SPRING FOLIAR APPLICATION OF NITROGEN FERTILIZERS AND PLANT GROWTH REGULATORS TO SWEET CHERRY (PRUNUS AVIUM) SPUR LEAVES By Feiran Li A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Horticulture ─ Master of Science 2019 ABSTRACT SPRING FOLIAR APPLICATION OF NITROGEN FERTILIZERS AND PLANT GROWTH REGULATORS TO SWEET CHERRY (PRUNUS AVIUM) SPUR LEAVES By Feiran Li The objective of this study was to investigate the effect of nitrogen fertilizers and plant hormones applied to sweet cherry (Prunus avium L.) spurs in early spring on spur leaf growth and morphological features. Preliminary studies (2017) with isolated spurs showed that gibberellic acid (GA3, 30 ppm), 6- benzylaminopurine (BA, 150 ppm) and 6-benzylaminopurine + gibberellic acid (150 ppm BA + 30 ppm GA4+7) increased total spur leaf area 30%, 37% and 47%, respectively. All three nitrogen fertilizer treatments showed no significant differences with the control. At the microscopic level, the leaf adaxial epidermis cell size was increased from 20% to 40% in the plant hormone treatments. Follow-up studies at the whole tree level (2018) showed that: 1) Rate Experiment. 150 ppm BA + 30 ppm GA3 and 75 ppm BA + 15 ppm GA3 applications increased total leaf area per spur by 59% and 55%, respectively. 2) Timing Experiment. 30 ppm GA3 applied twice (when three emerging leaves were present and after accrual of an additional 100 Growing Degree Days) increased total leaf area by 36%. 3) Gibberellins Experiment. 30 ppm GA3 increased total leaf area by 33%. The whole trees experiment did not affect the area of individual leaves; for the Timing and Rate Experiments, the larger total leaf area per spur was due primarily to the emergence of more leaves per spur. TABLE OF CONTENTS LIST OF TABLES ...................................................................................................... iv LIST OF FIGURES .....................................................................................................v Chapter 1: Introduction .............................................................................................. 1 The impact of new sweet cherry rootstocks on modern orchards ........................... 1 New sweet cherry training systems ........................................................................ 5 New sweet cherry varieties .................................................................................... 7 The importance of sweet cherry spur leaves ........................................................ 11 Leaf light interception and photosynthetic capacity .............................................. 14 Potential phytohormone treatments for increasing leaf photosynthetic capacity ... 16 Potential nutritional treatments for increasing leaf photosynthetic capacity .......... 21 Objectives ............................................................................................................ 23 Chapter 2: Foliar treatment studies at the spur level (2017) .................................... 25 Materials and methods ......................................................................................... 25 Results ................................................................................................................ 29 Discussion ........................................................................................................... 37 Chapter 3: Foliar treatment studies at the microscopic level (2017) ......................... 39 Material and Methods .......................................................................................... 39 Results ................................................................................................................ 41 Discussion ........................................................................................................... 42 Chapter 4: Foliar treatment studies at the whole tree level (2018) ........................... 44 Materials and methods ......................................................................................... 44 Timing experiment. ........................................................................................... 45 Rate Experiment ............................................................................................... 45 Gibberellins experiment. ................................................................................... 46 Results ................................................................................................................ 47 Timing experiment. ........................................................................................... 47 Rate experiment. .............................................................................................. 51 Gibberellins experiment. ................................................................................... 55 Discussion ........................................................................................................... 58 Chapter 5: Conclusions ........................................................................................... 60 LITERATURE CITED .............................................................................................. 64 LIST OF TABLES Table 1. Average 'Sam' sweet cherry spur leaf area at maturity following treatment at leaf emergence with 0.5% urea (Urea), 1.7% KNO3 (KNO3), 150 ppm 6-BA (BA), 30 ppm GA3 (GA3), 150 ppm 6-BA + 30 ppm GA4+7 (BA&GA4+7). .................................. 29 Table 2. Timing for foliar growth regulator treatments applied to ‘Burgundy Pearl’/Gi5 sweet cherry trees during Spring 2018. ................................................................... 45 Table 3. The percentage of spurs that elongated into shoots for each phytohormone treatment in the application timing experiment with 'Burgundy Pearl'/Gi5 sweet cherry trees. 0BA, 0BG and 0GA3 represent 6-BA (150 ppm), 6-BA (150 ppm) plus GA4+7 (30 ppm) and GA3 (30 ppm), respectively, applied May 6. BBA, BBG and BGA3 represent the same rates for 6-BA, 6-BA+GA4+7 and GA3, respectively, applied twice, on May 6 and May 10. HBA, HBG and HGA represent the same rates for 6-BA, 6-BA+GA4+7 and GA3, respectively, applied only on May 10. .............................................................. 51 Table 4. The percentage of spurs that elongated into shoots for each phytohormone treatment in the application rate experiment with 'DD'/Gi5 and 'EE'/Gi5 sweet cherry trees. 0.5BA, 1BA and 2BA represent 6-BA at 75 ppm, 150 ppm and 300 ppm, respectively. 0.5BG, 1BG and 2BG represent 6-BA + GA4+7 at 75 ppm plus 15 ppm, 150 ppm plus 30 ppm and 300 ppm plus 60 ppm, respectively. 0.5GA3, 1GA3 and 2GA3 represent GA3 at 15 ppm, 30 ppm and 60 ppm, respectively.......................... 55 Table 5. The percentage of spurs that elongated into shoots for each phytohormone treatment in the gibberellin type experiment with 'GG'/Gi5 sweet cherry trees. GA3, GA4+7, and PM represents 30 ppm GA3, 30 ppm GA4+7 and 30 ppm 6-BA + 30 ppm GA4+7, respectively. ................................................................................................. 57 iv LIST OF FIGURES Figure 1. Spur leaf area growth curves over 312 hours as determined by 24 hour measuring intervals (30 GDD). ................................................................................ 26 Figure 2. Comparison of 'Sam' sweet cherry spur leaf shape (length/width ratio) following nitrogen and phytohormone foliar treatments (0.5% urea [Urea], 1.7% potassium nitrate [KNO3], 150 ppm 6-BA [BA], 30 ppm GA3 [GA3], 150 ppm 6-BA + 30 ppm GA4+7 [BA&GA4+7]) applied during leaf emergence. All the bars represent standard error. ....................................................................................................................... 30 Figure 3. Average 'Sam' sweet cherry spur leaf growth curves following treatment with (A) 150 ppm 6-BA [BA], (B)150 ppm 6-BA + 30 ppm GA4+7 [BA&GA4+7]), (C) 30 ppm GA3 [GA3], (D) Control), (E) 1.7% potassium nitrate [KNO3], and (F) 0.5% urea [Urea]. Data are means of five replications. ........................................................................ 32 Figure 4. Combined mean growth curves of 'Sam' sweet cherry spur leaves by leaf size ranking: largest leaf (A), 2nd largest leaf (B), 3rd largest leaf (C), 4th largest leaf (D) and 5th largest leaf (E), following nitrogen and phytohormone foliar treatments (0.5% urea [Urea], 1.7% potassium nitrate [KNO3], 150 ppm 6-BA [BA], 30 ppm GA3 [GA3], 150 ppm 6-BA + 30 ppm GA4+7 [BA&GA4+7]) applied during leaf emergence. Data are means of five replications. ....................................................................................... 33 Figure 5. Statistical results of multiple comparison of area of the largest leaf (A), 2nd largest leaf (B), 3rd largest leaf (C), 4th largest leaf (D) and 5th largest leaf (E) following nitrogen and phytohormone foliar treatments (0.5% urea [Urea], 1.7% potassium nitrate [KNO3], 150 ppm 6-BA [BA], 30 ppm GA3 [GA3], 150 ppm 6-BA + 30 ppm GA4+7 [BA&GA4+7]) applied during leaf emergence. All the bars represent standard error. . 36 Figure 6. Examples of sweet cherry spur leaf A) adaxial epidermis cell and B) leaf cross-section. .......................................................................................................... 40 Figure 7. Comparison of mature 'Sam' sweet cherry leaf adaxial epidermis cell size following phytohormone and nitrogen foliar treatments (0.5% urea [Urea], 1.7% potassium nitrate [KNO3], 150 ppm 6-BA [BA], 30 ppm GA3 [GA3], 150 ppm 6-BA + 30 ppm GA4+7 [BA&GA4+7]) during leaf emergence. All the bars represent standard error. ................................................................................................................................ 41 Figure 8. Comparison of mature 'Sam' sweet cherry leaf cross-sectional thickness following phytohormone and nitrogen foliar treatments (0.5% urea [Urea], 1.7% potassium nitrate [KNO3], 150 ppm 6-BA [BA], 30 ppm GA3 [GA3], 150 ppm 6-BA + 30 ppm GA4+7 [BA&GA4+7]) during leaf emergence. All the bars represent standard error. ................................................................................................................................ 42 Figure 9. The total leaf area per spur at harvest for the phytohormone timing experiment with 'Burgundy Pearl'/Gi5 sweet cherry trees. 0BA, 0BG and 0GA3 v represent 6-BA (150 ppm), 6-BA (150 ppm) plus GA4+7 (30 ppm) and GA3 (30 ppm), respectively, applied May 6. BBA, BBG and BGA3 represent the same rates for 6-BA, 6-BA+GA4+7 and GA3, respectively, applied twice, on May 6 and May 10. HBA, HBG and HGA represent the same rates for 6-BA, 6-BA+GA4+7 and GA3, respectively, applied only on May 10. All the bars represent standard error. ................................. 48 Figure 10. The average spur single leaf area at harvest for the phytohormone timing experiment with 'Burgundy Pearl'/Gi5 sweet cherry trees. 0BA, 0BG and 0GA3 represent 6-BA (150 ppm), 6-BA (150 ppm) plus GA4+7 (30 ppm) and GA3 (30 ppm), respectively, applied May 6. BBA, BBG and BGA3 represent the same rates for 6-BA, 6-BA+GA4+7 and GA3, respectively, applied twice, on May 6 and May 10. HBA, HBG and HGA represent the same rates for 6-BA, 6-BA+GA4+7 and GA3, respectively, applied only on May 10. All the bars represent standard error. ................................. 