In order to explore the role of bioactive water in corn stover fermentation, the moisture content in corn stover fermentation was regulated by DW and bioactive water for this experiment, and the sensory comprehensive assessment, cellulose and hemicellulose degradation rates were analyzed. The results showed that the comprehensive score on the 5 d was higher than that on the 10 d from the sensory evaluation analysis, and the sterilized corn stover fermentation was superior to the unsterilized corn stover fermentation, with the total score of treat1 (sterilized stover + BW) and treat4 (sterilized stover + BW + MYB3) higher than the other treatment groups. For sterilized and unsterilized stover, the maximum cellulose and hemicellulose decomposition rates occurred in the four groups (sterilized / unsterilized stover + BW + MYB3) treated in 10 d, which were 39.38%, 45.25%, 32.25%, and 38.63%, respectively. It nearly doubled compared with the control (sterilized / unsterilized stover + DW). The results show that bioactive water could improve the decomposition rate of stover cellulose and hemicellulose, and it is of great significance to provide a new feasible method for the application of stover fermentation.
Data assimilation can be used to predict crop yield by coupling remote sensing information with the crop growth model, but it often grapples with the challenge of enhancing the computational efficiency for the integrated model. To address this issue, particularly in regional-scale studies, simulation zone partitioning can offer a viable solution to improve computational efficiency. In this study, we first extracted high-resolution rice planting areas in Jiangsu Province (JP), then conducted simulation zone partitioning in JP based on the fuzzy c-means clustering algorithm (FCM) combined with soil data, meteorological indices, and EVI. Finally, the hierarchical assimilation system was developed by using phenology and leaf area index (LAI) as state variables to predict rice yield in JP. The results showed that the coefficient of variation (CV) of the small subregion after simulation zone partitioning obtained by using FCM was less than the overall CV of each subregion at different period. Compared with a single assimilation system that only used LAI as the state variable (R2 was between 0.33 and 0.35, NRMSE was between 9.08 and 10.94%), the predicted yield of the hierarchical assimilation system (R2 was between 0.44 and 0.51, NRMSE was between 7.23 and 8.44%) was in better agreement with the statistic yield. The research findings can provide technical support for the prediction of rice yield at the regional scale.
Variations in water and soil backgrounds can affect canopy spectral reflectance, complicating canopy N status estimation. We created rice ( Oryza sativa L.) canopies with varying levels of leaf area index (LAI) and water and soil backgrounds using different rice varieties and a combination of different N rates and planting densities. The quantitative relationships between hyperspectral vegetation indices and leaf nitrogen accumulation (LNA) were analyzed to derive new spectral indices and models for estimating LNA. The sensitive spectral region of LNA significantly differed from that of leaf nitrogen concentration (LNC). All two‐band hyperspectral vegetation indices derived from the systematic combinations of bands in the 400 to 2500 nm range were correlated to canopy LNA. The new, simple vegetation index SR(R 770 , R 752 ) exhibited the highest correlation with LNA, with R 2 0.88 for model calibration and with R 2 0.80 for model validation, respectively. The SR(R 770 , R 752 ) was modified by incorporating a coefficient of soil/water line parameter, θ, yielding the simple vegetation index SR 2 (R 770 , R 752 ). This modified index provided slightly better estimates of rice LNA, with a calibration R 2 0.90 and a validation R 2 0.80. Datasets obtained for different sensor heights before and after canopy closure confirmed the superior performance of SR 2 (R 770 , R 752 ). Therefore, the SR(R 770 , R 752 ) and SR 2 (R 770 , R 752 ) can be used to estimate rice LNA. Since SR 2 (R 770 , R 752 ) is less dominated by soil background, this index is recommended for estimating LNA in rice under various cultivation conditions.
The emergence of rice panicle substantially changes the spectral reflectance of rice canopy and, as a result, decreases the accuracy of leaf area index (LAI) that was derived from vegetation indices (VIs). From a four-year field experiment with using rice varieties, nitrogen (N) rates, and planting densities, the spectral reflectance characteristics of panicles and the changes in canopy reflectance after panicle removal were investigated. A rice “panicle line”—graphical relationship between red-edge and near-infrared bands was constructed by using the near-infrared and red-edge spectral reflectance of rice panicles. Subsequently, a panicle-adjusted renormalized difference vegetation index (PRDVI) that was based on the “panicle line” and the renormalized difference vegetation index (RDVI) was developed to reduce the effects of rice panicles and background. The results showed that the effects of rice panicles on canopy reflectance were concentrated in the visible region and the near-infrared region. The red band (670 nm) was the most affected by panicles, while the red-edge bands (720–740 nm) were less affected. In addition, a combination of near-infrared and red-edge bands was for the one that best predicted LAI, and the difference vegetation index (DI) (976, 733) performed the best, although it had relatively low estimation accuracy (R2 = 0.60, RMSE = 1.41 m2/m2). From these findings, correcting the near-infrared band in the RDVI by the panicle adjustment factor (θ) developed the PRDVI, which was obtained while using the “panicle line”, and the less-affected red-edge band replaced the red band. Verification data from an unmanned aerial vehicle (UAV) showed that the PRDVI could minimize the panicle and background influence and was more sensitive to LAI (R2 = 0.77; RMSE = 1.01 m2/m2) than other VIs during the post-heading stage. Moreover, of all the assessed VIs, the PRDVI yielded the highest R2 (0.71) over the entire growth period, with an RMSE of 1.31 (m2/m2). These results suggest that the PRDVI is an efficient and suitable LAI estimation index.
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