Cercospora leaf spot (CLS) is a destructive foliar pathogen impacting sugarbeet (Beta vulgaris L.)  production worldwide (Weiland & Koch, 2004). The causal fungus,  Cercospora beticola Sacc., reduces root yield and recoverable sucrose while increasing sugar impurity concentrations resulting in revenue losses up to 40% (Shane & Teng, 1992; Lamey et al., 1996). Current management strategies rely heavily on fungicide application, host plant resistance, and tillage for inoculum reduction (Khan & Smith, 2005; Miller et al., 1994). Reduced fungicide efficacy and increased resistance to current control mechanisms have been attributed to the high genetic variability, prolific sporulation, and polycyclic life cycle of C. beticola (Rosenzweig et al., 2015; Shrestha et al., 2020). Alternative control measures including foliar applied micronutrients have shown efficacy for maize ear rot, coffee rust, and cucumber powdery mildew (Farahat et al., 2018; Pérez et al., 2020; M. Reuveni et al., 1997; R. Reuveni & M. Reuveni, 1998). New CLS management strategies may need to integrate a balanced plant nutrition program, fungicide rotation, resistant germplasm, and cultural practices to enhance sugarbeet plant health and grower profitability.

Primary C. beticola inoculum in sugarbeet is distributed through wind dispersal of asexual conidia from plant residue or long distance transfer by sugarbeet seed (Spanner et al., 2021; Weiland & Koch, 2004). After conidiation, water-splash, wind, and insects aid in spore transfer to leaf surfaces where hyphae elongate and infect via stomates (Weiland & Koch, 2004). Optimal conditions for CLS development are relative humidity > 60%, prolonged leaf wetness, and air temperatures > 16 °C (61 °F) (Shane & Teng, 1992; Tedford et al., 2018). Following hyphal establishment, toxins are produced within leaf tissue and necrotize cells in close proximity (Rathaiah, 1977; Steinkamp et al., 1979). Symptoms include grey-tan circular lesions with distinct borders that coalesce forming large necrotic areas and death of older leaves (Rangel et al., 2020). Protecting newly emerged leaves is vital for suppressing CLS disease progression and reducing plant stress. Cercospora leaf spot is characterized as a polycyclic disease. C. beticola produces the phytotoxins cercosporin and beticolin both known to debilitate cells and enhance fungal growth throughout the growing season (Weiland & Koch, 2004; Windels et al., 1998). Multiple application timings of foliar protection agents are often necessary to mitigate quality reductions through harvest. Conidia persistence and spore dispersal require a combination of contact and systemic protection via fungicides. While disease control is heavily reliant on fungicide rotation, questions persist regarding the use of foliar boron to aid in CLS leaf surface protection.

A knowledge gap exists regarding the use of B-containing compounds and the potential to aid in CLS management. While sugarbeets have become less responsive to B applications over time in Michigan, B-containing products have been reported to contain fungistatic properties with studies in Egypt observing reduced in vitro growth of C. beticola and decreased in-field CLS severity when utilizing sodium tetraborate and boric acid applications (El-Fawy, 2016; Warncke et al., 2009). Researchers suggested reductions in mycelial growth were related to cell membrane disruption of the pathogen leading to cytoplasmic leakage and death. Additionally, B may stimulate reactive oxygen species accumulation in fungal spores leading to mitochondrial damage (Qin et al., 2010; Shi et al., 2010). Fungistatic properties combined with the role of B in plant defense warrant further CLS management studies.

In addition to root yield and quality, foliar B may affect plant metabolism including cell wall and membrane structure, ion, hormone, and metabolite transfer (Brdar-Jokanović, 2020; Brown & Shelp, 1997; Camacho-cristóbal et al., 2008). Micronutrients such as B function as cofactors or activators of enzyme systems which are pivotal to disease resistance and the production of defense barriers (Datnoff et al., 2007). Key roles of B in cell wall structure and plasma membrane integrity are directly impacted by C. beticola colonization and necrotrophic disruption. Boron deficiencies may decrease root yield, sugar quality, and root quality by inducing ‘heart rot’ symptoms and subsequent ‘dry rot’ within the root (Armin & Asgharipour, 2012; Cox, 1940). Previously, B applications were utilized for preventative management of ‘heart rot’ disease which increased frequency of B application. The demand for B in sugarbeet as an essential nutrient is greater than other field crops. Sufficient leaf tissue concentrations range from 26-80 ppm with observed deficiency symptoms at < 20 ppm (Voth et al., 1979; Robertson & Lucas, 1981; Christenson et al., 1991). Current Michigan soil test B recommendations suggest < 0.7 ppm as deficient and > 1.0 ppm as sufficient with marginal likelihood for deficiency between these values (Warncke et al., 2009).

Boron fertilizer application practices have evolved over time. Previously, bulk fertilizers (i.e., urea, monoammonium phosphate, muriate of potash) contained B impurities which decreased need for supplemental B application (Nelson, 1965). As fertilizer processing and manufacturing improved to produce more highly concentrated fertilizers, the indirect B inclusion within bulk fertilizers no longer occurred. In high pH soils (>7.5) the borate anion (HBO₄^–) prevails and is subject to leaching. Some field crops grown in rotation with sugarbeet in Michigan (i.e., winter wheat (Triticum aestivum L.), dry beans (Phaseolus vulgaris L.), and soybeans (Glycine max L.)) are sensitive to excess soil B which may limit B application and accumulation within these crop rotations. Additionally, sugarbeet varietal response to supplemental B has decreased in modern varieties (i.e., 2000 and later) on fine-textured Michigan soils (Voth et al., 1979; Christenson et al., 1991; Warncke et al., 2009). The role of improved plant genetics on changes in nutrient demand have not been determined.

Lack of previous response to B application, limited B accumulation in the soil profile, changes in the soil microenvironment (i.e., warmer soil temperatures longer into autumn), and fungicide efficacy may all contribute to increased CLS occurrence. Sources of B and application timing may impact CLS reductions and sugarbeet response to B utilization. Approximately 96% of B uptake occurs as uncharged boric acid molecules with little uptake from borate anions (Bolaños et al., 2004). Application timing is reportedly most effective at 80-100 days after planting with sodium tetraborate and boric acid contributing to increases in yield and quality (Armin & Asgharipour, 2012; Gobarah & Mekki, 2005; Mekdad et al., 2015). Integrating foliar B to improve sugarbeet fertility and reduce CLS may result in synergistic improvements to sugarbeet quality, plant defense mechanisms, and reduced C. beticola growth and sporulation.

The objectives of the current study were to 1) evaluate in-field applications of sodium tetraborate with and without a standard fungicide program on CLS growth and development, and 2) evaluate in vitro growth of C. beticola isolates in response to a concentration gradient of sodium tetraborate and boric acid. The working hypothesis was application of B-containing compounds would reduce in-field CLS severity and incidence and decrease in vitro C. beticola growth.

Materials and Methods

Field trials were established in the 2020 and 2021 growing seasons at the Saginaw Valley Research and Extension center near Richville, MI (43°23’57.3”N, 83°41’49.7”W) on a Tappan-Londo loam (fine-loamy, mixed, active, calcareous, mesic Typic Epiaquoll). Located in northeastern Michigan, the site was non-irrigated, tile-drained, and representative of sugarbeet production across the state. Fields were previously cropped to corn (Zea mays L.) and autumn plowed followed by spring field cultivation (0-10 cm depth). Pre-plant soil characteristics (0-20 cm) were 6.2-7.2 pH (1:1 soil/water), 22-28 g kg^-1 soil organic matter (loss-on-ignition), 22-24 mg kg^-1 P (Olsen sodium bicarbonate extraction), and 138-178 mg kg^-1 K (ammonium acetate method) (Table 1). Prior to planting, soil samples (0-30 cm) for nitrate-N (NO₃-N) analysis were air-dried and ground to pass through a 2 mm sieve resulting in pre-plant concentrations of 5.5 and 6.3 mg NO₃-N kg^-1 soil (nitrate electrode method) in 2020 and 2021, respectively (Gelderman and Beegle, 2015). Monthly precipitation and temperature data were collected and recorded throughout the growing season from Enviro-weather (http://mawn.geo.msu.edu) Michigan State University, East Lansing, MI) (Table 2).

Experimental Procedures

Trials were planted on 7 April 2020 and 5 April 2021 to variety ‘Crystal G932NT’ (ACH Seeds, Inc., Eden Prarie, MN) with a John Deere planter (Deere & Company, Moline, IL). Trials were replanted 7 May 2021 due to a freezing event 23-24 April 2021. Plots measured 3.05 m in width by 10.7 m in length with 76-cm row spacing. Trial consisted of eight treatments arranged as a randomized complete-block design with four replications. Treatments consisted of 1) a non-treated check containing no fungicide or boron, 2) grower standard fungicide program (GS), 3-5) three rates of sodium tetraborate (low, medium, and high) in combination with a grower standard fungicide program (GS+ FBL, GS+FBM, GS+FBH), and 6-8) three rates of sodium tetraborate (low, medium, and high) individually excluding fungicide (FBL, FBM, FBH) (Tables 3 and 4). A CO₂-powered backpack sprayer with four TJ 8002XR nozzles (XR TeeJet^® Flat Fan Spary Tips; TeeJet^® Technologies, 220 Glendale Heights, IL) (76-cm spacing) at 140 L ha^-1 was utilized for application every 10-14 days starting 6 July and 28 June in 2020 and 2021, respectively. Fungicides were applied 6, 16, 27 July, 11, 24 August, and 4, 14 September in 2020. Fungicides were applied 28 June, 12, 26, July, 5, 16, 25 August, 9, and 27 September in 2021. All treatments received 101 kg N ha^-1 as pre-plant urea. Sidedress 67 kg N ha^-1 injected to a 12.7-cm depth halfway between the rows as 28% UAN was applied at the 4-6 leaf stage on 9 June 2020 and 1 June 2021.

Inoculation of C. beticola (1×10³ conidia mL^-1) was applied at 140 L ha^-1 using a tractor mounted sprayer after 20:00 on 9 and 23 July 2020 and 12 July 2021. Inoculum suspensions were prepared from rehydrated desiccated symptomatic leaves, naturally and artificially infested with a mixture of local isolates, collected the previous season (Ruppel & Gaskill, 1971). A precipitation event reduced inoculation efficacy in 2020 resulting in an additional inoculation. Bi-weekly disease ratings began 9 and 26 July and continued to 6 October and 27 September in 2020 and 2021, respectively. Plots were assigned a severity rating using the KWS scale based on infected leaf area: 1=0.1% (1-5 spots/leaf), 2=0.35% (6-12 spots/leaf), 3=0.75% (13-25 spots/leaf), 4=1.5% (26-50 spots/leaf), 5=2.5% (51-75 spots/leaf), 6=3%, 7=6%, 8=12% 9=25%, 10=50% (Kleinwanzlebener Saatzucht, 1970). Incidence (DI, 0-100%) and severity (DS) ratings were utilized to calculate disease index (DX): DI x DS/10 and quantify differences in CLS development among treatments. Disease incidence was recorded to represent the frequency of new lesion activity and severity ratings were used to calculate area under the disease progress curve for disease severity (AUDPC) (Madden et al., 2017).

Plant emergence was counted 20-30 days after planting and prior to harvest to confirm population (data not shown). Fractional green canopy coverage (FGCC) utilizing the software Canopeo and normalized difference vegetation index (NDVI) were collected every 10-14 days coinciding with fungicide application (Patrignani and Ochsner, 2015). The uppermost fully developed and extended leaf and petiole were collected from 25 plants plot^-1 at the 12-14 leaf growth stage in 2020.  Additional tissue samples were collected at 6-8 leaf, 12-14 leaf, and 18-20 leaf in 2021 to monitor B uptake throughout the growing season. Plant tissue samples were dried at 60°C, mechanically ground to pass through a 1-mm mesh screen and analyzed for total N using a micro-Kjeldahl digestion method and colorimetric analysis with a Lachat rapid flow injector autoanalyzer (Nelson and Sommers, 1973; Bremner, 1996). Beets from the center two rows of each plot were harvested on 14 October 2020 and 20 October 2021 with a mechanical plot harvester and weighed. Root subsamples were collected (10-12 roots plot^-1) analyzed for sucrose concentration, extraction percentage, and recoverable sucrose at the Michigan Sugar Co. (MSC) Laboratory (Bay City, MI).

Expected economic net return was calculated using both root yield and recoverable sucrose (kg Mg^-1) in addition to MSC’s average payment standard (2020-2021) (Michigan Sugar Company, Bay City, MI). Expected net return was based on US$48.58 Mg^-1 and US$24.25 Mg^-1 (fresh weight) for sugarbeets in 2020 and 2021, respectively which was later adjusted based on a ratio of observed recoverable sucrose (kg Mg^-1) to average MSC recoverable sucrose (kg Mg^-1) value. Michigan Sugar Company payment standards were calculated using adjustment factors based on harvest date to determine amount of sugar delivered (kg ha^-1). Adjustment factors used were 1.00 and 1.04 for root yield and recoverable sucrose (kg ha^-1) and then multiplied by US$0.16 kg^-1 and US$0.10 kg^-1 to equal total payment ha^-1 in 2020 and 2021, respectively. Variable costs including trucking (US$4.13 Mg^-1) were subtracted from expected net return across years.

Data were analyzed in SAS v. 9.4 (SAS, Cary, NC) using the GLIMMIX procedure (SAS Institute, 2012). Year and treatment were considered fixed effects and replication as random. The UNIVARIATE procedure in SAS was used to examine the normality of residuals (P ≤ 0.05). Squared and absolute values of residuals were examined with Levene’s Test to confirm homogeneity of variances (P ≤ 0.05). Least square means were separated using the LINES option when ANOVA indicated significance (P ≤ 0.10).

Experimental Procedures for Determining In Vitro Sensitivity of C. beticola

Sensitivity of C. beticola isolates to B was evaluated using a mycelial growth on solid media assay. Trials were arranged in a randomized complete block design with four replications and repeated twice for each isolate and concentration. Treatments consisted of a concentration gradient of 0, 1, 10, 50, 100, 500, and 1000 μg ml^-1 sodium tetraborate, boric acid, and thiophanate-methyl (FRAC Group 1). Thiophanate-methyl was selected as a positive control due to higher EC₅₀ value to achieve closest comparison to B compounds. Technical-grade thiophanate-methyl (Millipore Sigma, Burlington, MA) was dissolved in methanol to prepare a stock solution of 7,500 μg ml^-1. Technical grade boric acid (Fisher Scientific, Waltham, MA) and sodium tetraborate (20 Mule Team, Borax) were weighed and added to molten media to achieve desired concentration. The test medium was prepared by mixing potato dextrose agar (PDA) 39 g L^-1 for 15 minutes, autoclaving at 121°C for 30 minutes, and cooling to 60°C prior to adding appropriate dry boron product or thiophanate-methyl stock quantities to achieve desired concentrations. Media was mixed for 10 minutes (until homogenous) once products were added and maintained at 60 °C for plate transfer. Agar plates were prepared by transferring 20 mL of amended-agar solution to 100 mm x 20 mm Petri dishes for constant depths. Nonamended control plates consisted only of PDA.

