Abstract
Northern highbush blueberry (Vaccinium corymbosum L.) often requires frequent irrigation for commercial production, but irrigation is becoming increasingly challenging for many growers because of warmer and drier weather conditions, increased water regulations, and other water-use limitations. The purpose of this study was to develop improved methods of irrigation to prepare the industry more effectively against future water uncertainties. Treatments were applied for 2 years (2021 and 2022) and included a combination of weather-based or fixed irrigation schedules using continuous or pulse irrigation in a commercial field of ‘Draper’ blueberry in eastern Washington, USA. The soil at the site was a silt loam, and irrigation was applied using two laterals of drip tubing per row. Plants on a fixed schedule were irrigated for 12 to 13 hours per application (set by the grower), whereas those on a weather-based schedule were irrigated according to daily estimates of crop evapotranspiration (downloaded from an automated weather station). In both cases, irrigation was applied every 2 to 4 days as a single, continuous application or in 30- to 50-minute pulses every 2 hours (up to nine times per day) with the same amount of water as the continuous treatment. During the first year of the study, weather-based scheduling maintained greater stem water potentials in the plants and, on average, increased yield by 3.4 t⋅ha–1, berry weight by 0.14 g/berry, berry diameter by 0.4 mm, and fruit bud set by 4.3% when compared with fixed scheduling. Likewise, pulse irrigation maintained greater stem water potentials and, on average, increased berry weight and diameter by 0.10 g and 0.4 mm, respectively, fruit bud set by 3.3%, and canopy cover by 2.4% relative to continuous irrigation. Yield and canopy cover were unaffected by any treatment in the second year, which was likely a result of uncharacteristically cool, wet weather in the spring. However, weather-based scheduling continued to maintain greater stem water potentials and, when combined with pulse irrigation, increased berry weight and diameter by 3.7 g and 1.0 mm, respectively, relative to continuous irrigation on a fixed schedule. Pulse drip irrigation also increased fruit bud set by 5.1% during the second year. These results demonstrate the potential benefits of using weather-based scheduling and pulse drip in northern highbush blueberry, especially when the plants are grown on light-textured soils in hot, dry climates.
Northern highbush blueberry (Vaccinium corymbosum L.) is a water-intensive crop that requires irrigation to achieve suitable production in most commercial operations. However, with warmer and drier weather conditions, increased water regulations, and greater demand for water by other sectors, growers are facing serious water-use limitations (Dalton et al. 2013). Current methods of irrigation must be improved to prepare the industry more effectively against future water uncertainties. These improved methods should reduce water use while maintaining or increasing current yields and fruit quality if they are to be economically feasible for the blueberry industry.
Drip irrigation is currently the most common and efficient method to apply water to northern highbush blueberry (Retamales and Hancock 2018). Typically, the plants are irrigated using one or two lines of drip tape or tubing per row (Bryla 2011). This not only reduces water and energy use, but—unlike sprinklers—it also increases access to the field during irrigation and is more cost-effective than other irrigation methods (Bryla et al. 2011). Water-soluble fertilizers can also be applied directly through the drip lines, which increases growth and production relative to using soil-applied granular fertilizers in blueberry (Bryla and Strik 2015; Ehret et al. 2014; Vargas and Bryla 2015).
Blueberry growers often apply irrigation on a fixed schedule (duration and frequency) throughout the growing season and only adjust irrigation or stop watering when it rains or when the leaves begin to senesce in the fall (Bryla D, personal observations). The crop coefficient (Kc) approach is a simple alternative for estimating irrigation requirements based on weather conditions (Allen et al. 1998). This approach entails calculating crop evapotranspiration (ETc) by multiplying potential evapotranspiration (ETo) of a hypothetical surface, such as grass, by Kc values for a particular plant. These latter measurements are climatic variables and often available online from regional, automated agricultural weather networks, such as AgriMet (https://www.usbr.gov/pn/agrimet/) and AgWeatherNet (https://weather.wsu.edu/). Recent studies report that using weather-based estimates of ETc for irrigation scheduling help to maintain or improve yield, while reducing water use compared with using fixed schedules in many crops, including blueberry (Hunsaker et al. 2015; Johnson et al. 2016; Keen and Slavich 2012). By providing accurate estimates of crop water use, weather-based irrigation scheduling can help growers avoid over- or underirrigating their fields and provides a means of conserving water and fostering plant productivity during dry years or in years with other causes of water shortages.