49 Figure 11. The mean leaf number per spur at harvest for the phytohormone timing experiment with 'Burgundy Pearl'/Gi5 sweet cherry trees. 0BA, 0BG and 0GA3 represent 6-BA (150 ppm), 6-BA (150 ppm) plus GA4+7 (30 ppm) and GA3 (30 ppm), respectively, applied May 6. BBA, BBG and BGA3 represent the same rates for 6-BA, 6-BA+GA4+7 and GA3, respectively, applied twice, on May 6 and May 10. HBA, HBG and HGA represent the same rates for 6-BA, 6-BA+GA4+7 and GA3, respectively, applied only on May 10. All the bars represent standard error. ................................. 50 Figure 12. The total leaf area per spur at harvest for the phytohormone rate experiment with 'DD'/Gi5 and 'EE'/Gi5 sweet cherry trees, applied when most of the spurs had one to three leaves emerging. 0.5BA, 1BA and 2BA represent 6-BA at 75 ppm, 150 ppm and 300 ppm, respectively. 0.5BG, 1BG and 2BG represent 6-BA + GA4+7 at 75 ppm plus 15 ppm, 150 ppm plus 30 ppm and 300 ppm plus 60 ppm, respectively. 0.5GA3, 1GA3 and 2GA3 represent GA3 at 15 ppm, 30 ppm and 60 ppm, respectively. All the bars represent standard error. .................................................. 52 Figure 13. The mean single leaf area at harvest for the phytohormone rate experiment with 'DD'/Gi5 and 'EE'/Gi5 sweet cherry trees, applied when most of the spurs had one to three leaves emerging. 0.5BA, 1BA and 2BA represent 6-BA at 75 ppm, 150 ppm and 300 ppm, respectively. 0.5BG, 1BG and 2BG represent 6-BA + GA4+7 at 75 ppm plus 15 ppm, 150 ppm plus 30 ppm and 300 ppm plus 60 ppm, respectively. 0.5GA3, 1GA3 and 2GA3 represent GA3 at 15 ppm, 30 ppm and 60 ppm, respectively. All the bars represent standard error. ................................................................................. 53 Figure 14. The mean leaf number per spur at harvest for the phytohormone rate experiment with 'DD'/Gi5 and 'EE'/Gi5 sweet cherry trees, applied when most of the spurs had one to three leaves emerging. 0.5BA, 1BA and 2BA represent 6-BA at 75 ppm, 150 ppm and 300 ppm, respectively. 0.5BG, 1BG and 2BG represent 6-BA + GA4+7 at 75 ppm plus 15 ppm, 150 ppm plus 30 ppm and 300 ppm plus 60 ppm, respectively. 0.5GA3, 1GA3 and 2GA3 represent GA3 at 15 ppm, 30 ppm and 60 ppm, respectively. All the bars represent standard error. .................................................. 54 vi Figure 15. The total leaf area per spur at harvest for the gibberellins experiment with 'GG'/Gi5 sweet cherry trees, applied when most of the spurs had one to three leaves emerging. GA3, GA4+7, and PM represents 30 ppm GA3, 30 ppm GA4+7 and 30 ppm 6-BA + 30 ppm GA4+7, respectively. All the bars represent standard error. ............... 56 Figure 16. The mean single leaf area at harvest for the gibberellins experiment with 'GG'/Gi5 sweet cherry trees, applied when most of the spurs had one to three leaves emerging. GA3, GA4+7, and PM represents 30 ppm GA3, 30 ppm GA4+7 and 30 ppm 6-BA + 30 ppm GA4+7, respectively. All the bars represent standard error. ............. 56 Figure 17. The mean leaf number per spur at harvest for the gibberellins experiment with 'GG'/Gi5 sweet cherry trees, applied when most of the spurs had one to three leaves emerging. GA3, GA4+7, and PM represents 30 ppm GA3, 30 ppm GA4+7 and 30 ppm 6-BA + 30 ppm GA4+7, respectively. All the bars represent standard error. .. 57 vii CHAPTER 1: INTRODUCTION The impact of new sweet cherry rootstocks on modern orchards Sweet cherry (Prunus avium L.) production, yield, and fruit quality has advanced worldwide over the past two decades to meet growing market demands. United States sweet cherry production in 2017 totaled 437,600 tons, ranking as the second-largest producer (after Turkey) of sweet cherries in the world. Around 75% of U.S. sweet cherry production is destined for the fresh market (NASS 2017). Developments in new rootstocks, training systems and cultivars have played very important roles in yield improvement. In the 1960s and 1970s, several cherry rootstock breeding programs were started around the world with the main purpose of decreasing tree size, since hand-harvest of such small fruits on large trees requires extensive and inefficient labor (Hrotko and Rozpara, 2017). In rootstock breeding, the predominant source of parental materials has come from the subgenus Cerasus (Rehder, 1974). Major species in the parentage of rootstocks include P. avium, P. cerasus, P. canescens Bois, P. fruticosa, and P. mahaleb L. In addition to these four species, P. x dawyckensis Sealy, P. incisa Thunb., P. concinna Koehne, P. serrulata L., P. subhirtella Miq., P. pseudocerasus, P. tomentosa, and P. serrula also are used as rootstocks or in rootstock breeding programs (Webster and Schmidt, 1996). Some hybrids of the above species also are considered as useful materials in rootstock breeding (Kappel et al., 1 2012). Besides the subgenus Cerasus, two genotypes of P. cerasifera Ehrh., from the subgenus Prunophora, have been used as interstocks or rootstocks: Adara and Myrobalan ‘R1’ (Moreno et al., 1996; Lang, 2006). In the 1980s, cherry rootstock breeding objectives had been expanded beyond size control to include increased scion precocity and cropping, improved graft compatibility, uniformity in performance, cold hardiness, adaptation to a wide range of soils, and disease and pest tolerance (Perry, 1987). These breeding objectives are still of importance, although the order of priorities may vary. The ideal goal is to develop productive orchards that are “pedestrian orchards”, for which all work can be completed without the use of ladders. Although the major target is tree vigor reduction, the balance among multiple objectives needs to be considered. Major rootstock breeding sources for scion vigor control include P. fruticosa and P. cerasus, as well as P. canescens (Wolfram, 1971; Trefois, 1980; Gruppe,1985; Wolfram, 1996). Several species within section Pseudocerasus (P. pseudocerasus and P. serrulata) might be considered as further sources, but the hardiness and drought tolerance of these hybrids in continental climates need more investigation (Cummins, 1979a, b). Additionally, from standard to medium vigor rootstocks have been found in selections of P. mahaleb in the last 15 years (Hrotkó, 2004; Hrotkó and Magyar, 2004; Lang, 2006; Sotirov, 2012; Barać et al., 2014). Also, genetic dwarf scion genotypes may, the future, 2 be considered as potential strategies for scion vigor control. Rootstocks also influence branch growth habit. Wider branch angles were observed in some P. avium and P. pseudocerasus clones and P. mahaleb ‘Magyar’ (Webster and Schmidt, 1996; Hrotkó et al., 1999), as well as several of the Gisela series (e.g., Gi5, Gi6) of P. cerasus x P. canescens hybrids (Schaumberg and Gruppe, 1985). Conversely, scions on ‘MxM 14’ and ‘MxM 97’ (P. avium x P. mahaleb hybrids) showed narrower branch angles (Hrotkó et al., 1999). Tree precocity, cropping and fruit quality of scion cultivars can be positively influenced by rootstocks. Scions on Mahaleb seedling rootstock have been shown to produce fruit 1 ~ 2 years earlier than on Mazzard rootstocks (Perry, 1987; Hrotkó, 1990; Hrotkó et al., 2008; Stachowiak et al., 2014). However, precocity, cropping and fruit quality are controlled by the combined effects of rootstock, training and pruning, tree spacing and nutrition. Therefore, the effect can of rootstock on these can only be determined in specific field trials. Rootstock graft compatibility varies from species to species and genotype to genotype. In some cases, optimal growth practices (particularly use of virus- free plant materials) and environmental conditions reduce the occurrence of incompatible symptoms (Quero-Garcia et al., 2017). For rootstock tolerance to cold climate winter conditions, P. cerasus and P. 3 fruticosa are considered to confer the best cold hardiness for rootstock breeding, and Mahaleb is hardier than Mazzard. Also, P. avium is the least hardy species within the Eucerasus section (Perry, 1987). In drought and heat tolerance, the most tolerant rootstocks appear to be the P. mahaleb selections and their hybrids (e.g., Mazzard × Mahaleb [M×M] series) (Quero-Garcia et al., 2017). Rootstocks show different adaptabilities for diverse soil conditions. In general, cherry root systems are not well-adapted to poorly drained or wet soils. Specific rootstocks can be found to fit for some soil types. For example, if drought tolerance is required, Mahaleb can confer some benefits. And Mazzard is often used where poorer drainage is known to occur. In the northwest provinces of China, Mahaleb seedlings (e.g., ‘Cema’) have proven to be tolerant to calcareous and high pH soils (Hrotkó and Cai, 2014). Tree nutrient content and water status can be affected by rootstock. Less calcium was observed in the leaves of trees on the dwarf rootstocks ‘P-HL-A’ and ‘P-HL-C’ than those of control trees (Sitarek et al., 1998). Also, higher Fe, Cu, Zn elements were found in fruit from trees on Mahaleb roots on sandy soil, and higher Fe, Cu and Cr were found in leaves in Poland (Stachowiak et al. 2015). Rootstocks also can affect tree tolerance or resistance to pests and diseases. For instance, P. mahaleb showed less tolerance to root-knot nematodes 4 (Meloidogyne incognita) than Mazzard, but it was more resistant to root-lesion nematodes (Pratylenchus vulnus) than Mazzard (Webster and Schmidt, 1996; Hartmann et al., 2002). On the other hand, all rootstocks are sensitive to Verticillium spp., and there is no known source of resistance. In modern high-density orchards, tree height has decreased by as much as 2.5~4.0 m (compare with 12~18 m mature heights of seedling sweet cherry trees in nature), and typical tree density varies from 667 to 1250 trees ha–1 by using size-reduction rootstocks. Consequently, contemporary sweet cherry orchards can and must yield high quality fruit, usually on a precocious, productive, dwarfing to semi-dwarfing rootstock. The relative importance of these three key characteristics depends on the orchard site and the economic requirements of different orchard operations. As labor efficiency becomes increasingly important in modern high-density orchards, scion vigor control will be in higher demand. There can be disadvantages of scion vigor control, for example, some highly efficient dwarfing rootstocks can increase the fruit-to-leaf area ratio and thereby have a negative effect on fruit size (Quero-Garcia et al., 2017). For this reason, new training systems are another important factor in modern pedestrian orchard development. New sweet cherry training systems Traditional sweet cherry training systems have complex canopies, take years (usually 5~7) to fully develop, lack a systematic plan to renew fruiting 5 wood, and are difficult to clearly and specifically explain (Long et al., 2015). Consequently, a number of new sweet cherry tree training systems have been developed to meet modern orchard requirements. Seven major training systems have been described recently in detail (Long et al., 2015) and they can be divided into two parts: multiple leader canopy architectures and single leader canopy architectures. For multiple leader systems, the Kym Green Bush (KGB) and Spanish Bush (SB) are free-standing trees that consist of multiple temporary (renewable) vertical fruiting units or multiple permanent leaders with temporary small fruiting laterals, respectively. The Upright Fruiting Offshoots (UFO) is a multiple leader narrow fruiting wall that consists of permanent horizontal cordon-like structure and multiple temporary vertical fruiting units (similar to KGB) that are fully supported by a trellis system. Also, the UFO system can be trained as a single-cordon or a dual-cordon UFO canopy (Long et al., 2015). The Steep Leader (SL) training system is configured similar to a Tall Spindle Axe (see below), but instead of a single leader, it has three or four closely-spaced vertical leaders instead to create a wider canopy and capture more light. Each leader mimics a one-quarter of a spindle tree canopy. For single leader systems, the Vogel Central Leader (VCL) system is a freestanding, single leader tree with tiers of renewable fruiting scaffolds and it has a conical or pyramidal shape. The Tall Spindle Axe (TSA) is an evolution of the VCL system and is still a freestanding, single leader tree, but the canopy is 6 characterized by a continuous whorl of moderately vigorous lateral branches. The Super Slender Axe (SSA) is a very high-density system (up to 4,400 trees ha-1) of central axis trees with very short limbs that are pruned severely every year. It fruits primarily on non-spur flower buds at the base of 1-year-old shoots, which is quite different from other systems (Long et al., 2015). The minimization of spurs on SSA trees means that most fruit growth (i.e., from the basal buds) is supported by the relatively large leaves typical of new shoots on well- managed trees. Integration of tree training systems with dwarfing rootstocks must take into account anticipated fruit set, balancing leaf area with crop load, and the promotion of fruit quality suitable for the target market. Achieving a pedestrian orchard depends on the appropriate combination of dwarfing rootstocks and training systems. For example, the single leader systems can create fully- pedestrian orchards with dwarfing rootstocks or semi-pedestrian orchards with semi-dwarfing rootstocks (Long et al., 2015). Training systems also must be matched to cultivar growth habits. For example, non-spur type varieties such as ‘Regina’ and ‘Attika’ perform well on SB and SSA because the fruit are largely produced from the basal buds of 1-year-old shoots (Long et al., 2015). New sweet cherry varieties Early in the 20th century, sweet cherry breeding involving selection of elite parents was undertaken in the United State (USA) and Canada, followed in the middle of the century by European countries such as the United Kingdom (UK), 7 Russia, Ukraine, and others. The USDA cherry breeding program at Washington State University (Prosser, WA) had its first major release with the popular blush variety Rainier in 1952, and in the last 60~70 years, numerous breeding programs have been developed throughout the world to meet the increasing demands of growers for locally-adapted varieties. The release rate of new cultivars was relatively high during the period 1991~2004 (Sansavini and Lugli, 2008). In the last 30 years, breeding has progressed rapidly, partly due to the increased interest in high-density orchards and dwarfing rootstocks (Hrotko and Rozpara, 2017). Current sweet cherry breeding goals include precocity and productivity, resistance to rain-induced cracking, resistance to diseases and insects, improved fruit quality, extension of the ripening season, and resistance to environmental stress. Modern high-density orchards require higher capital investment, and therefore a quicker return on investment, and precocity can help meet this demand. For example, ‘Sweetheart’ is very precocious even on the standard rootstocks and can be used as a standard benchmark for sweet cherry cultivar precocity (Kappel et al., 2012). Productivity is another extremely important need of growers. Increased yield must be balanced with fruit quality (especially fruit size) and yield (Omeg and Omeg, 2005). That is, very high yields of small poor quality fruit are uneconomical for growers. Precocity also can be highly influenced by rootstock and training system. Rain-induced cracking is one of the most damaging problems in many cherry 8 growing regions of the world. In some years, the proportion of cracked fruits can be up to 90% in some susceptible sweet cherry cultivars, causing a complete economic loss (Christensen, 1996). Breeding for resistance to rain- induced cracking is a highly desirable goal, yet with little success thus far. There are very few commercial cultivars with a high and consistent tolerance to rain- induced cracking (e.g., ‘Regina’ and ‘Fermina’). Sweet cherry trees are susceptible to a range of pests and diseases. Major diseases include powdery mildew (Podosphaera clandestina), brown rot (Monilinia spp.), leaf spot (Blumeriella jaapii (Rehm)), bacterial canker (Pseudomonas spp.), Cytospora canker (Leucostoma spp.), and various viruses. Key insect pests include cherry fruit fly (Rhagoletis spp.), spotted wing drosophila (Drosophila suzukii), black cherry aphid (Myzus cerasi Fab.), and cherry slug (Caliroa cerasi L.). Studies of potential genetic resistance to bacterial canker and brown rot haven’t shown satisfactory results, but cultivars with different levels of tolerance have been discovered (Bargioni, 1996; Brown and Wilcox, 1989; Kappel and Sholberg, 2008; Kappel et al., 2012). No tolerance or resistance has been identified to cherry fruit fly or spotted wing drosophila. However, cultivars with black cherry aphid tolerance have been found in UK (Bargioni, 1996). Large size, firmness, and sweetness are all considered to be extremely important fruit quality traits for sweet cherries (Proebsting, 1992; Christensen, 1995; Ystaas and Frøynes, 1990). Other fruit quality traits have been 9 considered as specific requirements for certain breeding programs, such as self-fertility, fruit skin color and juiciness, low chilling requirements, and excellent postharvest performance. For export quality fruit, the optimum fruit size is about 12 g and the minimum soluble solids content is 17~19% (Kappel et al., 1996). The timing of fruit maturity is important for sweet cherry production. Fruits produced in the early or later part of the regular production season trend toward higher prices than in the mid-season (San Martino et al., 2008). Therefore, extension of harvest seasonality with improved early- and late-ripening cultivars is a primary objective in sweet cherry breeding across the world, especially in Europe (Piaskowski et al., 2018; Quero-Garcia et al., 2014). Currently, the benchmark earliest cultivar is ‘Burlat’, so breeding to combine early flowering with a short fruit ripening phase, without quality loss, is a major target (Quero- Garcia et al., 2017). The main environment stress risks, beyond rain-cracking, for sweet cherries include winter injury, spring frosts, and heat stress. Increasingly, sweet cherries have been planted at the margins of traditional production areas to meet growing worldwide market demands, and that is where new cultivars resistant to winter injury are primarily needed. ‘Windsor’, ‘Black Eagle’, ‘Vic’, ‘Kristin’ and ‘Hudson’ were considered as cold hardy by Bargioni (1996). Another cultivar breeding objective is to improve tolerance to spring frosts that can kill flower buds. Currently, the most effective breeding method to address this one of 10 indirect prevention, that is, avoidance of high frost risks by using of late flowering parental lines (a widely expressed character in the Prunus cerasus germplasm) (Dondini et al., 2018). Also, heat stress can affect the formation of fruit doubles and spurs the following year, but studies to determine the genetics of heat stress resistance have not yet been initiated (Quero-Garcia et al., 2017). The new sweet cherry rootstocks, training systems and cultivars have played similar essential roles in the expansion of cherry production capacity over the past two decades. Sweet cherry trees trained to high density production systems on dwarfing rootstocks have more regular and larger yields, larger fruit, smaller tree size, easier worker access, and especially earlier fruiting (Long et al., 2015). Thus, the future trend for sweet cherry growers is to adopt improved sweet cherry genetic materials that are integrated into new, efficient training systems to develop sustainable, competitive and pedestrian orchards. The importance of sweet cherry spur leaves The majority of the fruit typically produced on sweet cherry trees arises from spurs on wood that is two or more years old, with a lesser proportion produced at the base of shoots that grew the previous season. The fruiting spurs are predominately positioned on growth that is 3 to 7 years old, decreasing in productivity as wood ages and/or becomes heavily shaded. Floral initiation occurs within spur axillary buds in late spring and early summer of the year 11 preceding flowering and fruiting. A typical fruiting spur consists of one vegetative bud, which usually develops 6~8 spur leaves, and 0~10 flower buds, each of which can develop 1~4 fruits. On wood two or more years old, the number of flower buds per spur typically increases from base to apex of the branch's sections of annual growth (Lang, 2005). For the seven modern training systems noted above, two (KGB, UFO) bear fruit primarily on spurs, one (SSA) bears fruit primarily on the base of one-year-old branches, and four (SB, VCL, TSA, SL) bear fruit in varying proportions on a mix of both types, generally with the majority on spurs (Long et al., 2015). Since the majority of the yield on most mature trees occurs on fruiting spurs, several studies have focused on the factors controlling spur development and flowering. Both traits can vary by cultivar (Kramer, 1985; Maguylo et al., 2004), rootstock (Gruppe, 1985; Schaumberg and Gruppe, 1985; Maguylo et al., 2004) and training systems, as well as branch orientation (Kramer, 1985; Lauri et al., 1998). Further investigation of sweet cherry fruiting physiology has differentiated all the leaves on the tree canopy into three general populations: leaves on new (current year) shoot growth, spur leaves on the one-year-old section of shoots (without fruit buds in the spring of leaf emergence) and spur leaves on two-year and older sections of shoots with fruit buds (Ayala and Lang, 2005). Both types of spur leaves have a quite different growth period from that of shoot leaves: spur leaves develop in a very short time in spring, within 3~5 weeks after bud break, whereas formation of shoot leaves continues as new shoots elongate, 12 generally up to 10 weeks after bud break (Flore,1996; Ayala, 2004). Sweet cherry fruit development draws heavily upon the photosynthates supplied by the leaves on the same spur where the fruit is attached (Ayala and Lang, 2005; Ayala and Lang, 2018). However, fruit is known as a priority sink, especially during stages I and III, and leaves from neighboring non-fruiting spurs and long terminal shoots also become important sources of photosynthates for the growing fruit as early as stage I (Loescher et al., 1985; Roper et al., 1987; Roper and Loescher, 1987; Toldam-Andersen, 1998; Ayala and Lang, 2005; Ayala and Lang, 2018). Using 13C to track photosynthate partitioning from selected leaf populations, Ayala and Lang (2005) demonstrated that the more distant the 13C source, the lower the amount of 13C that was detected in fruit. In general, for a 