Este archivo ha sido creado el 18-12-2024 por Alfonso Moriana Elvira GENERAL INFORMATION ------------------ 1. Dataset title: Dataset of the article Identification of water stress conditions in olive trees through frequencies of trunk growth rate. 2. Authorship: Name: Martín-Palomo, María José Institution: Universidad de Sevilla Email:mjpalomo@us.es ORCID:0000-0002-0314-4363 Name:Corell, Mireia Institution:Universidad de Sevilla Email:mcorell@us.es ORCID:0000-0001-5955-0048 Name: Andreu, Luis Institution: Universidad de Sevilla Email:landreu@us.es ORCID:0000-0002-8741-127X Name: López-Moreno, YE Institution: Email:yestivenlm@outlook.com ORCID: Name: Galindo, A Institution: IMIDA Email:alejandro.galindo@carm.es ORCID:0000-0002-3724-2586 Name: Moriana, Alfonso Institution:Universidad de Sevilla Email:amoriana@us.es ORCID:0000-0002-5237-6937 DESCRIPTION ---------- 1. Dataset language: English 2. Abstract: El artículo presenta datos de 2 estaciones de riego en un olivar en seto adulto de arbequina (variedad de aceite) en 2018 y 2019. Se realizaron varios tratamientos de riego con diferentes niveles de estrés hídrico. Se caracterizó el potencial hídrico y la fluctuación del diámetro del tronco. El diámetro del tronco se midió de forma continua empleando dendrometros tipo D6. Los indicadores de la fluctuación del diámetro del tronco se compararon para ver su sensibilidad al estrés hídrico. La MDS no fue adecuado y la gran variabilidad de la TGR impedía un uso en continuo. En cambio el uso de tasas de frecuencias de aparición de diferentes rangos de valores sí podría ser empleado como indicador para la programación de riego 3. Keywords: Deficit irrigation, olive trees, irrigation scheduling 4. Date of data collection (fecha única o rango de fechas): Seasonal data of 2018 and 2019 5. Publication Date: 30-12-2020 6. Grant information: Agencia Española de Investigación (AEI) y Fondo Europeo de Desarrollo (FEDER) proyecto AGL2016-75794-C4-4-R. 7. Geographical location/s of data collection: Finca “El Morillo” en Carmona (37.49ºN, 􀀀 5.67ºW, Seville,España) ACCESS INFORMATION ------------------------ 1. Creative Commons License of the dataset: cc-BY 2. Dataset DOI: https://doi.org/10.12795/11441/166736 3. Related publication: Martín-Palomo, MJ, Corell, M, Andreu, L, López-Moreno, YE, Galindo, A, Moriana, A (2021). Identification of water stress conditions in olive trees through frequencies of trunk growth rate. Agricultural Water Management 247, doi: 10.1016/j.agwat.2020.106735 METHODOLOGICAL INFORMATION ----------------------- 1. Description of the methods used to collect and generate the data: The olive (Olea europaea L) orchard was 12 years old at the beginning of the experiment. This is a superhigh density (4 × 1.5 m) olive orchard cv Arbequina, irrigated daily through a single line with pressure compensated drips (3.4 l h􀀀 1) separated 0.4 m each. Preliminary works in failed installation of 1 m soil moisture probes allowed estimating that the soil was very variable in depth, with plots ranging between 0.4 m to more than 1 m and with a large number of stones. The soil had a sandy-loam texture with a high pH level (8.4) and a high percentage of carbonates (greater than 25%). The amount of P2O5 and K2O in the soil was adequate, and so was the percentage of organic matter (1.8%). The experimental design consisted of randomized complete blocks with 4 repetitions of 3 irrigation treatments. These treatments were based on phenology and water stress intensity. The irrigation season was divided into three different periods: from sprouting to the beginning of massive pit hardening, from pit hardening to the first week of September and from the first week of September to harvest. The beginning of pit hardening was estimated according to Rapoport et al. (2013). In brief, weekly longitudinal measurements of the fruit were determined from full bloom and the longitudinal growth showed to increase linearly until the maximum endocarp size was reached; then, it decreased sharply and pit hardening changes rapidly to increase the hardening rate (Rapoport et al., 2013). Recovery started the last week of August (day of the year, DOY, 242 in 2018 and DOY 237 in 2019). The amount of water applied in the irrigation treatments was based on the following approach, for a daily irrigation scheduling: • Control. No water stress conditions. The amount of water applied was estimated using the FAO approach for estimating the crop evapotranspiration (ETc) based on the crop coefficient (Kc) recommended for C´ordoba (Spain) and a reduction coefficient of 0.8. However, in previous works (Corell et al. 2019), this approach produced a slight decrease of the water potential in the mid-season. In order to ensure an optimum water status, the amount of water applied in the 2018 season was around 150% ETc and, in 2019, it was around 175% ETc. • Regulated deficit irrigation-1 (RDI-1). Deficit irrigation during the pit hardening period using dendrometers as irrigation decision tool. The irrigation scheduling was based on trunk growth rate (TGR) according to Corell et al. (2017, 2019). In order to clearly identify the water status, irrigation was provided when the TGR was lower than 􀀀 0.1 mm day􀀀 1 or when the weekly frequency of these values was greater than 20%. The irrigation rates changed according to the deviation for an optimum level and the phenological phase considered (Table 1). Regulated deficit irrigation-2 (RDI-2). This treatment had a maximum seasonal available amount of water (170 mm). In order to adjust irrigation scheduling to this maximum amount, the water stress was more severe during pit hardening than