Este archivo ha sido creado el 18-12-2024 por Alfonso Moriana Elvira GENERAL INFORMATION ------------------ 1. Dataset title: Dataset of the article Dataset. Trunk growth rate frequencies as water stress indicator in almond trees 2. Authorship: Name: Martín-Palomo, María José Institution: Universidad de Sevilla Email:mjpalomo@us.es ORCID:0000-0002-0314-4363 Name: Andreu, Luis Institution: Universidad de Sevilla Email:landreu@us.es ORCID:0000-0002-8741-127X Name: Pérez-López, D Institution: Universidad Politécnica de Madrid Email:david.perezl@upm.es ORCID:0000-0002-9293-4892 Name: Centeno, A Institution: Universidad Politécnica de Madrid Email:ana.centeno@upm.es ORCID:0000-0001-5592-5447 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 Name:Corell, Mireia Institution:Universidad de Sevilla Email:mcorell@us.es ORCID:0000-0001-5955-0048 DESCRIPTION ---------- 1. Dataset language: English 2. Abstract: El artículo presenta datos de tres estaciones de riego (2017, 2018 y 2019) en un a finca comercial de almendros de Dos Hermanas (Sevilla). En esta finca se llevaron a cabo cuatro tratamientos de riego diferentes, un control sin condiciones de estrés hídrico y tres tratamientos de riego deficitario. Se monitorizó el estado hídrico de los árboles empleando el potencial hídrico foliar (cámara de Scholander) y la variación del diámetro del tronco (con sensores tipo D6). El objetivo era evaluar el uso de frecuencias de ciertos valores de la tasa de crecimiento para monitorizar el riego. Se obtuvieron diferentes rangos de frecuencias que potencialmente podrían ser útiles. Sin embargo, se observó una evolución a lo largo del ensayo fruto, posiblemente, del crecimiento de los árboles que no permitió concluir en el uso de unos rangos en concreto 3. Keywords: Deficit irrigation, almond trees, irrigation scheduling 4. Date of data collection (fecha única o rango de fechas): Seasonal data of 2017, 2018 and 2019 5. Publication Date: 03-06-2022 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 “La Florida” en Dos Hermanas (37.23 °N, -5.91 °W, Seville,España) ACCESS INFORMATION ------------------------ 1. Creative Commons License of the dataset: cc-BY 2. Dataset DOI: https://doi.org/10.12795/11441/166719 3. Related publication: Martin-Palomo, MJ, Andreu, L, Pérez-López, D, Centeno, A, Galido A, Moriana, A, Corell, M. 2022. Trunk growth rate frequencies as water stress indicator in almond trees. Agricultural Water Management 271 DOI 10.1016/j.agwat.2022.107765 METHODOLOGICAL INFORMATION ----------------------- 1. Description of the methods used to collect and generate the data: The experiment was carried out during 3 consecutive seasons (from 2017–2019) in a commercial orchard located in Finca "La Florida" (37.23ºN, 􀀀 5.91ºW, Dos Hermanas, Seville, Spain). Orchard was 7 years old at the beginning of the experiment (2017 season) with a vase training system. Trees were 6 m x 8 m apart in the orchard, with coupled rows of 2 different cultivars (cv "Guara" and "Vairo" on GF-677 rootstock) to improve the pollination process. The experimental plots were a rectangle of 4 * 3 trees (4 rows of 3 trees each). Central rows were cv “Vairo” and measurements were taken from the two central trees of both rows of cv "Vairo". Trees around these two measured ones were guard lines. The experimental design consisted of randomized complete blocks with 4 repetitions of 4 irrigated treatments. Each block was included in 4 rows because the main soil variability was between rows. Blocks were located in adjacent rows. The irrigation system was a single line of drips (3.4 L h􀀀 1) located 0.4 m apart. Irrigation scheduling was performed weekly with a remote programming device (Ciclon, C-146 v 3.53, Maher, Almeria, Spain). The soil was clay loam with over 1 m depth, a high percentage of carbonate (higher than 30%) and pH around 8. The percentage of organic matter in the 0–30 cm layer was approximately 1.6%, with adequate levels of Phosphorus (10.4 ppm, Olsen Phosphorus) and Potassium (161 ppm, Ammonium acatete extractable Potassium) according to Delgado et al. (2016). Fertilizers and pest-management were conducted by the owner following the common practise of the zone. No deficient nutrient level in foliar analysis was detected during the 3-years of the experimental period (data not shown). Four irrigation treatments were applied during the 3 years of the experiment. These treatments considered the timing and the level of water stress throughout the season. Three phenological stages were considered for deficit irrigation scheduling (simplified from Nortes et al., 2009): Phase I, from flowering to the beginning of kernel filling (commonly February to mid-May); Phase II, from kernel filling to harvest (Mid-May to early August); Phase III, postharvest (early August to mid-October). The irrigation scheduling was applied during each treatment weekly and, in the case of the RDIs treatments, it was different for each plot: • Control. Full irrigated conditions. 