辽西北霜期气候变化及气象服务对策

为了霜期设施农业充分利用气候资源适应气候变化,规避气象灾害风险,利用辽宁西北部气象资料分析霜期气候资源变化,并探讨霜期设施农业气象服务对策。结果表明：辽西北地区霜期气温升高而不稳,极端最低气温天气更显突出,霜期降雪增加,阴天日数增加,日照时间减少,太阳总辐射减弱,大风强度增加,霜期极端天气现象对霜期设施农业影响显著。霜期设施农业气象服务势在必行,为了推动霜期设施农业的发展,应从设施农业工程、环境控制、利用气候资源节约能源、适应气候变化等方面入手,建立评估、监测、预警气象服务一体化服务系统,通过网络平台,搭建专业化、动态化、多元化、精细化的霜期设施农业气象服务预警平台,提高防灾减灾能力,确保霜期设施农业可持续发展。     

山西谷子品种主要农艺性状的相关和主成分分析

为了明确山西谷子品种主要农艺性状间的关系,采用相关分析和主成分分析方法对11个山西谷子品种的10个农艺性状进行分析。结果表明：农艺性状的变异范围为1.94%~13.39%,各农艺性状的变异程度由大到小依次为单穗重、穗粒重、产量、穗长、株高、千粒重、出苗期—抽穗期天数、抽穗期—成熟期天数、出谷率、生育期。抽穗期—成熟期天数、单穗重、穗粒重、千粒重、出谷率等5个性状指标与产量呈正相关,其中,抽穗期—成熟期天数与产量呈显著正相关(P<0.05),出苗期—抽穗期天数与抽穗期—成熟期天数呈极显著负相关(P<0.01),出苗期—抽穗期天数与生育期呈极显著正相关(P<0.01)。在所有的主成分分析中,主要集中在前3个主成分,其累积贡献率达75.20%。在对高产品种综合性状评价和选育过程中,穗粒重是提高产量的关键,但同时也应注重对其他性状的综合考虑,注重选择单穗重偏高、株高偏低、生育期、穗长和出谷率适中的材料。     

基于CLIMEX和ArcGIS的灰茶尺蠖在中国的潜在适生区预测

基于中国820个气象站点数据、灰茶尺蠖已知地理分布资料和生物学数据,结合CLIMEX模型与ArcGIS软件模拟预测灰茶尺蠖在中国目前及未来的潜在分布范围,评估灰茶尺蠖的潜在分布范围及气候变暖对其分布的潜在影响。结果表明,灰茶尺蠖在中国目前的适生区主要分布在3°51′N~40°6′N,适生区面积占全国总面积的34.27%。中国大部分省份的气候条件均适合灰茶尺蠖种群生长。随着气候的变化,灰茶尺蠖在中国的潜在适生区面积总体增幅不大,但其组成变化较大。至2050年,预测灰茶尺蠖高度适生区面积占比达最大（22.23%）。相比各种能源之间的平衡情景（A1B）,较高能源需求情景（A2）下灰茶尺蠖在陕甘宁地区向内陆扩张更快。灰茶尺蠖在中国适生区分布广泛,应加强灰茶尺蠖预测预报,趁早采取防控措施,以保障茶叶优质安全生产。     

基于Google Earth Engine平台的关中冬小麦面积时空变化监测

以关中地区为研究区,基于Google Earth Engine(GEE)平台,根据冬小麦生育期内归一化植被指数(NDVI)时序曲线和物候特征,采用NDVI重构增幅算法和光谱突变斜率,构建了关中地区冬小麦提取模型并实现了冬小麦种植面积的提取。用农业统计面积验证提取结果表明:在市级和县级尺度上,决定系数R~2分别为0.82和0.62,一致性指标d分别为0.95和0.84,提取结果与实地调查数据的空间一致性精度为93.4%。结果显示:关中地区冬小麦主要分布在中部关中平原,冬小麦种植面积在2011—2017年呈下降趋势,减少了83.22×10~3 hm~2(8.47%)。综合考虑冬小麦NDVI时序曲线的"峰""谷"特征,具有一定的普适性,可为大面积连续年份冬小麦种植面积时空监测提供参考。     

