Physiology and Genetic Variation of Nitrogen Use Efficiency in Spinach (Spinacia Oleracea L.)

Department of plant breeding – abiotic stress group

Physiology and genetic variation of nitrogen use efficiency in spinach (Spinacia oleracea L.)

July 2015

MSc thesis plant breeding

Remco Overeem Registration number: 900605-637-100

Wageningen University The Netherlands

Physiology and genetic variation of nitrogen use efficiency in spinach (Spinacia oleracea L.)

A literature and experimental study to gain insight in the physiology and genetic variation of nitrogen use efficiency in cultivated spinach (Spinacia oleracea L.)

Remco Overeem Registration number: 900605-637-100 MSc thesis plant breeding (PBR-80436)

Supervisors: Ir. J. Rafael Chan Navarrete Dr. C. Gerard van der Linden Prof. Dr. Ir. Edith T. Lammerts van Bueren Dr. Ir. Oene Dolstra

Examinors: Dr. C. Gerard van der Linden Prof. Dr. Ir. Edith T. Lammerts van Bueren

Wageningen University Laboratory of plant breeding Abiotic stress group Droevendaalsesteeg 1 6708 PB Wageningen The Netherlands Table of contents

Acknowledgement ...... 5 Abstract ...... 6 1. Introduction ...... 7 1.1. Nitrogen ...... 7 1.2. Spinach ...... 8 1.3. Nitrogen use efficiency ...... 8 1.3.1. Nitrogen uptake and utilization efficiency ...... 9 1.3.2. Breeding for NUE ...... 9 1.4. Hydroponics and Ingestad model ...... 10 1.5. Problem statement ...... 10 1.6. Research objectives and questions ...... 10 2. Literature study ...... 12 2.1. The origin of spinach ...... 12 2.2. Nitrogen use efficiency ...... 12 2.2.1. Nitrogen uptake efficiency ...... 12 2.2.1.1. External factors ...... 13 2.2.1.2. Internal factors ...... 15 2.2.2. Nitrogen utilization efficiency ...... 18 2.2.2.1. Nitrogen reduction and assimilation ...... 19 2.2.2.2. Nitrogen transport inside the plant ...... 22 2.2.2.3. Nitrogen accumulation ...... 22 2.3. Concluding remarks ...... 25 3. Materials and methods ...... 27 3.1. Pre-screening mapping population ...... 27 3.1.1. Plant material and cultural practices ...... 27 3.1.2. Measurements ...... 28 3.2. Physiology ...... 29 3.2.1. Plant material and cultural practices ...... 29 3.2.2. Measurements ...... 29 3.3. Statistical analysis ...... 30 4. Results ...... 31 4.1. Pre-screening ...... 31 4.1.1. General results ...... 31 4.1.2. Genetic variation ...... 34 4.2. Physiology ...... 37 4.2.1. General results ...... 37 4.2.2. Interaction effects ...... 39 4.2.3. Correlations ...... 42 5. Discussion ...... 43 5.1. Considerations ...... 43 5.2. Segregation mapping population ...... 43 5.3. Nitrogen level effect ...... 43 5.4. Effect of nitrogen application method ...... 45 5.5. Interaction effects ...... 46 5.6. Correlation of traits to NUE ...... 47 6. Conclusions ...... 49 References ...... 50 Appendix I. Genotypes pre-screening ...... 61 Appendix II. Hoagland solution ...... 61 Appendix III. Experimental design pre-screening ...... 62 Appendix IV. NAR pre-screening ...... 63 Appendix V. Experimental design physiology ...... 64 Appendix VI. NAR physiology Ingestad ...... 65 Appendix VII. NAR physiology depletion ...... 66 Appendix VIII. Graphs type of plant material effect ...... 67 Appendix IX. Variation of several traits per cross ...... 68 Appendix X. Correlation matrix pre-screening ...... 69 Appendix XI. REML table N level effect ...... 70 Appendix XII. REML table application method effect ...... 71 Appendix XIII. REML table cultivar x N level effect ...... 72 Appendix XIV. REML table cultivar x application method effect ...... 73 Appendix XV. Additional graphs physiology ...... 74 Appendix XVI. Correlations harvest 1 ...... 86 Appendix XVII. Correlations harvest 2 ...... 87

Acknowledgement It would not have been possible for me to carry out this thesis without the help of others. I would therefor use this opportunity to express my gratitude to some people who supported me during this thesis project. Firstly I would like to thank my daily supervisors Rafael Chan Navarrete and Gerard van der Linden. Rafael who gave me the opportunity to join in his PhD project and who helped me during the first part of the projects with the experiments. Gerard for his useful and practical comments during the period of writing my thesis report. Rafael and Gerard I am very grateful for that! I also would like to thank Oene Dolstra for his support with the statistical part of the thesis and Edith Lammerts van Bueren for her general support. Further I would like to thank Mayra Huzen who was my partner during the physiology experiment. Also thanks to Geurt Versteeg and Maarten Peters of Unifarm for their practical support during the experiments. I would also like to thank my friends Albert-Ruben and Roelof who supported me with practical help and useful advises. Also thanks to my family who supported me during the period of this thesis project. It is not possible to mention everybody with their name, but I would like to thank everybody who contributed to my thesis project. Above all however I would like to thank God who gave everything what I needed to finish my thesis.

“Every good gift and every perfect gift is from above and cometh down from the Father of lights” (James 1:17)

Abstract Nitrogen is the macronutrient that is consumed by plants in the largest amounts compared to other nutrients, and limits growth most often. Nitrogen use efficiency (NUE) of plants is in general low; for spinach it is even less than 50%. This great loss of nitrogen causes mainly two problems, firstly an environmental and secondly an economic problem. It is therefore important to enhance the efficiency of nitrogen fertilization. This can among others be done by breeding for crops (in this report spinach) with a higher NUE. For breeding for higher NUE it is in the first place essential to have a good insight in traits related to NUE and in the second place to know if there is sufficient genetic variation for these traits in the breeding material. To reach these goals a literature study and two hydroponic experiments were carried out. Cultivated spinach (Spinacia oleracea L.) has two wild relatives (S. tetrandra and S. turkestanica), but as far as known, these wild relatives have not been used for breeding for higher NUE in cultivated spinach. NUE can be separated into Nitrogen Uptake Efficiency (NUpE) and Nitrogen Utilization Efficiency (NUtE). Nitrogen is taken up from the soil by mass flow, diffusion or root interception. Nitrogen utilization can be divided into reduction and assimilation, internal transport and accumulation. Both NUpE and NUtE are influenced by several internal and external factors like: nitrogen level, nitrogen form, cultivar, plant demand, light, temperature etc. To investigate whether there is sufficient genetic diversity for QTL mapping in traits related to NUE in crosses between spinach cultivars, four crosses with four hybrid cultivars (Ranchero, Novico, Crocodile and Marabu) were made. The F1s of these crosses were selfed, resulting in so called I1F1 lines. In the first hydroponic experiment 32 plants from ten of these I1F1 lines, together with the parents and selfings of the parents, were grown under low and high nitrogen levels. In the second hydroponic experiment eight hybrid spinach cultivars were grown under two nitrogen levels (low and high) and two application methods (depletion and Ingestad). The aim of this experiment was to investigate the correlation between several traits and NUE and whether there exists interaction effects between cultivars and nitrogen levels and between cultivars and application methods. In the first experiment the largest variation between I1F1 lines was observed for the traits shoot fresh weight (SFW), root dry weight (RDW), shoot dry weight (SDW), total dry weight (TDW) and leaf area (LA). These traits are therefore most useful for QTL mapping. The crosses Novico x Crocodile and Marabu x Novico resulted in the highest segregation under low and high nitrogen level respectively. Based on factors like germination, mortality and the average performance of a cross, however, finally the cross Marabu x Ranchero was chosen to be advanced to the next generation. Under all investigated conditions, during the entire growth period, SDW, SFW, RDW, TDW, LA, RL and RSA were highly correlated to NUE and therefore good indicators of this complex trait. Besides these traits, DM% and SLA were also found to be good indicators of NUE under all treatments (except depletion low nitrogen), but only at the end of the growth period. In the second experiment an interaction effect was observed between cultivars and nitrogen levels and cultivars and application methods. Interestingly, tolerance to nitrogen limitations was not the same for both application methods and cultivars with a good tolerance under one of the two application methods does not necessarily show a good tolerance under the other application method. The highest tolerant cultivars, for both application methods, were not the best yielding ones. The challenge for breeding for suitable cultivars under low nitrogen is therefore to combine the high tolerance to nitrogen limitations of for example Cello, with the high responsiveness to nitrogen fertilization of for example Andromeda. Caution, however, has to be taken for generalization of the results to field conditions, because these results were obtained from hydroponic experiments.

1. Introduction

1.1. Nitrogen Nitrogen is a common element in nature. About 78 % of the earth’s atmosphere consists of nitrogen. Nitrogen is the macronutrient that is consumed by plants in the largest amounts compared to other nutrients, and limits growth most often (Crawford and Glass, 1998). Nitrogen is present in the soil in inorganic form e.g. ammonium and nitrate, but also in organic form e.g. urea, amino acids and microorganisms (Figure 1). Traditionally nitrogen can come into the soil by, among other processes, biological fixation and manure, as shown in Figure 1. Since the industrialization of the Haber-Bosch reaction, approximately 100 years ago, nitrogen can also be industrially fixed (Smil, 1999). Under a pressure of 10 MPa and a temperature of 500 ⁰C the following reaction takes place (Smil, 2001):

N2 + 3H2 → 2NH3

Through the reaction of ammonia with sulfuric acid the first chemical nitrogen fertilizer, ammonium sulfate, was produced (Smil, 2001):

2NH3 + H2SO4 → (NH4)2SO4

Figure 1. Nitrogen forms and pathways within an agricultural cropping system (McKague et al., 2005)

In the year 2011, the application of nitrogen fertilizers increased until 108 million tons globally, (FAO, 2012). This intensive use of chemical nitrogen fertilizers is one of the major reasons of the large increase in yield in agriculture in the past decades. This increase in food production resulted in a significant decrease of hunger, despite the fact that the world population has doubled (Godfray et al., 2010). At the moment, 50 % of the food production for the human population is depending on nitrogen fertilizers (Smil, 1999).

1.2. Spinach Spinach is an annual crop whose vegetative parts are harvested for human consumption. Spinach is wind-pollinated, bisexual and diploid with 2n=2x=12 (Ryder, 1979; J.R. Chan Navarrete personal communication, 2013). Spinach is in general a dioecious species, but also monoecism with both male and female flowers exist (Khattak et al., 2006). Normally male and female plants are expected to occur in a 1:1 ratio, but in spinach this ratio depends on genotype and is influenced by the environment (Morelock and Corell, 2008). In 2010, 1,821 ha of spinach was cultivated in the Netherlands with a total yield of 29,5 million kilograms (CBS, 2013). Current varieties are mostly hybrids, with yields up to 40 % higher than open- pollinated varieties (Ryder, 1979; Morelock and Corell, 2008). Spinach has a high need for nitrogen to grow in a few weeks to a dark green harvestable product (Biemond et al., 1996). A lack of nitrogen makes that spinach plants become weaker and light green, resulting in an unmarketable product. Elia et al. (1998), Stagnari et al. (2007) and Ahmadil et al. (2010) found that spinach yield increased with increasing N fertilizer amounts. However, the nitrate content also increased with increasing nitrogen fertilization (Stagnari et al., 2007 and Ahmadil et al., 2010). Nitrate in vegetables is toxic for humans when consumed in higher amounts. Therefor the EU made restrictions for the maximum allowed levels of nitrate in vegetables such as spinach (Table 1).

Table 1. Maximum levels of NO3 in different forms of spinach (Commission Regulation, 2011)

Form of spinach Maximum levels (mg NO3/kg) Fresh spinach 3500 Preserved, deep-frozen or frozen spinach 2000

A general trend in the Netherlands and the EU is the restriction of nitrogen fertilization by legislation. Except for some leachable soils, this is not the case for spinach till at least 2017 in the Netherlands, (Table 2). It has to be taken into account, however, that the high nitrogen demand of spinach, in time, cannot be met anymore because of limitations by legislation. In organic agriculture, growers are not allowed to use chemical fertilizers. Nitrogen deficiency is therefore also a significant problem in organic agriculture.

Table 2. Maximum allowed nitrogen application (kg ha-1) for spinach for different soils for the years 2014-2017 in the Netherlands. Distinction is made between a first and a second cultivation in one year (Rijksdienst voor Ondernemend Nederland, 2014).

Cultivation Clay Sand1 Loess1 Peat 1th cultivation 260 190 190 200 2th cultivation 185 145 145 150 1 For leachable sand and loess soils, in the south and east of the Netherlands, from 2015 on, the maximum application will be reduced by 20% (Staatscourant, 2014).

1.3. Nitrogen use efficiency Nitrogen use efficiency (NUE) can be defined in several ways (Good et al., 2004). In this report NUE is based on Moll et al (1982) and defined as the unit harvestable product (shoot dry weight in case of spinach) per unit nitrogen supplied. The NUE of plants is in general low. Peoples et al. (1995) stated in a review that the nitrogen taken up by the plants most of the times is less than 50 % of the applied nitrogen. Kant et al. (2010) described that typically more than 60 % of the nitrogen in the soil is lost through a combination of leaching, surface run-off, denitrification, volatilization and microbial consumption (see also Figure 1). For spinach it is known that when 250 kg N is applied, only 112.9 kg N is harvested with the shoot (Heins and Schenk, 1987). Wasting most of the applied nitrogen causes mainly two problems, first an environmental and second an economic problem.

The nitrogen that leaches out of the soil, mainly in the form of nitrate, into the ground and surface water has adverse effects on the water quality and water life (Ingestad, 1977; Gallais and Hirel, 2004; McKague et al., 2005). The leached nitrate is toxic to fish, but also to humans that consume the water. The maximum allowed concentration in drinking water in the European Union (EU) is therefore 50 mg l-1 for nitrate and 0.5 mg l-1 for nitrite (Council Directive, 1998). Nitrogen that is lost from the soil by volatilization, mainly in the form of N2O, NO and NO2 (the well-known greenhouse gasses), is also harmful to the environment (Duxbury, 1994). The second problem is the cost of the fertilizers. Nitrogen fertilizers form a significant part of the costs of production. Because of the, in general, low uptake efficiency, growers see only a small part of the costs back in the harvestable product (Gallais and Hirel, 2004). Therefore it can be concluded that is it desirable to acquire a better NUE.

1.3.1. Nitrogen uptake and utilization efficiency NUE can be separated into Nitrogen Uptake Efficiency (NUpE) en Nitrogen Utilization Efficiency (NUtE) (Good et al., 2004; Lea and Azevedo, 2006; Hirel et al., 2007a; Hirel et al., 2007b). Nitrogen is taken up by the roots of plants mainly in the form of nitrate or ammonium. This process of nitrogen uptake is influenced by a lot of plant and environmental factors. The ability of a plant to take up nitrogen from the soil is defined as the NUpE. When nitrate is taken up by spinach plants, most of it is transported to the shoots where it is metabolised (Olday et al., 1976). Nitrate metabolism also occurs in the roots, but to a lesser extent (Steingröver et al., 1986b). The efficiency of this process is determined by a lot of internal and external factors. The amount of dry weight a plant finally can produce with a unit of nitrogen taken up is defined as the NUtE. Plant factors related to NUpE and NUtE will be described in more detail in Paragraph 1.3.2 and 2.2.

1.3.2. Breeding for NUE Essential for breeding for higher NUE is to know which traits are related to NUE. In literature complete protocols to breed for higher NUE are found, for example for maize (Bänziger et al., 2000). But also papers are published describing single traits related to the nitrogen status of the plants. Liu et al. (2006), for example, found that chlorophyll measurements with a SPAD-502 meter in spinach - are closely correlated to leaf NO3 concentration, total nitrogen (in leaves and roots together) and nitrogen fertilizer concentration. Broadley et al. (2001) reported that nitrogen-limited growth in lettuce is caused by lower stomatal conductance. Liao et al (2004, 2006) found that early vigorous root growth, especially in the 0.2-0.7 m soil profile, is an important factor for nitrogen uptake of wheat. Also in previous studies within the PhD project of J.R. Chan Navarrete, entitled “The development of a breeding strategy for nitrogen efficiency in spinach”, several traits were found to be correlated to NUE, like: leaf area, shoot- and root dry weight (Chan Navarrete et al., 2014). Selection for these traits related to NUE, is traditionally done by phenotyping. In the last decades molecular markers have become more and more important and more applied in breeding for NUE (Agrama, 2005). Hirel et al. (2011), however, reported that for complex traits like NUE still several technical and scientific challenges have to be resolved before MAS can be routinely used in breeding. The two most important factors, limiting the routinely use of MAS in breeding for complex traits, are our confounded understanding of epistasis and genotype x environment interaction (GxE) (Xu and Crouch, 2008). Besides phenotypic selection of correlated traits, the mentioned PhD project also aims to develop a molecular map and molecular markers, to make marker assisted selection (MAS) possible. Crucial for breeding is that genetic diversity in the breeding material exists. Okazaki et al. (2008) describes that there is diversity in NUtE between spinach cultivars. Other authors also describe differences in nitrogen response between cultivars (Zornoza and González, 1998a; Stagnari et al., 2007 and Ahmadil et al., 2010). In the current PhD project also contrasting cultivars with respect to NUE were found (J.R. Chan Navarrete personal communication, 2013). Besides currently existing cultivars and varieties, also wild varieties can be a possible source of genetic variation for NUE. In

Paragraph 2.1 therefore a literature study about the origin of the cultivated spinach and its wild relatives is given.

1.4. Hydroponics and Ingestad model In a hydroponics system, climatic and nutritional factors are under better control than under field conditions. Moreover, below ground factors are easier to measure when plants are grown in a hydroponic system. For diverse studies in spinach, a hydroponic system has shown to be an useful tool (Smolders et al. 1991; Steege et al., 1998; Lasa et al., 2001; Zhang et al., 2009). Besides the advantages of growing plants in a hydroponic system there are also differences in comparison with soil grown plants, which makes comparisons of results from both growing systems complicated. Miller and Cramer (2004) mention seven points in which a hydroponic system differs from the soil: 1 the availability of water is mostly higher, 2 nutrients are more uniformly distributed, 3 the gas environment is very different, 4 exudates from the roots are immediately lost, 5 there is no soil flora and fauna, 6 no mycorrhizal infection and 7 often nodules are absent from legumes. Smolders et al. (1993) further state that the carbon cost per unit of root is lower in a hydroponic system and observed that the root weight ratio (g DW root per g-1 DW total plant) is lower in hydroponics. Finally, Heins and Schenk (1987) found that the root hair surface of spinach plants grown in the soil was ten times greater in comparison with spinach plants grown in a nutrient solution. By use of a proper fertilizer management, a steady state condition can be acquired. Under steady state conditions, the relative growth rate (RGR) is equal to the relative uptake rate (Ru), because the internal nutrient concentration and the root : shoot ratio (R:S) remains constant over time (Ingestad and Lund, 1986; Ingestad and Agren, 1988). The relative addition rate is then analogous to the relative growth rate (Ingestad and Lund, 1986). This means that the desired stable RGR can be acquired with a specific nutrient addition rate (NAR). The main advantage of testing plants in a hydroponic system, fertilized according to the Ingestad model, is that all plants within the same nitrogen treatment experience more or less the same internal nitrogen availability (Chan Navarrete et al., 2014).

1.5. Problem statement For conventional and organic growers it becomes more and more difficult to grow spinach with a good yield and marketable quality given the restrictions by legislation. Because of the environmental impact of nitrogen, nitrogen fertilization will be even more restricted in the future. Besides that, nitrogen fertilizers are a significant part of the costs of a grower. Therefore it is important to enhance the efficiency of nitrogen fertilization. NUE can be improved in several ways, among others by proper farming techniques and breeding (McKague et al., 2005; Hirel et al., 2011). This report will focus on improving NUE by breeding.

1.6. Research objectives and questions To breed for higher NUE in spinach, first it is important to investigate what already is known in literature. Secondly it is essential to know if there is sufficient genetic variation in the breeding material. Finally it is crucial to have a good insight in traits related to NUE. To reach these goals, the following questions need to be answered:

1) What is known about the origin of spinach? 2) What is known about NUpE? a) What is known about the uptake of nitrogen from the soil? b) What factors determine the efficiency of nitrogen uptake of spinach plants? 3) What is known about NUtE? a) What is known about transport of nitrogen in spinach plants? b) How is nitrogen used in spinach plants? c) Which factors determine the efficiency of nitrogen utilization in spinach plants?

4) Is there sufficient segregation in the traits of the progeny of the crosses for QTL mapping? 5) Is there GxE interaction between: a) High and low nitrogen treatment? b) Fertilization according to the Ingestad- and depletion model? 6) How, and how strong are the investigated traits correlated to NUE?

This report consists out of three parts. The first part is a literature study that will address the first three research questions; this is described in chapter 2. The second part is an experimental screening of a population consisting out of selfings of different F1s (for more details see Paragraph 3.1) in a hydroponic culture. This part will answer research questions 4 and 5a. The results of this experiment will be presented in Paragraph 4.1. The third part will deal with a hydroponic experiment with a few commercial cultivars (for more details see Paragraph 3.2) and will address the research questions 5 (a and b) and 6. The results of this part are presented in Paragraph 4.2.

2. Literature study

2.1. The origin of spinach Cultivated spinach (Spinacia oleracea L.) is a food crop of the family of Chenopodiaceae (Ryder, 1979; Andersen and Torp, 2011; Catalogue of life, 2013). However there is some inconsistency about which family spinach belongs to, because some authors classify spinach to the family of Amaranthaceae. There are two wild relatives known of the cultivated spinach, namely S. tetrandra and S. turkestanica (Andersen and Torp, 2011). But there is some unclarity about other species and subspecies. Sneep (1983) described in a review several species of the genus Spinacia, but he discussed already whether they are separate species. Currently a lot of the mentioned species are not found any more in literature or are classified as subspecies. The International Spinach Database (2013) has the following Spinacia species and subspecies available: • oleracea • oleracea var. inermis • oleracea occidentalis • oleracea var. oleracea • oleracea orientalis • oleracea subsp. spinosa • oleracea var. spinosa • tetrandra • turkenstanica

It is not clear whether these are real separate species and subspecies. Unclear, for example, is what the difference is between S. oleracea var. spinosa (available in Germany) and S. oleracea subsp. spinosa (available in Spain). The herbarium botanik (2013), used as resource by the Catalogue of life, state that S. oleracea inermis is a synonym for S. oleracea. They also claim that S. spinosa, which is not available at the International Spinach Database, is the same species as S. oleracea.

S. terandra originates from the south west of the Caspian sea in Armenia and Kurdistan and surrounding countries. S. turkestanica originates from the east of the Caspian sea in Turkmenistan, Uzbekistan and Kazakstan (Andersen and Torp, 2011). Other authors mention Iran as the region of origin of the genus Spinacia (Morelock and Corell, 2008). S. oleracea is not found in the wild, but because Spinacia oleracea is crossable with S. tetrandra and S. turkenstanica it is assumed that S. oleracea also originates from the region around the Caspian sea. It is not known whether S. terandra or S. turkestanica is the ancestor of S. oleracea (Andersen and Torp, 2011). The wild relatives of S. oleracea are limitedly used in breeding for resistance genes against diseases (Andersen and Torp, 2011). As far as known, the wild relatives are not used in breeding for higher NUE.

2.2. Nitrogen use efficiency As described in the introduction, NUE can be separated in NUpE and NUtE. Research is done in literature about both processes and about factors influencing the efficiency of processes. NUpE and NUtE will be described in Paragraph 2.2.1 and 2.2.2 respectively.

2.2.1. Nitrogen uptake efficiency Before nitrogen can be taken up, it has to come in contact with the root surface. This can occur in three different ways, namely root interception, mass flow and diffusion (Barber, 1984; Fageria et al., 2011). Root interception is the contact with nutrients (in this report nitrogen) that the root encounters in the soil. Mass flow is the uptake of nitrogen dissolved in the water absorbed by plants. This flow is induced by the plant’s transpiration. The uptake of nitrogen, by mass flow, depends on the transpiration rate of the plant and the nitrogen concentration in the water, but is also affected by

plant species, soil properties, solubility of the nutrient and climatic conditions. Diffusion is the movement from nutrients form a region with a high concentration to a region with a low concentration. Plant roots take up nitrogen from the soil, what causes a depletion of nitrogen in the rhizosphere, nitrogen out of the soil will therefore move into the rhizosphere where it becomes available for uptake. (Barber, 1984; Fageria et al., 2011). Barber (1984) estimated that for a corn yield of 9500 kg grain ha-1, 190 kg nitrogen ha-1 is needed. The nitrogen uptake of corn by root interception, mass flow and diffusion is in that case assumed to be 2, 150 and 38 kg nitrogen ha-1 respectively. For spinach or related crops, values for uptake rates by root interception, mass flow and diffusion, are not found in literature. In the rest of this paragraph factors influencing nitrogen uptake efficiency will be discussed. Distinction will be made between external and internal factors in Paragraph 2.2.1.1 and 2.2.1.2 respectively.

2.2.1.1. External factors The most important factors influencing the efficiency of nitrogen uptake are the nitrogen level, the nitrogen form available, soil properties, the availability of water and other nutrients and climatic circumstances.

