PGPR in Agriculture: A Sustainable Approach to Increasing Climate Change Resilience
Bibliographic record
Abstract
Growing environmental concerns are potentially narrowing global yield capacity of agricultural systems. Climate change is the most significant problem the world is currently facing. To meet global food demand, food production must be doubled by 2050; over exploitation of arable lands using unsustainable techniques might resolve food demand issues, but they have negative environmental effects. Current crop production systems are a major reason for changing global climate through diminishing biodiversity, physical and chemical soil degradation, and water pollution. The over application of fertilizers and pesticides contribute to climate change through greenhouse gas emissions (GHG) and toxic soil depositions. At this crucial time, there is a pressing need to transition to more sustainable crop production practices, ones that concentrate more on promoting sustainable mechanisms, which enable crops to grow well in resource limited and environmentally challenging environments, and also develop crops with greater resource use efficiency that have optimum sustainable yields across a wider array of environmental conditions. The phytomicrobiome is considered as one of the best strategies; a better alternative for sustainable agriculture, and a viable solution to meet the twin challenges of global food security and environmental stability. Use of the phytomicrobiome, due to its sustainable and environmentally friendly mechanisms of plant growth promotion, is becoming more widespread in the agricultural industry. Therefore, in this review, we emphasize the contribution of beneficial phytomicrobiome members, particularly plant growth promoting rhizobacteria (PGPR), as a strategy to sustainable improvement of plant growth and production in the face of climate change. Also, the roles of soil dwelling microbes in stress amelioration, nutrient supply (nitrogen fixation, phosphorus solubilization), and phytohormone production along with the factors that could potentially affect their efficiency have been discussed extensively. Lastly, limitations to expansion and use of biobased techniques, for instance, the perspective of crop producers, indigenous microbial competition and regulatory approval are discussed. This review largely focusses on the importance and need of sustainable and environmentally friendly approaches such as biobased/PGPR-based techniques in our agricultural systems, especially in the context of current climate change conditions, which are almost certain to worsen in near 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".