Effects of Different Crop Loads on Physiological, Yield and Fruit Quality of ‘JoyaTM’ Apple Trees: High Crop Load Decreases Maximum Daily Trunk Diameter and Does Not Affect Stem Water Potential

ABSTRACT This study aimed to evaluate the effects of different crop load levels on apple trees’ yield and physiological characteristics, emphasizing the relationship between stem water potential and crop load and the link between trunk diameter growth and crop load. It was conducted in 2014 at Station Expérimentale Fruits et Légumes (SUDEXPE-CEHM) in Southern France. The ‘JoyaTM’ apple on Pajam-1 rootstock was used as the plant material. Trees were subjected to hand thinning to obtain four final crop load levels as high (100%; 10 fruit/cm2 of branch cross-sectional area (BCSA)), medium-high (75%; 7.5 fruit/cm2 of BCSA), medium-low (50%; 5 fruit/cm2 of BCSA) and low (25%; 2.5 fruit/cm2 of BCSA). The four crop load levels were set up in a randomized block design with four replications, and each plot was formed of five uniform trees. Stem water potential was weakly affected by crop load but was mainly driven by vapor pressure deficit. The maximum daily trunk diameter, by and large, reduced while crop load level increased. The increase in crop load level was negatively related to fruit size, acidity and soluble solids content. Crop load did not affect bitter pit incidence. However, in parallel to the increasing crop load level, cumulative yield, yield efficiency and water productivity increased.


Introduction
The primary goal in fruit growing is to access as maximum yield as possible of better-quality crops at minimum cost (Westwood, 1995). Crop load management is a pivotal practice to reach this target. It influences many physiological processes in fruit orchards such as source-sink carbon relationships, water status and water consumption (Bustan et al., 2016). The fruit are strong carbohydrate sink, and high crop load levels can stunt the vegetative growth (Yuri et al., 2011) and return bloom of trees (Elsysy and Hirst, 2017).
The low crop load manipulates the tree to promote secondary growth in perennial plants (Lauri et al., 2010). Apple growers set crop load, as a vigor control tool, to bend the main leader and lateral branches down, especially in the training systems for high-density apple orchards such as Centrifugal Training, to achieve the balance between vegetative growth and fruiting (Atay, 2016). Thus, high crop load levels deplete nutrients in plants and especially carbohydrates required for vegetative growth (Goldschmidt, 1999).
Crop load management regulates regular annual cropping, next season's crop potential, vigor and fruit characteristics (Beya-Marshall and Fichet, 2017;Yuri et al., 2011). However, after physiological fruit drop, crop load adjustment by thinning has no or little effect on next season's crop potential because of already established flower initiation (Westwood, 1995).
Setting up an optimized crop load level can allow a correct size and quality for fruit. High crop load levels can penalize fruit quality, and therefore crop's market value. Dry matter accumulation, firmness, sugars and acidity in the fruit can decrease under high crop load levels (Atay, 2017;Serra et al., 2016). The fruit are relatively better colored in low yield trees (Gonzalez et al., 2020). There is a strong negative correlation between tree yield and fruit size (Forshey et al., 1992). High crop load extends ripening . Leaf and fruit nutrient concentrations can be affected by crop load (Neilsen et al., 2015). Reig et al. (2018) found that leaf and fruit minerals are moderate to low, correlate with cumulative crop load in 'Fuji' apple grafted on different rootstocks under New York conditions. Low crop load levels can increase physiological and storage disorders via increasing vegetative growth (Serra et al., 2016;Wünsche and Ferguson, 2005). As crop load increased in 'Golden Delicious'/M.9 apple, potassium and manganese concentrations in the leaf decreased correspondingly (Atay, 2016).
In that context, for a long time, fruit growers have sought to understand how crop load changes the yield and fruit quality characteristics of trees. With this background, our study aimed at evaluating tree water status, physiological and yield responses of 'Joya TM ' apple under varied crop load levels from low to high.

