Acta agriculturae Slovenica, 120/2, 1–17, Ljubljana 2024 doi:10.14720/aas.2024.120.2.17110 Original research article / izvirni znanstveni članek Soil water dynamics and olive yield (Olea europaea L.) under different surface drip irrigation treatments in northern Mediterranean Matic NOČ 1, 2, Urša PEČAN 1, Marina PINTAR 1, Maja PODGORNIK 3 Received December 13, 2023; accepted April 25, 2024. Delo je prispelo 13. decembra 2023, sprejeto 25. aprila 2024. 1 University of Ljubljana, Biotechnical Faculty, Department of Agronomy, Ljubljana, Slovenia 2 Corresponding author, e-mail: matic.noc@bf.uni.lj.si 3 Science and Research Centre Koper, Institute for Oliveculture, Koper, Slovenia Soil water dynamics and olive yield (Olea europaea L.) under different surface drip irrigation treatments in northern Medi- terranean Abstract: The use of modern irrigation systems and mon- itoring of soil water status can help improve crop performance and water use efficiency. The influence of different irrigation treatments on soil water content dynamics and olive oil yield was studied over two growing seasons using a surface drip ir- rigation system in an olive grove in northern Mediterranean climate. Irrigation treatments included optimal irrigation, sus- tained deficit irrigation (33 % of optimal irrigation), and rain- fed treatment. Based on the water applied, we calculated the percentage of replenished estimated evapotranspiration (ETc*) for each treatment using the Penman-Monteith method. Soil water content dynamics were monitored with capacitive probes at five depths (10 to 50 cm). The increase in soil water content at a depth of 30 to 50 cm, which was only achieved with optimal irrigation, resulted in a significantly higher olive oil yield. In contrast, deficit irrigation, despite the addition of water, did not lead to an increase in soil water in the layers below 30 cm, so that the yield was equal to that of rainfed treatment. In irrigated olive groves, it is beneficial to monitor the water content of the soil at several depths to ensure that a sufficient amount of water has been applied. Key words: diviner, evapotranspiration, irrigation man- agement, olive, soil depths, volumetric soil water content Dinamika vode v tleh in pridelek oljk (Olea europaea L.) pri različnih načinih površinskega kapljičnega namakanja v se- vernem Sredozemlju Izvleček: Uporaba sodobnih namakalnih sistemov ter spremljanje stanja vode v tleh lahko pripomore k izboljšanju učinkovitosti rastlinske pridelave in rabe vode. Vpliv različnih načinov namakanja na dinamiko vsebnosti vode v tleh in pri- delek oljčnega olja smo preučevali v dveh rastnih dobah z upo- rabo površinskega kapljičnega namakalnega sistema v oljčnem nasadu v severnem sredozemskem podnebju. Obravnavanja so vključevala optimalno namakanje, trajno namakanje s priman- jkljajem (33 % optimalnega namakanja) in brez namakanja. Na podlagi porabljene vode smo z uporabo metode Penman-Mon- teith izračunali odstotek nadomeščene ocenjene evapotranspi- racije (ETc*) za vsako obravnavo. Dinamiko vsebnosti vode v tleh smo spremljali s kapacitivnimi merilniki na petih globinah (od 10 do 50 cm). Povečanje vsebnosti vode v tleh na globini od 30 do 50 cm, ki je bilo doseženo le z optimalnim namakanjem, je povzročilo večji pridelek oljčnega olja. Nasprotno pa se pri namakanju s primanjkljajem kljub dodajanju vode ni povečala količina vode v tleh v plasteh pod 30 cm, zato je bil pridelek enak pridelku brez namakanja. V namakanih oljčnih nasadih je koristno spremljati vsebnost vode v tleh na več globinah, da se zagotovi, da je bila priskrbljena zadostna količina vode. Ključne besede: diviner, evapotranspiracija, upravljanje namakanja, oljke, globine tal, volumska vsebnost vode v tleh Acta agriculturae Slovenica, 120/2 – 20242 M. NOČ et al. 1 INTRODUCTION Olive (Olea europaea L.) is traditionally cultivated in regions with water scarcity (Rufat et al., 2014). The vul- nerability of the Mediterranean region to climate change has been highlighted by the increasing occurrence and intensity of agricultural droughts (Tramblay et al., 2020). In recent years, Slovenian olive growers and producers have struggled to achieve consistent yields and olive oil quality due to extreme weather conditions, particularly the more frequent occurrence of droughts (Podgornik et al., 2018; Valenčič et. al., 2018). Olive irrigation is a well-known agrotechnical measure to improve olive oil yield and quality (Rufat et al., 2018; Santos, 2018). Regulated deficit irrigation is a commonly studied management practice in water-scarce environments, however the optimal irrigation regime is not easy to define because it is a complex interaction of different factors, such as tree age, size, health, nutrition, weed cover, and others (Arampatzis et al., 2018; Carr, 2013). In northern Mediterranean climate, Podgornik et al. (2017) showed that the olive oil yield of the cultivar ‘Istrska Belica’ can still be significantly improved by ir- rigation. However, out of a total area of 2571 ha of ol- ive groves in Slovenia, only 47 ha were irrigated in 2023 (MKGP, 2024). Since 2008, most irrigation systems have been based on drip irrigation using public water as the main water source (Podgornik et al., 2022). The use of modern irrigation systems and monitor- ing of soil and crop water status can contribute to im- proved crop performance and water use efficiency in the face of a changing climate. Automated or decision- supported systems for irrigation scheduling based on soil water content (θ) measurement are commonly used to optimize water use in agriculture (Cvejić et al., 2020; Navarro-Hellín et al., 2016; Vera et al., 2021). The use of profile capacitance sensors inserted into an access tube has the added advantage that θ can be measured at multi- ple depths simultaneously (Arampatzis et al., 2018; Egea et al., 2016). In micro-irrigated heterogeneous crop sys- tem, such as Mediterranean tree crops, the variability of soil water content in the field depends on the spatial dis- tribution of roots and local water supply. Consequently, such heterogeneity affects crop water status and manage- ment strategies (Rallo et al., 2018). Despite predictions that olive growing areas will ex- pand to higher elevations and northward in the future (Tanasijević et al., 2014), there are currently few studies on the effects of different water regimes on olive trees in sub-humid and/or northern Mediterranean regions. Studies on the response of olive trees to water availabil- ity in sub-humid regions often focus on the aboveground part of the plant (D’andria et al., 2009; Podgornik et al., 2017; Tognetti et al., 2008) and the water balance of the olive grove (Zupanc et al., 2018). Despite the fact that crop yields are more closely related to soil water avail- ability than to any other soil or meteorological variable (de Jong and Bootsma, 1996), few studies have been con- ducted on the dynamics of soil water content in irrigated olive groves in the northern Mediterranean region. The objective of this study was to investigate how different amounts of water used in surface drip-irrigation (optimal irrigation, sustained deficit irrigation, and rain- fed) affect the dynamics of soil water content in the soil profile and how they influence olive oil yield. 2 MATERIALS AND METHODS 2.1 SITE DESCRIPTION The study was conducted during the 2016 and 2017 irrigation seasons in a 17-year-old olive grove (Olea euro- paea ‘Istrska Belica’) located in Slovenian Istria (Dekani: 45°33.541′N, 13°47.637′E; 96 m above sea level) (Fig. 1), a typical olive-growing area in southwestern Slovenia. The olive variety ‘Istrska Belica’ is the most widespread variety in the northern part of the Adriatic region and is intensively propagated in Slovenian Istria and in the Friuli-Venezia Giulia region in Italy. This is due to its ex- cellent adaptability to pedoclimatic conditions, its very good and regular fertility and its high oil content (Ban- delj et al., 2004). This olive oil has a high phenol con- tent, which gives the oil a special flavour characterised by bitterness and pungency. These sensory characteris- tics are very intense in oil from drought-stressed trees and are generally perceived as unpleasant by consumers. Irrigation can influence the content of phenols in olive oil and thus its sensory characteristics (Dag et al., 2008; Gómez-Rico et al., 2007; Romero et al., 2002). Southwestern Slovenia has a sub-mediterranean climate with an average annual precipitation of 969 mm (20-year mean, 1999-2019), although seasonal pre- cipitation varies greatly from year to year, especially in monthly distribution (Sušnik and Matajc, 2013). The daily mean temperature varied from −2 to 7 °C in winter (December/January) and 20 to 28 °C in summer (July/ August). The mean annual reference evapotranspiration (ET0) is 1035  mm. Mean precipitation data for the ex- perimental olive grove were obtained from the local me- teorological station (ARSO, 2022). Olive trees are spaced 6 m × 5 m apart, with an overall plantation density of 300 plants ha−1. The olive grove is covered with natural greenery and no tillage was used during the experiment. Acta agriculturae Slovenica, 120/2 – 2024 3 Soil water dynamics and olive yield (Olea europaea L.) under different surface drip irrigation treatments ... The soil characteristics for the experimental olive grove are given in Table 1. The soil type is clay loam with a mean depth of 0.74 m. Soil water content (θ) at field capacity (FC) and permanent wilting point (PWP) were determined for the 25 cm to 30 cm soil layer in the labo- ratory using a pressure plate extractor. The θ at FC at a soil matric potential of −0.033 MPa is 0.32 m3 m−3. The θ at PWP (−1.5 MPa) is 0.19 m3 m−3. Ratliff et al. (1983) suggested that if absolute accuracy is necessary for wa- ter-balance calculations, laboratory-estimated soil water limits (e.g., field capacity, wilting point) should be used with caution, and field-measured limits are preferred, if available. The phenological growth stages of the olive variety ‘Istrska Belica’ observed in the experiment in 2016 and 2017 growing seasons are listed in Table 2. Figure 1: Location of experimental olive grove in the region Table 1: Soil texture and organic matter content (OM) of the soil horizons of the olive grove in Dekani (Slovenia) (Podgornik et al., 2017) Soil horizon Depth (cm) Sand (%) Loam (%) Clay (%) Texture OM (%) Ah 0-2 31.7 43.5 24.8 Loam 18.0 P1 2-24 29.3 42.1 28.6 Clay loam 3.1 P2 24-51 28.7 43.4 27.9 Clay loam 2.2 P3 51-74 32.3 38.2 29.5 Clay loam 1.6 Acta agriculturae Slovenica, 120/2 – 20244 M. NOČ et al. 2.2 IRRIGATION REGIMES The surface drip irrigation system was established in April 2009 to provide different amounts of water throughout the season (i.e., June–October). Trees were surface drip-irrigated with different combinations of 2 l h−1 pressure-compensating drippers placed around the trees. They provided different irrigation treatments with distinct water regimes: optimal irrigation, in which sea- sonal irrigation