稻麦复种模式下氮肥与稻秸互作对小麦产量和N_(2)O排放影响及推荐施肥研究

优化氮肥施用和秸秆还田技术为途径的农业管理措施被认为是提升农业可持续性的有效手段,然而当前关于氮肥和秸秆还田对小麦产量和N_(2)O排放影响的研究仍十分有限。为此,本研究基于2000—2022年发表的关于长江中下游流域氮肥和秸秆投入下小麦产量和N_(2)O排放变化的文献,运用随机森林建模,定量分析氮肥和秸秆还田对小麦产量和N_(2)O排放的影响,并结合情景设置进行了特定地点的小麦产量和N_(2)O排放模拟,同时评估了碳排放强度(CEE)和净生态系统经济效益(NEEB)。结果表明,建立的区域尺度小麦产量与N_(2)O排放对氮秸互作响应的随机森林模型,验证结果R^(2)分别为0.66和0.65,RMSE分别为0.70和1.11。结果表明施氮量和土壤有机质是影响小麦产量和N_(2)O排放的重要因素。综合来看,达到最大产量所需的氮肥量为208~212 kg hm^(-2),达到最小CEE所需的氮肥量为113~130 kg hm^(-2),达到最高的NEEB所需的氮肥量为202~205 kg hm^(-2),其中在6.75 t hm^(-2)的秸秆投入下施用202 kg hm^(-2)的氮肥可以获得最高的生态收益1.37万元。优化氮肥和秸秆投入具备减少作物碳排放强度并获得最大净生态环境效益的潜力。

持续开挖效应下结构面剪切力学性质与能量特征研究

基于地下岩体工程开挖扰动的实际情况,开展考虑持续开挖效应,充分反映岩体结构面应力调整过程的结构面剪切试验更具理论意义和工程应用价值。采用人工劈裂方法制备岩体结构面,开展了常规应力路径和持续开挖效应下的结构面剪切试验,系统研究了两种条件下结构面剪切力学性质、声发射特征和能量的演化规律。研究结果表明:开挖扰动荷载强度越大,结构面发生剪切破坏时的剪应力降整体上越大,但持续开挖效应下的剪应力降最大值仅为常规应力路径下剪应力降的48.57%;考虑持续开挖效应的结构面剪切过程中声发射活动主要集中于结构面受剪破坏和剪应力降产生时,且声发射活动强度与开挖扰动强度正相关,声发射振铃计数变化率极值明显小于常规应力路径下的相应值;持续开挖效应下试样裂纹发育密集度、结构面磨损区域及破坏程度均随开挖扰动强度增加而增加,但试样裂纹发育的密集程度和结构面破坏程度相对常规应力路径下的情况轻;考虑持续开挖效应的结构面发生剪切破坏时弹性应变能随扰动荷载增大而增大,均低于常规应力路径条件下的弹性应变能值,持续开挖效应降低了结构面剪切破坏的强度,但更易引发结构面发生剪切破坏。

基于农田物联网设备CGMD-502的双季稻缺氮和干旱胁迫诊断研究

通过3个氮肥水平和2个水分处理的田间小区试验,模拟双季稻遭遇不同逆境胁迫的情景,利用农田物联网设备CGMD-502采集早、晚稻归一化红边指数(NDRE_(CGMD)),并分析了NDRE_(CGMD)与农学参数(SPAD值、LAI)之间的相关关系以及该植被指数对双季稻不同程度缺氮和干旱胁迫的响应。结果发现:NDRE_(CGMD)与双季稻LAI之间具有较高的相关性,两者之间线性监测模型(LAI=32.25×NDRE_(CGMD)-2.34)的建模决定系数(R2)达到了0.72,检验均方根误差(RMSE)和偏差(bias)分别为1.25和0.80;NDRE_(CGMD)对双季稻缺氮和干旱胁迫具有较强的响应能力,在不同胁迫情景下的数值变化幅度略低于LAI,但与SPAD值相当。由此表明,农田物联网设备CGMD-502在生产中具有替代SPAD值和LAI诊断双季稻缺氮和干旱胁迫的实际应用潜力。研究结果对于促进农田物联网技术在双季稻生产中的应用及提高江西双季稻生产智慧管理水平具有重要意义。