Performance and uncertainty analysis of a short-term climate reconstruction based on multi-source data in the Tianshan Mountains region,China

Short-term climate reconstruction, i.e., the reproduction of short-term(several decades) historical climatic time series based on the relationship between observed data and available longer-term reference data in a certain area, can extend the length of climatic time series and offset the shortage of observations. This can be used to assess regional climate change over a much longer time scale. Based on monthly grid climate data from a Coupled Model Inter-comparison Project phase 5(CMIP5) dataset for the period of 1850–2000, the Climatic Research Unit(CRU) dataset for the period of 1901–2000 and the observed data from 53 meteorological stations located in the Tianshan Mountains region(TMR) of China during the period of 1961–2011, we calibrated and validated monthly average temperature(MAT) and monthly accumulated precipitation(MAP) in the TMR using the delta, physical scaling(SP) and artificial neural network(ANN) methods. Performance and uncertainty during the calibration(1971–1999) and verification(1961–1970) periods were assessed and compared using traditional performance indices and a revised set pair analysis(RSPA) method. The calibration and verification processes were subjected to various sources of uncertainty due to the influence of different reconstructed variables, different data sources, and/or different methods used. According to traditional performance indices, both the CRU and CMIP5 datasets resulted in satisfactory calibrated and verified MAT time series at 53 meteorological stations and MAP time series at 20 meteorological stations using the delta and SP methods for the period of 1961–1999. However, the results differed from those obtained by the RSPA method. This showed that the CRU dataset produced a low degree of uncertainty(positive connection degree) during the calibration and verification of MAT using the delta and SP methods compared to the CMIP5 dataset. Overall, the calibrated and verified MAP had a high degree of uncertainty(negative connection degree) regardless of the dataset or reconstruction method used. Therefore, the reconstructed time series of MAT for the period of 1850(or 1901)–1960 based on the CRU and CMIP5 datasets using the delta and SP methods could be used for further study. The results of this study will be useful for short-term(several decades) regional climate reconstruction and longer-term(100 a or more) assessments of regional climate change.     

Mapping the current and future distributions of Onosma species endemic to Iran

Climate change may cause shifts in the natural range of species especially for those that are geographically restricted and/or endemic species. In this study, the spatial distribution of five endemic and threatened species belonging to the genus Onosma (including O. asperrima, O. bisotunensis, O. kotschyi, O. platyphylla, and O. straussii) was investigated under present and future climate change scenarios: RCP2.6 (RCP, representative concentration pathway; optimistic scenario) and RCP8.5 (pessimistic scenario) for the years 2050 and 2080 in Iran. Analysis was conducted using the maximum entropy (MaxEnt) model to provide a basis for the protection and conservation of these species. Seven environmental variables including aspect, depth of soil, silt content, slope, annual precipitation, minimum temperature of the coldest month, and annual temperature range were used as main predictors in this study. The model output for the potential habitat suitability of the studied species showed acceptable performance for all species (i.e., the area under the curve (AUC)>0.800). According to the models generated by MaxEnt, the potential current patterns of the species were consistent with the observed areas of distributions. The projected climate maps under optimistic and pessimistic scenarios (RCP2.6 and RCP8.5, respectively) of 2050 and 2080 resulted in reductions and expansions as well as positive range changes for all species in comparison to their current predicted distributions. Among all species, O. bisotunensis showed the most significant and highest increase under the pessimistic scenario of 2050 and 2080. Finally, the results of this study revealed that the studied plant species have shown an acute adaptability to environmental changes. The results can provide useful information to managers to apply appropriate strategies for the management and conservation of these valuable Iranian medicinal and threatened plant species in the future.     