attempted to compensate for all water loss so that the water content at 25 cm depth was main- tained near FC; sustained deficit irrigation, in which ir- rigation volume was 33 % of optimal irrigation; and rain- fed, in which the trees were not irrigated. The amount of water for deficit irrigation (33 % optimal) was chosen based on relatively high long-term annual precipitation (about 1000 mm). Optimal irrigation was achieved with 15 drippers spaced 0.47 m apart on the dripline around the tree at a distance of 1.5 m from tree trunk. Sustained deficit irrigation was achieved with 5 drippers placed 1.41 m apart. Timing and amount of irrigation were au- tomated based on continuous measurement of θ with two TRIME-Pico 32 sensors (IMKO micromodultechnik GmbH, Ettlingen, Germany) installed horizontally at a depth of 25 cm between two drippers under the drip line. Irrigation was triggered so that the θ at optimal irrigation in 2016 ranged from 0.25 m3 m−3 (start of irrigation) to 0.31 m3 m−3. Due to high water use in 2016, the irrigation regime was changed in 2017 and optimal irrigation was maintained only in the range of 0.23 m3 m−3 to 0.30 m3 m−3, resulting in less frequent irrigation events compared to 2016. Estimated crop evapotranspiration (ETc*) for ol- ive grove was calculated based on Penman-Monteith calculations with a single crop coefficient (Kc) (FAO-56 approach). The reference evapotranspiration ET0 was obtained from the local meteorological station (ARSO, 2022), and Kc = 0.7 (Kc mid) was used for olive groves with 40-60 % ground cover through the canopy (Allen et al., 1998). However, some authors have calculated lower val- ues of Kc mid = 0.45 (Pastor and Orgaz, 1994). The ratio of water applied by precipitation and/or irrigation (P + I) to calculated ETc was calculated for each treatment on a weekly basis. 2.3 STUDY DESIGN AND MEASUREMENTS The study design included four rows of trees. In each row, blocks of four trees were randomly selected for each irrigation treatment (total 16 trees per treatment). θ was measured near two randomly selected trees for each irrigation treatment, weekly during the irrigation season (from June to September) using a Diviner 2000 soil mois- ture sensor (Sentek Pty Ltd., Stepney, Australia), previ- ously calibrated for the experimental soil. The Diviner 2000 is a portable device with a hand-held logger and a capacitance sensor inserted into an access tube (Sentek, 2009). The measurement of θ was technically repeated three times, and the mean value was used for further analysis. Measurements of θ were taken at five different soil depths (10 cm, 20 cm, 30 cm, 40 cm, 50 cm) at a dis- tance of 1.5 m from the tree trunk. Diviner access tubes were installed near two TRIME-Pico 32 sensors, which triggered irrigation at a threshold θ (Fig. 2). Olive oil yield was measured in the 2016 season on eight randomly selected trees per treatment (2 per row). In 2017, yield was measured on the same trees as in the previous season. In both experimental years 2016 and 2017, harvesting was carried out in November (Novem- ber 7 and 9, respectively). Trees were harvested individu- ally by hand. The fruit mass of each tree was measured after harvest, and samples of 700 g of olives per treatment were taken for each year to determine the oil content. Oil Table 2: Phenological growth stages (Sanz-Cortés et al., 2002) of the olive variety ‘Istrska Belica’ in 2016 and 2017 BBCH Description 2016 2017 11 First leaves completely separated 10/04 08/04 31 Shoots reach 10 % of final length 14/04 15/04 51 Inflorescence buds start to swell 21/04 21/04 60 First flowers open 22/05 22/05 65 Full flowering: at least 50 % of flowers open 29/05 29/05 69 End of flowering, fruit set, non-fertilised ovaries fallen 04/06 05/06 71 Fruit about 10 % of final size 11/06 13/06 81 Beginning of fruit colouring 25/09 20/09 89 Harvest maturity: fruits are suitable for oil extraction 01/11 01/11 92 Overripe: fruits lose turgidity and start to fall 10/11 06/11 Acta agriculturae Slovenica, 120/2 – 2024 5 Soil water dynamics and olive yield (Olea europaea L.) under different surface drip irrigation treatments ... (“gls() function”) and accounting for the different vari- ances for each irrigation treatment. Post-hoc analysis was performed for both variables using the package “em- means” with “mvt” adjustment (multivariate t-distribu- tion) for pairwise comparisons. Statistical significance was assumed at the = 0.05 level. 3 RESULTS 3.1 ACTUAL IRRIGATION TREATMENTS Total precipitation (P), optimal irrigation (I), ref- erence evapotranspiration (ET0), estimated crop evapo- transpiration (ETc*; from single crop Kc), and estimated daily mean ratio of total P + I to ETc* for periods between consecutive Diviner measurements are shown for each irrigation treatment for the 2016 and 2017 growing sea- sons in Tables 3 and 4, respectively. Estimated mean daily ETc* ranged from 2.0 mm (September) to 4.4 mm (early August) in the 2016 season, and from 1.3 mm (late Sep- tember) to 4.8 mm (July) in 2017. The monthly ratio of P + I to ETc* for each irrigation treatment is shown in Table 5. In August 2016, well over 100 % of the estimated ETc* was applied (234.1 % from 02/08/2016 to 29/08/2016), while in August 2017, slight- ly more than 100  % of the calculated ETc* was applied (127.2 % from 01/08/2016 to 28/08/2016) under optimal irrigation. In July 2016, applied water under optimal irri- extraction was performed using a laboratory olive mill (Abencor, MC2 Ingeniería y Sistemas SL, Seville, Spain). The fruits were crushed with a hammer mill, the resulting olive pulp was malaxed at 25 °C for 20 min, and the oil was separated by centrifugation. The oil was then filtered and the oil yield and content were determined. 