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
{"title":"Multisite evaluation of microtensiometer and osmotic cell stem water potential sensors in almond orchards","authors":"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","doi":"10.1016/j.compag.2024.109547","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109547"},"PeriodicalIF":7.7000,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers and Electronics in Agriculture","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0168169924009384","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
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.