Soil variables and reflected light revealed the plasticity of grape and wine composition: Regulation of the flavoromics under inner row gravel covering.

Abundant gravel in vineyards bothers growers. To investigate the gravel covering inner-row effect on grapes and wines, a two-year experiment was carried out. Regional climate and vine microclimate were collected, the flavoromics of grapes and wines were determined by HPLC-MS and HS/SPME-GC-MS. Gravel covering reduced the soil moisture. Light-colored gravel covering (LGC) enhanced the reflected light by 7-16% and cluster-zone temperature by up to 2.5 °C. Dark-colored gravel covering (DGC) absorbed 13% of the solar radiation and cooled the cluster-zones. DGC promoted the accumulation of 3'4'5'-hydroxylated anthocyanins and C6/C9 compounds in grapes, while grapes of LGC were accompanied by higher flavonols. The phenolic profiles of grapes and wines among treatments were consistent. The overall aroma of grapes from LGC was lower, while DGC helped to weaken the negative effects of rapid ripening in warm vintages. Our results revealed the gravel regulate grape and wine quality through soil and cluster microclimate.

The formulation of irrigation and nitrogen application strategies under multi-dimensional soil fertility targets based on preference neural network.

With the aim of improving soil fertility, it is of great significance to put forward optimal irrigation and nitrogen fertilizer application strategies for improving land productivity and alleviating non-point source pollution effects. To overcome this task, a 6-hidden layer neural network with a preference mechanism, namely Preference Neural network (PNN), has been developed in this study based on the field data from 2018 to 2020. PNN takes soil total nitrogen, organic matter, total salt, pH, irrigation time and target soil depth as input, and irrigation amount and nitrogen application rate (N rate) as output, and the prior preference matrix was used to adjust the learning of weight matrix of each layer. The outcomes indicated that the predictive accuracy of PNN for irrigation amount were (R2 = 0.913, MAE = 0.018, RMSE = 0.022), and for N rate were (R2 = 0.943, MAE = 0.009, RMSE = 0.011). The R2 predicted by PNN at the irrigation amount and N rate were 40.03% to more than 99% and 40.33% to more than 99% higher than those obtained using support vector regression (SVR), linear regression (LR), logistic regression (LOR) and traditional back propagation neural network (BPNN), respectively. In addition, compared with the neural network (Reverse Multilayer Perceptron, RMLP) with the same structure but no preference structure, the R2 of the predicted irrigation amount and N rate by PNN increased by 25.81% and 27.99%, respectively. The results showed that, through the irrigation of 93 to 102, 92 to 98 and 92 to 98 mm, along with nitrogen applications of 65 to 71, 64 to 73 and 72 to 81 kg/hm2 at 17, 59 and 87 days after sowing, respectively, the organic matter, total nitrogen, total salt content and pH of the soil would reach high fertility levels simultaneously.