2.4 STATISTICAL ANALYSIS All statistical analyses were performed using R sta- tistical software version 4.2.1. To evaluate the effects of the three irrigation treatments: rainfed, deficit and opti- mal irrigation on soil water content during two growing seasons, a linear-mixed model (mixed model ANOVA) function lmer() (package “lme4”) was used for each of the two seasons (2016 and 2017) separately. A random effect of date (random intercept), a random effect of six Diviner 2000 access tube locations that have been repeat- edly sampled over time (random intercept), and an inter- action of two fixed factors - irrigation treatment (rainfed, deficit irrigation, optimal irrigation) and depth (10 cm, 20 cm, 30 cm, 40 cm, 50 cm) were included in the model. Homogeneity of variances was checked using residual plots for each treatment and depth. The normality as- sumption was checked using the Q-Q plot. For olive oil yield analysis, a linear model was used to analyze the data for each of the two seasons (2016, 2017) separately, using the generalized least squares Figure 2: Experimental design Acta agriculturae Slovenica, 120/2 – 20246 M. NOČ et al. Table 3: Precipitation (P) and irrigation (I) amount for optimal irrigation treatment with sum of reference ET0 and estimated evapotranspiration (ETc*), estimated mean daily ETc*, and ratio of sum of irrigation + precipitation to ETc* for all treatments. Data is shown for the 2016 growing season for periods between two consecutive Diviner 2000 soil water content measurements. ND is number of days Year 2016 ND P (mm) I optimal (mm) ET0 (mm) ETc* (mm) (Kc = 0.7) Daily mean ETc* (mm) P + I (mm) Ratio P + I / ETc* (%) Optimal Deficit Optimal Deficit Rainfed 08/06-13/06 6 45.9 0.0 20.8 14.6 2.4 45.9 45.9 315.2 315.2 315.2 14/06-20/06 7 45.9 21.5 28.7 20.1 2.9 67.4 53.0 335.4 263.8 228.5 21/06-27/06 7 0.1 71.3 40.7 28.5 4.1 71.4 23.6 250.6 82.9 0.4 28/06-05/07 8 0.5 62.7 46.1 32.3 4.0 63.2 21.2 195.9 65.7 1.5 06/07-15/07 10 10.1 3.4 60.4 42.3 4.2 13.5 11.2 32.0 26.6 23.9 16/07-18/07 3 0.0 0.0 15.3 10.7 3.6 0.0 0.0 0.0 0.0 0.0 19/07-26/07 8 5.6 3.0 45.2 31.6 4.0 8.6 6.6 27.1 20.8 17.7 27/07 - 01/08 6 1.7 35.4 33.2 23.2 3.9 37.1 13.4 159.7 57.6 7.3 02/08-09/08 8 1.0 53.8 50.0 35.0 4.4 54.8 18.7 156.5 53.6 2.9 10/08-16/08 7 7.3 58.9 35.3 24.7 3.5 66.2 26.7 267.7 108.2 29.5 17/08-22/08 6 31.3 50.8 27.9 19.5 3.3 82.1 48.1 420.2 246.1 160.3 23/08-29/08 7 0.0 43.9 37.5 26.3 3.8 43.9 14.5 167.4 55.2 0.0 30/08 - 05/09 7 2.2 47.6 32.2 22.5 3.2 49.8 17.9 220.8 79.4 9.8 06/09-12/09 7 9.1 37.2 30.5 21.4 3.1 46.3 21.4 217.1 100.2 42.6 13/09-19/09 7 53.5 7.6 20.4 14.3 2.0 51.1 46.0 357.5 322.1 374.6 20/09-26/09 7 0.0 0.0 23.7 16.6 3.4 0.0 0.0 0.0 0.0 0.0 gation was lower (73.0 % from 28/06/2016 to 26/07/2016) due to problems with the automated system. The results show that the ETc* calculation based on a single Kc ap- proach does not account for the additional evaporative losses at the surface, because more water than estimated ETc* was applied to increase θ. Deficit irrigation replenished approximately 100 % of calculated ETc* in August 2016 (102.4 % from 02/08/2016 to 29/08/2016) and 66.2  % in August 2017 (from 01/08/2017 to 28/08/2017). Comparison of the three-month mean water balance from June to Au- gust in 2016 and 2017 shows that more water was ap- plied for both irrigation treatments in 2016. Optimal irrigation (179.4  % of calculated ETc from 08/06/2016 to 29/08/2016) and deficit irrigation (91.6  % from 08/06/2016 to 29/08/2016) in 2016, while in 2017 opti- mal irrigation reached 116.2 % of calculated ETc* from 30/05/2017 to 28/08/2017 and deficit irrigation reached 60.5 % from 30/05/2017 to 28/08/2017. 3.2 EFFECT OF IRRIGATION TREATMENTS ON VOLUMETRIC SOIL WATER CONTENT Figures 3, 4, and 5 show the temporal dynamics of the θ measured during the 2016 and 2017 irrigation seasons (mean and standard error of two access tubes θ measurements for each depth at 34 time points), as well as the irrigation and precipitation events that occurred during the periods studied. Additional secondary axis for (I + P) to ETc * ratios was added, showing only ratios be- low 350 % ETc*. The dashed lines indicate the 100 % and 33 % ETc* ratios. The black dots represent the mean ratios I + P / ETc* during the selected period between two con- secutive Diviner 2000 measurements and are scaled on the secondary axis. From 04/07/2016 to 20/07/2016 and from 05/07/2017 to 18/07/2017, the automatic irrigation did not work properly, so the irrigation was applied man- ually, causing the θ to decrease at all depths. Soil water content increased after precipitation events. Optimal irrigation treatment resulted in higher θ Acta agriculturae Slovenica, 120/2 – 2024 7 Soil water dynamics and olive yield (Olea europaea L.) under different surface drip irrigation treatments ... Table 4: Precipitation (P) and irrigation (I) amount for optimal irrigation treatment with sum of reference ET0 and estimated evapotranspiration (ETc*), estimated mean daily ETc*, and ratio of sum of irrigation + precipitation to ETc* for all treatments. Data is shown for the 2017 growing season for periods between two consecutive Diviner 2000 soil water content measurements. ND is number of days Year 2017 ND P (mm) I optimal (mm) ET0 (mm) ETc* (mm) (Kc = 0.7) Daily