3-year-old section of branch with no lateral shoots, 75 to 90% of the carbon supplied to spur fruit during development originated in the two spur leaf populations (Ayala and Lang, 2018). Research on manipulation of canopy leaf to fruit ratio (L:F) with ‘Lapins’/‘Gisela 5’ sweet cherry trees established different L:F ratios of 0.7:1, 2:1 and 3:1, and found that high L:F ratios significantly resulted in the darkest fruit color, higher fruit mass and higher total soluble solids content (Usenik et al., 2010). Besides the influence of fruit quality, L:F ratio also affected the fruit ripening process; fruit maturity was delayed by a low L:F ratio. Also, Whiting and Lang (2004) reported that a leaf area to fruit ratio (LA: F) around 210 cm2 per fruit is needed to achieve adequate fruit size and sugar for good quality fresh market sweet 13 cherries. Furthermore, spur leaves tend to be smaller than new shoot leaves. Therefore, in spur-bearing training systems, a better understanding of factors that affect spur leaf development, as a key determinant of photosynthetic capacity to supply fruit growth, may provide cherry growers with important management tools for optimizing fruit growth potential. Leaf light interception and photosynthetic capacity In addition to the environmental factors that are limiting to photosynthesis, including light intensity, carbon dioxide concentration and temperature, photosynthesis is determined by leaf photosynthetic capacity. Leaf photosynthetic capacity is a measure of the maximum rate of carbon fixation during photosynthesis. For individual leaves, photosynthetic capacity is determined by physical features (particularly leaf area, leaf thickness and stomatal properties) and biochemical features (particularly leaf nitrogen concentration per unit area) (Boardman 1977; Field and Mooney, 1986; Schulze et al., 1994; Hirose et al., 1997). Stomatal density and conductance play a key role in the regulation of gas exchange and leaf CO2 assimilation rate, therefore having an important influence on leaf photosynthetic capacity. A high-yielding rice variety, Habataki, showed higher leaf photosynthetic capacity because of its higher stomatal 14 conductance (Adachi et al., 2011). Similarly, with the overexpression of StNF- YB3.1, a gene which promotes ABA-mediated stomatal closure, potato exhibited a reduction in photosynthetic capacity (Xuanyuan et al., 2017). Regarding stomatal density, Tanaka et al. (2013) reported that the stomatal density of soybean from 18 lines had positive correlations with their photosynthetic capacity. Additionally, Tanaka et al. (2013) conducted further research on Arabidopsis mutants with STOMAGEN-overexpressing and - silencing lines, and indicated the significant positive correlation between stomatal density and leaf photosynthetic capacity. Leaf thickness is associated with the mesophyll conductance to CO2 which is an essential component of photosynthetic capacity. Previous study found a negative relationship between leaf thickness and mesophyll diffusion conductance (Flexas et al., 2008). The explanation is that CO2 diffuses through leaf mesophyll via intercellular air spaces, cell wall and the intracellular liquid pathway, and all of these are strongly affected by the leaf thickness (Evans and Von Caemmerer, 1996). Mesophyll transport resistance was reported to be a limiting factor in leaf photosynthesis in the middle of the last century (Gaastra, 1959). Since then, more detailed positive relationships between mesophyll conductance and photosynthetic capacity have been found (Galmés et al., 2014a; Peguero-Pina et al., 2016a; Tosens et al., 2016). Within a species, it is not surprising to find the strong positive correlation 15 between photosynthetic capacity and leaf nitrogen content, because the proteins of the Calvin cycle and thylakoids represent the majority of leaf nitrogen (Evans 1989; Walcroft et al. 1997). Evans and Seemann (1989) found that nearly half of leaf nitrogen is allocated to the photosynthetic apparatus. The enzyme Rubisco is involved in the first major step of carbon fixation and requires a large amount (15～30%) of leaf nitrogen (Evans, 1989; Kellomäki and Wang, 1997; Evans and Seemann 1989). Rubisco content per unit area and maximum rate of Rubisco carboxylation increased as the nitrogen content per unit leaf area increased (Warren et al., 2003b). Thylakoids are the site of the light dependent portion of photosynthesis. Evans (1989) showed that higher rates of oxygen evolution (the oxidation of water during oxygenic photosynthesis) was related to a greater amount of thylakoid nitrogen per unit of chlorophyll. In a study of deciduous hardwood and evergreen coniferous tree species, Reich et al. (1995) found that both two groups showed a positive correlation of photosynthetic capacity and leaf nitrogen content. However, the deciduous broad-leaved group (r2=0.75) had a greater slope (photosynthetic capacity – nitrogen content) than the evergreen coniferous group (r2=0.59). Potential phytohormone treatments for increasing leaf photosynthetic capacity The gibberellins (GAs) are an important group of phytohormones which exert various effects on promotion and regulation of plant growth. These can affect stem elongation, seed germination, dormancy, flowering, sex expression, 16 enzyme induction, and leaf and fruit senescence (Hedden and Thomas, 2012). The application of GAs to stems produces a remarkable increase in cell division in the subapical meristem (Sachs et al. 1960). The occurrence of rapid growth resulted from both the greater number of cells formed and an increased elongation of the individual cells. Several studies reported similar results, showing that GAs are associated with leaf expansion via their direct effects on cell division and expansion as well as via integration of internal and external stimuli (Yoshida et al., 2014; Davie`re and Achard, 2016). Nagel et al. (2001) conducted research on mutant tomatoes with reduced gibberellin synthesis, and found a decrease in leaf photosynthetic capacity. The treatment of Plantago major with GA increased leaf photosynthetic capacity (Dijkstra and Kuiper, 1989; Dijkstra et al., 1990). Similarly, with spray applications of uniconazole (an inhibitor of GA biosynthesis) and GAs on Polygonum cuspidatum, Sugiura et al. (2014) found that GA increased leaf photosynthetic capacity, whereas uniconazole showed the opposite result. In fruit crop production, GAs can be used to improve early stages of plant development, such as stimulation of adventitious rooting of cuttings, termination of seed dormancy, and promotion of lateral branch development. Direct application of GAs to cuttings were found to have a negative effect on rooting (Hansen, 1988). However, Ford et al., (2002) found that pre-treatment with GA3 on ‘Stella’, ‘F12/1’ and ‘Charger’ sweet cherries increased the rooting percentage of cuttings subsequently taken from the treated plants by 80% or 17 more compared to the control group. Furthermore, GA3 pre-treatment increased the number of roots per rooted cutting. Both seed germination percentage and rate for ‘Lambert’ sweet cherry was improved to 47.1% and 1.9 seed d–1, respectively, with additional 500 ppm GA3 applications after seed washing compared to seed washing only (Javanmard et al., 2014). Also, 500 ppm GA3 treatment after 120 days of stratification for Mazzard cherry seeds without the seed coat resulted in a germination percentage of 79.7% which was 29% higher than the control group (Çetinbaş and Koyuncu, 2006). Three treatments (GA3, GA4, and GA4+7 at 5000 ppm) were applied to 1-year-old shoots of 'Skeena'/Mazzard sweet cherry trees at green-tip stage, and all treatments showed similar positive effects for stimulation of branching from lateral buds (Elfving et al., 2011). Additionally, GAs can elicit numerous effects on mature fruit trees, including termination of bud dormancy, floral formation, prevention or delay of flowering, and fruit development, ripening, quality and increasing yield. Applications of GA3 and GA4+7 (100 ppm) to mature ‘Bing’/‘Gisela 1’ trees at the end of both stage I and II of fruit development reduced the number of reproductive buds per spur by 67% the next spring (Lenahan et al.,2006). Also, applications of 200 ppm GA3 and GA4+7 at both dates delayed flowering the following spring by 4~5 days. Similar research was conducted by Engin et al. (2014) on mature ‘0900 Ziraat’/‘Gisela 5’ trees. GA3 (25 ppm, 50 ppm and 100 ppm) was applied at two growth stages (early flowering and beginning of fruit development) and caused 18 the number of flowers per bud to decrease in the dormant season in the comparison with control. Also, the number of flowers per bud was correlated negatively with GA3 concentration. Dissection of flower buds showed that differentiation of the floral primordia (sepal, petal, stamen and pistil) was considerably slower after GA3 applications (Engin et al., 2014). Applications of GA3 at 15~30 ppm at the beginning of stage III of fruit development has become a standard practice due to a well-documented delay in fruit maturation, and increased fruit size and firmness at maturity (Proebsting et al., 1973; Facteau et al., 1985; Looney, 1996; Kappel and MacDonald, 2002; Zhang and Whiting, 2011). But in some studies, larger fruit size was not found with GA3 applications. GA3 applied at straw color development on ‘Bing’ sweet cherry trees showed similar fruit size with untreated controls (Clayton and Biasi, 2003). Furthermore, GA can affect soluble solids content (SSC) and fruit color, which are also considered as important factors in fruit quality. Fruit color and SSC development were delayed by GA3 applications at 25 ppm applied 1 week before harvest in ‘Lapins’/‘Mazzard’, but not in ‘Regina’/‘Gisela 6’ (Dong et al., 2018). ‘Bing’ fruits treated with 50 and 100 ppm GA3 had 7% and 12% higher soluble solids content, respectively (Lenahan et al., 2006). In addition to the effects mentioned above, preharvest treatment with GAs can influence the postharvest quality of sweet cherry fruits. Fruits of ‘Aksehir Napolyon’ treated with GA3 (10, 20 and 30 ppm) showed less loss in fruit 19 firmness, delayed stem discoloration and maintained fruit color brightness after four weeks of cold storage (Özkaya et al., 2006). Also, ‘Lapins’ fruit with GA3 applications (25, 50 and 100 ppm) at pit hardening had significantly higher firmness compared to the control group after four weeks of storage (Einhorn et al., 2013). Stem discoloration after cold storage was reduced by 25 ppm GA3 treatment. The cytokinin 6-benzylaminopurine (6-BA) is a growth regulator that promotes cell division and has positive effects on leaf photosynthesis. Foliar treatment with 6-BA to sugar beet, pea, meadow fescue and reed fescue increased leaf net photosynthetic rate and Rubisco activity (Chernyad’ev, 1994). The effect of 6-BA on Vicia faba leaf structure was a stimulation of mesophyll cell elongation and it increased the amount of photosynthetic pigments (Ron’zhina, 2003). According to Harvey’s et al. (1974) research on detached cucumber cotyledons incubated in buffer, cotyledons in buffer containing 6-BA had greater photosynthetic enzyme activities. In Epipremnum aureum, 6-BA sprays increased net photosynthesis as well as leaf thickness (Di-Benedetto et al., 2015a). 