中间主应力影响下加锚试样应力-应变曲线特征参数分析

为探究锚杆加固、中间主应力和岩石强度对试样应力-应变曲线特征参数的影响,采用电液伺服双轴压缩试验机对不同中间主应力影响下无锚和两种强度的加锚类岩石试样的应力-应变曲线特征参数进行了试验研究。研究结果表明:加锚能够提高试样刚度和降低试样脆性,单轴抗压强度UCS=25.4 MPa的试样加锚后的刚度角、强化角和韧度角分别提高1.36%、0.89%和50.8%,脆度角和软化角分别降低0.2%和1.43%。中间主应力增加能够提高试样强度和刚度、降低试样脆性,中间主应力由4.44 MPa提高到17.78 MPa时,UCS=25.4 MPa的加锚试样屈服强度、峰值强度和残余强度分别提高94.2%、41.89%和20.16%;刚度角、强化角和韧度角分别提高1.69%、0.76%和74.21%;脆度角和软化角分别降低2.96%和2.44%。岩样强度提高对试样刚度和脆性有显著提升作用,和UCS=23.8 MPa的加锚试样比,UCS=25.4 MPa的加锚试样的刚度角、脆度角、强化角和软化角分别增加1.18%、0.72%、1.28%和3.95%,韧度角降低46.8%。研究结果促进了对中间主应力影响下加锚围岩强度和变形规律的认识,对巷道稳定性控制和灾变防控具有一定的参考作用。

不同产量水平下稻茬小麦的氮素营养指标特征

【目的】明确长江中下游地区不同产量水平稻茬小麦氮营养指标变化规律,为小麦氮素营养状况实时诊断提供理论依据。【方法】本研究通过江苏省多年多点田间不同氮肥、播期、密度和品种试验,构建不同产量水平小麦大数据,分析不同产量水平小麦在不同生育时期的干物质积累量、植株氮积累量和植株氮浓度的变化规律,并通过计算小麦临界氮浓度,得到累积氮亏缺和氮营养指数的动态变化规律,进而明确高产稻茬小麦氮素营养指标特征。【结果】干物质积累量和植株氮积累量的变化趋势一致,随着小麦生育进程的推进均逐渐增加,植株氮浓度逐渐减小,累积氮亏缺和氮营养指数会出现波动。整个生育期内,干物质积累量和植株氮积累量在高产小麦和中产小麦之间的差异不显著,但二者植株氮积累量均显著高于中低产小麦,中低产小麦又显著高于低产小麦。在抽穗期、开花期和灌浆期,高产和中产小麦干物质积累量显著高于中低产小麦,中低产小麦又显著高于低产小麦。依据累积氮亏缺值判断氮素营养状况,高产和中产小麦的累积氮亏缺变化趋势一致,在起身—孕穗期,高产小麦的累积氮亏缺值由0.3 kg/hm^2降低为-23.0 kg/hm^2,中产小麦由7.0 kg/hm^2降低为-14.6kg/hm^2,孕穗—抽穗期又呈升高趋势,高产小麦由-23.0 kg/hm^2升高为-11.4 kg/hm^2,中产小麦由-14.6 kg/hm^2升高为2.4 kg/hm^2,开花—灌浆期的波动较小。表明高产小麦氮营养除起身期之外均为过剩,中产小麦在拔节—孕穗期的累积氮亏缺小于0,其余时期累积氮亏缺均大于0,但该水平的累积氮亏缺值一直在适宜范围内波动。中低产小麦在起身—拔节期,累积氮亏缺值由14.2 kg/hm^2降低为9.5 kg/hm^2,之后逐渐升高,灌浆期达到最大为43.9 kg/hm^2;低产小麦在起身期到灌浆期,累积氮亏缺值由17.3 kg/hm^2升高为71.1 kg/hm^2。表明中低产和低产的小麦氮营养水平在整个生育期内逐渐降低,且整个生育期均处于亏缺状态(累积氮亏缺值>0)。在拔节期、孕穗期、抽穗期和灌浆期,高产水平的小麦植株实际氮浓度高于植株临界氮浓度,中产小麦在孕穗期的植株实际氮浓度高于临界氮浓度,而中低产和低产的小麦在整个生育期植株实际氮浓度低于植株临界氮浓度。高产和中产的小麦氮营养指数在1附近波动,其中高产小麦的氮营养指数在起身—孕穗期由0.9升高为1.1,在抽穗—灌浆期,氮营养指数呈现先降低后升高趋势,其值分别为1.0、0.9和1.0,中产小麦与高产小麦的变化趋势一致,起身—孕穗期的氮营养指数由0.8升高为1.0,之后逐渐下降,其值均小于1,抽穗—灌浆期分别为0.9、0.9和0.9。中低产和低产的小麦氮营养指数始终低于1,中低产小麦在起身—拔节期氮营养指数由0.7升高为0.8,之后则逐渐下降,低产小麦从起身—开花期均逐渐下降,而这两个产量水平的氮营养指数在灌浆期会呈现略微上升趋势。【结论】随着产量水平的提高,小麦植株干物质和氮积累量、植株氮浓度、氮营养指数等都相应增加,累积氮亏缺相应下降。较高的干物质积累量和植株氮积累量是提高小麦产量的主要原因,在小麦生长过程中氮营养指数和累积氮亏缺均能准确诊断小麦氮营养状况,可为小麦氮肥精准管理提供理论支持。