mean ETc* (mm) P + I (mm) Ratio P + I / ETc* (%) Optimal Deficit Optimal Deficit Rainfed 23/05-29/05 7 0.3 0.1 37.5 26.3 3.8 0.4 0.3 1.4 1.2 1.1 30/05-05/06 7 0.0 21.2 40.6 28.4 4.1 21.2 7.0 74.7 24.6 0.0 06/06-12/06 7 3.3 16.8 40.7 28.5 4.1 20.1 8.9 70.7 31.1 11.6 13/06-19/06 7 0.1 26.7 42.3 29.6 4.2 26.8 8.9 90.5 30.1 0.3 20/06-26/06 7 9.5 28.8 41.4 29.0 4.1 38.3 19.0 132.1 65.6 32.8 27/06-03/07 7 65.7 0.0 35.4 24.8 3.5 65.7 65.7 265.1 265.1 265.1 04/07-10/07 7 0.8 20.4 43.8 30.7 4.4 21.2 7.5 69.1 24.6 2.6 11/07 - 17/07 7 0.0 25.4 47.5 33.3 4.8 25.4 8.4 76.4 25.2 0.0 18/07-24/07 7 0.0 25.2 40.9 28.6 4.1 25.2 8.3 87.9 29.0 0.0 25/07-31/07 7 3.5 43.9 39.4 27.6 3.9 47.4 18.0 171.8 65.2 12.7 01/08-07/08 7 15.9 0.0 43.9 30.7 4.4 15.9 15.9 51.7 51.7 51.7 08/08 - 14/08 7 4.1 43.5 34.0 23.8 3.4 47.6 18.4 199.8 77.5 17.2 15/08-21/08 7 16.9 15.5 36.6 25.6 3.7 32.4 22.0 126.4 85.9 66.0 22/08-28/08 7 0.0 33.9 31.2 21.8 3.1 33.9 11.2 155.2 51.2 0.0 29/08-04/09 7 21.5 27.3 26.5 18.6 2.7 48.8 30.5 262.9 164.4 115.9 05/09-11/09 7 84 15.8 16.7 11.7 1.7 99.8 89.2 853.5 763.1 718.6 12/09-18/09 7 86.6 9.4 15.7 11.0 1.6 96.0 89.7 873.6 816.2 788.0 19/09-25/09 7 56.2 0.0 13.3 9.3 1.3 56.2 56.2 603.9 603.7 603.7 Table 5: Approximate monthly irrigation + precipitation (I + P) to ETc ratios for each irrigation treatment Year and month Mean ratio I + P / ETc* and amount of water (I + P) applied (mm) Optimal irrigation Deficit irrigation Rainfed June 2016 (08/06-27/06) 292.5% (184.7 mm) 194.0 % (122.5 mm) 145.5 % (91.9 mm) July 2016 (28/06-26/07) 73.0 % (85.3 mm) 33.4 % (39.0 mm) 13.9 % (16.2 mm) August 2016 (02/08-29/08) 234.1 % (246.9 mm) 102.4 % (108.8 mm) 37.5 % (39.6 mm) June – August 2016 (08/06-29/08) 179.4 % (554.0 mm) 91.6 % (282.9 mm) 48.4 % (149.4 mm) June 2017 (30/05-26/06) 92.2 % (106.4 mm) 37.9 % (43.8 mm) 11.2 % (12.9 mm) July 2017 (04/07-31/07) 99.2 % (119.1 mm) 35.1 % (42.2 mm) 3.6 % (4.3 mm) August 2017 (01/08-28/08) 127.2 % (129.7 mm) 66.2 % (67.5 mm) 36.2 % (36.9 mm) June – August 2017 (30/05-28/08) 116.2 % (421.0 mm) 60.5 % (219.2 mm) 33.1 % (199.8 mm) at deeper layers - 30 cm, 40 cm, and 50 cm compared to the rainfed treatment. In August 2016, more than 100 % of the estimated (single Kc) ETc* was applied during most periods (dots of ratios above 100 % ETc* line) to compen- sate for surface evaporative losses. In 2017, however, the ratios are closer to 100 % estimated ETc*. Interestingly, al- though deficit irrigation in August 2016 and 2017 replen- ished more than 33 % of estimated ETc*, θ at 20 cm depth did not increase but remained low. It is also interesting to note that under deficit irrigation, similar amounts of water were applied (18 mm I and 3.5 mm P; 27 mm ETc*) during the rainless period (25/7/2017 - 31/7/2017) as during the following rainy week (1/8/2017-7/8/2017; 0 mm I, 15.9 mm P; 30.7 mm ETc*), but θ at depths from 10 cm to 50 cm increased only during the second week (mainly rain), but not during the first week (mainly ir- rigation). A similar situation can be observed during 2/8/2016-9/8/2016 and 8/8/2017-14/8/2017. Acta agriculturae Slovenica, 120/2 – 20248 M. NOČ et al. Figure 3: Temporal dynamics of mean volumetric soil water content with standard error under optimal irrigation at different soil depths (10 cm, 20 cm, 30 cm, 40 cm, 50 cm) and weekly precipitation and irrigation during the 2016 and 2017 growing seasons. Black dots represent the ratio of rainfall to estimated ETc* (secondary axis). Field capacity and wilting point are also indicated, along with 100 % ETc and 33 % ETc Figure 4: Temporal dynamics of mean volumetric soil water content with standard error under deficit irrigation at different soil depths (10 cm, 20 cm, 30 cm, 40 cm, 50 cm) and weekly precipitation and irrigation during the 2016 and 2017 growing seasons. Black dots represent the ratio of rainfall to estimated ET * (secondary axis). Field capacity and wilting point are also indicated, along with 100 % ETc and 33 % ETc Acta agriculturae Slovenica, 120/2 – 2024 9 Soil water dynamics and olive yield (Olea europaea L.) under different surface drip irrigation treatments ... Fig. 6 shows the combined temporal dynamics of θ under rainfed, deficit irrigation, and optimal irrigation for each of the five depth layers. The black line represents the mean θ measurements from TRIME-Pico 32 under optimal irrigation. θ measurements made with two dif- ferent sensor types agree well within the standard errors of the Diviner measurements during most of the growing season. From 23/05/2017 to 10/07/2017, TRIME-Pico 32 measurements were not successfully transmitted (data was lost), although the irrigation regime was maintained throughout the 2017 growing season. There is a similar θ pattern between the different irrigation treatments in both growing seasons, however differences in mean θ are less obvious in 2017 due to the lower amount of water ap- plied. Mean θ was higher under optimal irrigation than under deficit irrigation and rainfed treatment at 30 cm, 40 cm, and 50 cm, but not at 10 and 20 cm. No clear dif- ferences were found between rainfed and deficit irriga- tion at any of the five depths. The interaction between treatment and depth was statistically significant (p < 0.05), as were the main ef- fects of treatment (p < 0.05) and depth (p < 0.001) in both growing seasons. Model prediction-means and 95% con- fidence intervals (CI) of soil water content measurements for each of the three treatments at different soil depths (10 cm, 20 cm, 30 cm, 40 cm, 50 cm) during the 