Review of the detasseling techniques for maize(Zea mays L.)hybrid seed production

Maize(Zea mays L.)is a critical staple crop globally,integral to human consumption,food security,and agricultural product stability.The quality and purity of maize seeds,essential for hybrid seed production,are contingent upon effective detasseling.This study investigates the evolution of detasseling technologies and their application in Chinese maize hybrid seed production,with a comparative analysis against the United States.A comprehensive examination of the development and utilization of detasseling technology in Chinese maize hybrid seed production was undertaken,with a specific focus on key milestones.Data from the United States were included for comparative purposes.The analysis encompassed various detasseling methods,including manual,semi-mechanized,and cytoplasmic male sterility,as well as more recent innovations such as detasseling machines,and the emerging field of intelligent detasseling driven by unmanned aerial vehicles(UAVs),computer vision,and mechanical arms.Mechanized detasseling methods were predominantly employed by America.Despite the challenges of inflexible and occasionally overlooked,applying detasseling machines is efficient and reliable.At present,China’s detasseling operations in hybrid maize seed production are mainly carried out by manual work,which is labor-intensive and inefficient.In order to address this issue,China is dedicated to developing intelligent detasseling technology.This study emphasizes the critical role of detasseling in hybrid maize seed production.The United States has embraced mechanized detasseling.The application and development of manual and mechanized detasseling were applied later than those in the United States,but latest intelligent detasseling technologies first appeared in China.Intelligent detasseling is expected to be the future direction,ensuring the quality and efficiency of hybrid maize seed production,with implications for global food security.

中国阿勒泰地区鸟类物种编目、丰富度格局和区系组成

中国阿勒泰地区位于新疆北部,与哈萨克斯坦、俄罗斯、蒙古国交界,该区包含阿尔泰山及山前荒漠和绿洲,属于全球200个生物多样性最丰富和最具代表性生态区之一的阿尔泰–萨彦岭生物热点地区。阿勒泰地区生境多样,鸟类物种资源丰富。尽管以往曾在阿勒泰地区进行过一些鸟类调查,但对于该地区不同景观和生境类型中鸟类物种丰富度和分布尚无详尽报道。本文通过2013–2016年在中国境内阿尔泰山及山前平原地区对不同生境类型中的鸟类进行实地调查,并总结文献资料及观鸟爱好者的记录数据,重新整理了阿勒泰地区鸟类名录及地理分布,分析了鸟类的物种组成、区系成分;通过鸟类分布位点数据,选取气候、土地覆被类型、人类足迹指数及地形共4类环境因子作为自变量建立MaxEnt生态位模型,通过模拟77种鸟类的适宜分布区并叠加分布图层,获得了阿勒泰地区的鸟类物种丰富度分布格局。结果表明:阿勒泰共记录鸟类344种,隶属19目55科149属,其中雀形目163种。在垂直海拔带上,鸟类物种数分别为高山裸岩带24种,高山草甸带35种,山地森林草原带172种,低山灌木带130种,荒漠草原带84种,平原绿洲带173种,以及各海拔带的湿地与水域生境中水鸟92种;在区系成分上,以北方型鸟类为主(170种,占49.4%),其次为广布种(93种,占27.0%)。阿尔泰山地的鸟类区系呈现出西伯利亚动物区系特征,山前平原地区呈现蒙新区分布特征,因此,阿勒泰地区动物地理区系属于古北界欧洲–西伯利亚亚界阿尔泰–萨彦岭区阿尔泰亚区;山前平原地区属于古北界中亚亚界蒙新区西部荒漠亚区。Max Ent模型推测阿勒泰地区山前平原绿洲地区、山地森林草原带和低山灌木带具有较高的鸟类物种丰富度,尤其是额尔齐斯河流域下游的绿洲带宽阔,鸟类物种丰富,而高山区和荒漠生境中鸟类物种相对较少。模型预测的结果与实际调查情况相符。阿勒泰地区应采用生态友好的经济发展策略,加强对乔木和灌木的保护,这有助于维持较高的鸟类物种多样性。此外,降低生境破碎化不仅对该地区物种保护有重要作用,也对维持阿尔泰–萨彦岭生物热点地区的山地鸟类多样性有重要意义。

