How do spatial scale and seasonal factors affect thermal-based water status estimation and precision irrigation decisions in vineyards?

IF 5.4 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Idan Bahat, Yishai Netzer, José M. Grünzweig, Amos Naor, Victor Alchanatis, Alon Ben-Gal, Ohali’av Keisar, Guy Lidor, Yafit Cohen
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Abstract

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.

Abstract Image

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?
作物水分胁迫指数(CWSI)被广泛用于评估葡萄园的水分状况,但其准确性会受到各种因素的影响。尽管CWSI存在已知的局限性,但问题是它是否不如目前直接测量少数代表性葡萄树Ψ茎的方法。本研究旨在解决三个关键的知识空白:(1) 确定Ψ茎(在少数葡萄树上测量)还是 CWSI(提供更大的空间代表性)更能代表葡萄园的水分状况;(2) 确定使用 CWSI 进行精确灌溉的最佳尺度;(3) 了解季节对 CWSI 与Ψ茎关系的影响,并根据 CWSI 和气象参数建立可靠的Ψ茎预测模型。从2017年到2020年,在单一葡萄园的五个空间尺度上进行的分析表明,CWSI-Ψ干模型的性能随着尺度的增加和气象变量的整合而提高。这种整合有助于减轻对 CWSI-Ψstem 关系的明显季节性影响。葡萄树和葡萄园尺度的 R2 分别为 0.36 和 0.57。加入气象变量后,这些值分别上升到 0.51 和 0.85。此外,在葡萄园(2.5 公顷)和管理单元(0.09 公顷)范围内,基于 CWSI 的模型在气象变量的增强下,优于当前的水分状况监测。尽管较小尺度的精确度有所降低,但与整个葡萄园的评估相比,管理单元尺度的水分状况评估产生的Ψ干误差要低得多。预计这将为实施精确灌溉的葡萄园小规模管理区提供更有效的灌溉决策。
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来源期刊
Precision Agriculture
Precision Agriculture 农林科学-农业综合
CiteScore
12.30
自引率
8.10%
发文量
103
审稿时长
>24 weeks
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.
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