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This study investigated the effects of different compost substitution ratios for chemical fertilizers and planting densities on wheat stem lodging resistance, and explored the feasibility of using vegetation indices derived from UAV-based multispectral imagery for lodging prediction, aiming to provide scientific guidance for fertilizer-density management and lodging forecasting in wheat production. Using the wheat cultivar Huaimai 43, a split-plot design was implemented during 2022-2024 with four compost substitution levels［0(T0), 10% (T1), 20% (T2), 30% (T3)］ and three planting densities (D1: 1.5 million plants·hm2; D2: 3.0 million plants·hm2; D3: 4.5 million plants·hm2). Stem lodging resistance traits were measured, and UVA multispectral images were acquired for lodging prediction. The results demonstrated that, under the same planting density, as the proportion of compost substitution increases, the height of wheat plants showed a trend of first decreasing and then increasing, the center of gravity height showed a fluctuating trend, and all were at the lowest in the T2 treatment;the lodging resistance index, fresh weight, mechanical strength, the number and area of large and small vascular bundles in the second basal internode, as well as hemicellulose, cellulose and lignin contents in basal internodes all first increased and then decreased, and peaked under T2. Under the same compost substitution level, as planting density increased, plant height and center of gravity height showed an increasing trend; whereas the lodging resistance index, fresh weight, mechanical strength, and the number and area of large and small vascular bundles in the second basal internode decreased. In contrast, hemicellulose, cellulose and lignin contents in basal internodes showed a trend of first increased and then decreased, reaching maxima under D2. Correlation analysis revealed that the lodging resistance index showed the strongest association with vegetation indices. Accordingly, key vegetation indices (NDVI, RVI, TVI, DVI, and RDVI) were selected to construct lodging resistance prediction models using three regression methods: Stepwise Multiple Linear Regression (SMLR), Partial Least Squares Regression (PLSR), and Principal Component Regression (PCAR). The SMLR model achieved the highest accuracy, with calibration and validation R2 values of 0.43 and 0.76, respectively. In conclusion, a compost substitution ratio of 20% combined with a planting density of 1.5 million plants·hm-2 conferred superior lodging resistance in wheat. The SMLR-based model utilizing vegetation indices effectively predicted the lodging resistance index.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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