Nitrogen level The amount of nitrogen available in the soil is closely related to the rate of nitrogen fertilization. The critical nitrogen concentration for nitrogen uptake is 100 µM (0.1mM) (Heins and Schenk, 1986). This is the concentration by which the nitrogen uptake per cm root is 100 %. That there is still an increase in yield above this nitrogen concentration is not because of an increase in nitrogen uptake per unit of root, but because an increase in plant demand and root and shoot biomass. Below a concentration of 3 µM nitrate, there is no uptake of nitrate possible anymore (Heins and Schenk, 1986). Increasing fertilizer rates result, till a certain maximum application, in increasing nitrogen uptake and therefore in an increased yield. Mondal and Nad (2012) found, for example, an increase in nitrogen uptake by spinach till the maximum fertilization level of 240 kg nitrogen ha-1, however not always significant. Ahmadil (2010) found increasing yields up to nitrogen applications of 150 kg ha-1. By further increase in nitrogen fertilizer rates no further increase in biomass is found. Plant nitrate concentration, in contrast to that, increased sharply by increasing fertilizer rates. The authors however, did not make clear whether there are differences between cultivars or not. Stagnari et al. (2007) found an increase in yield till the maximum investigated nitrogen level of 200 kg ha-1, but -1 advised doses of 130 and 150 kg ha , with Ca(NO3)2 and NH4NO3 respectively, because of accumulation of nitrates and oxalates, which are toxic to humans, at higher fertilizer rates. In hydroponic experiments Zhang et al. (2005; 2009) found an increase in biomass of spinach till a nitrogen concentration of 12 mM1. However they advised a nitrogen concentration of 8 mM because of a strong reduction of total oxalate content and only a small decrease in biomass in comparison with the 12 mM treatment. The optimum nitrogen level is therefore not per definition the nitrogen level which results in the highest yield, but also quality factors determine the optimum nitrogen level. Despite absolute increase of nitrogen uptake and an increase in yield, reduction of NUpE occurs with increasing fertilizer amounts and therefore with available nitrogen. Canali et al. (2011) found that the higher the amount of available nitrogen in the soil, the lower the efficiency of nitrogen uptake (kg nitrogen taken up per kg nitrogen applied).

1 For comparison, 100 kg ha-1 is similar to a nitrogen concentration of 22 mM (Heins and Schenk, 1987). This shows that the advised concentration of 8 mM is much lower than the advised doses of at least 130 kg ha-1. This is probably because the nitrogen in a nutrient concentration is more uniformly distributed than in the soil. Therefore roots come in better contact with the nitrogen. For a more detailed description of difference between a (hydroponic) nutrient solution, see Paragraph 1.4.

N form Most plant species are able to take up both nitrate and ammonium but prefer one of them. Spinach has a preference for nitrate above ammonium (Goh and Vityakon, 1986; Elia et al., 1998; Lasa et al., 2001). A higher proportion of nitrate in comparison to ammonium result therefore in a higher yield, but also in an increase in nitrate content of the plants (Stagnari et al., 2007). Zhang et al. (2005) found the highest yield at a nitrate-ammonium ratio of 50:50, however there was no significant difference with a 75:25 ratio. In this research a nitrate-ammonium ratio of 100:0 resulted in a small decrease of biomass, but a ratio of 25:75 and especially 0:100 resulted in strongly depressed growth and yield. Lasa et al. (2001) found a strong increase in photosynthesis under nitrate, as nitrogen source, instead of ammonium. They found, however, no effect of nitrogen form on stomatal conductance and transpiration rate. The nitrogen productivity (gram dry weight mol-1 nitrogen day-1) was also significantly higher under nitrate as nitrogen source. Zornoza and González (1998a) reported that diversity, in the degree of preference for nitrogen form, between cultivars exist. They used a cultivar with a smooth leaf type, a cultivar with a curly leaf type and a cultivar with a semi- curly leaf type, in combination with two nitrate-ammonium ratios (100:0, 80:20). In the 100:0 treatment, the curly leaf type spinach showed the highest nitrogen uptake, but in the 80:20 showed the semi-curly leaf type cultivar the highest nitrogen uptake. The authors, however, did not made clear whether these differences in nitrogen uptake are just cultivar differences or are correlated with the leaf type. Barker et al. (1974) found that nitrate accumulation was higher in the savoyed (=curly) leaf type spinach than in the smooth leaf type spinach. Olday et al. (1976) found that these differences are caused by differences in nitrogen assimilation rather than by differences in nitrogen uptake, because the nitrate reductase activity of the smooth leaf type spinach was two to three fold higher than in the savoyed leaf type spinach. So it is clear that there are differences in nitrogen uptake between cultivars, but no evidence is found that these differences are determined by the leaf type. The accumulation of nitrate will be discussed in more detail in Paragraph 2.2.2.3. Besides the ability of plants to take up inorganic nitrogen, also evidence exist that they are able to take up organic nitrogen in the form of amino acids (Muller and Touraine, 1992; Lambers et al., 2008). Results of Matsumoto et al. (1999) suggest even an efficient uptake of organic nitrogen by spinach in comparison with pimento and lettuce. Miller and Cramer (2004) in opposite state that plants are able to take up amino acids but in practice this will not occur because of competition with micro-organisms. The uptake of organic nitrogen is therefore probably of minor importance for plant growth.

Soil properties, availability of water and other nutrients and climatic circumstances. Soil pH is an important factor determining the availability of nutrients (Barber, 1984). The availability of nitrogen is influenced by pH because it affects ‘the biological processes that control production and consumption of nitrogen’ (Lambers et al., 2008). In general nitrogen is optimal available between a pH of 6 and 8 (Lambers et al., 2008). However some diversity between nitrogen form exists, because ammonium uptake by many plants is increased under higher pH (Miller and Cramer, 2004). Organic matter is known to improve nitrogen uptake and to increase crop yield. Kansal et al. (1981) investigated that the addition of farmyard manure (FYM) caused an increase in yield, of spinach, irrespective the rate of inorganic nitrogen fertilization. Maximum yield, however, is obtained in the treatment with highest inorganic nitrogen fertilization rate as well as the highest FYM rate. Ebid et al. (2008) investigated the effect of inorganic nitrogen and three different composts on yield and nitrogen uptake by among others spinach. The total nitrogen uptake was highest by spinach fertilised with inorganic nitrogen (IN), followed by tea leaf compost (TC), coffee compost (CC) and kitchen compost (KC). However dry matter of yield was not significantly different between IN and TC treatment, while yields of other treatments where significantly lower. The percentage of nitrogen derived, by the spinach leaf, from the different treatments also did not differed significantly between IN and TC, while the others being again lower. The authors state that the high uptake of TC is probably because of a high C/N ratio. Also Matsumoto et al. (1999) reported differences in nitrogen uptake between treatments fertilised with separate organic materials. Total nitrogen uptake was

several fold higher by spinach plants fertilised with rapeseed cake as with dried cattle feces. Uptake of total nitrogen, 28 days after transplanting, was even higher by spinach plants fertilised with rapeseed cake as with inorganic nitrogen in the form of ammonium sulphate. One of the reasons for the positive effects of humic substances is an auxin like function, which has a positive effect nutrient uptake and root architecture (Canellas et al., 2008; Trevisan et al., 2010). The availability of other nutrients can influence the uptake of nitrogen. Mondal and Nad (2012) found that both, phosphate and sulphur, caused an increase in nitrogen uptake by spinach. Nitrogen uptake was highest in the highest nitrogen (240 kg ha-1), phosphorus (90 kg ha-1) and sulphur (45 kg ha-1) fertilization rates. Prosser et al. (2001) found a decrease of nitrate uptake of ca. 30-60 %, depending on the nitrate concentration of the nutrient solution, under sulphate deprivation. The decrease of nitrate uptake was highest in the lowest nitrate treatment. Also Smatanová et al. (2004) found an increase of nitrogen uptake and yield under increasing sulphur levels till a maximum of 20.6 mg kg-1 water soluble sulphur. Since most of the nitrogen is brought to the root surface by the transpiration rate-induced water flux, the water availability in the soil is of major importance for the uptake of nitrogen from the soil. Abreu et al. (1993) found that the nitrogen uptake of wheat showed a strong increase under the highest investigated irrigation level. This increase in nitrogen uptake resulted in a strong increase of yield in comparison with the less irrigated treatments. This, of course, does not mean the more water the better. Thompson and Doerge (1995) found that an excess of water reduced nitrogen uptake in spinach. They found that the optimum soil water tension for spinach is 8 kPa by 300 kg N ha-1. Optimum is then defined as the situation at which at least 95 % of the optimum yield is reached and less than 150 kg N ha-1 is left in the soil. The authors gave no explanation for the reduced nitrogen uptake caused by an excess of water, but probably this is because nitrogen in that case leached to depths were it is not available to spinach roots (for spinach rooting see Paragraph 2.2.1.2). Low water availability, however, will also reduce nitrogen uptake. Lambers et al. (2008) report that diffusion rates of ions are strongly reduced by low water availability. Between a soil water potential of -0.1 and -1.0 MPa, ion mobility in soil can decrease two orders of magnitude. Barber (1984) described that temperature is an important factor determining the rate of ion uptake. The optimum temperature differs between species and is for fescue 25 ⁰C and for corn 30 ⁰C. Light is also known to influence nitrogen uptake. Terashima and Evans (1988) found that that NUE, for spinach, is highest under 100 % irradiance of natural light (it is unclear how many light this is). The authors did not investigated to which extent NUpE contributed to this high NUE.

2.2.1.2. Internal factors The most important internal factors influencing the efficiency of nitrogen uptake are the plant’s nitrogen demand, root development and nitrogen transporters. These subjects will be discussed in this paragraph.

Plant demand Steege et al. (1998) and Lambers et al. (2008) define the nitrogen demand of a plant (under non- limiting nitrogen conditions) as the relative growth rate (RGR) times the nitrogen concentration of a plant. Lefsrud et al. (2007) and Okazaki et al. (2008) report that differences in nitrogen response between spinach cultivars exist, from which it can be deduced that the plants nitrogen demand differs between species.

As described in Paragraph 1.4, the RGR can be controlled by a specific NAR. However, Steege et al. (1999), did not find a significant difference in RGR between spinach plants grown under 0.8 and 4.0 mM nitrate. In a second experiment, a replicate of the experiment with 0.8 mM nitrate, the RGR differed from the RGR of the previous two experiments. In the first experiment Steege et al. (1999) found a decrease in RGR over time. This is in agreement with results of Smolders and Merckx (1992) and of Smolders et al. (1993). In the second experiment of Steege et al. (1999) an increase of RGR over time was found. It is not clear why the results of the second experiment differ from the first

experiment. The only explanation Steege et al. (1999) give is that the differences were caused by an uncontrolled increase in CO2 concentration of the growth chamber. Buysse and Merckx (1995) report that the RGR is related to the levels of sugars and amino acids of spinach plants. These authors also showed a diurnal variation in RGR. The RGR was lowest during the dark period, mainly because a strong decrease of shoot RGR. Root RGR in opposite of that showed less variation between dark and light period.

Plant nitrogen concentration showed an increase over time (Smolders et al., 1993; Zornoza and González, 1998a). The plants nitrogen concentration follows an S-shaped curve, with a strong exponential increase in the beginning of the growth cycle and flattening of the curve in the end. From these results it can be deduced that nitrogen uptake increased at the beginning of the growth cycle, then stays stable for a period of time and finally at the end of the growth cycle decreased. Diurnal fluctuations in nitrogen uptake are less clear than fluctuations in nitrogen uptake over plants life span. Steingröver et al. (1986a) found a short but sharp peak in nitrate uptake during the first hours of darkness. Scaife and Schloemer (1994) discuss that this is unlikely and depends probably on a single observation. These authors did not found a clear diurnal pattern of nitrate uptake by spinach.

Mengel and Barber (1974) found that nitrogen uptake, by corn was strongly determined by plant age. Nitrogen uptake decreased from 226.9 µmol nitrogen m-1 day-1 at a plant age of 20 days, to 32.4 µmol m-1 day-1 at a plant age of 30 days. It decreased further to 5.7 µmol m-1 day-1 at a plant age of 60 days after where it continued to fluctuate slightly above 0, till a plant age of 100 days. Heins and Schenk (1987) found a decrease of nitrogen uptake, by spinach plants, over time. At the first measurement, two weeks before harvest by plants grown in the field, nitrogen uptake was 4.7 pmol s-1 cm-2. The nitrate uptake decreased almost linearly to 1.5 pmol s-1 cm-2 at harvest. It should be noted that the decrease of nitrogen uptake found by Mengel and Barber (1974) and Heins and Schenk (1987) are per unit of root length and are therefore not per definition contradictory to results of Smolders et al. (1993) and Zornoza and González (1998a) who describe nitrogen uptake per whole plant. Mengel and Barber (1974) also found an increase of total nitrogen uptake per plant till a plant age of 50 days. The reason for this is an extreme increase of root length in the beginning of the growth of the corn plants. At a plant age of 20 days, the root length doubled in one day, while it took five days to double the total root length when the plants are 50 days old.

Root age, in opposite of plant age, does not influence nutrient uptake (Barber, 1984). This statement, however, is based on experiments with phosphate and calcium uptake. Phosphate and nitrogen uptake show a lot of similarities (see later), which supports a generalization of the statements of Barber (1984) to nitrogen uptake. Mengel and Barber (1974) found that roots, of corn, of the same age absorb nutrients (among others nitrogen) faster at a plant age of 20 than of 70 days. That shows indeed that plant age contribute more to nutrient uptake than root age. Nazoa et al. (2003) found that the nitrate influx of young root parts of Arabidopsis is similar to that of older root parts, what supports the statement of Barber (1984).

The uptake of nitrate by roots costs 1-2 mol of ATP per mol of nitrogen (Bloom et al., 1992). The costs for nitrogen uptake reduce with increasing RGR, which is probably caused by a lower nitrogen efflux : nitrogen influx (E:I) ratio (Steege et al., 1999). The efficiency of net nitrate uptake rate (NNUR) is therefore higher under a higher RGR. In Paragraph 2.2.1.1, however, is written that NUpE increases with decreasing nitrogen levels. This seems contradictory, but is not. In Paragraph 2.2.1.1. NUpE is defined as the total amount of nitrogen taken up compared to the total amount of nitrogen applied. It may be clear that this is another parameter than NNUR, which is defined as the E:I ratio and can be expressed as the costs in mol of ATP per mol of nitrogen net taken up.

Root development A well-known phenomenon under decreasing nitrogen availability is an increase in root : shoot ratio. Heins and Schenk (1986) found a 2.3 fold increase of R:S ratio of spinach and corn plants under nitrogen deficiency. Also Bottrill et al. (1970) found a 2.6 fold increase in R:S ratio by spinach plants grown under nitrogen deficiency, compared to control plants.

In general by far the most roots are present in the upper soil layers. Heins and Schenk (1987) reported that about 84-87 % of the spinach roots are in the upper 15 cm of the soil. The 15-30 cm layer contains 12-14 % of the spinach roots. Deeper soil layers contain therefore no meaningful amount of roots. Schenk et al. (1991) even observed that spinach roots were only present in the 15- 30 cm soil layer in the last two weeks before harvest. This is confirmed by Smit and Groenewold (2005). These authors, however, reported a fast root growth of spinach. The first roots of spinach reached a depth of 60 cm in the soil at a thermal time of 350 ⁰C day, what is much faster than for example for Maize which needs almost double thermal time to reach that depth. The reason that spinach roots are mainly found in the upper soil layers, is because of the short growing season (Smit and Groenewold, 2005). Together with the fact that spinach is harvested in full growth, makes this the cultivation of the crop sensitive to leaching (Smit and Groenewold, 2005). Schenk et al. (1991) reported that the root density (cm root cm-3 soil) in the 0-15 and 15-30 cm soil layers is increased by deep nitrogen placement. Because of the shallow roots of spinach, deep nitrogen placement causes nitrogen deprivation. Root density in deeper soil layers is not affected which again confirms that spinach is not able to use nitrogen below 30 cm. Heins and Schenk (1986), however, report a higher root density in the high nitrogen treatment (166 mg kg-1 soil) compared to the low nitrogen treatment (9 mg kg-1 soil). This difference in root density is probably because of the enormous difference in nitrogen, which resulted in an almost nine fold difference in yield.

Föhse et al. (1988) found that spinach has a high phosphate uptake efficiency (PUpE). This was because of a very high phosphate influx and because of a relatively low R:S ratio. In a second paper, Föhse et al. (1991), investigated the differences in phosphate influx found in the previous paper. The very high phosphate influx of spinach was caused by root hairs. The root hairs of spinach contribute for 90 % to phosphate uptake. In comparison with the other investigated crops, spinach had more root hairs, longer root hairs and a bigger surface area (cm2 cm-1) of the root hairs. Heins and Schenk (1987) found that the five times higher uptake rate of nitrate, by spinach, in the field in comparison with spinach grown in nutrient solution is mainly due to a 10 fold increase in root hair surface by plants grown in the soil. Foehse and Jungk (1983) found an increase of root hair length under decreasing external nitrogen and phosphorus concentrations. Decreasing nitrate concentration from 1000 µM to 2 µM increased root hair length from 0.25 to 1.4 mm. Foehse and Jungk (1983) concluded therefore that the dependence of root hair formation on nitrate supply, is similar to that from phosphate. These authors did additional split-root experiments with rape and showed that when 90 % of the roots was exposed to a nitrate concentration of 2 µM and 10 % to a nitrate concentration of 1000 µM no root hairs were produced. The authors concluded therefore that the outside nutrient concentration has no influence on root hair formation. The importance of root hairs is confirmed by Miller and Cramer (2004). They state that number, size and location of root hairs are important because of an increase in absorptive surface. Föhse et al. (1991) state that, besides the increase in absorbing surface, root hairs are important because of an increase of a very low radius and an accessibility of a larger soil volume. From the above text reveals that there is a high extent of similarity between uptake of phosphate and nitrogen. There are, however, also differences. De diffusion coefficient of nitrate (order of magnitude of 10-6 cm2 s-1; Nye, 1969) is much higher than that of phosphate (order of magnitude of 10-11 cm2 s-1; Bhadoria et al., 1991). Steingrobe and Schenk (1991) concluded therefore that the increase in root hair length (0.25 to 1.4 mm), reported by Foehse and Jungk (1983), is meaningless for nitrogen uptake in comparison with the nitrate diffusion distance of several centimetres. Besides differences in diffusion coefficient and consequences, the uptake rate of nitrogen (order of

magnitude of 10-13 cm-1 s-1; Heins and Schenk, 1986) is clearly higher than that of phosphate (order of magnitude of 10-14-10-15 cm-1 s-1; Föhse et al., 1991). This might be an important reason for the fact that nitrogen is mainly brought to the roots by mass flow and phosphorus by diffusion, as was estimated by Barber (1984).

Transport systems As described in Paragraph 2.2.1., nitrogen is brought to the root by root interception, mass flow or diffusion. The uptake of nitrogen into the root is performed by transporters of the NRT transporter family (Touraine, 2004). Distinction can be made between three different transporter systems. A constitutively expressed high affinity transport system (cHATS), a inducible high affinity transport system (iHATS) and a low affinity transport system (LATS) (Touraine, 2004). The iHATS is induced by nitrate, however also nitrite is shown to induce these systems (Aslam et al., 1993; King et al., 1993). However, because the nitrite concentration is low under natural conditions in the soil (Glass, 2003), its relevance in inducing nitrate transporters is therefore probably negligible. At low external nitrate concentrations, the nitrate uptake is mainly performed by the HATS, but at concentrations of 0.2-0.5 mM this transport system becomes saturated in barley (Siddiqi et al., 1990). The LATS is constitutively expressed but start contributing significantly to nitrate uptake from an external nitrate concentration of 0.3-1.0 mM and does not become saturated till at least 50 mM (Siddiqi et al., 1990). The nitrate level from which LATS becomes meaningful differs between species. For spinach it is known that nitrate influx is mediated by LATS from a nitrate concentration of at least 0.8 mM (Steege et al., 1999).

The uptake of ammonium is mediated by the AMT transporter family (Loqué and von Wirén, 2004). Also for the uptake of ammonium into the roots, distinction can be made between a HATS and a LATS (Glass, 2003). The author described in his review, based on several papers of investigations in numerous crops, that HATS is most important at low ammonium concentrations, while at concentration above about 1 mM the non-saturating LATS becomes more evident.

For the uptake of amino acids also separate transport systems are known (Miller and Cramer, 2004). The role of these transporters in uptake from the soil is still uncertain (Miller and Cramer, 2004). As described before, amino acid uptake will probably not occur in practice. Amino acid transport systems will therefore be of limited importance for nitrogen acquisition of plants. Miller and Cramer (2004) describe that there is one important exception, namely transporters for urea, because this is currently a regularly used fertiliser.

The way in which the plants control the uptake of nitrate is by the amino acid concentration in the phloem sap. An increase in the amino acid concentration in the phloem sap causes a decrease of nitrate uptake (Imsande and Touraine, 1994). Muller and Touraine (1992) reported that differences in strength of inhibitory effects between amino acids exists. King et al. (1993) found that nitrate, nitrite and ammonium are capable of feedback regulation of nitrate influx. So nitrate uptake seems to be regulated at whole plant level. This is confirmed by Gansel et al. (2001) who found that nitrate uptake in Arabidopsis by the AtNrt2.1 gene was regulated by the whole plant nitrogen status. These authors, however, found in contrast to nitrate uptake that ammonium uptake by the AtAmt1.1 gene in Arabidopsis is regulated by local root nitrogen status and not by whole plant nitrogen status as by nitrate.

2.2.2. Nitrogen utilization efficiency As described earlier, NUtE is defined as the production of dry matter per unit of nitrogen supplied or taken up. Similar to NUpE, NUtE is lower with higher external nitrogen levels (Smatanová et al., 2004; Canali et al., 2011). The processes inside the plant contributing to NUtE can be roughly divided in the reduction and assimilation of nitrogen, the internal nitrogen transport and the accumulation of nitrogen. These subjects will be described in the paragraphs 2.2.2.1. till 2.2.2.3.

2.2.2.1. Nitrogen reduction and assimilation Nitrogen taken up in the form of nitrate is first reduced to nitrite by the enzyme nitrate reductase (NR). Nitrite is further reduced to ammonium by the enzyme nitrite reductase (NiR) (Lillo, 2004). This ammonium, together with ammonium taken up from the soil is assimilated by the glutamine synthetase/glutamate synthase (GS/GOGAT) pathway into glutamate, from which several amino acids are produced (Lillo, 2004). This reduction of nitrate to ammonium is an energy consuming process, which costs about 15% of the roots carbon catabolism of barley plants, while the assimilation of ammonium only costs 3% (Bloom et al., 1992). The enzymes NR, NiR, GS and GOGAT, as well as glutamate dehydrogenase (GDH), another enzyme involved in ammonium assimilation, will be discussed in this paragraph.

Nitrate reductase Of the different enzymes involved in the nitrogen reduction and assimilation pathway, NR is by far the most investigated one. NR reduces nitrate to nitrite in dependence of NAD(P)H (Lillo, 2004):

- + - + NO3 + NAD(P)H + H → NO2 + NAD(P) + H2O

NR is long thought to be the rate-limiting step in nitrogen assimilation, but there is now strong evidence that levels of NR (and other nitrogen assimilating enzymes) in general do not limit the growth at higher nitrogen levels (Andrews et al., 2004). Differences in nitrate reductase activity (NRA), however, exist. Olday et al. (1976) found that the NRA in the smooth leaf type cultivar ‘Hybrid 424’ is two to three fold higher than in the savoyed leaf type cultivar ‘America’. This is not necessarily contradictory to the statement of Andrews et al. (2004) because they talk about levels of NR enzyme and not about NRA. As can be deduced from the formula of nitrate reduction, NRA depends not only on the amount of NR enzyme but also on the availability of nitrate, NAD(P)H and H+. At higher external nitrate level nitrate is not limiting, but is accumulating at high amounts in the vacuole (see Paragraph 2.2.2.3.). Therefore, it may be possible that that NAD(P)H and therefore photosynthesis is limiting nitrogen assimilation in spinach at higher nitrate levels. Olday et al. (1976) tested two spinach cultivars and found for both that more than 90 % of the total NRA occurs in the shoot. Also Steingröver et al. (1986a; 1986b) found the highest NRA in the shoot. At cellular level, NR is located in the cytosol (Lillo, 2004). Ward et al. (1989), however, found NRA in the plasma membrane of corn roots. This activity was caused by another, unknown, enzyme than the cytosolic NR. Nitrate serves as the primary signal in regulating NRA (Crawford, 1995). Aslam et al. (1993) found that nitrite also could induce NRA, but the induction by nitrate was stronger. The induction of NR after exposure to nitrate occurs within minutes and needs only very low concentrations (down to 10 µM) of nitrate (Crawford, 1995). Steingröver et al. (1982) suggest that lowering the nitrate concentration of 8.0 mM to 0.16 mM affected the nitrogen uptake rather than the NRA of spinach. Chen et al. (2004) found that NR is active in spinach plants at a nitrate concentration of 0.15 g kg-1 (≈ 2.41 mM). These authors found no effect on NRA by a further increase of the external nitrogen concentration. Kandlbinder et al. (2000) however, found a clear increase in NRA when the nitrate concentration is increased from 1 mM to 3 mM. A further increase to 10 mM did not result in a different NRA. Differences in results of above mentioned authors might be caused by differences in experimental set up. Chen et al. has grown the plants in pots with field foil (which contained already nitrogen), while Kandlbinder et al. and Steingröver at al. has grown the plants in a growth solution. All three authors used moreover a large nitrogen range (till about 10 mM), while the induction of NRA occurs probably in the range of 0-0.5 mM (Steingröver et al., 1982; Crawford, 1995). So clear is that NRA is induced at a certain level, but to determine the exact level of nitrate needed for induction of NRA more research is necessary. Besides nitrate, also light is found to affect NRA. Riens and Heldt (1992) found a strong decrease in NRA of spinach plants after a transition of light to dark. This inactivation of NR is reversible within 40 minutes which indicates that this is not due to protein degradation, but to modification of the

enzyme (Riens and Heldt, 1992). Also Kandlbinder et al. (2000), Scaife and Schloemer (1994) and Steingröver et al. (1986a) found a diurnal rhythm for NRA. Steingröver et al. (1986b) confirmed the influence of light on NRA by investigating the effect of a low light treatment, at night, on NRA and nitrate accumulation. The low light treatment at night resulted in a strong increase in NRA and in a clear decrease of nitrate content of the leaf blades by 50 % and of the total shoot (leaf blades + petioles) of 25 %, compared to plants which did not received this low light treatment. Kaiser et al. (2004) state, based on some reports that reported similar results under the presence and absence of CO2, that the NRA is not directly regulated by light but by photosynthesis. They further substantiated this statement with reports about investigations of effects of drought and salinity stress, causing a stomata closure that indeed resulted in nitrate accumulation, which may be caused by reduced nitrate assimilation. The NRA is, however, immediately down regulated after removal of light. Riens and Heldt (1992) found that the NRA after the end of illumination decreased rapidly till about 15 % of the control rate with a half-time of only 2 min. It seems unlikely that the products of photosynthesis, used for NRA (NAD(P)H), are depleted in that time. So another signal might be responsible for that. A possible important factor in regulating the NRA is the 14-3-3 protein. Weiner and Kaiser (2001) found that under light conditions less than 5 % of the NR in spinach is bound to 14-3-3 proteins, while after 30 minutes under dark conditions about 70 % of the NR is bound to 14-3-3 proteins. This resulted in 60 % decrease in NR activation state. Weiner and Kaiser (2000) found that NR is only inhibited by 14- 3-3 proteins in the presence of magnesium. Kandlbinder et al. (2000) also found a reduction of NRA in spinach under presence of magnesium; increasing magnesium concentrations resulted in decrease of NRA. Yin et al. (2009) however reported that spinach plants grown in Hoagland solution without magnesium showed a strong decrease of about 60 % in NRA in comparison with control plants grown in the presence of magnesium. This decrease could be partly substituted when cerium is present in the medium. The difference between results of Yin et al. (2009) and the results of Weiner and Kaiser (2000) and Kandlbinder et al. (2000) might be caused by the fact that Yin et al. (2009) performed their treatment at the whole plant level, while the other authors performed their treatments on extracted NR proteins. The plants of Yin et al. (2009), grown without magnesium, showed repressed growth which has probably also negative effects on NRA. Besides magnesium, sulphate is another factor involved in the regulation of NRA. Prosser et al. (2001) found that in spinach the NRA is lower under sulphur deprivation. This was especially evident in young leaves at four to eight days after deprivation. These authors wrote that this decrease in NRA is not necessarily because of the repression of the NR, but could also reflect a general reduction in protein synthesis caused by a lack of sulphur amino acids. In shaded plants (< 60 µmol m-2 s-1) no effects of sulphur deprivation was found, however NRA was two- to three-fold lower in the shaded plants. Zornoza and González (1998b) found that the NRA is influenced by the nitrate-ammonium ratio, however the effect differs between cultivars. The NRA in the leaves of spinach plants grown with only nitrate is 25 and 17 % higher compared to a nitrate-ammonium ratio of 80:20 in the cultivars Viroflay and Butterflay respectively. The NRA in the cultivar Giant however was 40 % higher in the 80:20 treatment. The NRA of the roots showed also some differences, but because the NRA of the roots contributed at maximum about 5 % to total NRA, these changes are of limited importance. Kandlbinder et al. (2000) performed an extensive study of factors influencing NRA in among others spinach. NRA was found to depend on leaf age. In the youngest leaves, NRA was 0.1 µmol g-1 FW h-1, but increased strongly to a maximum of 3.5 µmol g-1 FW h-1 at the third leaf stage2, after which it decreased to 0.1 µmol g-1 FW h-1 at the sixth leaf stage. Finally, Kandlbinder et al. (2000) found, by adding ATP to spinach leaf extracts, that the NRA is strongly decreased by ATP. Xiao et al. (2008) found that NRA of spinach leaves is reduced when the leaves are sprayed with lead chloride (PbCl2). Increasing levels of lead caused a reduction of NRA. Kaiser et al. (2004) describe in their review that other internal factors like 5’ AMP, sugar phosphates and cytosolic ion concentrations are involved in controlling the activation and inactivation of NR, but our knowledge is limited on the precise influence of these factors

