Field application of beneficial microbes to ameliorate drought stress in maize
Bibliographic record
Abstract
Abstract Background and aims Drought stress is a challenge to maize ( Zea mays L.) production, especially in an era of unpredictable climate change and weather extremes. Maize is a source of calories for millions of people all over the world. It is a food security crop, and any yield loss has food security implications. This study examines the effects of soil microbes on maize growth and yield under drought conditions, focusing on field-based experiments. Methods This review follows PRISMA guidelines to systematically evaluate studies on the field effects of soil microbes on maize growth and yield under drought stress. A comprehensive search across multiple scientific databases using specific keywords and Boolean operators identified 78 manuscripts published between 2010 and 2024. After applying inclusion and exclusion criteria, only 9 studies met the criteria for microbial application in maize fields under drought conditions. Results Microbial Biofertilizer applications enhance maize performance compared to uninoculated plants. Complementary application of biofertilizer together with conventional fertilizers outperforms sole application of biofertilizer in ameliorating drought stress in maize under field conditions. This study highlights some mechanisms through which soil microbes contribute to drought tolerance, the influence of environmental factors and host plant characteristics on microbial inoculants' effectiveness. Conclusions The diverse array of growth-promoting microbial species available and their application methods offer significant potential for improving agricultural resilience. By integrating microbial technologies into farming practices, the challenges posed by climate change to food security may be reduced, thus contributing to sustainable agricultural production.
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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".