不同产量水平稻茬小麦氮素需求特征研究

明确长江中下游地区不同产量水平稻茬小麦氮素需求特征,可为小麦施肥管理提供理论依据。本研究通过在江苏开展的多年多点不同品种、氮肥水平以及播期播量的小麦试验,构建不同产量水平的实测数据集,分析不同产量水平下单位籽粒需氮量、干物质积累量、植株氮积累量、氮浓度(植株氮浓度、秸秆氮浓度、籽粒氮浓度)、收获指数、氮收获指数和氮营养指数的变化规律。结果表明,不同产量水平下单位籽粒需氮量无显著差异,中低产的单位籽粒需氮量最高,其值为27.8 kg t–1;低产水平最低,其值为24.8 kg t–1。随着产量水平的提高,成熟期干物质积累量、植株氮积累量、植株氮浓度均呈上升趋势,不同产量水平间差异显著。小麦产量与植株氮积累量呈显著正相关,播种期—拔节期、拔节期—开花期和开花期—成熟期的干物质积累量和氮积累量均随着产量的提高而提高,但不同生育阶段的植株干物质积累和氮积累占比呈现不同变化趋势。秸秆和籽粒氮浓度均随产量水平的提高而提高,高产水平下的秸秆氮浓度与中产无显著差异,但显著高于中低产和低产水平;而对于籽粒氮浓度,除中产和中低产水平外均存在显著差异。收获指数随产量水平的提高而逐渐提高,其变化范围为0.39~0.49,其中低产和中低产显著低于中产和高产;而不同产量水平间氮收获指数无显著差异,其变化范围为0.60~0.96。氮营养指数随着产量水平的提高逐渐提高,且在不同产量水平间差异显著,高产水平的氮营养指数较高,部分值大于1,表明有的试验氮肥供应过量。随着产量水平的提高,单位籽粒需氮量呈现先增加后下降趋势,而干物质积累量、植株氮积累量、植株氮浓度、秸秆氮浓度和籽粒氮浓度均逐渐提高,其中秸秆氮浓度增幅高于籽粒氮浓度,田间施肥应注意避免小麦对氮素的奢侈吸收。收获指数和氮收获指数的变化范围与前人研究一致,生长后期较高的干物质积累量和植株氮积累量是小麦获得高产的主要原因,利用氮营养指数可以对小麦田间氮肥管理起到较好的指导作用。

