| 2.1 Mechanism of soil nutrients on maize growth |
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| Maize yield is jointly controlled by soil nutrient |
32 |
| Nutrient deficiency, especially of nitrogen and ph |
32 |
| 2.2 Effects of climate factors on maize yield |
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| Temperature, precipitation, drought, and vapor pre |
32 |
| Beyond extremes, the balance between atmospheric e |
33 |
| 2.3 Synergistic mechanism of soil and climate fact |
33 |
| Soil fertility and climate interact to determine b |
33 |
| Nitrogen supply particularly modulates maize sensi |
33 |
| Maize yield depends on sufficient N, P, and K to b |
33 |
| 3.1 Natural and agricultural conditions of the stu |
34 |
| The major maize-producing regions of northern and |
34 |
| Soil conditions in the maize belt range from high- |
34 |
| 3.2 Data sources and acquisition methods |
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| Maize yield data and associated environmental vari |
34 |
| Climate data are typically derived from ground-bas |
34 |
| 3.3 Data preprocessing and quality control |
34 |
| Prior to model construction, environmental and yie |
34 |
| Remote sensing and soil datasets also undergo subs |
35 |
| 4.1 Construction of soil nutrient indicator system |
35 |
| A scientific soil nutrient indicator system should |
35 |
| For predictive modeling, soil indicators must also |
35 |
| 4.2 Extraction of climate variable features |
35 |
| Climate feature construction should represent both |
35 |
| Careful temporal aggregation and transformation of |
35 |
| 4.3 Feature selection and dimensionality reduction |
36 |
| High‑dimensional soil-climate datasets require eff |
36 |
| Comparative studies of dimensionality reduction fo |
36 |
| 5.1 Traditional statistical modeling methods |
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| Traditional statistical methods for yield predicti |
36 |
| More recent work has introduced penalized regressi |
36 |
| 5.2 Machine learning modeling methods |
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| Machine learning (ML) methods such as Random Fores |
37 |
| In some applications, ML models trained on relativ |
37 |
| 5.3 Deep learning and ensemble learning methods |
37 |
| Deep learning (DL) extends ML by learning complex, |
37 |
| Ensemble learning combines multiple base learners |
37 |
| 6.1 Dataset partitioning and validation strategies |
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| A reasonable partition of the maize yield dataset |
37 |
| For crop yield prediction with strong spatial and |
38 |
| 6.2 Model parameter optimization methods |
38 |
| Hyperparameters of machine learning models, such a |
38 |
| More advanced approaches treat hyperparameter tuni |
38 |
| 6.3 Model evaluation indicator system |
38 |
| Because maize yield prediction is a regression pro |
38 |
| To complement absolute error measures, goodness‑of |
38 |
| 7.1 Study area and sample construction |
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| In many recent maize yield prediction studies, the |
38 |
| Large‑area studies, such as county‑level maize ana |
39 |
| 7.2 Comparative Analysis Of Multi-Model Prediction |
39 |
| Comparative studies consistently show that model p |
39 |
| At regional scales, ensemble or tree‑based machine |
39 |
| 7.3 Result validation and agricultural application |
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| Robust validation is essential to ensure that mult |
39 |
| When rigorously validated, yield prediction models |
40 |