Pulse irrigation is another method that has potential for conserving water or increasing yield per unit of water applied in certain crops. The practice involves applying irrigation water, usually by drip, for a short duration, followed by a rest period multiple times a day, until the total amount of water required by the crop is added. When managed properly, pulse irrigation supplies water at an optimum rate for plant uptake, thereby reducing water loss through evaporation, deep percolation, and runoff (Cote et al. 2003; Phogat et al. 2013). In doing so, pulse irrigation increases plant growth and production relative to applying irrigation in single continuous applications per day or every few days (Elnesr et al. 2015; Segal et al. 2006). Pulse irrigation is usually most effective in sandy, well-drained soils, but could have benefits in other soil types (Assouline et al. 2006; El-Mogy et al. 2012). Recently, pulse drip irrigation has been shown to increase growth and production in strawberry (Fragaria ×ananassa Duch.) (Gendron et al. 2018; Létourneau and Caron 2019) and red raspberry (Rubus idaeus L.) (Carroll et al. 2024). However, there is no information available on whether this method of irrigation has any positive effects on blueberry. Blueberry is a shallow-rooted crop (Bryla and Strik 2007) and, therefore, is expected to benefit from pulsed water applications.
The objective of our study was to compare weather-based scheduling to a conventional, grower-determined fixed schedule and evaluate their impact in combination with pulse or continuous drip irrigation on fruit production in northern highbush blueberry. We hypothesized that weather-based scheduling and pulse irrigation would reduce water limitations and thereby increase plant growth and production relative to the standard practice of using a fixed schedule and continuous water applications with each irrigation.
Materials and Methods
Study site.
This study was conducted in a mature, 1.4-ha field of ‘Draper’ northern highbush blueberry. The field was established in Mar 2013 and was managed organically by a commercial farm in Prosser, WA, USA (lat. 46°15′N, long. 119°37′W, elevation 259 m). The soil at the site was a Scootney silt loam (coarse-loamy, mixed, superactive, mesic Xeric Haplocambids) with a gravelly subsoil. Plants were spaced 0.76-m apart on rows of raised beds (0.3-m high × 1-m wide at the base) centered 3.05-m apart (4305 plants/ha). Each row was 170 m in length and was oriented downslope in a north–south direction. The slope ranged from 2% at the upper end of each row to 15% at the lower end. The beds were covered with black geotextile landscape fabric for weed control. Permanent grass alleyways were maintained between the beds and were mowed as needed. Two trellis wires (0.3- and 1.0-m high) were used to support the plants on each side of the row.
Irrigation was applied using two laterals of drip tubing per row, each installed ≈0.2 m from the center of the row on either side of the plants. The tubing was located beneath the landscape fabric and had 1.14-L⋅h–1 pressure-compensating emitters every 0.9 m. Overhead microsprinklers were also installed at the site and were used for evaporative cooling whenever ambient air temperatures reached 30 to 35 °C (Yang et al. 2020). Plants were fertilized and managed for pests and diseases following standard certified organic practices for the region (DeVetter et al. 2015).
Experimental design.
Four treatments were arranged in a randomized complete block design with four replicates and included a combination of fixed or weather-based irrigation schedules and continuous or pulsed water applications (i.e., two irrigation schedules × two methods of water application). Plants placed on a fixed schedule were irrigated for 12 to 13 h per application as set by the grower, whereas those placed on a weather-based schedule were irrigated to replace water loss by ETc (adjusted for precipitation) throughout the growing season (April–October). Daily estimates of ETc were calculated by multiplying ETo downloaded from an AgWeatherNet station (Benton, WA, USA) located ≈8 km from the study site by the single Kc values for berry bushes in the Food and Agriculture Organization (FAO) guidelines for computing crop water requirements (Allen et al. 1998). The length of the crop development stages required for the calculations were obtained from remote images collected previously in commercial fields of northern highbush blueberry in Prosser, WA, USA (unpublished data). In both the fixed or weather-based schedules, water was applied every 2 to 4 d as a single continuous application or in 30- to 50-min pulses every 2 h (up to nine times per day) with the same amount of water as the continuous treatment. Each of the four treatments was applied to four 170-m-long rows in the field, for a total of 16 experimental rows. Irrigation schedules were programmed using a wireless irrigation controller (model 2772-WVP; Hunter Industries Inc., San Marcos, CA, USA), which was used to open and close electronic solenoid valves installed at the head of each experimental row. The irrigation treatments were initiated in early May 2021 and late May 2022 and continued to early October during both years of the study. Distribution uniformity of the drip irrigation system was estimated to be ≈90% and was used to adjust the water applications in each treatment accordingly (Holzapfel et al. 2004).
Measurements.
