气候变化对临沂冬小麦主要生育期的影响及适播期研究

为了研究气候变化对临沂市冬小麦生产的影响,确定合理播期和适宜的田间管理措施。利用2001—2021年临沂市气象资料和农业农村部门苗情调查数据,同时参考1962—2000年临沂市历史天气数据,采用线性趋势性预测对小麦生育期温度、积温以及主要生育期日期和持续时间进行变化规律分析。结果表明:临沂市冬小麦适播期和各生育期均对气候变化发生了不同的响应,适播期为10月8—19日,最佳播期为10月11—14日;分蘖期出现反复降温频率变高,促进了小麦冬前抗寒锻炼,利于形成壮苗;冬前积温增多和越冬期延迟,间接推迟了适播期,为上一季玉米晚收创造了条件;返青大幅提前和返青后积温显著增加,大幅缩短了小麦停止生长和越冬时间,增加了冬小麦实际生长时间,有利于冬小麦苗情转化;拔节期大幅提前,使小麦更容易遭受晚霜冻(冷)害和倒春寒;4月和5月温度下降为小麦抽穗、扬花、灌浆提供了更好的气候条件,特别是5月温度下降既减少了干热风的发生机率又利于小麦后期灌浆。确定了临沂市冬小麦适播期,为小麦全生育期田间管理和高产栽培提供了依据。     

气候变暖对临汾合欢物候期的影响

为揭示气候变暖对黄土高原城市典型木本植物物候的影响,以临汾合欢为研究对象,采用线性回归、小波分析、相关分析等方法,分析探讨气温、积温等气候因子对临汾合欢各物候期的影响。结果表明,由于气候变暖,临汾年平均气温呈上升趋势,1983—2021年上升了2.9℃;年降水量存在6年和15年2个周期。临汾合欢春季物候期提前,秋季物候期变化趋势不明显,生长季延长。气温和积温对合欢展叶始期和开花始期表现为极显著相关,对果实成熟期表现为显著相关。叶变色始期、落叶末期对气象条件反应不敏感,主要受果实成熟期的出现日期影响。     

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。     

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。     

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.     

夏玉米产量时空变化及气候年型分析

定量分析夏玉米同一品种产量及产量构成要素时空变化,探讨造成产量时空差异的气候年型组合变化特征。基于2004～2013年夏玉米种植区郑单958多点田间试验数据分析表明,夏玉米区域平均产量为9 055 kg/hm~2,年际差为1 635 kg/hm~2,变幅为18.1%;地点差4 258 kg/hm~2,变幅为47.1%。夏玉米区域平均千粒重为313 g,年际变幅为13.1%;地点变幅为27.8%。夏玉米区域平均穗粒数为479,年际变幅为18.0%,地点变幅为38.7%。千粒重的增加导致夏玉米产量显著增加。穗粒数显著降低和时空差异大是造成产量波动和时空差异变幅大的主要原因。夏玉米产量和产量构成要素,低产点受各气候要素变化的影响显著;平产点受温度和日最高温度大于33℃的天数影响显著;高产点受降水影响显著。     

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.     

水稻拔节期和抽穗期低温对稻米品质影响

【目的】为了探讨低温冷害对水稻品质的影响,以便为气候变化背景下辽宁地区水稻的高效栽培和品质认证提供理论依据。【方法】以水稻90-35为供试品种,分别于拔节期和抽穗期通过TRP-1000D型人工智能气候箱进行不同低温处理(比外界低5℃和低3℃,均持续5 d),以正常栽培管理作对照,5个处理,分别记为CK、A1、A2、B1、B2,分析拔节期和抽穗期不同等级低温胁迫处理对水稻的营养品质、研磨品质和外观品质的影响。【结果】低温胁迫下90-35水稻的蛋白质含量、直链淀粉含量较CK显著增加,处理A1、A2、B1、B2的蛋白质含量分别比CK增加3.45%、1.15%、6.90%、8.05%,除处理B1外直链淀粉含量分别比CK增加1.75%、0.44%、0.44%;脂肪酸含量和精米率较CK显著减少,处理A1、A2、B1、B2的脂肪酸含量分别比CK减少17.75%、7.5%、25%、11%,精米率分别比CK减少2.1%、6.95%、0.22%、7.5%。拔节期低温胁迫下糙米率和垩白粒率显著减少,处理A1、A2的糙米率分别比CK减少0.73%、1.09%,垩白粒率分别比CK减少33.3%、22.2%;抽雄期低温胁迫下糙米率和垩白粒率显著增加,处理B1、B2的的糙米率分别比CK增加0.6%、3.02%,垩白粒率分别比CK增加255.6%、133.3%,即低温胁迫使其营养品质和研磨品质中的精米率受到负面影响,而拔节期适当低温胁迫处理对其研磨品质中的糙米率和外观品质有益。【结论】水稻90-35在不同生育时期经受不同低温胁迫处理后,水稻的营养品质、研磨品质和外观品质均受到不同程度的影响。     

未来气温和降水变化对山西冬小麦产量的影响

【目的】冬小麦是山西省重要的粮食作物,气候变化对冬小麦产量影响显著。利用模型模拟评估气候变化对山西省冬小麦产量的影响,为山西省冬小麦种植规划提供重要科学依据。【方法】利用山西省 2017—2018 年气象资料和 2018 年县级冬小麦产量数据,采用 DNDC 区域模拟方法,对山西省冬小麦种植区单产进行模拟和验证;模拟未来 8 种气候变化情景（温度上升 1、2℃,降水减少 20%、10% 以及温度和降水耦合）对冬小麦产量产生的影响。【结果】DNDC 模型可以较好模拟出山西省冬小麦产量整体水平（4 335.6 kg/hm2）,总体平均误差为 4.63%,空间上的模拟误差主要由于山西省地形和气候差异导致。【结论】未来气候变化背景下,不同程度的增温对产量的影响不一,在温度平均升高 1℃情况下,山西省冬小麦单产下降 4.60%,而气温上升 2℃情景下,单产增加 16.44%;降水减少 10% 和 20% 情景下,产量均降低,说明水分依然是制约冬小麦产量的重要因子;但在增温和降水减少的耦合作用下产量增加 7.41%~15.84%。未来还需根据山西省种植特点,从轮作角度评估气候变化对农业的影响。