Efficient Irrigation of Maize Through Soil Moisture Monitoring and Modeling
Bibliographic record
Abstract
Agriculture is the major user of water resources, accounting for 70% of global freshwater demand. As the demand for clean water increases, so does the need to implement more efficient strategies for water management in irrigated agriculture. While the benefits of precision irrigation in high-value crops, such as cannabis, tomatoes, and potatoes, are fully recognized, there is still need to investigate and implement cheap and efficient irrigation strategies for widespread low-value crops such as maize. In this study, the soil moisture dynamics in a sprinkler-irrigated maize field in Veneto (Northeastern Italy) was monitored using six time domain reflectometry (TDR) probes for the entire growing season. The TDR sensors were positioned at different depths into two separate sites: an Uninformed Site irrigated based on the farmer's experience and an Informed Site in which a water balance irrigation strategy was applied based on soil moisture measurements. A parsimonious hydrological model was then implemented and calibrated to quantify the different water balance terms (precipitation, evapotranspiration, lateral fluxes, and deep percolation). The comparison between the water budget terms in the two sites highlights that soil moisture monitoring during agriculture activities leads to substantial savings in terms of irrigation water volumes requirements and cost, without compromising the productivity of the crop field. A simplified upscaling of the results at the regional scale, assuming average conditions as in this study site and growing season, reveals that potentially significant economic savings, compared to the total profits linked to maize crops, could be possible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".