The field was harvested on four dates per year: 6 Jul, 21 Jul, 29 Jul, and 9 Aug 2021, and 14 Jul, 29 Jul, 8 Aug, and 23 Aug 2022. At each harvest, ripe fruit were handpicked from a marked 85-m section near the upper end of each row (2–5% slope; 112 plants/plot) and weighed to determine total yield in each treatment. A random sample of 100 berries was also collected from each row and weighed to determine the average berry weight on each harvest date. The samples were frozen at –60 °C and stored for later analysis of total soluble solids and titratable acidity. An additional random sample of 32 berries was collected from each row on each date and measured for average diameter and firmness. These later samples were refrigerated at 0 to 2 °C and analyzed the following day using a fruit firmness tester (FruitFirm1000; CVM Inc., Pleasanton, CA, USA). Calibration was performed using the integrated calibration protocol with a weight of 111.9 g and a size offset of 25 mm. Berries were positioned on their side within divots on the turntable of the firmness tester, with the calyx facing inward toward the center. The turntable had 16 divots and was set to process 40 berries per minute. Approximately 150 g of the frozen berries were thawed and pureed in a blender. A 5.0-g sample of puree was then combined with 50 mL of degassed, deionized water and titrated with 0.1 mol⋅L–1 NaOH using an automated titration station (model 862; Metrohm AG, Ionenstrasse, Switzerland). Each sample was titrated to an endpoint pH of 8.1 and reported as citric acid equivalents. The puree was also measured for total soluble solids using a calibrated, digital temperature-compensating refractometer (model Palette PR-32; ATAGO Co., Ltd., Tokyo, Japan). Approximately 0.25 g of unstrained puree was placed on the refractometer aperture, ensuring it was covered completely, and was then read for percentage of soluble solids.
Stem water potential was measured on 18 May, 17 Jun, 6 Jul, 21 Jul, 29 Jul, 9 Aug, and 24 Aug 2021; and on 21 May, 13 June, 14 Jul, 29 Jul, and 8 Aug 2022 using a pressure chamber instrument (model 600; PMS Instrument Co., Albany, OR, USA), following the recommendations of Hsiao (1990). The measurements were taken before dawn (0230–0430 HR) on harvest days or on days when evaporative cooling was scheduled to be used (based on the weather forecast) and at midday (1245–1330 HR) on days with no harvest or cooling. In each case, two or three fully expanded leaves on the tip of a lateral shoot were sampled from the west side of one random plant per treatment row. Before taking the measurements, the leaves were enclosed for at least 45 min in heavy-duty, foil-laminate zip-lock bags to equilibrate their water potential with the stem (McCutchan and Shackel 1992). This procedure reduces variability in the water potential measurements (Bryla et al. 2011).
Leaves were sampled in late July each year for nutrient analysis. Fifty recently and fully expanded leaves were collected randomly from each row and dried at 73 °C in a forced-air oven. When dry, the leaves were ground in a Wiley mill (Thomas Scientific, Swedesboro, NJ, USA), filtered with a 40-mesh (425-μm) screen, and analyzed for N using a combustion analyzer (CN 828 Leco Corp., St. Joseph, MI, USA) and for other nutrients, including P, K, Ca, Mg, S, B, Cu, Fe, Mn, and Zn, using an inductively coupled plasma (ICP) optical emission spectrometry (Optima 8300; PerkinElmer, Waltham, MA, USA) (Gavlak et al. 2005).
Soil samples were collected from the center of the bed in each treatment row on 10 Nov 2021 and 11 Nov 2022. Each sample was taken at a depth of 0 to 30 cm using a stainless steel, 2.5-cm-diameter soil corer (JMC Soil Samplers, Newton, IA, USA). After collection, the samples were air-dried and sent to a commercial laboratory (Brookside Laboratories, New Bremen, OH, USA) for analysis of soil pH and nutrients. Soil pH was determined using a 1:1 ratio of soil and water (McLean 1982). Available N (NO3-N and NH4-N) was extracted and analyzed using methods developed by Dahnke and Johnson (1990). Other soil nutrients (P, K, S, Ca, Na, Mg, B, Fe, Mn, Cu, Zn, and Al) were extracted using the Mehlich III method (Mehlich 1984) and analyzed using an ICP spectrometer.
Fruit and vegetative buds were counted on two canes per plant from two randomly selected plants per treatment row on 26 Jan 2022 and 1 Feb 2023. In both cases, the buds were counted after leaf fall and before pruning. Fruit bud set was calculated by dividing the number of fruit buds by the total buds on each cane.
Canopy cover was measured remotely using an unoccupied aerial system (AgBot; Aerial Technology International, Oregon City, OR, USA) equipped with a multispectral camera (RedEdge; Micasense, Seattle, WA, USA) on 7 Sep 2021 and 3 Sep 2022. Flights were conducted within 1 h of solar noon (1300 HR) at an altitude of 150 m. Imagery was stitched into an orthomosaic of each spectrum using geospatial software (Pix4D SA ver. 1.9, Prilly, Switzerland) and imported into ArcGIS Pro ver. 3.0 (ESRI, Redlands, CA, USA) for analysis. To distinguish the plants from the soil, a modified version of the normalized difference vegetation index was used, whereby the difference between the 40-nm-wide, near-infrared spectral band centered at 840 nm and the 10-nm-wide, red spectral band centered at 668 nm was divided by their sum. Because shadows and glare can be a source of error in multispectral imagery, the equation was modified by squaring the near-infrared value in the numerator to augment the signature of the plants (Leblon et al. 1996). Subsamples of each treatment row were designated using a random point generator in ArcGIS. The image of each subplot was then categorized into plant and soil classes. To calculate the canopy cover, the number of pixels occupying the plant class was multiplied by the ground sampling distance squared and divided by the plot area. The final values were multiplied by 100 and reported as the percentage of the soil surface shaded by the canopy at midday.
