Site-specific soil hydraulic quality index to describe the essential conditions for the optimum soil water regime
Bibliographic record
Abstract
This study was conducted in northeast Thailand (i) to identify soil hydraulic quality (SHQ) factors under four common cropping systems in a specific soil series, (ii) to configure indicators from these factors that can be used to develop SHQ indices for each cropping system, and (iii) to describe the essential conditions for the optimum soil water process in the study area. The cropping systems were (i) maize (Zea mays L.)-maize, (ii) mungbean (Vigna radiate L.)-maize, (iii) maize-fallow and (iv) cassava (Manihot esculenta Crantz). Ten representative farms under each system were used to develop location-specific SHQ indices. Soil hydraulic quality indices were determined for two soil layers, such as the Ap (0-20 cm) and the EB (20-65 cm) horizons at each farm. Principal component analysis (PCA) and factor analysis identified eight factors affecting soil hydraulic quality, such as pore size and distribution frequency, water-stable aggregates, aggregate shape and grade, shrinking-swelling, infiltration rate (IR) and percent sand particle for the Ap horizon. Aggregate shape, size and grade, pore distribution frequency, shrinking-swelling, packing density, soil organic matter (SOM), porosity and root density influenced water movement in the sub-soils (EB horizon). A comprehensive SHQ index was developed for both soil layers under each of the cropping systems. The mungbean-maize (Mn-M) system scored the highest total SHQ index (0.726) in topsoil followed by maize-fallow (M-F) (0.708), cassava (C) (0.663) and maize-maize (M-M) (0.623). Finally, SHQ indices were ranked into three distinctive classes under the selected cropping systems, i.e., I - Mn-M; II - M-F and C; and III - M-M. The Mn-M system is better compared with the M-M in sustaining SHQ due to favorable individual soil parameters such as pore distribution frequency, root density, SOM (2.80%) and IR (494.95 mm h-1). For the rooted subsoil, no significant differences could be identified irrespective of cropping systems. All SHQ indices for sub-soils scored higher index values compared with their respective topsoil scores indicating less deteriorating effect of crop husbandry practices in this layer. Our findings could be used to advance the assessment of valid location-specific SHQ indicators to describe the essential conditions for sustainable soil water processes.Key words: Site-specific, soil hydraulic quality index, soil water process, cropping system, essential conditions
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".