6-BA can be integrated into orchard production systems for such effects as initiation and growth of new shoots, control of canopy formation and fruit thinning. It increases biosynthesis of nucleic acids and mitotic activity in bud apices. Treatments of 6-BA+GA4+7 at 100, 250, 500, 750 and 1000 ppm were 20 applied to one-year-old ‘0900 Ziraat’/‘Mahaleb’ sweet cherry trees. All treatments significantly increased tree diameter compared to the control, and the 500 ppm 6-BA+GA4+7 treatment was most effective for stimulating feathering of ‘0900 Ziraat’/‘Mahaleb’ sweet cherry trees (Koyuncu and Yildirim, 2008). Moghadam and Zamanipour (2012) investigated the influence of 6-BA on one-year-old ‘Siah Mashhad’/‘Mahaleb’ and ‘Dovomras’/‘ Mahaleb’ trees. All the treatments with 200, 400, or 600 ppm 6-BA applied in mid-June (when scion shoots were 60~65 cm tall) increased the number of lateral shoots compared to the control. Fruit thinning with 6-BA in mature apple trees can result in larger fruit size (Greene, 1989a; Elfving, 1989; Greene and Autio, 1989) and increased return bloom the following year (Williams and Stahly, 1969; Stembridge and Morrell, 1972; Unrath, 1974). Potential nutritional treatments for increasing leaf photosynthetic capacity Nitrogen supply has been strongly and positively correlated (r2=0.97) with corn leaf nitrogen content (Bennett et al., 1953). In addition to the obvious effect on increasing leaf nitrogen content, nitrogen fertilization can influence leaf photosynthetic capacity. Knops and Reinhart (2000) conducted research on three grass species in Minnesota with long-term nitrogen fertilization and found that leaf photosynthetic capacity increased as the level of nitrogen fertilization increased. On sandy soil of West Australia, the greatest leaf photosynthetic capacity of wheat occurred only with additional nitrogen inputs (Asseng et al., 21 2003). In maize, higher specific leaf area was related to higher nitrogen supply (Amanullah et al., 2007). There also are many studies on woody plants. Cork oak seedlings with two-month nitrogen fertilization significantly increased leaf photosynthetic capacity cmpared to the control group (Kachout et al. 2017). Treatment of Handroanthus impetiginosus, a tree species for restoration of degraded areas, with different levels of nitrogen fertilization resulted in a positive linear correlation of leaf photosynthetic capacity and nitrogen supply (Leite et al., 2017). Nitrogen supply plays a vital role in the growth and productivity of fruit trees (Titus and Kang, 1982). Fruit tree growth during early spring is supported by remobilization of stored nitrogen instead of current-year root uptake, so applications of nitrogen in the fall can be considered as one possible method to improve nitrogen reserves and subsequent early spring growth (Millard, 1996). Foliar application of urea in October to ‘Hedelfinger’/‘Gisela 5’ sweet cherry trees increased the storage nitrogen levels in flower buds (up to 40%), shoot apices (up to 20%) and bark (up to 29%) (Ouzounis and Lang, 2011). Since spur leaf growth in the spring draws upon storage nitrogen levels, spur leaf size and total leaf area per spur were increased in the next spring by up to 24%. With autumn foliar application of urea to apricot trees, fruit yield was increased by 26.1%, and the abortive flower ratio decreased by 27.1% compared with the control group (Karlidag et al., 2017). Moreover, numerous other studies support that autumn sprays of urea significantly increase yield in 22 various fruit species (Sanchez et al., 1990; Khemira et al., 1998; Cheng et al., 2004; Ebert, 2009). Both soil and foliar fertilizer applications are common in orchard management. Compared to soil application, foliar application can produce a more direct effect on the plant because the nutrient ions can be absorbed by the leaf immediately. Foliar application is applied most effectively taken up during dawn and dusk, especially in hot weather, because during those times, stomata are open to take up nutrients and the drying time is relatively long (resulting in a longer effective time for absorption). Also, rainy weather needs to be avoided to prevent washing off of the foliar spray. Objectives Given the importance of spur leaves to sweet cherry fruit growth potential and the lack of any previous studies on potential orchard management strategies in the spring that might increase spur leaf size during the 3-4 week window of spur leaf growth after budbreak, the objective of this project was to examine potential foliar applications that might increase spur leaf size. A preliminary study was undertaken in 2017 to determine spur leaf growth and quality responses following foliar application of several nitrogen fertilizers and phytohormones, including cytokinin (BA), gibberellins (GA3), or the mix of cytokinin and gibberellins (BA+GA4+7) applied at different concentrations to mature ‘Sam’ sweet cherry trees. Subsequently, follow-up studies were conducted in 2018 to 23 further investigate application parameters and potential fruit effects for foliar spring treatments of GA3, GA4+7, or BA combinations on spur leaf responses of mature ‘GG’, ‘DD’, ‘EE’, and ‘Burgundy Pearl’ sweet cherry trees. 24 CHAPTER 2: FOLIAR TREATMENT STUDIES AT THE SPUR LEVEL (2017) Materials and methods Before the foliar application experiment in the field, one preliminary study was undertaken in the growth chamber to determine the appropriate spur leaf growth measuring interval based on Growing Degree Days (GDD, base temperature 7°C and calculated by equation: GDD = Average daily temperature - Base temperature = [Tmax + Tmin] / 2 - Base temperature). Two spurs were chosen randomly from one potted two-year-old sweet cherry tree grown in the growth chamber at 25°C under a 16:8 hour day:night photoperiod. Leaf area measurements were begun when the first leaf began unfolding and measures were taken at time intervals of about 24 hours (30 GDD). The measuring method involved drawing an outline of the leaves, which was scanned into a computer for counting of pixels in Photoshop (Adobe Photoshop CS., Berkeley, CA) and quantifying the leaf area by pixel number. This pixel counting method provided extremely accurate for leaf area measurements, providing detailed data for determining a measuring interval. According to the data, most detectable differences between successive leaf area values were less than 100 mm2 (1 cm2), which is quite difficult to detect by the length and width measuring method (Demirsoy and Lang, 2010) in the field (Figure 1). Consequently, a leaf size measuring interval of 60 GDD was determined to be the most appropriate interval for future field measurements. 25 Figure 1. Spur leaf area growth curves over 312 hours as determined by 24 hour measuring intervals (30 GDD). The study to determine spur leaf growth, leaf thickness and adaxial epidermis cell size responses following foliar application of nitrogen fertilizers and phytohormones was initiated at bud break on sweet cherry trees at the Michigan State University (MSU) Horticulture Teaching and Research Center (HTRC, aka the Hort Farm) near East Lansing, Michigan. To clearly differentiate each leaf, markings were painted (BEHR MARQUEE (Eggshell Enamel Low Odor Interior Paint) Behr Paint Co., Santa Ana, Calif.) on the abaxial leaf surface to differentiate each leaf of one spur. However, even though this paint marking had previously been used successfully on mature sweet cherry leaves (T. Wilkinson, personal communication), it was phytotoxic on newly emerging 26 leaves, resulting in severe leaf injury including burned holes and shrinkage. Since this severe damage affected natural leaf growth, this initial experiment was abandoned. Consequently, the experiment was moved north to orchards less developed phenologically. The treatments were conducted at the MSU Northwest Michigan Horticultural Research Center near Traverse City, Michigan, using mature ‘Sam’ sweet cherry trees on Mazzard rootstocks. All experimental trees were cultivated with standard orchard practices (i.e., dormant and spring pruning, timely pesticide applications, irrigation, weed control as needed) in the years before imposition of the treatments. Each trial was arranged in a randomized complete block design with five single-spur replications. Spurs were selected for uniformity of flower bud number, attached branch diameter and height from ground. There were seven foliar treatments (aqueous solutions using distilled water and no additional surfactant): T1 = 0.5% urea; T2 = 150 ppm 6-BA (Maxcel); T3 = 30 ppm GA3 (ProGibb); T4 = distilled water as a control; T5 = 2.0% calcium nitrate; T6 = 1.7% potassium nitrate; and T7 = 150 ppm 6-BA plus 30 ppm GA4+7. All applications were applied to individual spurs before sunrise by using a hand-held trigger-pump sprayer until run off. The spray application dates (May 10 for 30 spurs, May 15 for an additional 5 spurs) were determined by spur leaf growth stage, that is, at least three spur leaves were eligible for measurement at the time of treatment, usually between full- bloom and petal fall stage. Paper cones with a narrow opening at the base were 27 used to shield each spur during treatment to protect the other spurs from contamination. The experimental design was completely randomized and individual spurs were used as a treatment unit, replicated five times. All the spurs were chosen within the canopy at a similar height (around 1.2 to 1.5 m from the ground) and both the flower bud number per spur and the diameter of branches were recorded. Before the treatments were applied, each spur leaf length and width were measured, and a small triangular section of the leaf margin was collected for microscopy (see Chapter 3). The leaf length and width were determined with a measurement tape, recording the width of the intact half of the leaf from the midrib to the margin, opposite the half of the leaf that had been sampled for leaf margin tissue. Leaf area was estimated following the procedure of Demirsoy and Lang (2010). After the treatment dates, additional tissue samples were collected from the same side as the initial sample, and area measurements were conducted every ~60 GDD. When the leaf length and width data did not change for three continuous dates, leaf growth was considered to be finished. All data were analyzed in R 3.5.3 (R Core Team, 2018) with mean separation using the Wilcox test for individual leaf size, ANOVA and Tukey test for leaf shape (ratio of length to width) and the area of the five largest leaves. 