strawberry irrigation strategy based on water
matric potential and a climatic model
Résumé
Les fraises sont produites dans beaucoup de régions où l’eau est limitante et les producteurs doivent choisir parmi plusieurs stratégies d’irrigation celle qui maximise le rendement et l’efficacité d’utilisation de l’eau (EUE). En Californie, sur des fraises cultivées en champ (Fragaria × annanasa), des irrigations basées sur le potentiel matriciel du sol (ψ) avec des données disponibles en temps réel ont été comparées à une gestion par bilan hydrique climatique, et un seuil de ψ variable ajusté à l’ETc prévisionnelle a été investigué. L’ETc
instantanée, étant reliée de près au flux matriciel de l’eau du sol, a été utilisée pour estimer le ψ critique journalier. Sur un sol à faible perméabilité des traitements initiant l’irrigation à -10 kPa, -35 kPa, un témoin, -35/-10 kPa (une gestion de l’irrigation à seuil variable utilisant un ψ plus sec de début de saison à -35 kPa suivie d’une gestion à -10 kPa) et un traitement à seuil variable ajusté à l’ETc prévisionnelle quotidien n’ont pas montré de
différences de rendement, tandis que sur un sol hautement perméable, des traitements correspondant à 100% ETc, 75% ETc, 50% ETc, un témoin, -10 kPa et -35/-10 kPa ont mené
à d’importantes différences d’EUE et de rendement. Les régies pleinement irriguées, -10 kPa et 100% ET, ont maximisé les rendements mais la régie à -10 kPa a atteint la meilleure EUE. En gérant l’irrigation par déficit, la gestion basée sur le ψ a permis d’augmenter le rendement et l’EUE contrairement au modèle climatique, montrant l’intérêt d’une telle approche relativement au contexte de restriction en eau affectant les régions sensibles à la sécheresse tel que la Californie.
Mots-clés. Irrigation, fraise, tensiomètre, humidité du sol, potentiel matriciel du sol, évapotranspiration, modèle climatique, irrigation de déficit, efficacité d’utilisation de l’eau.
Abstract
Strawberries are produced in a many areas where water is limited and growers have to choose among irrigation strategies which one offers the best yield and water use efficiency (WUE). In California, on field grown strawberry plants (Fragaria × annanasa) soil matric potential (ψ) based irrigation with real-time data was compared to the climatic water balance model (CWB) and a variable ψ threshold adjusted to the forecasted crop (ETc) was
investigated. Instantaneous ETc, closely related to soil water flux, was used for estimating
daily critical ψ threshold. On a low permeability soil, -10 kPa, -35 kPa, a control, -35/-10 kPa (a variable management using early dry ψ management of -35 kPa followed by -10 kPa) and a variable ψ threshold adjusted to daily forecast of ETc
did not result in any yield differences, whereas on a high permeability soil, 100% ETc, 75%
ETc, 50% ETc, a control, -10 kPa and -35/-10 kPa resulted in important WUE and yield
differences. Fully irrigated treatments, -10 kPa and 100% ETc, maximized seasonal yield
but -10 kPa obtained the best WUE. When applying deficit irrigation managements, ψ based irrigation allowed yield and WUE increase unlike the climatic model, showing the interest of such an approach in water shortage context affecting drought sensitive areas as California.
Keywords. Irrigation, strawberry, tensiometer, soil moisture, water matric potential, evapotranspiration, climatic model, deficit irrigation, water use efficiency.
Introduction
In many areas, water has been a relatively inexpensive input and strawberry is a high value production. Growers that have to deal with a shallow rooted crop sensitive to water deficit often opt to manage on the wet side applying more water than necessary. In California, that context is subject to change in light of the growing uncertainty of water supply that has been facing the state for the past few years. In light of that concern, irrigators must use water as efficiently as possible. Many irrigation tools are available to help determining precise crop needs. There are two main operating modes in the field: (1) refilling water losses that occurred through potential evapotranspiration and (2) adjusting irrigations directly onto soil moisture measurements whether it is done by the use of tensiometer or electrical conductance tools. The latest is more susceptible to mitigate crop water needs because of the nonlinear relation between water content and soil water matric potential in conjunction with a plant water assimilation process closer to water potential differences than water content differences. In the past few years, soil water monitoring technologies have evolved rapidly and irrigators now have the opportunity to work with real-time data. It is thus of interest to verify which approach, tensiometer or climatic water balance model (CWB), provides the best yield and water use efficiency (WUE). A number of studies were conducted in order to clarify the proper way of using one or the other of these two technologies, but in strawberries the results varied. Some studies observed that tensiometer measurement is preferable over the use of climatic model for scheduling irrigations (Evenhuis and Wilms, 2008; Kirschbaum and al., 2003) while other studies seem more in favor of a climatic model (e.g. Krüger and al., 1999; Yuan and al., 2004).