Cercospora beticola isolates ‘Blum 1-2’ and ‘Range A’ were obtained from the United States Department of Agriculture- ARS Sugar Beet Research Unit (SBRU) fungal collection. ‘Blum 1-2’ was obtained from symptomatic sugarbeet leaf lesions in Saginaw County, MI in 2017. ‘Range A’ was collected from a symptomatic sugarbeet leaf in Ingham County, MI in 2008. Single spore transfer protocols were utilized to obtain pure cultures with fungal ball storage at -20 ℃. Isolates were cultured on clarified V8 (CV8) agar medium to produced inoculum and incubated at room temperature (21-24°C) for 30 days. Five-mm agar discs were excised from the actively growing margin of the colony. Agar disks were inverted, and a single disk was placed in the center of each amended PDA plate and incubated at room temperature for 21 days. Cercospora beticola radial growth diameter was collected every seven days.  Diameters were corrected for the 5-mm agar disk. Diameters after 21 days were used to calculate growth relative to the control for each replicate and EC₅₀ values were generated using R version 4.1.2 (R Core Team, 2022) with the three-parameter log-logistic (LL.3) function in package ‘drc’ (Ritz et al., 2015). Mean EC₅₀ values were obtained from each experimental repetition. Means were further analyzed in a generalized linear mixed model (GLIMMIX) ANOVA in SAS v. 9.4 (SAS, Cary, NC). Isolate and compound were considered fixed effects while experimental repetition was considered a random effect.

Results and Discussion

­­­­­­Environmental Conditions

April through September growing season precipitation was 12.5% and 8.3% below the 30-yr mean in 2020 and 2021, respectively (Table 2). Cool April soil temperatures combined with deficit June 2020 precipitation (i.e., 55% below the 30-yr mean) slowed plant emergence and development. In 2021, April and May rainfall was 75% and 65% below the 30-yr mean, respectively, resulting in delayed emergence. June precipitation, however, was 50% above the 30-yr mean, contributing to favorable conditions for disease. Except for April 2020, monthly growing season air temperatures were near or above the 30-yr mean. Above average April 2021 soil temperatures resulted in a 5 April planting date but a frost on 21 April resulted in replanting the field trial on 5 May. The 2021 replant resulted in minimal impact on sugarbeet emergence and early season growth.

Effect of Sodium Tetraborate and Boric Acid on In Vitro Growth of Cercospora beticola

Relative radial growth of C. beticola grown in vitro decreased with inclusion of sodium tetraborate and boric acid (Table 5). Across both isolates, radial growth decreased 14-19% with sodium tetraborate as compared to the control. Boric acid reduced mean radial growth 5-10% in Range A and Blum 1-2. Thiophanate-methyl (i.e., positive control for both isolates) demonstrated 8 and 86% growth reductions in Range A and Blum 1-2, respectively. A polymerase chain reaction – restriction fragment length polymorphism (PCR-RFLP) analysis of the β-tubulin gene (Rosenzweig et al., 2015) confirmed the E198A point mutation conferring benzimidazole resistance in the Range A isolate resulting in minimal effectiveness by thiophanate-methyl as compared to Blum 1-2. Benzimidazole resistance was previously identified in Michigan C. beticola isolates in the 1990s further contributing to current CLS control challenges (Rosenzweig et al., 2015).

Estimated EC₅₀ values (i.e., value of half maximal concentration) for control of C. beticola were significantly lower with sodium tetraborate ranging from 772 and 876 mg kg^-1 for Blum 1-2 and Range A, respectively, indicating greater effectiveness than boric acid (Table 6). Estimated EC₅₀ values for boric acid exceeded 1,000 mg kg^-1 for Range A and Blum 1-2. Response of C. beticola may be impacted by pH as the pH of the boric acid and sodium tetraborate solutions were 5.1 and 9.3, respectively, indicating that growth of C. beticola decreased at greater pH. Iamandei et al. (2013) evaluated the influence of pH on in vitro development of C. beticola colonies observing a wide pH spectrum in which vegetative mass and condia growth began at a pH of 3.0 with optimal values falling between 4.0 and 7.0. Vegetative C. beticola growth declined as pH increased but continued to produce numerous conidia.

Labeled rates of current B-containing products indicate that EC₅₀ values could be achieved with in-field applications. However, labeled rates of boric acid and 100% sodium tetraborate (i.e., borax) products may range from 2,000-9,000 mg kg^-1 B or 200-900 mg kg^-1 B, respectively, for a single in-field application with variations in product use rate, active ingredient, and B concentrations strongly influencing concentration ranges. Current B-containing products are formulated to correct B nutrient deficiencies across a wide range of crops. With minimal B concentrations needed to correct soil and plant tissue nutrient deficiencies, current products may not be sufficient for disease management due to the higher required concentrations. Utilizing greater B concentrations for CLS control could result in secondary impacts including B toxicity to ensuing highly sensitive crop rotations.

Environmental fate of foliar B is a determining factor in both disease and nutritional response. Plant response is species-specific and highly dependent on method of application, soil characteristics, temperature, and humidity leading to discrepancies in environmental fate and plant B utilization (Brdar-Jokanović, 2020). Soil pH and trace element interaction are known to affect B availability and ion reactions in soil (Ibekwe et al., 2010). In arid and semiarid irrigated areas, high soil B concentrations are often associated with high salt concentrations and can be a limiting factor to plant growth (Ayars et al., 1993; Grieve & Poss, 2008; Shouse et al., 2006).  El-Fawy (2016) reported significant CLS reductions and increased root yield and recoverable sucrose with B application in El-Behera Governorate, Egypt. Yield reductions up to 60% have been attributed to salinity levels in similar regions of Egypt as compared to soils with reduced salinity levels (Ahmed Bakry et al., 2014). While C. beticola response was attributed to application of foliar B, soil salinity levels of this region may have increased plant B response for improved sugarbeet quality thus ultimately improving response to CLS.

Effect of Foliar Boron and Fungicide on Root Yield, Quality, and Expected Net Return­

Increased air temperatures combined with adequate precipitation resulted in root yields ranging between 44.8-89.3 Mg ha^-1 (20-40 T A^-1) in 2021 as compared to 40.0-60.9 Mg ha^-1 (17.9-27.2 T A^-1) in 2020. Replanting did not reduce yield in 2021. Across site years, application of foliar B did not increase root yield when compared to the GS treatment (Table 7, 8). In 2020, the FBH treatment reduced root yield while FBL and FBM yielded similar to GS. In 2021, B treatments with a GS fungicide program reduced root yield > 22.2 Mg ha^-1 ( > 9.9 T A^-1)across treatments.  In saline soils, Gobarah & Mekki (2005) observed application of up to 3.7 kg B ha^-1 (3.3 lb B A^-1) applied as sodium borate increased root length, diameter, and yield with the highest recoverable sucrose at rates of 4.9 kg ha^-1 (4.4 lb B A^-1). In Michigan, current soil test B recommendations suggest < 0.7 mg kg^-1 as deficient and > 1.0 mg kg^-1 as sufficient with marginal deficiency conditions in-between these values (Warncke et al., 2009). Current B recommendations indicate 1.1 kg B ha^-1 (1.0 lb B A^-1) may be beneficial with 2.2 kg B ha^-1 (2.0 lb B A^-1) in coarse-textured soils (Vitosh et al., 2006). Soil B concentrations of 1.2 and 0.8 mg kg^-1 in 2020 and 2021, respectively, indicate sufficiency (Table 1) and therefore less probability of a positive impact from foliar B applications on root yield across site years.

Similar to root yield, sugarbeet quality parameters indicated lack of response to foliar B. The addition of foliar B did not improve recoverable sucrose in 2020 with FBH individually decreasing recoverable sucrose per hectare (Table 7). In 2021, recoverable sucrose per hectare decreased > 48-57% with treatments excluding the GS program (Table 8). Plant response to foliar B is dependent upon soil physical and chemical properties (i.e., nutrient solubility, solution pH, surface tension, retention, and molecular structure of B source), environmental conditions, and leaf characteristics which may help determine the efficacy, uptake, and usage of foliar nutrient solutions (Fernandez & Brown, 2013; Fernandez & Eichert, 2009). Application of sodium borate and boric acid have been linked to increases in recoverable sucrose and improved juice purity by decreasing Na and K uptake (Abdallah & Mekdad, 2015; Armin & Asgharipour, 2012; Dordas et al., 2007). Tissue nutrient concentration of B remained sufficient (i.e., 32-46 ppm) throughout the growing season for all treatments (data not shown) indicating foliar B was not limiting and not likely to affect sugarbeet yield and quality. Root yield and recoverable sucrose results indicate foliar B applications did not provide or enhance protection from CLS.

Root yield and quality responses to foliar B were also reflected in economic return across site years. Expected net return was similar or reduced as compared to the grower standard fungicide program when including foliar B individually or when combined with a standard fungicide program (Table 9). Profitability was similar between the GS, GS+FBL, and GS+FBH treatments with reductions > $973.00 ha^-1 ($394 A^-1) when removing fungicide applications altogether in 2021. Increases in recoverable sucrose and root yield did not translate to profitability when considering both sugar volume and quality parameters (Table 9).

Effect of Foliar Boron and Fungicide on CLS Development

In 2020, no significant disease index (DX) differences were detected between foliar B rates for the entire growing season (Table 10). All treatments containing fungicide reduced DX values with no impact of foliar B. Similar results were recorded through 9 Sept. 2021. However, a final DX rating on 27 Sept. 2021 demonstrated reduced DX with FBM as compared to FBL. Disease development was delayed in 2020 with initial symptoms beginning 20 Aug. Despite inoculation, CLS did not develop until later in the season resulting in a smaller window for treatment effectiveness. Absence of disease during the first half of the growing season allowed increased canopy development thus reducing risk for production losses in treatments excluding fungicide. Decreased June precipitation and sporadic rainfall events in July likely extended the symptomless biotrophic phase of C. beticola colonization in 2020 (Table 2). Lesion development occurs as the fungus transitions to a necrotrophic phase (Ebert et al., 2021). Without adequate moisture, relative humidity below 90-95%, and overnight temperatures remaining < 16 °C, sporulation was reduced between June – August 2020 resulting in delayed infection. June 2021 precipitation was frequent with rainfall events taking place on 19 of 30 days resulting in improved conditions for early-season infection (initial symptoms 20 Jul) followed by near to above normal precipitation and above normal air temperatures for the remainder of the growing season.

Significant differences in fractional green canopy coverage (FGCC) occurred across both study years (Table 11). The grower standard fungicide program maintained greater canopy coverage and helped determine treatment differences when included within any treatment whereas foliar B applications individually resulted in significantly reduced canopy coverage across both years. No differences in normalized difference vegetation index (NDVI) occurred throughout 2020 but did occur in 2021 with fungicide application again producing greener plants with more biomass and lower CLS occurrence (Table 12). Area under the disease progress curve (AUDPC) values indicated reduced CLS control with treatments excluding fungicide in 2020 (Table 13). In 2021, AUDPC of FBL was greater than the grower standard fungicide program while FBM and FBH did not differ.

Due to coalescing lesions and loss of older leaves, CLS can be difficult to precisely measure (Steddom et al., 2007). Vegetative indices are largely impacted by percentage of photosynthetically active tissue resulting in difficulty monitoring treatment differences in canopy reflectance as affected by foliar B. Rating variability and physiological response of sugarbeet to C. beticola induce challenges to quantify differences among treatments without severe levels of infection. Early CLS pressure in 2021 had a greater impact on measurable response.

Effect of Foliar Boron and Fungicide on Tissue B Concentration

Across site years, tissue B concentrations remained > 32 ppm at the 14-16 leaf stage indicating sufficiency. In 2021, an additional sample timing was included to evaluate foliar B uptake throughout the growing season. Late season tissue samples demonstrated greater B tissue concentrations (43-46 mg kg^-1) when including fungicide as compared to no fungicide (38-40 mg kg^-1). Despite statistical differences, B tissue concentrations were above critical thresholds for all treatments. Sugarbeet has one of the larger B requirements among field crops with reported sufficient leaf tissue concentrations ranging between 26-80 mg kg^-1 and observed deficiency symptoms at < 20 mg kg^-1 (Voth et al., 1979; Robertson and Lucas, 1981; Christenson et al., 1991). Sufficient (i.e., > 0.7 mg kg^-1) B soil test levels, above critical tissue B concentrations (i.e., > 20 mg kg^-1), and application of B in the form of sodium tetraborate reduced the likelihood of plant response by means of foliar uptake.

Physical and chemical leaf surface conditions are fundamental to parasitic microorganism development that initiate at the leaf boundary and may also affect the efficiency and persistence of foliar applied pesticides (Oertli et al., 1977). Potential buffering of leaf surface pH may impact effectiveness of foliar B on CLS control. Hutchinson et al. (1986) examined neutralizing abilities of sugarbeet, radish (Raphanus sativus L.), sunflower (Helianthus L.), and wormwood (Artemisia tilesii L) to acid rain ranging in pH from 2.4-4.7. Radish, sunflower, and wormwood significantly increased pH in all droplets while sugarbeet resulted in little to no change. The mechanism behind acid rain neutralization may be facilitated by leaching and exchange of base cations (e.g., Ca²⁺, K⁺, Mg⁺ and Na⁺ for H⁺) on leaf surfaces induced by cell membrane and cuticle damage (Tukey, 1980). Lack of acidic droplet neutralization by sugarbeet was attributed to absence of leaf injury as compared to other species examined (Hutchinson et al., 1986). The sodium tetraborate product used in the current study has a pH range between 6-7 reducing the direct impact on leaf surface pH. However, a combination of cuticle injury due to necrotic lesions of CLS may influence sugarbeet leaf ion exchange resulting in neutralization of alternative B-containing compounds (i.e., boric acid).

In addition to chemically altering the foliar microenvironment, B-containing compounds may prevent or reduce parasitic spore germination by synthesis of substances such as phytoalexins (Oertli et al., 1977).  Researchers suggest foliar application of B, Mn, and Cu result in exchange of Ca²⁺ cations from cell walls and interact with salicylic acid (involved with phytoalexin response) to activate resistance mechanisms in the host plant (M. Reuveni et al., 1997; R. Reuveni & M. Reuveni, 1998). While foliar B may support natural plant resistance, application is unlikely to overcome rapid development of CLS. Intact cuticles of sugarbeet, slow rates of ion exchange, low susceptibility of inorganic ion leaching, and limited sodium borate uptake indicate that foliar B application may not be an effective strategy for CLS management (Bolaños et al., 2004; Tukey & Tukey, 1962). Lack of disease response, reductions in root yield, and decreased quality suggest foliar B failed to provide disease suppression in current field environments.