印度野牛在中国的分布及其栖息地适宜性分析

印度野牛(Bos gaurus)在中国分布在云南省南部和西藏藏南地区。2016年2–3月和2016年11–12月,我们在西双版纳州、普洱市及高黎贡山区域开展印度野牛调查,并对藏南地区进行文献调研,共获得47处印度野牛有效出现位点数据。目前云南地区印度野牛种群数量约180–210头,面临着严重的生存危机;在高黎贡山未发现印度野牛。利用印度野牛分布位点数据,选取地形、土地覆被类型、人类足迹指数、距水源和道路距离以及气候共5类14种因子作为自变量建立MaxEnt生态位模型,通过模拟云南和西藏印度野牛的适宜分布区,分析各环境因子对该物种分布的影响。结果表明:模型预测精度较高,平均AUC (area under the curve)值为0.994。印度野牛潜在适宜栖息地可划分为高适宜、次适宜、低适宜和不适宜4个等级。高适宜栖息地主要分布在云南省西双版纳和藏南地区,其中西双版纳部分镶嵌有次适宜和低适宜栖息地斑块,面积为4,987 km2;藏南部分高适宜栖息地面积为13,995 km2。次适宜栖息地主要分布于云南省南部、高黎贡山区域以及藏南高适宜栖息地区的边缘,总面积为32,778 km2。低适宜和不适宜栖息地区连接成片,位于云南省中部、北部地区和藏南地区北部。Jackknife检验结果显示,季节温度变化和等温线对印度野牛潜在分布区的影响较大,而地形因子和降水变化的影响较弱。遥感地物分类结果表明:橡胶林等人工经济林的种植占据了西双版纳野牛的适宜栖息地,降低了景观连接度。建议管理部门加大对天然林的保护力度,控制橡胶林等人工林在野牛适宜栖息地的扩张,提高景观连接度,以促进该物种种群的恢复。

中国哺乳动物形态、生活史和生态学特征数据集

物种特征是生物对生存环境适应和响应的表现,反映了物种的生态位、适合度和生态功能。特征数据库的建立和共享是研究生物多样性维持与丧失、物种进化与适应、生态过程与生态系统功能、物种对气候变化和人类干扰响应、种内与种间关系等的基础。中国是世界哺乳动物物种数最多的国家之一,然而目前中国还没有包含哺乳动物形态、生活史、生态学和地理分布等物种特征的数据库。我们系统查阅了文献和各种数据资料,共收集整理出中国有分布记录的754种哺乳动物(包括近些年野外绝灭种、分布存疑种)的体重、脑容量、体长、尾长、前臂长(翼手目)、后足长、耳长、性成熟时间、妊娠期、窝崽数、年窝数、世代长度、食性、活动模式、是否特有种、濒危等级、海拔范围、栖息地类型、栖息地宽度、动物地理界、生物群系、分布型、动物地理区划和分布省份或水域等24个生态特征数据。在这些特征中,除了分布省份或水域及是否特有种外,其余特征数据均存在不同程度的缺失,数据的完整度为30%‒100%。本数据库收录的哺乳动物种数为目前中国哺乳动物种数的上限,为中国哺乳动物研究提供了基础数据,推进中国哺乳动物多样性信息共享和深度挖掘。

An application of the LTP＿DSEF model to heavy precipitation forecasts of landfalling tropical cyclones over China in 2018

Recently, a track-similarity-based Dynamical-Statistical Ensemble Forecast(LTP_DSEF) model has been developed in an attempt to predict heavy rainfall from Landfalling Tropical cyclones(LTCs). In this study, the LTP_DSEF model is applied to predicting heavy precipitation associated with 10 LTCs occurring over China in 2018. The best forecast scheme of the model with optimized parameters is obtained after testing 3452 different schemes for the 10 LTCs. Then, its performance is compared to that of three operational dynamical models. Results show that the LTP_DSEF model has advantages over the three dynamical models in predicting heavy precipitation accumulated after landfall, especially for rainfall amounts greater than 250 mm. The model also provides superior or slightly inferior heavy rainfall forecast performance for individual LTCs compared to the three dynamical models. In particular, the LTP_DSEF model can predict heavy rainfall with valuable threat scores associated with certain LTCs, which is not possible with the three dynamical models. Moreover, the model can reasonably capture the distribution of heavier accumulated rainfall, albeit with widespread coverage compared to observations. The preliminary results suggest that the LTP_DSEF model can provide useful forecast guidance for heavy accumulated rainfall of LTCs despite its limited variables included in the model.