NATURAL VARIATION IN CAMELINA NITROGEN RESPONSES by Shreya Gautam A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Plant Science MONTANA STATE UNIVERSITY Bozeman, Montana July 2023 ©COPYRIGHT by Shreya Gautam 2023 All Rights Reserved ii DEDICATION I hereby dedicate my work to my family - my mother, father, and brother, whose unwavering love and support have been the foundation of my life. I would also like to express my gratitude to my advisor for his invaluable guidance and support throughout this journey. My friends Katie Sparks, Yi Zhou, and Maral Etesami have been a constant source of encouragement, and I dedicate this work to them as well. Their unwavering support and motivation have been a source of strength in my success, and for that, I am forever grateful. iii ACKNOWLEDGEMENTS I wish to express sincere gratitude to Dr. Chengci Chen, my advisor, for offering me the opportunity to work under his supervision. I am also appreciative of my committee members, Dr. Chaofu Lu and Dr. Jennifer Lachowiec, for providing invaluable mentorship and helping me improve my skills. I am thankful for my team, who generously offered their time, effort, patient guidance and constructive feedback throughout the project. I would like to express my appreciation to Dr. Charlie Lim, Calla Kowatch-Carlson, Thomas Gross, Dr. Maral Etesami, Dr. William Franck, Sooyoung Franck, and Amy Williams for their contributions to the fieldwork and data collection. Additionally, I am grateful to Dr. Jennifer Lachowiec and Dr. Fernando Correr for assisting me with the image analysis in the project. I would also like to acknowledge my lab mates, Yi Zhou and Marie Dorval, for their valuable input that significantly improved my presentation. I extend my sincere thanks to the Department of Energy grant program for their support of this project. Finally, I would like to thank everyone involved in the Enhancing Camelina oilseed project for their efforts in making the project a reality. iv TABLE OF CONTENTS 1. GENERAL INTRODUCTION ..................................................................................... 1 Camelina Production and Distribution.......................................................................... 3 Role of Nitrogen Fertilizer in Camelina ....................................................................... 6 Selection of Genotypes of Camelina............................................................................. 9 Relevance and Importance of the Research ................................................................ 10 Summary ..................................................................................................................... 10 References Cited ......................................................................................................... 12 2. SELECTION OF CAMELINA GENOTYPES WITH REMOTE SENSING DEVICES FOR HIGH PRODUCTIVITY ....................................................................................................... 21 Introduction ................................................................................................................. 21 Materials and Methods ................................................................................................ 24 Trial Site................................................................................................................ 24 Experimental Design ............................................................................................. 24 Data Collection ........................................................................................................... 25 Data Analysis .............................................................................................................. 26 Results ......................................................................................................................... 28 Conditions in Sidney 2021 and 2022 .................................................................... 28 Canopy Area ......................................................................................................... 29 Normalized Vegetative Index (NDVI).................................................................. 38 Discussion ................................................................................................................... 44 Identification of Lines that Have Robust Vegetative Growth .............................. 44 References Cited ......................................................................................................... 47 3. SELECTION OF CAMELINA LINES FOR HIGH NITROGEN USE EFFICIENCY ............................................................................... 52 Introduction ................................................................................................................. 52 Materials and Methods ................................................................................................ 55 Data Collection ........................................................................................................... 55 Data Analysis .............................................................................................................. 55 Results ......................................................................................................................... 56 Biomass ................................................................................................................. 56 Biomass and Canopy Imagery .............................................................................. 57 Nitrogen Use Efficiency (NUE) ............................................................................ 58 Discussion ................................................................................................................... 60 References Cited ......................................................................................................... 63 4. SUMMARY ................................................................................................................ 75 REFERENCES CITED ..................................................................................................... 77 v LIST OF TABLES Table Page Chapter 2 1. Table 2. 1 Weather conditions in Sidney, MT in year 2021 and 2022................................................................................................................................... 28 2. Table 2. 2 Available soil nitrogen after fertilization in the field in year 2021 and 2022. .......................................................................................................... 29 3. Table 2. 3. ANOVA table showing the effects of genotype, nitrogen, and their interaction on canopy area taken at three time points on camelina in Sidney for year 2021...................................................................... 29 4. Table 2. 4. Mean comparisons table showing the effects of nitrogen on canopy area (cm^2) taken at within each of three time periods on camelina in Sidney for year 2021. .................................................................................... 30 5. Table 2. 5. Top 30 genotypes with the largest canopy areas at times 1,2, and 3 in Sidney, during 2021 growing season. .......................................................... 31 6. Table 2. 6. ANOVA showing the effects of genotype, nitrogen and their interaction on canopy area growth rate between times 1 and 2 (Rate 1) and times 2 and 3 (Rate 2) in Sidney during 2021 growing season. ............................................................................................................................... 32 7. Table 2. 7. ANOVA showing the effects of genotype, nitrogen and their interaction on canopy area taken at two time periods on Camelina in Sidney for year 2022. ................................................................................... 35 8. Table 2. 8. Mean comparisons showing the effects of nitrogen on canopy area (cm2) within two time periods on camelina in Sidney for year 2022. .................................................................................................................... 35 9. Table 2. 9 Top 30 genotypes with the largest canopy areas at times 1, and 2 in Sidney, 2022.................................................................................................... 36 10. Table 2. 10. ANOVA showing the effects of genotype, nitrogen, and their interaction on growth rate of canopy area (cm2day-1) taken at two time periods in Sidney, 2022. ....................................................................... 37 11. Table 2. 11 .ANOVA showing the effects of genotype and nitrogen on Normalized Difference Vegetation Index (NDVI) taken at three time points in Sidney for year 2021. ................................................................................. 38 12. Table 2. 12. Top 30 genotypes with the highest NDVI values across three points (time 1, 2, and 3) in Sidney 2021. ...................................................... 39 vi LIST OF TABLES CONTINUED Table Page 13. Table 2. 13 Mean comparisons showing the effects of nitrogen on genotypes in NDVI taken at three time periods in Sidney for year 2021................................................................................................................................... 40 14. Table 2. 14 . ANOVA showing the effects of genotype and nitrogen on Normalized Difference Vegetation Index (NDVI) taken at three time points in Sidney for year 2022. .......................................................... 41 15. Table 2. 15. Top 30 genotypes with the highest NDVI values across three points (time 1, 2, and 3) in Sidney 2022. ...................................................... 