Statistical analysis.
Data were analyzed by analysis of variance using a mixed-model procedure in R (R Foundation for Statistical Computing, Vienna, Austria). Fixed effects included irrigation schedule, method of water application, harvest date, and their interactions; random effects included block. Data were assessed for normality (Shapiro-Wilk test) and homogeneity of variance (Levene’s test), and were transformed if needed. Transformed data were back-transformed after analysis to represent actual means. Means were separated at the 0.05 level using Tukey’s honestly significant difference test.
Results and Discussion
Weather conditions.
The weather conditions were generally warmer and drier before harvest in 2021 than in 2022 (Table 1). On average, mean daily air temperatures at the site were 1.6 to 3.9 °C greater than normal in Apr, Jun, and Jul 2021; and 3.2 °C less than normal in Apr and May 2022. Conversely, precipitation was 40 mm less than normal from Apr through Jun 2021 and 60 mm greater than normal in 2022. Consequently, ETo during that same period was 143 mm greater than normal in 2021 and 95 mm less than normal in 2022. This, coupled with warmer temperatures in July, resulted in the need for more irrigation with weather-based scheduling in 2021 than in 2022 (Table 1). Irrigation with the fixed schedule, on the other hand, was greater in 2022 than in 2021. In this case, the grower irrigated more frequently (i.e., fewer days between irrigation events) in 2022 based on evidence of water limitations to fruit production with the fixed schedule used in the previous year (as described in the next section). In 2021 and 2002, irrigation with the continuous and pulse treatments totaled 731 and 799 mm, respectively, with the fixed schedule, and 1226 and 1062 mm, respectively, with the weather-based schedule.
Mean daily average air temperature, precipitation, potential evapotranspiration, and the total amount of irrigation applied each month to a mature field of ‘Draper’ blueberry in Prosser, WA, USA.i
Yield and fruit quality.
Weather-based scheduling increased yield by 17%, or an average of 3.4 t⋅ha–1, relative to the fixed schedule during the first year of study (Table 2). However, yield was similar with fixed and weather-based scheduling the following year and was unaffected by pulse irrigation in either year. Berry weight, on the other hand, was greater with weather-based scheduling and pulse irrigation in 2021 and the combination of both treatments in 2022. Berry diameter was also affected by the treatments during both years of the study and was often greater with weather-based scheduling and/or pulse irrigation on most harvest dates (Fig. 1). Fruit were subsampled from each row to estimate the weight and diameter of the berries and, therefore, unlike yield, were less affected by differences in plant size in the field. Pulse drip irrigation also increased fruit size in tomato (Solanum lycopersicum L.) (Elnesr et al. 2015) and red raspberry (Carroll et al. 2024), but had no effect on berry weight in strawberry (Létourneau and Caron 2019).
Effects of fixed or weather-based scheduling using continuous or pulse drip irrigation on yield and berry weight of ‘Draper’ blueberry in Prosser, WA, USA.
Effects of fixed or weather-based scheduling using continuous or pulse drip irrigation on berry diameter and fruit firmness of ‘Draper’ blueberries in Prosser, WA, USA. Plants were irrigated once every 2 to 4 d at either a fixed duration of 12 to 13 h per application or according to weather-based estimates of crop evapotranspiration, and water was applied in either a single continuous application on a given day or in 30- to 50-min pulses every 2 h, up to nine times per day, using the same amount of water as the continuous treatment. Each symbol represents the mean of four replicates, and those with a different letter within a harvest are significantly different at P ≤ 0.05 (Tukey’s test).
Citation: HortScience 59, 5; 10.21273/HORTSCI17527-23
Fruit firmness was affected by a three-way interaction among irrigation schedule, irrigation method, and harvest date (P = 0.026) in 2021, and by two-way interactions between irrigation schedule and irrigation method (P = 0.024) and irrigation schedule and harvest date (P < 0.001) in 2022. Firmness varied among the treatments on most harvest dates and, on several dates, was less with weather-based scheduling and/or pulse irrigation than with the other treatments, including during the second and third harvest in 2021 and during the first and fourth harvest in 2022 (Fig. 1). In each case, the difference was < 24 g⋅mm–1 between any of the treatments. The fourth harvest in 2022 was delayed in effort to increase fruit size, and, consequently, fruit were overripe and much softer than usual. Fruit firmness in each treatment was typical for ‘Draper’ blueberry (Strik 2019), and, in all but the fourth harvest of 2022, was satisfactory for the fresh market (Oliver K, personal communication).
