Soybean crop plays an important role in world food production and food security, and agricultural production should be increased accordingly to meet the global food demand. Satellite remote sensing data is considered a promising proxy for monitoring and predicting yield. This research aimed to evaluate strategies for monitoring within-field soybean yield using Sentinel-2 visible, near-infrared and shortwave infrared (Vis/NIR/SWIR) spectral bands and partial least squares regression (PLSR) and support vector regression (SVR) methods. Soybean yield maps (over 500 ha) were recorded by a combine harvester with a yield monitor in 15 fields (3 farms) in Paraná State, southern Brazil. Sentinel-2 images (spectral bands and 8 vegetation indices) across a cropping season were correlated to soybean yield. Information pooled across the cropping season presented better results compared to single images, with best performance of Vis/NIR/SWIR spectral bands under PLSR and SVR. At the grain filling stage, field-, farm- and global-based models were evaluated and presented similar trends compared to leaf-based hyperspectral reflectance collected at the Brazilian National Soybean Research Center. SVR outperformed PLSR, with a strong correlation between observed and predicted yield. For within-field soybean yield mapping, field-based SVR models (developed individually for each field) presented the highest accuracies. The results obtained demonstrate the possibility of developing within-field yield prediction models using Sentinel-2 Vis/NIR/SWIR bands through machine learning methods.
By using PCR-based molecular method,four new pear SFBB-γ genes were isolated from 8 pear cultivars known as S-genotypes.Length of PCR products from eight pear cultivars was around 1 200 bp.Sequencing the specific PCR fragments revealed four new SFBB-γ genes that were respectively named as SFBB 16-γ (EU422956),SFBB 17-γ (EU422957),SFBB 28-γ (EU422960)and SFBB 35-γ (EU422958).As for different SFBB-γ genes,variation in amino acid was higher in F-box region and variable region 1,and lower in variable region 2 t... 相似文献