2 Unfortunately leaf age is given in letters (A, B, C etc) and not in units of time (e.g. days).

Nitrite reductase After the reduction of nitrate to nitrite by the enzyme NR, nitrite needs to be reduced into ammonium. This process is performed in the chloroplast by the enzyme NiR. The energy source for this reduction is reduced ferredoxin, a product of photosynthesis (Lillo, 2004):

- + + NO2 + 6 Fdred + 8H → NH4 + 6Fdox + 2 H2O

NiR activity (NiRA) in barley is induced by both nitrate and nitrite (Aslam et al., 1993). This induction of NiRA is, in opposite to the induction of NRA, as strong for nitrate as for nitrite. Light is also a crucial factor for the NiRA. Robinson (1986) found nitrite reduction in spinach to be dependent on light. Yin et al. (2009) found that, similar to NRA, NiRA in spinach plants is strongly decreased when grown without magnesium in comparison to plants grown with magnesium. This effect could be partly compensated for with cerium. Miller and Cramer (2004) state that a reduction in NiRA is due to a drop in amount of protein and not, as with NRA, because of an inactivation of the protein. The posttranscriptional mechanism, controlling the amount of protein remains to be determined.

Glutamine synthetase/glutamate synthase The reduction of nitrate by NR and NiR result in one mole of ammonium per mole of nitrate (Barneix and Causin, 1996). The major route for ammonium assimilation is believed to be the GS/GOGAT pathway (Cramer and Miller, 2004). The first step is the production of glutamine catalysed by GS, with ATP as energy source (Lea and Miflin, 2011). The following reaction takes place (Lillo, 2004):

+ Glutamate + NH4 + ATP → glutamine + ADP + Pi + H2O

Of GS two forms are reported, a cytoplasmic form (GS1) and a plastidic form (GS2) (Lea and Miflin, 2011). At cellular level GS1 is abundant in vascular tissues, while GS2 is located in chloroplasts of mesophyll cells (Yamaya and Oaks, 2004). Hirel et al. (1982) found in green leaves of spinach only the chloroplastic isoform. Dragićević et al. (2011) found in spinach one GS1 isoform in the root and two GS2 isoforms in the chloroplasts in the leaves. GS2 is strongly induced by light, while GS1 is more developmentally regulated (Lillo, 2004). In cauliflower GS1 is post translationally regulated by phosphorylation and binding to 14-3-3 proteins (Moorhead et al., 1999). GS2 is also found to be regulated by phosphorylation and binding to 14-3-3 proteins in Medicago (Lima et al., 2006) and tobacco (Riedel et al., 2001). In spinach GS activity is found to be repressed by ammonium nutrition (Zornoza and González, 1998b). Compared to a nitrate-ammonium ratio of 100:0, a 80:20 ratio resulted in a clear decrease in GS activity (GSA) in leaves and roots of three spinach varieties. In roots of the semi-curly spinach cultivar Giant, however, a threefold increase of GSA was found. The effects of these differences will be discussed at the end of this paragraph.

Of GOGAT two forms are known, one use NADH and the other uses ferredoxin (Fd) as electron source (Lea and Miflin, 2011). GOGAT catalyses the production of glutamate (Lillo, 2004):

+ + Glutamine + α-ketoglutarate + 2 Fdred(NADH + H ) → 2 glutamate + 2 Fdox (NAD )

Hayakawa et al. (2007) found NADH-GOGAT in young spinach leaves mainly located in companion cells of large vascular bundles and to a lesser extent also in vascular parenchyma cells and mesophyll cells. Fd-GOGAT was mainly found in mesophyll cells and to a lesser extent also in companion cells and xylem-parenchyma cells of large vascular bundles. The localisation of these enzymes may differ during growth; Yamaya and Oaks (2004) report that the localisation of GOGAT as well as GS depends on developmental stage in rice. Zornoza and González (1998b) found in spinach comparable levels of NADH-GOGAT in leaves and in roots. NADH-GOGAT activity in leaves is lower in the spinach cultivar Viroflay when grown with a nitrate-ammonium ratio of 100:0 compared to 80:20. In the other two

cultivars, especially in Giant, NADH-GOGAT is higher with the mixed nitrogen nutrition. NADH-GOGAT activity in roots was higher in all cultivars under mixed nitrogen nutrition. Limited information is found about the regulation of the GOGAT enzymes. In Arabidopsis Fd-GOGAT mRNA accumulates in response to light, but can in the dark also be induced by exogenous sucrose applications (Lam et al., 1996). In finger millet Gupta et al. (2012) found that GOGAT enzyme activity was influenced by nitrogen level; a higher nitrogen level resulted in a higher activity of the enzyme. Unfortunately these authors made not clear whether they measured the Fd-GOGAT or NADH-GOGAT activity. In rice roots, glutamine is found to be the signal for induction of NADH-GOGAT mRNA (Hirose et al., 1997).

Glutamate dehydrogenase Another enzyme involved in the ammonium assimilation is glutamate dehydrogenase (GDH). It is believed that GDH is a deaminating enzyme (Cramer and Miller, 2004). However it is also proposed that this enzyme is important for the detoxification of ammonium under higher external ammonium concentrations (Zornoza and González, 1998b). These authors found the GDH activity in leaves of the spinach cultivars Butterflay and Viroflay to be 74 % and 200 % higher when grown under the mixed nitrogen nutrition compared to only nitrate nutrition respectively. For roots this was 43 % and 93 % respectively. Spinach cultivar Giant showed an increase of 44 % in GDH activity in the leaves while it decreased with about 97 % in the roots. Giant, opposite to the other cultivars, did not accumulate free ammonium (Zornoza and González, 1998b). It is therefore suggested that the GS/GOGAT pathway of Giant is more efficient in the utilization and detoxification of ammonium than the other cultivars. Giant, moreover, was shown to be most tolerant to ammonium nutrition (Zornoza and González, 1998a).

2.2.2.2. Nitrogen transport inside the plant Nitrate in the plant is not transported in phloem (Imsande and Touraine, 1994), but in the xylem (Miller and Cramer, 2004). Schjoerring et al. (2002) found, in oilseed rape, also ammonium to be present in the xylem, which suggests that also this ion is transported via the xylem. The transport of ammonium, however, seems to be of minor importance because even under ammonium nutrition the concentration of ammonium in the xylem sap of Brassica napus was less than 11 % of the total nitrogen concentration of the xylem (Schjoerring et al., 2002). Nitrogen in the form of amino acids is transported by both the phloem and the xylem (Miller and Cramer, 2004). Amino acids cycle through the plant via the phloem and xylem and are an important source of organic nitrogen (e.g. 18 % of total plant nitrogen of wheat grown with limited supply of nitrogen; Lambers et al., 1982; Simpson et al., 1982). The CLC gene family is proposed to encode channels/transporters that are involved in nitrate homeostasis (De Angeli et al., 2009). Seven members of this family in the Arabidopsis genome are known; named AtCLC-a - AtCLC-g (Teardo et al., 2005; De Angeli et al., 2006). These authors found - + that the AtCLC-a was located in the tonoplast and behaved as a 2NO3 /1H antiporter which was able to accumulate nitrate in the vacuole. AtCLC-a was also able to transport other anions, but had a high - selectivity for NO3 . Teardo et al. (2005) found AtCLC-f to be located in the outer envelope of the chloroplast. The AtCLC-f was found to be expressed in the leaves and stems, but not in the roots. Teardo et al. (2005) did not investigated whether this gene was also involved in nitrate transport. Besides the CLC gene family, recently also a member of the NRT2 family was localised in the vacuolar membrane of Arabidopsis and shown to be involved in nitrate accumulation in the seeds (Chopin et al., 2007). Vacuolar ammonium transport is suggested to be mediated by tonoplast aquaporins (Orsel and Miller, 2011)

2.2.2.3. Nitrogen accumulation As described in Paragraph 2.2.2.1. nitrogen is reduced and assimilated into amino acids. Excess nitrogen that cannot be assimilated accumulates in the form of nitrate. This paragraph will focus on

nitrogen accumulation and will describe where it is stored and what factors influence the accumulation of it.

At plant level nitrate in spinach is mainly accumulated in the petioles (Barker et al., 1971; Biemond et al., 1996; Stagnari et al., 2007), followed by roots and leaf blades (Olday et al., 1976). At low nitrogen conditions, however, nitrate concentration in the roots can exceed that of petioles (Olday et al., 1976). At cellular level nitrate is stored in the vacuole (Steingröver et al., 1986b; Riens and Heldt, 1992).

Several authors found differences in nitrate accumulation between spinach cultivars (e.g.; Barker et al., 1974; Maynard and Barker, 1974; Stulen and Steege, 1988; Stagnari et al., 2007; Okazaki et al., 2008; Huang et al., 2010). Leaf type seems to be a factor influencing differences in nitrate accumulation in spinach cultivars. Barker et al. (1971), Barker et al. (1974), Maynard and Barker (1974) and Olday et al. (1976) found by several investigation with in total about 20 spinach cultivars that smooth leaf type spinach cultivars accumulated less nitrate then savoyed leaf type spinach. Olday et al. (1976) suggest that this is more likely related to differences in nitrate assimilation than in uptake or transport rates. Another explanation could be that the lower nitrate accumulation of the smooth leaf type spinach cultivars is because of a combination of desirable characteristics of these cultivars (Barker et al., 1974). These authors state that factors like fast growth rate, high yields, large protein content and high NRA are common among smooth leaf type cultivars. Interestingly, these authors found that semi savoyed leaf type spinach cultivars did not show an intermediate nitrate content, but the nitrate content of either the savoyed or the smooth leaf type spinach.

In addition to genotype the cropping system might influence nitrate accumulation as well. Koh et al. (2012) found that nitrate content is markedly lower in most of the 27 spinach cultivars grown under organic growing conditions compared to conventional growing conditions. These authors did not clarify what factors causes the differences between the cropping systems, but they excluded the effect of climatic circumstances because the field trials where positioned in the same area. Probably differences between organic and conventional cropping systems depends on several factors including organic fertilizers (which might result in a lower nitrogen availability), because other authors found nitrate accumulation by spinach to be affected by organic fertilizers. Ebid et al. (2008) found that the nitrate concentration was two to six fold higher in spinach plants grown under inorganic nitrogen than under different composts. The total nitrogen concentration in the soil was twofold higher by the compost treatments than by the inorganic nitrogen treatment, but they did not measured the plant available nitrogen, which is probably not higher with the composts. Kansal et al. (1981) investigated the effect of a combination of different levels of inorganic nitrogen fertilization and farmyard manure. They found that nitrogen content decreases with increasing levels of farmyard manure irrespective the level of inorganic nitrogen fertilizer. Kansal et al. (1981) state that this might be caused by the dilution effect of higher yields. Another reason could be the immobilization of available nitrogen by the soil microbes, which activities were increased by the addition of carbonaceous materials with farmyard manure.

Several authors report about an increase in nitrate and nitrogen accumulation in spinach with increasing external nitrogen concentrations (Cantliffe, 1973; Biemond et al., 1996; Lefsrud et al., 2007; Okazaki et al., 2008; Yamori et al., 2011; Mondal and Nad, 2012; Gutriérrez-Rodríguez et al., 2013). The increase of nitrate accumulation occurs continues even at very high external nitrate concentrations. Maynard and Barker (1974) found still an increase of plant nitrate concentration at external nitrate concentrations as high as 48 mM. Goh and Vityakon (1986) found in general an increase of nitrogen and nitrate concentration till the maximum investigated nitrogen level of 600 kg ha-1. Not only nitrogen level, but also nitrogen form (Stagnari et al., 2007) and nitrate-ammonium ratio (Conesa et al., 2009) are shown to effect nitrate accumulation. Conesa et al., (2009) investigated the effect of nitrate-ammonium ratios of 100/0, 75/25, 50/50, 25/75 on nitrate

accumulation in baby leaf spinach. The nitrate content increased with each increase of nitrate in growing solution. A higher nitrate content in spinach, in roots as well as in shoots, grown under nitrate in comparison to ammonium is confirmed by Lasa et al. (2001).

Other nutrients are shown to have a limited influence on nitrate and nitrogen accumulation. Barker and Maynard (1971) investigated the effect of phosphorus, potassium, calcium and magnesium on nitrate accumulation in spinach plants, but found no effect. Cantliffe (1973) investigated the effect of phosphorus and potassium on nitrate and total nitrogen concentration of spinach plants under different light intensities. Phosphorus was shown to have a decreasing effect on nitrate content, and seemed to have an increasing effect on total nitrogen content. The effects, however, were mostly not significant. Potassium had a decreasing effect on nitrate and nitrogen accumulation, except on nitrate content under high light were the opposite was true (3.23 x 104 lux) intensity. These effects were mostly significant. Smatanová et al. (2004) found spinach nitrate concentration to be affected by the application of sulphur. They used in their experiment two nitrogen and three sulphate levels. When both nutrients were applied in a low amount, or both in a high amount, nitrogen accumulation was relatively low. When one of the two nutrients was applied in a high amount and the other in a low amount, nitrogen accumulation was relatively high. These results suggests therefore an interaction effect between nitrogen and sulphur fertilization level. Mondal and Nad (2012) found for both phosphate and sulphur, a decreasing effect on nitrate accumulation in spinach. On total nitrogen content, phosphate and sulphate had almost no effect. Mondal and Nad (2012) state that the reduction in nitrate accumulation, caused by sulphur fertilization, was probably because of increased NRA. This is in agreement with what is written already in Paragraph 2.2.2.1. Explanations for differences between results of Cantliffe (1973), Smatanová et al. (2004) and Mondal and Nad (2012) are not found, but might be caused by differences in experimental set up. Results of Smatanová et al. (2004) suggests an interaction effect between nitrogen and sulphur, while Mondal and Nad (2012) did not took this possibility into consideration with their experimental set up. Moreover a considerable part of the effects was not significant. So more research is necessary to clarify the effect of phosphorus and sulphur on nitrogen and nitrate accumulation and to make clear whether there exists an interaction effect or not. As sulphur is found to increase NRA, however, it would be likely that at least sulphur application will reduce nitrate accumulation.

Besides nutritional factors, climatic factors were shown to influence nitrate and nitrogen accumulation as well. Cantliffe (1973) found that, in spinach plants, light intensity influenced nitrate and nitrogen accumulation. Both nitrate and nitrogen content were higher under low light (1.08 x 104 lux) than under high light (3.23 x 104 lux) intensity. Cantliffe (1972) found temperature affecting nitrate and nitrogen accumulation. At low nitrogen (0 and 50 mg N kg-1 soil) nitrate accumulation increased with increasing temperature till the maximum investigated temperature of 30 ⁰C. At high nitrogen (200 mg N kg-1 soil) nitrate accumulation increased with temperature till a maximum at 25 ⁰C, an further increase till 30 ⁰C caused a decrease in nitrate accumulation. Nitrogen accumulation also increased with increasing temperature till 25 ⁰C, except with spinach plants grown without nitrogen, which showed the highest nitrogen accumulation by 15 ⁰C (Cantliffe, 1972).

Nitrate accumulation can differ periodically. Steingröver et al. (1986a; 1986b), Riens and Heldt (1992) and Scaife and Schloemer (1994) found a clear diurnal pattern for nitrate accumulation. Nitrate concentration was higher in the dark, especially in leaves. This was probably, as described in Paragraph 2.2.2.1., because a reduced NRA in the dark. Mondal and Nad (2012) found nitrate as well as total nitrogen concentration of spinach plants in general to be higher at 65 days after sowing than at 50 days after sowing. Biemond et al. (1996), however, did not found a clear pattern of nitrate accumulation over time. The nitrate concentration decreased strongly the first five to ten days after emergence, after which it stabilized slightly above a nitrate concentration of 0 %. Approximately 20 days after emergence, nitrate concentration increased sharply, but decreased again about 5 days later. Biemond et al. (1996) found an increase of

total nitrogen over time till about 30 days after emergence, after which it decreased. Differences between the results of Biemond et al. (1996) and Mondal and Nad (2012) might be explained by the fact that the results of Biemond et al. concerned only the second leaf pair, while Mondal and Nad measured the whole plant. Besides that, Biemond et al. measured till 45 days after sowing, while Mondal and Nad measured till 65 days after sowing. Barker and Maynard (1971) found nitrate accumulation being higher in mature than in immature leaves. Mozafar (1996) also found nitrate concentration higher in older leaves. Biemond et al. (1996), however, found that the organic nitrogen in the first leaf pairs decreased with increasing leaf age. This is explained by the fact that nitrogen is redistributed to younger leaves. Differences with results of Barker and Maynard (1971) and Mozafar (1996) might be due to the fact that these authors harvested the plants before senescence and consequently nitrogen remobilization occurred. Additionally the results of Biemond et al. (1996) concerned organic nitrogen, while the others measured the nitrate-nitrogen concentration. That this could explain differences between the mentioned authors is supported by the fact that Maynard and Barker (1971) found the total nitrogen concentration to be higher in immature leaves compared to mature leaves.

Besides the ability to accumulate nitrate, it is also reported that spinach plants accumulate ammonium, especially under ammonium nutrition (Lasa et al., 2001). The extent of accumulation of nitrate and ammonium however clearly differs. Ammonium content of spinach roots under ammonium nutrition is a 30 fold lower than nitrate content of spinach roots under nitrate nutrition (Lasa et al., 2001). For spinach shoots this is only 3 times lower. Spinach plants, however, are in general sensitive to ammonium nutrition (Zornoza and González, 1998a; 1998b; Lasa et al., 2001; Conesa et al., 2009). In practice, ammonium accumulation will therefore only occur to a limited extent. Lasa et al. (2001) state that ammonium toxicity is correlated to ammonium accumulation in photosynthetic tissue, were it inhibits photosynthetic processes. These authors further suggest that ammonium tolerance can be improved by increased ammonium assimilation, especially in the roots. Zornoza and González (1998b) confirmed a higher tolerance to ammonium by spinach plants by an increase in ammonium assimilation in roots, but also in shoots.

Nitrate stored in the vacuole is important as an osmoticum, but also as nitrogen source (Orsel and Miller, 2011). When nitrogen supply becomes limiting, plants can remobilize the nitrogen stored in the vacuole. In literature only limited information is found about this nitrogen remobilization. Leij et al (1998) investigated the remobilization of nitrate in barley roots. After removal of nitrogen, nitrate concentration of both cytosol and vacuole decreased. 24 hours after removal of nitrogen, the mobilization of stored nitrate in the vacuole, was faster than that of the cytosol for both cortical and epidermal cells. Fan et al (2007) found a remobilization of vacuolar nitrate of epidermal cells of rice roots and leaves under nitrogen starvation. By remobilizing vacuolar nitrate, cytoplasmic nitrate concentration remained stable. Wang and Shen (2011) found a strong mobilization of vacuolar nitrate in lettuce. The nitrate concentration in the cytosol, however, remained constant to maintain normal plant growth. Lettuce cultivar Sx1 showed a faster remobilization of vacuolar nitrate than cultivar Nrnct. Wang and Shen (2011) therefore suggested that Sx1 is more adaptable to changes in external nitrate concentration. Sx1 moreover had a greater capacity to recover vacuolar nitrate concentration after resupply with nitrogen. About nitrate remobilization in spinach, unfortunately no information is found in literature.

2.3. Concluding remarks Little is known about the origin of spinach. Cultivated spinach (Spinacia oleracea L.) has two wild relatives (S. tetrandra and S. turkestanica), but it is unknown which one is the ancestor. The wild relatives are limitedly used in breeding for resistance, but are as far as known until now not been used for breeding for higher NUE in cultivated spinach.

NUE can be separated into Nitrogen Uptake Efficiency (NUpE) en Nitrogen Utilization Efficiency (NUtE). Nitrogen is taken up from the soil by mass flow, diffusion or root interception. The efficiency of nitrogen uptake is determined by several external and internal factors. Most important external factors are: nitrogen level, nitrogen form and several soil characteristics. The main internal factors are: plant demand, root development and transport systems. Nitrogen utilization can be divided into reduction and assimilation, internal transport and accumulation. Of reduction and assimilation, NR is far the most investigated enzyme. Over several spinach cultivars a large variation in NRA was found. About other enzymes and also internal transport is still much unknown. Nitrogen accumulation is intensively studied and large variation between spinach cultivars was found. Information about the remobilization of this (vacuolar stored) nitrogen, however, is still largely missing.

3. Materials and methods Two studies were carried out in this thesis project. The first one was a pre-screening of a mapping population. The materials and methods of this experiment will be described in Paragraph 3.1. The second experiment was done with only hybrid cultivars and had the purpose to get better insight in the physiology of several traits. The materials and methods of this experiment will be described in Paragraph 3.2. In the third paragraph of this chapter, the statistical methods used for analysis of both experiments will be described.

3.1. Pre-screening mapping population

3.1.1. Plant material and cultural practices A population, consisting of the progeny of four crosses, was used. The parents were Ranchero (ID: 7), Novico (ID: 22), Crocodile (ID: 27) and Marabu (ID: 41). These parents were chosen because they were contrasting in growth habit: Ranchero and Novico were fast growing cultivars while Crocodile and Marabu were slow growing (J.R. Chan Navarrete personal communication, 2013). With these parents four crosses were made: 41x7, 7x27, 41x22 and 22x27. From ten F1s of each cross, selfings were made Figure 2. Schematic presentation of crosses. resulting in ten I1F1 lines (Figure 2). Besides these I1F1 P=hybrid cultivars, I1F1=selfing of the F1. lines also the four parents were used in this experiment, as well as inbreds of these parents. In Appendix I the detailed list of the 48 genotypes and corresponding numbers are given. The seeds were first primed for three days at 14 ⁰C in the dark. Then the seeds were placed in rock wool for two weeks. In these rock wool trays the plants were rinsed manually with tap water every two days. After priming and germination, the seedlings were placed in the containers of the hydroponic system in the greenhouse of Unifarm in Wageningen. This was done at 21-11-2012, which in the rest of the experiment will be considered as 2 1 3 14 6 19 21 18 the day of planting. 24 plants were placed in each 30 22 48 41 24 39 15 11 container. Because in total 48 genotypes were used (40 8 45 7 42 16 29 23 5 I1F1 lines, 4 parents and 4 inbreds) and each genotype was 1 planted only once per plot, each plot consisted out of two 32 4 35 28 17 43 33 9 containers. Per treatments 16 plots were used divided over Figure 1. Randomization of the 48 genotypes 44 10 31 36 37 26 40 47 two units. The genotypes were randomly planted within over the two containers of 38 25 27 46plot34 1. 20 13 12 the plots (Appendix III; in Figure 3 plot 1 is shown as an Figure 3. Randomization of the 48 genotypes example). Around every container a protection box was over the two containers of plot 1. placed to prevent for a border effect. The plants were harvested four weeks after planting.

For nutrition, a modified Hoagland solution without nitrogen was used (Appendix III). Nitrogen was added separately in a nitrate-ammonium ratio of 3 : 1. Nitrogen was added every day around 10 am. Two nitrogen treatments were used. A high one to achieve a RGR of 0.18 and a low one to achieve a RGR of 0.10. As described in the introduction (Paragraph 1.4) a certain RGR can be achieved by a specific nitrogen additional rate (NAR). The NAR of the two treatments are given in Appendix IV.

