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。     

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。     

罗布泊盐湖沉积物磁性特征及其环境意义

以罗布泊盐湖沉积物为研究对象,以环境磁学为主要研究方法,探讨罗布泊盐湖沉积物磁性特征的影响因素和环境意义.根据磁性特征可将罗布泊LOP1剖面沉积物分为两类:磁铁矿主导和铁硫化物(胶黄铁矿和黄铁矿)主导.磁铁矿主要来源于物源区塔里木盆地.胶黄铁矿和黄铁矿是早期还原成岩作用的产物,对磁性特征产生显著影响,胶黄铁矿主导层具有...     

气候变化与大麦生产

分析了新洋农场近14年来的气候变化,年平均气温达14.4℃,比1990年前24年提高了0.6℃,且有升幅逐渐加大趋向。近5年平均气温比1990年前提高了0.9℃。气温升高以冬季最明显。近14年11月中旬至3月中旬升温1.15℃,而极端最低气温的升幅比平均气温更大。降水量变化的最显著特点是变率增大,近14年降水量的变异系数为35.0%,前24年为22.3%。从种植区划、品种、播期、耕作制度、密度、抗灾、病虫草害防治、种植结构调整、品质控制和秸秆还田等方面提出了应变对策。     

BUILDING CLIMATE-RESILIENT FOOD SYSTEMS IN EAST AND SOUTHEAST ASIA: VULNERABILITIES,RESPONSES AND FINANCING

Food system resilience to climate change is uniquely imperative for bringing Sustainable Development Goals within reach and leaving no one behind. Food systems in East and Southeast Asia are interacting with planetary boundaries and are adversely affected by extreme weather-related events. A practical question for East and Southeast Asian stakeholders is how to foster climateresilient food systems in the face of lingering food system vulnerabilities and policy gaps. This paper reviews food syste...     

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.     

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.     

石灰岩山地4种林分空气细菌动态变化及其影响因素分析

为探索石灰岩山地不同林分类型中空气细菌的动态变化规律及其影响因素,筛选出抑菌效果较好的林分,选取徐州石灰岩山地4种典型林分为试验材料,采用自然沉降法研究林分中细菌含量的季节变化和日变化,同步监测温湿度、风速、PM2.5和空气负离子浓度,分析空气细菌含量及其之间的关系。试验结果表明:(1)细菌含量季节性变化表现为春季秋季夏季冬季,除冬季日变化呈现"早晚低,中午高"的变化趋势,其他季节表现为"早晚高,中午低";(2)不同林分类型空气细菌含量存在显著差异(P0.05),总体来看,苦楝林对细菌的抑制效果最好;(3)细菌含量与空气湿度和PM2.5呈极显著正相关关系,与空气负离子呈极显著的负相关关系。综上,苦楝林的抑菌效果最好,湿度、PM2.5和负离子是影响空气细菌含量的主要因素。本研究可为此地城市保健林的营建以及生态旅游规划提供科学依据。     

气候变暖对我国水稻生产的综合影响及其应对策略

气候变暖已是不争的事实,预计到21世纪末全球地表平均气温仍将上升1.5℃以上。水稻是我国最重要的口粮作物,全国80%以上的人口以稻米为主食,探明气候变暖对我国“口粮绝对安全”的潜在影响意义重大。作者依据多年的田间增温试验及长期观察,并结合国内外现有研究,阐明了我国粮食主产区气候变暖的基本态势,总结发现温度升高1.5℃对我国水稻生产的潜在影响正负参半,并取决于具体的稻作季节和地区。但是,随着水稻种植制度调整,尤其是南方双季稻种植面积下降,温度升高对我国水稻生产的负面影响将逐步递增。最后,作者提出了应对气候变暖的气候智慧型稻作技术创新建议,并展望了该研究领域的重点内容和方向。