Toxin Role in C. beticola Development and Pathogenicity

The pathogenicity of C. beticola is driven by cercosporin, a photoactivated polyketide toxin that acts as a cell membrane sensitizer and producer of singlet oxygen (Daub & Briggs, 1983; Mitchell et al., 2002). Peroxidation of membrane lipids leads to membrane breakdown, cell death, and leakage of nutrients into leaf intercellular spaces allowing for fungal growth and sporulation (Daub & Briggs, 1983). In addition to cercosporin, beticolins are non-host-specific phytotoxins of C. beticola that induce loss of electrolytes, amino acids, and betacyanin via ion channel formation and permeabilization of host cell membranes (Goudet et al., 2000; Macrì & Vianello, 1979). Physiological parameters including pH, nutrient conditions, temperature, and C:N ratios all influence toxin production (Daub & Ehrenshaft, 2000). Toxin production in culture is highly variable among and within species. Cercospora beticola isolates are capable of producing cercosporin, beticolin, or both (Daub & Chung, 2007). While cercosporin and beticolin aid in host pathogenicity of C. beticola, auto resistance (AR) is essential for self-protection (Rangel et al., 2020). Cercospora AR is facilitated by toxin export and reductive detoxification of the cercosporin molecule (Daub et al., 1992; Leisman & Daub, 1992; Sollod et al., 1992). Cercosporin derivatives absorb less light and generate significantly less singlet oxygen (¹O₂) when stably methylated and acetylated compared to wild‐type cercosporin (Leisman & Daub, 1992). Herrero et al. (2007) conducted cercosporin toxicity assays to evaluate isolate strain AR sensitivity to pH and discovered the crg1-null strain of C. nicotianae to cercosporin was strongly impacted by pH. In the presence of cercosporin on media at pH levels < 6, observations included almost complete lack of growth in the presence of cercosporin suggesting certain isolates lack detection of acidic environments and inability to adjust intracellular pH creating cercosporin susceptibility (Herrero et al., 2007). Environmental conditions including changes in pH and ion concentration may influence methylation and acetylation of cercosporin or reduction in isolate AR resulting in altered pathogenicity.

Cercosporin and beticolin levels were not quantified in the current study. However, notable differences in isolate color were consistent with varying concentrations of sodium tetraborate and boric acid (Figs. 1, 2). Cercosporin is characterized by red pigments that turn green in alkaline conditions, while beticolins are yellow in color and turn orange with pH increase (Goodwin & Dunkle, 2010; Goudet et al., 1998). Changes in isolate color suggest that cercosporin and beticolin production may be influenced by presence of B-containing compounds and altered growth media pH. You et al. (2008) reported changing pH values to 4.0 –7.0 reduced cercosporin and isolate radial growth compared to non-buffered medium. However, addition of citrate or phosphate buffers caused cercosporin reduction regardless of the pH values indicating solution buffer directly impacts cercosporin production. Further examination of metal ions (Zn²⁺, Fe³⁺, Co²⁺, Mn²⁺, Cu²⁺, and Mg²⁺) slightly enhanced or had no effect on cercosporin production unlike high quantities of Na⁺ or K⁺ which inhibited cercosporin production (You et al., 2008). The role of cercosporin and beticolin in cell membrane disruption, nutrient leakage, and alteration of ion concentration suggests that leaf surface microenvironment directly impacts cercosporin and beticolin production in sugarbeet. Visual differences in isolate color indicate change in toxin production and suggest altered pathogenicity of C. beticola and potential for enhanced host defense by means of ion exchange.   

Conclusions

Foliar B applications did not reduce CLS in field environments across site years. Grower standard fungicide treatment increased root yield, recoverable sucrose, and canopy coverage with minimal differences detected among foliar B rates. Plant health indicators such as NDVI, fractional green canopy coverage (FGCC), and DX did not support improvement in CLS protection with foliar B. Radial growth of C. beticola decreased with increasing concentrations of B in vitro. Sodium tetraborate more effectively inhibited growth than boric acid. Differences in growth response and estimated EC₅₀ values could be attributed to secondary physiological effects based on increasing pH. Boron-compounds were not as effective as thiophanate-methyl with regard to mycelial growth reduction. Previous findings of reduced CLS with B application in sugarbeet may be due to increased plant health and nutritional improvement rather than improved disease resistance. Evaluation of soil test levels, sugarbeet varietal characteristics, and environmental and disease conditions are necessary to make appropriate B recommendations. Reduced control options, increased CLS resistance, and increased sugarbeet B requirements enhance the need for further evaluation of alternative CLS control measures. In-field evaluation of various B timings, increased B concentrations, and addition of B-containing compounds may contribute to future CLS control.

Acknowledgements

The authors would like to thank the USDA National Institute of Food and Agriculture, Michigan Sugar Company, Michigan State University College of Agriculture and Natural Resources, Michigan State University AgBioResearch, and MSU-Project GREEEN for partial funding and support of this research. In addition, the authors would like to thank Andrew Chomas, undergraduate research assistants, graduate research assistants, and research farm staff for their support and assistance.

LIST OF TABLES

Table 1. Soil physical and chemical properties including mean NO₃-N (0 – 30 cm), P (0 – 20 cm), and K soil test (0 – 20 cm) nutrient concentrations obtained prior to sugarbeet planting, Richville, MI, 2020-2021.

Year	Soil	NO3–N	Soil test†
	description		P	K	B	pH	OM
		mg kg–1					g kg–1
2020	Tappan-Londo Loam	5.5	24	138	1.2	7.2	22
2021	Tappan-Londo Loam	6.3	22	178	0.8	6.2	28

†P phosphorus (Olsen sodium bicarbonate extraction); K potassium (ammonium acetate extractable K).

 Table 2. Mean monthly and 30-yr precipitation† and temperature for the sugarbeet growing season, Richville, MI, 2020 – 2021.

Year	Apr.	May	Jun.	Jul.	Aug.	Sept.	Total
	cm (in
2020	5.3 (2.1)	9.5 (3.7)	3.4 (1.3)	8.2 (3.2)	8.6 (3.4)	7.1 (2.8)	42.1 (16.6)
2021	1.8 (0.7)	3.0 (1.2)	11.4 (4.5)	7.3 (2.9)	7.8 (3.1)	12.8 (5.0)	44.1 (17.4)
30-yr‡ avg.	7.3 (2.9)	8.6 (3.4)	7.6 (3.0)	6.6 (2.6)	8.1(3.2)	9.9 (3.9)	48.1 (18.9)
	°C (°F
2020	6.2 (43)	13.8 (57)	20.6 (69)	23.7 (75)	21.4 (71)	15.8 (60)	—
2021	9.3 (49)	14.1 (57)	21.8 (71)	21.3 (70)	22.8 (73)	17.6 (64)	—
30-yr avg.	7.4 (45)	13.2 (56)	18.7 (66)	20.9 (70)	19.7 (68)	15.8 (60)	—

†Precipitation and air temperature data were collected from Michigan State University Enviro-weather (https://mawn.geo.msu.edu).                                                                                                    ‡30-yr means were obtained from the National Oceanic and Atmospheric Administration (https://www.ncdc.noaa.gov/cdo-web/datatools/normals).

Table 3. Treatment design and application timings for sugarbeet field trial evaluating boron applications with and without standard fungicide program for control of Cercospora leaf spot, Richville, MI, 2020.

Treatment	Product Rate† and Timing‡
Grower Standard Fungicide	Manzate Max (3.7 L)[1.6 qt] ABCDEF + Inspire XT (0.5 L)[7 fl oz] ADF + Super Tin (0.6 L)[8 fl oz] BE + Priaxor (0.6 L)[8 fl oz], Topsin (1.5 L)[20 fl oz] C + Badge (2.3 L)[2 pt] G
Foliar Boron – Low No Fungicide	SprayBor (112 g)[0.1 lb] ABCDEFG
Foliar Boron – Medium No Fungicide	SprayBor (280 g)[0.25 lb] ABCDEFG
Foliar Boron – High No Fungicide	SprayBor (560 g)[0.5 lb] ABCDEFG
Grower Standard + Foliar Boron Low	SprayBor (112 g)[0.1 lb] ABCDEFG +Manzate Max (3.7 L) [1.6 qt] ABCDEF + Inspire XT (0.5 L)[7 fl oz] ADF + Super Tin (0.6 L)[8 fl oz] BE + Priaxor (0.6 L)[8 fl oz], Topsin (1.5 L)[20 fl oz] C + Badge (2.3 L)[2 pt] G
Grower Standard + Foliar Boron Medium	SprayBor (280 g)[0.25 lb] ABCDEFG +Manzate Max (3.7 L) [1.6 qt] ABCDEF + Inspire XT (0.5 L)[7 fl oz] ADF + Super Tin (0.6 L)[8 fl oz] BE + Priaxor (0.6 L)[8 fl oz], Topsin (1.5 L)[20 fl oz] C + Badge (2.3 L)[2 pt] G
Grower Standard + Foliar Boron High	SprayBor (560 g)[0.5 lb] ABCDEFG +Manzate Max (3.7 L) [1.6 qt] ABCDEF + Inspire XT (0.5 L)[7 fl oz] ADF + Super Tin (0.6 L)[8 fl oz] BE + Priaxor (0.6 L)[8 fl oz], Topsin (1.5 L)[20 fl oz] C + Badge (2.3 L)[2 pt] G
Check	No Fungicide, No Foliar Boron

†All rates, unless otherwise specified, are listed as a measure of product per hectare followed by product per acre.                                                                                                                              ‡Application letters code for the following dates: A=6 Jul, B=16 Jul, C=27 Jul, D=11 Aug, E=24 Aug, F=4 Sept, G=14 Sept.

Table 4. Treatment design and application timings for sugarbeet field trial evaluating boron applications with and without standard fungicide program for control of Cercospora leaf spot, Richville, MI, 2021.

Treatment	Product Rate† and Timing‡
Grower Standard Fungicide	Manzate Max (3.7 L)[1.6 qt] ABCDEFG + Inspire XT (0.5 L)[7 fl oz] BEG + Super Tin (0.6 L)[8 fl oz] CF + Priaxor (0.6 L)[8 fl oz], Topsin (1.5 L)[20 fl oz] D + Badge (2.3 L)[2 pt] H
Foliar Boron – Low No Fungicide	SprayBor (112 g)[0.1 lb] ABCDEFGH
Foliar Boron – Medium No Fungicide	SprayBor (280 g)[0.25 lb] ABCDEFGH
Foliar Boron – High No Fungicide	SprayBor (560 g)[0.5 lb] ABCDEFGH
Grower Standard + Foliar Boron Low	SprayBor (112 g)[0.1 lb] ABCDEFGH +Manzate Max (3.7 L)[1.6 qt] ABCDEFG + Inspire XT (0.5 L)[7 fl oz] BEG + Super Tin (0.6 L)[8 fl oz] CF + Priaxor (0.6 L)[8 fl oz], Topsin (1.5 L)[20 fl oz] D + Badge (2.3 L)[2 pt] H
Grower Standard + Foliar Boron Medium	SprayBor (280 g)[0.25 lb] ABCDEFGH +Manzate Max (3.7 L)[1.6 qt] ABCDEF + Inspire XT (0.5 L)[7 fl oz] BEG + Super Tin (0.6 L)[8 fl oz] CF + Priaxor (0.6 L)[8 fl oz], Topsin (1.5 L)[20 fl oz] D + Badge (2.3 L)[2 pt] H
Grower Standard + Foliar Boron High	SprayBor (560 g)[0.5 lb] ABCDEFGH +Manzate Max (3.7 L)[1.6 qt] ABCDEF + Inspire XT (0.5 L)[7 fl oz] BEG + Super Tin (0.6 L)[8 fl oz] CF + Priaxor (0.6 L)[8 fl oz], Topsin (1.5 L)[20 fl oz] D + Badge (2.3 L)[2 pt] H
Check	No Fungicide, No Foliar Boron

†All rates, unless otherwise specified, are listed as a measure of product per hectare followed by product per acre.                                                                                                                                                 ‡ Application letters code for the following dates: A=28 Jun, B=12 Jul, C=26 Jul, D=5 Aug, E=16 Aug, F=25 Aug, G=9 Sept, H=27 Sept.

 Table 5. Relative radial growth for Blum 1-2 and Range A as affected by isolate and compound.

Isolate	Compound	Relative Growth
Blum 1-2	Boric Acid	0.90 ab
Blum 1-2	Sodium Tetraborate	0.86 bc
Blum 1-2	Thiophanate-Methyl	0.14 d
Range A	Boric Acid	0.95 a
Range A	Sodium Tetraborate	0.81 c
Range A	Thiophanate-Methyl	0.92 ab
P > F		< 0.01

†Means followed by the same lowercase letter are not significantly different at (α=0.1).                                                                                                                                                                                                  ‡ Relative growth (21 days) calculated as compared to control.

Table 6. Estimated EC₅₀ values for Blum 1-2 and Range A as affected by compound.

Isolate	Compound	EC † Estimate mg kg-1 50
Blum 1-2	Boric Acid	>1000
Blum 1-2	Sodium Tetraborate	772
Blum 1-2	Thiophanate-Methyl	0.35
Range A	Boric Acid	>1000
Range A	Sodium Tetraborate	876
Range A	Thiophanate-Methyl	>1000

†Value of half maximal effective concentration i.e., 50% growth reduction as compared to control.

 Table 7. Sugarbeet root yield, recoverable sucrose (kg ha-1 and kg Mg-1), sucrose concentration, and extraction in response to fungicide and foliar boron, Richville, MI, 2020.

Treatment	Root Yield	Recoverable Sucrose		Sucrose	Extraction
	-Mg ha-1– (T A-1)	-kg ha–1– (lb A–1)	-kg Mg-1– (lb T–1)	–%–	–%–
Grower Standard (GS) Fungicide	55.2 abc† (24.6)	7389 ab (6591)	134 a (268)	17.9 a	95.9
Foliar Boron – Low (FBL), No Fungicide	59.5 ab (26.5)	7561 ab (6745)	127 b (254)	17.1 b	95.5
Foliar Boron – Medium (FBM), No Fungicide	46.6 cd (20.8)	5900 bc (5263)	126 b (252)	16.9 b	95.6
Foliar Boron – High (FBH), No Fungicide	40.0 d (17.9)	5107 c (4556)	126 b (252)	17.0 b	95.7
Grower Standard + FBL	52.5 abc (23.4)	7109 ab (6342)	135 a (270)	18.0 a	95.9
Grower Standard + FBM	55.0 abc (24.5)	7361 ab (6567)	133 a (266)	17.7 a	95.8
Grower Standard + FBH	60.9 a (27.2)	8172 a (7290)	134 a (268)	17.9 a	95.8
Check – No Fungicide, No Boron	47.2 bcd (21.1)	5878 bc (5244)	124 b (248)	16.7 b	95.7
P > F	= 0.09	<0.01	= 0.06	< 0.01	NS

†Means in the same column following by the same lowercase letter are not significantly different at P ≤ 0.10.

 Table 8. Sugarbeet root yield, recoverable sucrose (kg ha-1 and kg Mg-1), sucrose concentration, and extraction in response to fungicide and foliar boron, Richville, MI, 2021.

