Effects of temperature and water limitation on the germination of Stipagrostis ciliata seeds collected from Sidi Bouzid Governorate in Central Tunisia

Most ecological studies in North Africa reveal a process of continuous degradation of rangeland ecosystems as a result of overgrazing. This degradation appears across the decreasing of perennial grass diversity. Indeed, the majority of steppe ecosystems are characterized by a low density of perennial grass species at present. This study evaluated the effects of temperature and water limitation on the seed germination of Stipagrostis ciliata(Desf.) de Winter, a perennial grass species. The seeds were collected from the Bou Hedma Park, Sidi Bouzid Governorate, Central Tunisia. The thermal time and hydrothermal time models were used to describe the seed germination of S. ciliata under different water potentials and temperatures. The germination response of S. ciliata seeds in darkness was evaluated over a range of temperatures(15°C, 20°C, 25°C, 30°C and 35°C) and across a wide range of osmotic potentials(0.0, –0.2, –0.6, –1.2, –1.6 and –2.0 MPa) of the polyethylene glycol(PEG6000) solutions at each temperature level. Among the tested temperatures, 25°C was found to be the optimal temperature to the germination of S. ciliata seeds. The final germination percentage(75.2%) was obtained with distilled water. The progressive decrease of osmotic potential of the PEG6000 solutions inhibited the seed germination. However, the number of days to first germination was increased with a reduction of osmotic potential. A significant positive relationship was identified between final germination percentage of S. ciliata seeds and osmotic potential of the PEG6000 solutions, with R~2 ranging from 0.5678 to 0.8761. Furthermore, a high degree of congruency between predicted and observed germination time course curves was observed. In general, S. ciliata exhibits a significant adaptation capacity for water limitation and high temperature in arid ecosystems.     

农牧交错带柠条锦鸡儿根系与土壤水分空间关系研究

以内蒙古农牧交错带柠条锦鸡儿(Caragana korshinskii Kom.)人工林为研究对象,在秋季对其根系分布特征及土壤水分采用四分之一圆法分层采样分析,研究了柠条锦鸡儿根系生物量与土壤含水率空间分布规律。结果表明:(1)柠条锦鸡儿根系呈浅层化分布,根系主要集中在0~60 cm土层;(2)根系生物量与土壤水分的空间分布规律基本一致,且根系分布与同空间内土壤水分存在正相关关系,均表现为随着土层深度的增加而逐渐减少;(3)在垂直、水平方向,各实验地柠条锦鸡儿根系生物量与土壤含水率均呈显著相关,构建根系生物量与土壤含水率的经验模型,验证发现该模型可以很好地说明二者的关系。研究结果旨在为干旱区人工柠条林的栽植管理及植被恢复提供理论依据。     

气候变化对新疆意大利蝗潜在分布的影响

意大利蝗Calliptamus italicus(L.)是新疆草原主要优势蝗虫之一,每年给新疆畜牧业经济带来严重损失,气候变化对其潜在分布影响的预测对其科学防治有重要意义。本研究采用意大利蝗的分布数据和生物气候数据,结合MaxEnt模型和ArcGIS软件,预测了BCC_CSM1.1气候模式下政府间气候变化专门委员会第五次工作报告(IPCC AR5)采用的RCP2.6、RCP4.5和RCP8.5三种气候情景在2021-2040年(2030s)、2041-2060年(2050s)和2061-2080年(2070s)的意大利蝗新疆潜在适生区分布范围。结果表明:在BCC_CSM1.1的各情景下,意大利蝗适生区在北疆及天山一带分布格局基本保持不变,但高度适生区面积都有所增加,其中在天山和阿尔泰山地区,意大利蝗中、高度适生区范围将向更高海拔区域蔓延,在北疆阿勒泰地区高度适生区明显增加。极端水分条件和水热条件对意大利蝗在新疆潜在分布发挥主要作用,其中4月、10月、3月和11月降水量对意大利蝗在新疆潜在分布影响最大,因其直接影响土壤相对含水量和土壤温度,从而决定意大利蝗卵的存活量。     

Spatial pattern of plant species diversity and the influencing factors in a Gobi Desert within the Heihe River Basin,Northwest China

Understanding the spatial pattern of plant species diversity and the influencing factors has important implications for the conservation and management of ecosystem biodiversity. The transitional zone between biomes in desert ecosystems, however, has received little attention in that regard. In this study, we conducted a quantitative field survey(including 187 sampling plots) in a 40-km~2 study area to determine the spatial pattern of plant species diversity and analyze the influencing factors in a Gobi Desert within the Heihe River Basin, Northwest China. A total of 42 plant species belonging to 16 families and 39 genera were recorded. Shrub and semi-shrub species generally represented the major part of the plant communities(covering 90% of the land surface), while annual and perennial herbaceous species occupied a large proportion of the total recorded species(71%). Patrick richness index(R), Shannon-Wiener diversity index(H'), Simpson's dominance index(D), and Pielou's evenness index(J) were all moderately spatially variable, and the variability increased with increasing sampling area. The semivariograms for R and H' were best fitted with Gaussian models while the semivariograms for D and J were best fitted with exponential models. Nugget-to-still ratios indicated a moderate spatial autocorrelation for R, H', and D while a strong spatial autocorrelation was observed for J. The spatial patterns of R and H' were closely related to the geographic location within the study area, with lower values near the oasis and higher values near the mountains. However, there was an opposite trend for D. R, H', and D were significantly correlated with elevation, soil texture, bulk density, saturated hydraulic conductivity, and total porosity(P0.05). Generally speaking, locations at higher elevations tended to have higher species richness and diversity and the higher elevations were characterized by higher values in sand and gravel contents, bulk density, and saturated hydraulic conductivity and also by lower values in total porosity. Furthermore, spatial variability of plant species diversity was dependent on the sampling area.     