播期播量及施氮量对冬小麦生长及光谱指标的影响

为阐明播期、播量及施氮量对冬小麦生长与光谱指标的影响规律,本研究通过开展连续两年不同播期、播量及施氮量的冬小麦田间试验,系统地研究了三因素及其互作对冬小麦产量、关键生育时期叶面积指数(leaf area index,LAI)和归一化红边指数(normalized difference red edge,NDRE)的影响,并进一步分析了三因素对冬小麦冠层NDRE时序曲线的影响。另一方面建立了不同产量水平下冬小麦冠层NDRE适宜时序曲线,以便于实时监测不同产量水平下冬小麦长势动态。结果表明,冬小麦冠层NDRE与LAI随关键生育期的变化相似,且三因素对关键生育时期2个指标的影响规律基本一致。2018—2019年冬小麦产量、不同生育时期LAI和冠层NDRE均随播期推迟而下降;2019—2020年除灌浆期外,晚播冬小麦产量、LAI及冠层NDRE峰值最大。2年冬小麦不同生育时期LAI、冠层NDRE随施氮量增加而增加;而不同播量间无明显差异。三因素中播期、施氮量对冬小麦冠层NDRE时序曲线有显著影响。冬小麦冠层NDRE时序曲线随施氮量增加被纵向拉长;曲线下降部分随播期推迟向左平移,同时2018—2019年随播期推迟曲线峰值下降,2019—2020年晚播、过晚播冬小麦曲线峰值高于适播期冬小麦。将2个年份数据融合在一起建立了3个产量水平下冬小麦冠层NDRE适宜时序曲线(产量水平分别为:小于6.75、6.75~8.25、大于8.25 t hm^(-2));发现随产量水平升高,NDRE时序曲线峰值及幅宽均增大。综上所述,冬小麦应适期早播,但若冬前积温较高,应适当推迟播期;且可通过增加一定播量和施氮量来改善晚播冬小麦群体长势。研究结果可为不同播期及不同产量水平下冬小麦长势监测提供技术支撑。

Common Spectral Bands and Optimum Vegetation Indices for Monitoring Leaf Nitrogen Accumulation in Rice and Wheat

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 hyperspeetral 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 （Rs22, R73s） 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.

A Knowledge Model System for Wheat Production Management

A knowledge model with temporal and spatial characteristics for the quantitative design of a cultural pattern in wheat production, using systems analysis and dynamic modeling techniques, was developed for wheat management, as a decision-making tool in digital farming. The fundamental relationships and algorithms of wheat growth indices and management criteria to cultivars, ecological environments, and production levels were derived from the existing literature and research data to establish a knowledge model system for quantitative wheat management using Visual C++. The system designed a cultural management plan for general management guidelines and crop regulation indices for time-course control criteria during the wheat-growing period. The cultural management plan module included submodels to determine target grain yield and quality, cultivar choice, sowing date, population density, sowing rate, fertilization strategy, and water management, whereas the crop regulation indices module included submodels for suitable development stages, dynamic growth indices, source-sink indices, and nutrient indices. Evaluation of the knowledge model by design studies on the basis of data sets of different eco-sites, cultivars, and soil types indicated a favorable performance of the model system in recommending growth indices and management criteria under diverse conditions. Practical application of the knowledge model system in comparative field experiments produced yield gains of 2.4% to 16.5%. Thus, the presented knowledge model system overcame some of the difficulties of the traditional wheat management patterns and expert systems, and laid a foundation for facilitating the digitization of wheat management.

A New Method to Determine Central Wavelength and Optimal Bandwidth for Predicting Plant Nitrogen Uptake in Winter Wheat

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.

Leaf area index based nitrogen diagnosis in irrigated lowland rice

Leaf area index （LAI） is used for crop growth monitoring in agronomic research, and is promising to diagnose the nitrogen （N） status of crops. This study was conducted to develop appropriate LAI-based N diagnostic models in irrigated lowland rice. Four field experiments were carried out in Jiangsu Province of East China from 2009 to 2014. Different N application rates and plant densities were used to generate contrasting conditions of N availability or population densities in rice. LAI was determined by LI-3000, and estimated indirectly by LAI-2000 during vegetative growth period. Group and individual plant characters （e.g., tiller number （TN） and plant height （H）） were investigated simultaneously. Two N indicators of plant N accumulation （NA） and N nutrition index （NNI） were measured as well. A calibration equation （LAI=1.7787LAI2o00-0.8816, R2=0.870＂） was developed for LAI-2000. The linear regression analysis showed a significant relationship between NA and actual LAI （R2=0.863^＊＊）. For the NNI, the relative LAI （R2=0.808-） was a relatively unbiased variable in the regression than the LAI （R^2=0.33^＊＊）. The results were used to formulate two LAI-based N diagnostic models for irrigated lowland rice （NA=29.778LAI-5.9397; NNI=0.7705RLAI＋0.2764）. Finally, a simple LAI deterministic model was developed to estimate the actual LAI using the characters of TN and H （LAI=-0.3375（THxHx0.01）2＋3.665（TH×H×0.01）-1.8249, R2=0.875＊＊）. With these models, the N status of rice can be diagnosed conveniently in the field.