the in RDI-1 (Table 2). In addition, rehydration was stopped when the water applied reached maximum availability. However, during the 2019 season, the important scarcity of rainfall during spring and autumn forced to irrigate this treatment during rehydration with more than the maximum amount of water indicated. Afterwards, irrigation was provided from DOY 250 to maintain frequencies of TGR values below 􀀀 0.1 between 40% and 60%. These management actions increased the seasonal amount of water to 270 mm. 3. Software or instruments needed to interpret the data: Excel program 4. Information about instruments, calibration and standards: The water relations were characterized weekly using soil moisture, midday stem water potential (SWP), gas exchange measurements and continuously with trunk diameter fluctuations. All these methodologies were used in each plot of the experiment. Soil moisture was measured at a 0.2 and 0.4 m depth with FDR sensors (Echo20 HS10, Decagon Device, USA) which were installed around 30 cm from an emitter (Fern´andez et al., 1991). SWP was measured in a leaf per plot, with the leaves covered for at least 2 h before, using a pressure chamber (PMS instruments, model 1000, Albany, USA). Two different methodologies were used in gas exchange determinations because there were several damage problems. When an infrared gas analyzer was available (CI-340, CID BioScience, USA), this was the instrument used to measure net photosynthesis at midday in one fully expanded sunny leaf per plot. In some periods of the experiment, abaxial leaf conductance was measured with a porometer (SC-1, Decagon Device, USA) at midday in a fully expanded sunny leaf per plot. Trunk diameter fluctuations were measured in one tree per plot using a band dendrometer (5 μm accuracy, D6, UMS, Germany) attached to the main trunk. The band dendrometer is an extensiometric gauge that rests on a section of the trunk perimeter. The ends of the band were joined with Invar steel, an alloy of Ni and Fe with a thermal expansion coefficient close to zero (Katerji et al., 1994). The band and the Invar steel encircled the trunk. They have a Teflon net underneath to prevent friction with the bark surface. Each band dendrometer was plugged into a node (Widhoc smart solution SL, Spain) near the sensor. The nodes generated a stabilized power supply of 10 Vdc to the band dendrometer and measurements were made every 15 min These nodes were integrated by two different parts. One being the measurement interface and other the processing, recording and communication system. Data from each sensor node were sent wirelessly to the cloud. These devices provided a daily curve of shrinkage and swelling. Trunk growth rate (TGR) was calculated as the difference between two consecutive daily maximums (Goldhamer et al., 1999). Therefore, the TGR on day “n” was the difference between the daily maximum value on day “n + 1′′ and “n”. Daily TGRs are very changeable and the graphs generated are extremely confusing. In order to improve clarity, maximum diameter values were presented. In the resulting figure, the rate for each curve is the daily TGR. In this way, the statistical analysis can be easily presented in a more comprehensible way. Daily values of TGR were grouped in different ranges according to Corell et al. (2019) and the weekly frequency for each range was calculated. The selected ranges were: below 􀀀 0.3 mm day􀀀 1, between 􀀀 0.3 and 􀀀 0.2, between 􀀀 0.2 and 􀀀 0.1, between 􀀀 0.1 and 0.3, and greater than 0.3 mm day􀀀 1. Therefore, 5 weekly frequencies of each range were obtained on each date. In order to increase clarity in the paper, frequencies below 􀀀 0.3 mm day􀀀 1 were called “Severe FR”, between 􀀀 0.1 and 0.3 mm day􀀀 1 were “Good FR” and greater than 0.3 mm day􀀀 1 were “Alert FR”. These are the main ones that will be used when showing the results and in the discussion section. The final approach derived from the use of this frequencies was named SGA approach. 5. Environmental or experimental conditions: Climatic data were obtained from the web page of the “Sistema de informaci´on clim´atica para el regadío” (Spanish Agriculture Ministry) and originated in the Andalusian weather station network, at the station “Villanueva de Rio y Minas”, which is around 9.4 km away from the experimental plot (http://eportal.mapa.gob.es/websiar/SeleccionPa rametrosMap.aspx?dst=1). Potential evapotranpiration (ETo) and rain distribution were selected to characterize the locations. Average seasonal data were also obtained from these data for the period 2008–2017. Data showed that the rainfall and reference evapotranspiration (ETo) distribution were typical of a Mediterranean climate. Seasonal rainfall was greater than the average in 2018 (705 vs 538 mm) but lower in 2019 (328 mm). There was a lengthy dry period from early spring to mid autumn in both seasons with very low, even null, rainfall. ETo data were greater than 6 mm day􀀀 1 from late spring until the end summer in both seasons. The 2018 season presented a shorter period of maximum ETo values than the 2019 one. Deficit irrigation was performed, in both seasons, during dry and very high evaporative demand periods. The recovery of the trees also occurred under high evaporative demand conditions, generally greater than 4 mm day􀀀 1, and almost absence of rain, at least at the beginning. FILE OVERVIEW ---------------------- 1. Explain the file naming conversion, si es aplicable: There is a unique file with all data. Each sheet is data of each table at the reference publication 2. File list: File name:Dataset SDanchez-Piñero 2024 Description: Excel file 4. File format: Excel