杏园微张力计和渗透细胞茎水势传感器的多点评估

IF 7.7 1区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Computers and Electronics in Agriculture Pub Date : 2024-11-05 DOI:10.1016/j.compag.2024.109547
Isaya Kisekka , Srinivasa Rao Peddinti , Peter Savchik , Liyuan Yang , Mae Culumber , Khalid Bali , Luke Milliron , Erica Edwards , Mallika Nocco , Clarissa A. Reyes , Robert J. Mahoney , Kenneth Shackel , Allan Fulton
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引用次数: 0

摘要

面对气候变化,优化杏仁灌溉管理对于确保坚果生产和水资源的长期可持续性至关重要。要实现优化灌溉管理,持续监测植物水分状况对安排灌溉至关重要。使用茎干水势(SWP)来衡量杏仁等多年生木本植物的水分状况是一种广为接受的做法。然而,通常用来进行这些测量的压力室(PC)是劳动密集型的,如果不付出大量额外的劳动,就无法提供连续的数据。在这项研究中,我们评估了最近开发的两种茎干水势传感器(微张力计 [MT] 和渗透细胞 [OC]),这两种传感器嵌入茎干边材组织(通常在树干或树枝中)后几乎可以连续测量 SWP。在加利福尼亚中央谷地的九个商业杏仁园中对 SWP 传感器进行了评估。使用名为 FITEVAL 的统计软件将两种传感器获得的 SWP 值与 PC 测量值进行了比较。总体而言,MT 和 OC 传感器的性能分别从良好到可接受以及从可接受到不可接受。MT 传感器的精度更高,其纳什-苏特克利夫效率系数 (NSE) 为 0.84(95 % CI:0.78-0.88),均方根误差 (RMSE) 为 -0.24 兆帕(95 % CI:-0.21 至 -0.28 兆帕),而 OC 传感器的 NSE 为 0.68(95 % CI:0.61-0.74),RMSE 为 -0.32 兆帕(95 % CI:-0.29 至 -0.35 兆帕)。MT 传感器具有提供亚小时数据和显示灌溉后树木从水胁迫中恢复的额外优势,因此在杏园精确水分管理方面具有潜在优势。如果得到广泛应用,SWP 传感器有可能优化杏仁生产中的用水。
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Multisite evaluation of microtensiometer and osmotic cell stem water potential sensors in almond orchards
In the face of climate change, optimization of almond irrigation management is critical for ensuring the long-term sustainability of nut production and water resources. To achieve optimal irrigation management, continuous monitoring of the plant water status is critical in scheduling irrigation. It is a widely accepted practice to use stem water potential (SWP) as a measure of plant water status in woody perennials like almonds. However, the pressure chamber (PC) commonly used to make these measurements is labor-intensive and does not provide continuous data without significant additional labor. In this study, we evaluated two recently developed stem water potential sensors (Microtensiometer [MT], and Osmotic Cell [OC]), both of which can measure the SWP nearly continuously when embedded in stem sapwood tissue (typically in the trunk or branch of a tree). SWP sensors were evaluated in nine commercial almond orchards in the Central Valley of California. The SWP values obtained from both sensors were compared to the values measured using a PC using statistical software called FITEVAL. Overall, sensor performance varied from good to acceptable and from acceptable to unacceptable for MT and OC sensors respectively. The MT sensors demonstrated higher accuracy with a Nash-Sutcliff Coefficient of Efficiency (NSE) of 0.84 (95 % CI: 0.78–0.88) and a Root Mean Square Error (RMSE) of −0.24 MPa (95 % CI: −0.21 to −0.28 MPa), while the OC sensor had an NSE of 0.68 (95 % CI: 0.61–0.74) and an RMSE of −0.32 MPa (95 % CI: −0.29 to −0.35 MPa). MT sensors exhibited the added advantage of providing sub-hourly data and displaying tree recovery from water stress following irrigation, positioning them as potentially superior for precision almond orchard water management. If widely adopted, SWP sensors have the potential to optimize water use in almond production.
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来源期刊
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture 工程技术-计算机:跨学科应用
CiteScore
15.30
自引率
14.50%
发文量
800
审稿时长
62 days
期刊介绍: Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.
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