42 16. Table 2. 16. Mean comparisons showing the effects of nitrogen on genotypes in NDVI taken at three time periods in Sidney for year 2022................................................................................................................................... 43 Chapter 3 17. Table 3. 1. ANOVA table showing the effects of year, genotype, nitrogen, and its interaction on biomass yield (grams) in Sidney on year 2021 and 2022. .......................................................................................................... 57 18. Table 3. 2. ANOVA table showing the effects of genotype on NUE (g plant-1 kgN-1 ) in Sidney for years 2021 and 2022. ...................................................... 58 19. Table 3. 3 Top 30 genotypes with the highest NUE(g plant-1kgN-1) in Sidney in 2021 and 2022. ............................................................................................. 59 vii LIST OF FIGURES Figure Page Chapter 1. 1. Figure 1. 1. (a) Camelina seedlings (5-leaf stage), (b) rossette stage of camelina after transplantation, (c) camelina at initiation of flowering, and (d) camelina during pod setting. ................................................................. 2 2. Figure 1. 2. Comparison of (a) camelina annual crop production (Eynck et al., 2021; Kon’kova et al., 2021; McVay & Lamb, 2008b), and (b) camelina seed yield (Adiele et al., 2021; Gesch et al., 2018; Kirkhus et al., 2013; Zanetti et al., 2017) ........................................................... 5 Chapter 2. 3. Figure 2. 1. Digital image analysis by ImageJ: (a) raw image capture; (b) ImageJ color threshold settings; (c)Image after cropping and applying color threshold; (d) green tissue in the image is selected; (e) green tissue is re-selected by “mask” function when analyzing the selected pixels. .................................................................... 27 4. Figure 2. 2 . Effect of genotype on canopy area in square centimeters of camelina taken one week after transplantation (time 1) in year 2021. ................................................................................................................. 30 5. Figure 2. 3. Effect of nitrogen on canopy area growth rate (cm2/day) of camelina taken between times 2 and 3 in year 2021.................................... 33 6. Figure 2. 4. Effect of genotype on canopy area growth rate (cm^2/day) of camelina taken between times 2 and 3 in year 2021. ................................. 34 7. Figure 2. 5. Effect of nitrogen on canopy area growth rate (cm2/day) of camelina taken in year 2022. ....................................................................... 37 8. Figure 2. 6. Effect of nitrogen on NDVI of camelina at three time periods after transplantation in year 2021, first week as NDVI 1, second week as NDVI 2 and third week as NDVI 3......................................................... 40 9. Figure 2. 7. Effect of nitrogen on NDVI of camelina at three time periods after transplantation in year 2022, first week as NDVI 1, second week as NDVI 2 and third week as NDVI 3......................................................... 44 10. Figure 2. 8. Images changing morphology and phenology of genotype CS070 in week 1 (06/02/2022) (a); week 2 (07/15/2022) (b); week 3 (07/22/2022) (c) ;and week 4 (07/29/2022) (d),in 2022................................................................................................................................... 46 viii LIST OF FIGURES CONTINUED Figure Page Chapter 3. 11. Figure 3. 1 Correlation of Canopy Area taken at different time points in year 2021 (time 1,2 and 3) and year 2022(time 1 and 2) with Biomass. .................................................................................................................... 58 12. Figure 3. 2 Mean NUE of camelina genotypes on NUE in year 2021................................................................................................................................... 60 13. Figure 3. 3 Mean NUE of camelina genotypes on NUE in year 2022................................................................................................................................... 60 ix ABSTRACT Camelina (Camelina sativa L.Crantz) is an oilseed crop with the potential to be planted for biofuel production. It is crucial to select camelina genotypes with higher nitrogen use efficiency (NUE) so that the superior cultivar has higher crop productivity. To select genotypes of camelina that exhibit higher biomass yield and nitrogen use efficiency, two field experiments were conducted in 2021 and 2022 in Sidney, MT with different nitrogen regimes, low (unfertilized) and high (fertilized). Distinct projects were carried out, one of them emphasizing canopy area and normalized difference vegetation index (NDVI), and the other focusing on biomass yield and NUE. The experiments highlighted the response of camelina to nitrogen application and the variation among genotypes. The study identified canopy image analysis effectively differentiated the canopy size and growth rate of camelina genotypes under two nitrogen regimes, demonstrating the influence of nitrogen on camelina growth. The NDVI measurement proved to be useful in evaluating plant health and greenness, offering a time-saving and efficient approach. Additionally, some of the genotypes were identified that exhibited high canopy area, NDVI, and nitrogen use efficiency in both 2021 and 2022, providing potential for enhancing crop productivity. This study reveals the potential to use canopy area, NDVI for biomass yield and nitrogen use efficiency screening in camelina. 1 CHAPTER ONE GENERAL INTRODUCTION Camelina (Camelina sativa L.) is an oilseed crop from the Brassicaceae family native to Eastern Europe and Western Asia (Vollmann & Eynck, 2015). Camelina was introduced to cultivation in North Africa (Tunisia), Australia (Tasmania, South Australia, Victoria, Western Australia), North America (USA, Canada), and South America (Argentina, Uruguay) (Sydor et al., 2022). Camelina was commonly grown in Europe until the Middle Ages, and for a long time camelina was known primarily in North America as a weed (false flax).Recently, camelina is recognized for its value as an oilseed crop, and its cultivation is increasing (Guy et al., 2014).Camelina is characterized as a short-season crop which requires 85 to 100 days to mature and has high adaptability to various climatic and soil conditions (Hunter & Roth, 2010). It is well adapted to production in the temperate climate zone (Hunter & Roth, 2010). Camelina is an annual plant with both spring and winter biotypes. The spring biotype is the most widespread globally and is grown as an early summer annual oilseed crop, but camelina can also be grown as a winter annual in milder climates(Hunter & Roth, 2010). Camelina attains heights of 0.3-0.9 m, has branched stems that become woody at maturity, and stems are generally smooth or only sparsely hairy near the base (Hitchcock & Cronquist, 2018). Leaves are arrow-shaped, sharp- pointed, 5 to 8.9 centimeters long with smooth edges. Camelina produces prolific small, pale yellow or greenish-yellow flowers with four petals. Seed are contained in pear shaped pods known as silicles resembling flax bolls and have a squared off tip (Klinkenberg, 2008). The 2 seeds have a rough surface and are small, with 1000- seed weight in the range of 0.8- 2.0 grams (Ehrensing & Guy). Figure 1. 1. (a) Camelina seedlings (5-leaf stage), (b) rossette stage of camelina after transplantation, (c) camelina at initiation of flowering, and (d) camelina during pod setting. Because of the oil content research is inclined towards the development of camelina as a crop. The oil content of the seed, on a dry weight basis, is typically between 30 and 40 percent, which contains about 64 percent polyunsaturated, 30 percent monounsaturated, and 6 percent saturated fatty acids (McVay & Lamb, 2008a). Camelina oil can be used in both edible and industrial products. Historically, the seeds of camelina were crushed and boiled to release oil for food, medicinal use, and lamp oil (Ehrensing & Guy, 2008). More recently camelina has been grown as a source of vegetable oil high in omega-3 fatty acids. Camelina has been marketed in Europe as salad dressing and as cooking oil and has been approved for use in cattle, chicken, and pig feed in USA (Ehrensing & Guy, 2008). It is also used in cosmetics, skincare products, soaps, 3 and soft detergents (Ehrensing & Guy, 2008). The oil has been used successfully as an adjuvant in agricultural spraying applications, and as biodiesel (Ehrensing & Guy, 2008). The oil content in camelina seeds are rich in ω-3 (α-linolenic acid ;C18:3 ω-3) and ω-6 acids (linoleic acid ;C18:2 ω-6), phytosterols, and phenolic compounds, which makes it attractive for the production of food and biofuels (Berti et al., 2016). These high levels of long-chain hydrocarbons in camelina oil are used for an aviation biofuel and have been reported to reduce CO2 emissions compared with traditional petroleum jet fuels (Belayneh et al., 2015; Kwiatek et al., 2021; Shonnard et al., 2010; Walia et al., 2018; Yang et al., 2016). This raw material made it attractive for the production of food and it’s potential to be planted for advanced biofuel production on marginal land in Northern Great Plains(NPG) and also as a rotation crop on fallow land (Shonnard et al., 2010). Camelina Production and Distribution The earliest discoveries of camelina as a plant was in Central Europe dated to 4000 BCE in Auvernier, Switzerland (Zohary & Hopf, 2000). Further discoveries in south-eastern Europe dated back to 1800–1200 