Variable Rate Nitrogen and Water Management for Irrigated Maize in the Western US
Bibliographic record
Abstract
Nitrogen (N) and water continue to be the most limiting factors for profitable maize (Zea Mays L.) production in the western US Great Plains. Precision application of N and water has the potential to significantly enhance input use efficiency without impairing yields. The overall objective of this study was to determine the most productive and efficient nitrogen and water management strategy for irrigated maize by using site-specific management zones and a proximal remote sensing approach. This study was conducted over 2016, 2017, 2018 and 2019 crop growing seasons near Fort Collins, Colorado, USA. Six nitrogen rates (0, 56, 112, 168, 224, and 280 kg N ha−1) were applied along experimental strips across three delineated management zones (low, medium, and high productivity). Four rates of irrigation were applied to maize (60%, 80%, 100%, and 120% of evapotranspiration) using a center pivot precision irrigation system equipped with zone control. Optical proximal sensor readings were acquired on all experimental strips four times during the growing season to assess four nitrogen management strategies (uniform, management zone (MZ), remote sensing (RS), and management zone remote sensing (MZRS)) on grain yield and nitrogen use efficiency (NUE). Results from this three-year study showed the significant interaction (p = 0.05) of zone vs. irrigation and irrigation vs. nitrogen across all years. In two of the three years (2016 and 2018), the high productivity zone benefitted from high irrigation rates, demonstrated by 16% and 18% yield increase from the lowest irrigation rate. In 2016, yield plateau was reached at 168 kg N ha−1 with 80% and 100% irrigation rates, whereas the plateau was reached at 112 kg N ha−1 in the 120% irrigation rate. These results demonstrate the possibility of fine-tuning zones, irrigation, and nitrogen to achieve optimum yield. While uniform and MZ nitrogen management strategies produced the highest grain yield, the best NUE was achieved via the RS strategy, followed by the MZ strategy. In this study, the MZRS strategy (combined MZ and RS) did not produce superior yield and NUE as compared to uniform and other strategies. However, there is a tremendous opportunity to fine-tune these two strategies, using other algorithms that are not explored in this study to improve the sustainability of maize production under irrigated conditions.
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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".