Treatment	Root Yield	Recoverable Sucrose		Sucrose	Extraction
	-Mg ha-1– (T A-1)	-kg ha-1– (lb A–1)	-kg Mg-1– (lb T–1)	–%–	–%–
Grower Standard (GS) Fungicide	89.3 a† (39.8)	10759 a (9598)	121 a (241)	16.4 a	94.9
Foliar Boron – Low (FBL), No Fungicide	54.3 c (24.2)	5577 c (4975)	103 b (205)	14.2 b	94.5
Foliar Boron – Medium (FBM), No Fungicide	44.8 d (20.0)	4571 c (4078)	101 b (202)	14.0 b	94.2
Foliar Boron – High (FBH), No Fungicide	52.2 cd (23.3)	5331 c (4756)	102 b (204)	14.2 b	94.7
Grower Standard + FBL	82.7 ab (36.9)	10045 ab (8961)	122 a (243)	16.4 a	94.5
Grower Standard + FBM	77.4 b (34.5)	8962 b (7995)	116 a (232)	15.8 a	94.7
Grower Standard + FBH	76.5 b (34.1)	9797 ab (8740)	121 a (241)	16.3 a	95.0
Check – No Fungicide, No Boron	54.0 cd (24.1)	5526 c (4930)	103 b (205)	14.2 b	94.5
P > F	<0.01	<0.01	<0.01	< 0.01	NS

†Means in the same column following by the same lowercase letter are not significantly different at P ≤ 0.10.

Table 9. Sugarbeet expected net return and expected net return minus trucking costs as affected by foliar boron and fun- gicide combinations, Richville, MI, 2020-21.

Treatment	Expected Net Return ‡		Expected Net Return Minus Trucking Costs
	US$ ha-1 ( US$ A–1
	2020	2021	2020	2021
Grower Standard (GS) Fungicide	2929 ab† (1186)	2481 a (1005)	2701 ab (1094)	2112 a (855)
Foliar Boron – Low (FBL), No Fungicide	2999 ab (1215)	1286 c (520)	2753 ab (1115)	1061 c (430)
Foliar Boron – Medium (FBM), No Fungicide	2338 bc (947)	1054 c (427)	2146 bc (869)	868 c (352)
Foliar Boron – High (FBH), No Fungicide	2024 c (820)	1229 c (498)	1859 c (753)	1013 c (410)
Grower Standard + FBL	2818 ab (1141)	2316 ab (938)	2601 ab (1053)	1975 ab (800)
Grower Standard + FBM	2917 ab (1181)	2066 b (837)	2690 ab (1089)	1746 b (707)
Grower Standard + FBH	3196 a (1294)	2259 ab (915)	2945 a (1193)	1923 ab (779)
Check – No Fungicide, No Boron	2330 bc (944)	1274 c (516)	2134 bc (864)	1051c (426)
P > F	= 0.08	< 0.01	= 0.08	< 0.01

†Means in the same column following by the same lowercase letter are not significantly different at P ≤ 0.10.                                                                                                                                              ‡Expected net returns based upon MSC payment adjustment with volume and quality incentives and trucking costs of $US$4.13 Mg-1 or $US$3.75 T-1 .

Table 10. Sugarbeet final disease index (DX, %) ratings Richville, MI 2020-21.

Treatment	2020		2021
	Sept. 14‡	Oct. 6	Sept. 9	Sept. 27
Grower Standard (GS) Fungicide	0.88 b†	1.8 b	17.3 b	41.5 c
Foliar Boron – Low (FBL), No Fungicide	73.5 a	90.3 a	89.0 a	73.8 a
Foliar Boron – Medium (FBM), No Fungicide	70.4 a	85.3 a	87.5 a	61.3 b
Foliar Boron – High (FBH), No Fungicide	71.5 a	83.5 a	88.8 a	71.3 ab
Grower Standard + FBL	2.1 b	4.0 b	21.3 b	30.0 cd
Grower Standard + FBM	1.0 b	2.5 b	20.3 b	31.3 cd
Grower Standard + FBH	1.3 b	2.1 b	12.5 b	28.0 d
Check – No Fungicide, No Boron	77.5 a	87.5 a	90.0 a	80.0 a
P > F	<0.01	<0.01	<0.01	<0.01

†Means followed by the same lowercase letter are not significantly different within rating date at (α=0.1).                                                                                                                                                               ‡ Disease index calculated from disease incidence and severity ratings recorded every 10-14 days post infection.

Table 11. Sugarbeet fractional green canopy coverage (FGCC) as affected by fungicide and foliar boron Richville, MI 2020-21.

Treatment	2020		2021
	Sept. 14	Oct. 6	Sept. 9	Sept. 27
	canopy
Grower Standard (GS) Fungicide	75.4 a	77.3 a	87.0 a	87.0 a
Foliar Boron – Low (FBL), No Fungicide	49.7 b	37.4 c	35.1 c	29.0 c
Foliar Boron – Medium (FBM), No Fungicide	54.5 b	39.5 c	33.7 c	30.0 c
Foliar Boron – High (FBH), No Fungicide	48.7 b	37.0 c	35.6 c	32.0 c
Grower Standard + FBL	70.9 a	67.5 b	82.6 ab	82.0 ab
Grower Standard + FBM	72.2 a	68.5 ab	80.0 b	79.0 b
Grower Standard + FBH	70.9 a	71.9 ab	82.6 ab	79.0 b
Check – No Fungicide, No Boron	55.3 b	38.7 c	34.6 c	33.0 c
P > F                                                                                                               < 0.01                      < 0.01                      <0.01                       <0.01

†Means followed by the same lowercase letter are not significantly different at (α=0.1).

Table 12. Sugarbeet normalized difference vegetation index (NDVI) as affected by fungicide and foliar boron Richville, MI 2020-21.

Treatment	2020		2021
	Sept. 14	Oct. 6	Sept. 9	Sept. 27
Grower Standard (GS) Fungicide	0.80	0.74	0.85 a	0.89 a
Foliar Boron – Low (FBL), No Fungicide	0.73	0.63	0.62 b	0.74 b
Foliar Boron – Medium (FBM), No Fungicide	0.82	0.72	0.61 b	0.77 b
Foliar Boron – High (FBH), No Fungicide	0.71	0.61	0.56 b	0.74 b
Grower Standard + FBL	0.83	0.72	0.81 a	0.86 a
Grower Standard + FBM	0.82	0.77	0.82 a	0.86 a
Grower Standard + FBH	0.76	0.68	0.81 a	0.87 a
Check – No Fungicide, No Boron	0.80	0.68	0.57 b	0.73 b
P > F	NS	NS	<0.01	<0.01

†Means followed by the same lowercase letter are not significantly different at (α=0.1).

Table 13. Area under the disease progress curve (AUDPC) as affected by fungicide and foliar boron Richville, MI 2020-21.

Treatment	2020	2021
Grower Standard (GS) Fungicide	62.4 c	285.1 bc
Foliar Boron – Low (FBL), No Fungicide	356.8 b	371.6 a
Foliar Boron – Medium (FBM), No Fungicide	550.0 a	339.0 ab
Foliar Boron – High (FBH), No Fungicide	337.8 b	343.4 ab
Grower Standard + FBL	57.9 c	201.3 d
Grower Standard + FBM	37.9 c	280.4 bc
Grower Standard + FBH	41.4 c	223.1 cd
Check – No Fungicide, No Boron	355.6 b	337.4 a
P > F	< 0.01	< 0.01

†Means followed by the same lowercase letter are not significantly different at (α=0.1).

 LIST OF FIGURES

Figure 1. Day 21 radial growth of C. beticola isolate ‘Blum 1-2.’

 

 

 

 

                                                                                                                                                                                                                                                                                                                                                                                                                                                         †Sodium tetraborate (1A), boric acid (1B), thiophanate-methyl (1C) concentrations displayed left to right (0, 1, 10, 50, 100, 300, 500 ppm).

Figure 2. Day 21 radial growth of C. beticola isolate ‘Range A.’

 

 

 

 

 

†Sodium tetraborate (2A), boric acid (2B), thiophanate-methyl (2C) concentrations displayed left to right (0, 1, 10, 50, 100, 300, 500 ppm).

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INTRODUCTION

Approximately 5.2 million short tons of beet sugar were produced in the U.S. in the fiscal year 2021-22, accounting for 56.3% of total domestic sugar production (Abadam, 2023). During growth and storage of sugar beets, microbial infection of plant roots can cause devastating crop loss, sucrose degradation and carryover of microbes and associated soil into the factory processing streams that can cause sucrose losses and operational challenges (Majumdar et al., 2022; Solomon, 2009; Strausbaugh, 2016; Strausbaugh et al., 2011). Various microbial contaminants have been reported to reduce processing efficiency during  sugar beet extraction, necessitating control measures such as the addition of antimicrobial agents,  maintenance of high temperatures in the diffusion tower of 70-73℃, and heating of press water to 90℃ (Arvanitis et al., 2004; Asadi, 2007; Holland et al., 1990; McGinnis, 1982; Šereš et al., 2017). Culture-dependent microbiological studies to isolate and identify microbes present and characterize their behaviors are central to ongoing efforts to reduce microbial contamination and improve factory sanitation, but these approaches also have limitations such as potential culture bias and low throughput (Abdel-Rahman et al., 2023; Robles-Gancedo et al., 2009). Additionally, culture-independent methods such as amplicon-based sequencing provide a more comprehensive profile of microbes present including those that may be unculturable in the laboratory (Bill, et al., 2024). Commonly reported microbes include lactic acid bacteria such as Leuconostoc and Lactobacillus, thermophilic species of Bacillus and Clostridium, and yeasts (Pollach et al., 2002; Robles-Gancedo et al., 2009; Tallgren et al., 1999). Additionally, microbes infecting the beet roots in the field or storage piles such as Candida, Fusarium, and Penicillium, which are associated with increased invert sugars and raffinose, may also carry over into the extraction process  (Bill, et al., 2024 under review; Kusstatscher et al., 2021; Kusstatscher et al., 2019). In addition to consumption of sucrose, microbial metabolism results in various byproducts that can interfere with factory operations. These biproducts include ethanol, organic acids that lower the pH of raw juice (lactic acid, acetic acid, butyric acid), gases that affect heat exchange (H₂S, H₂, CO₂), and polysaccharides that block filtration (primarily dextran) (Kohout et al., 2020; Pollach et al., 2002; Tallgren et al., 1999; Wojtczak et al., 2013). Microbial contaminants may also form biofilms on factory surfaces that necessitate cleaning and increase operating costs (Abdel-Rahman et al., 2023; Galié et al., 2018)).

Recent advances in high-throughput sequencing have greatly advanced the present understanding of the sugar beet-associated microbiome in growing fields and storage piles (Mendes et al., 2011; Wolfgang et al., 2023), and will increase our understanding of the factory beet juice microbiome (Bill, et al., 2024; Zhang et al., 2022). Further research may elucidate the microbes which have the most detrimental effect on sugar extraction processes and environmental factors that lead to the greatest levels of such microbes. In the present study, microbial isolations were carried out using diffusion tower juice and biofilm samples collected at 18 sugar beet factories in North America. While various microbiological studies have previously been performed at beet sugar factories, microbes were often classified in broad categories such as “Aerobic mesophiles” or “Anaerobic thermophiles” (Kohout et al., 2020; Robles-Gancedo et al., 2009),which can contain overlapping groups of bacteria and do not provide a complete picture of microbial diversity. Another understudied aspect this study attempted to address was the sampling of biofilms, which are commonly found on exposed factory surfaces (Abdel-Rahman et al., 2023; Galié et al., 2018). Biofilms are laborious to sanitize and may also be sources of microbial contamination. The microbial isolates collected in this study lay the groundwork for future studies on how various microbes contribute to factory losses, and the efficacy of various biocides to reduce their impact in factories. A portion of isolates also underwent preliminary assessment for their ability to produce polysaccharides in flask culture that increase culture viscosity that may be predictive of impacts during sugar beet processing.

MATERIALS AND METHODS

Sample collection. In total, 18 sugar beet factories (Table 1) contributed samples to the study during the 2022-2023 campaign. Factory staff collected diffusion tower juice from the bottom of the tower and biofilm samples from multiple factory locations and added these to cryotubes containing sterile glycerol solution to yield a final concentration of 20% glycerol (Cabrera et al., 2020; Amberg, 2005). Many factories collected multiple samples during the processing campaign from as early as December 2022 and as late as August 2023 depending on location with the intent to capture as much microbial diversity as possible in the samples given that many microorganisms are “unculturable” (Vartoukian et al., 2010). One-third of biofilm collection sites were noted by factory operators (Table 2).

The samples were then frozen at −20 °C and shipped overnight on ice packs to the USDA-ARS laboratory in Fargo, ND, where they were held at −80 °C until being shipped overnight on dry ice to the USDA-ARS laboratory in New Orleans, LA. Upon arrival, the sample cryovials were stored at −80 °C.

Preparation of beet sugar growth media. Factory beet juice was used to prepare agar plates to aid in recovery of a greater diversity of microbes present in factory samples. Diffusion tower juice was collected from American Crystal Sugar Company, Moorhead Factory, MN and stored at −80 °C and shipped overnight on dry ice to the USDA-ARS laboratory in New Orleans, LA and stored at −20 ℃. Raw juice was thawed at 4 ℃ and centrifuged at 10,000 × g for 20 minutes to remove solid debris. To the supernatant, 3 g/L yeast extract, 6 g/L peptone, and 20 g/L agar were added before autoclaving as similarly performed previously (Bruni et al., 2022).

Microbial isolation & identification. Small amounts (<50 µL) of the frozen factory samples were serially diluted in sterile water and spread onto beet agar plates, which were incubated aerobically at 28 ℃ until colonies appeared. Approximately 12 colonies were picked for isolation and identification from each factory sample that was cultured. Whenever possible we tried to pick as many different colony morphologies as possible.  However, if most of the colonies looked the same on the plate, colonies were then randomly picked. Due to the tendency for large mucoid colonies to form on the beet juice agar, de Man, Rogosa, and Sharpe (MRS) (DeMan et al., 2003), and nutrient agar (Research Products International, Mt. Prospect, IL USA) plates were used for re-streaking to obtain single colony axenic cultures. All culturing was done aerobically at 28 ℃. After 3 rounds of streaking, an isolated colony was picked and suspended in 50 µL of autoclaved ultrapure water to use as a PCR template. The 16S rRNA gene was PCR-amplified with primers 27F (5’-AGAGTTTGATCCTGGCTCAG-3’) and 1492R (5’-GGTTACCTTGTTACGACTT-3’) (Heuer et al., 1997), using Extaq Hot Start DNA polymerase (Takara, San Jose, CA USA)). PCR products were purified with Clean and Concentrator-5 kits (Zymo Research, Irvine, CA USA) and Sanger sequenced at Eurofins Genomics, LLC (Louisville, KY USA). The chromatogram files were imported into Geneious Prime Version 2023.0.1 (Dotmatics, Boston, MA USA), which was used to trim the primer-binding regions, assemble the forward and reverse reads, and BLASTn query the consensus sequences against the NCBI 16S RefSeq database to identify the nearest related organism. In a few cases, fungal isolates were identified and thus the ITS1F (5’-CTTGGTCATTTAGAGGAAGTAA-3’) and ITS2R (5’-GCTGCGTTCTTCATCGATGC-3’) primers were used for PCR and sequencing (Smith & Peay, 2014; Walters et al., 2016), and the consensus sequences were BLASTn queried against the NCBI ITS RefSeq database. The isolates were identified by their closest BLAST hit in the NCBI RefSeq 16S and ITS databases and had an average of 99.48% sequence identity to their best hits. Additionally, the colony morphology of bacterial isolates on beet juice agar plates was recorded as either mucoid (slimy or gummy), rough (dull and irregular), or smooth (circular and generally translucent) (Ayers et al., 1979; Breakwell et al., 2017).