A simple and very common way to estimate crop water needs among growers is by calculating the water balance model a posteriori by adding potential evapotranspiration of the crop to water losses and irrigations are usually applied on predetermined schedule. In many regions meteorological services, such as CIMIS in California (California Irrigation Management Information System), provide ET0 at the end of each day. ET0 is then
multiplied by a crop coefficient (Kc) whether this value is estimated as a function of the
plantation date or as a function of the crop coverage (e.g. Grattan and al., 1998; Hanson and Bendixen, 2004). Kc estimation using crop coverage is more reliable since transpiration is
closely related to canopy size but is unfortunately less likely to be used by growers because of the complexity of the estimation (Hanson and Bendixen, 2004). When using water balance model, it is important to consider that there might be environmental differences between the site where ET0 is measured and the actual crop site; that sufficient water must
be available in the soil so that plant transpiration is not reduced by water stress; that a Kc
estimation takes into account specific phenological characteristics of the cultivars from which it has been established and that avoiding any of these considerations might contribute to miscalculate crop water needs. Moreover, CWB often has the inconvenient of not taking into account real-time soil water flux within the root zone, which is necessary for quantifying plant water uptake use and avoid hydric stress of the crop, especially during dry spells (Rekika and al., 2014).
Determination of optimal water matric potential (ψ) with tensiometers to trigger the irrigation in strawberries was studied several times and has been proven to increase yield (Cormier and al., 2015; Létourneau and al., 2015; Serrano and al., 1992). However, in several strawberry studies, ψ follow-up was often unpaired with a monitoring system but was instead performed by visual readings (Krüger and al., 1999; Serrano and al., 1992). The lack of real-time data could therefore have led to fragment the ψ information and possibly misidentify the moment at which the actual critical ψ was reached and thus the appropriate irrigation set time. While studies using tensiometer unpaired with a monitoring system and performed under different conditions - i.e. soil texture, climate, depth of tensiometer probes ranging from 10 to 15 cm and strawberry grown in tunnel or in field - point out the range maximizing the yield as between -10 and -15 kPa (Hoppula and Salo, 2007; Savé and al., 1993; Guimerà and al., 1995; Serrano and al., 1992; Evenhuis and Wilms, 2008), recent studies using real-time data, were able to pin point the critical ψ threshold of strawberry as between -8 et -10 kPa for field grown strawberries with plastic mulch under varied climatic conditions (Létourneau and al., 2015).
On the other hand, even when monitoring systems were used, ψ experiments that aimed at finding the optimal irrigation ψ threshold for triggering irrigations only used fixed thresholds. Recently, Rekika and al. (2014) noted that the use of a fixed ψ threshold does not necessarily take into account instantaneous ETc requirements within the root zone, a
condition linked to the unsaturated hydraulic conductivity of the soil and the rooting depth among many factors. Using an analytical solution to Richard’s equation, first derived by Yuan and Lu (2005) and considering a uniform root water uptake, Rekika and al. (2014) isolated a threshold estimate from the Yuan and Lu (2005) analytical solution and Gardner function. This threshold estimate corresponds to an approximation of the critical irrigation threshold necessary in order to maintain the soil ψ in plant comfort zone. Lower ψ could be targeted early season, because at this time, lower ETc requirements result in a lower flux
into the root zone. Indeed, early season, Liu and al. (2007) observed that there were no important yield differences and Savé and al., (1993) observed no significant differences in physiological response when comparing different tensiometer managements and noted that this might be due to low evaporation demands and few accumulated stress cycles. Applying early season lower matric potential might thus allow maintaining yield and then result in an increased WUE. In order to achieve such a management, root depth, forecast of ETc, and
soil properties are needed.
The aim of this study was (1) to identify which of ψ triggered irrigation or water balance model based on ETc managements offers the best yield and WUE, and (2) to compare yield
and water performances between an irrigation management using a variable ψ threshold adjusted to forecast of ETc with managements where fixed ψ thresholds are applied.
Materials and methods
Experimental sites and crop details
This study covers two experiments that were performed during one crop season on two different California sites planted with two different cultivars. At site 1 short-day strawberry plants (Fragaria × annanasa) were planted mid-October and grown on raised beds covered with black plastic mulch according to the conventional farming practice in the area on an Entisol of Hueneme series (O'Geen, 2014) in South Coast California (34 8'N °, 119 ° 9'W). Each bed consisted of 4 rows with plants set 30 cm apart, giving an equivalent plant density of 10 298 plants per hectare. Water was supplied by trickle irrigation consisting of three
laterals of 16mm per bed diameter and located 5 cm below soil surface. Emitters were spaced every 20 cm with a 2.98 lph / m flow rate of a 0.44 kg / cm2 operating pressure.