3.1.2. Measurements

Pre-harvest Leaf number Leaf number was counted twice a week in the first two weeks and once a week in the last two weeks before harvest. Leaf pairs were counted, see Figure 4 for a schematic presentation.

Figure 4. A schematic drawing of the leaf rosette of spinach. Leaf pairs were counted.

Chlorophyll content Chlorophyll content was measured with a SPAD 502 Plus chlorophyll meter (Spectrum Technologies, Inc., USA). Chlorophyll content measurements were started at the first leaf pair by the end of the second week after planting. In the third week also measurements at the second leaf pair were started. Measurements were performed twice a week. Measurements were performed at the corner of the leaf as shown in Figure 5.

Stomatal conductance Stomatal conductance was measured with a leaf porometer (Decagon Devices, Inc., USA). Stomatal conductance was measured only once during the growth, in the third week.

Flowering At the moment of harvest flowering was scored. Figure 5. Chlorophyll Post-harvest measurements were Fresh weight performed within the Immediately after harvest, total plant fresh weight (TFW) was determined. red border. Main nerve is avoided. Thereafter the shoot was cut from the root and also weighed. Root fresh weight (RFW) was calculated by subtracting the shoot fresh weight (SFW) of the total fresh weight.

Leaf area After weighing the shoot, the total leaf area (LA) was measured with a Licor Leaf Area Scanner (LI- 3100C). For this measurement all leaves were put in this scanner with the adaxial side down.

Dry weight When the measurements with the fresh material was finished, shoots and roots were put in paper bags and placed in the oven at 40 ⁰C for 24 hours, followed by 70 ⁰C for 24 hours. The dried shoots and roots are weighed. By adding shoot and root dry weight (SDW and RDW), total dry weight is (TDW) calculated.

Calculated traits With the obtained weights and LA, the root : shoot ratio (R:S), specific leaf area (SLA) and the dry matter percentage of the shoot (DM%) was calculated by the following formulas:

R:S = RDW / SDW

DM% = (SDW/SFW)*100%

SLA = LA / SDW

3.2. Physiology

3.2.1. Plant material and cultural practices For this experiment eight cultivars were used, namely Ranchero, Chevelle, Andromeda, Sparrow, PV 9273/Cello, Novico, Crocodile and Marabu. The first four cultivars were fast growing, the last four slow growing cultivars. The priming and germination of the seeds of these cultivars was performed in the same way as for the previous experiment. Priming was performed at 7-1-2013, germinating at 9- 1-2013. The plants were, similar to the previous experiment, then planted in the hydroponic system of the greenhouse of Unifarm in Wageningen at 25-1-2013. The eight genotypes were randomized within each row of eight holes of the container, with three replicates per container, see Figure 6 (for complete experimental design see Appendix V).

27 41 16 33 23 22 7 9 1 23 16 41 7 27 33 22 9 7 41 16 9 22 23 33 27 Figure 6. Randomization container 1. The eight cultivars were randomized within each row. Cultivar 7 is Ranchero, cultivar 9 is Chevelle, cultivar 16 is Cello, cultivar 22 is Novico, cultivar 23 is Andromeda, cultivar 33 is Sparrow and cultivar 41 is Marabu.

A modified Hoagland solution was used (Appendix II). The nitrogen was added separately by two different methods: according to the Ingestad and depletion model. With the Ingestad model a high and low nitrogen level was maintained by NAR given in Appendix VI. With the depletion model, an equal total amount of nitrogen is given but all at the beginning of the experiment (see Appendix VII). All four treatments consisted of eight containers. Three of these containers were harvested two weeks after planting, three four weeks after planting and two containers were extra (Appendix V).

3.2.2. Measurements

Pre-harvest The pre-harvest measurements were performed as described in Paragraph 3.1.1. The only difference is that leaf number was also counted twice a week in the last two weeks of the growth. The pre- harvest measurements were only performed by the plants of the second harvest to get a good overview of the development of these traits over time.

Post-harvest Similar to the previous experiment, after harvest the fresh and dry weight of root, shoot and total and the leaf area of the plants was determined. These measurements were done in the same way for harvest 1 as for harvest 2. Similar to the previous experiment also R:S ratio, SLA and DM% was calculated. For methodical details see Paragraph 3.1.2. In this experiment also the relative growth rate (RGR) was calculated with the following formula:

RGR = [Ln(SDWt2/SDWt1)]/t2 - t1

SDWt1 and SDWt2 were the SDW at first and second harvest respectively. T2-t1 was the difference in days between the second and first harvest.

Root characteristics After fresh weight was determined, the roots were stored in a 50 ml tube with water. The root measurements were performed within two weeks after harvest. When not possible to carry out the measurements within two weeks, roots were stored in tubes filled with 90 % water and 10 % ethanol. The root characteristics were determined by scanning the roots and analysing the traits with the software program: WinRHIZO Pro 2005. With this program the average root diameter (RD), the total root length (RL) and the surface area of the roots (RSA) was determined. Also the root length per class of 0.05 cm was determined. The images were scanned with grey levels at 400dpi. Because of the labour intensity of this job, only the plants of the middle row of each container were used for these measurements.

3.3. Statistical analysis The statistical analysis was performed with software program GenStat (15th edition). Before analysis outliers were removed from the dataset. Outliers were, in general, defined as individual measurements that were bigger or smaller than four times the standard error. If the obtained results agree with the assumptions of normality, homogeneity and independence, statistical analysis was done with ANOVA. If not, REML was used for analysis. Multiple comparisons were done by the Bonferonni test. In some cases it was necessary to perform a square root or natural logarithm transformation.

4. Results Two experiments were carried out in this thesis project. The first one was a pre-screening of a mapping population. The results of this experiment will be described in Paragraph 4.1. The second experiment was done with only hybrid cultivars and had the purpose to get better insight in the physiology of several traits. The results of this experiment will be described in Paragraph 4.2.

4.1. Pre-screening In the pre-screening experiment 48 genotypes ((40 I1F1 lines, 4 parents and 4 inbreds) were grown hydroponically under two nitrogen levels (low and high). The aim of this experiment was to investigate whether sufficient segregation in the traits of the progeny of the crosses for QTL mapping exists.

4.1.1. General results Table 3 shows the germination percentage of the four hybrid Table 3. Germination percentage of the cultivars used as parents and of their selfings. The germination four parents and the selfings of these at 10 days after sowing (DAS). percentage of Crocodile, Marabu and Novico was below 60 %, while that of Ranchero was clearly higher. Remarkable is that Cultivar Germination (%) the germination percentage of the selfings of Crocodile, Crocodile 54 Marabu and Novico was higher than that of the hybrid cultivars. Crocodile Self 55 Table 4 shows the mean germination percentage of the 10 I1F1 Marabu 46 lines per cross, the germination of the line with the lowest Marabu Self 56 (min) and highest (max) germination percentage and the ratio Novico 55 of max/min at 10 days after sowing (DAS). The cross Marabu x Novico Self 79 Ranchero had the highest average germination rate, Novico x Ranchero 76 Crocodile the lowest. Remarkable is the smaller variation (max/min) in germination between lines of the cross Marabu x Ranchero Self 55 Ranchero, in comparison with the other crosses.

Table 4. Mean germination percentage of the 10 I1F1 lines per cross, germination percentage of the line with lowest (min) and highest (max) germination percentage, the ratio of max/min and the standard deviation (st. dev.) at 10 DAS.

Cross Mean Min Max Max/Min Marabu x Ranchero 80 69 92 1.34 Marabu x Novico 71 51 92 1.80 Novico x Crocodile 61 45 83 1.86 Ranchero x Crocodile 76 47 90 1.91

Figure 7 shows the nitrogen effect on several traits averaged over all plants used in the experiment. RDW was higher at low compared to high nitrogen levels while for SDW the opposite was true (Figure 7a and b). This consequently results in a higher R:S at low nitrogen levels (Figure 7d). SFW and TDW were, similar to SDW, clearly higher at high compared to low nitrogen levels (not shown). The differences between SFW and SDW, however, were slightly larger at high compared to low nitrogen levels, which resulted in the higher DM% at low nitrogen levels (Figure 7e). Plants at high nitrogen levels, however, had larger LA and SLA (Figure 7c and f). This was mainly caused by larger leaves, as the leaf number, even at 22 DAP, was hardly influenced by nitrogen level (Figure 7g). For measurements earlier in the experiment, no nitrogen effect was observed on leaf number (data not shown). Chlorophyll content at harvest was higher at high compared to low nitrogen levels for both the first and the second leaf (Figure 7h and i).

0.12 a 0.6 b 200 c 0.10 0.5 )

2 150 0.08 0.4 0.06 0.3 100 0.04 0.2

Leaf area (cm 50 Root dry weight (g) 0.02 Shoot dry weight (g) 0.1 0.00 0.0 0

0.6 8 e 400 f d ) 1 - ) 1 g -

0.5 2 6 300 0.4 0.3 4 200 0.2

Dry matter (%) 2 100 0.1 Root shoot ratio (g g Specific leaf area (cm 0.0 0 0

3.0 g 40 h 40 i 2.5 30 30 (SPAD) 2.0 (SPAD) st nd 1.5 20 20 1.0 10 10 0.5 Leaf number (22 DAP) Chl. content 1 Chl. content 2 0.0 0 0

Figure 7. Average root dry weight (a), shoot dry weight (b), leaf area (c), root shoot ratio (d), dry matter percentage (e), specific leaf area (f), leaf number at 22 DAP (g), chlorophyll content of the first leaf at harvest (h), chlorophyll content of the second leaf at harvest (i), of all plants under low (blue bars) and high (red bars) nitrogen level. Error bars represent the standard error of the mean.

Genotype had a strong significant effect for all traits (Table 5). Remarkable is that nitrogen level was shown to have a clear effect on SFW, RDW, SDW, TDW and LA (Figure 7), but that this effect was mostly not significant (Table 5). This may be caused by large a unit effect for the two units that received the high nitrogen treatments (see Table 6, SFW as an example). The nitrogen level effect was significant for R:S, DM%, SLA and CC (chlorophyll content) of both leaves at harvest. The interaction effect between genotype and nitrogen level was significant for all traits, except for the leaf number measurements and CC at 20 DAP.

Table 5. F probabilities of the ANOVA for shoot fresh weight (SFW), root dry weight (RDW), shoot dry weight (SDW), total dry weight (TDW) root shoot ratio (R:S), dry matter percentage (DM%), leaf area (LA), specific leaf area (SLA) leaf number (L. No.) at 9, 12 and 22 DAP and chlorophyll content (CC) of the second (2nd) leaf at 20 DAP and of the first (1st) and second leaf at harvest. p<0.05.

Trait Genotype N-Level Genotype x N-Level SFW <.001 0.030 <.001 RDW <.001 0.332 0.007 SDW <.001 0.063 <.001 TDW <.001 0.121 <.001 LA <.001 0.034 <.001 R:S <.001 <.001 <.001 DM% <.001 0.003 0.002 SLA <.001 0.016 0.011 L. No. 9 DAP* <.001 0.513 0.075 L. No. 12 DAP* <.001 0.127 0.485 L. No. 22 DAP* <.001 0.416 0.351 CC 2nd 20 DAP <.001 0.318 0.086 CC 1st harvest <.001 0.019 <.001 CC 2nd harvest <.001 0.017 0.022 * ANOVA was performed for the statistical analysis of leaf number. By the observations, however, a limited number of results were possible, namely 1, 2, 3 etc. leaves. These binominal results gave some problems concerning the assumptions of normality and homogeneity of the ANOVA. Because there was almost no variation by the first two measurements and by de last three measurements the normality of the distribution was acceptable, it was decided to perform an ANOVA for the last three measurements. Caution, however, has to be taken with the interpretation of the results of the ANOVA, because the residuals were not homogeneously distributed.

Table 6. Average SFW per unit. Units 1 and 3 received a high nitrogen treatment (RGR=0.18), units 2 and 4 a low (RGR=0.10).

Unit N-level Mean s.e.mean 1 High 7.28 0.25 2 Low 2.87 0.10 3 High 10.00 0.35 4 Low 2.76 0.09

Figure 8 shows the average performance of the hybrid cultivars (used as parents), the I1F1 lines and of the selfings of the hybrid cultivars for selected traits. SFW, RDW, SDW and LA of the cultivars were clearly higher than for the I1F1s and the selfings (8a-e). The I1F1s tended to score higher for the mentioned traits than the selfings, but differences were small. For SLA and CC of the first leaf at harvest the opposite was found (8f). TDW and Leaf number at 22 DAP showed a very similar pattern to SFW, SDW and LA and were therefore only shown in Appendix VIII. For the other traits only small differences between the different types of plant materials were found (Appendix VIII).

20 0.30 1.2 a b c 0.25 1.0 15 0.20 0.8 10 0.15 0.6 0.10 0.4 5 0.05 0.2 Root dry weight (g)

0 0.00 Shoot dry weight (g) 0.0 Shoot fresh weight (g)

350 ) 500 40

1 f

d - e ) g

2 300

2 400 250 30 (SPAD)

200 300 st 20 150 200 100 10 Leaf area (cm 50 100 0 0 0 Chl. content 1 Specific leaf area (cm

Figure 8. Average shoot fresh weight (a), root dry weight (b), shoot dry weight (c), leaf area (d), specific leaf area (e) and chlorophyll content of the first leaf at harvest (f) of the cultivars, the I1F1 lines and the selfings, under low (blue bars) and high (red bars) nitrogen level. Error bars represent the standard error of the mean.

4.1.2. Genetic variation

Table 7 shows statistics data per cross for the traits SFW, RDW, SDW, TDW and LA. For these five traits under low nitrogen levels the progeny of the cross Novico x Crocodile had the largest segregation (max/min), followed by Marabu x Novico. Under high nitrogen levels Marabu x Novico had the widest segregating progeny, Novico x Crocodile was second. For the five mentioned traits the progeny of the cross Marabu x Ranchero had highest biomass and largest LA, under both nitrogen levels. For the traits R:S, DM%, SLA, and CC of the first and second leaf at harvest, only very little variation was observed for all cross progenies (Appendix IX). Data of the leaf number measurements and of CC of the second leaf at 20 DAP were not shown because no significant nitrogen level effect was observed.

Table 7. Descriptive statistics, per nitrogen level, of the progeny of the four crosses for shoot fresh weight (SFW), root dry weight (RDW), shoot dry weight (SDW), total dry weight (TDW) and leaf area (LA). Mean is the average of all I1F1 plants of the same cross, min is the worst performing line of a cross and max the best performing one. Max/min is the ratio of max divided by min.

Low N High N Trait Cross Mean Min Max Max/Min Mean Min Max Max/Min Marabu x Ranchero 3.31 2.22 4.69 2.1* 11.05 7.73 14.25 1.8* Marabu x Novico 2.41 0.98 3.73 3.8* 7.14 2.67 14.10 5.3*

SFW Novico x Crocodile 1.94 0.70 3.72 5.3* 5.27 3.09 9.30 3.0 Ranchero x Crocodile 2.82 1.70 4.04 2.4* 8.06 3.97 10.85 2.7* Marabu x Ranchero 0.123 0.091 0.169 1.9 0.098 0.062 0.131 2.1*

Marabu x Novico 0.087 0.033 0.130 4.0 0.072 0.029 0.135 4.6

RDW Novico x Crocodile 0.068 0.016 0.146 9.0* 0.051 0.030 0.087 2.9 Ranchero x Crocodile 0.107 0.057 0.152 2.7 0.077 0.040 0.110 2.8* Marabu x Ranchero 0.25 0.16 0.34 2.1* 0.59 0.41 0.78 1.9

Marabu x Novico 0.18 0.07 0.27 3.8* 0.39 0.13 0.79 5.8*

SDW Novico x Crocodile 0.15 0.05 0.28 6.1* 0.30 0.18 0.57 3.2* Ranchero x Crocodile 0.21 0.12 0.30 2.6* 0.44 0.20 0.60 3.0* Marabu x Ranchero 0.37 0.25 0.51 2.0* 0.69 0.47 0.90 1.9

Marabu x Novico 0.27 0.11 0.40 3.6* 0.47 0.17 0.92 5.4*

TDW Novico x Crocodile 0.22 0.06 0.43 6.8* 0.36 0.22 0.45 2.1* Ranchero x Crocodile 0.32 0.18 0.45 2.5* 0.52 0.26 0.71 2.8* Marabu x Ranchero 63 47 83 1.8 187 133 254 1.9*

Marabu x Novico 49 19 73 3.8* 134 59 250 4.2* LA Novico x Crocodile 39 14 76 5.6* 104 63 200 3.2* Ranchero x Crocodile 54 33 76 2.3* 142 67 205 3.0* *Significant differences between the I1F1 lines of these cross exists (p<0.05).

Table 8 shows the correlations between SFW, RDW, SDW, TDW and LA under low and high nitrogen levels. These traits were under both nitrogen levels strongly correlated. The correlations were slightly stronger under high compared to low nitrogen levels, especially for RDW with SFW, SDW and LA. The correlations of the other traits are shown in Appendix X. The correlations of R:S with the other traits were slightly stronger under low compared to high nitrogen levels, for DM% and SLA the opposite was true. For CC of both leaves no difference in strength of correlations between the two nitrogen levels was observed. Neither for R:S, DM%, SLA or CC, strong correlations were observed.

Table 8. Correlations between shoot fresh weight (SFW), root dry weight (RDW), shoot dry weight (SDW), total dry weight (TDW) and leaf area (LA). Correlations in white and grey refer to low and high nitrogen conditions, respectively. All correlations were significantly different from zero (p<0.001).

Trait SFW RDW SDW TDW LA SFW - 0.941 0.987 0.982 0.974 RDW 0.819 - 0.951 0.964 0.925 SDW 0.960 0.782 - 0.995 0.969 TDW 0.955 0.918 0.965 - 0.966 LA 0.928 0.784 0.907 0.906 - The correlation calculations were besides the traits out of this table based on R:S, DM%, SLA and CC of the first and second leaf at harvest. The calculations could only be performed for plants of which none of the measurements was missing. The calculations were therefore based on 52 and 80 % of the plants under low and high nitrogen level respectively.

4.2. Physiology In the physiology experiment eight hybrid cultivars were grown hydroponically under two nitrogen levels (low and high) according to two application methods (depletion and Ingestad). The aim was to investigate possible interaction effects between nitrogen levels and application methods and the correlations of the measured traits to NUE.

4.2.1. General results Figure 9 shows the germination percentage of the eight cultivars over time. The final germination percentage was highest for the cultivar Andromeda and lowest for Marabu and Ranchero. Ranchero is typically a good germinating cultivar, as was confirmed in the pre-screening experiment. The yield and other characteristics of Ranchero were also much lower than what could be expected based on previous experiments (J.R. Chan Navarrete personal communication, 2013). This suggests that there was something wrong with the seeds of this cultivar in the current experiment. Ranchero was therefore excluded from all analysis in this experiment. Germination of Marabu was also worse than in the pre-screening experiment, but this cultivar shows typically a bad germination. Moreover since yield and other characteristics were not lower than what could be expected based on previous experiments (C.G. van der Linden personal communication, 2015), this cultivar was not excluded from analysis. 100 90 Ranchero 80 70 Chevelle 60 Cello 50 Novico 40 Andromeda Germination (%) 30 20 Crocodile 10 Sparrow 0 1 3 5 7 9 11 13 Marabu Time (DAS)

Figure 9. Germination percentage of the eight cultivars over time. DAS means days after sowing.

Table 9 shows per treatment the expected and observed relative growth rate (RGR). Remarkable is that under high nitrogen levels the observed RGR was close to expected, while under low nitrogen levels the observed RGR was clearly higher than the expected RGR, irrespective of the application method.

Table 9. Observed and expected relative growth rate (RGR) per treatment.

Application method N level Expected RGR Observed RGR Ingestad Low 0.10 0.137 Ingestad High 0.18 0.188 Depletion Low 0.10 0.139 Depletion High 0.18 0.173

Figure 10 shows for several traits the average performance of all plants used in the experiment (except of Ranchero for reasons described above) for both harvests. RDW showed only little variation

between the nitrogen levels and application methods (Figure 10a). SDW (Figure 10b), LA (Figure 10c), SFW and TDW (data not shown) were at both harvests higher under depletion than under Ingestad at both nitrogen levels, and higher at high than low nitrogen levels for both application methods. Since SDW was clearly lower under low nitrogen level and RDW showed only little variation, R:S was consequently higher under low nitrogen levels (Figure 10d). R:S was at both harvests higher under Ingestad, especially under low nitrogen levels. At the first harvest almost no variation in DM% was observed (Figure 10e). At the second harvest more variation was observed and especially DM% under depletion low nitrogen levels was clearly higher than for any of the other conditions. SLA was decreased under low compared to high nitrogen levels at the second harvest (Figure 10f). Chlorophyll content of both leaf pairs showed a decrease over time (Figure 10g and h). At harvest chlorophyll content of both leaves was highest at high nitrogen levels for both application levels. At harvest, chlorophyll content of both leaves was higher under Ingestad compared to depletion, both at low and high nitrogen levels.

The cultivar effect was significant at both harvests for most traits (Appendix XI and Appendix XII). The nitrogen level was shown to influence most traits (Figure 10), but this effect was only significant at the second harvest (Appendix XI). At the first harvest, the nitrogen application method effect was significant for the traits SFW, SDW, TDW, LA and R:S under both nitrogen levels (Appendix XIIappendix xii). At the second harvest the application method effect was clearly weaker than the nitrogen level effect for most traits, but for the mentioned traits it was still significant. Besides this, the application method effect at the second harvest was significant for DM% and SLA under low nitrogen levels and for some chlorophyll content measurements under both nitrogen levels.

0.15 a 1.0 b 0.8 0.10 0.6 0.05 0.4 0.2 0.00 0.0 Root dry weight (g)

Low N High N Low N High N Shoot dry weight (g) Low N High N Low N High N Depletion Ingestad Depletion Ingestad

300 ) 0.6

) c 1 d - 2 200 0.4

100 0.2 Leaf area (cm 0 0.0 Low N High N Low N High N Low N High N Low N High N Root shoot ratio (g g Depletion Ingestad Depletion Ingestad

f 20 e ) 400 1 - g

15 2 300 10 200 5 100 Dry matter (%) 0 0 Low N High N Low N High N Low N High N Low N High N

Depletion Ingestad Specific leaf area (cm Depletion Ingestad

40 g 40 h 30 30

20 20

10 Depl. Low N Depl. High N 10 Depl. Low N Depl. High N Ing. Low N Ing. High N Ing. Low N Ing. High N Chl. content (SPAD) Chl. content (SPAD) 0 0 13 18 23 28 13 18 23 28 Time (DAP) Time (DAP)

Figure 10. Average root dry weight (a), shoot dry weight (b), leaf area (c) root shoot ratio (d), dry matter percentage (e), specific leaf area (f), chlorophyll content of the first leaf (g), chlorophyll content of the second leaf (h), of the seven cultivars at two (blue bars) and four weeks (red bars) after planting. Error bars represent the standard error of the mean.

4.2.2. Interaction effects Figure 11 displays the SDW of the cultivars at the first and second harvest. A similar trend is visible at both harvests: cultivars Novico and Andromeda were the most responsive to nitrogen, cultivar Cello the least. For all treatments, at both harvests, an interaction effect was observed between cultivars and nitrogen levels and cultivars and application methods. At first harvest, however, the application method effect was stronger than the nitrogen level effect and the interaction effect between

cultivars and application methods was therefore stronger than the interaction effect between cultivars and nitrogen levels. At the second harvest the opposite was true and the interaction effect between cultivars and nitrogen levels was stronger than between cultivars and application methods. For SFW, RDW, TDW and LA a similar trend as for SDW was observed (Figures 15-18 in Appendix XV).

0.09 a 0.08 0.07 0.06 0.05 0.04 0.03

Shoot dry weight (g) 0.02 0.01 0.00

Depletion low N Ingestad low N 1.4 Depletion high N Ingestad high N b 1.2

1.0

0.8

0.6

Shoot dry weight (g) 0.4

0.2

0.0 Chevelle Cello Novico Andromeda Crocodile Sparrow Marabu Average

Figure 11. Shoot dry weight at the first (a) and second (b) harvest for the seven cultivars and the average of all plants, under low and high nitrogen level for both depletion and Ingestad.

DM% and SLA had almost no variation between the treatments at the first harvest, but at the second harvest clear differences were observed (Figure 20 and Figure 21 in Appendix XV). Considerable interaction effects were also only visible at the second harvest. Root diameter (RD) and leaf number (L. no.) showed at both harvests almost no variation between the treatments and therefore also no considerable interaction effects (Figure 22 and Figure 26 in Appendix XV). SC, RL and RSA showed a lot of variation and interaction between treatments and cultivars(Figures 23-25 in Appendix XV). The only consistent pattern that could be detected was that cultivars Cello and Sparrow, which under depletion had a small difference in SDW between low and high nitrogen levels, showed an increase in RL. At the first measurements, CC of both leaves showed almost no variation and interaction effects between cultivars and treatments. These variation and interaction effects, however, increased over

time and were largest at the final measurements, which is the reason that only the final measurements of both leaves were shown (Figure 27 in Appendix XV). Table 10 shows the cultivar x nitrogen level interaction effect for several traits under depletion and Ingestad for both harvests. The interaction effect was, with a few exceptions, for none of the traits significant at the first harvest for both application methods. At the second harvest, however, this interaction effect was for most of these traits significant under both depletion and Ingestad. The cultivar x nitrogen level interaction effect for the other traits was on a few exceptions in no case significant (Appendix XIII).

Table 10. F probability for the cultivar x nitrogen level effect under depletion and Ingestad, for first and second harvest separately.

Table 11 shows the F probability for the cultivar x application method interaction under low and high nitrogen levels for both harvests separately. For the shown traits, with a few exceptions, the interaction effects were only significant at the first harvest at high nitrogen levels. Remarkable exceptions were R:S, DM% and SLA at the second harvest under low nitrogen levels which showed a (close to) significant interaction effect. For these traits also a strong application method effect was observed (Figures 19-21 in Appendix XV). The cultivar x application method effect was for the other traits mostly not significant (Appendix XIV).

