为理解长期有机物料与化学氮肥配施对土壤有机碳和全氮的影响，在黄淮海平原开展了长达30年的小麦-玉米轮作田间试验（1990–2019年）。试验包含5个施肥处理：不施肥（control）、单施化肥（NPK）、化肥与秸秆配施（NPKS）、化肥与有机肥配施（NPKM）和1.5倍化肥与有机肥配施（1.5NPKM），NPK、NPKS和NPKM处理氮投入总量相同。测定试验期间作物产量，采集土壤表层(0–10 and 10–20 cm)和下层(20–40 cm)样品，进行土壤团聚体分级并测定团聚体碳氮含量。有机无机配合处理（NPKS, NPKM和1.5NPKM）与NPK处理相比，30年平均作物产量无显著差异，土壤表层和下层有机碳和全氮均显著提高，表层土壤有机碳和全氮分别提高24.1–44.4%和22.8–47.7%，下层土壤有机碳和全氮分别提高22.0–47.9%和19.8–41.8%%。与NPK处理相比，NPKS处理0-10cm土层和NPKM处理20-40cm土层具有显著较高的大团聚体组分质量比例，提高幅度分别为19.8和27.0%；然而，1.5NPKM处理在0-10cm和20-40cm土层的土壤大团聚体组分质量比例较对照显著降低，下降幅度分别为-19.2和-29.1%。分析表明有机无机配合处理较NPK处理有显著较高土壤有机碳和全氮主要与自由态微团聚体和大团聚体中的微团聚体等土壤稳定性组分的碳氮增加有关，稳定性组分对土壤碳氮含量增加的贡献率分别达81.1–91.7%和 83.3–94.0%。不同施肥处理0-40cm土层的土壤稳定性碳库和氮库与处理年平均碳投入量均呈显著正相关，回归系数分别为0.74和0.72（P<0.01），表明土壤氮对碳贮存的重要性。我们研究为长期有机物料与化学氮肥配施措施在保持合理总氮投入下有利于上层和下层土壤碳氮的保蓄提供了证据。

本研究以来源于辽宁、吉林和黑龙江三个省份的200个粳稻品种为实验材料，对碾磨和外观品质相关的性状进行考察。材料的系谱分析和遗传多样性分析结果表明，来自吉林省的品种遗传多样性最高。稻米品质的评价结果表明，来自辽宁省的品种具较好的碾磨品质，而来自黑龙江的品种具较好的外观品质。本研究同时用单位点和多位点的全基因组关联分析（GWAS）对碾磨和外观品质相关的基因位点进行计算，结果共检测到99个显著的SNP位点。其中，共3个SNP位点同时在混合线性模型（MLM）、mrMLM和FASTmrMLM这3种计算模型中检测到，进一步利用连锁不平衡分析获得对应的3个候选区域（qBRR-1、qBRR-9和qDEC-3），以便于后续的候选基因分析。由于候选区域内的候选基因超过300个，研究还结合基因GO分析以鉴定潜在的候选基因。此外，候选区域的遗传多样性分析结果表明，qBRR-9很可能在东北粳稻的育种过程中受到了较强的选择。这些结果为水稻育种和品质改良提供了具有参考意义的信息。

了解作物氮（N）素利用率（NUE）的时空动态及其与环境变量的关系可以有效指导土地利用管理和相关政策制定。然而，有关应用统计模型评估作物NUE时空变化的解释变量的研究较少。因此，本研究采用逐步多元线性回归（SMLR）模型和随机森林（RF）模型来评价1990—2015年间中国东北地区（黑龙江、辽宁、吉林）县域尺度下NUE的时空变化。其中，NUE包括N素偏生产力（PFP_N）和N素偏因子养分平衡（PNB_N）两个指标，解释变量包括农业管理措施、地形、气候、经济、土壤和作物类型。结果表明，1990-2015年间，东北地区PFP_N以北部较高，中部较低，PNB_N由南向北逐渐增加。而多数县的NUE随着时间的变化逐渐降低。SMLR和RF的模型效率系数对于PFP_N分别为0.44和0.84，PNB_N分别为0.67和0.89。与SMLR模型相比，RF模型中土壤类型和气候的相对重要性较高，而作物类型的相对重要性较低。蔬菜和豆类种植面积指数、土壤粘土含量、饱和含水量、11-12月植被增强指数、土壤容重和年最低气温是NUE的主要解释变量。本文首次使用SMLR和RF模型对中国东北县级NUE解释变量的相对重要性进行定量研究。研究结果为改善作物NUE提供了重要参考，有利于氮素优化管理、农业可持续发展、保障粮食安全、缓解环境恶化和提高农民收入。