Rarefaction analysis. Rarefaction analysis is a method to examine the effect of sample size on species richness (Raup, 1975; Sanders, 1968).The analysis presented in this study was performed in Microsoft Excel using a custom function to randomly sample a numbered list of isolates or samples without replacement. The list of isolates and samples were divided into increasing intervals of 51 (612 total isolates, divided by 12) and 5 (55 total samples, divided by 11), respectively. At each interval, 10 random samplings were performed. For the purposes of the rarefaction analysis, isolates with different species as the best 16S rRNA gene or ITS1 region BLAST hit were considered unique taxa, though we acknowledge that these methods are no longer considered sufficient to properly classify isolates at the species level. Nevertheless, we argue that having enough sequence variation to change the top BLAST hit is enough for estimations of microbial diversity, similar to how amplicon-based sequencing studies now use amplicon sequence variants (ASVs) in similar analyses (Callahan et al., 2017) .

Measurement of microbial culture viscosity. Bacterial isolates were grown overnight at 28 ℃ at 250 RPM as precultures in 5 mL of either MRS broth or  tryptone sucrose yeast (TSY) broth containing 50 g/L sucrose (adapted from tryptone glucose yeast extract (TGY) medium) (Haynes et al., 1955) ). Leuconostoc and Weissella isolates were grown in MRS as precultures since these strains sometimes became too viscous to pipette in the presence of sucrose. All other precultures were grown in TSY. The precultures were then used to inoculate 50 mL TSY medium containing 120 g/L sucrose in 250 mL culture flasks to OD₆₀₀ of 0.05. The flask cultures were shaken at 250 RPM for 24 hours at 28 ℃. OD₆₀₀ was read with a spectrophotometer and culture viscosity was measured using a Brookfield model DV-II+ viscometer equipped with a UL spindle and small sample adapter. Culture samples were first visually classified among one of three groups: watery, intermediate, and viscous. Watery samples had no significant visual increase in viscosity, while viscous samples had consistencies resembling gels (with intermediate class falling in between). Viscous samples required dilution prior to viscometry analysis, whereby 100 mL of deionized water was added to the 50 mL cultures and mixed thoroughly by shaking the flask; watery and intermediate class samples were analyzed as-is. Based on their initial qualitative classification, samples were measured with at least two viscometer speeds (20 and 50 RPM for watery; 1 and 2 RPM for intermediate; 5, 10 and 20 RPM for diluted viscous samples). Multiple speeds were used to assess for shear thinning phenomena, which is characteristic of polysaccharide solutions (Evageliou, 2020; Xu et al., 2009; Yang et al., 2019). An observation of shear thinning would provide evidence in support of the presence of polysaccharides within the culture broth, showing that the bacteria studied are responsible for EPS formation. All viscosity measurements (for individual 50 mL cultures) were done in duplicate.

RESULTS

In total, 18 sugar beet factories contributed 55 cryostock vial samples to this study (Table 3). This number includes 33 diffusion tower juice samples, 21 biofilm samples, and 1 unknown sample, which was only identified by factory but not sample type. The factories are geographically distributed across the sugar beet producing regions of North America, representing 9 U.S. states and 1 Canadian province. The samples were collected as early as December 2022 and as late as August 2023. This design was intended to maximize the microbial diversity in the samples to ensure as many representative organisms as possible could be collected. Relating to this, factories that begin their processing campaign earlier in the year were asked to wait to collect samples until at least December, when microbial contamination typically increases in most factories with the exception of the factory in Brawley, CA, which processes on an alternative schedule (English, 2020; Strausbaugh, 2018).

The main goal of this work was to obtain representative isolates of the most abundant microbial contaminants of beet sugar juice and biofilms, rather than a comprehensive profiling of the microbiome recently reported by others (Bill, et al., 2024). As such, greater emphasis was placed on sample count rather than the depth of sampling, i.e. the number of isolates per sample. Based on this logic, roughly 12 colonies were picked from each sample, eventually resulting in 612 isolates. The isolates were identified by sequencing of their 16S or ITS1 rRNA genes and found to belong to 37 genera, including many previously reported genera such as Leuconostoc, Bacillus, and Rahnella (Figure 1). There were 499 Gram-positive bacteria compared to 103 Gram-negative bacteria. Leuconostoc was the most abundant genus by far, represented by 365 isolates. Only 10 fungal isolates were obtained. Proportional to the number of viable samples, 379 isolates were obtained from 33 diffusion tower juice samples, 223 isolates were obtained from 21 biofilm samples, and 10 isolates were obtained from the 1 unlabeled sample. There were 14 genera common to both juice- and biofilm- derived isolates while 9 were unique to juice samples and 13 were unique to biofilm samples. Additionally, 2 genera were unique to the “unknown” sample. Rarefaction analysis was applied to examine the recovery rate of novel taxa as a function of sampling (Figure 2). The data did not appear to have reached an asymptote, suggesting that additional taxa would continue to be recovered as more samples are collected and more isolates are picked per sample.

A set of 54 bacterial isolates, which represent 7 of the 8 most abundant genera, were assessed for their ability to increase the viscosity of the growth medium when grown as flask cultures. The viscosity of flask cultures were both visually classified and measured using a Brookfield model DV-II+ viscometer (see methods). Roughly equal numbers of juice- (n=25) and biofilm- (n=29) derived isolates were characterized overall, but certain genera were more abundant in either juice or biofilm samples, such as Rahnella and Acinetobacter. In total, 37 flask cultures were classified as watery (Table 4), 7 flask cultures were classified as intermediate (Table 5), and 10 flask cultures were classified as viscous (Table 6). In total, the juice and biofilm isolate flask culture samples had similar absolute numbers of watery and intermediate class samples (Figure 3; juice, watery: n=18; juice, intermediate: n=4; biofilm, watery: n=19; biofilm, intermediate: n=3). However, the biofilm-derived isolates had roughly double the number of viscous culture samples compared to juice (juice, viscous: n=3; biofilm, viscous: n=7). Although the sample size in this case is relatively small, these results seem consistent with previous observations that that EPS from biofilm isolates results in higher viscosities than EPS from juice (planktonic) isolates (Yang et al., 2019). Finally, there seemed to be a weak correlation (Cramers V, 0.316) between increased viscosity and earlier observations of mucoid colony morphology on beet juice agar during the microbial isolations (Cramer, 1946). To verify this, the isolates were streaked again onto beet juice agar plates. Indeed, mucoid morphology appeared to be more common in strains producing viscous (8 of 10) and intermediate viscosity (7 of 7) flask cultures when compared to the watery cultures (17 of 37). The highest viscosity was observed in flask cultures of Leuconostoc isolate 48-3 followed by Weissella isolate 15-1 and Pantoea isolate 19-7. Interestingly, isolates obtained from both juice and biofilm samples produced viscous cultures. Furthermore, a mild shear thinning effect was observed in most of the viscous culture samples as the RPMs were increased. Shear thinning is a typical effect reported for polysaccharide solutions (Evageliou, 2020; Xu et al., 2009).

DISCUSSION

The negative impact of microbial contaminants on sugar crop processing has long been recognized (Solomon, 2009), and the sugar production industry continues to seek improvements in methods to detect and reduce microbial load (Abdel-Rahman et al., 2023; Bill, et al., 2024; Holland et al., 1990; Kusstatscher et al., 2019; Robles-Gancedo et al., 2009). The isolation work in this study provided hundreds of relevant isolates that are central to performing future crucial experiments testing the efficacy of antimicrobial agents and characterizing bacterial exopolysaccharides. Towards this goal, a broad sampling of diffusion tower juice and biofilms from sugar beet factories across North America was undertaken to obtain a representative collection of microbial contaminants. The broad design of the sampling scheme has resulted in a remarkably diverse collection of isolates. This diversity likely reflects the wide geographic and temporal range of sampling. Additionally, this study revealed significant microbial diversity present in biofilms throughout the sugar beet factories, which do not seem to have been systematically studied previously. Biofilms are specialized microbial communities encased in extracellular matrix comprised of exopolysaccharides, proteins, and extracellular DNA that usually exhibit unique rheological and structural characteristics that typically have increased resistance to antimicrobial measures (Galié et al., 2018; Jeon et al., 2023). These biofilms are of interest as potential sources of re-contamination, and a more systematic study would be required to understand the factors affecting biofilm formation and composition in beet sugar factories.

Although the rarefaction curves suggest that additional sampling would produce more isolates belonging to novel taxa, the goal of this work was to obtain representative and relevant isolates for experimental characterization. This appears to have been generally achieved, as the isolates obtained in this study include diverse genera and are taxonomically similar to those previously identified in both culture-dependent and culture-independent studies (Bill, et al., 2024; Pollach et al., 2002; Robles-Gancedo et al., 2009; Tallgren et al., 1999; Zhang et al., 2022). Admittedly, the culturing conditions used in this study did likely lead to the omission of some previously reported genera such as the strictly anaerobic Clostridium and Thermoanaerobacter (Wiegel, 1981),  Lactobacillus that can be microaerophilic or anaerobic requiring addition of reducing agent and anaerobic culture conditions (De Angelis, 2016), and thermophiles like Thermoanaerobacterium and Thermoanaerobacter (Bill, et al., 2024; Kohout et al., 2020; Lee et al., 1993). It is also possible that fewer fungi were isolated if some of these microorganisms were less able to tolerate elevated temperatures during processing and collection from the diffusion tower (Robles-Gancedo et al., 2009). Such strains could be obtained through more targeted sampling or isolation strategies from the remaining frozen factory samples or other culture collections.

A major impact of microbial contamination is the production of viscous polysaccharides (Hector et al., 2016), which interfere with filtration and other processing steps during raw sugar extraction (Ernst et al., 2024; Evageliou, 2020; Soliman, 2007; Borji et al., 2019). A polysaccharide of special concern has been dextran, a polymer composed primarily of α-1,6 linked glucose subunits (Díaz-Montes, 2021; Ernst et al., 2024; Passerini et al., 2015; Purama et al., 2009). Indeed, many of the strains that caused significant viscosity in flask cultures were lactic acid bacteria such as Leuconostoc and Weissella, whose genomes tend to encode for dextran production (Qi et al., 2023; Yu et al., 2022). In contrast, it would be interesting to identify the composition of the viscous polysaccharides produced by Pantoea spp. isolates 7-5, 19-4, and 19-7, as this genus is not known to produce dextran. It is also worth noting differences in apparent polysaccharide production between isolates identified as the same genus through 16S rRNA sequence similarity. This shows further studies are needed to associate particular microbes with increased viscosity, which is crucial to mitigating viscosity problems during processing. While microbes are likely killed during juice heating, microbial-derived exopolysaccharides (EPS) such as dextran are likely to persist and cause operational challenges downstream of the initial microbial degradation of sucrose. Furthermore, some studies suggest that microbes producing higher viscosity EPS may be more likely to adhere to surfaces and form biofilms (Yang et al., 2019) . In summary, the microbial isolates collected in this study are valuable for future studies aimed at identifying microbial susceptibility to antimicrobials as well as characterizing exopolysaccharide production and providing potential solutions to the operational challenges such as increased viscosity and biofilm formation resulting from microbial contamination.

DATA AVAILABILITY

The 16S rRNA and ITS1 sequences for the isolates have been deposited in the National Center for Biotechnology Information GenBank database and can be found by the accession numbers PQ691418-PQ692019 and PQ687024-PQ687033.

ACKNOWLEDGEMENTS

We thank the staff at each of the sugar beet factories for participating through collection and preservation of juice and biofilm samples. We also thank Ms. Sarah Newton, Ms. Heidi Giles, and Ms. Anna Murphy of the Beet Sugar Development Foundation for coordinating factory sampling and for administrative support. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by USDA. USDA is an equal opportunity provider and employer.

FUNDING

This work was supported by the U.S. Department of Agriculture (USDA), Agricultural Research Service (ARS), under project number 6054-41000-114-000-D. This work was also supported by the Beet Sugar Development Foundation project number 501 to G.B.

Table 1. Participating factories and their locations and parent companies.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 2. Collection sites of biofilm samples were reported for one-third of sugar beet factory biofilm samples (SN)`.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 3. Summary of sugar beet factory samples and resulting numbers of isolates identified, with distribution of genera based on full-length 16S rDNA or ITS1 region rDNA Sanger sequencing.

 

 

 

 

 

                                                                                                                                                                                                                  ^a Only the factory name was legible.

Table 4: Measured viscosities of culture broths from biofilm- and diffuser juice-derived isolates from sugar beet factories in North America classified as “watery” viscosity (1-20 cP at 20 RPM).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

                                                                                                                                       ^a Higher viscosity samples could not be measured at 50 RPM due to exceeding the upper limit for instrument torque required to determine viscosity.

Table 5: Measured viscosities of culture broths classified as “intermediate” viscosity (30-230cP at 1 RPM) from biofilm- and diffuser juice-derived isolates from sugar beet factories in North America.

 

 

 

 

 

 

Table 6: Measured viscosities of culture broths from biofilm- and diffuser juice-derived isolates classified as “viscous” that required dilution to enable measurement (diluted with 100 mL water added to 50 mL cultures) of viscosity (2-57cP at 5 RPM).

 

 

 

 

 

 

 

 

 

^a Higher viscosity samples could not be measured at 20 RPM due to exceeding the upper limit for instrument torque required to determine viscosity.

LIST OF FIGURES

Figure 1. Microbial isolates tallied by genus from sugar beet factories in North America. Gram-positive bacteria are indicated by (+), Gram-negative bacteria are indicated by (−), and fungi are indicated by (f). The 9 genera that had only 1 isolate each were grouped into the category “other”. Note: The NCBI taxonomy database entry indicates that the type strain [Curtobacterium] plantarum ATCC 49174 should be transferred into the genus Pantoea.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 2. Rarefaction analysis estimating the isolation rate of novel taxa from sugar beet factories in North America. Each point represents the average of 10 random subsamples at intervals of A) isolates and B) factory samples. Error bars denote the standard deviation.

 

 

 

 

 

 

 

 

 

Figure 3. Relative proportion of juice and biofilm samples across different viscosity classifications from sugar beet factories in North America.