Table 11. F probability for the cultivar x application method effect under low and high nitrogen level, for first and second harvest separately.

4.2.3. Correlations Table 12 shows the correlations of several traits of plants fertilized according to a) the depletion and b) the Ingestad model at the second harvest. Correlations below and above the diagonal refer to low and high nitrogen levels respectively. Under all treatments strong correlations existed between SFW, RDW, SDW, TDW, LA, RL and RSA. In general, the correlations with R:S under the Ingestad model were stronger under low nitrogen levels, while under the depletion model these were stronger under high N. Plants fertilized according to the depletion model, however, showed under both nitrogen levels a significant correlation between RDW and R:S, while under Ingestad this was not observed. The correlation between R:S and RD was for both fertilization methods only significant under low nitrogen levels. The correlations between R:S and RL and RSA was for both nitrogen levels stronger under depletion than under Ingestad. For both fertilization methods, DM% and SLA were stronger correlated to SFW, RDW, SDW, TDW, LA, RL and RSA under high compared to low nitrogen levels. For L. no. and CC only the final measurements are shown in Appendix XVII. L. no. showed under most treatments only a significant correlation with SFW, RDW, SDW, TDW, LA, RL and RSA. CC of the second leaf was significantly correlated with most other traits. Further no significant correlations for CC were observed. At the first harvest also strong positive correlations were observed between SFW, RDW, SDW, TDW, LA, RL and RSA (Appendix XVI). These traits were mostly significant negative correlated with RD, except under depletion low nitrogen. Under Ingestad low nitrogen, R:S and SLA were strongly positive correlated with SFW, RDW, SDW, TDW, LA, RL and RSA. Under depletion high nitrogen, DM% was strongly positive correlated with SFW, RDW, SDW, TDW, LA, RL and RSA.

Table 12. Correlations at the second harvest between shoot fresh weight (SFW), root dry weight (RDW), shoot dry weight (SDW), total dry weight (TDW), leaf area (LA), root shoot ratio (R:S), dry matter percentage (DM%), specific leaf area (SLA), average diameter of the root (RD), total root length (RL) and root surface area (RSA) of plants fertilized according to a) the depletion or b) the Ingestad model. Correlations in white and grey below and above the diagonal refer to low and high nitrogen levels respectively. Correlations in black were significantly diferent from zero (p<0.05), in red not. a) SFW RDW SDW TDW LA R:S DM% SLA RD RL RSA SFW - 0.967 0.991 0.991 0.989 0.552 0.577 -0.696 0.210 0.945 0.959 RDW 0.890 - 0.985 0.985 0.957 0.687 0.662 -0.686 0.193 0.972 0.993 SDW 0.897 0.802 - 1.000 0.985 0.595 0.659 -0.716 0.186 0.963 0.976 TDW 0.897 0.802 1.000 - 0.985 0.595 0.659 -0.716 0.186 0.963 0.976 LA 0.965 0.896 0.908 0.908 - 0.551 0.603 -0.662 0.194 0.941 0.952 R:S 0.240 0.562 0.000 0.000 0.284 - 0.641 -0.436 0.345 0.625 0.679 DM% -0.393 -0.356 0.020 0.020 -0.329 -0.555 - -0.781 0.194 0.620 0.644 SLA 0.021 0.116 -0.294 -0.294 0.107 0.684 -0.726 - -0.300 -0.631 -0.672 RD -0.011 0.222 -0.025 -0.025 0.022 0.485 -0.124 0.174 - 0.007 0.175 RL 0.867 0.906 0.768 0.768 0.850 0.424 -0.357 0.094 -0.103 - 0.978 RSA 0.874 0.984 0.793 0.793 0.878 0.558 -0.366 0.120 0.243 0.926 - b) SFW RDW SDW TDW LA R:S DM% SLA RD RL RSA SFW - 0.973 0.988 0.988 0.972 -0.114 0.417 -0.650 0.162 0.918 0.945 RDW 0.882 - 0.987 0.987 0.966 0.056 0.558 -0.695 0.227 0.907 0.975 SDW 0.966 0.792 - 1.000 0.965 -0.085 0.528 -0.694 0.164 0.928 0.971 TDW 0.966 0.792 1.000 - 0.965 -0.085 0.528 -0.694 0.164 0.928 0.971 LA 0.958 0.846 0.919 0.919 - -0.056 0.425 -0.524 0.102 0.916 0.940 R:S -0.177 0.254 -0.367 -0.367 -0.175 - 0.364 -0.155 0.324 -0.062 0.033 DM% 0.318 0.083 0.547 0.547 0.306 -0.777 - -0.723 0.278 0.477 0.593 SLA -0.396 -0.233 -0.558 -0.558 -0.200 0.526 -0.737 - -0.516 -0.547 -0.670 RD 0.417 0.709 0.321 0.321 0.446 0.553 -0.177 0.106 - -0.150 0.114 RL 0.824 0.784 0.712 0.712 0.798 0.041 0.023 -0.155 0.209 - 0.946 RSA 0.887 0.964 0.766 0.766 0.867 0.229 -0.005 -0.145 0.582 0.907 -

5. Discussion

5.1. Considerations In the physiology experiment eight containers per unit were used (three for harvest 1, three for harvest 2 and two with reserve plants; Appendix V). The three containers of the first harvest were removed after harvest and with these containers about 21 % of the growth solution including the nitrogen. The solution, in the second half of the experiment, was therefore probably faster depleted than planned. With the NAR calculation, namely, it was taken into account that the plants of the first harvest would be removed from the system (Appendix VII). The plants of the second harvest under depletion therefore had less nitrogen than planned, which complicates a comparison between Ingestad and depletion. The second consideration has to do with the nitrate-ammonium ratio used. In the literature study is already described that the nitrate-ammonium ratio has a strong influence on the growth of spinach (Paragraph 2.2.1.1). Zornoza and Gonzalez (1998a) found even for two of the three investigated cultivars a lower yield under a nitrate-ammonium ratio of 80:20 compared to 100:0. In the current experiment a nitrate-ammonium ratio of 75:25 was used. This raises the question whether a (unwanted) selection for ammonium tolerance took place. Further research is necessary to clarify the sensitivity of the currently used cultivars to ammonium nutrition.

5.2. Segregation mapping population For QTL mapping, a population is needed that, among others, shows sufficient segregation for the traits under investigation. In the pre-screening experiment, under both nitrogen levels, substantial variation was found between I1F1 lines within a cross for SFW, RDW, SDW, TDW and LA. The segregation for these traits under low nitrogen levels was highest for the progeny of the cross Novico x Crocodile, followed by Marabu x Novico. Under high nitrogen level Marabu x Novico was the most segregating cross, while Novico x Crocodile was second. Based on segregation only, these crosses would therefore be most suitable for QTL mapping. To choose the optimal cross, however, more factors have to be considered in addition to segregation. As the spinach cultivars used in the current experiment were sensitive to inbreeding depression (Figure 8), factors like germination, mortality and the average performance of a cross are also important to consider with respect to maintaining sufficient viable plants in the next generation. The highly segregating crosses Novico x Crocodile and Marabu x Novico resulted in I1F1 lines with a low germination percentage (Table 4), a high mortality rate (data not shown) and a low average performance (Table 7), in contrast to Marabu x Ranchero and Ranchero x Crocodile. Al things considered, the cross Marabu x Ranchero was therefore chosen to be advanced to the next generation (J.R. Chan Navarrete, personal communication, 2013).

5.3. Nitrogen level effect In both experiments, for both application methods, SDW was in general much lower and RDW slightly higher under low compared to high nitrogen level, consequently resulting in a higher R:S under low N levels. Under low nitrogen levels, spinach plants are known to produce longer root hairs (Foehse and Jungk, 1983). The dependence of root hair formation of spinach plants on nitrate availability is similar to that of phosphate. Root hair density (number of root hairs mm-1 root) is therefore also expected to be higher under low compared to high nitrogen levels (Paragraph 2.2.1.2 and Foehse and Jungk, 1983). Because root hairs are the thinnest roots, average root diameter is therefore expected to be smaller under low nitrogen level. RD, however, was hardly affected by nitrogen level in the current experiment (Figure 22 in Appendix XV). Moreover the first two root length classes (which contain probably most of the root hairs), contained only relatively small amounts of roots and were limited influenced by nitrogen level, especially at the second harvest (data not shown). The spinach plants in the current experiment therefore likely contained limited amounts of root hairs, which moreover are hardly influenced by nitrogen level. This was probably caused by growing the plants in the

hydroponic culture solution, were among others the availability of water is mostly higher and nutrients are more uniformly distributed compared to field conditions (Paragraph 1.4). Heins and Schenk (1987) also found the root hair surface of spinach plants, grown in a nutrient solution, was tenfold lower in comparison with soil grown plants. Therefore caution has to be taken in the interpretation of the current results, especially of the root characteristics. Field trials are needed to show to which extent the results can be generalized to field conditions. The higher investment in shoot under high nitrogen level was accompanied by a higher LA, SLA and CC and a lower DM%. A higher SLA does not necessarily mean that the leaves of these plants are thinner, because SLA is calculated based on SDW, and leaf thickness is measured on fresh leaves. Since DM% is lower under high nitrogen level, these leaves still can be thicker than the leaves with a high DM% and low SLA under low nitrogen level. Gutiérrez-Rodríguez et al. (2013) found for one of two investigated spinach cultivars that the number of palisade mesophyll cell layers was reduced at increasing nitrogen levels. The length of these palisade cells, however, was strongly increased. In the second investigated cultivar the number of palisade cell layers was not affected by nitrogen level, but the palisade cell length was slightly increased. The first cultivar showed therefore a clear increase in leaf thickness with increasing nitrogen levels, while for the other cultivar the increase of leaf thickness was very small. A decrease in leaf thickness with increasing nitrogen levels, as suggested by others, was not observed by Gutiérrez-Rodríguez et al. (2013). Cui et al. (1991) found leaves of spinach plants to be thicker under sunny (723 µmol m-2 s-1) compared to shaded (186 µmol m-2 s-1) conditions. The leaves were mainly thicker due to more palisade cell layers. Despite that these sun-exposed leaves were thicker and had higher CC, light still penetrated deeper in these leaves than in shade leaves. These authors conclude therefore that the palisade cells have, among others, a facilitating function in the transmission of light into the leaf, which enhances the scattering of light in the spongy mesophyll cells. The functional significance of these differences in leaf anatomy is, however, under debate. Evans (1999) states that leaf absorptance can be described by CC regardless of SLA and suggests therefore that internal leaf structure can influence the profile of light capturing through a leaf3, but does not alter the absolute amount captured by the leaf. Evans and Poorter (2001) state that leaf porosity and mesophyll structure can influence scattering, but are not related to SLA. This latter statement, however, seems to be unlikely, since based on results of Gutiérrez-Rodríguez et al. (2013) SLA seems to be related to the number of palisade cell layers. Whether it was caused by the higher SLA, the higher CC or both is unknown, but photosynthesis per unit area of spinach was in a hydroponic experiment found to be higher under high compared to low nitrogen level (Noguchi an Terashima, 2006). A possible explanation may be that plants under high nitrogen level reduce the investment in palisade cell layers in favour of LA, to increase total photosynthetic surface area. Despite of the lower number of palisade cell layers, the plant tries to maintain the positive effects of a thick palisade mesophyll due to more cell layers (as described by Cui et al., 1991) by elongating the remaining palisade cells. These more elongated cells could also explain the lower DM% under high nitrogen level. Besides a controlled reduction in photosynthesis, spinach plants under low nitrogen level might invest more in defence against biotic and abiotic stress since the low nitrogen leaves (with more but thinner palisade cell layers) are known to be less fragile than the high nitrogen leaves (Gutiérrez-Rodríguez et al., 2013). Boese and Huner (1990) moreover found that the thicker spinach leaves (mainly due to more palisade cell layers), under low compared to high temperature, were more resistant to photoinhibition caused by high light intensities. Further research, however, is necessary to clarify to which extent the higher photosynthesis per leaf area under high nitrogen level is influenced by the higher SLA, the higher CC or by both. Another question that arises is whether the less, but thicker palisade cell layers found by Gutiérrez-Rodríguez et al. (2013) have a similar facilitating function in the transmission of light into the leaf, as the more but thinner palisade cell layers found by Cui et al. (1991). Further research should therefore focus on the

3 The profile of light captured through a leaf is the light absorption per depth in the leaf (Evans, 1999).

relations between SLA, leaf texture and anatomy, CC and photosynthesis under different nitrogen levels for several spinach cultivars. Finally, as a higher SLA implies a larger photosynthetic surface per unit of mass, photosynthetic activity expressed per unit biomass should necessarily be higher under high nitrogen level. For hydroponically grown spinach this is indeed confirmed by Bottrill et al. (1970).

Despite the clear effect of nitrogen level on SFW, RDW, SDW, TDW and LA, this effect was, except for SFW, not significant for these traits at the pre-screening experiment. Probably this was caused by large differences between the two units that received the high nitrogen treatment. Between the two units that received the high nitrogen treatment 37 – 50 % difference (Table 6 and data not shown) for the traits SFW, RDW, SDW, TDW and LA was observed. For the units that received low nitrogen treatment this was at most about 5 %. It is not likely that these differences were caused by wrong nitrogen additions, because nitrogen was added every day manually. Given the order of magnitude of difference between the two units, most of the days a similar mistake should be made, which is not likely. It is also not likely that the order of harvesting (units 1 and 2 at 28 DAP and units 3 and 4 at 29 DAP) had caused these differences because if that were true also differences between the units with the low nitrogen treatment would be observed. Position in the greenhouse might have influenced the results, but a 37 – 50 % difference is given the experimental setup (Appendix III) unlikely large. And if the position had affected the results this would also be expected for the low nitrogen units. Finally random variation between the units or a combination of all mentioned possibilities might have caused the differences. In addition to the large differences between the units, the low number of degrees of freedom (just one because of only two nitrogen treatments) reduced the statistical power and might therefore be a reason for the relatively high P-values (C.A. Maliepaard, personal communication, 2014).

5.4. Effect of nitrogen application method With the Ingestad model, nitrogen is added daily to the plants, while with the depletion model all nitrogen for the growing period is added at once (Paragraph 3.2.1). With the depletion model, nitrogen was abundant available to the plants at the beginning of the growth period. Spinach plants in the depletion experiment therefore had a higher biomass than the Ingestad-grown plants at the first harvest, at both N levels (Figure 10). At both nitrogen levels and at both harvests, R:S was lower under the depletion than under the Ingestad model (Figure 10). The availability of excess N caused the plants under depletion to invest in shoots relatively more than in roots, resulting in a low R:S irrespective of nitrogen level. At the end of the growth period nitrogen became limiting, but plants were not able to increase their R:S, at the second harvest. Plants grown according to the Ingestad model had a relatively high R:S at both harvests. As these plants at both harvests under both nitrogen levels also had a lower SDW than plants grown according to the depletion model, it can be concluded that nitrogen was a limiting factor for these plants. The R:S of plants grown according to the Ingestad model should remain constant over time (see Paragraph 1.4). In the current experiment this was not the case (Figure 10). This implies that also the RGR was not constant over time (see Paragraph 1.4). The RGR however is only assumed to be constant during the exponential growth phase. As spinach shows a short S-shaped growth curve, the RGR will also decline when the growth curve starts flattening. That Ingestad and co-workers achieved a constant RGR during their experiments is probably because they used woody perennials for their experiments, which have a long exponential growth phase (Smolders and Merckx, 1992). The observed RGR under low nitrogen levels was for both application methods clearly higher than expected (Table 9). An explanation for this could be that the NAR was higher than what is needed for a RGR of 0.10. If this was the case, however, the RGR under high nitrogen level should also have been higher than expected because these NAR was calculated in the same manner, but this was not the case. The RGR was measured between the first and second harvest, in which the plants, both under depletion and Ingestad, were suffering from nitrogen deficiency. A more likely explanation for the

higher RGR under low nitrogen levels could therefore be that the NUE was higher under low compared to high nitrogen level. This is supported by results of Smatanová et al. (2004) and Canali et al. (2011) who found NUpE and NUtE to be higher under low compared to high nitrogen levels.

The nitrogen level of the plants fertilized according to the depletion model probably became depleted during growth, which will cause nitrogen shortage to the plants. At the second harvest this had no marked effect on the plants grown under high nitrogen level, but under low nitrogen level it had. DM% under depletion low nitrogen was extremely high at the second harvest (Figure 10). SDW of these plants is also relatively high, which may be the result of abundant availability of nitrogen at the beginning of the growth. The extremely high DM% at the end of the growth period might therefore be caused by the low SFW. This indicates a great loss of water of the plants at the end of the growth period. CC of these plants reduced also stronger at the end of the growth period than under the other treatments. These changes are therefore probably the result of leaf senescence due to nitrogen deficiency.

When nitrogen becomes limiting, plants remobilize the vacuolar nitrogen to maintain growth (Paragraph 2.2.2.3). Through senescence, plants are also able to remobilize nitrogen of the photosynthetic proteins, which contain the majority of leaf nitrogen (Masclaux-Daubresse et al., 2010). This loss of chlorophyll can be initiated by many biotic and abiotic stress factors (Hörtensteiner and Kräutler, 2011). Nitrogen shortage is known to accelerate leaf senescence and chlorophyll breakdown (e.g. Liu et al., 2006). In the current experiment, CC of both measured leaves was also found to decrease over time. This effect was for both nitrogen levels stronger when fertilized according to the depletion than to the Ingestad model (Figure 10). This is another indication that the spinach plants grown according to the depletion model (especially under low nitrogen) at the second harvest suffered more from nitrogen deficiency than grown according to the Ingestad model.

5.5. Interaction effects Especially under low nitrogen level, plants grown according to the Ingestad model were exposed to structural nitrogen limitations, while plants grown according to the depletion model only had to deal with limited nitrogen availability at the end of the growth period. These different kinds of nitrogen limitations seems to require different types of tolerance of the plants, since distinct cultivars responded differently to the two application methods. At the second harvest under the depletion model, cultivar Sparrow showed a relatively small decrease in SDW and large increase in RDW under low compared to high nitrogen level. Under the Ingestad model, however, this cultivar showed a strong decrease in SDW under low compared to high nitrogen level, while RDW was hardly affected (Appendix XV). This cultivar seemed therefore better able to adapt to nitrogen limitations at the end of the growth period (depletion) than to structural nitrogen limitations (Ingestad). For cultivar Crocodile for both application methods, the opposite was observed. This cultivar seemed therefore better able to adapt to structural nitrogen limitations (Ingestad) than to nitrogen limitations at the end of growth period (depletion). The tolerance to both kinds of nitrogen limitations was characterized by a relatively small decrease in SDW and strong increase in RDW. The increase in RDW of the plants with a high tolerance under depletion, was accompanied by an increase in RL (Figure 23 in Appendix XV). This increase in RL was probably the result of nitrogen stress and had the purpose to increase nitrogen uptake. For this increase in RL, however, also nitrogen is needed. A possibility is that the last available nitrogen mainly is used for this increase in RL. Since nitrogen is limiting however, the nitrogen needed for the increase in RL could also be (partly) originating from the plant itself. The decrease in SDW, however, of these plants was relatively low and also CC showed no strong decrease compared to the other cultivars (Figure 11 and data not shown), which implies that this investment in roots was not directly at the expense of the shoot. A possibility is that the nitrogen invested in the roots originates from remobilization of vacuolar stored nitrogen. In literature a large variation in nitrate accumulation

between spinach cultivars was found (Paragraph 2.2.2.3). Further research however is necessary to see whether also variation in vacuolar nitrate remobilization efficiency between cultivars exists and whether cultivars with a high tolerance to nitrogen stress later on during the growth period have superior nitrogen storage in and remobilization from the vacuole. The higher RDW of plants with a high tolerance to constant nitrogen limitation (Ingestad) was not accompanied by an higher RL or RD (Figure 22 and Figure 23). This indicates that the weight per unit root length increased, which results in a lower specific root length (SRL; m g-1). Roots with a low SRL have in general a greater longevity, a higher uptake per unit root length under field conditions and under very low resource supply lower respiration costs in comparison with high SRL roots (for review see Eissenstat, 1992). Spinach plants under low nitrogen levels invest probably also more in defence against biotic and abiotic stress and thus in longevity of the shoots than plants grown under high nitrogen level (see Paragraph 5.3). The current results, however, give no evidence that the more tolerant cultivars, neither grown according to the depletion or to the Ingestad model, invested more in longevity of the shoots than the other cultivars. Caution, however, has to be taken, especially in the interpretation of the root characteristics, because the current results were derived from a hydroponics experiment4. Field trials are needed to show to which extent the results can be generalized to field conditions.

Cultivars Sparrow and Crocodile showed only tolerance to one of the two kinds of nitrogen limitations. Cultivar Cello showed a relatively high tolerance to nitrogen limitations under both application methods. It is also possible that a cultivar showed a poor tolerance to both kind of nitrogen limitations, which was visible for cultivars Andromeda and Marabu. For breeding it is therefore important to have clear which kind of tolerance is desired and to realize that a cultivar which shows a high tolerance to one kind of nitrogen limitation, is not necessarily also tolerant to the other kind.

A high tolerance to nitrogen limitations does not necessarily mean that these plants also have a high NUE. In the current report, NUE is defined as the biomass production per unit nitrogen applied (g SDW g-1 N). Given the current experimental setup, NUE is per definition highest in the best yielding (SDW) cultivar under a certain treatment. High tolerance to nitrogen limitations in contradiction to that, is characterized by the smallest difference in yield (SDW) between high and low nitrogen treatment, and is not consequently related to a high yield. The highest tolerant cultivars, for both application methods, were not the best yielding ones. The challenge for breeding for suitable cultivars under low nitrogen is therefore to combine the high tolerance to nitrogen limitations of for example Cello, with the high responsiveness to nitrogen fertilization of for example Andromeda.

5.6. Correlation of traits to NUE For correlation calculations only the plants were used for which data of all measurements was available. For the pre-screening and physiology experiments this was always at least 75 %, except for the pre-screening under low nitrogen were this was 52 %. The lower percentage for the pre- screening under low nitrogen was because the first leaves of a lot of plants (mainly I1F1 lines and selfings of the hybrid cultivars) were not large enough to measure CC. As the correlations of the pre- screening mostly were similar to that of the physiology experiment, this lower percentage of plants probably did not influenced the correlations markedly. As described in Paragraph 5.5, NUE is closely related to SDW. For both experiments, at both harvests and for all treatments, SFW, RDW, TDW, LA, RL and RSA were strong positively correlated to SDW and with each other (Table 8, Table 12, Appendix XVI, Appendix XVII). At the second harvest of the physiological experiment as well as in the pre-screening, SDW was significantly positively correlated to DM% and negative to SLA under all treatments, except depletion low nitrogen. This exception might be caused by the considerable loss of water of these plants at the end of the growth period

4 For differences between soil and hydroponic conditions, see Paragraph 1.4.

which had a strong influence on DM% and SLA. This loss of water was probably the result of leaf senescence due to nitrogen deficiency (Paragraph 5.4). So on average, under all investigated hydroponic conditions and at both harvests, SDW, SFW, RDW, TDW, LA, RL and RSA are good indicators of NUE. In addition, at the second harvest, under most treatments (excluding depletion low nitrogen) and for the pre-screening, DM% and SLA are also good indicators of NUE. Caution, however, has to be taken for generalization to field conditions. Except for CC of the second leaf under depletion high nitrogen level, on average weak and mostly not significant correlations of CC of both leaves, with SDW and any of the other traits were observed. For the pre-screening the correlation of the CC of both leaves with most other traits was on average also mostly weak. The general absence of a correlation between SDW and CC might be explained by the fact that high yielding cultivars focus more on total photosynthetic capacity (LA) than in photosynthetic capacity per unit area (CC). Liu et al. (2006), however, found a strong significant correlation between CC and SDW. The difference with the current findings is probably caused by different calculations. Liu et al. (2006) calculated the correlations for one cultivar and several nitrogen levels. In the current report the correlations are calculated for several cultivars and just one treatment. Since no apparent reason for the deviating results of the CC of the second leaf under depletion high nitrogen level was found, this might be coincidence. Further research, however, should clarify that.

6. Conclusions

1) For the mapping population the largest range was observed for the traits SFW, RDW, SDW, TDW and LA. These traits are therefore most useful for QTL mapping. The crosses Novico x Crocodile and Marabu x Novico resulted in the highest segregation under low and high nitrogen level respectively. Based on factors like germination, mortality and the average performance of a cross, however, the cross Marabu x Ranchero was chosen to be advanced to the next generation. 2) Cultivar x environment interaction effect was observed for both nitrogen level and application method. Interestingly, tolerance to nitrogen limitations was not the same for both application methods and cultivars with a high tolerance under one of the two application methods do not necessarily show a high tolerance under the other. The highest tolerant cultivars, for both application methods, were not the best yielding ones. The challenge for breeding for suitable cultivars under low nitrogen is therefore to combine the high tolerance to nitrogen limitations of for example Cello, with the high responsiveness to nitrogen fertilization of for example Andromeda. 3) SDW, SFW, RDW, TDW, LA, RL and RSA were under all investigated conditions, during the entire growth period, highly correlated to NUE and therefore good indicators of this complex trait. At the end of the growth period DM% and SLA are also good indicators of NUE under all treatments, except depletion low nitrogen because the plants lost a lot of water at the end of the growth period. Caution, however, has to be taken for generalization to field conditions, because these results were obtained from hydroponic experiments.

References

Abreu, J.D. De M.E., Flores, I., Abreu, F.M.G. De and Madeira, M.V., 1993. Nitrogen uptake in relation to water availability. Plant and soil 154: 89-96.

Agrama, H.A., 2005. Application of molecular markers in breeding for nitrogen use efficiency. In: Enhancing the efficiency of nitrogen utilization in plants (ed: Goyal, S.S., Tischner, R. and Basra, A.S.). Food product press, an imprint of The Haworth Press, Inc., New York/London /Victoria, 175-211.

Ahmadil, H., Akbarpour, V., Dashti, F. and Shojaeian, A., 2010. Effect of different levels of nitrogen fertilizer on yield, nitrate accumulation and several quantitative attributes of five Iranian spinach accessions. American-Eurasian journal of agriculture and environmental sience 8 (4): 468-473.