基于作物生长监测诊断仪的双季稻叶片氮含量和氮积累量监测

为了验证作物生长监测诊断仪(CGMD)监测双季稻氮素营养指标的准确性和适用性,构建基于CGMD的双季稻叶片氮含量(LNC)和氮积累量(LNA)的监测模型。选用8个不同早、晚稻品种,设置4个不同施氮水平,利用CGMD采集冠层差值植被指数(DVI)、归一化植被指数(NDVI)和比值植被指数(RVI),同步利用ASD FH2高光谱仪采集冠层光谱反射率,并计算DVI、NDVI和RVI;通过比较CGMD和ASD FH2采集的冠层植被指数变化特征,验证CGMD的测量精度,构建基于CGMD的LNC和LNA监测模型,并利用独立试验数据对模型进行检验。结果表明:早、晚稻LNC、LNA、DVI、NDVI和RVI随施氮水平的增加而增大,随生育进程的推进呈先升后降的趋势;CGMD与ASD FH2采集的DVI、NDVI和RVI间拟合的决定系数(R^2)分别为0.9350、0.9436和0.9433,表明CGMD的测量精度较高,可替代ASD FH2采集冠层植被指数。基于CGMD的3个冠层植被指数相比,NDVI_(CGMD)与LNC的相关性最高,RVI_(CGMD)与LNA的相关性最高;基于NDVI_(CGMD)的指数模型可较准确地预测LNC,模型R^2为0.8581~0.9318,模型检验的均方根误差(RMSE)、相对均方根误差(RRMSE)和相关系数(r)分别为0.1%~0.2%、4.0%~8.5%和0.9041~0.9854;基于RVI_(CGMD)的幂函数模型可较准确地预测LNA,模型R^2为0.8684~0.9577,模型检验的RMSE、RRMSE和r分别为0.37~0.89 g·m^(-2)、6.7%~20.4%和0.9191~0.9851。与化学分析方法相比,利用CGMD可便捷准确地获取早、晚稻的LNC和LNA,在双季稻丰产高效栽培和氮肥精确管理中具有应用价值。

试以气机论肠道菌群

肠道菌群是近年来医学界的研究重点,既往研究者在构建中医理论模型时多以“脾胃”立论,强调其在营养代谢方面的功能,突出了肠道菌群的主要特点,但也忽略了肠道菌群在免疫、内分泌及神经等系统功能中发挥的作用。笔者认为,中医理论之哲学基础完备精微,且在不同语境下矛盾双方时常变化,需细心界定其差别。在中医语境下,免疫功能亦有阴阳之分,并赖其动态平衡维持机体的稳定。然而脾胃系统仅能参与解释其“阳”,无法解释其“阴”,也就无所谓“调衡”。故文章从气机立论,试全面解释肠道菌群的功能及其调节过程,为之构建更为完备的中医理论模型。

Estimating Leaf Chlorophyll Content Using Red Edge Parameters

Hyperspectral remote sensing makes it possible to non-destructively monitor leaf chlorophyll content(LCC).This study characterized the geometric patterns of the first derivative reflectance spectra in the red edge region of rapeseed(Brassica napus L.) and wheat(Triticum aestivum L.) crops.The ratio of the red edge area less than 718 nm to the entire red edge area was negatively correlated with LCC.This finding allowed the construction of a new red edge parameter,defined as red edge symmetry(RES).Compared to the commonly used red edge parameters(red edge position,red edge amplitude,and red edge area),RES was a better predictor of LCC.Furthermore,RES was easily calculated using the reflectance of red edge boundary wavebands at 675 and 755 nm(R675 and R755) and reflectance of red edge center wavelength at 718 nm(R718),with the equation RES =(R718-R675)/(R755-R675).In addition,RES was simulated effectively with wide wavebands from the airborne hyperspectral sensor AVIRIS and satellite hyperspectral sensor Hyperion.The close relationships between the simulated RES and LCC indicated a high feasibility of estimating LCC with simulated RES from AVIRIS and Hyperion data.This made RES readily applicable to common airborne and satellite hyperspectral data derived from AVIRIS and Hyperion sources,as well as ground-based spectral reflectance data.