Bio-organic fertilization is a widely used, eco-friendly, sustainable approach to increasing plant productivity in the agriculture and fruit industries. However, it remains unclear whether the promotion of fruit productivity is related to specific changes in microbial inoculants, the resident microbiome, and/or the physicochemical properties of rhizosphere soils.

Watermelon (Citrullus lanatus) is susceptible to wilt disease caused by the fungus Fusarium oxysporum f. sp niveum (FON). Intercropping management of watermelon/aerobic rice (Oryza sativa) alleviates watermelon wilt disease, because some unidentified component(s) in rice root exudates suppress FON sporulation and spore germination. Here, we show that the phenolic acid p-coumaric acid is present in rice root exudates only, and it inhibits FON spore germination and sporulation. We found that exogenously applied p-coumaric acid up-regulated the expression of ClPR3 in roots, as well as increased chitinase activity in leaves. Furthermore, exogenously applied p-coumaric acid increased β-1,3-glucanase activity in watermelon roots. By contrast, we found that ferulic acid was secreted by watermelon roots, but not by rice roots, and that it stimulated spore germination and sporulation of FON. Exogenous application of ferulic acid down-regulated ClPR3 expression and inhibited chitinase activity in watermelon leaves. Salicylic acid was detected in both watermelon and rice root exudates, which stimulated FON spore germination at low concentrations and suppressed spore germination at high concentrations. Exogenously applied salicylic acid did not alter ClPR3 expression, but did increase chitinase and β-1,3-glucanase activities in watermelon leaves. Together, our results show that the root exudates of phenolic acids were different between rice and watermelon, which lead to their special ecological roles on pathogenic fungus and watermelon defense.

Non-destructive and timely determination of leaf nitrogen (N) concentration is urgently needed for N management in pear orchards. A two-year field experiment was conducted in a commercial pear orchard with five N application rates: 0 (N0), 165 (N1), 330 (N2), 660 (N3), and 990 (N4) kg·N·ha⁻¹. The mid-portion leaves on the year’s shoot were selected for the spectral measurement first and then N concentration determination in the laboratory at 50 and 80 days after full bloom (DAB). Three methods of in-field spectral measurement (25° bare fibre under solar conditions, black background attached to plant probe, and white background attached to plant probe) were compared. We also investigated the modelling performances of four chemometric techniques (principal components regression, PCR; partial least squares regression, PLSR; stepwise multiple linear regression, SMLR; and back propagation neural network, BPNN) and three vegetation indices (difference spectral index, normalized difference spectral index, and ratio spectral index). Due to the low correlation of reflectance obtained by the 25° field of view method, all of the modelling was performed on two spectral datasets—both acquired by a plant probe. Results showed that the best modelling and prediction accuracy were found in the model established by PLSR and spectra measured with a black background. The randomly-separated subsets of calibration (n = 1000) and validation (n = 420) of this model resulted in high R² values of 0.86 and 0.85, respectively, as well as a low mean relative error (<6%). Furthermore, a higher coefficient of determination between the leaf N concentration and fruit yield was found at 50 DAB samplings in both 2015 (R² = 0.77) and 2014 (R² = 0.59). Thus, the leaf N concentration was suggested to be determined at 50 DAB by visible/near-infrared spectroscopy and the threshold should be 24–27 g/kg.