Andersen, S.B. and Torp, A.M., 2011. Spinacia. In: Wild crop relatives:genomic and breeding resources, vegetables (ed: Kole, C.). Springer-verlag, Berlin/Heidelberg, 273-276.

Andrews, M., Lea, P.J., Raven, J.A. and Lindsey, K., 2004. Can genetic manipulation of plant nitrogen assimilation enzymes result in increased crop yield and greater N-use efficiency? An assessment. Annals of applied biology 145: 25-40.

Aslam, M., Travis, R.L. and Huffaker, R.C., 1993. Comparative induction of nitrate and nitrite uptake and reduction systems by ambient nitrate and nitrite in intact roots of barley (Hordeum vulgare L.) seedlings. Plant physiology 102: 811-819.

Barneix, A.J. and Causin, H.F., 1996. The central role of amino acids on nitrogen utilization and plant growth. Plant physiology 149: 358-362.

Barber, S.A., 1984. Soil nutrient bioavailability. A mechanistic approach. John Wiley & Sons, Inc., New York, 398 pp.

Bänziger, M., Edmaedes, G.O., Beck, D. and Bellon, M., 2000. Breeding for drought and nitrogen stress tolerance in maize: From theory to practice. Mexico, D.F.: CIMMYT.

Barker, A.V. and Maynard, D.N., 1971. Nutritional factors affecting nitrate accumulation in spinach. Communications in soil science and plant analysis 2 (6): 471-478.

Barker, A.V., Maynard, D.N. and Mills, H.A., 1974. Variations in nitrate accumulation among spinach cultivars. Journal of the American society of horticultural science 99 (2): 132-134.

Barker, A.V., Peck, N.H. and MacDonald, G.E., 1971. Nitrate accumulation in vegetables. 1. Spinach grown in upland soils. Agronomy journal 63: 126-129.

Bhadoria, P.B.S., Kaselowsky, J., Claassen, N. and Jungk, A., 1991. Soil phosphate diffusion coefficients: their dependence on phosphorus concentration and buffer power. Soil science society of America journal 55: 56-60.

Biemond, H., Vos, J. and Struik, P.C., 1996. Effects of nitrogen on accumulation and partitioning of dry matter and nitrogen of vegetables. 3. Spinach. Netherlands journal of agricultural science 44: 227-239.

Bloom, A.J., Sukrapanna, S.S. and Warner, R.L., 1992. Root respiration associated with ammonium and nitrate absorption and assimilation by barley. Plant physiology 99: 1294-1301.

Boese, S.R. and Huner, N.P.A., 1990. Effect of growth temperature and temperature shifts on spinach leaf morphology and photosynthesis. Plant physiology 94: 1830-1836.

Bottrill, D.E., Possingham, J.V. and Kriedeman, P.E., 1970. The effect of nutrient deficiencies on photosynthesis and respiration in spinach. Plant and soil 32: 424-438.

Broadley, M.R., Escobar-Gutiérrez, A.J., Burns, A. and Burns, I.G., 2001. Nitrogen-Limited Growth of Lettuce Is Associated with Lower Stomatal Conductance. New Phytologist 152 (1): 97-106.

Buysse, J. and Merckx, R., 1995. Diurnal variations in growth rate and growth and substrate levels of spinach (Spinacia oleracea L.) under nitrogen-limiting conditions. Plant, cell and environment 18: 1419-1425.

Canali, S., Montemurro, F., Tittarelli, F. and Masetti, O., 2011. Is it possible to reduce nitrogen fertilization in processing spinach? Journal of plant nutrition 34 (4): 534-546.

Canellas, L.P., Teixeira Junior, L.R.L., Dobbss, L.B., Silva, C.A., Medici, L.O., Zandonadi, D.B. and Façanha, A.R., 2008. Humic acid crossinteractions with root and organic acids. Annals of applied biology 153: 157-166.

Cantliffe, D.J., 1972. Nitrate accumulation in spinach grown at different temperatures. Journal of the American society of horticultural science 97 (5): 674-676.

Cantliffe, D.J., 1973. Nitrate accumulation in table beets and spinach as affected by nitrogen, phosphorus, and potassium nutrition and light intensity. Agronomy journal 65: 563-565.

Catalogue of life, 2013. Spinacia oleracea L. http://www.catalogueoflife.org/testcol/details/species/id/10790755 (visited on September 19, 2013)

CBS, 26-4-2013. Groenteteelt; oogst en teeltoppervlakte per groentesoort. http://statline.cbs.nl/StatWeb/publication/?DM=SLNL&PA=37738&D1=a&D2=0-1,6- 47&D3=2,7,10-12&HDR=T,G2&STB=G1&VW=T (visited on September 9, 2013)

Chan Navarrete, R., Kawai, A., Dolstra, O., Lammerts van Bueren, E.T. and Linden, C. van der, 2014. Genetic diversity for nitrogen use efficiency in spinach (Spinacia oleracea L.) cultivars using the Ingestad model on hydroponics. Euphytica 199: 155-166.

Chen, B.M., Wang, Z.H., Li, S.X., Wang, G.X., Song, H.X. and Wang, X.N., 2004. Effects of nitrate supply on plant growth, nitrate accumulation metabolic nitrate concentration and nitrate reductase activity in three leafy vegetables. Plant science 167: 635-643.

Chopin, F., Orsel, M., Dorbe, M.F., Chardon, F., Truong, H.N., Miller, A.J., Krapp, A. and Daniel-Vedele, F., 2007. The Arabidopsis ATNRT2.7 nitrate transporter controls nitrate content in seeds. The plant cell 19: 1590-1602.

Commission Regulation, 2011. Commission Regulation (EU) No 1258/2011 of 2 December 2011 amending Regulation (EC) No 1881/2006 as regards maximum levels for nitrates in foodstuffs. Official journal of the European Union 320: 15.

Conesa, E., Niñirola, D., Vicente, M.J., Ochoa, J., Bañon, S. and Ferández, J.A., 2009. The influence of nitrate/ammonium ratio on yield quality and nitrate, oxalate and vitamin C content of baby leaf spinach and bladder campion plants grown in a floating system. In: Proceedings of the International Symposium on Soilless Culture and Hydroponics (ed. Rodríquez-Alfín, A. and Martínez, P.F.) Acta horticulturae 843: 269-274.

Council Directive, 1998. Council Directive 98/83/EC of 3 November 1998 on the quality of water intended for human consumption. Official journal of the European Communities: 32-54.

Crawford, N.M., 1995. Nitrate: nutrient and signal for plant growth. The plant cell 7: 859-868.

Crawford, N.M. and Glass, A.D.M., 1998. Molecular and physiological aspects of nitrate uptake in plants. Trends in plant science 3 (10): 389-395.

Cui, M., Vogelmann, T.C. and Smith, W.K., 1991. Chlorophyll and light gradients in sun and shade leaves of Spinacia oleracea. Plant, cell and environment 14: 493-500.

De Angeli, A., Monachello, D., Ephritikhine, G., Frachisse, J.M., Thomine, S., Gambale, F. and Barbier- Brygoo, H., 2006. The nitrate/proton antiporter AtCLCa mediates nitrate accumulation in plant vacuoles. Nature 44: 939-942.

De Angeli, A., Monachello, D., Ephritikhine, G., Frachisse, J.M., Thomine, S., Gambale, F. and Barbier- Brygoo, H., 2009. CLC-mediated anion transport in plant cells. Philosophical transactions of the royal society biological sciences 364: 195-201.

Dragićević, M., Tanacković, V., Mišić, D., Cvetić, T., Todorović, S., Bogdanović, M. and Simonović, A., 2011. Coupling native page/activity-staining with SDS-page/immunodetection for the analysis of glutamine synthetase isoforms in spinach. Archives of biological science Belgrade 63 (4): 965-969.

Duxbury, J.M., 1994. The significance of agricultural sources of greenhouse gases. Fertilizer research 38: 151-163.

Ebid, A., Ueno, H., Ghoneim, A. and Asagi, N., 2008. Nitrogen uptake by radish, spinach and “chingensai” from composted tea leaves, coffee waste and kitchen garbage. Compost science & utilization 16 (3): 152-158.

Eissenstat, D.M., 1992. Costs and benefits of constructing roots of small diameter. Journal of plant nutrition 15 (6-7): 763-782.

Elia, A., Santamaria, P. and Serio, F., 1998. Nitrogen nutrition, yield and quality of spinach. Journal of the science of food and agriculture 76: 341-346.

Evans, J.R., 1999. Leaf anatomy enables more equal access to light and CO2 between chloroplasts. New phytologist 143: 93-104.

Evans, J.R. and Poorter, H., 2001. Photosynthetic acclimation of plants to growth irradiance: the relative importance of specific leaf area and nitrogen partitioning in maximizing carbon gain. Plant, cell and environment 24: 755-767.

Fan, X., Jia, L., Smith, S.J., Miller, A.J. and Shen, Q., 2007. Comparing nitrate storage and remobilization in two rice cultivars that differ in their nitrogen use efficiency. Journal of experimental botany 58 (7): 1729-1740.

Fageria, N.K., Baligar, V.C. and Jones, C.A., 2011. Growth and mineral nutrition of field crops. Third edition. CRC press, Taylor & Francis Group, Boca Raton/London/New York, 560 pp.

FAO, 2012. Current world fertilizer trends and outlook to 2016. Food and Agriculture Organization of the United Nations, Rome, 43 pp.

Foehse, D. and Jungk, A., 1983. Influence of phosphate and nitrate supply on root hair formation of rape, spinach and tomato plants. Plant and soil 74: 359-368.

Föhse, D., Claassen, N. and Jungk, A., 1988. Phosphorus efficiency of plants. 1. External and internal P requirement and P uptake efficiency of different plant species. Plant and soil 110: 101-109.

Föhse, D., Claassen, N. and Jungk, A., 1991. Phosphorus efficiency of plants. 2. Significance of root radius, root hairs and cation-anion balance for phosphorus influx in seven plant species. Plant and soil 132: 261-272.

Gallais, A. and Hirel, B., 2004. An approach to the genetics of nitrogen use efficiency in maize. Journal of experimental botany 55 (395): 295-306.

- + Gansel, X., Muños, S., Tillard, P. and Gojon, A., 2001. Differential regulation of the NO3 and NH4 transporter genes AtNrt2.1 and AtAmt1.1 in Arabidopsis: relation with long-distance and local controls by N status of the plant. The plant journal 26 (2): 143-155.

Glass, A.D.M., 2003. Nitrogen use efficiency of crop plants: physiological constraints upon nitrogen absorption. Critical reviews in plant sciences 22 (5): 453-470.

Godfray, H.C.J., Beddington, J.R., Crute, I.R., Haddad, L., Lawrence, D., Muir, J.F., Pretty, J., Robinson, S., Thomas, S.M. and Toulmin, C., 2010. Food security: the challenge of feeding 9 billion people. Science 327: 812-818.

Goh, K.M. and Vityakon, P., 1986. Effects of fertilisers on vegetable production 2. Effects of nitrogen fertilisers on nitrogen content and nitrate accumulation of spinach and beetroot. New Zealand journal of agricultural research 29: 485-494.

Good, A.G., Shrawat, A.K. and Muench, D.G., 2004. Can less yield more? Is reducing nutrient input into the environment compatible with maintaining crop production? Trends in plant science 9 (12): 597-605.

Gupta, N., Gupta, A.K., Gaur, V.S. and Kumar, A., 2012. Relationship of nitrogen use efficiency with the activities of enzymes involved in nitrogen uptake and assimilation of finger millet genotypes grown under different nitrogen inputs. The scientific world journal 2014, 10 pp.

Gutriérrez-Rodríguez, E., Lieth, H.J., Jernstedt, J.A., Labavitch, J.M., Suslow, T.V. and Cantwell, M.I., 2013. Texture, composition and anatomy of spinach leaves in relation to nitrogen fertilization. Journal of the science of food and agriculture 93: 227-237.

Hayakawa, T., Ishiyama, K. and Yamaya, T., 2007. Tissue and cellular localization of NADH-dependent glutamate synthase protein in leaves of spinach. Tohoku journal of agricultural research 57 (3-4): 1-11.

Heins, B. and Schenk, M., 1986. Nitrate-uptake characteristics of roots as affected by nitrate supply. In: Fundamental, Ecological and Agricultural Aspects of Nitrogen Metabolism in Higher Plants (ed: Lambers, H., Neeteson, J.J. and Stulen, I.) Martinus Nijhoff publishers, Dordrecht/Boston/Lancaster, 41-45.

Heins, B. and Schenk, M., 1987. Nitrogen. Journal of plant nutrition 10 (9-16): 1743-1751.

Herbarium botanik, 2013. Taxon Spinacia oleracea L. http://herbarium.botanik.univie.ac.at/annonaceae/listSynonyms.php?ID=8123 (visited on September 20, 2013)

Hirel, B., Chardon, F. and Durand, J., 2007b. The contribution of molecular physiology to the improvement of nitrogen use efficiency in crops. Journal of crop science and biotechnology 10 (3): 123-132.

Hirel, B., Le Gouis, J., Ney, B. and Gallais, A.,2007a. The challenge of improving nitrogen use efficiency in crop plants: towards a more central role for genetic variability and quantitative genetics within integrated approaches. Journal of experimental botany 58 (9): 2369-2387.

Hirel, B., Perrot-Rechenmann, C., Suzuki, A., Vidal, J. and Gadal, P., 1982. Glutamine synthetase in spinach leaves. Immunological studies and immunocytochemical localization. Plant physiology 69: 983-987.

Hirel, B., Tétu, T., Lea, P.J. and Dubois, F., 2011. Improving nitrogen use efficiency in crops for sustainable agriculture. Sustainability 3: 1452-1485.

Hirose, N., Hayakawa, T. and Yamaya, T., 1997. Inducible accumulation of mRNA for NADH- dependent glutamate synthase in rice roots in response to ammonium ions. Plant cell physiology 38 (11): 1295-1297.

Hörtensteiner, S. and Kräutler, B., 2011. Chlorophyll breakdown in higher plants. Biochimica et biophysica acta 1807: 977-988.

Huang, C., Wang, Z., Li, S. and Malhi, S.S., 2010. Nitrate in leaf petiole and blade of spinach cultivars and its relation to biomass and water in plants. Journal of plant nutrition 33 (8): 1112-1123.

Imsande, J. and Touraine, B., 1994. N demand and the regulation of nitrate uptake. Plant physiology 105: 3-7.

Ingestad, T., 1977. Nitrogen and plant growth; maximum efficiency of nitrogen fertilizers. Ambio, 6 (2/3): 146-151.

Ingestad, T. and Agren, G.I., 1988. Nutrient uptake and allocation at steady-state nutrition. Physiologia plantarum 72: 450-459.

Ingestad, T. and Lund, A.B., 1986. Theory and techniques for steady state mineral nutrition and growth of plants. Scandinavian journal of forest research 1: 439-453.

International Spinach Database, 2013. http://documents.plant.wur.nl/cgn/pgr/spinach/default.htm (visited on September 20, 2013)

Kaiser, W.M., Planchet, E., Stoimenova, M. and Sonoda, M., 2004. Modulation of nitrate reduction – environmental and internal factors involved. In: Nitrogen acquisition and assimilation in higher plants (ed. Amâncio, S. and Stulen, I.) Kluwer Academic Publishers, Dordrecht/Boston/London, 185-205.

Kandlbinder, A., Weiner, H. and Kaiser, W.M., 2000. Nitrate reductases from leaves of Ricinus (Ricinus communis L.) and spinach (Spinacia oleracea L.) have different regulatory properties. Journal of experimental botany 51 (347): 1099-1105.

Kansal, B.D., Singh, B., Bajaj, K.L. and Kaur, G., 1981. Effect of different levels of nitrogen and farmyard manure on yield and quality of spinach (Spinacia oleracea L.). Qualitas plantarum, plant foods for human nutrition 31: 163-170.

Kant, S., Bi, Y.M. and Rothstein, S., 2010. Understanding plant response to nitrogen limitation for the improvement of crop nitrogen use efficiency. Journal of experimental botany 1-11.

Khattak, J.Z.K., Torp, A.M. and Andersen, S.B., 2006. A genetic linkage map of Spinacia oleracea and localization of a sex determination locus. Euphytica 148: 311-318.

King, B.J., Siddiqi, M.Y., Ruth, T.J., Warner, R.L. and Glass, A.D.M., 1993. Feedback regulation of nitrate influx in barley roots by nitrate, nitrite, and ammonium. Plant physiology 102: 1279- 1286.

Koh, E., Charoenprasert, S. and Mitchell, A.E., 2012. Effect of organic and conventional cropping systems on ascorbic acid, vitamin C, flavonoids, nitrate, and oxalate in 27 varieties of spinach (Spinacia oleracea L.). Journal of agricultural and food chemistry 60: 3144-3150.

Lam, H.M., Coschigano, K.T., Oliveira, I.C., Melo-Oliveira, R. and Coruzzi, G.M., 1996. The molecular- genetics of nitrogen assimilation into amino acids in higher plants. Annual review of plant physiology and plant molecular biology 47: 569-593.

Lambers, H., Chapin III, F.S. and Pons, T.L., 2008. Plant physiological ecology. Springer Science+Business Media, LLC, New York, 622 pp.

Lambers, H., Simpson, R.J., Beilharz, V.C. and Dalling, M.J., 1982. Growth and translocation of C and N in wheat (Triticum aestivum) grown with a split root system. Physiologia plantarum 56: 421- 429.

Lasa, B., Frechilla, S., Lamsfus, C. and Aparicio-Tejo, P.M., 2001. The sensitivity to ammonium nutrition is related to nitrogen accumulation. Scientia Horticuturae 91: 143-152.

Lea, P.J. and Azevedo, R.A., 2006. Nitrogen use efficiency. 1. Uptake of nitrogen from the soil. Annals of applied biology 149: 243-247.

Lea, P.J. and Miflin, B.J., 2011. Nitrogen assimilation and its relevance to crop improvement. In: Annual plant reviews 42: 1-40. Nitrogen Metabolism in Plants in the Post-Genomic Era (ed. Foyer, C.H. and Zhang, H.). Wiley-Blackwell and Sons, Ltd, publication, Oxford/Chichester/Ames.

Lefsrud, M.G., Kopsell, D.A. and Kopsell, D.E., 2007. Nitrogen levels influence biomass, elemental accumulations, and pigment concentrations in spinach. Journal of plant nutrition 30(2): 171- 185.

Leij, M. van der, Smith, S.J. and Miller, A.J., 1998. Remobilisation of vacuolar stored nitrate in barley root cells. Planta 205: 64-72.

Liao, M., Fillery, I.R.P. and Palta, J.A., 2004. Early vigorous growth is a major factor influencing nitrogen uptake in wheat. Functional plant biology 31: 121-129.

Liao, M., Palta, J.A. and Fillery, I.R.P., 2006. Root characteristics of vigorous wheat improve early nitrogen uptake. Australian journal of agricultural research 57: 1097-1107.

Lima, L., Seabra, A., Melo, P., Cullimore, J., Carvalho, H., 2006. Phosphorylation and subsequent interaction with 14-3-3 proteins regulate plastid glutamine synthetase in Medicago truncatula. Planta 223: 558-567.

Lillo, C., 2004. Light regulation of nitrate uptake, assimilation and metabolism. In: Nitrogen acquisition and assimilation in higher plants (ed. Amâncio, S. and Stulen, I.) Kluwer Academic Publishers, Dordrecht/Boston/London, 149-184.

Liu, Y.J., Tong, Y.P., Zhu, Y.G., Ding, H. and Smith, F.A., 2006. Leaf Chlorophyll Readings as an Indicator for Spinach Yield and Nutritional Quality with Different Nitrogen Fertilizer Applications. Journal of Plant Nutrition, 29: 1207–1217.

Loqué, D. and Wirén N. von, 2004. Regulatory levels for the transport of ammonium in plant roots. Journal of experimental botany 55 (401): 1293-1305.

Masclaux-Daubresse, C., Daniel-Vedele, F., Dechorgnat, J., Chardon, F., Gaufichon, L. and Suzuki, A., 2010. Nitrogen uptake, assimilation and remobilizoation in plants: challenges for sustainable and productive agriculture. Annals of botany 105: 1141-1157.

Matsumoto, S., Ae, N. and Yamagata, M., 1999. Nitrogen uptake response of vegetable crops to organic materials. Soil science and plant nutrition 45 (2): 269-278.

Maynard, D.N. and Barker, A.V., 1974. Nitrate accumulation in spinach as influenced by leaf type. Journal of the American society of horticultural science 99 (2): 135-138.

McKague, K., Reid, K. and Simpson, H., 2005. Environmental impacts of nitrogen use in agriculture. Factsheet ministry of agriculture, food and rural affairs Ontario, 4 pp.

Mengel, D.B. and Barber, S.A., 1974. Rate of nutrient uptake per unit of corn root under field conditions. Agronomy journal 66: 399-402.

Miller, A.J. and Cramer, M.D., 2004. Root nitrogen acquisition and assimilation. Plant and soil 274: 1-36

Moll, R.H., Kamprath, E.J. and Jackson, W.A., 1982. Analysis and interpretation of factors which contribute to efficiency of nitrogen utilization. Agronomy journal 74:562-564.

Mondal, S. and Nad, B.K., 2012. Nitrate accumulation in spinach as influenced by sulphur and phosphorus application under increasing nitrogen levels. Journal of plant nutrition 35 (14) : 2081-2088.

Moorhead, G., Douglas, P., Cotelle, V., Harthill, J., Morrice, N., Meek, S., Deiting, U., Stitt, M., Scarabel, M., Aitken, A. and MacKintosh, C., 1999. Phosphorylation-dependent interactions between enzymes of plant metabolism and 14-3-3 proteins. The plant journal 18 (1): 1-12.

Morelock, T.E. and Corell, J.C., 2008. Spinach. In: Handbook of plant breeding. Vegetables 1 (Prohens, J. and Nuez, F., ed.), 189-218.

Mozafar, A., 1996. Decreasing the NO3 and increasing the vitamin C contents in spinach by a nitrogen deprivation method. Plant foods for human nutrition 49: 155-162.

- Muller, B. and Touraine, B., 1992. Inhibition of NO3 uptake by various phloem-translocated amino acids in soybean seedlings. Journal of experimental botany 43 (250): 617-623.

Nazoa, P., Vidmar, J.J., Tranbarger, T.J., Mouline, K., Damiani, I., Tillard, P., Zhuo, D., Glass, A.D.M. and Touraine, B., 2003. Regulation of the nitrate transporter gene AtNRT2.1 in Arabidopsis thaliana: responses to nitrate, amino acids and developmental stage. Plant molecular biology 52: 689-703.

Noguchi, K. and Terashima, 2006. Responses of spinach leaf mitochondria to low N availability. Plant, cell and environment 29: 710-719.

Nye, P.H., 1969. The soil model and its application to plant nutrition. In: Ecological aspects of the mineral nutrition of plants (ed: Rorison, I.H.) Blackwell, London, 105-114.

Okazaki, K., Oka, N., Shinano, T., Osaki, M. and Takebe, M., 2008. Differences in the metabolite profiles of spinach (Spinacia oleracea L.) leaf in different concentrations of nitrate in the culture solution. Plant cell physiology 49 (2): 170-177.

Olday, F.C., Barker, A.V. and Maynard, D.N., 1976. A physiological basis for different patterns of nitrate accumulation in two spinach cultivars. Journal of the American society of horticultural science 101 (3): 217-219.

- + Orsel, M. and Miller, A.J., 2011. Transport systems for NO3 and NH4 . In: Annual plant reviews 42: 83-102. Nitrogen Metabolism in Plants in the Post-Genomic Era (ed. Foyer, C.H. and Zhang, H.). Wiley-Blackwell and Sons, Ltd, publication, Oxford/Chichester/Ames.

Peoples, M.B. and Freney, J.R., 1995. Minimizing gaseous losses of nitrogen. Nitrogen fertilization in the environment (Bacon, P.E., ed.), 565-602.

Prosser, I.M., Purves, J.V., Saker, L.R. and Clarkson, D.T., 2001. Rapid disruption of nitrogen metabolism and nitrate transport in spinach plants deprived of sulphate. Journal of experimental botany 52 (354): 113-121.

Riedel, J., Tischner, R. and Mäck, G., 2001. The chloroplastic glutamine synthetase (GS-2) of tobacco is phosphorylated and associated with 14-3-3 proteins inside the chloroplast. Planta 213: 396-401.

Riens, B. and Heldt, H.W., 1992. Decrease of nitrate reducase activity in spinach leaves during a light- dark transition. Plant physiology 98: 573-577.

Rijksdienst voor Ondernemend Nederland, 2014. Tabel 1 Stikstofgebruiksnormen, 6 pp. https://mijn.rvo.nl/documents/13225/132100/Tabel+1+Stikstofgebruiksnormen+2014- 2017/f69ec23a-4524-47b6-9089-e08977ecf317 (visited on September 25, 2014)

Robinson., J.M., 1986. Carbon dioxide and nitrite photoassimilatory processes do not intercompete for reducing equivalents in spinach and soybean leaf chloroplasts. Plant physiology 80: 676- 684.

Ryder, E.J., 1979. Leafy salad vegetables. Avi Publishing Company, Westport, 266pp.

Scaife, A. and Schloemer, S., 1994. The diurnal pattern of nitrate uptake and reduction by spinach (Spinacia oleracea L.). Annals of botany 73: 337-343.

Schenk, M., Heins, B. and Steingrobe, B., 1991. The significance of root development of spinach and kohlrabi for N fertilization. Plant and soil 135: 197-203.

Schjoerring, J.K., Husted, S., Mäck, G. and Mattsson, M., 2002. The regulation of ammonium translocation in plants. Journal of experimental botany 53 (370): 883-890.

Sneep, J., 1983. The domestication of spinach and the breeding history of its varieties. Euphytica supplement 2, 27 pp.

Siddiqi, M.Y., Glass, A.D.M., Ruth, T.J. and Rufty Jr., T.W., 1990. Studies of the uptake of nitrate in barley I. Kinetics of 13NO3- influx. Plant physiology 93: 1426-1432.

Simpson, R.J., Lambers, H. and Dalling, M.J., 1982. Translocation of nitrogen in a vegetative wheat plant (Triticum aestivum). Physiologia plantarum 56: 11-17.