At site 2, day neutral strawberry plants (Fragaria × annanasa) were planted mid-October and grown on raised beds covered with silver plastic mulch according to the conventional farming practice in the area on a Mollisol of Salinas series (O'Geen, 2014) in Central Coast California (36°53'N, 121°40'W). Each bed was constituted of 2 rows planted bed with plants set 30 cm apart, giving an equivalent plant density of 8814 plants per hectare. Water was supplied by trickle irrigation consisting of two laterals of 16mm diameter per bed and located on top of soil surface. Emitters were spaced every 20 cm with a 3.72 lph / flow rate at a 0.70 kg / cm2 operating pressure.
Irrigation treatments and experimental design
Treatments
Figure 1 shows the treatment structure of the two sites for more clarity. (The experimental design is not shown on this figure). Both experimental designs consisted in complete randomized blocks. Site 1 was designed with five repetitions of four ψ treatment plus a control. Site 2 was designed with four repetitions of two ψ treatments, three water balance model treatments based on ETc and a control. A control, a -10 kPa ψ threshold applied all
season and a -35 kPa early season dry management (from January 1st to March 20th at site 1
and from March 21st to May 23rd at site 2) followed by a -10 kPa wet management until the
end of the season were applied on both sites (this treatment will further be mentioned as - 35/-10 kPa). At site 1, a dry management of -35 kPa and a “variable” ψ threshold were also applied.
The variable treatment used the critical matric potential threshold (hc) adjusted to forecast
of ETc as described in the appendix of Rekika and al. (2014). Saturated hydraulic
conductivity (KsG) and the exponent of the Gardner function were fitted on the exponential
part of the shape of the unsaturated hydraulic conductivity curve into the -3.5 to -35 kPa range. Soil surface evaporation (q0) was estimated as 0 cm d-1 because of plastic mulch
covering crop rows and root depth was estimated relatively to root measurements performed every two weeks. ETc was calculated using daily value from the experimental
factor was estimated with the crop canopy coverage regression developed by Grattan and al. (1998).
At site 2, three treatments using water balance model were applied. Water reserve was refilled at 50%, 75% and 100% of potential ETc, regardless of soil matric potential. ET0 was
determined from the nearby CIMIS weather station 129. The Kc was derived from the
calculation method explained in Gallardo and al. (1996) and the cover percentage for using this method was measured monthly using a photographic technique.
Irrigation management
A combination of sprinkler and drip-irrigation systems is the conventional method that was used to ensure a good plant establishment after planting and this phase was managed by the grower. At site 1, this period covered from planting date to January 1st 2014, and at site 2,
from planting date to March 21st 2014. Afterward, the application of the treatments started
and water from trickle irrigation was monitored weekly.
Irrigations from ψ based treatments were triggered via automated valves (Irrolis-WEB; Hortau Inc., QC, Canada) at site 2, and via manual valves at site 1. Throughout the season, fertilization, insect control and disease control benefited from the usual care of the grower. Wireless tensiometers (TX3; Hortau Inc., QC, Canada) with continuously recorded data at a 15 minutes interval and transmitted to a web base (Irrolis-WEB; Hortau Inc., QC, Canada) were used as a tool for determining irrigation set times and durations. Each treatment was equipped with 3 pairs of tensiometers at site 1, and 2 pairs of tensiometers at site 2. They were installed at 15 and 30 cm (6 and 12 inches) below the surface which respectively correspond to the center and the bottom of the root zone. Tensiometers were randomly distributed in three blocks at site 1 and in two blocks at site 2.
Irrigations were triggered when the average ψ measured by the sensors placed 15 cm (6 inches) below the surface reached the targeted threshold. Irrigation durations were then adjusted so the higher ψ reached by the 30 cm (12 in) sensors corresponded to field capacity. Field capacity was estimated in situ with the ψ remaining in the soil two to three days after irrigation was made, at this moment free drainage was negligible. This value was about -5 kPa at site 1 and -3 kPa at site 2. The application of a matric potential higher than
this value (i.e. closer 0 kPa at soil saturation) at the bottom of the root zone would result an increased risk of leaching.
Yield and plant measurements
On both sites, plant yield started shortly after the end of the implantation phase also corresponding to the beginning of treatment application, at site 1 first harvest was on January 2nd, and at site 2, it was on April 10th 2014. Sugar content, total marketable berry
weight and number of marketable fruits were measured on 50 subplots at site 1 and on 48 subplots at site 2 that contained respectively 16 plants and 10 plants each. Fruit selection was adjusted to grower’s criteria as the season when on to match market requirements. At site 1, fruits were picked for fresh market until April 25th, after that date, fruits were
picked for cannery market resulting in a less severe fruit criteria selection. At site 2, fruits were picked for fresh market all season long. Sugar content (expressed in °Brix) was evaluated with a refractometer. On both sites, strawberries yield is partial because it was collected once a week the day before producer’s harvest and subsequent harvests of the same week were made by the producer team relatively to the regular schedule.
