Idan Bahat, Yishai Netzer, José M. Grünzweig, Amos Naor, Victor Alchanatis, Alon Ben-Gal, Ohali’av Keisar, Guy Lidor, Yafit Cohen
{"title":"空间尺度和季节因素如何影响基于热量的葡萄园水分状况估算和精确灌溉决策?","authors":"Idan Bahat, Yishai Netzer, José M. Grünzweig, Amos Naor, Victor Alchanatis, Alon Ben-Gal, Ohali’av Keisar, Guy Lidor, Yafit Cohen","doi":"10.1007/s11119-024-10120-5","DOIUrl":null,"url":null,"abstract":"<p>The crop water stress index (CWSI) is widely used for assessing water status in vineyards, but its accuracy can be compromised by various factors. Despite its known limitations, the question remains whether it is inferior to the current practice of direct measurements of Ψ<sub>stem</sub> of a few representative vines. This study aimed to address three key knowledge gaps: (1) determining whether Ψ<sub>stem</sub> (measured in few vines) or CWSI (providing greater spatial representation) better represents vineyard water status; (2) identifying the optimal scale for using CWSI for precision irrigation; and (3) understanding the seasonal impact on the CWSI-Ψ<sub>stem</sub> relationship and establishing a reliable Ψ<sub>stem</sub> prediction model based on CWSI and meteorological parameters. The analysis, conducted at five spatial scales in a single vineyard from 2017 to 2020, demonstrated that the performance of the CWSI- Ψ<sub>stem</sub> model improved with increasing scale and when meteorological variables were integrated. This integration helped mitigate apparent seasonal effects on the CWSI-Ψ<sub>stem</sub> relationship. R<sup>2</sup> were 0.36 and 0.57 at the vine and the vineyard scales, respectively. These values rose to 0.51 and 0.85, respectively, with the incorporation of meteorological variables. Additionally, a CWSI-based model, enhanced by meteorological variables, outperformed current water status monitoring at both vineyard (2.5 ha) and management cell (MC) scales (0.09 ha). Despite reduced accuracy at smaller scales, water status evaluation at the management cell scale produced significantly lower Ψ<sub>stem</sub> errors compared to whole vineyard evaluation. This is anticipated to enable more effective irrigation decision-making for small-scale management zones in vineyards implementing precision irrigation.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"80 1","pages":""},"PeriodicalIF":5.4000,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"How do spatial scale and seasonal factors affect thermal-based water status estimation and precision irrigation decisions in vineyards?\",\"authors\":\"Idan Bahat, Yishai Netzer, José M. Grünzweig, Amos Naor, Victor Alchanatis, Alon Ben-Gal, Ohali’av Keisar, Guy Lidor, Yafit Cohen\",\"doi\":\"10.1007/s11119-024-10120-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The crop water stress index (CWSI) is widely used for assessing water status in vineyards, but its accuracy can be compromised by various factors. Despite its known limitations, the question remains whether it is inferior to the current practice of direct measurements of Ψ<sub>stem</sub> of a few representative vines. This study aimed to address three key knowledge gaps: (1) determining whether Ψ<sub>stem</sub> (measured in few vines) or CWSI (providing greater spatial representation) better represents vineyard water status; (2) identifying the optimal scale for using CWSI for precision irrigation; and (3) understanding the seasonal impact on the CWSI-Ψ<sub>stem</sub> relationship and establishing a reliable Ψ<sub>stem</sub> prediction model based on CWSI and meteorological parameters. The analysis, conducted at five spatial scales in a single vineyard from 2017 to 2020, demonstrated that the performance of the CWSI- Ψ<sub>stem</sub> model improved with increasing scale and when meteorological variables were integrated. This integration helped mitigate apparent seasonal effects on the CWSI-Ψ<sub>stem</sub> relationship. R<sup>2</sup> were 0.36 and 0.57 at the vine and the vineyard scales, respectively. These values rose to 0.51 and 0.85, respectively, with the incorporation of meteorological variables. Additionally, a CWSI-based model, enhanced by meteorological variables, outperformed current water status monitoring at both vineyard (2.5 ha) and management cell (MC) scales (0.09 ha). Despite reduced accuracy at smaller scales, water status evaluation at the management cell scale produced significantly lower Ψ<sub>stem</sub> errors compared to whole vineyard evaluation. 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How do spatial scale and seasonal factors affect thermal-based water status estimation and precision irrigation decisions in vineyards?
The crop water stress index (CWSI) is widely used for assessing water status in vineyards, but its accuracy can be compromised by various factors. Despite its known limitations, the question remains whether it is inferior to the current practice of direct measurements of Ψstem of a few representative vines. This study aimed to address three key knowledge gaps: (1) determining whether Ψstem (measured in few vines) or CWSI (providing greater spatial representation) better represents vineyard water status; (2) identifying the optimal scale for using CWSI for precision irrigation; and (3) understanding the seasonal impact on the CWSI-Ψstem relationship and establishing a reliable Ψstem prediction model based on CWSI and meteorological parameters. The analysis, conducted at five spatial scales in a single vineyard from 2017 to 2020, demonstrated that the performance of the CWSI- Ψstem model improved with increasing scale and when meteorological variables were integrated. This integration helped mitigate apparent seasonal effects on the CWSI-Ψstem relationship. R2 were 0.36 and 0.57 at the vine and the vineyard scales, respectively. These values rose to 0.51 and 0.85, respectively, with the incorporation of meteorological variables. Additionally, a CWSI-based model, enhanced by meteorological variables, outperformed current water status monitoring at both vineyard (2.5 ha) and management cell (MC) scales (0.09 ha). Despite reduced accuracy at smaller scales, water status evaluation at the management cell scale produced significantly lower Ψstem errors compared to whole vineyard evaluation. This is anticipated to enable more effective irrigation decision-making for small-scale management zones in vineyards implementing precision irrigation.
期刊介绍:
Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming.
There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to:
Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc.
Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc.
Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc.
Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc.
Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc.
Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.