28 Results There were several unexpected outcomes encountered with this experiment. The first was phytotoxicity caused by calcium nitrate, which was manifested by young leaf marginal burning and cupping. Consequently, this treatment and data were discarded for the analysis and further discussion. The second was leaf damage due to feeding by oblique banded leafrollers; data was not collected from any of these damaged samples. Finally, the 150 ppm 6-BA plus 30 ppm GA4+7 treatment stimulated 60% of the spurs to elongate into shoots in early June (around 20 days after the foliar application). Thus, the extra leaves on the shoots were not included in the spur leaf measurements. Several of the treatments resulted in larger average leaf areas (Table 1). Most effective were the plant hormones, 6-BA, BA+GA4+7 and GA3, which promoted average leaf areas that were 37%, 47% and 30% larger than the control leaves, respectively, all of which were significantly different from the control. However, the nitrogen treatments resulted in average leaf areas not significantly different from the control. Table 1. Average 'Sam' sweet cherry spur leaf area at maturity following treatment at leaf emergence with 0.5% urea (Urea), 1.7% KNO3 (KNO3), 150 ppm 6-BA (BA), 30 ppm GA3 (GA3), 150 ppm 6-BA + 30 ppm GA4+7 (BA&GA4+7). P-values of Wilcox test are significantly (*) or not significantly (NS) different from the 29 TreatmentsControlUreaKNO3BAGA3BA&GA4+7Leaf area (cm2)34.236.339.74744.350.5Wilcox test0.3348 NS0.1333 NS0.0129 *0.0294 *0.0182 * control at a = 0.05. Fully expanded leaf shape (ratio of length to width) was compared and there was no significant difference between any of the treatments and the control (Figure 2), suggesting that none of the treatments (even the gibberellins) caused differential leaf cell division or elongation. Figure 2. Comparison of 'Sam' sweet cherry spur leaf shape (length/width ratio) following nitrogen and phytohormone foliar treatments (0.5% urea [Urea], 1.7% potassium nitrate [KNO3], 150 ppm 6-BA [BA], 30 ppm GA3 [GA3], 150 ppm 6-BA + 30 ppm GA4+7 [BA&GA4+7]) applied during leaf emergence. All the bars represent standard error. In addition to the final leaf size comparison, the leaf growth process from first measurement to full expansion of each emerging leaf was examined with repeated measures to create growth curves. For each spur, the leaf area data were arranged from largest to smallest, and the average growth curves for the 30 five largest leaves are depicted for each treatment with different colors (Figure 3; red, orange, yellow, green, blue, from largest to smallest). For the hormone treatments (6-BA, BA+GA4+7 and GA3), the largest leaves were 170%, 270% and 200% larger in area than the fifth largest leaf, respectively. The differences in average area between the largest and fifth largest leaves for the potassium nitrate and urea treatments were 130% and 220%, respectively. This wide variation in leaf size within each spur is expected, as the earliest 3~5 leaves to emerge often include both the smallest and largest leaves and the leaves that emerge later always rank in the middle; in fact, the area of individual leaves within the control spurs varied by 200%. The 150 ppm 6-BA plus 30 ppm GA4+7 treatment might have increased the area of the largest leaves more than that of the smallest leaves, and the 1.7% potassium nitrate treatment may have increased the area of the relatively small leaves more than the large leaves. 31 Figure 3. Average 'Sam' sweet cherry spur leaf growth curves following treatment with (A) 150 ppm 6-BA [BA], (B)150 ppm 6-BA + 30 ppm GA4+7 [BA&GA4+7]), (C) 30 ppm GA3 [GA3], (D) Control), (E) 1.7% potassium nitrate [KNO3], and (F) 0.5% urea [Urea]. Data are means of five replications. 32 Figure 4. Combined mean growth curves of 'Sam' sweet cherry spur leaves by leaf size ranking: largest leaf (A), 2nd largest leaf (B), 3rd largest leaf (C), 4th largest leaf (D) and 5th largest leaf (E), following nitrogen and phytohormone foliar treatments (0.5% urea [Urea], 1.7% potassium nitrate [KNO3], 150 ppm 6-BA [BA], 30 ppm GA3 [GA3], 150 ppm 6-BA + 30 ppm GA4+7 [BA&GA4+7]) applied during leaf emergence. Data are means of five replications. 33 Since spur leaf area varied widely within a spur, the comparative effects of each of the treatments were depicted with separate graphs for each leaf size class within the spur (Figure 4). The curves in Figure 4 (A) and (B) showed a consistent result for the effect of the treatments on the two largest leaves within each spur. The 6-BA+GA4+7 treatment promoted about 50% larger leaf area for the two largest leaves compared to the control, and both the 6-BA and GA3 treatments promoted about 20% larger leaf area for those leaves compared to control. However, the differences in area of the two largest leaves following the nitrogen fertilizer treatments were quite small in comparison with the control. The effect of all treatments diminished as leaf size class decreased. The curves in Figure 4 (C) for the 3rd largest leaf show that the 6-BA+GA4+7 and 6-BA treatments promoted larger leaf area than the GA3 and nitrogen fertilizer treatments, which were only slightly larger (<10%) than the control. The average area of the fourth and fifth largest leaves are depicted in Figure 4 (D) and (E), with the best promotion by 6-BA treatment, followed by the 6-BA+GA4+7, GA3 and nitrogen fertilizer treatments in the comparison with the control. The GA3 treatment resulted in fourth and fifth largest spur leaves that were second and third best in promotion of leaf area. For the nitrogen treatments, only potassium nitrate increased leaf size, and then only of the fifth largest leaf (by 30%) compared to the control (Figure 4E). The five largest leaves in each spur were grouped by final leaf size for each 34 treatment for further statistical analysis (Figure 5). For the largest and second largest leaves, the 6-BA+GA4+7 treatment was the only treatment that differed significantly from the control. The three smaller leaves, however, did not exhibit significant differences within their leaf size classes among the treatments and the control. 35 Figure 5. Statistical results of multiple comparison of area of the largest leaf (A), 2nd largest leaf (B), 3rd largest leaf (C), 4th largest leaf (D) and 5th largest leaf (E) following nitrogen and phytohormone foliar treatments (0.5% urea [Urea], 1.7% potassium nitrate [KNO3], 150 ppm 6-BA [BA], 30 ppm GA3 [GA3], 150 ppm 6-BA + 30 ppm GA4+7 [BA&GA4+7]) applied during leaf emergence. All the bars represent standard error. 36 Discussion The nitrogen fertilizer treatments did not differ significantly in spur leaf area compared with the control, indicating that nitrogen fertilizer treatments applied between full bloom and petal fall have little influence on sweet cherry spur leaf growth. It is well-known that initial vegetative growth in temperate fruit trees depends on storage reserves (including carbon and nitrogen) accumulated during the previous season (Keller and Loescher, 1989; McCammant, 1988; Neilsen et al., 1997). Ouzounis and Lang (2011) found that nitrogen imported into sweet cherry spurs in the spring preceded bud swell. Grassi et al. (2002) and Millard et al. (2006) indicated that nitrogen remobilization in sweet cherry started immediately after bud break based on the observation of sharply increased sap concentrations of both glutamine and asparagine amides which are responsible for nitrogen remobilization. Nitrogen in leaves derived from root uptake was not found until three weeks after bud break. According to our spur leaf growth data, the rapid growth phase for the earliest 3~5 leaves is around 300 GDD (three weeks). Therefore, our results imply that in the first three weeks, those new spur leaves preferentially use storage nitrogen from previous season and foliar applications of nitrogen (in the forms used in this study) during this period are unable to provide additional nitrogenous resources for leaf growth. Viewed another way, this may suggest that nitrogen 37 supply was not a limiting factor for spur leaf growth in the trees used for this experiment. However, the fact that the plant growth regulator treatments significantly increased the size of some spur leaves suggests that while nitrogen may not have been a limiting factor, plant growth regulators may be able to increase spur leaf demand for or response to storage reserves. Thus, larger spur leaves occurred after application of 6-BA, 6-BA+GA4+7 and GA3 treatments. Although this is promising, the effects must be repeated, application parameters refined, and effective application rates studied further for potential unanticipated side effects. For example, Engin et al. (2014) reported that 30 ppm GA3 applied at full bloom affected flower bud initiation, with floral differentiation in the summer delayed and the flower number per bud decreased in the following year. In highly productive cultivars, this may be a desirable effect, whereas in cultivars of low productivity or in growing regions with a high risk of spring frost, this may be an undesirable effect. 38 CHAPTER 3: FOLIAR TREATMENT STUDIES AT THE MICROSCOPIC LEVEL (2017) Material and Methods The triangular leaf margin tissue samples were cut with razor blades (sections varied from 0.5 cm2 to 2 cm2, relative to sampled leaf size) and stored in FAA (10% formaldehyde [37%], 5% acetic acid [100%], 50% ethanol [95%], 35% distilled water) solution immediately after cutting. After immersion in the FAA solution for at least 24 h, the samples were dehydrated with a 50%, 75%, 90%, 95%, 100% alcohol series and infiltrated with 50% alcohol/50% n-Butanol, then 25% alcohol/75% n-Butanol, and finally 100% n-Butanol; each step required at least 1 h of immersion (Ruzin, 1999). The infiltrated samples were observed under a Nikon H600L Upright Microscope at 200X magnification to measure the adaxial epidermis cell size. The mean cell size was calculated by counting the cell numbers in a quantified area (100 µm x 100 µm) instead of measuring the length and width of each cell. For each treatment sample, the average cell size was calculated from four randomly chosen zones (Figure 6A). 39 Figure 6. Examples of sweet cherry spur leaf A) adaxial epidermis cell and B) leaf cross-section. Additionally, the thickness of whole leaf and mesophyll tissue was determined by measuring cross-sections of 174 spur leaf samples under the Nikon H600L Upright Microscope at 200X magnification (Figure 6B). The leaf cross-sections were obtained by using a Leica CM1850 Cryostat at the MSU Center for Advanced Microscopy. The difference between a regular microtome and the cryostat is the medium and temperature. The leaf samples were immersed in the medium (10.24% polyvinyl alcohol, 4.26% polyethylene glycol and 85.5% nonreactive) on the cold plate at -20°C. The medium would be liquid under room temperature and freezes at -10°C. For sectioning, the leaf sample was oriented perpendicular to the plate. After the medium had been frozen, the leaf cross- sections were obtained as with a regular microtome except in at -20°C (Dey, 2018). All data were analyzed in R 3.5.3 (R Core Team, 2018) with mean separation using the ANOVA and Tukey test. 40 Results Larger cells were observed in all of the plant growth regulator treatments (Figure 7). The BA+GA4+7, 6-BA and GA3 treatments resulted in 39%, 22% and 26% larger epidermis cells, respectively, compared to the control. The epidermis cells in the BA+GA4+7 treatment were significantly larger than the other two phytohormone treatments, which were significantly larger than the nitrogen or control treatments. Figure 7. Comparison of mature 'Sam' sweet cherry leaf adaxial epidermis cell size following phytohormone and nitrogen foliar treatments (0.5% urea [Urea], 1.7% potassium nitrate [KNO3], 150 ppm 6-BA [BA], 30 ppm GA3 [GA3], 150 ppm 6-BA + 30 ppm GA4+7 [BA&GA4+7]) during leaf emergence. All the bars represent standard error. In addition to greater leaf area and epidermal cell size, leaf thickness is another key parameter for leaf quality. The average leaf thickness of each group was 41 similar (Figure 8), with all treatments exhibiting less than 10% differences in comparison with the control, none of which were significant. Figure 8. Comparison of mature 'Sam' sweet cherry leaf cross-sectional thickness following phytohormone and nitrogen foliar treatments (0.5% urea [Urea], 1.7% potassium nitrate [KNO3], 150 ppm 6-BA [BA], 30 ppm GA3 [GA3], 150 ppm 6-BA + 30 ppm GA4+7 [BA&GA4+7]) during leaf emergence. All the bars represent standard error. Discussion All plant hormone treatments increased leaf area by promoting larger cell size. Leaf thickness was not affected. Cell elongation is the combination of two events, including biochemical and biophysical processes such as water absorption and cell expansion (Taiz, 1984; Cosgrove, 1986). Cell wall extensibility plays an important role in cell expansion and it is probably controlled by the orientation of both cellulose microfibrils and the cell wall matrix. The orientation of cellulose microfibrils can be regulated by gibberellins 42 (Shibaoka, 1994). Also, GAs have been shown to stimulate cell elongation by altering the rheological properties of the cell wall (Jones, 1983). Calculations of the different increased ratios of leaf area and epidermis cell size infer that epidermis cell numbers increased by 12%, 3% and 3% for the 6-BA, BA+GA4+7 and GA3 treatments, respectively. The higher cell numbers for the 6-BA treatment may be due to an acceleration of cell division by regulation of the cell cycle, including activating RNA synthesis, stimulating protein synthesis and the activities of some enzymes (Kulaeva, 1980; Kulaeva et al., 1996). Although the 6-BA+GA4+7 treatment had the same concentration of 6-BA, the leaf cell division may have been lower due to the 60% of those spurs that exhibited shoot elongation. 