BCE (Kroll, 1991), while camelina was also found in Scandinavia between 500 BCE and 1000 CE (Larsson, 2013). In southeast Europe and southwest Asia, it is believed to have originated as weed in flax and some grain crops (Haldane, 1990). In recent decades camelina has been grown widely since the interest in low-input oilseed crop elevated, with the majority of commercial production occurring in North America, Russia and Europe (Gugel & Falk, 2006; Guy et al., 2014; Shonnard et al., 2010; Zubr, 1997). In 2020, Canada cultivated approximately 4,050 hectares of camelina, predominantly in Saskatchewan (Eynck et al., 2021). The United States produced about 9,700 hectares of camelina in 2007, 4 primarily in Montana (McVay & Lamb, 2008b), which is similar to Europe's production of 10,000 hectares. In 2019, Russia had the largest cultivation of camelina, with an estimated 75,600 hectares (Kon’kova et al., 2021). Global camelina yields vary due to weather conditions, cultivars, and other parameters (Arshad et al., 2022). Yield of camelina seed in Canada is about 3 tons per hectare (Zanetti et al., 2017), while in the United States, it is 2.3 tons per hectare (Gesch et al., 2018). In Russia and Europe, camelina yields are approximately 0.69 tons per hectare and 3.3 tons per hectare, respectively (Arshad et al., 2022; Gesch et al., 2018; Kirkhus et al., 2013; Zanetti et al., 2017). The figure below shows the cultivation of camelina in different parts of the world (Figure.1.2 (a)), also illustrates the yield of camelina seed (Figure.1.2. (b)). 5 Figure 1. 2. Comparison of (a) camelina annual crop production (Eynck et al., 2021; Kon’kova et al., 2021; McVay & Lamb, 2008b), and (b) camelina seed yield (Adiele et al., 2021; Gesch et al., 2018; Kirkhus et al., 2013; Zanetti et al., 2017) In North America , the acreage expanded to 8100 hectares in the Northern Great Plains in 2011 (Nass, 2012). In Montana, there was no commercial production of camelina before 2004, and the production increased quickly to more than 20,000 hectares in 2007 and to around 30,000 ha in 2009 (Pilgeram, 2007).Similarly the grain yields of camelina was reported in different countries ranging from 1.65 to 3.58 t ha-1 in Austria (Vollmann et al., 1996; Vollmann et al., 2007) and from 2.87 to 3.64 t ha-1 in Denmark (Zubr, 1997). While in the United States, grain 6 yield was reported from 0.70 to 1.76 t ha-1 in Montana (McVay & Lamb, 2008a), from 0.66 to 1.87 t ha-1 in Rosemount, Minnesota (Putnam et al., 1993), from 0.79 to 2.20 t ha-1 in North Dakota, and about 1.10 t ha-1 in Arizona (French et al., 2009). Role of Nitrogen Fertilizer in Camelina Several authors have reported positive yield response to nitrogen (N) fertilizer application. The estimation of application dose is influenced by location, soil type and genotype (Malhi et al., 2014; Solis et al., 2013; Zubr & Matthäus, 2002).A prerequisite to maintaining high crop productivity under lower N fertilization input is to determine whether it is possible to select for genotypes that are adapted to low or high N fertilization, or that can perform well under both N fertilization conditions (Hirel et al., 2007). In oilseed production, nitrogen accounts for the largest energy input (Mohammed et al., 2017), reflecting the need to improve N use efficiency and minimize production costs (Chen et al., 2015; Gan et al., 2008). Compared with other biofuel crops such as rapeseed, camelina required less energy input in dryland farming systems (Keshavarz-Afshar et al., 2015). One study showed that N has the biggest share of production costs in camelina production when N was applied at a rate of 75 kg ha−1 (Chen et al., 2015). Nitrogen is essential for a crop’s metabolic activity and transformation of energy, and chlorophyll and protein synthesis. It also affects uptake of other essential nutrients and helps in the better partitioning of photosynthates to reproductive parts thereby increasing the seed: stover ratio(Singh & Meena, 2004). Camelina has been described as a crop with low capital expenditure, modest chemical inputs, and the ability to achieve moderate yields on less fertile soils (Solis et al., 2013). However, under N deficiency, camelina plants are thin and upright, and 7 the leaves are small and pale-yellow green. Ripening tends to be premature, and fewer pods and seed-bearing branches are developed (Agegenehu & Honermeier, 1997). Nitrogen application promoted the onset and development of yield components such as branches plant−1, pods plant−1, pods per unit area, seed weight plant−1, and seeds pod−1in camelina (Agegenehu & Honermeier, 1997; Stolarski et al., 2019). Camelina cultivar selection and applied N levels are important factors in obtaining optimum yield (Urbaniak et al., 2008). During the vegetative stage, the leaves represent a major nitrogen source and sink with the remobilization of nutrients from older to younger leaves or senescing leaves to reproductive tissues during bolting, flowering and seed fill (Jensen et al., 1996). As a result, N deficiency reduces plant growth by restricting leaf area development (Albert et al., 2012; Gammelvind et al., 1996), branching (Momoh et al., 2004) and dry matter accumulation. A positive linear relationship between seed yield and applied N rate up to 100 kg ha-1 was found by (Wysocki et al., 2013). However, significant variation in N requirement for camelina production have been documented under different environmental conditions in the world. Camelina has been shown to increase in seed yield with the application of 80 kg N ha−1 in Montana (McVay & Lamb, 2008a) .The optimum N input for camelina was found to be between 60 and 80 kg ha−1. Crowley and Fröhlich (1998) found that camelina yields peaked by using 75 kg ha−1 N in Ireland. Camelina seed yield was maximized at 100 kg ha−1 N in Europe (Zubr 1997) and 90 kg ha−1 N in US (Budin et al. 1995). Most recent research showed that optimum yields of camelina required relatively high N application. For example, the maximum seed yield of camelina was attained with the application of 185–300 kg ha−1 N in Chile (Solis et al. 2013). The optimum N rate for the highest yield ranged from 120 to 160 kg ha−1 N in eastern Canada (Jiang et al. 2013) and the maximum seed yield was achieved at a rate 8 of 170 kg ha−1 N on the Canadian prairies (Malhi et al. 2014). In general, there are many production in different areas of the world (Hirel et al., 2007).However, further research is needed to optimize the N fertilization rate and to increase N use efficiency ; moderate amount of fertilization was reasonable for efficient uptake of nitrogen (N) in spring camelina, but higher level of N do not always result in higher yield (Johnson et al., 2019). Brassicas are highly responsive to N application (Hocking et al., 1997); however, for optimizing seed yield they require relatively high rates of mineral N fertilizers (Malagoli et al., 2005). Managing N application for uptake and utilization efficiency requires an understanding of growth and resource allocation in response to N limitation. Brassicas have relatively high N uptake during vegetative growth until flowering followed by reduced N uptake during flowering and finally incomplete N translocation from the leaves and stems to the developing seeds (Wiesler et al., 2001). Although brassicas have a high capacity for N uptake, many species have a low nitrogen use efficiency and remobilization during the vegetative phase partly due to freeze- induced abscission of N-rich leaves during cold winter months; this is different for the spring and winter varieties, the spring varieties are susceptible to cold (Albert et al., 2012; Malagoli et al., 2005; Rossato et al., 2001). During the vegetative stage, the leaves represent a major nitrogen source and sink with the remobilization of nutrients from old to younger leaves or senescing leaves to reproductive tissues during bolting, flowering and seed fill (Jensen et al., 1996). In canola, nitrogen mobilized from leaves and stems contributes 70% of the total N required for seed filling with the remainder mobilized from other tissues ,22% from inflorescence and 8% from roots (Malagoli et al., 2005). Nitrogen uptake is usually greatest during the vegetative stage and declines at flowering and pod fill stages in canola (Rossato et al., 2001).Abiotic factors that 9 affect the uptake, assimilation and allocation capacity during the pre-bolting period will modulate the reproductive performance and seed yield of oilseed brassicas (Jackson, 2000; Malagoli et al., 2004; Malagoli et al., 2005). For example, removal of 50% of the leaves present at the end of the vegetative stage resulted in a 30% decrease in seed yield in canola (Noquet et al., 2004). Comparing camelina seed yields to those of other Brassicaceae oilseeds, including Brassica napus and B. juncea found in most cases that yields were comparable with fewer inputs (Robinson, 1987). Selection of Genotypes of Camelina Cultivars differ in their absorption and translocation of soil moisture, plant nutrients, photosynthates, and most importantly, interactions with environmental factors (Sintim et al., 2016). Both selection and breeding for better genotypes and transgenic techniques to develop varieties with changed gene expression have been used to increase the nitrogen use efficiency (NUE) in Brassica crops (Abberton et al., 2016; Chen et al., 2015; Fischer et al., 2013). It is important to establish its genetic potential and cultivation peculiarities for maximum yield considering the broad range of camelina uses (Bonjean A 1999). Brassica populations exhibit significant genetic variation of NUE (Ahmad et al., 2008). An evaluation of genotypic variation