The irrigation treatments also affected the composition of the berries during both years of the study (Table 3). On average, weather-based scheduling and pulse irrigation resulted in less soluble solids contents in the fruit than the fixed schedule and continuous irrigation, respectively, in 2021. Weather-based scheduling and pulse irrigation also resulted in less titratable acidity than the fixed schedule and continuous irrigation, respectively, in 2022, although only at harvest 3 in the latter case. Often, soluble solids and titratable acidy decrease as a result of more water in larger fruit (Acevedo-Opazo et al. 2010; Crisosto et al. 1994), which would explain why both measurements were less with weather-based scheduling and pulse irrigation in blueberry (Fig. 1; Table 3). Soluble solids and titratable acidity in each treatment fell within the range reported previously for ‘Draper’ blueberries produced in conventional and organic cropping systems (Strik 2019; Strik et al. 2017).
Effects of fixed or weather-based scheduling using continuous or pulse drip irrigation on soluble solids and titratable acidity of ‘Draper’ blueberries in Prosser, WA, USA.
Plant water status.
Stem water potential was affected significantly by irrigation schedule (P < 0.001) and method of water application (P = 0.001) in 2021 and by two-way interactions between irrigation schedule and measurement date (P = 0.012) and method of water application and measurement date (P = 0.038) in 2022. In general, weather-based scheduling and pulse irrigation resulted in greater readings (i.e., less water stress) than a fixed schedule and continuous irrigation, respectively, during both years of the study. Based on independent analysis of each measurement date, significant differences among the treatments were observed after harvest (i.e., late August) in 2021 and during harvest (i.e., mid-July through early August) in 2022 (Fig. 2). Basically, both weather-based scheduling and pulse irrigation resulted in greater stem water potentials than a fixed schedule or continuous irrigation in Aug 2021, and Jul and Aug 2022. Pulse drip irrigation also resulted in greater stem water potentials than continuous irrigation in red raspberry (Carroll et al. 2024).
Effects of fixed or weather-based scheduling using continuous or pulse drip irrigation on leaf water potential of ‘Draper’ blueberry in Prosser, WA, USA. Plants were irrigated once every 2 to 4 d at either a fixed duration of 12 to 13 h per application or according to weather-based estimates of crop evapotranspiration, and water was applied in either a single continuous application on a given day or in 30- to 50-min pulses every 2 h, up to nine times per day, using the same amount of water as the continuous treatment. Each symbol represents the mean of four replicates, and those with a different letter within a harvest are significantly different at P ≤ 0.05 (Tukey’s test).
Citation: HortScience 59, 5; 10.21273/HORTSCI17527-23
Canopy development and fruit bud set.
Pulse irrigation increased canopy cover by 2.4% relative to continuous irrigation in 2021, but had no effect on percent cover in 2022 (Table 4). Canopy cover was also unaffected by irrigation scheduling during either year of study. Because of abnormally hot weather in 2021, it is likely that canopy cover increased because of greater soil water availability with pulse irrigation than with continuous irrigation, as shown previously in other crops, including red raspberry (Carroll et al. 2024). Greater soil water availability reduces water stress and enables the plants to produce more growth. However, this effect was not observed in 2022. That year, cooler and wetter weather in the spring delayed growth and resulted in less water stress, thus reducing differences among the irrigation treatments.
Effects of fixed or weather-based scheduling using continuous or pulse drip irrigation on canopy cover of ‘Draper’ blueberry in Prosser, WA, USA.
On average, weather-based scheduling increased fruit bud set relative to a fixed schedule in 2021, whereas pulse irrigation increased fruit bud set relative to continuous irrigation in 2021 and 2022 (Table 5). Fruit bud set is considered a useful predictor of yield the following year in northern highbush blueberry (Salvo et al. 2012; Strik et al. 2017). Therefore, greater yields were expected with both weather-based scheduling and pulse drip in 2022, further indicating that the lack of treatment differences in yield the second year could be attributed to variability in plant size.
Effects of fixed or weather-based scheduling using continuous or pulse drip irrigation on fruit bud set of ‘Draper’ blueberry in Prosser, WA, USA.
Plant and soil nutrition.
None of the treatments had any effect on the concentration of nutrients in the leaves or soil during either year of the study (data not shown). Soil pH was also similar among the treatments and averaged 4.88 in 2021 and 4.89 in 2022. In each case, leaf and soil nutrients were within the range recommended for northern highbush blueberry (Hart et al. 2006; Strik and Davis 2023).
Irrigation water use.