氮素营养指数（NNI）是作物氮素诊断的可靠指标。然而，目前还没有适用于多生育时期NNI反演的光谱指数。为克服传统NNI直接反演方法（NNI_T1）和通过反演生物量（AGB）和植株氮浓度（PNC）进行NNI间接反演方法（NNI_T2）在多生育期应用的局限性，本文构建了一个新的NNI遥感指数（NNI_RS）。本文基于连续四年（2012–2013（Exp.1），2013–2014（Exp.2），2014–2015（Exp.3）和2015–2016（Exp.4））的冬小麦田间试验，采用交叉验证方法利用氮素相关植被指数和生物量相关植被指数构建了遥感关键氮浓度稀释曲线（N_{c_RS}）和根据NNI构建原理得到的NNI_RS进行综合评价。结果表明：（1）由标准叶面积指数决定指数（sLAIDI）和红边叶绿素指数（CI_{red edge}）构建的NNIR_S模型表达式为NNI_RS=CI_{red edge}/（a×sLAIDI^b），在Exp.1/2/4，Exp.1/2/3，Exp.1/3/4和Exp.2/3/4中参数“a”分别等于2.06，2.10，2.08和2.02，参数“b”分别等于0.66，0.73，0.67和0.62；（2）与NNI_T1和NNI_T2模型相比，NNI_RS模型的精度最高（R²的范围为0.50–0.82，RMSE的范围为0.12–0.14）；（3）NNI_RS在验证数据集中也达到了较好的精度，RMSE分别为0.09，0.18，0.13和0.10。因此，本文认为NNI_RS模型在氮素遥感诊断中具有较大的潜力。

本研究从中国东南、华北和东北花生产区收集了大量的田间试验数据研究了1993-2018年间花生产量、养分吸收和植株体分布特征的变化。从1993至2018年，三大区域的花生荚果产量均逐步增加，东南、华北和东北产区的平均产量分别为4148、5138和4635 kg ha^–1。三个区域的氮肥内效率相似，磷肥内效率在区域间从低到高变化为：华北<东南<东北，而钾内效率表现出东南<华北<东北的变化模式。根据养分内效率，生产1吨的荚果产量，花生地上部的平均氮、磷和钾需求量分别为：东南区44.8、5.7和20.6 kg，华北区47.2、5.1和25.5 kg，东北区44.6、4.4和14.7 kg。区域间N/P比以华北<东南<东北的模式变化，东北和华北区N/K比相似，但东南较低。在高量养分吸收情况下，三区域氮、钾收获指数均随养分吸收量的增加而下降。华北高的秸秆磷积累可解释其低的磷肥内效率和N/P比，而东北区高的钾内效率和N/K归因于低的土壤钾供应。前沿分析方法表明养分收获指数随着养分吸收量的增加而下降。本研究揭示了中国不同花生产区的养分供应的限制和盈余状况。

微生物是秸秆的分解者，气候条件和秸秆化学组成影响微生物的生长及其群落组成，并终影响秸秆分解。然而，对我国东北地区秸秆分解过程中的细菌演化特征并不清楚。为了阐明东北地区秸秆分解过程中细菌群体演化特征及其影响因子，我们于2014年10月份将玉米秸秆包埋入田间土壤，在随后的2年中不定期取样并分析秸秆生物量及其中细菌群落组成变化。秸秆埋入土壤5、12和24月后，其生物量积累损失率分别为起始量的18、69和77%；秸秆氮磷养分释放表现出与生物量相似的变化，而秸秆钾素1个月后释放了总量的79%。秸秆细菌丰度和群落组成多样性在埋入土壤后迅速增加，于9或20月后达到最高值。秸秆前期分解主要受环境温度和秸秆化学组成调控，后期主要受秸秆化学组成影响。细菌Actinobacteria、Bacteroidetes和Firmicutes门主导秸秆分解前期的群落组成，而Chloroflexi、Acidobacteria和Saccharibacteria门的丰度在分解后期逐步增加。总之，我国东北地区秸秆还田后的分解速率主要受环境温度和秸秆化学组成调控，秸秆分解过程中细菌群落从前期的富营养型群落主导向后期的贫营养型群落主导演化。