Modelling the impact of climate change on rangeland forage production using a generalized regression neural network: a case study in Isfahan Province,Central Iran

Monitoring of rangeland forage production at specified spatial and temporal scales is necessary for grazing management and also for implementation of rehabilitation projects in rangelands. This study focused on the capability of a generalized regression neural network(GRNN) model combined with GIS techniques to explore the impact of climate change on rangeland forage production. Specifically, a dataset of 115 monitored records of forage production were collected from 16 rangeland sites during the period 1998–2007 in Isfahan Province, Central Iran. Neural network models were designed using the monitored forage production values and available environmental data(including climate and topography data), and the performance of each network model was assessed using the mean estimation error(MEE), model efficiency factor(MEF), and correlation coefficient(r). The best neural network model was then selected and further applied to predict the forage production of rangelands in the future(in 2030 and 2080) under A1 B climate change scenario using Hadley Centre coupled model. The present and future forage production maps were also produced. Rangeland forage production exhibited strong correlations with environmental factors, such as slope, elevation, aspect and annual temperature. The present forage production in the study area varied from 25.6 to 574.1 kg/hm~2. Under climate change scenario, the annual temperature was predicted to increase and the annual precipitation was predicted to decrease. The prediction maps of forage production in the future indicated that the area with low level of forage production(0–100 kg/hm~2) will increase while the areas with moderate, moderately high and high levels of forage production(≥100 kg/hm~2) will decrease both in 2030 and in 2080, which may be attributable to the increasing annual temperature and decreasing annual precipitation. It was predicted that forage production of rangelands will decrease in the next couple of decades, especially in the western and southern parts of Isfahan Province. These changes are more pronounced in elevations between 2200 and 2900 m. Therefore, rangeland managers have to cope with these changes by holistic management approaches through mitigation and human adaptations.     

Meteorological impacts on evapotranspiration in different climatic zones of Pakistan

Arid regions are highly vulnerable and sensitive to drought. The crops cultivated in arid zones are at high risk due to the high evapotranspiration and water demands. This study analyzed the changes in seasonal and annual evapotranspiration(ET) during 1951–2016 at 50 meteorological stations located in the extremely arid, arid, and semi-arid zones of Pakistan using the Penman Monteith(PM) method. The results show that ET is highly sensitive and positively correlated to temperature, solar radiation, and wind speed whereas vapor pressure is negatively correlated to ET. The study also identifies the relationship of ET with the meteorological parameters in different climatic zones of Pakistan. The significant trend analysis of precipitation and temperature(maximum and minimum) are conducted at 95% confidence level to determine the behaviors of these parameters in the extremely arid, arid, and semi-arid zones. The mean annual precipitation and annual mean maximum temperature significantly increased by 0.828 mm/a and 0.014℃/a in the arid and extremely arid zones, respectively. The annual mean minimum temperature increased by 0.017℃/a in the extremely arid zone and 0.019℃/a in the arid zone, whereas a significant decrease of 0.007℃/a was observed in the semi-arid zone. This study provides probabilistic future scenarios that would be helpful for policy-makers, agriculturists to plan effective irrigation measures towards the sustainable development in Pakistan.     

轴流泵马鞍区水力性能与压力脉动测试与分析

为了分析轴流泵在马鞍区工况的运行特性,对一轴流泵不同工况下的外特性和压力脉动进行了测试,重点分析了轴流泵马鞍区水力特性和压力脉动特性.试验结果表明:模型泵H-Q曲线在0.50Q_d~0.60Q_d内表现出明显的马鞍形,且扬程在马鞍区内0.55Q_d工况时达到最小值,较0.60Q_d工况扬程降低0.33 m,为设计工况下扬程的5.5%;叶轮进口和泵出口处压力脉动具有较为明显的周期性,单个周期内压力脉动表现出明显的4波峰4波谷特征;0.55Q_d工况时,叶轮进口处压力脉动峰峰值为设计工况的2.3倍;各工况下导叶中间和出口处压力脉动规律较为复杂;叶轮进口压力脉动主频为叶片通过频率,0.55Q_d工况叶频处的幅值最大,高于设计工况27.6%.小流量工况下,导叶中间、导叶出口处压力脉动在频域内出现较多低频信号,压力脉动频率成分较复杂.泵出口压力脉动主频在1.00Q_d工况下明显表现为叶频.研究成果可为轴流泵不稳定运行特性的优化提供参考.     