2016 and 2017 growing seasons are shown in Fig. 7. Mean model prediction data are shown in Table 8 in the Appendix. Differences in mean θ over two growing seasons be- tween different irrigation treatments are shown by meas- urement depth for each growing season (Table 6). At 30 cm, mean θ was 0.12 m3 m−3 higher under optimal irriga- tion compared with deficit irrigation in 2016 (95  % CI from 0.03 m3 m−3 to 0.20 m3 m−3) and 0.09 m3 m−3 higher in 2017 (95 % CI from 0.09 m3 m−3 to 0.17 m3 m−3). At 30 cm, the difference in mean θ between optimal irriga- tion and rainfed treatment was statistically significant (p = 0.023) only in the 2016 growing season, with a higher mean θ under optimal irrigation, 0.11 m3 m−3 (95 % CI from 0.03 m3 m−3 to 0.19 m3 m−3). At 40 cm, the differ- ence in mean θ between optimal and deficit irrigation was statistically significant in both growing seasons (p < 0.05), with optimal irrigation having 0.12 m3 m−3 higher mean θ in 2016 (95% CI from 0.04 m3 m−3 to 0.20 m3 m−3) and 0.10 m3 m−3 higher mean θ in 2017 (95% CI from 0.02 m3 m−3 to 0.19 m3 m−3). At 40 cm, mean θ was 0.11 m3 m−3 higher under optimal irrigation than under rain- fed treatment (95 % CI from 0.03 m3 m−3 to 0.19 m3 m−3) in 2016 and 0.09 m3 m−3 higher in 2017 (95 % CI from 0.00 m3 m−3 to 0.17 m3 m−3). At 50 cm, mean θ was 0.09 Figure 5: Temporal dynamics of mean volumetric soil water content with standard error under rainfed treatment at different soil depths (10 cm, 20 cm, 30 cm, 40 cm, 50 cm) and weekly precipitation during the 2016 and 2017 growing seasons. Black dots rep- resent the ratio of rainfall to estimated ETc* (secondary axis). Field capacity and wilting point are also indicated, along with 100 % ETc and 33 % ETc Acta agriculturae Slovenica, 120/2 – 202410 M. NOČ et al. Figure 6: Temporal dynamics of the mean soil water content with standard error under three treatments at different soil depths (10 cm, 20 cm, 30 cm, 40 cm, and 50 cm) measured weekly with Diviner and continuous measurement of soil water content with TRIME-Pico 32 Figure 7: Model predictions of mean values and 95% confidence intervals of volumetric soil water content measurements for each of three treatments at different soil depths (10 cm, 20 cm, 30 cm, 40 cm, 50 cm) for the 2016 and 2017 growing seasons Acta agriculturae Slovenica, 120/2 – 2024 11 Soil water dynamics and olive yield (Olea europaea L.) under different surface drip irrigation treatments ... m3 m−3 higher under optimal irrigation than under defi- cit irrigation (95% CI from 0.01 m3 m−3 to 0.18 m3 m−3) in 2016 and 0.10 m3 m−3 higher in 2017 (95 % CI from 0.02 m3 m−3 to 0.19 m3 m−3). At 50 cm in both growing sea- sons, mean θ was higher under optimal irrigation than under rainfed treatment. Thus, the θ under the optimal irrigation treatment was higher compared to deficit irrigation and rainfed treatments in both growing seasons. Mean differences are higher in growing season 2016. The level of soil water content under the optimal irrigation treatment reflected the amount of water applied in each growing season. However, this was not the case in the deficit irrigation and rainfed treatments, between which no significant differences in θ were found at any depth, although more water was applied in the deficit irrigation treatment (Fig- ures 4 and 5). Table 6: Pairwise comparisons of differences in mean soil water content between irrigation treatments for each depth of monitor- ing for the 2016 and 2017 growing seasons Year Depth Pairwise comparison 2.5 % percentile (m3 m−3) Mean differences (m3 m−3) 97.5 % percentile (m3 m−3) p-value 2016 10 cm optimal - deficit −0.08 0.00 0.08 1.000 optimal - rainfed −0.05 0.03 0.12 0.455 deficit - rainfed −0.05 0.03 0.12 0.493 20 cm optimal - deficit −0.02 0.06 0.14 0.122 optimal - rainfed −0.00 0.08 0.16 0.051 deficit - rainfed −0.06 0.02 0.10 0.826 30 cm optimal - deficit 0.03 0.12 0.20 0.015 optimal - rainfed 0.02 0.10 0.19 0.023 deficit - rainfed −0.10 −0.01 0.07 0.968 40 cm optimal - deficit 0.04 0.12 0.20 0.013 optimal - rainfed 0.03 0.11 0.19 0.019 deficit - rainfed −0.09 −0.01 0.07 0.987 50 cm optimal - deficit 0.01 0.09 0.18 0.032 optimal - rainfed 0.02 0.11 0.19 0.021 deficit - rainfed −0.07 0.01 0.10 0.966 2017 10 cm optimal - deficit −0.11 −0.02 0.06 0.770 optimal - rainfed −0.07 0.02 0.10 0.918 deficit - rainfed −0.05 0.04 0.12 0.384 20 cm optimal - deficit −0.07 0.02 0.10 0.892 optimal - rainfed −0.05 0.04 0.12 0.455 deficit - rainfed −0.07 0.02 0.10 0.876 30 cm optimal - deficit 0.00 0.09 0.17 0.045 optimal - rainfed −0.02 0.06 0.15 0.131 deficit - rainfed −0.11 −0.03 0.06 0.692 40 cm optimal - deficit 0.02 0.10 0.19 0.026 optimal - rainfed 0.00 0.09 0.17 0.045 deficit - rainfed −0.10 −0.02 0.07 0.923 50 cm optimal - deficit 0.02 0.10 0.19 0.026 optimal - rainfed 0.00 0.09 0.17 0.045 deficit - rainfed −0.10 −0.02 0.07 0.924 Acta agriculturae Slovenica, 120/2 – 202412 M. NOČ et al. 3.3 EFFECT OF IRRIGATION TREATMENTS ON OLIVE OIL YIELD Mean fruit yield, oil content and olive oil yield are shown in Fig. 8. Fruit yield and olive oil yield of the dif- ferent irrigation treatments in each of the two growing seasons reflect the observed differences in θ. However, mean oil content is the highest under deficit irrigation treatment in 2016. In 2017 mean values of oil content ap- pear higher under rainfed and deficit than under optimal irrigation treatment. The mean olive oil yield with 95 % percentiles for the studied trees is shown in Table 9 in the Appendix. Pair- wise comparisons of