for plant traits contributing to N efficiency under low versus high N supply might be required to enable selection of genotypes showing optimal adaptation to the given growing conditions (Ceccarelli, 1994). Various parameters in plants affect the yield of the crops, such as flowers, vegetation indices (VIs), plant height, and canopy area (Bai et al., 2016; Sun et al., 2018; Tattaris et al., 2016; Thorp et al., 2016). The canopy area and NDVI (Normalized Difference Vegetative 10 Indices) vary with the condition and health of plant, under stress conditions, plant physiology and structural properties undergo complex changes, which in turn alter the reflectance spectra of leaves (Wahabzada et al., 2016). By applying appropriate spectral analysis, the differences in the reflectance signal can be used to characterize the plant’s physiological state and to assess plant genotype-specific responses to biotic and abiotic stresses (Wahabzada et al., 2015). The vegetation biomass directly or indirectly reflects crop vigor and photosynthetic capacity thus can be naturally used to indicate crop yield (Siegmann & Jarmer, 2015). Relevance and Importance of the Research My research aims to select camelina genotypes with high nitrogen use efficiency and yield. The growth and greenness; and biomass yield are examined to identify the desirable camelina lines. This research explores the image analysis to evaluate the growth and greenness of camelina population with wide genetic backgrounds. Comparing the plants grown under low and high doses of nitrogen fertilizers provides information on response of camelina genotypes to different nitrogen doses. The work should be beneficial in identifying camelina lines that are efficient in producing greater biomass yield and higher nitrogen use efficiency. Summary The research projects were carried out in years 2021 and 2022 in Sidney, Montana. The first project involved in remote sensing techniques, and the second project involved with measuring biomass yield and nitrogen use efficiency (NUE). The details of the studies are reported in this thesis as Chapter 2 and Chapter 3 respectively. 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Industrial crops and products, 15(2), 155-162. 21 CHAPTER TWO SELECTION OF CAMELINA GENOTYPES WITH REMOTE SENSING DEVICES FOR HIGH PRODUCTIVITY Introduction Phenotyping and quantifying nitrogen response can aid in the identification of target traits to screen for N use efficiency in N-limited conditions and serve as a precursor to breeding for those traits for improved oilseed productivity. Therefore, understanding the effects of N deprivation on growth, development and physiology of oilseed is essential for optimum N management and seed productivity (Seepaul et al., 2016). Plant breeders usually rely on traditional approaches when collecting phenotypic data, such as using a measurement stick to collect plant height data and visual ratings to evaluate flowering intensity, to collect phenotypic data to evaluate breeding lines. But these traditional phenotyping approaches are often low-throughput, labor-intensive, time-consuming, and sometimes subjective and/or destructive. These phenotyping bottlenecks have slowed the development of new cultivars (Furbank and Tester, 2011). Development of high-throughput phenotyping or phenomics technologies, using sensing and computer vison to collect data and evaluate plant traits qualitatively and quantitatively (Dhondt et al., 2013; Furbank and Tester, 2011), could alleviate these bottlenecks. Remote sensing (RS) can collect object or area related information from a distance without having direct physical contact (Shanmugapriya et al., 2019). Image-based phenotyping are usually connected to some greenness-related biophysical parameters in crop such as chlorophyll content, vegetation biomass and leaf area index, all of 22 which directly or indirectly reflect crop vigor and photosynthesis capacity thus can be naturally used to indicate crop health and yield (Siegmann & Jarmer, 2015). Electromagnetic radiation is a form of energy released and absorbed by charged particles, which has specific electrical and magnetic properties. The wavelength range corresponding to the electromagnetic radiation is termed the ‘electromagnetic spectrum.’ The human eye can only detect small portion of the spectrum. The interaction of electromagnetic spectrum with any material can be used in qualitative and quantitative analysis of various materials. The most commonly used quantitative index to assess the vegetation condition is the Normalized Difference Vegetation Index (NDVI) and was introduced by Rouse (Rouse et al., 1974). NDVI is defined by the reflectance of Red band (625 nm to 740 nm) and NIR band (750 nm to 1400 nm). The Red channel is the strong chlorophyll absorption region while NIR channel has high vegetation canopy reflectance in this area. The absorption and reflection vary with different depths through vegetation canopies. Hence, this index can be applied to classify the crop land cover and vigorousness (Zhang et al., 2017). The Green Seeker handheld is an instrument that directly provides the NDVI index, contributing to a fast and targeted diagnosis of nutritional and physiological state, the incidence of stress, and the potential yield of crops. Unlike aerial and satellite imagery, this system provides information obtained locally and quickly by terrestrial determinations. Despite the great interest in the implementation of image-based methods in agriculture, research is still in its early stages for applying the technology in alternative oil seed crops such as camelina (ANGELOPOULOU et al., 2020). Prediction of yield in oilseed rape is quite challenging, since oilseed rape has distinct developmental stages (e.g., seedling, bolting, 23 flowering, pod formation) that are very spectrally different (Domínguez et al., 2015) thus increasing uncertainties of yield prediction by spectral indices. Many studies showed that the presence of yellow flowers on top of the canopy in oilseed rape caused a decline in the relationship between rape spectra and biophysical parameters (Behrens et al., 2006; Fang et al., 2016) and timing to acquire spectral data for yield prediction in oilseed rape is important to achieve high accuracy (Piekarczyk et al., 2011). Digital image analysis is also widely used to determine the morphometric parameters of plants. This technique supports the identification and discrimination of various taxa (Cope et al., 2012). The shapes of leaves, petals and whole plants are of great significance to plant science, as they can help to distinguish between different species, to determine plant health, and even to model climate change (Cope et al., 2012). The plant phenomics technologies are used in many studies to evaluate plant traits in field conditions, such as early vigor (Kipp et al., 2014; Sankaran et al., 2015; Sankaran et al., 2018), canopy area and temperature (Bai et al., 2016; Patrignani & Ochsner, 2015), plant height (Madec et al., 2017; Wang et al., 2018), heading and flower intensity (Sadeghi-Tehran et al., 2017; Zhang et al., 2020), yield (Donohue et al., 2018; Lai et al., 2018), and phenological stages (Yang et al., 2017). Different image-based plant traits, such as flowers, vegetation indices (VIs), plant height, and canopy area (Bai et al., 2016; Sun et al., 2018; Tattaris et al., 2016; Thorp et al., 2016), have been used to monitor and predict crop yield. Among these plant traits, yield and agronomic traits are of great importance to agronomists, plant breeders and physiologists. The objectives of this study were to investigate if 1) NDVI and 2) canopy area images can be used for phenotyping and identifying camelina genotypes for high productivity. 24 Materials and Methods Trial Site The experiments were carried out at the Eastern Agricultural Research Center (EARC), irrigated farm (47◦73′ N, 104◦15′ W; 594 m asl), near Sidney, Montana, in 2021 and 2022. The soil is Savage clay loam (fine, smectitic, frigid Vertic Argiustolls) with less than 3% organic matter (OM) and pH of 7.8. Experimental Design The 212 accessions from the germplasm collection of camelina from Montana State University (MSU) and seven spring camelina cultivars (Ligena, Soshone, Calena, Licalla, Pronghorn, Suneson, and Blainecreek) in 2021 and five cultivars (Ligena, Soshone, Licalla, Pronghorn, Suneson, and Suneson) in 2022 were planted as single-plant trail in Split-plot Randomized Complete Block design with six replications. The main plots are nitrogen doses, and the split plots are accessions. Camelina seeds were sown into paper pots in the greenhouse on April 22nd, 2021 and June 7th , 2022. After sowing, all the seeds were thinly covered with potting mix soil and then soaked in water. The camelina emerged three days after planting. More than 95% emergence was observed on April 26th, 2021, and on June 10th, 2022.Right after the emergence, thinning was carried out to thin the plants down to one seedling per paper pot on April 28th, 2021, and June 15th ,2022. The seedlings were fertilized with 1 tablespoon of 20-20-20 (N-P-K) per gallon of water ten days after emergence. A week before transplanting, seedlings were hardened by taking them out of the greenhouse and into the open outside environment. Before the field preparation 25 for transplantation, soil samples were taken for the initial soil residual nutrients determination. Four composite soil samples, which is four soil cores per composite, were taken across the whole field at 0-6-, 6-12-, 12-24-, and 24–36-inch depths. The field was divided into low and high N blocks. Urea fertilizer was applied at a target rate of 100 kg N per acre or 223 