Liming remediates soil acidity and improves crop yield and profitability - a meta-analysis
Bibliographic record
Abstract
Soil acidity reduces base cations required for plant growth and may result in phytotoxic concentrations of soluble aluminum. Liming acidic soils is generally promoted as an effective management practice to increase soil pH, base cation concentrations, and ameliorate toxicity caused by aluminum and manganese. Through a global literature review using data published from field experiments on liming, the objective of this paper is to understand the effects of liming on soil pH, crop yields, and economic profitability. The results show that liming positively influenced crop yields and soil pH, implying that various lime sources can increase soil pH and crop productivity. The effect sizes of liming on crop yields when lime was incorporated into soils were higher than surface application irrespective of tillage practice. Liming under no-tillage (NT) compared to conventional tillage (CT) management showed higher effect sizes for crop yields. Liming increased effect sizes for crop yields in fertilized compared with unfertilized trials. Gypsum, calcium hydroxide and calcium carbonate showed higher effect sizes when compared with Cement Klin Dust (CKD), dolomite and wood ash. The results show that liming increased yields for all crops except potatoes and oats. Liming generally increases soil pH and changes in soil pH increased with higher lime application rates and yield increases were proportional to the magnitude of increases in soil pH. The profitability of liming differed with crop type and liming rate, being more profitable at lower liming rates. Overall, this meta-analysis shows that liming decreases soil acidity and improves crop yields. Attaining maximum gains from liming agricultural crops under acidic soil conditions requires an understanding of the appropriate lime rates required for specific crops and soil types to ensure overall profitability for producers and sustainable improvement of soil health.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.025 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".