Impact of waterlogging and temperature on autumn growth,hardening and freezing tolerance of timothy (Phleum pratense)

Precipitation has generally increased in Norway during the last century, and climate projections indicate a further increase. The growing season has also become longer with higher temperatures, particularly in autumn. Previous studies have shown negative effects of high temperatures and, depending upon temperature conditions, contrasting effects of waterlogging on hardening capacity of timothy. We studied effects of waterlogging on seedlings of timothy (Phleum pratense, cv. Noreng) under three pre-acclimation temperatures: 3°C, 7°C, 12°C, and in autumn natural light in a phytotron at Holt, Tromsø (69°N). After temperature treatments, all plants were cold acclimated at 2°C for three weeks under continued waterlogging treatments. Freezing tolerance was determined by intact plants being frozen in pots at incremental temperature decreases in a programmable freezer. Waterlogging resulted in a higher probability of death after freezing, and a significantly reduced regrowth after three weeks at 18°C, 24 hrs light in a greenhouse. Increasing pre-acclimation temperatures also had a clear negative effect on freezing tolerance, but there was no interaction between temperatures and waterlogging. The results indicate that waterlogging may have negative implications for hardening of timothy and may contribute to reduced winter survival under the projected increase in autumn temperatures and precipitation.     

基于GODAS资料的南海上层海洋热状态气候变化特征分析

为了深入了解南海上层海洋热力状态的变化规律,利用1980—2015年共36年GODAS月平均海温资料,将5~366 m的垂直平均海温表征南海地区海洋上层的热含量,分析了南海海洋上层热状态的水平和垂直分布特征以及季节和年际变化特征。结果表明：年平均南海热含量水平分布表现为东高西低的形势,垂直纬向平均分布表现为暖水厚度和温跃层深度东厚（深）西薄（浅）,垂直经向平均表现为暖水厚度南厚北薄,温跃层深度中间浅两边深;南海地区海温变化幅度在75~200 m处最大,不同深度海温距平均具有明显的年际和年代际变化特征;南海区域平均热含量在秋季最高,春夏次之,冬季最低,其年际变化明显,且在1998年之后出现明显的突变,由负值转变为正值,表现出明显的增温趋势;热含量季节EOF主模态空间分布形势表现为东高西低的特征,对应的时间序列在20世纪90年代末存在年代际转折,由主要为负值转化为主要为正值,表现在空间分布上,则为南海地区热含量由西高东低型转化为东高西低型。     

基于无人机多光谱遥感的冬小麦叶绿素含量反演及监测

旨在实现冬小麦各生育期叶绿素含量的准确估测,探究其时空变化规律。利用无人机获取冬小麦越冬期、返青期、拔节期、孕穗期和灌浆期的高分辨率多光谱图像,同时采集地面SPAD数据。选取三类光谱参数建立反演模型,优选出各生育期的最佳预测模型,并定量监测试验区冬小麦叶绿素含量时间变化和空间分布。结果表明：原始波段模型和波段倒数对数模型分别为越冬期及其他生育期叶绿素含量预测的最佳模型,拟合精度R²>0.59;时空分布上,灌浆期前试验区冬小麦叶绿素含量呈南北高、中部低特点,灌浆期则呈北高南低的趋势,叶绿素含量从越冬期到拔节期逐步增加,拔节期到孕穗期开始降低,孕穗期到灌浆期则大幅度降低。本研究建立的倒数对数预测模型,精度较高,且适用于返青到灌浆的4个生育期,对于试验区冬小麦叶绿素含量有较好的时空监测效果。     

根腐病菌侵染对黄芪苯丙烷途径关键酶活性的影响

以蒙古黄芪-根腐病菌（Fusarium solani和F. acuminatum）为互作体系,测定苯丙烷途径关键酶苯丙氨酸解氨酶(PAL)、肉桂酸-4-羟化酶(C4H)及4-香豆酸-辅酶A连接酶(4CL)活性的变化,以期获得黄芪抗根腐病菌侵染机制的信息,为抗病育种工作提供参考。试验采用幼苗浸根法接种病菌,分光光度法测定3种酶的动态变化。结果表明,F. solani侵染黄芪后,PAL和C4H活性与对照相比明显升高,并在7、21天出现2个酶活高峰;4CL活性则随着时间的延长持续上升,7、21天时也显著高于对照。F. acuminatum侵染黄芪后,PAL、C4H和4CL的活性均在21天显著高于对照。可见2种病原菌在侵染黄芪过程中,苯丙烷途径的关键酶被不同程度地激活,但2种病菌激活各酶的时间和规律有所差异,表明黄芪对这2种病菌的抗性机制明显不同。