lbs urea per acre on May 10th, 2021 and on June 24th ,2022 to the high N block, and no nitrogen fertilizer was applied to the low N block. For field preparation, soil was roto-tilled at least twice to make a uniform and loose seedbed. The TerraTek single row transplanter was used to carry out transplantation. The seedlings were transplanted into the field on May 12th, 2021, and on June 27th ,2022. Each transplanted seedling was inspected to ensure that the roots were covered with soil. Single camelina plants were planted at a foot apart and three feet between the rows. Sprinkler irrigation of half an inch was applied the day after transplantation. Data Collection The data collection included NDVI and canopy RGB images. NDVI value provides the information on the leaf area and leaf chlorophyll content which relates to grain yield (Labus et al., 2002). An NDVI plant health rating between 0 and 0.33 indicates unhealthy or stressed plant material, 0.33 to 0.66 moderately healthy, and 0.66 to 1 very healthy. These numbers are just rules of thumb and vary based on type of plant and other conditions. For NDVI, a handheld device Green Seeker was used to collect readings once every week after a week of transplanting for three weeks until flowering, which gives us the NDVI readings between 0-1. The NDVI device was held 0-1 cm over the canopy area while collecting the data. The NDVI- value is computed as: 26 (NIR – Red) / (NIR + Red) For canopy images, RGB images of the camelina single plants were taken using a RGB camera, (Canon DS126311 and Nikon D7100). A picture frame of 1 ft2 quadrat was prepared. It was placed as a base frame while taking pictures of camelina plant at the center. The frame was used as a reference to extract the green area to measure change in canopy growth over time. In 2022, the camera was placed 1m distance from the ground. This non-destructive method allowed us to monitor the progression of growth in individual plant to evaluate the plant. The pictures were taken after transplanting for three weeks until flowering, when the canopy areas become dense and plants are overlapping each other, and the frame is not visible when taking pictures. Data Analysis The Image Analysis was done with the software Image J for the images of 2021 and Plant Computer Vision (PlantCV) was used for image analysis in 2022. ImageJ is a freely available (https://imagej.nih.gov/ij/) Digital Image Analysis (DIA) program that was developed originally for medical research (Schindelin et al., 2015). The canopy images were transferred from the camera to computer folders and were processed individually by using ImageJ (Version 1.54c). The images are cropped after taking the measurements from the frame by: “select Analyze > Set Scale”, which helps setting the scale in the centimeter’s metric. The color threshold is then adjusted following the ImageJ user Guide (Ferreira & Rasband, 2012). The selected image particles were reselected and analyzed by the following operations: select “Analyze > Analyze Particles. The results were transferred to a Microsoft Excel worksheet (.xlsx format) for future analysis. 27 Figure 2. 1. Digital image analysis by ImageJ: (a) raw image capture; (b) ImageJ color threshold settings; (c)Image after cropping and applying color threshold; (d) green tissue in the image is selected; (e) green tissue is re-selected by “mask” function when analyzing the selected pixels. Images from 2022 were processed using PlantCV an open-source, open-development suite of analysis tools capable of analyzing high-throughput image-based phenotyping data (Ferreira & Rasband, 2012). The pipeline is created in such a way that it works for all the images taken at a time-period in a batch. The results are received in .csv format in pixels. The reading for NDVI was taken through Greenseeker and the readings were saved in .txt file and later transferred to a Microsoft Excel worksheet (.xlsx format) for future analysis. Analysis of variance (ANOVA) was performed on canopy area and NDVI for data of 2021 and 2022. The effects were considered statistically significant at P < 0.05. Tukey’s HSD test was conducted for mean comparisons with 5% significance levels. All statistical analysis were performed using R version 4.2.3 (R core team, 2021). Data were fitted to a linear model to 28 test for normality. The canopy area data were not normally distributed and, therefore, square root transformed for the data. The 212 genotypes of camelina and spring varieties and nitrogen were explanatory variables and canopy area and NDVI were taken as response variables. Data were analyzed separately by year due to different configurations among them. Results Conditions in Sidney 2021 and 2022 The weather conditions of Sidney were slightly different in year 2021 and 2022 planting seasons, recording different temperatures, wind speed and rainfall (Table 2.1). The available nitrogen in the soil before the application of the nitrogen also varied in year 2021 and 2011 (Table 2.2). Table 2. 1 Weather conditions in Sidney, MT in year 2021 and 2022. Year 2021 Avg Max Min Avg Bare Avg Turf Wind Total Temp Temp Soil Temp Soil Temp Speed Rainfall Degrees Degrees Month C C Degrees C Degrees C Kmph mm May 18.79 4.52 12.54 9.63 13.10 41.66 June 28.50 11.91 20.32 16.27 11.02 52.60 July 32.40 15.76 24.56 21.01 7.82 0.51 August 28.21 11.89 21.14 21.45 9.89 27.18 Year 2022 June 24.28 10.75 17.22 17.42 12.50 67.97 July 29.21 14.03 23.73 21.55 8.30 45.57 August 30.57 13.73 23.76 19.59 8.78 3.63 29 Table 2. 2 Available soil nitrogen after fertilization in the field in year 2021 and 2022. Available soil nitrogen after fertilization (lbs/Acre) Year 2021 2022 Low N 34.8 31 High N 134.8 131 Canopy Area The canopy areas were assessed at three times at the early growing seasons each year. They were analyzed separately by year. Year 2021:The analysis of variance (ANOVA) results showed that canopy area of camelina was significantly affected by genotype and nitrogen in all three time points of year 2021 (Table 2.3). The mean canopy area of the genotypes was higher when nitrogen was applied than no additional supply of nitrogen (Table 2.4). We did not detect an interaction between nitrogen and genotype. Table 2. 3. ANOVA table showing the effects of genotype, nitrogen, and their interaction on canopy area taken at three time points on camelina in Sidney for year 2021. 2021Canopy 2021Canopy 2021Canopy Source of Variance Df Area(time1) Area(time2) Area(time3) 05/25/2021 06/01/2021 06/07/2021 P>F P>F P>F Genotype 215 0.0001 0.0116 0.0001 Nitrogen 1 <0. 0001 0. 0001 <0. 0001 Genotype x 0.9387 215 0.7588 Nitrogen 0.9977 30 Table 2. 4. Mean comparisons table showing the effects of nitrogen on canopy area (cm^2) taken at within each of three time periods on camelina in Sidney for year 2021. Nitrogen 2021Canopy Area 2021Canopy Area 2021Canopy Area (time1) (time2) (time3) High 4.329 a 6.490 a 9.968 a Low 3.243 b 5.901 b 8.282 b The mean canopy area varied by genotypes. Ligena, CS210 and CS023 had the highest mean canopy area at time 1, time 2 and time 3 respectively. The figure below represents the canopy area at time 1, which is a week after the transplantation (Figure 2.2). The results of differences in mean canopy area at high and low N regimes are shown in Table 2.5, where top 30 genotypes with higher canopy area were listed from all three time periods. Figure 2. 2 . Effect of genotype on canopy area in square centimeters of camelina taken one week after transplantation (time 1) in year 2021. 31 Table 2. 5. Top 30 genotypes with the largest canopy areas at times 1,2, and 3 in Sidney, during 2021 growing season. Time 1 Time 2 Time 3 Canopy Canopy Canopy # Genotypes # Genotypes # Genotypes area area area 1 Ligena 22.318 1 CS210 58.963 1 CS023 128.420 2 CS065 20.868 2 CS018 57.744 2 CS217 118.550 3 CS210 20.708 3 CS024 56.931 3 CS050 115.632 4 CS024 20.545 4 CS222 55.581 4 CS133 115.289 5 CS246 20.243 5 CS173 55.526 5 CS009 115.118 6 CS050 19.846 6 CS037 55.441 6 CS204 114.328 7 CS138 19.829 7 CS030 54.357 7 CS098 114.249 8 CS139 19.665 8 CS114 54.311 8 CS249 113.810 9 CS077 18.980 9 CS007 53.835 9 CS065 112.809 10 CS190 18.594 10 CS077 53.499 10 CS114 112.521 11 CS235 18.576 11 CS008 53.269 11 CS220 112.305 12 CS044 18.553 12 CS226 52.609 12 CS147 112.141 13 CS220 18.548 13 CS132 52.585 13 CS007 111.969 14 CS049 18.451 14 CS128 52.276 14 CS101 110.650 15 CS108 18.374 15 CS217 51.752 15 CS146 110.411 16 CS063 18.370 16 CS009 51.429 16 CS201 110.077 17 CS223 18.343 17 CS201 51.377 17 CS033 109.877 18 CS007 18.308 18 CS043 51.127 18 CS192 109.664 19 CS098 18.133 19 CS246 50.927 19 CS142 109.583 20 CS226 18.037 20 CS143 50.733 20 CS246 109.142 21 CS123 18.003 21 CS213 50.577 21 CS248 108.790 22 CS132 17.928 22 CS096 49.855 22 CS143 108.490 23 CS003 17.877 23 CS035 49.838 23 CS128 108.232 24 CS101 17.863 24 CS233 49.736 24 CS228 107.617 25 CS228 17.857 25 CS230 49.717 25 CS116 107.524 26 CS202 17.811 26 CS058 49.607 26 CS188 107.399 27 CS136 17.724 27 CS151 49.603 27 CS093 107.104 28 CS008 17.703 28 CS220 49.574 28 CS027 107.034 29 CS159 17.622 29 CS050 49.384 29 CS071 106.874 30 CS151 17.593 30 CS019 49.299 30 CS223 106.516 32 Growth rates were calculated as the difference of canopy areas taken at different time points divided by the time lapse. The ANOVA results showed that growth rate of camelina was significantly affected by genotype and nitrogen between the 2nd and 3rd time points of year 2021 (Table 2.6). Table 2. 6. ANOVA showing the effects of genotype, nitrogen and their interaction on canopy area growth rate between times 1 and 2 (Rate 1) and times 2 and 3 (Rate 2) in Sidney during 2021 growing season. 