No abstract

In this study, the effects of Ligusticum chuanxiong extract as herbicide safeners were determined on glutathione-S-transferase (GST) activity of rice crop. Two main extract compounds, Z-ligustilide and senkyunolide A, were identified by high performance liquid chromatography and liquid chromatography-mass spectrometry. In both bioassays conducted in agar and soil, the whole L. chuanxiong extract, as well as two active components individually were effective in safening the growth of rice seedlings against S-metolachlor toxicity. Z-Ligustilide was a better safener than senkyunolide A, and both compounds were more protective of shoots than roots. After herbicide-inhibited rice seedlings were treated with Z-ligustilide, GST activity significantly increased, suggesting the safening effect of this L. chuanxiong extract component involves GST.

Crop growth and development is a dynamic and complex process, and the essence of yield formation is the continuous accumulation of photosynthetic products from multiple fertility stages. In this study, a new stacking method for integrating multiple growth stages information was proposed to improve the performance of the winter wheat grain yield (GY) prediction model. For this purpose, crop canopy hyperspectral reflectance and leaf area index (LAI) data were obtained at the jointing, flagging, anthesis and grain filling stages. In this case, 15 vegetation indices and LAI were used as input features of the elastic network to construct GY prediction models for single growth stage. Based on Stacking technique, the GY prediction results of four single growth stages were integrated to construct the ensemble learning framework. The results showed that vegetation indices coupled LAI could effectively overcome the spectral saturation phenomenon, the validated R2 of each growth stage was improved by 10%, 22.5%, 3.6% and 10%, respectively. The stacking method provided more stable information with higher prediction accuracy than the individual fertility results (R2 = 0.74), and the R2 of the model validation phase improved by 236%, 51%, 27.6%, and 12.1%, respectively. The study can provide a reference for GY prediction of other crops.