 

 

 

 

 

 

 

 

 

 

 

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Introduction

Cercospora leaf spot (CLS) caused by the fungal pathogen Cercospora beticola Sacc., is considered one of the most destructive foliar diseases of sugar beet (Beta vulgaris L.) and closely related relatives such as table beets, spinach, and swiss chard (Panella and Lewellen 2007; Vogel et al., 2018; Rangel et al., 2020; Tan et al., 2023). Moderate to severe epidemics causing extensive foliage damage and regrowth can result in considerable sugar losses unless applications of fungicides are used throughout the growing season (Lamey et al., 1987; Shane and Teng, 1992; Rangel et al., 2020; Liu et al., 2023). While efforts to breed enhanced CLS resistance into sugar beet is showing promising results, it is still ongoing and the commercial varieties that are currently available provide only partial crop protection that still require multiple applications of fungicides during the growing season to keep CLS symptoms to a minimum (Panella and Lewellen 2007; Khan et al., 2008; Vogel et al., 2018; Song et al., 2023).

Deployment of the most current integrated pest management (IPM) strategies such as disease prediction models, precisely timed fungicide applications, crop rotation practices/extensive field tillage, and the utilization of superior genetics with enhanced disease resistance traits in sugar beet have helped significantly in reducing CLS in sugar beet (Windels et al., 1998; Wolf and Vereet, 2005; Khan et al., 2007; Vogel et al., 2018; Kiniec et al., 2020; Memic et al., 2020; El-Jarroudi et al., 2021; Hernandez et al., 2023; Song et al., 2023). However, in warm humid growing regions, such as in the North Central and Great Lakes growing region of the United States, as well as the southern European growing region, heavy CLS outbreaks still occur yearly (Windels et al., 1998; Skaracis et al., 2010; Gummert et al., 2015; Vogel et al., 2018; Rangel et al., 2020; Spanner et al., 2022; Liu et al., 2023).

The most resilient and sustainable CLS reduction strategy from a circular agriculture perspective is to incorporate robust CLS resistance traits into sugar beet hybrid cultivars (Brown 2002; Taguchi et al., 2011; Vyska et al., 2016; Vogel et al., 2018). Progress is being made on this front with the recent incorporation of a new CLS resistance trait into commercially available sugar beet hybrid cultivars currently in the fields (Vogel et al., 2018; Emam et al., 2022). While this new trait has helped significantly in combating CLS pressure in sugar beet fields, the trait does not offer complete disease protection, and still requires the use fungicides (though less) to limit damage and maximize sugar yields (Emam et al., 2022).

Because of the biannual nature of the sugar beet lifecycle—compounded by the fact that all commercially available sugar beet cultivars are hybrids generated through complex cytoplasmic male sterile (CMS) crossings—breeding new and novel disease resistance traits into sugar beet can take close to a decade to be commercialized (Karakotov et al., 2021). Traits that are polygenic or recessive in nature can take even longer to be commercially realized. Marker assisted selection (MAS), next generation sequencing (NGS), and other molecular techniques have revolutionized sugar beet breeding programs by allowing early seedling selection of plants introgressed with disease resistant traits or even stacked events, thus shortening the breeding pipeline by months or even years (Karakotov et al., 2021). However, these molecular-based methods have limited utility if there is no prior knowledge of the genetic underpinnings for the observed disease resistant trait such as when introgressing from wild beet relatives or from mutagenized populations.

There is still no substitute for careful phenotyping when identifying new and novel disease resistance traits when the genetic causal variants in the genome are not known. When testing for CLS resistance in the field under natural epidemics of CLS, unsatisfactory outcomes can result because of low disease intensity. Even with artificial inoculations of Cercospora beticola in field CLS nurseries, favorable environmental conditions are still required for producing moderately severe and uniform disease epiphytotic conditions (Ruppel and Gaskill, 1971; Jansen et al., 2014). Furthermore, selecting individual plants with resistance out of heterogenous breeding populations in the field is difficult because of the masking effects from environmental and nutritional factors (Xu 2016; Louwaars et al., 2018). Wild beet relatives or mutagenized populations that are frequently incorporated into pre-breeding lines in hopes of finding new and novel disease resistance traits often lack sufficient agronomic traits—such as uniform germination or early seedling vigor (Lewellen and Panella, 2007). This substantially reduces uniform field plot establishment and complicates evaluation and selection of the CLS resistance traits.

As a way to expedite the early-stage phenotyping when identifying and characterizing new and novel disease resistance traits, we initiated this study to determine the utility of using greenhouse Cercospora leaf spot (CLS) screening assays in a sugar beet pre-breeding pipeline. Recently, the sugar beet germplasm, KEMS06 (PI-663873), was found to have high CLS resistance in two separate field trials (Eujayl et al., 2022a; Eujayl et al., 2022b). The KEMS06 germplasm was derived from an ethyl methanesulfonate (EMS)-mutagenized population whose sibling cohorts have shown diverse and tractable phenotypes in many disease resistance breeding studies (Eujayl and Strausbaugh, 2014; Eujayl and Strausbaugh, 2016; Eujayl and Strausbaugh, 2018; Majumdar et al., 2023), suggesting that the KEMS06 CLS resistance trait may contain genetic underpinnings that have not been leveraged before in commercial sugar beet cultivars. Therefore, we investigated the utility of using greenhouse CLS resistance screening assays to phenotypically track the introgression of the KEMS06 CLS resistance trait into the double haploid line KDH13 (PI 663862) and its full sibling double haploid cohort, KDH4-9 (Eujayl et al., 2016). Because plant double haploid genomes are nearly homogenous (Baenziger, 1996; Gurel et al., 2021), crosses with double haploids allow the generation of ‘clean genomic scaffolds’ to genetically track the causal variants linked to observed phenotypes in genome-wide association studies (GWAS) and to generate accurate molecular markers for marker assisted selection (MAS) breeding approaches (Taguchi et al., 2011; Bernardo 2020). Both KDH4-9 and KDH13 are also extremely susceptible to CLS (Eujayl et al., 2022a; Eujayl et al., 2022b) allowing us to observe and measure the segregation processes from one successive filial generation to the next. In this study, when using greenhouse CLS resistance screening assays, the important plant selection criteria metrics that we used for successful phenotyping outcomes in a segregating population are discussed as well.

 

Materials and Methods

Plant Material:

Sugar beet lines used in this study were developed by the United States Department of Agriculture-Agricultural Research Service (USDA-ARS) sugar beet program at Kimberly, Idaho, USA (Eujayl et al., 2016; Eujayl et al., 2022a; Eujayl et al., 2022b). The CLS-resistant parent used in this study is KEMS06 (PI-683514), a germplasm line isolated from an ethyl methanesulfonate (EMS)-mutagenesis population study (Eujayl and Strausbaugh, 2014). KEMS06 is homozygous self-fertile (SF), homozygous for multigerm seed (MM) and red hypocotyl color (RR). We used these traits to phenotypically track successful hybridizations in the hybrid progeny from one filial generation to the next. Two separate resistant x susceptible hybrid crosses were performed using KDH13 (PI 663862) and its full sibling cohort (KDH4-9) as the respective CLS-susceptible parental lines. Both KDH13 and KDH4-9 are homozygous self-fertile (SF), and homozygous for the recessive traits monogerm seed (mm) and green hypocotyl color (rr). KEMS06 was the pollen donor in these two cross-pollination events to generate the following hybrids:

Cross1 = KDH4-9 x KEMS06

Cross2 = KDH13 x KEMS06

In this report, the shorthand names Cross1 and Cross2 were used interchangeably for full hybrid names, KDH4-9 x KEMS06 and KDH13 x KEMS06, respectively. Filial generation was denoted in a suffix when necessary. Phenotypic analysis of the progeny from these two crosses was carried out to the F₃ generation for Cross1 and F₄ generation for Cross2. Seeds from both hybridizations were allowed to mature, then seeds were harvested from the KDH4-9 and KDH13 parental lines only. F₁ progeny from these crosses were identified by red hypocotyl color in the newly emerged seedlings and proceeded forward with these only.

Standard plant growth conditions:

Unless specifically noted, all plant material discussed in this report used the following plant growth conditions. Sugar beet seeds were destemmed, scarified, and sown at a rate of 2-3 seeds per 5 x 7.6 cm pot (24 pots per flat) into soil-less potting mix (Sungrow; Agawam, MA, USA). Plants were thinned as necessary to 1 plant per pot (24 per flat). For the F₁ seed from Cross1 and Cross2 in which the red hypocotyl seedlings were selected, all the green (rr) hypocotyl seedlings were removed. Plants were then grown for eight weeks under greenhouse conditions. Slow-release fertilizer (Osmocote 14:14:14; Scotts-Sierra Horticultural Products Company, Marysville, OH) was incorporated into the potting medium before planting at a rate of 17.5g/liter of medium to supply adequate nutrients. Supplemental lighting (LED broad spectrum grow lights; PHOTOBIO, Hydrofarm, Petaluma, CA) was used in the fall/winter/spring months to maintain an 18 hr. light-cycle. Greenhouse temperatures were regulated to 21-27 °C unless noted. Plants were vernalized at 4 °C for 16 weeks in a growth chamber (supplemented with constant dim fluorescent light). After 16 weeks, plants were acclimated to bolting/flowering conditions by slowly ramping up the temperature from 4 °C to 18 °C over a period of four weeks (22 hr. light-cycle) supplemented with constant red light (~660 nm) using a single red LED bulb for a 3 m² space. Plants were then placed back into the greenhouse and maintained under long-day (22 hr.) light conditions supplemented with constant red light (~660 nm). Adequate water was supplied to keep the soil moist but not water-logged and a second dose of 14:14:14 slow-release fertilizer was applied at a rate of 17.5g/liter of medium. For seed production from Cross1 and Cross2, unless specifically noted in the report: when flowering commenced, the plants were grouped according to the hybrid crosses and placed inside enclosed fabric tents to promote bulk cross-pollination within each cross. Bulk F₂ seed was allowed to mature and then harvested. For hybrid Cross2 (KDH13 x KEMS06), a second round of bulk cross-pollination was performed and bulk F₃ seed was harvested prior to any phenotypic selection occurring.

Greenhouse Cercospora leaf spot (CLS) assay:

Plant growth conditions: Using our standard protocol described above with the following modifications: Seedlings were thinned to one plant per pot and allowed to grow for 3-4 weeks until the 8-10 leaf growth stage. Previously published greenhouse based CLS screening methods have established that high humidity (≥95%) and temperatures in the range of 27-32 °C are required to establish Cercospora beticola infection in sugar beet (Rossi et al., 1999, Rossi et al., 2000, Khan 2008). However, in order to achieve a more uniform and consistent C. beticola leaf infection on sugar beet leaves for individual plant selections, we found it necessary to also acclimate the plants to near 95% humidity and 27-32 °C two weeks prior to treatment and maintain plants inoculated with C. beticola at high humidity throughout the duration of the experiment. Pilot experiments determined that open benchtop greenhouse experiments could not consistently achieve this environmental condition throughout the day and night. For CLS studies in the greenhouse, clear plastic tents (60 cm. x 60 cm. x 60 cm; BugDorm Mfg., Taiwan) were wrapped tightly on the outside with a layer of clear plastic sheeting to maintain a constant 95% humidity and 27-32 °C throughout the duration of the experiment. During the summer months, venting had to be monitored during the hottest times of the day to maintain these conditions without overheating.

Source of inoculum: Sugar beet leaves infected with Cercospora beticola were harvested from select fields in southern Idaho that have shown yearly outbreaks for the last 10 years (areas endemic to Idaho). The leaves were laid out as a single layer on the lab bench and air dried at ambient temperatures for two to three days. Leaves were hand crushed and stored in air-tight containers at 4 °C until ready to use in the greenhouse CLS index ratings assay. Dried leaves were stored no longer than 12 months (harvested from the previous field season) before being used in the CLS greenhouse assays. To verify the presence of Cercospora spores within the leaf spots, microscopic examination was routinely performed on freshly harvested leaves from the field as well as leaves from the greenhouse CLS assay.

To make the inoculum and application: 30 grams of crushed leaves were added to 500 ml of deionized water in a 1 L beaker and allowed to gently shake at room temperature for eight hours. The inoculum was filtered through three layers of cheese cloth, and spores were counted using a hemocytometer and diluted with deionized water to application strength (~1×10³-1×10⁴ spores/ml). Inoculum was applied using a manual hand sprayer (ZEP, Atlanta, GA) on both sides of all the the sugar beet leaves of the plant until dripping. 500 ml of inoculum was enough to treat eight flats of plants (four tents worth). Plants were arranged in the flats in a random block design to minimize error in application or variability in the environmental conditions. A susceptible check (KDH13 or KDH4-9) was used in every experiment to verify the efficacy of the treatments. Because greenhouse experiments are not afforded C. beticola’s cyclic spore reinfection process that field nurseries rely on to achieve robust disease outcomes (Ruppel and Gaskill, 1971), a second inoculation is applied to the leaves 10 days later as outlined above to allow for a second potential infection period to occur per treatment.

Scoring CLS on sugar beet: After the second application of the inoculum, the sugar beet plants remained inside the clear plastic tents throughout the duration of the experiment. Three weeks after the second application of the inoculum, plants were removed from the plastic tents and scored following established methods (Ruppel and Gaskill, 1971; Hanson et al., 2014; Atoum et al., 2015; Jay et al, 2020). Briefly, plants were individually inspected and scored using a disease index rating from 1 to 9 (whole numbers) as described in Table 1. Three separate experts were employed when rating the individual plants in the Cross1-F₂ and Cross2-F₃ segregating populations. For subsequent ratings in the progeny families, ratings were done by a single expert. Plants that were selected to proceed in the breeding pipeline were removed from the diseased flat, cleaned up by removing all symptomatic leaves and put into clean greenhouse nursery flats for four weeks to recover. They were then moved to vernalization conditions for bolting and flowering as described in our standard plant growth conditions section of this report.

Data analysis:

Raw data was presented wherever possible in this report. Statistical analysis following established methods was performed on select data sets as indicated in the report when necessary to clarify findings or conclusions. P-values were determined using paired t-test (two-tailed) analysis at either the .05 confidence level (*) or .005 confidence level (**). For greenhouse assays, the mean disease index () of the population was determined using the following equation:

Where (is the sum of the individual ratings divided by the total of number of individual plants () in the population (Ruppel and Gaskill, 1971; Ali et al., 2016). To determine the variance () of the population the following formula was used:

Where () is the sum of all the squared differences between each data point and the mean, divided by the total number of individual plants () in the analyzed population (Ruppel and Gaskill, 1971; Ali et al., 2016).