农用无人机避障技术的应用现状及展望

随着中国农业现代化稳步推进,农业生产对农业机械的需求日益增长,农用无人机因其广泛适用于地面机械难以耕作的农业区域,在实践推广应用中已表现出明显特点和优势。随着精准农业航空技术的进一步发展,实现障碍物的自主识别与实时避障是农用无人机智能化发展的必然趋势之一。然而,农田作业环境复杂、障碍物类型多变,实现农用无人机的实时避障仍任重道远。为提高航空植保作业的安全性,对农田环境中的障碍物进行了分类,并提出了作业视场的避障分区及各区避障策略。通过对比分析国内外农用无人机避障技术的应用现状及各类避障传感器,并剖析中国农用无人机避障问题中存在的不足,展望未来农用无人机避障技术的研究方向和研究热点。该文可为中国农用无人机避障技术的有序发展提供参考。     

中华猕猴桃在中国潜在分布及其对气候变化响应的研究

中华猕猴桃为中国特有果种,由于其独特的口感和较高的经济价值,近年来种植规模逐年扩大。在引种过程中,由于缺乏合理的布局规划和适生性分析,出现了品种单一化、易感病虫害等问题。近年来四川、陕西、贵州、重庆和湖北等猕猴桃主产省份相继开展了猕猴桃气候适宜性区划的研究,但目前的研究多未考虑未来气候变化对猕猴桃种植分布的影响,且伴随着气候变化的加剧,已有的研究结果已不能完全适应实际生产的需求。本文运用生态位模型软件MaxEnt,模拟和预测气候变化背景下大尺度范围中华猕猴桃适生区分布及其变化的可行性,以利于科学地优化产业结构、促进产业发展。基于当前数据和IPCC AR5提出的3种气候情景以及中华猕猴桃的分布信息,采用MaxEnt生态位模型和ArcGIS预测了中华猕猴桃的适生区及未来的变化趋势,用受试者工作特征曲线(receiver operating characteristic curve,ROC曲线)检测模型精度、刀切法(Jackknife test)筛选主导环境变量。结果表明,基于当前和未来情景构建的中华猕猴桃地理分布模型的AUC(area under curve)值均达到"极好"的标准,说明模型预测结果可用于本研究。当前气候条件下,中华猕猴桃的高适生区主要在四川、陕西、重庆、湖北、贵州、浙江、湖南、安徽、河南、江苏和甘肃等省份,面积达1.01×10~6 km~2。中适生区则以高适生区为中心向外扩散,包括河南、湖北、安徽、江苏和山东等地,面积为6.79×10~5 km~2。RCP2.6和RCP4.5排放情景下,中华猕猴桃高适生区的分布、面积及中心点位置都有所不同,面积均呈增加趋势;RCP8.5排放情景下,高适生区面积呈减少趋势。RCP4.5和RCP8.5排放情景下,中华猕猴桃高适生区中心点均有向北移动趋势。MaxEnt模型对未来气候变化条件下中华猕猴桃适生区的准确模拟与预测具有潜在应用价值,对该果树的气候适宜性区划具有重要指导意义。     

中国农业氨排放的时空演变趋势与减排潜力分析

中国雾霾成因比发达国家更为复杂,人为源氨气(NH_3)污染是中国PM2.5指数被持续推高的重要因素,却一直被全社会所忽视。已有研究表明,人为源NH_3排放主要来自农业,农业NH_3减排是雾霾治理最经济有效的方法,因此,研究中国农业NH_3减排潜力对中国控氨治霾具有重要现实意义。本文基于各类统计年鉴和研究成果中的相关数据,参考《大气氨源排放清单编制技术指南(试行)》,构建农业NH_3减排潜力分析模型,应用排放因子法和情景分析法,测算并分析了中国2004—2013年农业NH_3排放演变和2020—2030年农业NH_3减排潜力。结果表明:1)2013年中国农业NH_3排放总量为1 193.92万t,比2004年增长18.59%。2)农业年NH_3排放总量在60万t以上的省市,2004年有河南、山东、河北和内蒙古4省,到2013年演变为河南、内蒙古、河北、山东、新疆和四川6省。3)趋势照常情景(business-as-usual,BAU)下,2020年、2025年和2030年中国农业NH_3排放将比2013年分别提高15.26%、23.60%和30.23%。4)减排情景下,2020年、2025年和2030年的中国农业NH_3排放将比BAU情景分别减少319.40万t、501.31万t和660.40万t,将比2013年分别下降11.49%、18.39%和25.08%。5)未来中国农业NH_3减排的关键取决于中国居民消费畜禽产品的数量和结构,其次是中国畜禽养殖的饲料营养水平改变。6)未来中国农业NH_3减排重点区域在河南、山东、河北、内蒙古和四川。由此可见,BAU情境下未来中国农业NH_3排放将失控,未来中国农业NH_3减排必须从大力削减重点区域排放和加速转变居民畜禽产品消费行为两方面入手。