Smatanová, M., Richter, R. and Hlušek, J., 2004. Spinach and pepper response to nitrogen and sulphur fertilization. Plant soil environment 50 (7): 303-308.

Smil, V., 1999. Detonator of the population explosion. Nature 400: 415.

Smil, V., 2001. Enriching the Earth. MIT press, Cambridge/Massachesetts/London/England, 338 pp.

Smit, A.L. and Groenwold, J., 2005. Root characteristics of selected field crops: data from the Wageningen rhizolab (1990-2002). Plant and soil 272: 365-384.

Smolders, E., Buysse, J. and Merckx, R., 1993. Growth analysis of soil-grown spinach plants at different N-regimes. Plant and soil 154: 73-80.

Smolders, E. and Merckx, R., 1992. Growth and shoot:root partitioning of spinach plants as affected by nitrogen supply. Plant cell and environment 15: 795-807.

Smolders, E., Merckx, R., Schoonvaerts, F. and Vlassak, K., 1991. Continuous shoot growth monitoring in hydroponics. Physiologia plantarum 83: 83-92.

Staatscourant, 2014. Nr. 22547, 4 augustus 2014. Officiële uitgave van het Koninkrijk der Nederlanden sinds 1814, 11 pp.

Stagnari, F., Bitetto, V.D. and Pisante, M., 2007. Effects of N fertilizers and rates on yield, safety and nutrients in processing spinach genotypes. Scientia horticulturae 114: 225-233.

Steege, M.W. Ter, , Stulen, I., Wiersema, P.K., Paans, A.J.M., Vaalburg, W., Kuiper, P.J.C. and Clarkson, D.T., 1998. Growth requirement for N as a criterion to assess the effects of physical manipulation on nitrate uptake fluxes in spinach. Physiologia plantarum 103: 181-192.

Steege, M.W. Ter, , Stulen, I., Wiersema, P.K., Posthumus, F., Vaalburg, W., 1999. Efficiency of nitrate uptake in spinach: impact of external nitrate concentration and relative growth rate on nitrate influx and efflux. Plant and soil 208: 125-134.

Steingrobe, B. and Schenk, M.K., 1991. Influence of nitrate concentration at the root surface on yield and nitrate uptake of kohlrabi (Brassica oleracea gongyloides L.) and spinach (Spinacia oleracea L.). Plant and soil 135: 205-211.

Steingröver, E., Ratering, P. and Siesling, J., 1986a. Daily changes in uptake, reduction and storage of nitrate in spinach at low light intensity. Physiology plantarum 66: 550-556.

Steingröver, E., Siesling, J. and Ratering, P., 1986b. Effect of one night with “low light” on uptake, reduction and storage of nitrate in spinach. Physiologia plantarum 66: 557-562.

Steingröver, E., Oosterhuis, R. and Wieringa, F., 1982. Effect of light treatment and nutrition on nitrate accumulation in spinach (Spinacia oleracea L.). Zeitschrift für Pflanzenphysiologie 107: 97-102.

Stulen, I. and Steege, M.W. ter, 1988. Nitrate accumulation in spinach varieties. Proceedings of the international congress of plant physiology, New Delhi, India, 2: 1046-1049.

Teardo, E., Frare, E., Segalla, A., De Marco, V., Giacometti, G.M. and Szabò, I., 2005. Localization of a putative ClC chloride channel in spinach chloroplasts. FEBS letters 579: 4991-4996.

Terashima, I. and Evans, J.R., 1988. Effects of light and nitrogen nutrition on the organization of the photosynthetic apparatus in spinach. Plant cell physiology 29 (1): 143-155.

Thompson, T.L. and Doerge, T.A., 1995. Nitrogen and water rates for subsurface trickle-irrigated collard, mustard, and Spinach. HortiScience 307 (7): 1382-1387.

Touraine, B., 2004. Nitrate uptake by roots – transporters and root development. In: Nitrogen acquisition and assimilation in higher plants (ed. Amâncio, S. and Stulen, I.) Kluwer Academic Publishers, Dordrecht/Boston/London, 1-34.

Trevisan, S., Francioso, O., Quaggiotti, S. and Nardi, S., 2010. Humic substances biological activity at the plant-soil interface. From environmental aspects to molecular factors. Plant signaling and behavior 5: 635-643.

Ward, M.R., Grimes, H.D. and Huffaker, R.C., 1989. Latent nitrate reductase activity is associated with the plasma membrane of corn roots. Planta 177: 470-475.

+ - - Wang, B. and Shen, Q., 2011. NH4 -N/NO3 -N ratios on growth and NO3 -N remobilization in root vacuoles and cytoplasm of lettuce genotypes. Canadian journal of plant science 91: 411-417.

Weiner, H. and Kaiser, W.M., 2000. Binding to 14-3-3 proteins is not sufficient to inhibit nitrate reductase in spinach leaves. FEBS letters 480: 217-220.

Weiner, H. and Kaiser, W.M., 2001. Antibodies to assess phosphorylation of spinach leaf nitrate reductase on serine 543 and its binding to 14-3-3 proteins. Journal of experimental botany 52 (359): 1165-1172.

Xiao, W., Chao, L., Chunxiang, Q., Hao, H., Xiaoqing, L., Liang, C., Mingyu, S. and Fashui, H., 2008. Influences of lead (2) chloride on the nitrogen metabolism of spinach. Biological trace element research 121: 258-265.

Xu, Y. and Crough, J.H., 2008. Marker-assisted selection in plant breeding: from publications to practice. Crop science 48: 391-407.

Yamaya, T. and Oaks, A., 2004. Metabolic regulation of ammonium uptake and assimilation. In: Nitrogen acquisition and assimilation in higher plants (ed. Amâncio, S. and Stulen, I.) Kluwer Academic Publishers, Dordrecht/Boston/London, 35-63.

Yamori, W., Nagai, T. and Makino, A., 2011. The rate-limiting step for CO2 assimilation at different temperatures is influenced by the leaf nitrogen content in several C3 crop species. Plant, cell and environment 34: 764-777.

Yin, S., Ze, Y., Liu, C., Li, N., Zhou, M., Duan, Y. and Hong, F., 2009. Cerium relieves the inhibition of nitrogen metabolism of spinach caused by magnesium deficiency. Biological trace element research 132: 247-258.

Zhang, Y., Li, Y., Wei, J., Sun, M., Tian, Y. and Li, Z., 2009. Effects of nitrogen and calcium nutrition on oxalate contents, forms, and distribution in spinach. Journal of plant nutrition 32 (12): 2123- 2139.

Zhang, Y., Lin, X., Zhang, Y., Zheng, S.J. and Du, S., 2005. Effects of nitrogen levels and nitrate/ammonium ratios on oxalate concentrations of different forms in edible parts of spinach. Journal of plant nutrition 28 (11): 2011-2025.

Zornoza, P. and González, M., 1998a. Varietal specificity in growth, nitrogen uptake, and distribution under contrasting forms of nitrogen supply in spinach. Journal of plant nutrition 21 (5): 837- 847.

Zornoza, P. and González, M., 1998b. Intraspecific differences in nitrogen assimilating enzymes in spinach under contrasting forms of nitrogen supply. Journal of plant nutrition 21 (6): 1129- 1138.

Appendix I. Genotypes pre-screening

Table 13. Detailed list of plant material used in the pre-screening experiment.

Genotype Cross Type of Genotype Cross Type of material material 1 Marabu x Ranchero I1F1 25 Novico x Crocodile I1F1 2 Marabu x Ranchero I1F1 26 Novico x Crocodile I1F1 3 Marabu x Ranchero I1F1 27 Novico x Crocodile I1F1 4 Marabu x Ranchero I1F1 28 Novico x Crocodile I1F1 5 Marabu x Ranchero I1F1 29 Novico x Crocodile I1F1 6 Marabu x Ranchero I1F1 30 Novico x Crocodile I1F1 7 Marabu x Ranchero I1F1 31 Ranchero x Crocodile I1F1 8 Marabu x Ranchero I1F1 32 Ranchero x Crocodile I1F1 9 Marabu x Ranchero I1F1 33 Ranchero x Crocodile I1F1 10 Marabu x Ranchero I1F1 34 Ranchero x Crocodile I1F1 11 Marabu x Novico I1F1 35 Ranchero x Crocodile I1F1 12 Marabu x Novico I1F1 36 Ranchero x Crocodile I1F1 13 Marabu x Novico I1F1 37 Ranchero x Crocodile I1F1 14 Novico x Crocodile I1F1 38 Ranchero x Crocodile I1F1 15 Marabu x Novico I1F1 39 Ranchero x Crocodile I1F1 16 Novico x Crocodile I1F1 40 Ranchero x Crocodile I1F1 17 Novico x Crocodile I1F1 41 Self Crocodile Selfing 18 Marabu x Novico I1F1 42 Crocodile Hybrid 19 Marabu x Novico I1F1 43 Marabu Hybrid 20 Marabu x Novico I1F1 44 Self Marabu Selfing 21 Marabu x Novico I1F1 45 Novico Hybrid 22 Marabu x Novico I1F1 46 Self Novico Selfing 23 Marabu x Novico I1F1 47 Ranchero Hybrid 24 Novico x Crocodile I1F1 48 Self Ranchero Selfing

Appendix II. Hoagland solution

Table 14. Composition of Hoagland solution without nitrogen.

Nutrient Amount Nutrient Amount K 7.9 Fe 24 P 1.94 Mn 12 Ca 3.9 Zn 4.4 Mg 1.5 B 9.8 µmol l-1 mmol l-1 S 2.9 Cu 0.7 Cl 0.6 Mo 0.3

HCO3 0.4 Si 20 Na 0.4 pH 5.7 EC 2.1 mS cm-1

Appendix III. Experimental design pre-screening

Unit 1 (high N) Unit 2 (low N) Unit 3 (high N) Unit 4 (low N)

2 1 3 14 6 19 21 18 1 11 47 33 16 14 44 8 38 18 20 1 45 23 43 33 43 20 41 23 2 28 7 40 30 22 48 41 24 39 15 11 23 31 29 9 37 18 40 20 7 16 41 36 8 14 17 26 5 3 11 32 14 25 17 44 8 45 7 42 16 29 23 5 35 45 32 3 7 36 30 22 27 44 39 28 15 10 2 6 30 31 29 36 22 6 21 4 1 9 17 25 32 4 35 28 17 43 33 9 4 15 12 43 46 19 25 34 13 25 35 5 30 48 34 21 42 12 39 24 34 15 48 37 44 10 31 36 37 26 40 47 17 27 48 2 26 39 28 41 3 9 37 42 47 31 40 29 19 27 8 33 10 45 16 18 38 25 27 46 34 20 13 12 5 42 24 13 6 38 21 10 22 32 24 12 46 11 19 4 1 35 46 47 13 26 9 38

8 40 37 11 26 21 41 23 35 36 26 43 2 38 31 22 23 17 11 26 13 1 40 29 36 5 20 24 33 19 42 8 47 18 13 1 14 34 42 16 23 7 25 32 45 16 40 47 34 31 43 5 41 7 46 42 6 44 31 12 1 13 17 47 32 35 33 12 36 46 9 29 21 34 19 30 14 29 48 42 15 21 45 25 37 32 18 9 39 45 11 34 43 37 27 46 2 10 18 26 39 43 17 6 45 3 19 44 18 28 37 44 15 27 3 24 35 8 19 44 4 3 38 36 16 48 30 2 3 18 22 29 25 7 30 28 31 24 2 4 9 46 13 10 11 8 4 1 12 2 33 16 10 14 22 47 41 32 23 9 21 15 14 28 48 27 20 15 5 38 10 22 5 20 12 6 39 33 41 17 20 6 27 30 28 48 24 39 40 25 35 38 4 7 10 26

18 27 32 12 30 13 9 41 18 39 19 44 21 22 36 30 17 37 19 11 20 39 16 32 7 30 44 27 40 23 22 18 47 38 40 2 44 6 24 14 27 35 1 5 48 25 26 31 38 34 9 41 3 8 24 25 1 21 13 16 26 34 17 4 21 1 25 31 8 26 43 33 24 16 32 6 14 4 9 8 1 40 31 29 30 46 35 10 8 48 39 41 11 12 15 46 3 11 19 27 10 22 15 34 46 36 37 29 40 34 15 12 28 11 23 47 23 14 47 28 7 27 15 4 42 37 33 10 29 43 20 38 23 20 5 7 45 4 35 28 20 43 3 41 33 38 37 46 13 12 42 36 18 21 2 22 32 6 9 31 47 45 19 24 39 17 19 16 3 11 42 48 7 10 29 17 13 42 2 45 45 26 6 33 48 44 43 5 25 28 5 2 14 3 35 36

21 31 9 37 8 15 45 48 28 12 29 31 5 45 15 44 29 5 37 33 23 48 32 20 45 33 20 35 27 34 30 36 18 7 16 36 42 32 12 10 48 4 19 13 21 23 40 34 35 26 25 44 3 6 14 46 43 21 23 26 14 10 4 5 40 46 29 2 35 43 14 38 36 35 30 3 47 42 11 7 39 1 30 11 47 12 4 13 12 41 18 44 24 13 1 16 4 12 20 28 41 25 44 26 11 6 47 1 10 26 27 1 32 2 39 14 27 45 19 17 8 28 9 10 47 9 29 8 32 11 7 40 33 22 30 3 28 23 24 17 20 43 33 25 24 16 6 41 40 21 43 18 36 24 16 34 31 22 46 39 2 3 19 28 19 5 20 13 4 34 27 39 38 17 46 8 9 37 18 22 38 15 42 7 2 22 31 41 42 25 38 6 15 17 48 37

47 37 30 35 3 17 22 28 37 20 25 38 10 28 41 13 1 15 37 45 6 48 41 4 16 9 19 37 34 35 10 7 39 27 23 21 6 36 44 31 7 22 9 12 30 36 42 34 31 47 27 42 26 12 2 40 29 31 13 30 2 5 15 14 29 32 9 45 11 46 24 4 18 23 35 8 47 27 31 2 23 29 33 10 11 25 32 22 36 42 40 33 22 43 28 47 5 13 21 29 20 15 5 41 18 19 26 13 19 1 5 32 33 6 14 17 5 36 43 9 3 39 13 38 6 38 1 48 4 45 32 39 34 2 25 16 43 8 7 12 26 21 48 11 46 43 4 3 21 7 20 18 17 8 34 16 8 3 46 12 26 44 17 27 48 42 14 10 40 33 38 1 24 16 45 44 40 29 39 15 28 35 30 19 24 44 14 46 20 23 18 41 21 11 24 25

39 16 22 42 48 25 7 32 7 27 14 25 29 37 30 15 9 13 43 5 28 4 20 30 24 44 37 25 1 39 14 45 8 13 30 19 11 6 17 2 45 5 3 38 26 22 28 20 31 48 2 22 17 19 47 44 34 47 7 41 10 23 22 17 41 15 5 27 10 40 46 21 18 33 16 47 44 10 6 35 3 35 11 26 6 37 15 18 4 32 2 31 43 33 16 29 6 14 22 30 28 36 4 43 24 26 44 35 2 39 46 21 31 36 11 34 46 36 29 10 34 7 42 12 36 15 12 3 21 13 9 18 31 12 29 23 47 18 9 1 41 48 43 1 24 42 40 8 41 14 38 33 24 23 40 1 27 48 19 35 20 5 28 38 20 45 33 37 3 38 14 34 32 9 12 4 19 23 17 13 16 45 25 27 39 32 21 8 8 11 46 30 42 40 26 6

46 41 36 3 6 20 5 34 22 20 10 13 43 32 40 31 12 9 25 42 15 29 21 6 28 16 13 6 47 45 21 41 37 28 27 13 40 11 32 7 28 41 29 12 30 33 11 44 22 24 2 7 36 19 14 11 26 24 37 4 36 31 14 5 12 33 38 16 45 8 47 39 24 47 23 45 15 3 16 34 38 20 31 28 32 37 35 40 48 25 43 9 8 44 35 46 7 15 23 31 15 43 19 22 1 35 48 23 17 48 8 26 18 1 14 19 1 16 8 43 5 45 33 27 29 19 38 10 3 30 20 17 10 17 24 26 44 9 29 14 38 27 46 21 2 35 6 9 44 17 4 23 30 34 18 3 32 42 15 12 1 11 34 22 18 31 2 30 25 42 4 21 37 42 5 4 25 7 36 39 47 26 48 10 13 39 46 41 27 2 18 23 33 7 40 39

20 37 3 23 11 48 30 13 30 11 41 21 31 7 47 20 14 8 43 47 12 5 40 3 24 35 6 7 8 29 47 18 45 44 34 24 4 19 5 22 44 40 24 27 19 33 9 46 20 6 15 11 34 24 7 4 39 43 16 46 21 1 13 38 36 25 27 41 46 21 39 15 8 25 48 34 2 12 35 17 41 39 35 16 27 10 32 37 33 17 14 31 37 15 9 5 8 16 24 32 38 26 10 42 8 32 12 47 6 38 3 13 29 43 36 39 2 48 45 9 18 42 28 29 23 28 3 27 45 2 42 25 7 35 1 18 43 40 28 29 14 26 18 4 22 15 23 37 33 31 44 36 22 17 46 25 44 22 36 30 26 4 40 11 31 33 16 14 17 2 6 9 32 42 45 10 1 5 28 16 26 21 13 38 30 19 1 23 32 19 34 20 10 12 41 48

Figure 12. Experimental design pre-screening. 48 genotypes were randomized over two containers. Two containers together are one plot. Each unit consists out of eight plots. For genotypes corresponding to the numbers see Appendix I.

Appendix IV. NAR pre-screening

Table 15. Daily nitrogen addition per unit for low (RGR=0.10) and high (RGR=0.18) nitrogen levels. Each unit contained 384 plants.

RGR=0.10 RGR=0.18 Date Day KNO3 (mg) NH4Cl (mg) Day KNO3 (mg) NH4Cl (mg) 21-nov 1 116.747 20.590 1 218.924 38.611 22-nov 2 129.025 22.756 2 262.100 46.225 23-nov 3 142.595 25.149 3 313.790 55.342 24-nov 4 157.591 27.794 4 375.675 66.256 25-nov 5 174.165 30.717 5 449.765 79.323 26-nov 6 192.483 33.947 6 538.466 94.967 27-nov 7 212.726 37.518 7 644.661 113.696 28-nov 8 235.099 41.463 8 771.800 136.119 29-nov 9 259.824 45.824 9 924.012 162.964 30-nov 10 287.150 50.643 10 1106.243 195.103 1-dec 11 317.350 55.970 11 1324.414 233.581 2-dec 12 350.726 61.856 12 1585.611 279.647 3-dec 13 387.612 68.361 13 1898.321 334.799 4-dec 14 428.378 75.551 14 2272.703 400.827 5-dec 15 473.431 83.497 15 2720.920 479.877 6-dec 16 523.222 92.278 16 3257.532 574.517 7-dec 17 578.250 101.983 17 3899.974 687.821 8-dec 18 639.065 112.709 18 4669.117 823.472 9-dec 19 706.276 124.563 19 5589.947 985.875 10-dec 20 780.555 137.663 20 6692.382 1180.306 11-dec 21 862.647 152.141 21 8012.236 1413.083 12-dec 22 953.372 168.142 22 9592.388 1691.768 13-dec 23 1053.639 185.826 23 11484.174 2025.414 14-dec 24 1164.452 205.369 24 13749.052 2424.860 15-dec 25 1286.918 226.968 25 16460.604 2903.085 16-dec 26 1422.264 250.839 26 19706.921 3475.624 17-dec 27 1571.845 277.219 27 23593.468 4161.077 18-dec 28 1737.158 306.375 28 28246.509 4981.714 19-dec 29 1919.856 338.597 29 33817.211 5964.194 20-dec 30 2121.769 374.207 30 40486.552 7140.437 21-dec 31 2344.917 413.563 31 48471.204 8548.655

Appendix V. Experimental design physiology

Unit 1 (Ingestad low N) Unit 2 (Ingestad high N) Unit 3 (depletion low N) Unit 4 (depletion high N)

27 41 16 33 23 22 7 9 22 27 33 23 7 16 9 41 23 33 7 41 9 22 27 16 27 7 23 9 16 22 41 33 1 23 16 41 7 27 33 22 9 9 9 7 41 22 27 16 23 33 17 27 33 9 41 7 16 22 23 25 23 9 41 7 27 22 33 16 7 41 16 9 22 23 33 27 7 16 22 23 27 41 33 9 22 9 33 27 16 7 41 23 16 27 22 33 9 41 7 23

16 22 27 41 7 23 33 9 27 23 16 41 9 7 22 33 16 7 27 33 9 22 23 41 9 27 41 7 33 16 22 23 2 33 27 22 41 9 16 23 7 10 16 9 33 22 23 27 41 7 18 16 33 41 22 23 7 27 9 26 41 9 22 7 16 27 33 23 7 16 33 41 9 22 23 27 41 27 33 9 22 23 16 7 9 22 33 27 41 16 7 23 16 23 27 33 22 9 7 41 Harvest 1

16 22 41 33 9 23 27 7 16 23 41 22 27 33 7 9 22 16 23 9 33 41 7 27 33 27 9 16 7 23 41 22 3 9 7 23 22 41 33 27 16 11 23 41 22 33 9 27 16 7 19 33 9 27 23 22 7 41 16 27 22 27 33 16 9 41 7 23 27 41 16 9 22 7 33 23 23 7 33 16 27 22 9 41 23 9 41 7 22 27 33 16 9 7 16 41 23 27 33 22

7 27 23 33 9 16 22 41 23 9 22 7 41 16 27 33 41 27 16 7 33 9 22 23 7 41 27 9 16 22 23 33 4 7 9 16 41 27 33 23 22 12 23 22 41 27 16 33 7 9 20 22 16 23 9 33 41 27 7 28 7 23 9 33 27 16 22 41 41 22 9 23 33 16 7 27 7 33 22 23 9 16 41 27 9 7 22 27 23 16 33 41 41 16 7 23 33 9 27 22

7 16 9 33 27 23 41 22 7 22 23 16 33 41 9 27 27 33 22 7 41 16 23 9 41 27 7 16 33 23 9 22 5 7 33 22 27 23 41 16 9 13 27 41 16 9 33 7 22 23 21 9 7 41 27 33 16 23 22 29 22 16 33 9 7 41 23 27 9 27 41 33 7 23 22 16 9 16 27 23 22 33 41 7 23 41 9 7 16 22 27 33 41 9 16 7 33 23 22 27 Harvest 2

27 9 16 41 33 22 23 7 22 16 33 27 41 9 7 23 41 23 16 27 33 7 9 22 27 16 7 33 9 23 41 22 6 7 33 9 41 22 27 16 23 14 9 27 22 33 7 41 23 16 22 33 7 27 9 22 41 16 23 30 33 9 41 16 23 22 7 27 22 33 23 27 9 16 7 41 41 23 22 33 7 9 16 27 27 7 9 41 22 16 23 33 9 16 41 33 22 27 23 7

22 9 33 23 16 27 23 33 23 16 33 22 16 9 22 27 23 27 9 22 16 22 33 16 9 23 27 16 33 16 33 22 7 33 23 22 27 9 22 16 22 15 9 9 33 23 22 23 16 27 23 16 27 33 9 23 23 22 33 31 22 33 22 22 9 23 16 27 16 9 33 16 27 23 9 22 33 33 23 9 27 22 22 16 16 27 9 33 9 23 27 22 33 27 22 23 9 16 23 9

22 27 22 9 23 16 33 23 16 9 33 27 33 22 22 23 22 27 9 23 16 23 27 33 27 16 23 22 33 9 23 9 Reserve 8 22 9 16 23 16 9 27 33 16 23 33 16 27 9 22 16 23 24 23 33 22 16 27 9 33 22 32 33 27 9 22 16 23 22 33 9 23 33 33 27 16 22 22 22 16 9 33 9 23 27 22 16 33 9 9 23 27 22 16 9 16 23 27 22 22 33 16

Figure 13. Experimental design physiology experiment. Each container consists out of three rows. The eight cultivars were randomized within each row. Per harvest three containers were harvested per unit. Each unit had two reserve containers. The yellow marked cultivars were replaced by other cultivars because of an insufficient number of plants due to bad germination. Cultivar 7 is Ranchero, cultivar 9 is Chevelle, cultivar 16 is Cello, cultivar 22 is Novico, cultivar 23 is Andromeda, cultivar 33 is Sparrow and cultivar 41 is Marabu.

Appendix VI. NAR physiology Ingestad

Table 16. Daily nitrogen addition per unit for low and high nitrogen levels. Each unit contained 192 plants before and 120 after the first harvest (day 13).

Number Ingestad low N Ingestad high N

Date of plants Day KNO3(mg) NH4Cl (mg) KNO3(mg) NH4Cl (mg) 25-jan 192 1 58.373 10.295 109.462 19.305 26-jan 192 2 64.512 11.378 131.050 23.113 27-jan 192 3 71.297 12.574 156.895 27.671 28-jan 192 4 78.796 13.897 187.838 33.128 29-jan 192 5 87.083 15.358 224.882 39.662 30-jan 192 6 96.241 16.974 269.233 47.483 31-jan 192 7 106.363 18.759 322.331 56.848 1-feb 192 8 117.549 20.732 385.900 68.059 2-feb 192 9 129.912 22.912 462.006 81.482 3-feb 192 10 143.575 25.322 553.122 97.552 4-feb 192 11 158.675 27.985 662.207 116.791 5-feb 192 12 175.363 30.928 792.806 139.824 6-feb 120 13 121.129 21.363 593.225 104.625 7-feb 120 14 133.868 23.610 710.220 125.258 8-feb 120 15 147.947 26.093 850.287 149.961 9-feb 120 16 163.507 28.837 1017.979 179.536 10-feb 120 17 180.703 31.870 1218.742 214.944 11-feb 120 18 199.708 35.222 1459.099 257.335 12-feb 120 19 220.711 38.926 1746.859 308.086 13-feb 120 20 243.924 43.020 2091.369 368.846 14-feb 120 21 269.577 47.544 2503.824 441.588 15-feb 120 22 297.929 52.544 2997.621 528.677 16-feb 120 23 329.262 58.071 3588.804 632.942 17-feb 120 24 363.891 64.178 4296.579 757.769 18-feb 120 25 402.162 70.928 5143.939 907.214 19-feb 120 26 444.458 78.387 6158.413 1086.132 20-feb 120 27 491.202 86.631 7372.959 1300.337

Appendix VII. NAR physiology depletion

Table 17. Nitrogen addition per unit for low and high nitrogen levels. Each unit contained 192 plants before and 120 after the first harvest (day 13).

