From Factory to Field: Effects of a Novel Soil Amendment Derived From Cheese Production on Wheat and Corn Production
Bibliographic record
Abstract
To meet the nutritional demands of a rapidly growing population in the face of increasing climate variability, innovative tools are needed to rapidly regenerate soil health in agricultural systems. Using food wastes to improve soil health presents a viable opportunity to improve soils and efficiently manage waste. In a previous laboratory study, we found that potassium lactobionate, a byproduct of cheese production, greatly enhanced soil water holding capacity and nutrient availability. To further explore its potential as a soil amendment, we conducted an agronomic trial in winter wheat and corn at the USDA-ARS Central Great Plains Research Station in Akron, Colorado. Lactobionate was applied using different application modes (broadcast and subsurface banding) and rates in the field and soil health indices were measured at two soil depths (0-5 cm, 5-15 cm). Four weeks after the broadcast application, we observed a significant increase in soil moisture and microbial biomass in the 5-15 cm-depth and a decrease in soil nitrate for the wheat trial at both soil depths and across rates (p<0.1). We also saw a non-significant 14% increase in corn yield with subsurface banding of lactobionate but no observed changes in soil health parameters in the corn trial. We found no significant changes in soil pH, total soil carbon and nitrogen, and soil ammonium concentration between treatments for both trials. Our observations suggest the potential for lactobionate to modify soil water content, microbial biomass, nitrate, and yield varied by crop trial, amendment rates, and year. This implies that timing, mode and frequency of application needs to be optimized for maximal effects of lactobionate on soil health. It is also clear that the benefits of soil amendments may vary among years depending on weather and other factors.
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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".