differences in mean olive oil yield between different irrigation treatments for two growing seasons are shown in Table 7. A linear model accounting for different variances for each treatment was used for each growing season, and statistically significant differ- ences in olive oil yield between treatments were observed (p = 0.022). Pairwise comparisons between treatments in the 2016 season showed statistically significant differenc- es in mean yield between optimal and deficit irrigation treatment (p = 0.045) with a 2.24 l tree-1 higher olive oil yield under optimal irrigation compared to deficit (95 % CI from 0.06 l tree-1 to 4.43 l tree−1). Differences between optimal and rainfed treatment in 2016 season (p = 0.084) were not statistically significant, although olive oil yield has been 1.95 l tree−1 0.53 l tree−1 higher under optimal irrigation (Table 7). A similar pattern was observed in the 2017 growing season. Differences in mean olive oil yield between op- timal irrigation and rainfed treatment were statistically significant (p = 0.048), with mean olive oil yield under optimal irrigation being 1.56 l tree−1 higher (95  % CI from 0.01 l tree−1 to 3.31 l tree−1). Differences in mean olive oil yield between optimal and deficit irrigation were nearly statistically significant (p = 0.058), with mean olive oil yield higher under optimal irrigation by 1.50 l tree−1 (±0.57 l tree−1). Figure 8: Mean olive fruit yield, oil content and olive oil yield per tree with standard errors for eight olive trees per treatment for the 2016 and 2017 growing seasons Acta agriculturae Slovenica, 120/2 – 2024 13 Soil water dynamics and olive yield (Olea europaea L.) under different surface drip irrigation treatments ... 4 DISCUSSION Although deficit irrigation is often advantageous compared to rainfed olive groves (Fereres and Soriano, 2007; Fernandes-Silva et al., 2010), it was not superior to rainfed treatment in terms of θ and olive oil yield in the present study. However, a surface drip irrigation sys- tem was used in the present study, which, according to Martínez and Reca (2014), results in lower olive oil yields compared to the subsurface irrigation system due to wa- ter loss through soil evaporation. A similar observation regarding water evaporation was made for citrus irriga- tion in Mediterranean climate (Martínez-Gimeno et al., 2018). Caruso et al. (2013), using subsurface drip irriga- tion, obtained 82 % of olive oil yield with deficit irrigated olives (46–52  % of water supply) compared to optimal irrigation. Potential water savings from switching from surface to subsurface drip irrigation were also described by Bonachela et al. (2001). Since θ did not differ at any depth under rainfed treatment and deficit irrigation in either growing sea- son (Fig. 4), this raises the question of the effectiveness of such sustained deficit irrigation with a surface drip system. Similar soil water content values between rainfed and deficit irrigation can be explained by advective heat transfer from the dry soil surface surrounding the small wet surface around the surface emitters (Matthias et al., 1986). Bonachela et al. (2001) measured evaporation with microlysimeters and found that it can be as high as 8 mm day−1 near the wetter surface (0.2 m from the emit- ter) and 6 mm day−1 at a distance of 0.2 to 0.35 m from the emitter. This is much higher than our maximum esti- mated daily ETc* calculated from the reference ET0 using Penman-Monteith method and single crop coefficient Kc mid for olive orchard, a method that assumes complete and uniform soil wetting. An irrigation study conducted on a 9-year-old olive orchard (‘Coregiolo’) in Australia showed that evapotranspiration during the irrigation was higher in irrigated than in rainfed trees because evapo- transpiration was limited in rainfed trees due to low wa- ter content in the soil during summer (Zeleke, 2014). Measured olive oil yields and θ at depths of 10 to 50 cm in two growing seasons, indicate that it is important to measure θ at different depths to assess whether the ir- rigation system achieves an increase in θ at the root depth (Datta et al., 2017). In our case, it was critical to increase the water content at a depth of 30 to 50 cm to increase the olive oil yield. Relying only on replenishing the estimated ETc* with a single crop coefficient and the reference ET0 value of the previous day or week does not necessar- ily guarantee an increase in soil water content and thus yield. Estimation of the true ETc value may be erroneous due to non-uniform soil wetting during surface drip ir- rigation (Matthias et al., 1986; Bonachela et al., 2001), er- rors in estimating Kc values when calculating ETc (Allen et al., 2005), and the distance between the weather station and the location of the irrigated area (Fernández García et al., 2020). The irrigation water used could be wasted, as in our case of surface deficit irrigation. A better estimate of ETc could be obtained with the double crop coefficient approach, which includes a separate prediction of soil evaporation (Allen et al., 1998). However, this approach could not be used in the present study because daily ir- rigation data were not available. Dual crop coefficient ap- proach is also more complicated and more computation- ally intensive, especially because of the determination of daily Ke values for surface evaporation. The total Kc for non-uniformly wetted surfaces can be as high as Kc = 1.3 (Allen et al., 1998), which in our case would better cor- respond to