南方稻田冬季种植绿肥可通过构建微生物群落有效改善土壤性状和水稻产量。然而，不同冬种绿肥土壤中丛枝菌根真菌（Arbuscular mycorrhizal fungi，AMF)群落对土壤性状和水稻产量的影响尚不清楚。本研究探讨了冬闲、冬种黑麦草和冬种紫云英三种常见的华南地区冬季种植模式下稻田土壤AMF群落对后作水稻生产的影响。与冬闲相比，冬种黑麦草和冬种紫云英能缓解土壤酸化，显著增加土壤AMF孢子密度，改善土壤AMF群落结构。在灭菌土壤中，与未接种AMF孢子的处理相比，接种冬种紫云英土壤AMF孢子后的水稻千粒重、理论产量、稻米直链淀粉和总糖含量等指标提高了6.68-53.57%；接种冬种黑麦草土壤中AMF孢子后的水稻穗重、结实率和理论产量提高了15.38-22.71%，稻米蛋白质、直链淀粉和总糖含量分别为14.92、104.82和802.23 mg kg^-1，比未接种AMF孢子的处理分别高出31.31、14.25和34.47%。冬种紫云英土壤中的AMF优势属无梗囊霉属（Acaulospora）和球囊霉属（Glomus）对水稻产量的提高有更积极的作用。而冬种黑麦草土壤中的AMF优势属球囊霉属（Glomus）更有利于稻米品质的提高。研究结果揭示了冬种绿肥土壤AMF群落对水稻生产的关键作用，为促进南方冬季农业的可持续发展奠定了理论基础。

Received  8 September, 2017    Accepted  13 March, 2018

    Grain traits are major constraints in rice production, which are key factors in determining grain yield and market values. This study used two recombinant inbred line (RIL) populations, RIL-JJ (japonica/japonica) and RIL-IJ (indica/japonica) derived from the two crosses Shennong 265/Lijiangxintuanheigu (SN265/LTH) and Shennong 265/Luhui 99 (SN265/LH99). Sixty-eight quantitative trait loci (QTLs) associated with 10 grain traits were consistently detected on the 12 chromosomes across different populations and two environments. Although 61.75% of the QTLs clustered together across two populations, only 16.17% could be detected across two populations. Eight major QTLs were detected on the 9, 10 and 12 chromosomes in RIL-JJ under two environments, a novel QTL clustered on the 10 chromosome, qGT10, qBT10 and qTGW10, have a higher percentage of explained phenotypic variation (PVE) and additive effect; 15 major QTLs were detected on the 5, 8, 9, and 11 chromosomes in RIL-IJ under two environments, a novel clustered QTL, qGT8 and qTGW8, on the 8 chromosome have a higher additive effect. Finally, the analysis of major QTL-BSA mapping narrowed the qTGW10 to a 1.47-Mb region flanked by simple sequence repeat markers RM467 and RM6368 on chromosome 10. A comparison of QTLs for grain traits in two different genetic backgrounds recombinant inbred line populations confirmed that genetic background had a significant impact on grain traits. The identified QTLs were stable across different populations and various environments, and 29.42% of QTLs controlling grain traits were reliably detected in different environments. Fewer QTLs were detected for brown rice traits than for paddy rice traits, 7 and 17 QTLs brown rice out of 25 and 43 QTLs under RIL-JJ and RIL-IJ populations, respectively. The identification of genes constituting the QTLs will help to further our understanding of the molecular mechanisms underlying grain shape.

Plant nitrogen (N) uptake is a good indicator of crop N status. In this study, a new method was designed to determine the central wavelength, optimal bandwidth and vegetation indices for predicting plant N uptake (g N m-2) in winter wheat (Triticum aestivum L.). The data were collected from the ground-based hyperspectral reflectance measurements in eight field experiments on winter wheat of different years, eco-sites, varieties, N rates, sowing dates, and densities. The plant N uptake index (PNUI) based on NDVI of 807 nm combined with 736 nm was selected as the optimal vegetation index, and a linear model was developed with R2 of 0.870 and RMSE of 1.546 g N m-2 for calibration, and R2 of 0.834, RMSE of 1.316 g N m-2, slope of 0.934, and intercept of 0.001 for validation. Then, the effect of the bandwidth of central wavelengths on model performance was determined based on the interaction between central wavelength and bandwidth expansion. The results indicated that the optimal bandwidth varies with the changes of the central wavelength and with the interaction between the two bands in one vegetation index. These findings are important for prediction and diagnosis of plant N uptake more precise and accurate in crop management.

Real-time monitoring of nitrogen status in rice and wheat plant is of significant importance for nitrogen diagnosis, fertilization recommendation, and productivity prediction. With 11 field experiments involving different cultivars, nitrogen rates, and water regimes, time-course measurements were taken of canopy hyperspectral reflectance between 350-2 500 nm and leaf nitrogen accumulation (LNA) in rice and wheat. A new spectral analysis method through the consideration of characteristics of canopy components and plant growth status varied with phenological growth stages was designed to explore the common central bands in rice and wheat. Comprehensive analyses were made on the quantitative relationships of LNA to soil adjusted vegetation index (SAVI) and ratio vegetation index (RVI) composed of any two bands between 350-2 500 nm in rice and wheat. The results showed that the ranges of indicative spectral reflectance were largely located in 770-913 and 729-742 nm in both rice and wheat. The optimum spectral vegetation index for estimating LNA was SAVI (R822,R738) during the early-mid period (from jointing to booting), and it was RVI (R822,R738) during the mid-late period (from heading to filling) with the common central bands of 822 and 738 nm in rice and wheat. Comparison of the present spectral vegetation indices with previously reported vegetation indices gave a satisfactory performance in estimating LNA. It is concluded that the spectral bands of 822 and 738 nm can be used as common reflectance indicators for monitoring leaf nitrogen accumulation in rice and wheat.