Number Depletion low N Depletion high N

Date of plants Day KNO3(mg) NH4Cl (mg) KNO3(mg) NH4Cl (mg) 25-jan 192 1 5297.717 934.335 46007.649 8114.169 26-jan 192 2 27-jan 192 3 28-jan 192 4 29-jan 192 5 30-jan 192 6 31-jan 192 7 1-feb 192 8 2-feb 192 9 3-feb 192 10 4-feb 192 11 5-feb 192 12 6-feb 120 13 7-feb 120 14 8-feb 120 15 9-feb 120 16 10-feb 120 17 11-feb 120 18 12-feb 120 19 13-feb 120 20 14-feb 120 21 15-feb 120 22 16-feb 120 23 17-feb 120 24 18-feb 120 25 19-feb 120 26 20-feb 120 27

Appendix VIII. Graphs type of plant material effect

1.4 a ) 0.7 10

1 b c - 1.2 0.6 8 1.0 0.5 0.8 0.4 6 0.6 0.3 4 0.4 0.2

0.2 0.1 Dry matter (%) 2 Total dry weight (g) 0.0 0.0 0 Root shoot ratio (g g

4 d 40 e 3 30 (SPAD)

2 nd 20

1 10

0 0 Leaf number (22 DAP) Chl. content 2

Figure 14. Average total dry weight (a), root shoot ratio (b), dry matter percentage (c), leaf number at 22 DAP (d), and chlorophyll content of the second leaf at harvest (e) of the cultivars, the I1F1 lines and the selfings, under low (blue bars) and high (red bars) nitrogen level. Error bars represent the standard error of the mean.

Appendix IX. Variation of several traits per cross

Table 18. Descriptive statistics, per nitrogen level, of the progeny of the four crosses for root shoot ratio (R:S), dry matter percentage (DM%), specific leaf area (SLA) and for chlorophyll content of the first and second leaf at harvest (CC 1st H and CC 2nd H respectively). Mean is the average of plants of the same cross, min is the worst performing line of a cross and max the best performing one. Max/min is the ratio of max divided by min.

Low N High N Trait Cross Mean Min Max Max/Min Mean Min Max Max/Min Marabu x Ranchero 0.50 0.41 0.57 1.4 0.16 0.15 0.18 1.2

Marabu x Novico 0.48 0.40 0.53 1.3 0.19 0.16 0.22 1.4 R:S Novico x Crocodile 0.45 0.38 0.54 1.4 0.17 0.15 0.20 1.3 Ranchero x Crocodile 0.51 0.42 0.62 1.5 0.17 0.16 0.20 1.2 Marabu x Ranchero 7.52 6.84 8.52 1.2* 5.29 4.95 5.74 1.2

Marabu x Novico 7.28 6.60 8.25 1.3* 5.36 4.94 5.75 1.2

DM% Novico x Crocodile 7.32 6.42 7.82 1.2 5.37 4.80 5.85 1.2* Ranchero x Crocodile 7.28 6.80 7.81 1.1 5.27 4.93 5.65 1.1 Marabu x Ranchero 254 239 275 1.2 332 291 378 1.3* Marabu x Novico 281 257 293 1.1 364 316 418 1.3*

SLA Novico x Crocodile 280 265 309 1.2 379 351 418 1.2 Ranchero x Crocodile 271 236 307 1.3 351 319 401 1.3

Marabu x Ranchero 28.5 27.1 31.2 1.1 32.7 31.3 35.8 1.1* H

st Marabu x Novico 28.3 25.2 30.2 1.2 32.3 29.6 35.5 1.2 Novico x Crocodile 29.6 23.9 34.3 1.4* 33.9 30.1 37.2 1.2* CC 1 Ranchero x Crocodile 31.3 28.1 34.6 1.2* 36.1 33.7 38.2 1.1

Marabu x Ranchero 31.0 28.2 32.6 1.2 36.5 32.2 39.0 1.2* H

nd Marabu x Novico 29.2 27.4 32.3 1.2 34.2 32.1 37.9 1.2* Novico x Crocodile 28.2 25.9 31.2 1.2* 34.5 30.9 37.8 1.2* CC 2 Ranchero x Crocodile 31.0 29.5 38.2 1.3* 37.2 34.0 45.1 1.3*

Appendix X. Correlation matrix pre-screening

Table 19. Correlations between shoot fresh weight (SFW), root dry weight (RDW), shoot dry weight (SDW), total dry weight (TDW), leaf area (LA), root shoot ratio (R:S), dry matter percentage (DM%), specific leaf area (SLA) and chlorophyll content (CC) of the first (1st) and second (2nd) leaf at harvest. Correlations in white and grey refer to low and high nitrogen conditions, respectively. Correlations in black were significantly diferent from zero (p<0.05), in red not.

Trait SFW RDW SDW TDW LA R:S DM% SLA CC 1st CC 2nd SFW - 0.941 0.987 0.982 0.974 0.018 0.326 -0.595 -0.374 0.012 RDW 0.819 - 0.951 0.964 0.925 0.262 0.410 -0.581 -0.331 0.039 SDW 0.960 0.782 - 0.995 0.969 0.013 0.445 -0.633 -0.356 0.039 TDW 0.955 0.918 0.965 - 0.966 0.057 0.441 -0.625 -0.355 0.035 LA 0.928 0.784 0.907 0.906 - 0.027 0.365 -0.492 -0.381 -0.050 R:S 0.189 0.631 0.093 0.325 0.184 - -0.051 0.078 0.023 0.008 DM% -0.093 -0.084 0.157 0.064 -0.034 -0.367 - -0.595 -0.058 0.167 SLA -0.188 -0.122 -0.303 -0.244 0.048 0.191 -0.499 - 0.122 -0.330 CC 1st -0.309 -0.246 -0.348 -0.325 -0.338 0.030 -0.161 0.050 - 0.291 CC 2nd 0.101 0.073 0.110 0.100 0.027 0.014 0.057 -0.215 0.190 - The correlation calculations were besides the traits out of this table based on R:S, DM%, SLA and CC of the first and second leaf at harvest. The calculations could only be performed for plants of which none of the measurements was missing. The calculations were therefore based on 52 and 80 % of the plants under low and high nitrogen level respectively.

Appendix XI. REML table N level effect

Table 20. F probabilities of the cultivar and nitrogen level effect of the REML for shoot fresh weight (SFW), root dry weight (RDW), shoot dry weight (SDW), total dry weight (TDW), leaf area (LA), root shoot ratio (R:S), dry matter percentage (DM%), specific leaf area (SLA), average root diameter (RD), total root length (RL), total root surface area (RSA), stomatal conductance (SC), leaf number (L No.) at 4, 7, 10, 14, 17, 20 and 24 DAP and chlorophyll content (CC) of the first (1st) leaf at 14 DAP and of the first and second (2nd) leaf at 18, 21 and 27 DAP. Statistics were shown for depletion and Ingestad and for the first and second harvest separately. p<0.05.

Harvest 1 Harvest 2 Depletion Ingestad Depletion Ingestad Trait Cultivar N level Cultivar N level Cultivar N level Cultivar N level SFW <0.001 0.083 <0.001 0.077 0.003 <0.001 <0.001 <0.001 RDW <0.001 0.066 <0.001 0.292 0.024 0.449 <0.001 0.397 SDW <0.001 0.057 <0.001 0.105 0.007 <0.001 <0.001 <0.001 TDW <0.001 0.495 <0.001 0.389 0.014 <0.001 <0.001 <0.001 LA <0.001 0.073 <0.001 0.103 0.003 <0.001 <0.001 <0.001 R:S <0.001 0.006 0.001 0.014 <0.001 <0.001 <0.001 <0.001 DM% 0.004 0.689 0.150 0.870 <0.001 <0.001 0.003 <0.001 SLA <0.001 0.879 0.006 0.595 <0.001 <0.001 0.002 <0.001 RD 0.009 0.639 0.253 0.018 0.180 0.248 0.347 0.877 RL 0.037 0.251 0.009 0.081 0.073 0.825 0.011 0.661 RSA 0.131 0.177 0.010 0.101 0.045 0.907 0.05 0.964 SC 0.861 0.946 0.204 0.994 L No. 4 DAP 0.010 0.327 0.014 0.747 L No. 7 DAP <0.001 0.468 <0.001 0.062 L No. 10 DAP <0.001 0.046 <0.001 0.440 L No. 14 DAP <0.001 0.423 <0.001 0.368 L No. 17 DAP <0.001 0.655 <0.001 0.077 L No. 20 DAP <0.001 0.101 <0.001 0.012 L No. 24 DAP <0.001 0.106 <0.001 0.070 CC 1st 14 DAP <0.001 0.809 <0.001 0.007 CC 1st 18 DAP <0.001 0.029 <0.001 <0.001 CC 2nd 18 DAP <0.001 0.009 <0.001 0.002 CC 1st 21 DAP <0.001 <0.001 <0.001 <0.001 CC 2nd 21 DAP <0.001 0.006 0.002 0.015 CC 1st 27 DAP 0.002 0.009 <0.001 0.001 CC 2nd 27 DAP 0.005 <0.001 <0.001 0.002

Appendix XII. REML table application method effect

Table 21. F probabilities of the cultivar and application method (app. method) effect of the REML for shoot fresh weight (SFW), root dry weight (RDW), shoot dry weight (SDW), total dry weight (TDW), leaf area (LA), root shoot ratio (R:S), dry matter percentage (DM%), specific leaf area (SLA), average root diameter (RD), total root length (RL), total root surface area (RSA), stomatal conductance (SC), leaf number (L No.) at 4, 7, 10, 14, 17, 20 and 24 DAP and chlorophyll content (CC) of the first (1st) leaf at 14 DAP and of the first and second (2nd) leaf at 18, 21 and 27 DAP. Statistics were shown for low and high nitrogen level and for the first and second harvest separately. p<0.05.

Harvest 1 Harvest 2 Low N High N Low N High N App. App. App. App. Trait Cultivar method Cultivar method Cultivar method Cultivar method SFW <0.001 0.007 <0.001 0.005 0.475 0.010 <0.001 0.015 RDW <0.001 0.489 <0.001 0.981 0.208 0.183 <0.001 0.389 SDW 0.002 0.006 <0.001 0.005 0.098 <0.001 <0.001 0.012 TDW <0.001 0.024 <0.001 0.014 0.415 0.001 <0.001 0.017 LA <0.001 0.007 <0.001 0.010 0.274 0.187 <0.001 0.016 R:S 0.002 0.046 <0.001 <0.001 <0.001 <0.001 <0.001 0.022 DM% 0.005 0.173 0.058 0.889 <0.001 <0.001 0.392 0.495 SLA <0.001 0.201 0.038 0.428 <0.001 <0.001 0.001 0.064 RD 0.167 0.498 0.095 0.090 0.234 0.631 0.194 0.396 RL 0.022 0.223 0.024 0.166 0.182 0.743 0.004 0.574 RSA 0.088 0.136 0.041 0.204 0.077 0.720 0.011 0.866 SC 0.971 0.921 0.205 0.993 L No. 4 DAP 0.018 0.588 0.055 0.106 L No. 7 DAP <0.001 0.206 <0.001 0.297 L No. 10 DAP <0.001 0.011 <0.001 0.898 L No. 14 DAP <0.001 0.342 <0.001 0.461 L No. 17 DAP <0.001 0.109 <0.001 0.491 L No. 20 DAP <0.001 0.587 <0.001 0.236 L No. 24 DAP <0.001 0.269 <0.001 0.254 CC 1st 14 DAP <0.001 0.045 <0.001 0.170 CC 1st 18 DAP <0.001 0.017 <0.001 0.266 CC 2nd 18 DAP <0.001 0.748 <0.001 0.118 CC 1st 21 DAP <0.001 0.004 <0.001 0.004 CC 2nd 21 DAP <0.001 0.101 0.002 0.301 CC 1st 27 DAP 0.005 0.008 <0.001 0.021 CC 2nd 27 DAP 0.022 <0.001 <0.001 0.122

Appendix XIII. REML table cultivar x N level effect

Table 22. F probabilities of the cultivar x nitrogen level interaction effect of the REML for shoot fresh weight (SFW), root dry weight (RDW), shoot dry weight (SDW), total dry weight (TDW), leaf area (LA), root shoot ratio (R:S), dry matter percentage (DM%), specific leaf area (SLA), average root diameter (RD), total root length (RL), total root surface area (RSA), stomatal conductance (SC), leaf number (L No.) at 4, 7, 10, 14, 17, 20 and 24 DAP and chlorophyll content (CC) of the first (1st) leaf at 14 DAP and of the first and second (2nd) leaf at 18, 21 and 27 DAP. Statistics were shown for depletion and Ingestad and for the first and second harvest separately. p<0.05.

Harvest 1 Harvest 2 Trait Depletion Ingestad Depletion Ingestad SFW 0.319 0.209 0.002 <0.001 RDW 0.188 0.289 0.057 0.001 SDW 0.282 0.408 0.025 <0.001 TDW 0.272 0.373 0.064 <0.001 LA 0.234 0.378 0.001 <0.001 R:S <0.001 0.174 <0.001 <0.001 DM% 0.043 0.409 0.016 0.081 SLA 0.848 0.750 0.055 0.283 RD 0.787 0.826 0.172 0.559 RL 0.771 0.573 0.305 0.282 RSA 0.857 0.613 0.213 0.558 SC 0.944 0.422 L No. 4 DAP 0.654 0.986 L No. 7 DAP 0.106 0.062 L No. 10 DAP 0.973 0.312 L No. 14 DAP 0.229 0.068 L No. 17 DAP 0.266 0.203 L No. 20 DAP 0.781 0.792 L No. 24 DAP 0.568 0.211 CC 1st 14 DAP 0.657 0.002 CC 1st 18 DAP 0.388 0.303 CC 2nd 18 DAP 0.262 0.140 CC 1st 21 DAP 0.302 0.663 CC 2nd 21 DAP 0.814 0.487 CC 1st 27 DAP 0.098 0.143 CC 2nd 27 DAP 0.955 0.010

Appendix XIV. REML table cultivar x application method effect

Table 23. F probabilities of the cultivar x application method interaction effect of the REML for shoot fresh weight (SFW), root dry weight (RDW), shoot dry weight (SDW), total dry weight (TDW), leaf area (LA), root shoot ratio (R:S), dry matter percentage (DM%), specific leaf area (SLA), average root diameter (RD), total root length (RL), total root surface area (RSA), stomatal conductance (SC), leaf number (L No.) at 4, 7, 10, 14, 17, 20 and 24 DAP and chlorophyll content (CC) of the first (1st) leaf at 14 DAP and of the first and second (2nd) leaf at 18, 21 and 27 DAP. Statistics were shown for low and high nitrogen level and for the first and second harvest separately. p<0.05.

Harvest 1 Harvest 2 Trait Low N High N Low N High N SFW 0.384 0.056 0.603 0.417 RDW 0.521 0.023 0.303 0.225 SDW 0.577 0.027 0.685 0.307 TDW 0.557 0.039 0.694 0.297 LA 0.389 0.019 0.601 0.518 R:S 0.573 0.044 0.065 0.923 DM% 0.673 0.905 0.006 0.101 SLA 0.661 0.988 0.082 0.232 RD 0.629 0.271 0.395 0.516 RL 0.306 0.647 0.848 0.798 RSA 0.393 0.639 0.844 0.736 SC 0.955 0.135 L No. 4 DAP 0.939 0.098 L No. 7 DAP 0.568 0.202 L No. 10 DAP 0.719 0.573 L No. 14 DAP 0.018 0.637 L No. 17 DAP 0.223 0.827 L No. 20 DAP 0.757 0.123 L No. 24 DAP 0.443 0.197 CC 1st 14 DAP 0.751 0.438 CC 1st 18 DAP 0.545 0.062 CC 2nd 18 DAP 0.104 0.080 CC 1st 21 DAP 0.024 0.167 CC 2nd 21 DAP 0.268 0.914 CC 1st 27 DAP 0.058 0.127 CC 2nd 27 DAP 0.645 0.252

Appendix XV. Additional graphs physiology

1.4 a

1.2

1.0

0.8

0.6

Shoot fresh weight (g) 0.4

0.2

0.0 Chevelle Cello Novico Andromeda Crocodile Sparrow Marabu Average

Depletion low N Ingestad low N 30 b Depletion high N Ingestad high N 25

10 Shoot fresh weight (g)

0 Chevelle Cello Novico Andromeda Crocodile Sparrow Marabu Average

Figure 15. Shoot fresh weight at the first (a) and second (b) harvest for the seven cultivars and the average of all plants, under low and high nitrogen level for both depletion and Ingestad.

0.030 a

0.025

0.020

0.015

0.010 Root dry weight (g)

0.005

0.000 Chevelle Novico Crocodile Marabu

Depletion low N Ingestad low N 0.25 b Depletion high N Ingestad high N 0.20

0.15

0.10 Root dry weight (g) 0.05

0.00

Figure 16. Root dry weight at the first (a) and second (b) harvest for the seven cultivars and the average of all plants, under low and high nitrogen level for both depletion and Ingestad.

0.12 a

0.10

0.08

0.06

0.04 Total dry weight (g) 0.02

0.00

Depletion low N Ingestad low N 1.6 b Depletion high N Ingestad high N 1.4

1.2

1.0

0.8

0.6 Total dry weight (g) 0.4

0.2

0.0 Chevelle Cello Novico Andromeda Crocodile Sparrow Marabu Average

Figure 17. Total dry weight at the first (a) and second (b) harvest for the seven cultivars and the average of all plants, under low and high nitrogen level for both depletion and Ingestad.

35 a

25 ) 2 20

15 Leaf area (cm 10

0 Chevelle Cello Novico Andromeda Crocodile Sparrow Marabu Average

Depletion low N Ingestad low N 450 b Depletion high N Ingestad high N 400

350

) 300 2

250

200

Leaf area (cm 150

100

0 Chevelle Cello Novico Andromeda Crocodile Sparrow Marabu Average

Figure 18. Leaf area at the first (a) and second (b) harvest for the seven cultivars and the average of all plants, under low and high nitrogen level for both depletion and Ingestad.

0.70 a 0.60 ) 1 - 0.50

0.40

0.30

0.20 Root shoot ratio (g g 0.10

0.00

Depletion low N Ingestad low N 0.80 b Depletion high N Ingestad high N 0.70 ) 1 - 0.60 0.50 0.40 0.30 0.20 Root shoot ratio (g g 0.10 0.00

Figure 19. Root shoot ratio at the first (a) and second (b) harvest for the seven cultivars and the average of all plants, under low and high nitrogen level for both depletion and Ingestad.

12 a

4 Dry matter shoot (%)

0 Chevelle Cello Novico Andromeda Crocodile Sparrow Marabu Average

Depletion low N Ingestad low N 20 Depletion high N Ingestad high N b 18 16 14 12 10 8 Dry matter (%) 6 4 2 0 Chevelle Cello Novico Andromeda Crocodile Sparrow Marabu Average

Figure 20. Dry matter percentage at the first (a) and second (b) harvest for the seven cultivars and the average of all plants, under low and high nitrogen level for both depletion and Ingestad.

450 a 400 )

1 350 - g 2 300

250

200

150

Specific leaf area (cm 100

0 Chevelle Cello Novico Andromeda Crocodile Sparrow Marabu Average

Depletion low N Ingestad low N 500 Depletion high N Ingestad high N b 450

) 400 1 - g

2 350 300 250 200 150

Specific leaf area (cm 100 50 0 Chevelle Cello Novico Andromeda Crocodile Sparrow Marabu Average

Figure 21. Specific leaf area at the first (a) and second (b) harvest for the seven cultivars and the average of all plants, under low and high nitrogen level for both depletion and Ingestad.

0.6 a

0.5

0.4

0.3

0.2 Root diameter (mm)

0.1

0 Chevelle Cello Novico Andromeda Crocodile Sparrow Marabu Average

0.5 Depletion low N Ingestad low N Depletion high N Ingestad high N b

0.4

0.3

0.2 Root diameter (mm) 0.1

0.0 Chevelle Cello Novico Andromeda Crocodile Sparrow Marabu Average

Figure 22. Root diameter at the first (a) and second (b) harvest for the seven cultivars and the average of all plants, under low and high nitrogen level for both depletion and Ingestad.

70 a 60

30 Root length (m) 20

0 Chevelle Cello Novico Andromeda Crocodile Sparrow Marabu Average

Depletion low N Ingestad low N 800 Depletion high N Ingestad high N b 700

600

500

400

300 Root length (m)

200

100

0 Chevelle Cello Novico Andromeda Crocodile Sparrow Marabu Average

Figure 23. Total root length at the first (a) and second (b) harvest for the seven cultivars and the average of all plants, under low and high nitrogen level for both depletion and Ingestad.

9.0 a 8.0

7.0 ) 2 6.0

5.0

4.0

3.0

Root surface area (cm 2.0

1.0

0.0 Chevelle Cello Novico Andromeda Crocodile Sparrow Marabu Average

Depletion low N Ingestad low N 70 Depletion high N Ingestad high N b 60 ) 2 50

20 Root surface area (cm

0 Chevelle Cello Novico Andromeda Crocodile Sparrow Marabu Average

Figure 24. Root surface area at the first (a) and second (b) harvest for the seven cultivars and the average of all plants, under low and high nitrogen level for both depletion and Ingestad.

Depletion low N Ingestad low N 300 Depletion high N Ingestad high N ) 1 - s

2 250 -

200

150

100

50 Stomatal conductance (mmol m

0 Chevelle Cello Novico Andromeda Crocodile Sparrow Marabu Average

Figure 25. Stomatal conductance at 24 DAP for the seven cultivars and the average of all plants, under low and high nitrogen level for both depletion and Ingestad.

Depletion low N Ingestad low N 6 Depletion high N Ingestad high N

3 Leaf number 2

0 Chevelle Cello Novico Andromeda Crocodile Sparrow Marabu Average

Figure 26. Leaf number at 24 DAP for the seven cultivars and the average of all plants, under low and high nitrogen level for both depletion and Ingestad.

35 a 30

10 Chlorophyll content (SPAD) 5

0 Chevelle Cello Novico Andromeda Crocodile Sparrow Marabu Average Depletion low N Ingestad low N 45 Depletion high N Ingestad high N b 40

Chlorophyll content (SPAD) 10

0 Chevelle Cello Novico Andromeda Crocodile Sparrow Marabu Average

Figure 27. Chlorophyll content of the first (a) and second (b) leaf at harvest for the seven cultivars and the average of all plants, under low and high nitrogen level for both depletion and Ingestad.

Appendix XVI. Correlations harvest 1

Table 24. Correlations at the first harvest between shoot fresh weight (SFW), root dry weight (RDW), shoot dry weight (SDW), total dry weight (TDW), leaf area (LA), root shoot ratio (R:S), dry matter percentage (DM%), specific leaf area (SLA), average diameter of the root (RD), total root length (RL) and root surface area (RSA) of plants fertilized according to a) the depletion or b) the Ingestad model. Correlations in white and grey refer to low and high nitrogen conditions, respectively. Correlations in black were significantly diferent from zero (p<0.05), in red not. a) SFW RDW SDW TDW LA R:S DM% SLA RD RL RSA SFW - 0.858 0.971 0.974 0.968 0.406 -0.344 -0.172 -0.704 0.939 0.923 RDW 0.855 - 0.837 0.894 0.879 0.779 -0.299 -0.015 -0.467 0.906 0.936 SDW 0.954 0.829 - 0.994 0.958 0.333 -0.128 -0.285 -0.650 0.914 0.910 TDW 0.960 0.906 0.988 - 0.968 0.435 -0.167 -0.236 -0.630 0.937 0.941 LA 0.975 0.832 0.944 0.946 - 0.457 -0.283 -0.030 -0.730 0.966 0.944 R:S 0.276 0.675 0.190 0.332 0.259 - -0.403 0.342 -0.121 0.544 0.590 DM% -0.297 -0.232 -0.022 -0.081 -0.245 -0.387 - -0.530 0.413 -0.298 -0.254 SLA 0.057 0.020 -0.152 -0.110 0.170 0.238 -0.684 - -0.109 -0.023 -0.070 RD -0.131 -0.070 -0.103 -0.098 -0.142 -0.106 0.138 -0.076 - -0.688 -0.587 RL 0.885 0.925 0.859 0.908 0.872 0.544 -0.242 0.033 -0.387 - 0.986 RSA 0.920 0.976 0.899 0.952 0.895 0.550 -0.235 -0.003 -0.113 0.956 - b) SFW RDW SDW TDW LA R:S DM% SLA RD RL RSA SFW - 0.891 0.988 0.985 0.963 -0.169 -0.616 0.165 -0.562 0.888 0.878 RDW 0.927 - 0.893 0.932 0.899 0.185 -0.611 0.258 -0.520 0.948 0.963 SDW 0.984 0.936 - 0.995 0.971 -0.218 -0.527 0.137 -0.580 0.891 0.884 TDW 0.980 0.969 0.994 - 0.973 -0.136 -0.554 0.166 -0.578 0.920 0.917 LA 0.977 0.919 0.980 0.975 - -0.128 -0.555 0.351 -0.585 0.896 0.888 R:S 0.489 0.706 0.459 0.542 0.477 - -0.423 0.387 0.258 0.064 0.115 DM% -0.481 -0.349 -0.346 -0.352 -0.411 -0.443 - -0.373 0.311 -0.565 -0.579 SLA 0.635 0.526 0.573 0.566 0.700 0.435 -0.731 - -0.307 0.253 0.238 RD -0.434 -0.431 -0.446 -0.447 -0.452 -0.321 0.444 -0.484 - -0.636 -0.560 RL 0.773 0.853 0.798 0.826 0.781 0.585 -0.326 0.435 -0.687 - 0.992 RSA 0.794 0.873 0.812 0.842 0.794 0.625 -0.358 0.449 -0.642 0.993 -