 

Results and Discussion

Disease Indices of the Parental Lines:

In this study we first wanted to establish the phenotypic selection criteria at the individual plant disease rating level for the parental lines. By using this methodology, we could then apply the appropriate individual plant selection parameters to the phenotypically segregating populations Cross1-F₂ and Cross2-F₃ to give as stringent phenotypic selection of the individual plants being assayed as possible without missing any of the genetic determinants underlying the CLS resistance of the KEMS06 line. The greenhouse CLS ratings of the parental lines are presented in Table 2 as individual plant frequency distribution across the disease rating scale. The CLS-resistant KEMS06 parental line, had a frequency distribution that ranged from 1 to 5 with a mean disease index of 1.9 to 2.0 between the three separate biological replications (47-48 plants per rep; Table 2). The two CLS-susceptible parental lines (KDH4-9 and KDH13) used in this study had frequency distributions on the opposing end of the disease rating scale spectrum with frequency distributions between 5 and 9, and mean disease index scores between 8.3 and 8.5. We compared the results from two CLS field nursery experiments that were performed in 2015 and 2016 with our greenhouse CLS ratings results (Table 2). Field trial disease ratings for KEMS06 were slightly higher (more susceptible) with a 2 year mean average of 2.4 compared to the greenhouse assay mean of 2.0. In the field, KEMS06 showed significantly more CLS resistance than the resistant check used in the field assay in both years. On the other end of the ratings spectrum, the field results presented in Table 2 show that the susceptible parental line, KDH4-9, in the field was equal in disease severity to the CLS susceptible check with a disease index rating of 9 for 2 years in a row. The greenhouse disease index rating for KDH4-9 for all three biological reps was between 8.3 and 8.4.

To analyze the disease indices of the parental lines with more scrutiny, the individual plant frequency distributions of the parental lines were plotted in histogram format in order to establish the individual plant selection stringency parameters (Figure 1A). For the resistant KEMS06 parental line, the difference in the number of plants in disease rating 1 vs 2 was statistically insignificant (p-value 0.46), but then drops significantly at rating 3 on the disease scale (p-value 0.0006). Both susceptible parents also showed a tight clustering result in the histograms at the susceptible end of the rating scale (Figure 1B and 1C). No individual plant selection overlapped on the ratings scale between the susceptible parental lines and resistant KEMS06 parental line which gave us additional confidence that we would be able to select individual plants with CLS resistance trait from in the segregating hybrid populations (Table 2). Based on these results, the individual plant selection stringency parameters were set to a selection cutoff level of less than or equal to 2, so that when we began scoring the Cross1-F₂ and Cross2-F₃ segregating populations for CLS resistance, individual plants that scored 1-2 (more resistant) would be selected to proceed forward in our breeding pipeline and plants that scored 3 or higher (more susceptible) would be

Disease Indices of the segregating hybrid populations

To have a large enough population of segregating disease resistant plants with a rating of 2 or less to move forward with in the breeding pipeline, the sampling size that we used in the parental lines was scaled up to 121 plants in the Cross1-F₂ segregating population and 119 plants in the Cross2-F₃ segregating population. The greenhouse CLS ratings for the phenotypically segregating populations of these two hybrid crosses are presented on the first lines of Tables 3 and 4 as individual plant disease rating frequency distribution across the rating scale. In contrast to the results with the parental lines, the segregating populations for both hybrid crosses in this study showed a rating distribution that was spread across the disease index scale (ratings 1 through 9) with a greater proportion of individual plants scoring between 3-5. The mean population disease indices were calculated to be 5.0 and 3.7 for Cross1-F₂ and Cross2-F₃

The disease ratings were visualized by plotting the individual plant disease index frequency distributions in histogram format for the segregating populations of Cross1 and Cross2 (Figure 1D and 1E). The observed phenotypic variance () was calculated for these two segregating populations, with Cross1-F₂ having an observed disease rating variance of 4.38 and Cross2-F₃ having a variance of 3.79 (Figure 1D and 1E insets). This is in contrast to the parental lines had very low variance scores with the resistant KEMS06 parent line having a variance score of 0.98 and the two susceptible parental lines, KDH4-9 and KDH13, having respective 0.80 and 0.42 variance scores (Figure 1A-C insets). This higher variance ratio of the segregating populations to the parental lines suggests that this CLS resistance trait is a quantitative trait made up of two or more genetic determinants, especially when environmental contributions to the observed variance scores are minimized as is the case with our controlled environment greenhouse experiments (Bernardo 2020).

Using the individual plant selection stringency parameters that we established when analyzing the parental lines (cutoff being a disease score of 2 or less), there were 16 plants that scored 2 or less in the Cross1-F₂ segregating population and there were 33 plants that scored 2 or less in the Cross2-F₃ segregating population. By using these stringent selection criteria, there were twice the number of Cross2-F₃ segregating population plants that had a disease index score of 2 or less. There are various reasons why this could be the case including inconsistencies in the greenhouse-controlled environment or that the KDH13 sugar beet germplasm is positively contributing genetically to the observed CLS resistance in the Cross2 hybrid. However, a major difference between Cross1-F₂ and Cross2-F₃ is that Cross2-F₃ is in the third filial generation before selection took place.

When we initiated this study to characterize at the CLS resistance trait in KEMS06, Cross2 was farther along in our pre-breeding pipeline. Because Cross2 was kept isolated during the first bulk pollination (F₂) cycle, this was considered a ‘bulk increase’ of the Cross2 family. However, with closer scrutiny to our plant vernalization methods, we realized that in order for us to be selecting the CLS resistance trait in the F₃ generation, Cross2-F₃ actually proceeded through an additional vernalization period (two vernalizations in total) that Cross1-F₂ did not go through. Considering that each vernalization period has a duration period of 4 months at 4 °C with near 100% humidity, vernalization storage is typically stressful on sugar beets, and we have observed upwards of 10-20% loss to root rot or desiccation. While we have not fully investigated this aspect with KEMS06 yet, this ‘loss to root rot’ during vernalization may be inadvertently selecting for plants that are more resistant to root rot than the other individual plants in the Cross2-F₂ cohort. Cross-platform speaking, the KEMS06 trait may actually be a broad-spectrum fungal disease resistance trait with pleiotropic effects—improving both root rot resistance and Cercospora beticola resistance in sugar beet. In support of this hypothesis, the KEMS06 germplasm was noted in a previous study as having ‘superior’ storage rot resistance—with 50% better than the commercial resistant check used in the study (Eujayl and Strausbaugh 2018). While vernalization stress may have enriched the number of plants resistant to some fungal pathogens in the Cross2-F₃ on the outset, it did not change our CLS rating selection criteria in the greenhouse of having 2 or less on the disease rating scale. Thus, our conclusion here is that the extra vernalization cycle just increased the number of plants we selected in the Cross2-F₃ segregating population.

While all 49 plants that were selected from both of the segregating populations (Cross1-F₂ and Cross2-F₃) proceeded forward in our breeding pipeline, there was non-uniformity in the bolting and flowering in our greenhouses which resulted in unsynchronized flowering events within larger groups of plants as a whole. Consequently, this resulted in the progeny families being separated into two groups and batch-processed sequentially based on earlier flowering (30 plants) and later flowering (19 plants). Both batches had sufficient numbers of plants from the two segregating populations (including susceptible examples) to give us confidence to report on the results from the first batch here.

Disease Indices of the Progeny families

While the large phenotypic variances measured in the Cross1-F₂ and Cross2-F₃ segregating populations may result from underlying genetic determinates segregating in-step across the population in a quantitative manner, this may not be the case. Phenotypic segregation alone does not allow us to model the segregation of the underlying genetic determinants for this CLS resistant trait. Other factors, such as the environment and epistatic mechanisms, can impact the observed variances across a population even in controlled environments such as greenhouse assays (Doust et al., 2014). Therefore, the first steps to uncover whether possible genetics determinants are indeed underpinning this CLS-resistance trait is to look at inheritance. Can the 49 plants that we stringently selected from our greenhouse CLS assay faithfully pass on to the next generation the CLS resistance? If the progeny display the same level of CLS resistance as the phenotypically selected parent, then this would be strong evidence of genetic control, and it is heritable. Because both parental lines in this study were homozygous for self-fertility (SF), each individual plant of the 49 plants that we selected was self-fertile. We took advantage of this individual self-fertility trait to generate single plant descent progeny families as a way to track the genetics of this CLS resistance to the next filial generation.

Therefore, we repeated our greenhouse CLS assays on individual plants in the progeny families for both hybrid crosses by testing 12 plants from each progeny family (Tables 3 and 4). We chose this sampling number for two reasons: a) greenhouse capacity constraints limited how many progeny families could screen concurrently, and by using a half flat setup (12 plants) more progeny families could be screened at the same time, and b) based on our stringent selection criteria and single plant descent methodology, we hypothesized (at least for some families) a digital response in the screening—with 12 out of 12 sampled plants per progeny family having more or less the same CLS resistance rating as the KEMS06 parental line.

Beginning with the results from Cross1-F₃ (KDH4-9 x KEMS06-F₃ progeny) in Table 3, there was a wide spectrum of CLS response in the progeny. To organize these results, we sorted the progeny families by mean disease index (lowest to highest; far right column on Table 3). The results show a distinct segregation of the progeny families based on, not only the mean disease index results, but also by the clustering pattern of the individual plants within each family as visualized in Figure 2A. There was a segregation pattern that can be broadly defined as resistant, segregating, and susceptible. 1) resistant: individual plants in 5 progeny families from Cross1-F₃ showed nearly identical disease mean indices (~2.0) as the KEMS06 parent as well as the scores clustering at the low end of the disease rating scale (more resistant). 2) segregating: individual plants in 4 progeny families had mean disease index ratings between 3.7 to 5.0, well above the stringent CLS resistance selection cutoff requirements of less than 2 that was established earlier and displayed an evenly distributed segregation pattern of the CLS trait from the low end of the disease rating scale to the high end of the disease rating scale. 3) susceptible: individual plants in the last 3 progeny families were quite susceptible with mean disease index ratings between 7.9-8.0 indicating that CLS resistance was not inherited from the previous filial generation. In fact, all three of these progeny families showed a level of susceptibility very similar to the selected susceptible progeny line at the bottom of the list on Table 3. A similar pattern emerged for the Cross2-F₄ (KDH13 x KEMS06-F₄) progeny families which could also be organized by sorting the mean disease index (lowest to highest; far right column on Table 4) and the clustering pattern of the individual plants within each family (Figure 3A). There were six Cross2-F₄ progeny families that could be grouped into the resistant category; 7 progeny families that could be grouped into segregating

When the Cross1-F₂ and Cross2-F₃ progeny were tested in the greenhouse for CLS resistance, the individual plants always remained within their families, and it was the collective families that were sorted based on the three groups discussed: resistant, segregating, and susceptible group. However, sugar beet crop domestication is still considered to be in its infancy, so it is not uncommon to find breeding ‘families’ to be segregating for challenging reproductive traits such as genetic male sterility or self-incompatibility traits (Panella and Lewellen, 2007). This is especially true when broadening the genetic base of breeding lines with wild beets accessions that naturally have these reproductive traits to promote outcrossing within a population to maintain genetic diversity (Panella and Lewellen, 2007). Therefore, it is important to discuss recurrent selection breeding which would have resulted in equally successful outcomes to single plant descent breeding, but usually requires additional generations to arrive at complete introgression of the desired trait (Panella and Lewellen 2007; Karakotov et al., 2021). Recurrent selection breeding method focuses on pooling phenotypically selected plants together as a group then bulk cross-pollinating together with repeated selection/generation cycles until complete introgression of the desired trait is achieved (Panella and Lewellen 2007; Karakotov et al., 2021). As mentioned above, sometimes this is required if the population is segregating for reproductive deficits that prevents the use of single plant descent/self-fertilization.

For an approximated recurrent selection outcome in these experiments, if our phenotypic selection criteria that we used in the greenhouse CLS assay was at a high level to give us confidence that we were enriching for the trait then all selected individual plants could have been combined to generate a bulk seed production on the selected plants (bulk the 16 plants together for Cross1-F₂ and bulk the 33 plants together for Cross2-F₃). The individual plant progeny within each bulk seed production would have then been scored for CLS response resulting in the unsegregated progeny analysis for both Cross1-F₃ and Cross2-F₄ as shown in Figures 2C and 3C, respectively. While the number of plants that scored 2 or lower (more resistant) on the CLS rating scale increased, a recurrent selection method would have required additional generations to reach full introgression of the CLS resistance trait.

Heritability of the CLS resistance trait is one of the most important results from the greenhouse testing of the progeny families. This is clearly seen in the resistant category. Five out of 11 families (45%) from Cross1-F₃ and 6 out of 16 families (38%) from Cross2-F₄ showed that the KEMS06 CLS resistance trait was fully introgressed and fixed—fixed as defined as all individual plants within a single progeny family displaying the same KEMS06 CLS resistance trait without a broad segregating distribution (Figures 2B and 3B). Progeny testing in the greenhouse further refined our selection of families to move forward with in the breeding pipeline by selecting the top 11 progeny families that had nearly identical disease mean indices (~2.0) as the KEMS06 parent as well as having a similar score clustering at disease resistant end of the rating scale.

In our pre-breeding pipeline, we intentionally switched from bulk seed production that was done during pre-phenotypic selection of the segregating population to single plant selfing seed production post-phenotypic selection so that if our phenotypic selection was stringent enough, some or all of the progeny families would be fixed for the desired CLS resistance trait. This outcome is only possible by starting with two parental lines homozygous for self-fertility so that all plants that were phenotypically selected (above cutoff 2 on the disease rating scale) in the segregating filial generation would be guaranteed to be fertile when bolting commenced four months later.

If homozygous self-fertile parental lines had not been utilized and were segregating for male sterility, then there would have been a 1:3 to 1:9 chance that the plants being phenotypically selected for CLS resistance may also have been phenotypically pollen sterile preventing self-fertilization and the lineage from that single plant would have ended—unable to capture the fixed CLS resistance trait for the next generation. The breeding work-around often used for this is to cross-pollinate sterile or self-incompatible siblings (when identified) to one of the cohorts that are pollen fertile (sib-mating). In our case, other plants in the same CLS resistant selection cohort that are pollen fertile would be the pollen donors to other cohorts that are pollen sterile (or self-incompatible). The downside to sib-mating is that even with stringent selection criteria that we used, the progeny could still be segregating for the CLS resistance trait resulting in a sibling pair that would keep an exchange of genetic diversity within the population, but not be fixed for the disease resistance trait. This would require a second round of phenotypic selection in the following (F₄ or F₅) generations and possibly running again into male sterile cohorts.

It is important to note that the hypocotyl color (red or green) and seed germ-type (monogerm or multigerm) traits that were used to verify that we had successfully generated our hybrid Cross1 and Cross2 are still segregating in the successive filial generations (F₃ for Cross1 and F₄ for Cross2) as shown in Table 5. These visual markers will continue to be used for successive backcrossing/introgressing experiments in our pre-breeding pipeline.  In addition, when this Cercospora resistant pre-breeding line is released, seed breeders can use these visual traits for increased flexibility in their breeding pipelines as well.

 

Summary

In this report we describe the use of greenhouse CLS assays to characterize a CLS resistance trait from the KEMS06 sugar beet germplasm. Within two different hybrid populations derived from KEMS06 (Cross1 and Cross2), the measured segregation patterns with corresponding variance scores strongly suggest that there are genetic determinants directly linked to the CLS resistance trait and are segregating in-step across the population in a quantitative manner. Heritability of the KEMS06 CLS resistance trait was tractable into the F₂, F₃, and F₄ filial generations as demonstrated by our greenhouse CLS assay. By following a single plant descent methodology—expedited by the use of self-fertility traits in the parental lines—complete introgression was observed in the progeny families.