2021Canopy Area Growth 2021Canopy Area Growth Source of Variance Df Rate 1 Rate 2 P>F P>F Genotype 215 0.5191 0.0001 Nitrogen 1 0.1630 <0. 0001 Genotype x Nitrogen 215 0.9372 0.9910 The figure 2.3 below represents the mean canopy area growth rate at high and low N regimes taken between times 2 and 3, where the growth of canopy area is higher with high nitrogen and lower with low nitrogen. Figure 2.4 represents the variation of growth rate of 30 genotypes with larger canopy area growth rate between time 2nd and 3rd periods. 33 Figure 2. 3. Effect of nitrogen on canopy area growth rate (cm2/day) of camelina taken between times 2 and 3 in year 2021. 34 Figure 2. 4. Effect of genotype on canopy area growth rate (cm2/day) of camelina taken between times 2 and 3 in year 2021. Year 2022:The ANOVA results showed that canopy area of camelina was significantly affected by genotype and nitrogen in both time periods of year 2022 (Table 2.7). The mean canopy area of the genotypes was higher when nitrogen was applied than when there was no nitrogen application (Table 2.8). The mean canopy area varies with the genotypes, genotype CS144 (20.4755) and CS158 (69.173) had highest mean canopy area in time 1 and time 2 respectively (Table 2.9). 35 Table 2. 7. ANOVA showing the effects of genotype, nitrogen and their interaction on canopy area taken at two time periods on Camelina in Sidney for year 2022. 2022Canopy Area(time1) 2022Canopy Area(time2) Source of Variance Df 06/02/2022 07/15/2022 P>F P>F Genotype 215 0.0002 0.0060 Nitrogen 1 <0.0001 0.0001 Genotype x Nitrogen 215 0.7782 0.8380 Table 2. 8. Mean comparisons showing the effects of nitrogen on canopy area (cm2) within two time periods on camelina in Sidney for year 2022. Nitrogen 2022Canopy Area(time1) 2022Canopy Area(time2) 06/02/2022 07/15/2022 High 14.09 a 55.26 a Low 5.81 b 37.20 b 36 Table 2. 9 Top 30 genotypes with the largest canopy areas at times 1, and 2 in Sidney, 2022. 2022 Canopy Area taken at different times in cm2 Time 1 Time 2 # Genotypes Mean # Genotypes Mean 1 CS144 20.476 1 CS158 69.173 2 CS220 18.229 2 CS157 67.225 3 CS166 18.138 3 CS144 67.128 4 CS075 17.063 4 CS166 65.160 5 CS087 16.488 5 CS050 63.658 6 CS024 16.446 6 CS067 63.018 7 CS109 16.182 7 CS192 58.983 8 CS215 16.030 8 CS024 56.770 9 CS223 15.465 9 CS132 54.207 10 CS077 15.246 10 Shoshone 53.529 11 CS050 15.011 11 CS220 52.533 12 CS116 14.699 12 CS094 52.042 13 CS182 14.693 13 CS202 50.613 14 CS222 14.676 14 CS116 50.517 15 CS203 14.315 15 CS075 49.553 16 CS094 14.171 16 CS143 49.412 17 CS230 13.983 17 CS042 48.741 18 CS201 13.733 18 CS230 48.252 19 CS105 13.630 19 CS049 47.353 20 CS254 13.454 20 CS053 46.265 21 CS158 13.255 21 CS223 46.154 22 CS211 13.153 22 CS215 45.200 23 Suneson 13.142 23 CS218 45.157 24 CS157 12.990 24 CS055 44.331 25 CS092 12.964 25 CS226 44.224 26 CS131 12.831 26 CS108 43.758 27 CS034 12.734 27 CS102 43.633 28 CS066 12.700 28 CS137 42.589 29 CS053 12.555 29 CS188 42.303 30 CS086 12.512 30 CS105 42.094 37 Table 2. 10. ANOVA showing the effects of genotype, nitrogen, and their interaction on growth rate of canopy area (cm2day-1) taken at two time periods in Sidney, 2022. Source of Variance Df 2022 Canopy Area Growth Rate 1 P>F Genotype 215 0.0001 Nitrogen 1 0.0001 Genotype x Nitrogen 215 0.3069 Figure 2.5 represents the mean canopy area growth rate taken between times 1 and 2, where the growth of canopy area is higher with high nitrogen and lower with low nitrogen (Table 2.8). Figure 2. 5. Effect of nitrogen on canopy area growth rate (cm2/day) of camelina taken in year 2022. 38 Normalized Vegetative Index (NDVI) The canopy of the plants was assessed also by NDVI for each year 2021 and 2022. The data were analyzed separately by year. The NDVI readings were taken three times after transplantation, once every week, for three weeks. Year 2021:The ANOVA results (Table 2.11) show that NDVI is highly significantly affected by the genotypes in all three time periods. NDVI is significant with nitrogen during first and second period. There is no significance with NDVI and interaction between genotype and nitrogen. Table 2. 11 .ANOVA showing the effects of genotype and nitrogen on Normalized Difference Vegetation Index (NDVI) taken at three time points in Sidney for year 2021. 2021 NDVI (time1) 2021 NDVI (time2) 2021 NDVI (time3) Source of Variance Df 06/15/2021 06/24/2021 06/28/2021 P>F P>F P>F Genotype 215 0. 0001 0. 0001 0. 0001 Nitrogen 1 0.0135 0.0006 0.1040 Genotype x 215 Nitrogen 0.4439 0.5263 0.3773 39 Table 2. 12. Top 30 genotypes with the highest NDVI values across three points (time 1, 2, and 3) in Sidney 2021. 2021 NDVI # Genotypes NDVI 1 # Genotypes NDVI 2 # Genotypes NDVI 3 1 CS133 0.918 1 CS254 0.975 1 CS133 0.942 2 CS202 0.915 2 CS023 0.947 2 CS245 0.930 3 CS019 0.910 3 CS220 0.940 3 CS229 0.903 4 CS008 0.906 4 CS019 0.933 4 CS023 0.897 5 CS030 0.905 5 CS215 0.932 5 CS101 0.897 6 CS223 0.904 6 CS201 0.926 6 CS030 0.896 7 CS042 0.904 7 CS036 0.925 7 CS201 0.879 8 CS044 0.901 8 CS121 0.924 8 CS071 0.872 9 CS235 0.900 9 CS033 0.919 9 CS210 0.870 10 CS087 0.900 10 CS101 0.918 10 CS202 0.866 11 CS003 0.899 11 CS142 0.915 11 CS248 0.857 12 CS016 0.895 12 CS024 0.908 12 CS003 0.855 13 CS204 0.894 13 CS015 0.905 13 CS215 0.855 14 CS004 0.889 14 CS075 0.904 14 CS220 0.853 15 CS018 0.887 15 CS202 0.902 15 CS122 0.850 16 CS142 0.884 16 CS210 0.901 16 CS235 0.847 17 CS137 0.882 17 CS166 0.891 17 CS193 0.844 18 CS217 0.879 18 CS077 0.888 18 CS230 0.843 19 CS220 0.878 19 CS037 0.886 19 CS144 0.841 20 CS190 0.876 20 CS003 0.882 20 CS254 0.839 21 CS131 0.871 21 CS230 0.876 21 CS024 0.838 22 CS116 0.870 22 CS071 0.862 22 CS025 0.830 23 CS056 0.869 23 CS217 0.860 23 CS033 0.826 24 CS051 0.869 24 CS030 0.859 24 CS162 0.824 25 CS216 0.868 25 CS188 0.854 25 CS222 0.817 26 CS143 0.868 26 CS122 0.853 26 CS166 0.817 27 CS236 0.867 27 CS050 0.852 27 CS116 0.808 28 CS170 0.867 28 CS236 0.844 28 CS027 0.805 29 CS037 0.866 29 CS096 0.842 29 CS236 0.804 30 CS039 0.866 30 CS126 0.839 30 CS019 0.803 40 Table 2. 13 Mean comparisons showing the effects of nitrogen on genotypes in NDVI taken at three time periods in Sidney for year 2021. Nitrogen 2021 NDVI (time1) 2021 NDVI (time2) 2021 NDVI (time3) 06/15/2021 06/24/2021 06/28/2021 Low 0.793 a 0.733 a 0.668 a High 0.772 b 0.698 b 0.652 a The mean of NDVI readings had a trend of decreasing with the time after transplanting (NDVI1 vs. NDVI2 vs. NDVI3; Figure 2.6; Table 2.13). The mean NDVI of camelina planted at low nitrogen regimes was slightly higher than with nitrogen supply (Table 2.13). Figure 2. 6. Effect of nitrogen on NDVI of camelina at three time periods after transplantation in year 2021, first week as NDVI 1, second week as NDVI 2 and third week as NDVI 3. Year 2022:The ANOVA results (Table 2.14) shows that NDVI is significantly affected by the genotypes in all second and third time periods. NDVI is significant with nitrogen during 41 first and third period. There is no significance with NDVI and interaction between genotype and nitrogen. Table 2. 14 . ANOVA showing the effects of genotype and nitrogen on Normalized Difference Vegetation Index (NDVI) taken at three time points in Sidney for year 2022. Source of 2022 NDVI (time1) 2022 NDVI (time2) 2022 NDVI (time3) Df Variance 07/21/2022 07/28/2022 08/04/2022 P>F P>F P>F Genotype 215 0.7094 0.0057 0.0826 Nitrogen 1 0. 0001 0.6204 0. 0001 Genotype x 215 Nitrogen 0.5932 0.8840 0.9060 42 Table 2. 15. Top 30 genotypes with the highest NDVI values across three points (time 1, 2, and 3) in Sidney 2022. 2022 NDVI # Genotypes NDVI 1 # Genotypes NDVI 2 # Genotypes NDVI 3 1 CS009 0.786 1 CS050 0.892 1 CS202 0.755 2 CS250 0.774 2 CS164 0.845 2 CS245 0.745 3 CS052 0.767 3 CS182 0.831 3 CS126 0.743 4 CS033 0.743 4 CS133 0.827 4 CS085 0.739 5 CS084 0.740 5 CS228 0.806 5 CS137 0.734 6 CS122 0.738 6 CS062 0.803 6 CS220 0.723 7 CS094 0.736 7 CS019 0.802 7 CS101 0.707 8 CS050 0.732 8 CS047 0.802 8 CS253 0.705 9 CS137 0.725 9 CS030 0.797 9 CS230 0.700 10 CS024 0.725 10 CS077 0.793 10 CS254 0.698 11 CS129 0.722 11 CS116 0.790 11 CS102 0.697 12 CS163 0.717 12 CS220 0.790 12 CS175 0.694 13 CS144 0.716 13 CS109 0.784 13 CS067 0.694 14 CS146 0.713 14 CS254 0.784 14 CS142 0.693 15 CS030 0.705 15 CS071 0.782 15 CS133 0.686 16 CS081 0.697 16 CS132 0.780 16 CS112 0.681 17 CS218 0.693 17 CS137 0.775 17 CS030 0.676 18 CS119 0.692 18 CS084 0.768 18 CS051 0.668 19 CS027 0.689 19 CS230 0.762 19 CS165 0.668 20 CS028 0.686 20 CS009 0.758 20 CS076 0.667 21 CS046 0.685 21 CS049 0.755 21 CS008 0.662 22 CS102 0.684 22 CS175 0.754 22 CS105 0.647 23 CS116 0.681 23 CS115 0.745 23 CS025 0.645 24 CS196 0.679 24 CS039 0.743 24 CS222 0.642 25 Ligena 0.676 25 CS087 0.739 25 CS055 0.640 26 CS139 0.676 26 CS085 0.736 26 CS044 0.638 27 CS191 0.670 27 CS245 0.734 27 CS218 0.637 28 CS051 0.667 28 CS159 0.734 28 CS071 0.637 29 CS214 0.664 29 CS188 0.727 29 CS223 0.637 30 CS121 0.663 30 CS064 0.726 30 CS210 0.637 43 Table 2. 16. Mean comparisons showing the effects of nitrogen on genotypes in NDVI taken at three time periods in Sidney for year 2022. Nitrogen 2022 NDVI (time1) 2022 NDVI (time2) 2022 NDVI (time3) 07/21/2022 07/28/2022 08/04/2022 Low 0.631 a 0.606 a 0.557 a High 0.506 b 0.600 a 0.496 b The mean NDVI reading was higher for high N than low N at one week after transplantation (time1), but it was higher for low N (time2) than high N at second week after transplantation (Table 2.16; Figure 2.7).The mean NDVI of camelina planted at low nitrogen regimes was slightly higher than that of those provided with nitrogen, except at second week after transplantation where mean NDVI of low N was similar to that of higher N. 44 Figure 2. 