43 CHAPTER 4: FOLIAR TREATMENT STUDIES AT THE WHOLE TREE LEVEL (2018) Based on the 2017 results of the nitrogen fertilizer treatments and plant growth regulator treatments, the nitrogen fertilizer applications were omitted for additional study in 2018. The main objectives of the 2018 experiments were to examine application parameters for the promising plant hormone treatments and to treat entire trees in the orchard rather than individual spurs, as well as to determine whether the hormone treatments had any adverse effects on fruiting or fruit quality. Among the phytohormone application parameters studied were the timings (from bud break to fully expanded leaves), rates of BA, GA3 and GA4+7, and a comparison of GA3 and GA4+7. Since the largest 3 leaves showed the greatest response to plant growth regulator treatments in 2017, the question of whether repeated applications would extend these growth promotive effects to additional, later-developing leaves (such as those that emerge around 100 GDD later) is of particular interest. Materials and methods All experiments in 2018 were conducted at the MSU HTRC near East Lansing, Michigan. Treatments were applied to nine-year-old trees of ‘GG’/Gi5, ‘DD’/Gi5, ‘EE’/Gi5, and ‘Burgundy Pearl’/Gi5 sweet cherry. All experimental trees were cultivated with standard cultural practices (i.e., dormant and spring pruning, 44 irrigation, weed control, and bird netting), with the exception of pesticide applications which were minimal due to limited tractor access (the plot was originally grown organically under plastic-covered high tunnels, but organic and high tunnel management had ceased several years earlier). Timing experiment. To study the effect of phytohormone application timing, a block of ‘Burgundy Pearl’/Gi5 trees was selected, and whole trees were treated with foliar applications of 150 ppm 6-BA (Maxcel), 30 ppm GA3 (ProGibb), and 150 ppm 6-BA + 30 ppm GA3. The experimental design was completely randomized with seven replications. For each application, sprays were applied either when most of the spurs had one to three leaves emerging (May 6th) or at 100 GDD (base 7.2°C) later (May 10th), alone (single applications) or in combination (double applications) (Table 2). A tank sprayer with a handgun was used to apply the spray treatments to run-off in the early morning. Control trees were left unsprayed. Table 2. Timing for foliar growth regulator treatments applied to ‘Burgundy Pearl’/Gi5 sweet cherry trees during Spring 2018. Rate Experiment. To study the effect of phytohormone application rates, a block 45 Treatment Application TimingUntreated150 ppm 6-BA + 30 ppm GA4+7150 ppm 6-BA30 ppm GA33 leaves√√√100 GDD√√√3 leaves+100 GDD√√√Control√ of ‘DD’/Gi5 and ‘EE’/Gi5 sweet cherry trees were selected, and whole trees were treated with foliar applications of three plant growth regulators at different concentrations: 6-BA (75 ppm, 150 ppm and 300 ppm), GA3 (15 ppm, 30 ppm, 60 ppm), and combination of 6-BA and gibberellins (6-BA+ GA4+7) (75 ppm+15 ppm, 150 ppm+30 ppm, 300 ppm+60 ppm). All treatments were applied in the morning when most of the spurs had one to three leaves emerging, using a tank sprayer. The experimental design was completely randomized with eight replicatations and control trees were left unsprayed. Gibberellins experiment. To study the effect of gibberellin type, treatments were applied to a plot of ‘GG’/Gi5 sweet cherry trees in a completely randomized design with five replications. Foliar applications of 30 ppm GA3 (ProGibb), 30 ppm GA4+7 (ProVide), and 30 ppm 6-BA+GA4+7 (Promalin) were applied when most of the spurs had one to three leaves emerging. Treatments were applied in the morning with a tank sprayer. Before treatment, four spurs were selected for measurements from each tree and marked with flags. All sampled spurs were at a similar canopy height (between 1.25 m and 1.75 m) and in different quadrants of the canopy (north, south, west and east). After fruit harvest, the leaf area of the marked spurs was estimated by measuring length and width according to the method of Demirsoy and Lang (2010). In late June, cherries were harvested from all of the experimental trees. Besides the whole tree yield data, the weight and size of 25 46 fruits from each tree were recorded. Due to the rapid spread of a severe brown rot fungus infection, other fruit qualities such as the firmness and brix were not measured. In the cases where spurs elongated into shoots, data were not collected since these became marked as “shoots”. Treatment data were analyzed in three fields: total leaf area per spur, individual leaf area and leaf number per spur. The data were analyzed using SAS software (SAS Institute Inc., Cary, NC). A linear mixed model was fitted by GLIMMIX procedure with tree effects and spur effects treated as random effects according to the experiment design. Multiple comparisons of all treatments with Tukey contrasts were conducted in the above linear mixed model. Results Timing experiment. The 30 ppm GA3 treatment applied twice resulted in the largest total leaf area per spur, which was significantly 36% larger than the control (Figure 9). It was also significantly more than the 150 ppm 6-BA and 30 ppm GA3 treatments that were only applied at the second timing, resulting in 46% and 51% larger total leaf area than those two treatments, respectively. Also, the 150 ppm 6-BA+30 ppm GA4+7 treatment applied twice was significantly different from the single 150 ppm 6-BA and 30 ppm GA3 treatments. There were no significant differences in mean individual leaf area between the phytohormone treatments and the control (Figure 10). 47 The results for mean leaf number per spur were similar to those for total leaf area per spur (Figure 11). Treatment of 30 ppm GA3 applied twice had the greatest significant difference from the control, resulting in 33% more leaves per spur. Also, there were significant differences between 30 ppm GA3 applied twice with the 150 ppm 6-BA and 30 ppm GA3 treatments that were only applied at the second timing. Figure 9. The total leaf area per spur at harvest for the phytohormone timing experiment with 'Burgundy Pearl'/Gi5 sweet cherry trees. 0BA, 0BG and 0GA3 represent 6-BA (150 ppm), 6-BA (150 ppm) plus GA4+7 (30 ppm) and GA3 (30 ppm), respectively, applied May 6. BBA, BBG and BGA3 represent the same rates for 6-BA, 6-BA+GA4+7 and GA3, respectively, applied twice, on May 6 and May 10. HBA, HBG and HGA represent the same rates for 6-BA, 6-BA+GA4+7 and GA3, respectively, applied only on May 10. All the bars represent standard error. 48 Figure 10. The average spur single leaf area at harvest for the phytohormone timing experiment with 'Burgundy Pearl'/Gi5 sweet cherry trees. 0BA, 0BG and 0GA3 represent 6-BA (150 ppm), 6-BA (150 ppm) plus GA4+7 (30 ppm) and GA3 (30 ppm), respectively, applied May 6. BBA, BBG and BGA3 represent the same rates for 6-BA, 6-BA+GA4+7 and GA3, respectively, applied twice, on May 6 and May 10. HBA, HBG and HGA represent the same rates for 6-BA, 6-BA+GA4+7 and GA3, respectively, applied only on May 10. All the bars represent standard error. 49 Figure 11. The mean leaf number per spur at harvest for the phytohormone timing experiment with 'Burgundy Pearl'/Gi5 sweet cherry trees. 0BA, 0BG and 0GA3 represent 6-BA (150 ppm), 6-BA (150 ppm) plus GA4+7 (30 ppm) and GA3 (30 ppm), respectively, applied May 6. BBA, BBG and BGA3 represent the same rates for 6-BA, 6-BA+GA4+7 and GA3, respectively, applied twice, on May 6 and May 10. HBA, HBG and HGA represent the same rates for 6-BA, 6-BA+GA4+7 and GA3, respectively, applied only on May 10. All the bars represent standard error. The 150 ppm 6-BA treatment applied at the first timing and the 30 ppm GA3 treatment applied at the second time appeared to stimulate more spurs to elongate into shoots, but results were highly variable between replications and none of the treatments were significantly different from each other (Table 3). 50 Table 3. The percentage of spurs that elongated into shoots for each phytohormone treatment in the application timing experiment with 'Burgundy Pearl'/Gi5 sweet cherry trees. 0BA, 0BG and 0GA3 represent 6-BA (150 ppm), 6-BA (150 ppm) plus GA4+7 (30 ppm) and GA3 (30 ppm), respectively, applied May 6. BBA, BBG and BGA3 represent the same rates for 6-BA, 6-BA+GA4+7 and GA3, respectively, applied twice, on May 6 and May 10. HBA, HBG and HGA represent the same rates for 6-BA, 6-BA+GA4+7 and GA3, respectively, applied only on May 10. P-values of ANOVA test are significantly (*) or not significantly (NS) different from the control at a = 0.05. Rate experiment. The 75 ppm 6-BA+15 ppm GA4+7 and 150 ppm 6-BA+30 ppm GA4+7 treatments resulted in 55% and 59%, respectively, larger total leaf area per spur compared to the control, which was significant (Figure 12). The 300 ppm 6-BA treatment also differed significantly from the control, with 42% larger total leaf area per spur. The other treatments had high variability and were not statistically different from the control, even though some were also not statistically different from the best treatments, either. 51 Treatment0BA0BG0GA3BBABBGBGA3CHBAHBGHGA3Shoot Percentage17.9% NS14.3% NS14.3% NS7.1% NS3.6% NS10.7% NS7.1% NS3.6% NS3.6% NS17.9% NS Figure 12. The total leaf area per spur at harvest for the phytohormone rate experiment with 'DD'/Gi5 and 'EE'/Gi5 sweet cherry trees, applied when most of the spurs had one to three leaves emerging. 0.5BA, 1BA and 2BA represent 6-BA at 75 ppm, 150 ppm and 300 ppm, respectively. 0.5BG, 1BG and 2BG represent 6-BA + GA4+7 at 75 ppm plus 15 ppm, 150 ppm plus 30 ppm and 300 ppm plus 60 ppm, respectively. 