Effects of Fertilizer Levels on the Absorption, Translocation, and Distribution of Phosphorus and Potassium in Rice Cultivars with Different Nitrogen-Use Efficiencies
Bibliographic record
Abstract
<p>The fertilizer management and the selection of rice cultivars play a vital role in rice production to maximize yield and minimize fertilizer cost. Many researches have elucidated the combined increase of nitrogen (N) accumulation and N use efficiency (NUE) in different rice genotypes, however, the accumulation, translocation, distribution of phosphorus (P) and potassium (K), and the correlation of N, P and K absorption characteristics and their relationships with grain yield in rice cultivars with different NUE is still obscure. For this purpose, two rice cultivars differing in NUE were chosen for this study, one with high-NUE (Dexiang 4103) and the other with low-NUE (Yixiang 3724). Fertilizers were applied at three levels, including low (75 kg N·hm<sup>-2</sup>, 37.5 kg P<sub>2</sub>O<sub>5</sub>·hm<sup>-2</sup>, 75 kg K<sub>2</sub>O·hm<sup>-2</sup>), medium (150 kg N·hm<sup>-2</sup>, 75 kg P<sub>2</sub>O<sub>5</sub>·hm<sup>-2</sup>, 150 kg K<sub>2</sub>O·hm<sup>-2</sup>), high rate (225 kg N·hm<sup>-2</sup>, 112.5 kg P<sub>2</sub>O<sub>5</sub>·hm<sup>-2</sup>, 225 kg K<sub>2</sub>O·hm<sup>-2</sup>). A no-N treatment was included for each level as the control. The results showed that there were obvious interacting effects of cultivars and fertilizer levels on grain yield, as well as the absorption and translocation of P and K. Rice cultivars exhibited markedly stronger effects on total spikelets and the translocation of P and K in leaves, compared to fertilizer levels. The opposite trend was observed for grain yield, P and K accumulation at the main growth stages, and P and K translocation in stem and leaf sheaths. Compared with other treatments, the combined application of NPK fertilizers at medium level promoted nutrient accumulation, increased the nutrient harvest index, facilitated nutrient translocation in vegetative organs, and ultimately improved grain yield in both cultivars. The equilibrium relationship between N, P and K accumulation and grain yield indicated that the grain yield associated with high-NUE cultivar could reach more than 10,000 kg hm<sup>-2</sup>, with N, P, and K requirements of 180.8-213.3, 47.3-54.7, and 223.5-259.1 kg hm<sup>-2</sup>, respectively. Additionally, the correlation analysis revealed that accumulation and translocation of P, K during different growth stages was significantly (P &lt; 0.05) related to grain yield and nutrient accumulation in different NUE cultivars. This study suggested that varieties with high-NUE also has high P and K use efficiencies, indicating that the conventional screening of varieties with high P or K use efficiencies can be included in the selection of high-NUE varieties. The increase of P and K accumulation and translocation during the period from heading to maturity was helpful to maintain a high-yield and NUE in rice production.</p>
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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".