Plant biomass including roots and leaves dry masses, were sampled at mid-season (at the end of the -35 kPa ψ step in the -35/-10 kPa treatment) and at the end of the season. It thus allowed verifying the impact of an early dry season management on plant growth. Root biomass was measured by digging 4 replicates per treatment of soil cores having 20 cm deep by 30 cm in diameter and having their axes centered on the collar of the plants. Roots in soil cores were then washed from soil, dried and weighed. Percentage of ground cover and plant collar circumferences were also measured bi-monthly. Individual plants and plant density measurement (number of plants/area) thus allowed calculating average area covered by the crop.
Soil properties
Salinity
Strawberries is sensitive to salinity (Barroso and Alvarez, 1997), thus soil solution and soil solute salts were followed to determine whether or not irrigation treatments could have resulted in salinity build-up. Soil soluble salts were measured using a 1: 1 (volume:volume)
Soil:Water extract method (Dahnke and Whitney, 1988) for characterizing initial, midseason and final soil electrical conductivity. Soil solution was also measured weekly from three blocks in both sites using suction lysimeters (Soil Moisture Equipment Corp., Santa Barbara, California) installed at a depth of 15 cm (6 in).
Soil texture
Soil texture characterisation was performed on soil samples randomly distributed within both trials themselves analyzed in laboratory with the Bouyoucos hydrometer method to measure the smaller than 53 microns fraction. The same samples were subsequently sieved to measure the larger than 53 microns fraction (Centre d’expertise en analyse environnementale du Québec, 2010). At site 1 the soil series was a Hueneme Sandy Loam characterized with poor drainage as classified in the USDA classification system and at site 2 was found a well drained Salinas clay loam, as identified by the National Cooperative Soil Survey U.S.A. (O’Geen, 2014).
Water desorption curve, unsaturated hydraulic conductivity, and variable threshold estimation
In order to characterize matric potential at which water is limiting for the crop and for deriving threshold estimates of the variable treatment (Rekika and al., 2014), the water retention curves (WRC), the saturated and the unsaturated hydraulic conductivity (Ksat and
Kunsat) were characterized prior to the application of treatments. Because those analyses are
time consuming, Kunsat for the estimation of site 1 variable threshold was estimated by
inverse modeling performed on the WRC of an adjacent site that had already been analysed in a previous study and that had much similarities in soil properties. At sites 1 and 2, soil cores sampling was performed at the beginning and at the end of the season on undisturbed soil columns of 5.5 cm height and 8.0 cm diameter. Validation of the variable ψ treatment calculation was thus verified a posteriori.
Ksat was measured on soil samples previously saturated from the bottom at a very slow rate
(12h), then saturated hydraulic conductivity was measured using a vertical constant head soil core method (Caron and al., 1997). On the same samples, retention curves were characterized in desorption phase in the range -2 kPa to -20 kPa (using the steps of -2, -5,- 10 and -20 kPa) with the multistep outflow method (Dane and Hopmans ,2002) in Tempe
cells (Soil Moisture Equipment Corp., USA) equipped with pressure monitors. Higher steps (-30, -50 and -100 kPa) were determined with a pressure plate extractor (Soil Moisture Equipment Corp., É.-U.). Multistep outflow method allowed the calculation of unsaturated hydraulic conductivity by inverse modeling in HYDRUS-1D (PC-Progress). WRC were also analyzed on tension tables in the -2 kPa to -20 kPa range (using the steps of -1.5, -3, -5, -7.5, -10, -15 and -20 kPa.) as described in Carter (1993).
Statistical analysis
Data were analyzed using the MIXED procedure of the SAS software package (SAS Institute, 2012). The least significant difference test was used for mean separation, and letter indicating significant differences were assigned using the macro DANDA procedure (O’Brien, 1998). Statistical comparisons were considered significant at P < 0.05.
Results and discussion
Soil propertiesSoil texture
Textural analysis (Table 1) showed site 1 to be a fine sandy loam and site 2 to be a silty clay according to the USDA soil classification. The results were consistent with the National Cooperative Soil Survey U.S.A. (O’Geen, 2014).
Water desorption curve
Figure 2 shows the WRC from both tension table and Tempe cells analyzed for samples taken at the beginning and at the end of the season on both sites. There was no remarkable difference in WRC between the two analytical methods neither between early nor end