A wireless sensor network for precision viticulture: The NAV system

In the last decade, wireless technologies have been increasingly applied in precision agriculture. Wireless monitoring systems in particular have been used in precision viticulture in order to understand vineyard variability, and therefore suggest appropriate management practices for improving the quality of the wines.The NAV (Network Avanzato per il Vigneto – Advanced Vineyard Network) system is a wireless sensor network designed and developed with the aim of remote real-time monitoring and collecting of micro-meteorological parameters in a vineyard. The system includes a base agrometeorological station (Master Unit) and a series of peripheral wireless nodes (Slave Units) located in the vineyard. The Master Unit is a typical single point monitoring station placed outside the vineyard in a representative site to collect agrometeorological data. It utilizes a wireless technology for data communication and transmission with the Slave Units and remote central server. The Slave Units are multiple stations placed in the vineyard and equipped with agrometeorological sensors for site-specific environmental monitoring, which store and transmit data to the Master Unit. Software was developed for setup and configuration functionality. A graphical user interface operating on the remote central server was implemented to collect and process data and provide real-time control. The devices were tested in a three-step process: hardware functionality and data acquisition, energy consumption and communication. The NAV system is a complete monitoring system that gave flexibility for planning and installation, which fully responded to the objectives of the work in terms of energy efficiency and performance.     

华北平原灌溉麦田水分利用效率的SEM多因素影响研究

水分利用效率(WUE)常被嵌入到多种生态系统模型中,用于评估生态系统对气候变化的响应。然而,自然条件下多种因素不仅直接影响WUE,还通过影响冠层结构等间接影响WUE,其中的影响机制仍不明晰。为了明确多种因素对冬小麦WUE的协同影响,本研究基于2015年(温暖湿润年)和2016年(温暖干旱年)涡度相关系统观测的小麦关键生育期(返青、拔节、抽穗、灌浆)的数据,分析了WUE的变化,并借助结构方程模型(SEM),以叶面积指数(LAI)为中间变量,分析了多种因素[净辐射(R_n)、空气温度(T_a)、饱和水汽压差(VPD)、风速(WS)、土壤含水量(SWC)]对WUE的影响机制。结果表明,2015年平均WUE为1.52g(C)·kg~(-1)(H_2O),2016年平均WUE为1.22g(C)·kg~(-1)(H_2O)。不管在温暖湿润年还是温暖干旱年, T_a、LAI和VPD均是影响WUE的主要因素。WUE随LAI增加而增加, Ta增加也有助于提高WUE,而当温度相近时, VPD增加会降低WUE。T_a、LAI和VPD对WUE的影响在温暖湿润年和温暖干旱年重要性程度不同,温暖湿润年最重要的影响因素为LAI,温暖干旱年为T_a; VPD在温暖湿润年既直接影响WUE,同时又通过影响LAI的变化间接作用于WUE,但在温暖干旱年仅具有直接影响。R_n在温暖干旱年和温暖湿润年表现也不相同:在温暖湿润年对WUE具有显著的影响,在温暖干旱年影响不显著,这与温暖湿润年降雨量大及降雨频次高有关。显然,模拟WUE时考虑不同年份气象条件会使结果更为准确。WS未对WUE产生显著的影响,潜在原因可能是其对冠层上部接收辐射充足的叶片影响较大,而对冠层内部叶片无显著影响。农田生态系统不同生育阶段对辐射、温度等的耐受性及响应方式不同,SEM可以将LAI设置为中间变量以综合这种阶段性的变化,因此,对于冠层结构季节变幅大的生态系统, SEM是研究其环境控制机制的有力工具。这些研究结果可为今后精确模拟生态系统WUE以及预测WUE对气候变化的响应提供科学依据。