Este archivo ha sido creado el 27-07-2023 por Alfonso Moriana
 

GENERAL INFORMATION
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1. Dataset title:Managing Water Stress in Olive (Olea europaea L.) Orchards
Using Reference Equations for Midday Stem Water Potential [DATASET]


2. Authorship:

Name: Marta Sánchez-Piñero
	Institution: University of Seville
	Email:msanchez20@us.es
	ORCID:  

        Name: María José Martín-Palomo
	Institution: University of Seville
	Email:mjpalomo@us.es
	ORCID:0000-0002-0314-4363 

        Name: Pedro Castro-Valdecantos
	Institution: University of Seville
	Email:pcvaldecantos@us.es
	ORCID:0000-0002-8543-9391

        Name: Alfonso Moriana
	Institution: University of Seville
	Email:amoriana@us.es
	ORCID:0000-0002-5237-6937 

        Name: Mireia Corell
	Institution: University of Seville
	Email:mcorell@us.es
	ORCID:0000-0001-5955-0048
	




DESCRIPTION
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1. Dataset language:
Español


2. Abstract:
Dataset is organied in different sheets. In each one, data for the figures of the publication has been included.  


3. Keywords: 
Water relations, water potential, baseline, irrigation scheduling, olive trees


4. Date of data collection (fecha única o rango de fechas):
Several seasons (2020, 2021, 2022)

5. Publication Date:
2023


6. Grant information:


	Grant Agency:Junta de Andalucia
	Grant Number:p20-00492


7. Geographical location/s of data collection:

Coria del Río (near Seville, Spain, 37170 N, 630 W, 30 m altitude)


ACCESS INFORMATION
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1. Creative Commons License of the dataset:
CC-0


2. Dataset DOI:https://doi.org/10.12795/11441/148243



3. Related publication:
Sánchez-Piñero, M., Corell, M., Moriana, A., Castro-Valdecantos, P., Martín-Palomo, M.J. 2023. Managing water stress in olive (Olea europaea L.) orchards using reference equations for midday stem water potential. Horticulturae 9, 563, 

4. Link to related datasets:
[Recomendado si es aplicable | (Ej. Otros conjuntos de datos del mismo proyecto) siguiendo el formato.] 

	DOI/URL: doi 10.3390/horticulturae9050563.



VERSIONING AND PROVENANCE
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1. Last modification date:



2. Were data derived from another source?:
NO 


3. Additional related data not included in this dataset:



METHODOLOGICAL INFORMATION
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1. Description of the methods used to collect and generate the data:
Water relations were characterized using the SWP and leaf conductance. The SWP
was measured with the pressure bomb technique (PMS 1000, Albany, OR, USA) on fully
expanded, healthy leaves that were covered around 2 h before the measurement was taken,
and this was performed on one tree per plot at midday. Leaf conductance was measured at
the same time as the SWP with a porometer (SC-1, Decagon, Pullman,WA, USA) on fully
expanded sunny leaves. Both measurements were taken weekly. In order to determine
the different SWP reference equations, the maximum value of leaf conductance every
date was identify. The leaf conductance reduction was estimated as the ratio between the
measurement on each plot and this maximum value. The SWP values used to determine
the reference equations were the average of the SWP on the same date and in the same leaf
conductance reduction interval. Three different levels of water stress were selected: values
of leaf conductance between 90–100% of the daily maximum, 70–80%, and 45–55%.
Soil moisture was measured in each repetition with a portable FDR probe (HH2.
Delta-T, Cambridge, UK) in 1 m depth. Access tubes were located in the irrigation line
around 30 cm far from a drip. In addition, the percentage of soil cover was estimated at
the beginning, after pruning, and at the end of each season. Three measurements of the
horizontal dimensions in each plot were carried out with a scope.
Reference equations were estimated using linear regressions of these data vs. maximum
daily temperature or maximum daily vapor pressure deficit (VPD). Meteorological
data were obtained from the “La Puebla” Station, which is approximately 6 km away
from the experimental station. Slopes of these regressions were compared to published
baselines, for temperature (Corell et al 2016) and for VPD (Shackel et al 2021) using a T-test (p < 0.05).

2. Data processing methods:


3. Software or instruments needed to interpret the data:


4. Information about instruments, calibration and standards:



5. Environmental or experimental conditions:



6. Quality-assurance procedures performed on the data:




FILE OVERVIEW 
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[Se han de mencionar todos los archivos incluidos en el conjunto de datos, con el nombre y la extensión (.csv, .pdf, etc.) de cada archivo. Incluya la estructura de directorios].

1. Explain the file naming conversion, si es aplicable:
There is just one file. In this file, data were separated into sheets. Each sheets has the name of the studied variable.


2. File list:
There is just one file   


3. Relationship between files:
There is just one file  


4. File format:
EXCEL


5. If the dataset includes multiple files, specify the directory structure and relationships between the files:
There is just one file      



SPECIFIC INFORMATION FOR TABULAR DATA
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1. Name file:



2. Number of rows and columns: 



3. Variables list:


4. Codes or symbols for missing data:

        
5. Special formats or abbreviations used:



MORE INFORMATION
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