0.5GA3, 1GA3 and 2GA3 represent GA3 at 15 ppm, 30 ppm and 60 ppm, respectively. All the bars represent standard error. In the statistical analysis of individual spur leaf areas, there were no significant differences between any of the hormone treatments and the control (Figure 13). 52 Figure 13. The mean single leaf area at harvest for the phytohormone rate experiment with 'DD'/Gi5 and 'EE'/Gi5 sweet cherry trees, applied when most of the spurs had one to three leaves emerging. 0.5BA, 1BA and 2BA represent 6-BA at 75 ppm, 150 ppm and 300 ppm, respectively. 0.5BG, 1BG and 2BG represent 6-BA + GA4+7 at 75 ppm plus 15 ppm, 150 ppm plus 30 ppm and 300 ppm plus 60 ppm, respectively. 0.5GA3, 1GA3 and 2GA3 represent GA3 at 15 ppm, 30 ppm and 60 ppm, respectively. All the bars represent standard error. Statistical analysis of the leaf number per spur showed results similar to that for total leaf area per spur (Figure 14). The 75 ppm 6-BA+15 ppm GA4+7 and 150 ppm 6-BA+30 ppm GA4+7 treatments had significantly more than the control, 44% and 52% more, respectively. The 75 ppm 6-BA group had 33% more leaves per spur than control, which was also significant, as was the 30 ppm GA3 treatment which was 37% more. The other treatments were more variable, resulting in no significant differences from either the control or the best treatments. 53 Figure 14. The mean leaf number per spur at harvest for the phytohormone rate experiment with 'DD'/Gi5 and 'EE'/Gi5 sweet cherry trees, applied when most of the spurs had one to three leaves emerging. 0.5BA, 1BA and 2BA represent 6-BA at 75 ppm, 150 ppm and 300 ppm, respectively. 0.5BG, 1BG and 2BG represent 6-BA + GA4+7 at 75 ppm plus 15 ppm, 150 ppm plus 30 ppm and 300 ppm plus 60 ppm, respectively. 0.5GA3, 1GA3 and 2GA3 represent GA3 at 15 ppm, 30 ppm and 60 ppm, respectively. All the bars represent standard error. As in the Timing experiment, it appeared that some treatments (especially 300 pm 6-BA and 300 ppm 6-BA + 60 ppm GA4+7) stimulated more spurs to elongate into shoots, but due to high variability between replications, none of the treatments differed significantly from the others (Table 4). 54 Table 4. The percentage of spurs that elongated into shoots for each phytohormone treatment in the application rate experiment with 'DD'/Gi5 and 'EE'/Gi5 sweet cherry trees. 0.5BA, 1BA and 2BA represent 6-BA at 75 ppm, 150 ppm and 300 ppm, respectively. 0.5BG, 1BG and 2BG represent 6-BA + GA4+7 at 75 ppm plus 15 ppm, 150 ppm plus 30 ppm and 300 ppm plus 60 ppm, respectively. 0.5GA3, 1GA3 and 2GA3 represent GA3 at 15 ppm, 30 ppm and 60 ppm, respectively. P-values of ANOVA test are significantly (*) or not significantly (NS) different from the control at a = 0.05. Gibberellins experiment. The 30 ppm GA3 treatment was significantly different from the control, resulting in 33% more total leaf area per spur (Figure 15). The 30 ppm GA3 and 30 ppm 6-BA + 30 ppm GA4+7 treatments were significantly different from the 30 ppm GA4+7 treatment, which had the lowest total leaf area per spur. There was no significant difference between any of the hormone treatments and the control for comparisons of either individual leaf area (Figure 16) or leaf number per spur (Figure 17). Unlike the close relationship between total leaf area and leaf number per spur, the larger total leaf area per spur was not primarily explained as more leaves per spur. However, the treatment effects on total leaf area per spur might reflect the combined effects on leaf number and individual leaf area. 55 Treatment0.5BG1BG2BG0.5BA1BA2BA0.5GA1GA2GACShoot Percentage18.75% NS28.1% NS31.3% NS25.0% NS18.8% NS31.3% NS25.0% NS25.0% NS25.0% NS6.3% NS Figure 15. The total leaf area per spur at harvest for the gibberellins experiment with 'GG'/Gi5 sweet cherry trees, applied when most of the spurs had one to three leaves emerging. GA3, GA4+7, and PM represents 30 ppm GA3, 30 ppm GA4+7 and 30 ppm 6-BA + 30 ppm GA4+7, respectively. All the bars represent standard error. Figure 16. The mean single leaf area at harvest for the gibberellins experiment with 'GG'/Gi5 sweet cherry trees, applied when most of the spurs had one to three leaves emerging. GA3, GA4+7, and PM represents 30 ppm GA3, 30 ppm GA4+7 and 30 ppm 6-BA + 30 ppm GA4+7, respectively. All the bars represent standard error. 56 Figure 17. The mean leaf number per spur at harvest for the gibberellins experiment with 'GG'/Gi5 sweet cherry trees, applied when most of the spurs had one to three leaves emerging. GA3, GA4+7, and PM represents 30 ppm GA3, 30 ppm GA4+7 and 30 ppm 6-BA + 30 ppm GA4+7, respectively. All the bars represent standard error. The stimulation by the various forms of GA for spur elongation into shoots was much lower than for the most active treatments in the Rate experiments, and none of the treatments were significantly different from the others (Table 5). Table 5. The percentage of spurs that elongated into shoots for each phytohormone treatment in the gibberellin type experiment with 'GG'/Gi5 sweet cherry trees. GA3, GA4+7, and PM represents 30 ppm GA3, 30 ppm GA4+7 and 30 ppm 6-BA + 30 ppm GA4+7, respectively. P-values of ANOVA test are significantly (*) or not significantly (NS) different from the control at a = 0.05. Although fruiting data (yields and fruit quality) were planned for each of the 57 TreatmentGA3GA4+7PMCShoot Percentage10.7% NS0.0% NS3.6% NS7.1% NS three experiments in 2018, rampant brown rot infections affected the quality of the data and so these were discarded. Since this project was developed for a Masters degree program, the degree timeline was not sufficient to follow the spring 2019 bloom to determine whether any of the hormone treatments significantly affected flower bud initiation and differentiation in 2018. Discussion The whole tree applications in 2018 resulted in much more variability and less effect of the plant hormone treatments on spur leaf area. However, in three different experiments (Timing, Rate and Gibberellin types), GA3 and 6-BA + GA4+7 resulted in significant differences with the control, showing a promotion of up to 50% larger total leaf area per spur. In the Timing Experiment, the first spray timing was the same time as in 2017, but none of these treatments gave similar results in 2018. Previous research on different timings of plant hormone applications to leaves was conducted on cashew (Anacardium occidentale) trees. Using foliar application of eight different plant growth regulators during cashew flushing, flowering and fruiting, Lakshmipathi et al. (2017) found that the largest leaf area was recorded for trees sprayed with GA3 at 50 ppm and Ethrel at 50 ppm, regardless of timing. Even though cashew and sweet cherry trees have different leaf growth patterns, application timing should be investigated further, beyond the two timings of the present study to optimize the spur leaf size. In both the Timing and Rate experiments, individual leaf size did not differ 58 significantly between treatments. Microscopic quantification of leaf thickness was not measured in the 2018 experiments. Falcioni et al. (2017) conducted experiments with GA3 and paclobutrazol (PAC, a GA biosynthesis inhibitor) treatments on tobacco plants. The results found greater leaf area, thinner leaves and reduced pigment content (expressed on a leaf area basis) in GA3- treated plants. More work needs to be conducted to determine the potential for plant growth regulators to reliably alter sweet cherry spur leaf size properties. 59 CHAPTER 5: CONCLUSIONS Using isolated spurs in 2017, spring foliar applications of the plant hormones 6- BA, GA3, and GA4+7 had variable but positive effects (up to 30% larger leaves) on spur leaf area, especially the BA+GA4+7 (150 ppm + 30 ppm) treatment. Also, this mix had a promotive effect on spur elongation into shoots, which could be useful in some training systems (e.g., TSA, SSA, VCL, SB) and undesirable in other systems (e.g., KGB, UFO. Based on light microscope analysis, all plant hormone treatments increased leaf size by promoting larger epidermal cell size. Epidermis cell numbers increased by 12%, 3% and 3% for the 6-BA, BA+GA4+7 and GA3 treatments, respectively. The 2017 and 2018 experiments shared several application timings and concentrations (30 ppm GA3, 150 ppm 6-BA and 150 ppm BA+30 ppm GA4+7 applied at the stage of one to three leaves emerging), but 2018 data did not show similarly significant different results as in 2017. Possible reasons might be differences between the cultivars used, the higher temperature at application (10°C) in 2018 than in 2017 (4°C), or the different soil types between the MSU Hort Farm near East Lansing (2018) and the MSU Northwest Michigan Horticultural Research Center near Traverse City (2017). Another possibility for the different results of 2017 and 2018 is rootstock. The experimental trees in 2017 were on vigorous rootstocks with a canopy 5 m in 60 height and 3 m in width. The 2018 experimental trees were on ‘Gisela 5’ dwarfing rootstock with a much smaller canopy 3 m in height and 1.5 m in width. There are several factors can influence the fruit tree vigor, and rootstock is a strong factor (Sorce et al., 2007), which can alter root-to-shoot and shoot-to- root chemical signaling (Gregory et al., 2013). Dwarfing rootstocks may influence scion growth by reducing endogenous concentrations of growth- promoting hormones (e.g., auxin, GAs, and cytokinins) and/or increasing endogenous concentrations of growth-inhibiting hormones (e.g., abscisic acid [ABA] and ethylene) at the active meristematic zones for leaf and shoot growth (Gregory et al., 2013). Several studies focused on peach, apple and pear dwarfing rootstocks have demonstrated that vigor reduction could be affected by deficiencies in gibberellin levels or signaling (Cristoferi and Filiti, 1981; Erez, 1984; Webster, 2004).) Van Hooijdonk et al. (2011) conducted research on ‘Royal Gala’ apple scions which were grafted onto dwarfing apple rootstock (M.9) to investigate whether changes in scion architecture were explained by changes in endogenous hormones within the scion. The GA19 concentration in the xylem sap of the scion on M.9 was significantly lower than in the control. Even though GA19 is biologically inactive, it can be converted to GA20 by GA20-oxidase. Furthermore, the GA20 can be converted to bioactive GA1 by GA3-oxidase (Yamaguchi, 2008). Hence, Van Hooijdonk et al. concluded that GA19 might be an important 61 root-produced signal regulating scion vigor. Considering the overall similar and different results in 2017 and 2018, in which individual leaf area was increased in 2017, was not in 2018, and yet overall leaf area increased in some 2018 trials due to spur extension into shoots, suggests that additional research is clearly warranted and necessary before any conclusions regarding potential orchard management recommendations can be made. Only two seasons of research are rather limited with respect to plant growth regulator effects in highly variable orchard environments. However, this research project does show that 6-BA and GAs appear to be more promising than nitrogen-based early spring foliar treatments for potentially positive effects on sweet cherry spur leaf size. 62 LITERATURE CITED 63 LITERATURE CITED Adachi, S., Tsuru, Y., Nito N., Murata, K., Yamamoto, T., Ebitani, T., Ookawa, T. and Hirasawa, T. (2011). 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