甘蓝型油菜是食用植物油和饲用蛋白的重要来源，然而种子中色素严重影响菜籽油的品质和饼粕的饲用价值。本研究以甘蓝型油菜黄籽母本GH06和黑籽父本中油821构建的重组自交系群体为研究对象，对不同环境下种子（种胚和种皮）色素组份进行QTL定位分析和候选基因的鉴定。结果共检测到94个影响种皮和种胚色素组份含量的QTL位点，44个在种胚中被检测到，50个在种皮中被检测到，分别位于甘蓝型油菜15条不同染色体上。其中包括28个花色素含量相关的QTL，单个QTL可解释2.41-44.46％的表型变异；24个类黄酮含量相关的QTL，单个QTL可解释2.41-20.26％的表型变异；16个总酚含量相关的QTL，单个QTL可解释2.74–23.68％的表型变异；26个黑色素含量相关的QTL，单个QTL可解释表型变异的2.37–24.82％，说明这些性状是由多基因控制的数量性状。同时，在A06，A09和C08染色体上存在多个QTL集中分布的现象，分别包含15个、19个和10个色素相关的QTL，且大多数QTL解释的表型变异>10%被认为是主效QTL。根据甘蓝型油菜“Darmor-bzh”参考基因组注释信息，在被重复检测到的QTL区间内筛选到67个候选基因，通过RNA-seq和qRT-PCR分析结果推断12个差异表达基因可能是参与种子色素合成相关的重要候选基因。本研究结果为甘蓝型油菜种子色素合成遗传机理提供了新的认识并为深入解析甘蓝型油菜粒色形成的分子机制奠定了基础。

量化当前作物的可获得产量和肥料需求可为评估粮食供应能力提供详细信息以及制定农业决策提供数据支持。本研究收集和汇总了从2000年到2015年中国小麦主产区的5408个田间试验，结合克里格插值和小麦养分专家支持决策系统，分析了小麦产量、土壤养分供应能力（采用相对产量表示，其定义为不施氮（N）、磷（P）或钾（K）肥处理的产量与氮磷钾肥全施处理的产量的比值）和氮、磷和钾肥需求的空间分布。结果表明，所有研究区域的小麦平均可获得产量为6.4 t ha⁻¹，变异系数(CV)为24.9%。华北地区（NCC）和长江中下游地区（MLYR）北部的产量普遍高于7 t ha⁻¹，而西南地区（SWC），东北地区（NEC）和西北地区（NWC）东部的小麦产量通常小于6 t ha⁻¹。氮、磷和钾肥的相对产量大于0.70、0.85和0.85的分布面积分别占研究区域的52.3%、74.7%和95.9%。氮、磷和钾肥的需要量存在差异，其变异系数分别为24.8%、23.9%和29.9%。与其它地区相比，华北地区（NCC）和长江中下游地区（MLYR）北部需要更多的肥料。在所有研究区域中，平均的N、P₂O₅和K₂O的需用量分别为162、72和57 kg ha⁻¹。将可获得产量和肥料需求的空间差异纳入小麦生产实践，将有利于小麦可持续生产和环境安全。

A crop growth model, integrating genotype, environment, and management factor, was developed to serve as an analytical tool to study the influence of these factors on crop growth, production, and agricultural planning.  A major challenge of model application is the optimization and calibration of a considerable number of parameters.  Sensitivity analysis (SA) has become an effective method to identify the importance of various parameters.  In this study, the extended Fourier Amplitude Sensitivity Test (EFAST) approach was used to evaluate the sensitivity of the DSSAT-CERES model output responses of interest to 39 crop genotype parameters and six soil parameters.  The outputs for the SA included grain yield and quality (take grain protein content (GPC) as an indicator) at maturity stage, as well as leaf area index, aboveground biomass, and aboveground nitrogen accumulation at the critical process variables.  The key results showed that: (1) the influence of parameter bounds on the sensitivity results was slight and less than the impacts from the significance of the parameters themselves; (2) the sensitivity parameters of grain yield and GPC were different, and the sensitivity of the interactions between parameters to GPC was greater than those between the parameters to grain yield; and (3) the sensitivity analyses of some process variables, including leaf area index, aboveground biomass, and aboveground nitrogen accumulation, should be performed differently.  Finally, some parameters, which improve the model’s structure and the accuracy of the process simulation, should not be ignored when maturity output as an objective variable is studied.