Irrigation water-use efficiency (IWUE) was less with weather-based scheduling than with the fixed schedule in 2021 and 2022; but, like yield, was unaffected by pulse irrigation in either year (Table 6). In both years, the plants irrigated on a fixed schedule produced an average of 0.81 and 0.44 kg more fruit/m3 of water applied, respectively, than those irrigated on a weather-based schedule. This suggests that the single Kc values for berry bushes in the FAO’s guidelines for computing crop water requirements (Allen et al. 1998) used in weather-based scheduling may overestimate the amount of water needed per day in blueberry. However, IWUE often increases when plants are underirrigated (e.g., Madane et al. 2018), which was likely the case with the fixed schedule. In Australia, irrigation scheduled using the Kc approach reduced water use without reducing yield or fruit quality in southern highbush blueberry (V. corymbosum interspecific hybrid) and thereby increased IWUE by 0.4 kg⋅m–3 relative to a fixed schedule of 4 to 5 L/plant at each irrigation commonly adopted by many of their growers (Keen and Slavich 2012). In other crops, pulse irrigation increased IWUE by 0.8 kg⋅m–3 in orange (Abdelraouf et al. 2019), 1.2 kg⋅m–3 in potato (Bakeer et al. 2009), and 2.0 to 3.2 kg⋅m–3 in onion (Madane et al. 2018). Thus, in these crops, pulse irrigation is a means to increase growth and production with less water; however, this did not appear to be the case for northern highbush blueberry.
Effects of fixed or weather-based scheduling using continuous or pulse drip irrigation on irrigation water use efficiency of ‘Draper’ blueberry in Prosser, WA, USA.
Conclusion
Relative to using a conventional fixed irrigation schedule, weather-based scheduling reduced water stress in the plants as we hypothesized and, during the first year of this 2-year study, resulted in greater yield and better fruit bud set and, in both years, produced larger, heavier berries. Pulse irrigation was likewise beneficial and resulted in more canopy cover, larger and heavier berries, and better fruit bud set than continuous irrigation during one or both years of the study. In contrast, fruit quality metrics, including firmness, soluble solids, and titratable acidity, were slightly less on occasion with weather-based scheduling or pulse irrigation; but, apart from the final harvest in 2022, each measurement was suitable for the fresh market and within the typical range for ‘Draper’ blueberries. Thus, growers who adopt the use of weather-based scheduling or pulse drip irrigation could potentially increase yield and fruit size with little to no impact on fruit quality, particularly during hot, dry years in fields with light-textured soils. The costs of implementing these practices, including an irrigation controller and electronic solenoid valves, are likely manageable for many growers, provided they have access to an automated weather station for daily estimates of ETc and pressurized water available for pulse irrigation. In the northwestern United States, daily estimates of ETc are available for blueberry and other crops on the websites for AgWeatherNet and AgriMet. However, weather-based scheduling required 33% to 67% more water per year than the fixed schedule in our study, increasing both economic and environmental expenses associated with greater water allocations (e.g., water and pumping costs, labor availability, sustainability of water resources). Results of this research form a basis for determining cost efficiency given the potential benefit of greater yields, but more research is needed to determine whether one or both practices are beneficial to blueberry plants grown in other regions, such as those with cooler and wetter climates or heavier soil types.
References Cited
Abdelraouf RE, Ahmed A, Tarabye HHH, Refaie KM. 2019. Effect of pulsed drip irrigation and organic mulching by rice straw on yield, water productivity and quality of orange under sandy soils conditions. Plant Arch. 19:2613–2621.
Acevedo-Opazo C, Ortega-Farias S, Fuentes S. 2010. Effects of grapevine (Vitis vinifera L.) water status on water consumption, vegetative growth, and grape quality: An irrigation scheduling application to achieve regulated deficit irrigation. Agric Water Manag. 97:956–964. https://doi.org/10.1016/j.agwat.2010.01.025.
Allen RG, Pereira LS, Raes D, Smith M. 1998. Crop evapotranspiration: Guidelines for computing crop water requirements. FAO irrigation and drainage paper 56. Food and Agriculture Organization of the United Nations, Rome, Italy.
Assouline S, Möller M, Cohen S, Ben-Hur M, Grava A, Narkis K, Silber A. 2006. Soil-plant system response to pulsed drip irrigation and salinity: Bell pepper case study. Soil Sci Soc Am J. 70:1556–1568. https://doi.org/10.2136/sssaj2005.0365.
Bakeer GAA, El-Ebabi FG, El-Saidi MT, Abdelghany ARE. 2009. Effect of pulse drip irrigation on yield and water use efficiency of potato crop under organic agriculture in sandy soils. Misr J Agric Eng. 26:736–765. https://doi.org/10.21608/mjae.2009.109488.
Bryla DR. 2011. Crop evapotranspiration and irrigation scheduling in blueberry, p 167–186. In: Gerosa G (ed). Evapotranspiration: From measurements to agricultural and environmental applications. Intech, Rijeka, Croatia. https://doi.org/10.5772/18311.
