Shallow Incorporation of Lime and Gypsum has Limited Benefit over the Sole-surface Application of Lime for Improving Grain Yield and Water Use Efficiency in the Low Rainfall Region of Western Australia
Bibliographic record
Abstract
Soil acidity is one of the major soil constraints for the grain-growing industry in Australia and around the globe. While surface liming is widely adopted, it has been proven ineffective for the timely amelioration of subsoil acidity. There is a growing interest in finding alternative approaches for the effective amelioration of subsoil acidity, especially for low-rainfall regions. In a controlled environment and a field experiment, we examined whether the combined application of lime and gypsum would be more effective than lime alone under no-till (NT) and shallow strategic tillage (ST) systems for reducing the impact of soil acidity and increasing grain yield. The controlled environment experiment highlighted that lime increased soil pH and decreased the soil exchangeable aluminium concentration (EAC) which resulted in significantly better root growth. In the field experiment, we found that the lime plus gypsum treatment, in most cases, did not significantly affect grain yield, water use efficiency (WUE) or grain quality compared to the lime treatment alone. Lime incorporation with a shallow ST was more effective in increasing soil pH and decreasing EAC at 10–20 cm depth, compared to the surface application of lime without tillage. However, ST did not affect the grain yield and WUE of wheat in 2017 and 2018 and significantly decreased the grain yield and WUE of canola in 2019 and barley in 2020. We found that measurements of either soil pH or EAC were equivalent in their ability to explain and predict the root growth of major grain crops. The results indicate that soil pH is the simplest indicator for grain growers to measure the improvement of soil acidity with liming and its impact on root growth and crop productivity. We recommend the application of lime as the preferred amendment on acidic sands, while shallow ST should be avoided in the low rainfall region. Further studies involving deep ST are warranted.
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 | 0.002 | 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".