Experimental site and plant materials
This study was conducted in 2014 in an experimental orchard at Station Expérimentale Fruits et Légumes (SUDEXPE-CEHM; http://www.sudexpe.net/), located in Marsillargues in the South-East Mediterranean region of France (latitude 43° 37ʹ N -longitude 4° 09ʹ E -altitude 5 m). The area has a Mediterranean climate (Figure 1). The soil of the orchard plot was the clay-loam texture. The 'Fuji' trees on 'Pajam-1ʹ rootstock were planted at 4 m × 2 m spacing (1250 trees per ha) in 1994. The trees regrafted from 'Fuji' to 'Joya TM ' in 2005. 'Joya TM ' (formerly as known as 'Cripps Red' and 'Sundowner TM '), a very late season cultivar, was bred by Western Australian breeding programme (Cripps et al., 1993). It has the same parentage ('Lady Williams' x 'Golden Delicious') as Cripps Pink ('Pink Lady®') (Brown and Maloney, 2003). It is bitter pit prone cultivar and has a regular bearing pattern. Trees were trained to a Biaxis Centrifugal System since grafting (Lauri et al., 2004). The orchard was irrigated daily (4 pulses a day) at 100% of the estimated crop evapotranspiration (ET c ). ET c based on the replacement of reference evapotranspiration (ET o ) ( Figure 2). Irrigation at ET o was controlled thanks to soil moisture content probes (Decagon EC-5 probes, Decagon Devices, inc., Pullman, USA) inserted at two depths (30 and 60 cm) the soil. The four crop load levels (see below) were displayed in a randomized block design with four replications, including five uniform, possibly adjacent, trees. The three central trees were considered experimental, and two exterior trees functioned as borders, yielding to 12 (3 × 4) experimental trees per crop load level. In 2014, all trees in the orchard were chemically thinned by ammonium thiosulfate (ATS) (Floristar®, Agronutrition, France). Agricultural practices other than crop load management were provided according to commercial procedures in the region.

Data collection
Stem Water Potential (SWP): As a proxy of tree water status, leaf water potential at noon was measured, following protocols established previously by Corell et al. (2013) and Naor et al. (2008). Measurements were performed by a pressure chamber (Arimad-3000; Plant Water Potential Measurement Device for Agriculture, Israel). Before the measurements, selected leaves from the outer part of the canopy (as possible) were covered with an aluminum foil (at least 1 hour before measurements) to ensure equilibrium between leaf and stem. Vapor pressure deficit (VPD) as the measure of the air's drying power (Murray, 1967) was evaluated to check whether it affects SWP. Trunk diameter growth: Over the growth season (125 DAFB (2/8) and 262 DAFB (17/12) included), the micrometric trunk diameter fluctuations were measured with a dendrometer (Ecomatik, Dachau/ Munich, Germany) on a single tree per treatment. Data were monitored at 15 min intervals via an Internet-of-Things (IoT) sensor networks of Sud Agro Météo (SAM; http://www.sudagrometeo.fr/). Maximum daily trunk shrinkage (MDS) and maximum daily trunk diameter (MXTD) were calculated following procedures of Garcia- Garcia-Tejero et al. (2012) and Goldhamer and Fereres (2001). Briefly, MDS was the difference between the maximum and minimum daily trunk diameter values, and MXTD was the maximum trunk diameter value over a 24-h cycle.
Water productivity: Irrigation water productivity was calculated kg/m 3 by using irrigation input and yield values.
Fruit growth dynamics: The fruit diameter was measured eleven times by a digital caliper between the dates on 79 DAFB (17/6) and 221 DAFB (6/11). For this, twenty randomly selected fruit (i.e. 20 × 4 = 80 fruit per crop load level) in terminal position were used for each replication.
Fruit quality: The fruit quality characteristics analyzed here have been described elsewhere . In summary, average fruit weight of all crop harvested was determined by dividing yield (kg/tree) to fruit number (fruit number/tree). At each picking, all fruit per tree were graded in terms of diameter (mm) and red color index (%) using an automatic fruit sorting machine (Calibreuse Pomone II 1 L, http://www. maf-roda.com). The starch index, ranging from 1 (high starch) to 10 (high sugar), was scored using CTIFL (France) starch conversion chart to indicate the relative amounts of starch and sugar. The fruit weight (g), titratable acidity (g/L), soluble solids content (SSC) (%), fruit firmness (kg/cm 2 ) and juiciness which is the ratio of the juice to dry matter, were measured using a computer-controlled quality control machine (Setop Giraud Technologie, Pimprenelle, France, www.setop.fr). Bitter pit incidence (%) was calculated using the weight values of fruit with the signs of bitter pit (at least one pit), divided by tree yield.

Data analysis
All statistical analyses were performed using R statistical software version 3.6.2 (R Core Team, 2019). When the F-test was significant in the one-way analysis of variance (ANOVA), means were separated using the Least Significant Difference (LSD) test (P < .05) ('agricolae' package). The break-point of fruit growth was determined by piecewise linear regression analysis ('segmented' package). Regression and correlation analyses were performed to investigate the relationship level among variables. Principal components analysis (PCA) was carried out to investigate relationships among fruit quality characteristics ('FactoMineR' package).

