[Effects of combined applications of pig farm slurry and chemical fertilizer on medium- and micro-element contents and quality of wheat].

Taking Yangmai 18, widely cultivated in south of Huaihe River, as experimental material, this study investigated the effects of application of 0, 30, 60, 90 and 120 m3 x hm(-2) pig farm slurry (PS) at the wintering stage combined with the application of 0, 30, 60, 90 kg x hm(-2) urea at the heading stage on medium- and micro-element contents and quality of wheat. The results showed that the Ca, Mg and Fe contents of plant firstly decreased then increased with the extension of the growth period, but the Cu, Zn and Mn contents decreased continuously through the growth period. Ca, Mg, Cu and Zn reached the highest value at every growth stage except the jointing stage when applying 120 m3 x hm(-2) PS with 90 kg x hm(-2) urea. Fe and Mn reached the highest value at every growth stage when applying 120 m3 x hm(-2) PS with 90 kg x hm(-2) urea. The contents of Ca, Mg, Cu, Zn, Fe and Mn all increased with increasing PS application across the growth period. Regarding the quality of wheat, applying 90 m3 x hm(-2) PS with 60 kg x hm(-2) urea was the best fertilization pattern. Combined applications of anaerobically treated PS with N fertilizer could increase medium- and micro-element contents and quality of wheat. Generally, it was recommended to apply 60-120 m3 x hm(-2) PS at the wintering stage and 90 kg x hm(-2) urea at the heading stage under field conditions.

[Hyperspectral remote sensing diagnosis models of rice plant nitrogen nutritional status].

The correlations of rice plant nitrogen content with raw hyperspectral reflectance, first derivative hyperspectral reflectance, and hyperspectral characteristic parameters were analyzed, and the hyperspectral remote sensing diagnosis models of rice plant nitrogen nutritional status with these remote sensing parameters as independent variables were constructed and validated. The results indicated that the nitrogen content in rice plant organs had a variation trend of stem < sheath < spike < leaf. The spectral reflectance at visible light bands was leaf < spike < sheath < stem, but that at near-infrared bands was in adverse. The linear and exponential models with the raw hyperspectral reflectance at 796.7 nm and the first derivative hyperspectral reflectance at 738.4 nm as independent variables could better diagnose rice plant nitrogen nutritional status, with the decisive coefficients (R2) being 0.7996 and 0.8606, respectively; while the model with vegetation index (SDr - SDb) / (SDr + SDb) as independent variable, i. e., y = 365.871 + 639.323 ((SDr - SDb) / (SDr + SDb)), was most fit rice plant nitrogen content, with R2 = 0.8755, RMSE = 0.2372 and relative error = 11.36%, being able to quantitatively diagnose the nitrogen nutritional status of rice.