Physiological tools for irrigation scheduling in grapevine (Vitis vinifera L.)

Cifré, J.; Bota, Josefina; Escalona, José M.; Medrano, H.; Flexas, Jaume

doi:10.1016/j.agee.2004.10.005

Cited by 297 publications

(263 citation statements)

References 68 publications

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“…T 3 remained under 0.05 mol H 2 O·m −2 ·s −1 , like the most water stressed treatment, and T 1 and T 2 remained constant in almost all measurement days between 0.15 and 0.05 mol H 2 O·m −2 ·s −1 , except for DOYs 54 and 61, when T 1 was close to T 0 ( Figure 6). Similar levels of g s were found by Cifre et al [34], Grant et al [16], and Jara-Rojas et al [35], where well-watered vines had over 0.15 mol H 2 O·m −2 ·s −1 , vines under moderate water stress were between 0.05 and 0.15 mol H 2 O·m −2 ·s −1 , and below 0.05 mol H 2 O·m −2 ·s −1 were vines under severe water stress. The leaf temperature (T leaf ) was registered with the thermocouple placed inside the leaf chamber of the infrared gas analyzer (LI-6400, LI-COR Inc., Lincoln, NB, USA).…”

Section: Results Of Plant-based Variablessupporting

confidence: 78%

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Selecting Canopy Zones and Thresholding Approaches to Assess Grapevine Water Status by Using Aerial and Ground-Based Thermal Imaging

et al. 2016

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Aerial and terrestrial thermography has become a practical tool to determine water stress conditions in vineyards. However, for proper use of this technique it is necessary to consider vine architecture (canopy zone analysis) and image thresholding approaches (determination of the upper and lower baseline temperature values). During the 2014-2015 growing season, an experimental study under different water conditions (slight, mild, moderate, and severe water stress) was carried out in a commercial vineyard (Vitis vinifera L., cv. Carménè). In this study thermal images were obtained from different canopy zones by using both aerial (>60 m height) and ground-based (sunlit, shadow and nadir views) thermography. Using customized code that was written specifically for this research, three different thresholding approaches were applied to each image: (i) the standard deviation technique (SDT); (ii) the energy balance technique (EBT); and (iii) the field reference temperature technique (FRT). Results obtained from three different approaches showed that the EBT had the best performance. The EBT was able to discriminate over 95% of the leaf material, while SDT and FRT were able to detect around 70% and 40% of the leaf material, respectively. In the case of canopy zone analysis, ground-based nadir images presented the best correlations with stomatal conductance (g s ) and stem water potential (Ψ stem ), reaching determination coefficients (r 2 ) of 0.73 and 0.82, respectively. The best relationships between thermal indices and plant-based variables were registered during the period of maximum atmospheric demand (near veraison) with significant correlations for all methods.

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Section: Results Of Plant-based Variablessupporting

confidence: 78%

“…Figure 6). Similar levels of gs were found by Cifre et al [34], Grant et al [16], and Jara-Rojas et al [35], where well-watered vines had over 0. …”

Section: Results Of Plant-based Variablessupporting

confidence: 74%

Selecting Canopy Zones and Thresholding Approaches to Assess Grapevine Water Status by Using Aerial and Ground-Based Thermal Imaging

et al. 2016

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“…Thus, all deficit irrigation strategies improve WUE i . However, even for a physiological analysis, when different irrigation strategies are compared in WUE i terms, no differences were found between them (Cifre et al 2005;Chaves et al 2007; De la Hera et al 2007). Moreover, WUE i could not be an appropriate proxy for the real plant water use efficiency, since this value does not describe the whole canopy behavior of g s during the whole day and at the whole plant level.…”

Section: Effects On Plant Physiologymentioning

confidence: 93%

“…On the other hand, g s play a critical role on the regulation of water loss. Under mild to moderate water stress, g s reduction is the earliest response Medrano et al 2003) and has been identified in grapevine as a suitable parameter to detect the degree of water stress (Cifre et al 2005;Medrano et al 2003). In general, no differences have been observed in g s between partial root zone drying and deficit irrigation strategies when the same amount of water was applied to the soil (Sadras 2009).…”

Section: Effects On Plant Physiologymentioning

confidence: 99%

Improving water use efficiency of vineyards in semi-arid regions. A review

Medrano

Tomás

Martorell

et al. 2014

Agron. Sustain. Dev.

249

158

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Water is critical for viticulture sustainability since grape production, quality and economic viability are largely dependent on water availability. The total water consumption of vineyards, 300 to 700 mm, is generally higher than the annual average precipitation in many viticultural areas, which induces a risk for sustainability of vineyards. Improving vineyard water use efficiency (WUE) is therefore crucial for a sustainable viticulture industry in semi-arid regions. Increased sustainability of water resources for vineyards can be achieved using both agronomical technology and cultivar selection. Here, we review advances in grapevine water use efficiency related to changes in agronomical practices and genetic improvements. Agronomical practices focus on increasing green water use by increasing soil water storage capacity, reducing direct soil water loss, or limiting early transpiration losses. Cover crops for semi-arid areas show a favorable effect, but careful management is needed to avoid excessive water consumption by the cover crop. Canopy management practices to reduce excessive water use are also analyzed. This is a genetic based review focused on identifying cultivars with higher WUE.

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“…As far as the authors are aware, this is the first attempt to contribute to ' precision viticulture ' through the use of a multi-agent cellular automaton that take into account detailed information concerning both resources (energy, water) as well as important botanical features (leaves, buds, etc). In the longer term, fitting the data and making predictions about growth will need to be related to quality of wine (15). With the inclusion of a wine quality module vintage ratings as well could be predicted under different possible weather and other atmospheric conditions.…”

Section: Resultsmentioning

confidence: 99%