100% of crop evapotranspiration (ETc) was estimated according to the recommendations for almond (Goldhamer and Girona, 2012) and only a dry period a few weeks before harvest was applied. Crown volume was included in ETc estimation with reduction coefficient (Kr), according to Steduto et al. (2012). The midday stem water potential (SWP) was also used to determine the irrigation scheduling. The SWP was compared to the McCutchan and Shackel (1992) baseline, and irrigation was increased to 125% ETc when the measured values were lower than the estimated threshold. • Regulated deficit irrigation-1 (RDI 1). This treatment and the next one (RDI-2) considered the MDS and the SWP for irrigation scheduling during 2017 (described in Martín-Palomo et al., 2019). However, the approach was changed to using only the SWP due to a lack of results with the MDS during this first season. Water stress conditions were applied from kernel filling to harvest (Phase II). Irrigation scheduling considered a threshold value of 􀀀 1.15 MPa in order to avoid values lower than 􀀀 1.5 MPa, which have been reported as limiting to yield. The rest of the season irrigation scheduling was as in Control treatment. • Regulated deficit irrigation-2 (RDI-2). The period of water stress was the same as in RDI-1, but the level of water stress was more severe because this treatment had seasonal limitation of the applied water (2017 and 2018, 100 mm; 2019 120 mm). In order to adjust this low amount of water, the water stress during Phase II was increased by moving the SPW threshold down to 􀀀 2 MPa. Also, the rehydration during Phase III was stopped when seasonal amount of water was reached. • Sustained deficit irrigation (SDI). The same amount of water as in RDI-2 was applied, but with an almost constant rate throughout the pre-harvest period (Phase I and Phase II). 3. Software or instruments needed to interpret the data: Excel program 4. Information about instruments, calibration and standards: Water stress of the trees was measured using the midday stem water potential (SWP) and the trunk diameter fluctuations (TDF). SWP was measured at midday using the pressure chamber technique (Scholander et al., 1965). Two leaves near the main trunk were covered with aluminium bags two hours before measurements were taken with a pressure chamber (Model 1000, PMS, USA). The daily cycles of TDF were measured using a band dendrometer (D6, UMS, Germany) attached to the main trunk. This device works like a beam when bending. These data were recorded in a datalogger and wireless network sent them to a remote destination. The sensor’s metallic band rested on a part of the trunk circumference, and their ends were connected by a metallic Invar cable, an alloy of Ni and Fe with a thermal expansion coefficient close to zero (Katerji et al., 1994), that encircled the trunk. A Teflon net below the steel prevented friction with the bark surface. Each band dendrometer was plugged into a node (Widhoc Smart Solution SL, Spain) near the sensor. Each node consisted of two different parts: one being the measurement interface and the other the processing, recording and communication system. The nodes generated a stabilized power supply of 10Vdc to the band dendrometer. The data from each sensor node were sent wirelessly to the cloud. Averages of ten measurements of each band dendrometer were taken every fifteen minutes. Two different indicators were obtained from the TDF daily curves. MDS was calculated as the difference between the maximum and minimum daily diameter. TGR was the difference between two consecutive maximum diameters. Several ranges of TGR were considered to compare with SWP. The weekly frequency of these TGR ranges were calculated and compared to different ranges of SWP according to previous works in olive trees (Corell et al., 2020; Martín-Palomo et al., 2021). The range of SWP variation identified was narrower than those for olive trees, several threshold were selected in order to secure very different water status. García-Tejero et al. (2018) suggested 􀀀 1.5 MPa as the threshold value for deficit irrigation, this was considered severe water stress threshold. According to the leaf conductance vs SWP relationship reported by Shackel et al. (2021), a great reduction of the leaf conductance would be expected from -1 MPa. In order to obtain distinct level of water stress, two different intermediate threshold were considered. The first, 􀀀 1.15 MPa of SWP, similar to the ones suggest in the current work, was a moderate water stress level. Second, 􀀀 0.8 MPa of SWP would be more positive that the one reported by Shackel et al. (2021) and near to mild or even full irrigated conditions. Then, the different ranges of SWP were: more positive than -0.8 MPa, considered mild water stress conditions; between -0.8 and -1.15 MPa, considered a moderate water stress level; between - 1,15 and -1.5 MPa, considered as an intermediate water stress; and more negative than -1.5 MPa, severe water stress 5. Environmental or experimental conditions: Climatic data were obtained from the Andalusian weather station networks (IFAPA "Los Palacios" station, SIAR, 2022). This station is located approximately 6 km away from the experimental orchard. The seasonal pattern of potential evapotranspiration (ETo) and rainfall is common of Mediterranean conditions. During the 3 years, there was a dry period from mid-spring until the end of summer season, when maximum ETo data were measured (around 7 mm day-1). The seasonal amount of rainfall was very variable, and two seasons (2017, 366.3 mm and 2019, 237.6 mm) received lower rain than the average annual precipitation rate (589 mm, AEMET, 2022) but 2018 had higher values (667.9 mm). 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 Martin-Palomo et al 2022 Description: Excel file 4. File format: Excel