quality and become an environmental problem in the future.
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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 | 0.001 | 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".