Sensitivity analysis (SA) is an effective tool for studying crop models; it is an important link in model localization and plays an important role in crop model calibration and application.  The objectives were to (i) determine influential and non-influential parameters with respect to above ground biomass (AGB), canopy cover (CC), and grain yield of winter wheat in the Beijing area based on the AquaCrop model under different water treatments (rainfall, normal irrigation, and over-irrigation); and (ii) generate an AquaCrop model that can be used in the Beijing area by setting non-influential parameters to fixed values and adjusting influential parameters according to the SA results.  In this study, field experiments were conducted during the 2012–2013, 2013–2014, and 2014–2015 winter wheat growing seasons at the National Precision Agriculture Demonstration Research Base in Beijing, China.  The extended Fourier amplitude sensitivity test (EFAST) method was used to perform SA of the AquaCrop model using 42 crop parameters, in order to verify the SA results, data from the 2013–2014 growing season were used to calibrate the AquaCrop model, and data from 2012–2013 and 2014–2015 growing seasons were validated.  For AGB and yield of winter wheat, the total order sensitivity analysis had more sensitive parameters than the first order sensitivity analysis.  For the AGB time-series, parameter sensitivity was changed under different water treatments; in comparison with the non-stressful conditions (normal irrigation and over-irrigation), there were more sensitive parameters under water stress (rainfall), while root development parameters were more sensitive.  For CC with time-series and yield, there were more sensitive parameters under water stress than under no water stress.  Two parameters sets were selected to calibrate the AquaCrop model, one group of parameters were under water stress, and the others were under no water stress, there were two more sensitive parameters (growing degree-days (GDD) from sowing to the maximum rooting depth (root) and the maximum effective rooting depth (rtx)) under water stress than under no water stress.  The results showed that there was higher accuracy under water stress than under no water stress.  This study provides guidelines for AquaCrop model calibration and application in Beijing, China, as well providing guidance to simplify the AquaCrop model and improve its precision, especially when many parameters are used.  

Phenazines are secondary metabolites with broad spectrum antibiotic activity and thus show high potential in biological control of pathogens. In this study, we identified phenazine biosynthesis (phz) genes in two genome-completed plant pathogenic bacteria Pseudomonas syringae pv. tomato (Pst) DC3000 and Xanthomonas oryzae pv. oryzae (Xoo) PXO99^A. Unlike the phz genes in typical phenazine-producing pseudomonads, phz homologs in Pst DC3000 and Xoo PXO99^A consisted of phzC/D/E/F/G and phzC/E1/E2/F/G, respectively, and the both were not organized into an operon. Detection experiments demonstrated that phenazine-1-carboxylic acid (PCA) of Pst DC3000 accumulated to 13.4 μg L^–1, while that of Xoo PXO99^A was almost undetectable. Moreover, Pst DC3000 was resistant to 1 mg mL^–1 PCA, while Xoo PXO99^A was sensitive to 50 μg mL^–1 PCA. Furthermore, mutation of phzF blocked the PCA production and significantly reduced the pathogenicity of Pst DC3000 in tomato, while the complementary strains restored these phenotypes. These results revealed that Pst DC3000 produces low level of and is resistant to phenazines and thus is unable to be biologically controlled by phenazines. Additionally, phz-mediated PCA production is required for full pathogenicity of Pst DC3000. To our knowledge, this is the first report of PCA production and its function in pathogenicity of a plant pathogenic P. syringae strain.

