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P. Verma

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Open access Jul 2026

Influence of Crop Establishment Methods, Organic Manures and Bio-stimulants on Nutrient Content and Uptake by Ashwagandha Roots under Rainfed Ecologies

Efficient nutrient management is essential for improving the productivity and quality of medicinal crops in rainfed ecosystems, where moisture and nutrient availability frequently limit crop performance. The present investigation evaluated the influence of crop establishment methods, organic manures and bio-stimulants on the nutrient content and uptake of ashwagandha roots under rainfed ecologies over two consecutive years. The treatments comprised two crop establishment methods, namely broad bed and furrow and ridge and furrow; two organic manure sources, namely poultry manure and vermicompost; and four bio-stimulant treatments consisting of a control, seaweed extract granules, humic and fulvic acid granules and their combined application. The content and uptake of nitrogen, phosphorus, potassium and sulphur in the roots were estimated at harvest. Root nutrient concentrations were not significantly affected by crop establishment methods, organic manures or bio-stimulants in either experimental year. However, nutrient uptake varied significantly among treatments. Broad bed and furrow recorded significantly greater uptake of nitrogen (7.56 and 8.13 kg ha-1), phosphorus (0.90 and 1.02 kg ha-1), potassium (7.80 and 8.38 kg ha-1) and sulphur (1.86 and 2.11 kg ha-1) than ridge and furrow. Poultry manure significantly enhanced nutrient uptake compared with vermicompost, whereas the combined application of seaweed extract and humic-fulvic acid granules produced the highest nutrient uptake among bio-stimulant treatments. The results indicated that nutrient uptake in ashwagandha roots was governed primarily by biomass production rather than nutrient concentration and that integrated management involving broad bed and furrow, poultry manure and combined bio-stimulant application may improve nutrient-use efficiency and support the sustainable production of ashwagandha under rainfed conditions.

Ambikesh Tripathi, S. K. Rajpoot, S. Choudhary et al. · 0 citations
Open access Aug 2026

A Guided AI Framework for Customizable and Efficient Harmonization to the OMOP Common Data Model

Getting clinical data from different sources to “talk” to each other within the OMOP Common Data Model (CDM) is arguably the most tedious part of multi-center research. While this integration is essential, the transformation process is frequently a manual grind, requiring a rare overlap of deep clinical knowledge and technical expertise. In this paper, we present a framework designed to alleviate some of the burden on the researcher by automating data harmonization through two distinct steps: structural schema mapping and terminological standardization. For the structural piece, we moved away from “black box” logic in favor of a stateful workflow managed by large language models (LLMs) and directed acyclic graphs. By profiling EHR data at the source, our system generates context-aware dictionaries that offer ranked mapping suggestions alongside confidence scores. While our benchmarking showed a 97.5% agreement rate at the schema level and an 84% agreement rate at the value level when compared with human experts, the system appears most effective when treated as a “co-pilot” rather than a total replacement for human oversight. To handle value-level standardization, we implemented a hybrid search strategy that pairs the semantic depth of SapBERT embeddings with the literal precision of fuzzy string matching. By using FAISS for rapid similarity retrieval, the engine attempts to resolve messy or “noisy” clinical descriptions to standard OMOP concepts. This approach seems particularly promising for handling the non-standardized labels that often plague smaller, local datasets. Ultimately, our results suggest that this guided approach can shift the timeline for OHDSI-compliant warehousing from weeks of manual curation to a more manageable and scalable pipeline, potentially lowering the barrier to entry for smaller research teams.

Nishu Nehra, Rohit Swami, Dharani Dadi et al. · 0 citations