Bryla DR, Gartung JL, Strik BC. 2011. Evaluation of irrigation methods for highbush blueberry: I. Growth and water requirements of young plants. HortScience. 46:95–101. https://doi.org/10.21273/HORTSCI.46.1.95.
Bryla DR, Strik BC. 2007. Effects of cultivar and plant spacing on the seasonal water requirements of highbush blueberry. J Am Soc Hortic Sci. 132:270–277. https://doi.org/10.21273/JASHS.132.2.270.
Bryla DR, Strik BC. 2015. Nutrient requirements, leaf tissue standards, and new options for fertigation of northern highbush blueberry. HortTechnology. 25:464–470. https://doi.org/10.21273/HORTTECH.25.4.464.
Carroll JL, Orr ST, Benedict CA, DeVetter LW, Bryla DR. 2024. Feasibility of using pulse drip irrigation for increasing growth, yield, and water productivity of red raspberry. HortScience. 59:332–339. https://doi.org/10.21273/HORTSCI17467-23.
Cote CM, Bristow KL, Charlesworth PB, Cook FJ, Thorburn PJ. 2003. Analysis of soil wetting and solute transport in subsurface trickle irrigation. Irrig Sci. 22:143–156. https://doi.org/10.1007/s00271-003-0080-8.
Crisosto CH, Johnson RS, Luza JG, Crisosto GM. 1994. Irrigation regimes affect fruit soluble solids concentration and rate of water loss of ‘O’Henry’ peaches. HortScience. 29:1169–1171. https://doi.org/10.21273/HORTSCI.29.10.1169.
Dahnke WC, Johnson GV. 1990. Testing soils for available nitrogen, p 127–140. In: Westerman RL (ed). Soil testing and plant analysis (3rd ed). American Society of Agronomy–Crop Science Society of America–Soil Science Society of America, Madison, WI, USA.
Dalton MM, Mote PW, Snover AK (eds). 2013. Climate change in the Northwest: Implications for our landscapes, waters, and communities. Island Press, Washington, DC, USA. https://doi.org/10.5822/978-1-61091-512-0.
DeVetter LW, Granatstein D, Kirby E, Brady M. 2015. Opportunities and challenges of organic blueberry production in Washington State. HortTechnology. 25:796–804. https://doi.org/10.21273/HORTTECH.25.6.796.
Ehret DL, Frey B, Forge T, Helmer T, Bryla DR, Zebarth BJ. 2014. Effects of nitrogen rate and application method on early production and fruit quality in highbush blueberry. Can J Plant Sci. 94:1165–1179. https://doi.org/10.4141/CJPS2013-401.
El-Mogy MM, Abuarab ME, Abdullatif AL. 2012. Response of green bean to pulse surface drip irrigation. J Hortic Sci Ornam Plants. 4:329–334. https://doi.org/10.5829/idosi.jhsop.2012.4.3.263.
Elnesr MN, Alazba AA, Zein El-Abedein AI, El-Adl MM. 2015. Evaluating the effect of three water management techniques on tomato crop. PLoS One. 10(6):e0129796. https://doi.org/10.1371/journal.pone.0129796.
Gavlak RG, Horneck DA, Miller RO. 2005. The soil, plant and water reference methods for the western region 3rd ed. Western Reg. Ext. Publ. 125. University of Alaska, Fairbanks, AK, USA. https://www.naptprogram.org/files/napt/western-states-method-manual-2005.pdf. [accessed 8 Jun 2021].
Gendron L, Létourneau G, Cormier J, Depardieu C, Boily C, Levallois R, Caron J. 2018. Using pulsed water application and automation technology to improve irrigation practices in strawberry production. HortTechnology. 28:642–650. https://doi.org/10.21273/HORTTECH04001-18.
Hart J, Strik B, White L, Yang W. 2006. Nutrient management for blueberries in Oregon. Oregon State University Extension Service EM 8918. https://catalog.extension.oregonstate.edu/sites/catalog/files/project/pdf/em8918.pdf. [accessed 15 Feb 2019].
Holzapfel EA, Hepp RF, Marino MA. 2004. Effect of irrigation on fruit production in blueberry. Agric Water Manag. 67:173–184. https://doi.org/10.1016/j.agwat.2004.02.008.
Howell TA. 2000. Irrigation role in enhancing water use efficiency, p 66–80. In: Evans RG, Benham BL, Trooien TP (eds). Proceedings of the 4th decennial national irrigation symposium, November 14–16, 2000, Phoenix, AZ. American Society of Agricultural Engineers, St. Joseph, MI, USA.