evapotranspiration losses. Conesa et al. (2021) compared an automated surface drip irrigation system, based on management allowed depletion threshold to trigger irrigation using θ values obtained with multi-depth capacitance sensors, with a conventional irrigation scheduling using estimated ETc for nectarine trees grown in the Mediterranean region under two water availability scenarios. Similar to our study, irrigation dose based on the 100 % ETc method did not necessarily increase θ close to FC at a depth of 0.5 Table 7: Pairwise comparisons of yield (litres of olive oil) between rainfed, deficit irrigation, and optimal irrigation treatment for the 2016 and 2017 growing seasons Growing season Contrast 2.5 % percentile (l tree−1) Mean differences (l tree−1) 97.5 % percentile (l tree−1) p-value 2016 optimal - deficit 0.1 2.2 4.4 0.045 optimal - rainfed −0.3 2.0 4.2 0.084 deficit - rainfed −1.3 −0.3 0.7 0.709 2017 optimal - deficit −0.1 1.5 3.1 0.058 optimal - rainfed 0.01 1.6 3.1 0.048 deficit - rainfed −0.7 0.06 0.8 0.967 Acta agriculturae Slovenica, 120/2 – 202414 M. NOČ et al. m from May to July (unlike an automated system with a threshold trigger). The 100 % ETc method supplied water only to the upper soil layer. By measuring soil water content at relevant depths (the main root water uptake zone) with properly installed θ sensors to maintain adequate soil water content during the critical period, we can ensure that the irrigation sys- tem replenishes sufficient water, even without knowing and calculating the estimation of the true ETc values. 5 CONCLUSIONS This research addresses the influence of different ir- rigation treatments on the dynamics of soil water content and olive oil yield. A surface drip irrigation system was used in an olive grove in a northern Mediterranean cli- mate, an olive growing area that has not been yet well studied. An increase in soil water content at a depth of 30 to 50 cm, achieved only with optimal irrigation, re- sulted in significantly higher olive oil yield. In contrast, sustained deficit irrigation did not increase soil water in the layers below 30 cm, despite the addition of water, so the yield was equal to that of rainfed treatment. Therefore it is advisable for olive oil producers to monitor soil water content in layers deeper than 30 cm to verify that enough water was applied to compensate for evapotranspiration losses. Policymakers and legislators should also be aware of the benefits of monitoring soil water content in a giv- en soil layer, especially when deficit surface irrigation is used, as water is wasted if it does not reach the roots at the desired depth. Irrigation scheduling based on esti- mated ETc using a single Kc approach can be problematic when using surface drip irrigation systems. In addition, the placement of drip emitters can also be an important contributor to water allocation. The shortcomings of this study are that the experiment was conducted in a single olive grove, with a single olive tree variety, with a specific soil type and a specific configuration of the surface drip irrigation system. Therefore, it is not necessarily trans- ferable to sites with other characteristics. Under different growing conditions, further studies are needed to more accurately determine best irrigation practices, including irrigation system, timing, frequency, water quantity, and to evaluate the effects of different deficit irrigation strate- gies on olive tree growth, olive oil quantity and quality. Future work should also investigate deficit subsurface drip irrigation in olive groves in the northern Mediter- ranean climate. 5.1 ACKNOWLEDGMENTS We would like to thank the Hlaj family for allowing us to perform our study in their olive grove in Dekani. We are grateful to Peter Korpar and our colleagues from the Science and Research Centre Koper, the Institute for Oliveculture for their technical assistance. We would like to thank dr. 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Acta agriculturae Slovenica, 120/2 – 2024 17 Soil water dynamics and olive yield (Olea europaea L.) under different surface drip irrigation treatments ... 7 APPENDIX Table 8: Model predictions of mean values and 95 % confidence intervals of soil water content of irrigated treatments at different depths for the 2016 and 2017 growing seasons Growing season Depth Treatment 2.5 % percentile (m3 m−3) Mean θ (m3 m−3) 97.5 % percentile (m3 m−3) 2016 10 cm rainfed 0.10 0.14 0.17 deficit 0.13 0.17 0.20 optimal 0.14 0.17 0.20 20 cm rainfed 0.18 0.21 0.25 deficit 0.20 0.23 0.27 optimal 0.26 0.30 0.33 30 cm rainfed 0.21 0.25 0.28 deficit 0.20 0.23 0.27 optimal 0.32 0.35 0.39 40 cm rainfed 0.22 0.25 0.29 deficit 0.21 0.24 0.28 optimal 0.33 0.36 0.40 50 cm rainfed 0.21 0.24 0.28 deficit 0.22 0.26 0.29 optimal 0.32 0.35 0.39 2017 10 cm rainfed 0.16 0.12 0.21 deficit 0.20 0.16 0.24 optimal 0.18 0.14 0.22 20 cm rainfed 0.24 0.20 0.29 deficit 0.26 0.22 0.30 optimal 0.28 0.23 0.32 30 cm rainfed 0.28 0.24 0.32 deficit 0.26 0.21 0.30 optimal 0.34 0.30 0.39 40 cm rainfed 0.29 0.24 0.33 deficit 0.27 0.23 0.31 optimal 0.37 0.33 0.42 50 cm rainfed 0.29 0.24 0.33 deficit 0.27 0.23 0.31 optimal 0.37 0.33 0.42 Table 9: Mean olive oil yield with 95 % percentiles for eight olive trees per treatment for the 2016 and 2017 growing seasons Growing season Treatment 2.5% percentile (l tree−1) Mean (l tree−1) 97.5 % percentile (l tree−1) 2016 optimal 2.5 4.2 6.0 deficit 1.7 2.0 2.3 rainfed 1.5 2.3 3.1 2017 optimal 2.6 3.9 5.1 deficit 1.8 2.4 2.9 rainfed 2.0 2.3 2.6