ArticleHeliyon2024
Identifying hidden factors influencing soil Olsen-P in an alkaline calcareous soil using machine learning and geostatistical techniques.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Soil-Plant-Animal Mineral Dynamics in Extensive Goat Production Systems: A Critical Review of Geochemical Availability, Soil-Plant Transfer, and Ruminal Bioavailability.Animals : an open access journal from MDPI · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Phosphorus (P) deficiency is one of the major constraints for sustainable crop production in calcareous soils. This study aimed to elucidate the key soil characteristics modulating the variability of soil Olsen P in these typical soils. A comprehensive soil sampling initiative (1.5 samples per hectare) was conducted on a 100-ha farm, considering 31 attributes that included soil physical and chemical properties, and geographic attributes. Three machine learning algorithms-partial least squares regression (PLSR), random forest (RF), and cubist regression (CR)-were employed to understand key variables controlling soil Olsen P. Furthermore, the same data set was used to spatially map the variations in Olsen P levels using ordinary kriging. The results revealed that soil chemical factors, specifically exchangeable manganese and zinc, cation exchange capacity, and carbonate, played a crucial role in controlling P levels. Among the machine learning models, the best performing model was RF (R
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.