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INTRODUCTION
Water influences
vegetative growth and reproductive growth, yield and fruit composition through
their effects on fruit size and on photosynthetic activity and Water
availability is a critical factor for assessing the impact of climate change on
agriculture and agricultural water management. Agriculture producers and crop
managers place great care on the water supply to their crops and, as water
availability might become less reliable under the actual forecasts of climate
change [1], they try to maximize the water use efficiency (WUE) even at the
cost of some yield loss to gain environmental and economic sustainability of
their operations. Many evaluations of water use efficiency are based on
instantaneous measurements on the assumption that they are representative of
whole-plant WUE. Examples of instantaneous measurements are stomatal
conductance, leaf photosynthesis and transpiration and canopy temperature.
These measurements either can´t be automated and continuously recorded or it
would be technologically too complex and extremely expensive. The relationship
between instantaneous and whole-plant values sometimes are clear but often it
is not, and the lack of correspondence is a methodological limitation to the
applicability of the obtained results [2]. On woody plants it is possible to
evaluate WUE from measurements of sap flow rate (SF) and diameter changes in
conductive organs (maximum daily shrinkage (MDS) was the parameter used) that
are considered good predictors of plant water status. Sap flow rate and
diameter changes can be continuously and automatically measured with high
resolution and recorded online; the technological development has made their
use reliable and affordable.
SF and MDS are
reported to be closely related to climatic variables such as solar radiation,
temperature, vapor pressure deficit and evaporative demand of the atmosphere
which is evaluated by the reference evapotranspiration (ETo) as computed by
Allen et al. [3]. Given the relationships between SF and MDS with weather
parameters that are included in the calculation of ETo [4,5], we assumed a
statistical interdependency between a series of physiological indicators
chronologically recorded and a simultaneously time spaced ETo data. The
assumption was tested on several grapevines, with different amounts of
available soil water that were fitted with dendrometers and sap flow needles.
An in situ meteorological station
collected weather data and automatically computed daily ETo. All data was sent
wireless to a gateway that made it available online. The relationship between
physiological indicators series and ETo series was tested by event coincidence
analysis (ECA) that is concerned with quantifying the statistical
interrelationships between pairs of event series [6].
SF and MDS could
predict clearly the water status of the grapevines with highly significant
differences and they both correlated significantly with ETo. ECA showed that
and ETo high or low event triggered equivalent SF or MDS events, but not both
simultaneously, one step time later with a coincidence far higher than would be
expected by mere randomness. The results show that SF and MDS are good
predictors of water status and they are dependent on ETo but they do not
respond equally to ETo. SF and MDS are still complex predictors that can be
recorded from a very limited number of plants with all the problems related to
extrapolations to a large population. On other hand, ETo is a common meteor
computed from data of meteorological stations, today almost conspicuous everywhere
and relying data online on real time, that is valid for large areas. Following
the course of ETo along the crop growing seasons, the manager knows when and
how to intervene preventing damaging crop losses, especially when ETo is high
that will trigger hours later events of high plant water loss.
This analysis
establishes empirical relationships, but it does not explain the mechanisms
underlying the phenomena that are dependent on several factors both biotic and
abiotic. However, the use of computed ETo
to plan just
on time
A detailed report
was found by Oliveira et al. [7].
1. Benzie M, Carter TR, Carlsen H,
Taylor R (2019). Cross-border climate change impacts: Implications for the
European Union. Regional Environmental Change 19: 1-14.
2. Medrano H, Tomas M, Martorell S,
Flexas J, Hernández E, et al. (2015) From leaf to whole-plant water use
efficiency (WUE) in complex canopies: Limitations of leaf WUE as a selection
target. Crop J 3: 220-228.
3. Allen RG, Pereira L, Raes D, Smith
M (1998). Crop evapotranspiration. Guidelines for computing crop water
requirements. FAO Irrigation and Drainage 56. Rome, Italy, pp: 15-27.
4. Conesa MR, Torres R, Domingo R,
Navarro H, Soto F (2016) Maximum daily trunk shrinkage and stem water potential
reference equations for irrigation scheduling in table grapes. Agriculture
Water Management 172: 51-61.
5. Mirás-Avalos JM, Pérez-Sarmiento
F, Alcobendas R, Alarcón JJ, Mounzer O (2016) Reference values of maximum daily
trunk shrinkage for irrigation scheduling in mid-late maturing peach trees.
Agriculture Water Management 171: 31-39.
6. Donges JF, Schleussner CF,
Siegmund JF, Donner RV (2016). Event coincidence analysis for quantifying
statistical interrelationships between event time series. Eur Phys J Special
Topics 225: 471-487.
7. Oliveira MT, Oliveira AC, Castro
CB (2017) Dependence of sap flow and stem diameter fluctuation of grapevines on
reference evapotranspiration: An event coincidence analysis. Adv Plants Agric
Res 7: 279-287.
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