Hsiao TC. 1990. Measurements of plant water status, p 243–279. In: Steward BA, Nielsen DR (eds). Irrigation of agriculture crops. Agronomy monograph no. 30. American Society of Agronomy–Crop Science Society of America–Soil Science Society of America Publications, Madison, WI, USA. https://doi.org/10.1017/S0014479700021220.
Hunsaker DJ, French AN, Waller PM, Bautista E, Thorp KR, Bronson KF, Andrade-Sanchez P. 2015. Comparison of traditional and ET-based irrigation scheduling of surface-irrigated cotton in the arid southwestern USA. Agric Water Manag. 159:209–224. https://doi.org/10.1016/j.agwat.2015.06.016.
Johnson LF, Cahn M, Martin F, Melton F, Benzen S, Farrara B, Post K. 2016. Evapotranspiration-based irrigation scheduling of head lettuce and broccoli. HortScience. 51:935–940. https://doi.org/10.21273/HORTSCI.51.7.935.
Keen B, Slavich P. 2012. Comparison of irrigation scheduling strategies for achieving water use efficiency in highbush blueberry. N Z J Crop Hortic Sci. 40:3–20. https://doi.org/10.1080/01140671.2011.599398.
Leblon B, Gallant L, Granberg H. 1996. Effects of shadowing types on ground-measured visible and near-infrared shadow reflectances. Remote Sens Environ. 58:322–328. https://doi.org/10.1016/S0034-4257(96)00079-X.
Létourneau G, Caron J. 2019. Irrigation management scale and water application method to improve yield and water productivity of field-grown strawberries. Agronomy. 9(6):286. https://doi.org/10.3390/agronomy9060286.
Madane DA, Mane MS, Kadam ES, Thokal RT. 2018. Study of white onion (Allium cepa L.) on yield and economics under pulsed irrigation (drip) for different irrigation levels. Int J Agric Eng. 11:128–134. https://doi.org/10.15740/HAS/IJAE/11.1/128-134.
McCutchan H, Shackel KA. 1992. Stem water potential as a sensitive indicator of water stress in prune trees (Prunus domestica L. cv. French). J Am Soc Hortic Sci. 117:607–611. https://doi.org/10.21273/JASHS.117.4.607.
McLean EO. 1982. Soil pH and lime requirement, p 199–224. In: Page AL, Miller RH, Keeney DR (eds). Methods of soil analysis: Part 2: Chemical and microbiological properties (2nd ed). American Society of Agronomy–Crop Science Society of America–Soil Science Society of America, Madison, WI, USA.
Mehlich A. 1984. Mehlich 3 soil test extractant: A modification of Mehlich-2 extractant. Commun Soil Sci Plant Anal. 15:1409–1416. https://doi.org/10.1080/00103628409367568.
Phogat V, Skewes MA, Mahadevan M, Cox JW. 2013. Evaluation of soil plant system response to pulsed drip irrigation of an almond tree under sustained stress conditions. Agric Water Manag. 118:1–11. https://doi.org/10.1016/j.agwat.2012.11.015.
Retamales JB, Hancock JF. 2018. Blueberries (2nd ed). CABI International, Cambridge, MA, USA.
Salvo S, Muñoz C, Ávila J, Bustos J, Ramírez-Valdivia M, Silva C, Vivallo G. 2012. An estimate of potential blueberry yield using regression models that relate the number of fruits to the number of flower buds and to climatic variables. Sci Hortic. 133:56–63. https://doi.org/10.1016/j.scienta.2011.10.020.
Segal E, Ben-Gal A, Shani U. 2006. Root water uptake efficiency under ultra-high irrigation frequency. Plant Soil. 282:333–341. https://doi.org/10.1007/s11104-006-0003-6.
Strik BC. 2019. Frequency of harvest affects berry weight, firmness, titratable acidity, and percent soluble solids of highbush blueberry cultivars in Oregon. J Am Pomol Soc. 73:254–268.
Strik BC, Davis AJ. 2023. Revised leaf tissue nutrient sufficiency standards for northern highbush blueberry in western Oregon. Acta Hortic. 1357:99–106. https://doi.org/10.17660/ActaHortic.2023.1357.15.
Strik BC, Vance AJ, Finn CE. 2017. Northern highbush blueberry cultivars differed in yield and fruit quality in two organic production systems from planting to maturity. HortScience. 52:844–851. https://doi.org/10.21273/HORTSCI11972-17.
Vargas OL, Bryla DR. 2015. Growth and fruit production of highbush blueberry fertilized with ammonium sulfate and urea applied by fertigation or as granular fertilizer. HortScience. 50:479–485. https://doi.org/10.21273/HORTSCI.50.3.479.
Yang F-H, Bryla DR, Orr ST, Strik BC, Zhao Y. 2020. Thermal cooling with sprinklers or microsprinklers reduces heat damage and improves fruit quality in northern highbush blueberry. HortScience. 55:1355–1371. https://doi.org/10.21273/HORTSCI15119-20.