7. Effect of nitrogen on NDVI of camelina at three time periods after transplantation in year 2022, first week as NDVI 1, second week as NDVI 2 and third week as NDVI 3. Discussion Identification of Lines that Have Robust Vegetative Growth The canopy area significantly increased by nitrogen applied in the field in both years 2021 and 2022. The growth rate calculated using image analysis were also significant with the nitrogen applications in both years. Therefore, it is crucial to consider the effect of nitrogen on growth of camelina during early season as it is essential for optimum N management and seed productivity (Seepaul et al., 2016). Also, canopy areas and growth rates measured were significantly different among genotypes in 2021 and 2022, resembling Brassica populations that exhibit significant genetic variation (Ahmad et al., 2008). However, the interaction between genotype and nitrogen was not seen in camelina. Similarly, the NDVI measurements, which help classify the crop cover and vigorousness (Zhang et al., 2017), suggest that nitrogen had significant effects on camelina during vegetative 45 state before flowering in 2021 and 2022. The NDVI, interpretation can aid in quickly and accurately diagnosing the nutritional and physiological state, stress levels, and potential yield of crops (Gutiérrez-Soto et al., 2011). As indicated by Labus et al. (2002), NDVI value provides the information on the leaf area and chlorophyll content which may relate to grain yield. The presence of yellow flowers in a vegetation canopy can lead to a decrease in NDVI values caused by red light (yellow = green + red) (Behrens et al., 2006; Piekarczyk et al., 2011; Shen et al., 2009). In this study, the NDVI seems to decrease later in the vegetative stage, which corresponds to the beginning of flowering stage. In research performed in canola, the NDVI data acquired between the six-leaf stage and the beginning of flowering were correlated to canola seed yield (R2 = 0.35; p < 0.001) (Holzapfel et al., 2007). Here, several genotypes were selected based on high canopy area and NDVI because they are reflective of health ,vigor and greenness of the plant thus can be used to indicate crop yield (Siegmann & Jarmer, 2015). The few genotypes were selected resulting high canopy area and NDVI. For elucidation of plant N response across the entire vegetation, destructive measurements are not appropriate. Instead, non-destructive high-throughput techniques, for example by unmanned aerial vehicles, can be a feasible option, and these initial image-based findings provide a foundation for those future efforts. Those data, coupled with elucidation of genome diversity (Voss‐Fels & Snowdon, 2016). The few genotypes were selected resulting high canopy area and NDVI. In this study, there were differences in the genotype ranking based on canopy area and NDVI at different measuring time points, which was likely caused by the differences in morphology and phenology of different biotypes of camelina. 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Frontiers in Plant Science, 7, 1131. Thorp, K., Wang, G., Badaruddin, M., & Bronson, K. (2016). Lesquerella seed yield estimation using color image segmentation to track flowering dynamics in response to variable water and nitrogen management. Industrial crops and products, 86, 186-195. Urbaniak, S., Caldwell, C., Zheljazkov, V., Lada, R., & Luan, L. (2008). The effect of cultivar and applied nitrogen on the performance of Camelina sativa L. in the Maritime Provinces of Canada. Canadian journal of plant science, 88(1), 111-119. Wang, X., Singh, D., Marla, S., Morris, G., & Poland, J. (2018). Field-based high-throughput phenotyping of plant height in sorghum using different sensing technologies. Plant Methods, 14(1), 1-16. Wiesler, F., Behrens, T., & Horst, W. (2001). Nitrogen efficiency of contrasting rape ideotypes. Plant nutrition: food security and sustainability of agro-ecosystems through basic and applied research, 60-61. 51 Yang, Z., Shao, Y., Li, K., Liu, Q., Liu, L., & Brisco, B. (2017). An improved scheme for rice phenology estimation based on time-series multispectral HJ-1A/B and polarimetric RADARSAT-2 data. Remote Sensing of Environment, 195, 184-201. Zhang, C., Craine, W. A., McGee, R. J., Vandemark, G. J., Davis, J. B., Brown, J., Hulbert, S. H., & Sankaran, S. (2020). Image-based phenotyping of flowering intensity in cool- season crops. Sensors, 20(5), 1450. 52 CHAPTER THREE SELECTION OF CAMELINA LINES FOR HIGH NITROGEN USE EFFICIENCY Introduction Although camelina is a low-input new oilseed crop, it responds well to fertilization (Putnam et al., 1993). Various researchers have observed the effect of applied nitrogen (N) on yield, protein and oil content (Gugel & Falk, 2006; Jiang et al., 2013; Johnson & Gesch, 2013; Kirkhus et al., 2013; Lošák et al., 2011; Urbaniak et al., 2008). While focusing on maximizing seed and oil yield response, most studies lack information on balancing agronomic performance against the environmental risk associated with nitrogen fertilization, e.g., nitrate-N and ammonium-N remaining in the soil after harvest, which can be transported off-site (Randall et al., 1997). Furthermore, nitrogen is one of the most expensive nutrients to supply, therefore it is essential to measure and maximize nutrient use efficiency (Good et al., 2004). Nitrogen application should provide enough N to optimize yield while minimizing any unused N that could be lost to the environment (Robertson & Vitousek, 2009). The efficiency of a crop utilizing nitrogen fertilizer determines economic sustainability of cropping system and response of crops to applied N and use efficiency is important criteria for evaluating crop (Neupane et al., 2018). Spring camelina was relatively efficient at taking up N at moderate levels of fertilization, but higher levels do not necessarily translate to higher yields (Johnson et al., 2019). The selection of plants with more efficient nitrogen usage is, therefore, an important research goal in achieving greater agricultural sustainability (Gifford et al., 1984). This challenge must be approached through the selection of the high nitrogen use efficient crops and applying optimal rates of N fertilizers. 53 Nitrogen (N) is a macronutrient that significantly affects yield and growth in plants; it is involved in the metabolism and transformation of energy, chlorophyll, and protein synthesis. It also affects uptake of other essential nutrients and helps in the optimal partitioning of photosynthates to reproductive parts which increases the seed: stover ratio (Singh A, 2004). However, plant requirements for N vary with cultivar, growth stage of plant, N utilization efficiency, soil type, climate, and type of N application (Berry et al., 2010; Sidlauskas & Tarakanovas, 2004). The observed variability in the nutrient use efficiency among plants is partly due to the inherent natural genetic variability within the germplasm (Baligar et al., 2001). The variability influences nutrient use efficiency of plants is based on the nutrient uptake, incorporation, and utilization efficiency of the plant (Baligar VC 2015). Definitions of nitrogen use efficiencies have been grouped or classified as agronomic efficiency, physiological efficiency, agro‐physiological efficiency, apparent recovery efficiency, and utilization efficiency (Fageria & Baligar, 2001; Fageria & Baligar, 2003). Agronomic efficiency: defined as the economic production obtained per unit of nitrogen applied and calculated by: Agronomic efficiency (AE; kg kg-1) = ((Gf – Gu)/Na) where Gf is the grain yield of the fertilized plot (kg), Gu is the grain yield in the unfertilized plot (kg), and Na is the quantity of nitrogen applied (kg). Physiological efficiency: defined as the biological yield obtained per unit of nitrogen uptake and calculated by: Physiological efficiency (PE; kg kg-1) = ((Yf – Yu)/(Nf – Nu)) 54 where Yf is the total biological yield (grain plus straw) of the fertilized plot (kg), Yu is the total biological yield in the unfertilized plot (kg), Nf is the nitrogen accumulation in the fertilized plot in grain and straw (kg), and Nu is the nitrogen accumulation in the unfertilized plot in grain and straw (kg). Agro-physiological efficiency: defined as the economic production (grain yield in case of annual crops) obtained per unit of nitrogen uptake and calculated by: Agro-physiological efficiency (APE; kg kg-1) = ((Gf – Gu)/(Nf – Nu)) where Gf is the grain yield in the fertilized plot (kg), Gu is the grain yield in the unfertilized plot (kg), Nf is the nitrogen accumulation by straw and grain in the fertilized plot (kg), and Nu is the nitrogen accumulation by straw and grains in the unfertilized plot (kg). Apparent recovery efficiency is defined as the quantity of nitrogen uptake per unit of nitrogen applied and calculated by: Apparent recovery efficiency (ARE; %) = ((Nf – Nu)/Na) × 100 where Nf is the nitrogen accumulation by the total biological yield (straw plus grain) in the fertilized plot (kg), Nu is the nitrogen accumulation by the total biological yield (straw plus grain) in the unfertilized plot (kg), and Na is the quantity of nitrogen applied (kg). For field had no fertilizer applied, the NUE may be evaluated as: Nitrogen Use Efficiency (NUE; kg kg-1) = (Y/N) where Y is the biomass yield of the unfertilized plot (kg), and N is the quantity of nitrogen available in field (kg) (Badr et al., 2016; Hammad et al., 2017; Jin et al., 2012; Lu et al., 2016; Mon et al., 2016). 