In order to study the molecular mechanism involved in cashmere regeneration, this study investigated the gene expression profile of skin tissue at various stages of the cashmere growth cycle and screen differentially expressed genes at proangen in 10 cashmere goats at 2 years of age using agilent sheep oligo microarray. Significance analysis of microarray (SAM) methods was used to identify the differentially expressed genes, Hierarchical clustering was performed to clarify these genes in association with different cashmere growth stages, and GO (Gene ontology) and the pathway analyses were con-ducted by a free web-based Molecular Annotation System3.0 (MAS 3.0). Approximately 10200 probe sets were detected in skin tissue of 2-yr-old cashmere goat. After SAM analysis of the microarray data, totally 417 genes were shown to be differentially expressed at different cashmere growth stages, and 24 genes are significantly up-regulated (21) or down-regulated (3) at proangen concurrently compared to angen and telogen. Hierarchical clustering analysis clearly distinguished the differentially expressed genes of each stage. GO analysis indicated that these altered genes at proangen were predominantly involved in collagen fibril organization, integrin-mediated signaling pathway, cell-matrix adhesion, cell adhesion, transforming growth factor-β (TGF-β) receptor signaling pathway, regulation of cell growth. Kyoto encyclopedia of genes and genomes (KEGG) analysis showed that the significant pathways involved mainly included focal adhesion and extracellular matrixc (ECM)-receptor interaction. Some important genes involved in these biological processes, such as COL1A1, COL1A2, COL3A1, SPARC, CYR61 and CTGF, were related to tissue remolding and repairing and detected by more than one probe with similar expression trends at different stages of cashmere growth cycle. The different expression of these genes may contribute to understanding the molecular mechanism of cashmere regeneration.

Winter wheat freeze injury is one of the main agro-meteorological disasters affecting wheat production. In order to evaluate the severity of freeze injury on winter wheat systematically, we proposed a grey-system model (GSM) to monitor the degree and the distribution of the winter wheat freeze injury. The model combines remote sensing (RS) and geographic information system (GIS) technology. It gave examples of wheat freeze injury monitoring applications in Gaocheng and Jinzhou of Hebei Province, China. We carried out a quantitative evaluation method study on the severity of winter wheat freeze injury. First, a grey relational analysis (GRA) was conducted. At the same time, the weights of the stressful factors were determined. Then a wheat freezing injury stress multiple factor spatial matrix was constructed using spatial interpolation technology. Finally, a winter wheat freeze damage evaluation model was established through grey clustering algorithm (GCA), and classifying the study area into three sub-areas, affected by severe, medium or light disasters. The evaluation model were verified by the Kappa model, the overall accuracy reached 78.82% and the Kappa coefficient was 0.6754. Therefore, through integration of GSM with RS images as well as GIS analysis, quantitative evaluation and study of winter wheat freeze disasters can be conducted objectively and accurately, making the evaluation model more scientific.

Pale, soft, exudative-like (PSE-like) broiler muscle is a growing problem for meat industry all over the world. However, limited studies have been made to assess broiler meat quality in China. The aim of this study was to investigate the characteristics and incidence of PSE-like broiler muscle commercially produced in China. A total of 1 274 Pectoralis muscles of Arbor Acre broiler were randomly obtained from the processing line to determine the commercial incidence of PSE-like muscle based on color. Furthermore, broiler Pectoralis muscles selected from the 1 274 muscle samples were classified as PSE-like muscle (L*>53, n=33) and normal muscle (L*>48 and L*=53, n=33) to assess meat quality. It was determined that PSE-like muscle had lower muscle pH values, lower water-holding capacity (WHC), lower sarcoplasmic protein solubility, and lower total protein solubility than the normal muscle did. SDS-PAGE profile also showed that bands of approximate 96 and 24 kDa in sarcoplasmic protein and myofibrillar protein varied between these two groups, suggesting partial denaturation of sarcoplasmic proteins and precipitation on myofillarments. Correlation analysis showed that L* values have significant correlation with WHC and protein solubility. Furthermore, the distribution of L* values exhibited a normal curve with range varying from 42.70 to 58.37. It was considered that approximately 23.39% of the population was PSE-like muscle. These results suggest that PSE-like meat can represent a significant portion of commercially processed broiler breast meat in China and that the L* value measurement could be used to sort broiler meat quality using a cut-off point.