Stem water potential
SWP showed no significant differences between crop load levels except at two measurement dates (117 and 130 DAFB). CL(100%) had a higher absolute SWP at 130 DAFB while not at 117 DAFB (Figure 3a). When considering the whole-season, SWP was not affected by the crop load level (Figure 3b). The relationships between SWP and VPD were significant in all crop load levels ( Figure 4).

Trunk diameter growth
The MDS did not run in parallel to the crop load level during the growth season ( Figure 5a). However, the tendency in MXDT was quite consistent across crop load levels. CL(25%) showed higher MXDT values during the season. It was followed by CL(50%), CL(75%) and CL(100%), respectively. Toward the end of the season, the layout between CL(50%) and CL(75%) had changed (Figure 5b). All relationships between crop load level and MDS ( Figure 6a) and MXTD (Figure 6b) were not significant at P < .05.

Yield and water productivity
CL(100%) had the greatest values for the number of fruit and yield at the second and third harvest as well as in cumulated yield (Table 1; Figure 7).

Stem water potential and trunk diameter growth
Trees with no fruit show a lower stomatal conductance and transpiration rates than the trees with fruit in deciduous species (DeJong, 1986;Hansen, 1971). Thus, the higher absolute SWP values are expected at high crop load levels because of the higher transpiration and the high resistance to water flow from soil to the stem (Bustan et al., 2016;Naor et al., 2008). In our study, crop load did not create a noticeable effect on SWP. Due to stress-free conditions (such as full irrigation (100% of ET c ) and proper fertilization) SWP might not have changed significantly in the study orchard. Besides, differences in SWP results between our study and previous studies may be attributed to tree training system we implemented. Leaf samples for SWP measurements are recommended to be taken from the    shaded interior part of the tree canopy (Correa-Tedesco et al., 2010;Naor et al., 2008). Shaded leaves in the interior canopy would be expected to have relatively low stomatal conductance, transpiration and post excision desiccation compared to the outer canopy (Fulton et al., 2001). However, in our study, we trained the trees to Centrifugal System. In this training system, buds that are close to the tree trunk are removed. In this study, SWP is driven by VPD. The stomatal closure, and thus SWP, is encouraged by high VPD (Hernandez et al., 2016). The sensors for MXTD monitoring were installed without repetitions. Sensors in commercial orchards are commonly used without any statistical design. In this study, MXTD, in general, decreased with increasing crop load level. MXTD inclined to decrease under water stress, especially during the midsummer period (Cuevas et al., 2013). The effect of high crop level inducing an increase in MXTD can be attributed to stimulated water stress.

Yield, water productivity, fruit growth and quality
It is safe to say that increasing crop load level, the number of fruit, cumulative yield, yield efficiency and water productivity increased. However, fruit size, acidity and SSC decreased. Crop load management is one of the most crucial vital practices determining the yield and fruit quality in fruit tree orchards (Anthony et al., 2019). Low crop load levels do not increase fruit size, beyond a saturation point (Corelli-Grappadelli and Lakso, 2004). High crop load penalizes SSC and coloring due to a shortage of carbohydrate supply and sinks for fruit (Atay, 2017;Bustan et al., 2016;Robinson et al., 2009). Besides, high crop load levels can negatively affect ripening, firmness, sugars and acidity in the fruit (Serra et al., 2016). As based on our previous experiences, 'Joya TM ' is a bitter pit prone cultivar. There is a relationship between crop load and bitter pit incidence, and relatively low crop load levels can produce large and bitter-pit prone fruit (Wünsche and Ferguson, 2005). However, in this study, crop load did not significantly affect on bitter pit incidence. Pajam-1, the rootstock of the trial trees, may have prevented the effect of crop load on the bitter pit incidence from being seen more clearly. Rootstocks can affect fruit nutrient composition and bitter pit susceptibility by affecting the nutrient absorption and distribution of trees (Fazio et al., 2020;Reig et al., 2018;Valverdi and Kalcsits, 2021). Donahue et al. (2021) found no direct relationship between crop load and bitter pit incidence, and rootstock and climatic conditions were influential in the rate of bitter pit incidence.
To conclude, our study conducted in a well-irrigated apple orchard trained to Centrifugal Training in South-East France showed that SWP was not affected by crop load levels whereas VPD did. MXTD was negatively correlated to crop load.