55 The yield estimation under different N regimes is commonly used as the indicator of NUE. In this study, experiments were conducted with two nitrogen doses and with different genotypes to study NUE and select genotypes by their performance on these conditions. Materials and Methods Data Collection In the same study described in Chapter 2, after pod setting the plants were bagged and tied well to minimize seed loss. The plants were uprooted or cut near to the ground surface after the plants reached physiological maturity. The roots were removed from the uprooted plants, and the plants were kept in a greenhouse and airdried. The above ground biomass was weighed after airdrying, and agronomic (NUE) were calculated. Data Analysis The data for biomass were taken and saved to a Microsoft Excel worksheet (.xlsx format), the data was also used to calculate Nitrogen Use Efficiency (NUE). The statistical analysis was performed using R version 4.2.3 (R core team, 2021). The biomass yields of the 212 genotypes of camelina plus the check and spring varieties were accessed for their response to N and data were fitted to a linear model to test for normality. Since the biomass data were not normally distributed, the square root transformation was used for data transform. The analysis of variance (ANOVA) was performed on biomass after transformation to determine the effects of N and genotype. The NUE was calculated with agronomic efficiency (AE; kg kg-1) = ((Gf – Gu)/Na) 56 where Gf is the biomass yield of the fertilized plot (kg), Gu is the biomass yield in the unfertilized plot (kg), and Na is the quantity of nitrogen applied (kg). Because the difference of biomass yield between high N and low N treatment were small and negative in some cases, and since the ANOVA showed that N effect was not significant, NUE was calculated using the biomass yields from the unfertilized plots, i.e., NUE (kg kg-1) = Plant Biomass/ N where, Plant Biomass is the biomass yield of the unfertilized plot (g), and N is the quantity of nitrogen in the field (kg) (Moll et al., 1982) ANOVA was performed for NUE. Data were tested for normality and square root transformation was performed. The effects were considered statistically significant at p < 0.05. Tukey’s HSD test was conducted for mean comparisons with 5% significance levels. Results Biomass The biomass of MSU collection of 212 genotypes of camelina and check spring varieties with different nitrogen treatments were assessed for year, N, genotype, and their interactive effects for 2021 and 2022. The ANOVA results showed that biomass of camelina was significantly affected by year, genotype and interaction between genotype and year (Table 3.1), but no significant nitrogen effects. Therefore, NUE was calculated using the biomass from unfertilized plots only and analyzed by individual years (Table 3.2). 57 Table 3. 1. ANOVA table showing the effects of year, genotype, nitrogen, and its interaction on biomass yield (grams) in Sidney on year 2021 and 2022. Source of Variance Df Biomass in grams P>F Year 1 0.0001 Genotype 215 0.0001 Nitrogen 1 0.8681 Year x Genotype 215 0.0222 Year x Nitrogen 1 0.4689 Genotype x Nitrogen 215 0.3453 Year x Genotype x Nitrogen 215 0.4743 Biomass and Canopy Imagery In this experiment we could see that the correlation of biomass with canopy area varied with measuring time points with coefficients of correlation ranging between 0.24 and 0.50. The correlation coefficient with the collective canopy area of year 2021 and year 2022 in time point 1 and 2 (Ca1 and Ca2) was 0.50 and 0.24, respectively, and the correlation of canopy area at third time point (Ca3) in 2021 is 0.29. Canopy area at time point 1 (Ca1) was quite a decent predictor when including data from both years (Figure 3.1). 58 Figure 3. 1 Correlation of Canopy Area taken at different time points in year 2021 (time 1,2 and 3) and year 2022(time 1 and 2) with Biomass. Nitrogen Use Efficiency (NUE) ANOVA results showed that NUE of camelina was significantly affected by genotype in 2021 and 2022 (Table 3.2). The NUE varied greatly among genotypes (Figure 3.2; Figure 3.3). The highest NUE in g plant−1 kgN-1was of genotype CS144 (0.36444) and CS248 (0.3454) in 2021 and 2022, respectively (Table 3.3). Table 3. 2. ANOVA table showing the effects of genotype on NUE (g plant−1 kgN-1) in Sidney for years 2021 and 2022. Source of Variance Df 2021 NUE 2022 NUE P>F P>F Genotype 215 <2.20E-16 0.03434 59 Table 3. 3 Top 30 genotypes with the highest NUE (g plant−1 kgN-1) in Sidney in 2021 and 2022. 2021 and 2022 Genotypes with NUE 2021 2022 # Genotypes Mean # Genotypes Mean 1 CS144 0.3644 1 CS248 0.3454 2 CS229 0.3508 2 CS088 0.3120 3 CS230 0.3277 3 CS133 0.3082 4 CS142 0.3249 4 CS047 0.3035 5 CS215 0.3142 5 CS213 0.2942 6 CS143 0.3076 6 CS071 0.2781 7 CS101 0.3032 7 CS067 0.2758 8 CS254 0.3020 8 CS142 0.2666 9 CS150 0.3010 9 CS009 0.2662 10 CS217 0.2982 10 CS131 0.2650 11 CS202 0.2846 11 CS060 0.2622 12 CS235 0.2832 12 CS116 0.2561 13 CS075 0.2800 13 CS063 0.2552 14 CS185 0.2716 14 CS246 0.2538 15 CS193 0.2701 15 CS044 0.2445 16 CS030 0.2641 16 CS058 0.2391 17 CS133 0.2630 17 Shoshone 0.2340 18 CS122 0.2568 18 CS189 0.2309 19 CS210 0.2564 19 CS103 0.2295 20 CS226 0.2515 20 CS162 0.2284 21 CS220 0.2511 21 CS183 0.2275 22 CS071 0.2511 22 CS037 0.2263 23 CS162 0.2457 23 CS217 0.2244 24 CS049 0.2379 24 CS220 0.2236 25 CS218 0.2326 25 CS210 0.2229 26 CS201 0.2310 26 CS145 0.2184 27 CS192 0.2309 27 CS136 0.2151 28 CS060 0.2305 28 CS081 0.2150 29 CS042 0.2299 29 CS027 0.2122 30 CS044 0.2284 30 CS182 0.2073 60 Figure 3. 2 Mean NUE of camelina genotypes on NUE in year 2021. Figure 3. 3 Mean NUE of camelina genotypes on NUE in year 2022. Discussion Camelina as an important, under-utilized oilseed crop has great potential for biodiesel production and other industrial applications. The estimation of the crop biomass harvested from above the ground is vital in improving the agronomic management efficiency and predicting crop yield (Chen et al., 2010; Cilia et al., 2014; Gilles et al., 2008). In one of the previous studies in 61 oilseed rape, higher plant biomass until flowering and increase of number of seeds per plant were identified as the major contributors for higher seed yield, and thus enhanced NUE (Stahl et al., 2019). Plant biomass accumulation is mainly determined by the fast-growing organs, which are termed as growth center, those falls on stem and branches in budding and early flowering periods, but shifts to reproductive organs during flowering and podding periods in oilseed crop (Chi-yun, 1975; Li et al., 2016; Schjoerring et al., 1995).The result of this study suggested that the genotype influence significantly on the above ground biomass yield of the camelina, indicating that genotype selection is important. The comparative biomass yield was higher in 2021 than that of 2022. There were weak to moderate correlations between biomass and canopy area with maximum coefficients of correlation ranging between 0.24 and 0.50. The nitrogen use efficiency (NUE) varies among the genotypes. The NUE was calculated with the biomass of crops with low N, the plant utilizes the nitrogen available in the soil during their growth period when nitrogen input is not provided. Nitrogen applied at pre-sowing has the highest benefit to increase seed yield and NUE (Li et al., 2016). The result suggested there was highest growth in genotype CS144 with 0.3644g increase in biomass per lbs of N available in soil in 2021 meanwhile, highest growth was found in genotype CS248 with 0.3454 g increase in biomass per lbs of N available in soil in 2022.The NUE is lower in 2021 than 2022 because the nitrogen availability varied in the field in both years. The determination of NUE in crop plants is an important approach to evaluate fertilizers and their role in improving crop yields (Baligar et al., 2001).In this study 30 genotypes with higher NUE were selected from 2021 and 2022 (Table 3.4.a.; 3.4.b.). Among those there are total 62 of 9 genotypes that appeared doing well in both year 2021 and 2022, they are CS14, CS217, CS133, CS210, CS220, CS071, CS162, CS060, and CS044. 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The rank order of canopy size and growth rate at different time points indicates difference among the genotypes and effect of nitrogen in growth. Other research has showed that the canopy images were efficient in identifying the growth stage, such as initiation of flowering stage, flowering stage, and pod setting stages without using any destructive measurement, with much more efficient way. For canopy area and growth analysis through images has been accurate and less biased. Though it took long period of time for image analysis in 2021, increasing the consistency in the height of the camera and using same camera for retrieving the images helped making a pipeline which could provide results in few minutes in 2022. NDVI was a useful measure to identify the greenness of the plants. The NDVI above 0.6 when plants were healthier and was much lower NDVI indicate the plants being damaged or dead. There was a decline in the NDVI as the plants grew in this study likely due to plant bolding or flowering. 76 Accurate estimation of above-ground biomass production is crucial for enhancing agronomic management efficiency and predicting crop yield. Final biomass yield and NUE data aid in identifying the optimal amount of fertilization required, particularly in addressing nitrogen deficiencies in crops. Notably, certain genotypes demonstrated higher biomass yield and nitrogen use efficiency (NUE). These genotypes, including CS014, CS217, CS133, CS210, CS220, CS071, CS162, CS060, and CS044, possess characteristics that hold promise for enhancing crop productivity. The results of remote sensing technology and traditional biomass measurements contribute valuable insights into the relationship between camelina biomass yield, canopy growth rate, NDVI and NUE. 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