To make this CLS resistance trait as translatable as possible to the sugar beet research community as a whole, we are also pursuing this trait at the molecular level in order to elucidate the genetic underpinnings in the sugar beet genome.  By using the double haploid parental lines (KDH4-9 and KDH13) in this study, we are establishing a genomic framework for us to genetically track any causal variants linked to the observed phenotypes in genome-wide association studies (GWAS). This work also is expected to facilitate the identification of tractable molecular markers for marker assisted selection (MAS) breeding approaches to expedite the introgression of the CLS resistance trait into other sugar beet breeding lines for research and commercial purposes.

 

The utility of pre-breeding pipelines for sugar beet development

While the primary focus of this study was to investigate the possible genetic underpinnings to the KEMS06 CLS resistance trait using greenhouse amendable methods and assays, there are some general guidelines that we have found that are applicable to many other sugar beet pre-breeding initiatives with similar goals to identify new and novel causal variants to other disease resistant traits (e.g. Curly top virus or Rhizoctonia root and crown rot), or other goals such as the introgression of wild beet relatives or mutagenized populations for germplasm enrichment purposes. None of the methods below are new and novel per se but by combining them together—a pre-breeding pipeline emerges. Collectively, the methods described within this report accelerated our CLS resistance pre-breeding timeline by reducing the time that would normally be required to introgress and fix disease resistance traits if limited to field trials and recurrent selection methods only. The three general guidelines for our greenhouse pre-breeding pipeline in selecting for CLS resistance and progressing in our pre-breeding pipeline are outlined below and illustrated in the flowchart on Figure 4:

 

1. Using Individual plant selections to phenotypically ‘sieve through’ segregating populations: It is important to select parental lines that are sufficiently resistant/susceptible across the disease spectrum being studied as well as establish or adapt disease rating metrics for stringent selection. Hybrid crosses were then brought to phenotypically segregating populations before selecting for the desired traits. In the current study, for Cross1 this was the F₂ generation and for Cross2 this was the F₃ generation. Once a population is phenotypically segregating, individual plants can then be selected. Since greenhouse screens allow for close inspection of individual plants or even young seedlings, stringent selection can act like a “phenotypic sieve” by finding the needle in a haystack within heterogenous populations or highly segregating populations controlled by polygenic genetic traits. Individual plants if given proper care, can then have swift vernalization turn-around times and bolting within four months. This is in contrast with field selection which is usually a once-a-season turnaround timeline.

 

2. Greenhouse assays reduce environmental stress and confounding observations: Pre-breeding brings unique challenges that greenhouse assays can overcome. This includes working with wild beet relatives and mutagenized populations that often lack sufficient agronomic traits for general field trials—such as uniform germination or early seedling vigor. In the field, the lack of these agronomic traits could substantially reduce uniform field plot establishment complicating evaluation and selection. A well-developed greenhouse assay can uncouple some of these field-desired agronomic traits without affecting the screening for disease resistance. An example of this would be to germinate under filter paper and transplant only germinated seedlings to the greenhouse for disease rating assays. Furthermore, selecting individual plants with resistance out of heterogenous breeding populations in the field is difficult because of the masking effects from environmental and nutritional factors (Doust et al., 2014)—both of which can be tightly controlled in the greenhouse.

 

3. Leveraging self-fertility to fix a desired trait in the progeny: When establishing this pre-breeding pipeline study, we wanted to, as quickly as possible, fix the target trait in the progeny as soon as the phenotyping selections were made. Single seed/plant descent post-phenotypic selection is the most straightforward method in achieving this goal, especially if the selected plants are capable of self-fertilization. However, self-fertilization is a double-edged sword with modern sugar beet breeding programs. On one hand, it is not possible to generate hybrid sugar beet seed on a commercial scale without eliminating self-fertility from the final crosses. But on the other hand, in pre-breeding pipelines such as the one we are reporting here, self-fertility allows immediate single seed/plant descent as soon as phenotypic selections are made—rapidly fixing desired traits that can be verified in the progeny lines. We do believe that both can and should co-exist, but that in pre-breeding pipelines, self-pollination should be used as much as possible.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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1 Introduction

Sugarbeet production in the Northwest U.S. is located primarily in southern Idaho and southeastern Oregon. Sugarbeet growers produce beets for the Amalgamated Sugar Company (ASCO), a grower-owned cooperative. From 2017 to 2021 an average of 67,340 ha yr^-1 (166,400 ac yr^-1) of sugarbeets were harvested in this growing area (USDA-NASS, 2022). Annually, ASCO grows between 30 and 40% of total U.S. sugarbeet production (USDA-NASS, 2022).

During sugarbeet processing, large amounts of precipitated calcium carbonate (PCC) is produced as the by-product from impurity removal during the purification of the sucrose juice stream. Impurities that need to be removed include organic molecules, phosphorus, magnesium, calcium, potassium and sodium (Hergert et al. 2017). To remove these impurities from the sucrose liquid juice stream, calcium oxide and carbon dioxide are added to form calcium carbonate (CaCO₃) which then precipitates out of the liquid juice stream with the impurities included. This combination of CaCO₃ and impurities form the PCC which is removed as a solid material and stored on site in large stockpiles.

The phosphorus (P) concentrations in PCC are high relative to other nutrients [potassium (K) and zinc (Zn)]. Across sugarbeet production areas in the U.S. P concentrations in PCC ranged from 24 to 34 kg P₂O₅ ha^-1 (21 to 30 lbs P₂O₅ ac^-1), 2.7 to 5.2 kg K₂O ha^-1 (2.4 to 4.6 lbs K₂O ac^-1), and 0.08 to 0.10 kg Zn ha^-1 (0.07 to 0.09 lbs Zn ac^-1), respectively (Table 1). The P, K, Zn and other elements in PCC originate from the sugarbeet root and are extracted along with other non-sucrose constituents in the sugar juice stream (Sailsbery & Hills, 1987). Past studies have reported sugarbeet root P and K concentrations ranging from 200 to 1700 mg P kg^-1 and 1700 to 7900 mg K kg^-1 (Soine, 1968; Hlisnikovský et al., 2021; Bravo et al., 1989; Carter, 1986; Dudley and Powers, 1960; Doxtator & Calton, 1951). The relatively high concentrations of P in PCC, together with its low cost to the grower, make this product an attractive alternative to fertilizer P, particularly as the price of fertilizer P continues to rise (Figure 1). The prices of fertilizer reached a historic high in 2022 due to increases in the cost of natural gas and transportation (Figure 1) (Smith, 2022).

In 2018, the ASCO processing factories (Paul, ID; Twin Falls, ID; and Nampa, ID) had PCC stockpiles totaling approximately 12.6 million tons. These factories produce approximately 351,081 Mg (387,000 tons) of PCC annually (ASCO, personal communication). Calcium carbonate is not a recommended amendment for the high pH soils in the growing area resulting in PCC being stockpiled. Without an offsite beneficial use or disposal method these stockpiles will continue to grow. The difficulty in finding more land to stockpile PCC and potential environmental issues have resulted in the need for ASCO to prioritize finding uses for this product. Agricultural land application may be a practical method to dispose the PCC. Research conducted in southern Idaho demonstrated that PCC applications did not negatively affect sugarbeet, spring malt barley, or dry bean yields (Tarkalson et al, 2022). Other research has shown that PCC does not negatively affect crop growth and yield (Christenson et al., 2000). The PCC used in the study did not add significant amounts of heavy metals or other harmful constituents and did not increase the soil pH. The overall conclusion from Tarkalson et al. 2022 was that PCC applied to a calcareous silt loam soil in southern Idaho at rates up to 90 Mg ha^-1 (40 tons ac^-1) did not negatively affect crop production and could serve as a P source. Other research has shown that PCC applications did not negatively affect soil chemistry or crop production (Sailsbery & Hill, 1987; Sims, 2010).

Similar to fertilizer P, applications of PCC can increase soil test P (STP) concentrations (Sims, 2010; Hergert et al., 2017). Modern P fertilizers have been developed to be highly water soluble, and thus plant available, when initially added to soils. However, over time this P will start to react with soil minerals and gradually become less available to plants. In high pH calcareous western U.S. soils, the major P reaction is the formation of calcium phosphates which dominates the soil P cycle. Although PCC is not produced with P solubility as a consideration, PCC does raise STP concentrations, thus increasing plant P availability. Because STP is tied to crop production response, P fertilizer and PCC P economic values can be linked (Sailsbery and Hill, 1987; Sims, 2010). Potassium additions to soil can increase the overall concentrations in soils and contribute to one of the four K pools: mineral, nonexchangeable, exchangeable, and soluble. Although we do not have data on the forms of K in PCC, it is added to these pools which over time will become plant available (exchangeable and soluble) (Tisdale et al.,1993).

The objective of this study was to estimate the P and K fertilizer value of PCC for sugarbeet production in the Northwest U.S. assuming equivalent availability of P and K in PCC to that of P and K in fertilizer.

Figure 1. The average U.S. retail P [diammonium phosphate (DAP) and monoammonium phosphate (MAP)] and K (Potash) fertilizer prices (2018-2021). Fertilizer price data from DTN (2022).

Figure 2. Soil bicarbonate extractable P concentrations for precipitated calcium carbonate (PCC) treatments (PCC applied at 0 tons/ac (control), 3 tons/ac, 10 tons/ac, and 40 tons/ac) in three studies. All application rates are on a dry weight basis. For each study, soil test P concentrations were measured in the 0 to 0.3 m depth in the fall prior to PCC application and the following year in the spring. Error bars are the standard error of the treatment means.

2 MATERIALS AND METHODS

2.1 Agronomic Data

Soil sample analysis and fertilizer data for agricultural land in the Northwest U.S. entering sugarbeet production in 2018, 2019, 2020 and 2021 was collected by ASCO in their AgriData database. Data input was at the field level. For each year, data entered represent fields going into sugarbeet for the next crop. However, all these fields are in a crop rotation with other common crops (e.g. small grain, potato, corn, dry bean). Annually, the approximate number of acres with sugarbeet in a rotation growing other crops is 153,376 ha (379,000 ac). Data were filtered to remove outlier data that potentially were a result of soil test analysis errors or data entry errors (deleted data: soil free lime > 15%, soil pH < 5, soil pH > 8.5, STP = 0 mg kg^-1, and soil test K (SPK) = 0 mg kg^-1, field ha < 4 (field ac < 10). The ASCO AgriData fields used in the analysis from were crop year, growing district, field ID, acres, soil sample date, soil sample excess lime content, bicarbonate extractable soil test P (STP, Olson, 1954), and ammonium acetate extractable soil test K (STK) (USEPA, 1996).

Based on STP and STK levels, P and K nutrient input recommendations were determined for sugarbeet, corn, spring malt barley, and potato (Russet Burbank) according to research-based recommendations (Amalgamated Sugar Company, 2020; Walsh et al., 2019; Brown et al., 2020; Robertson et al., 2003; Stark et al., 2004). Sugarbeet and potato (Russet Burbank) K recommendations are linked to yield goals. A sugarbeet yield goal of 90 Mg ha^-1 (40 tons ac^-1) and a potato (Russet Burbank) yield goal of 112 kg ha^-1 (100 cwt ac^-1) were used. The remaining P and K recommendations for all crops are not linked to a yield goal. The P and K fertilizer prices over time were obtained by DTN, a data acquisition and analysis company (DTN, 2022, Figure 1).

2.2 PCC Analysis

Samples were collected from two PCC stockpiles at the Paul, ID sugarbeet processing factory, the largest sugarbeet factory in the Northwest U.S. The PCC was sampled at four locations on top of each stockpile at 0.3 m (1 ft) depth increments to 1.5 m (5 ft) (Table 2). The PCC was analyzed for total P, K, and Zn with ICP-OES detection (USEPA, 1996).

Selected STP data from Tarkalson et al. (2022) and continuing research at our research locatioin was used to show the effect of PCC on STP. This work was comprised of three separate studies established in consecutive years near Kimberly ID. The studies received PCC applications in the fall to silt loam soil plots (6.7 m × 18.3 m [22 ft × 60 ft]) at rates of 7, 22, 90 Mg ha^-1 (3, 10, and 40 tons ac^-1). The PCC treatments were replicated 4 times in a randomized block design. Soil from each plot was sampled in the fall prior to PCC application and in the following spring prior to sugarbeet planting. Soil samples were analyzed for sodium bicarbonate extractable STP (Olsen, 1954).

The ASCO AgriData, P and K fertilizer price data, and PCC P and K lab analysis results were used to determine PCC Fertilizer P value, PCC needed to meet crop P requirements, P Fertilizer Savings from PCC, and total PCC Fertilizer P value for sugarbeet, corn, spring malt barley, and potato (Russet Burbank) (ASCO, 2020; Walsh et al., 2019; Brown et al., 2020; Robertson et al., 2003; Stark et al., 2004).

2.3 PCC Economic Analysis

This paper assessed the P and K quantity in PCC and determined the economic value of PCC based on the value of equivalent P and K in fertilizer. The ASCO payment for PCC removal from factory stockpiles, transportation and application costs of PCC and P fertilizers was not accounted for in the economic analyses. The P fertilizer replacement value of PCC was based on annual average fertilizer P prices (monoammonium phosphate and diammonium phosphate) in 2018, 2019, 2020, 2021, and 2022 (Figure 1). Other value-based uses such as Aphanomyces cochlioides suppression (Breshnahan et al. 2003) will not be assessed in this paper. In our analysis, the P and K fertilizer replacement value of PCC was based on the following scenarios:

1. Fields requiring only P, and fields requiring P and K: PCC was assumed to be applied at a rate to meet the crop P requirement and the P in PCC was given fertilizer replacement value. When the PCC application rate was insufficient to meet the crop K recommendation, all the applied K was given fertilizer replacement value. When the PCC application rate applied excess K relative to the K requirement, only the K applied to meet the crop K requirement was given fertilizer replacement value.
2. Fields requiring only K: PCC was applied at a rate to meet the crop K requirement and the K in PCC was given fertilizer replacement value. No P from the PCC application was given fertilizer replacement value.

Table 1. Reported plant nutrient concentrations in precipitated calcium carbonate (PCC) across locations and time.
Study	Location	Year	P2O5	K2O	Zn
			kg Mg-1 (lbs ton-1)	kg Mg-1 (lbs ton-1)	kg Mg-1 (lbs ton-1)
Sailsbery and Hills, 1987†	California	1976	27 (24)	—	—
Sims et al., 2010	Minnesota‡	2005	27 (24)	2.8 (2.5)	—
Sims et al., 2010	North Dakota§	2005	25 (22)	4.3 (3.8)	—
Hergert  et al., 2017	Nebraska	2012	24 (21)	3.0 (2.7)	0.10 (0.09)
Hergert  et al., 2017	Wyoming	2012	27 (24)	5.0 (4.5)	0.10 (0.09)
Hergert  et al., 2017	Colorado	2012	24 (21)	5.2 (4.6)	0.10 (0.09)
Tarkalson et al., 2022	Idaho	2016	34 (30)	2.7 (2.4)	0.08 (0.07)
† No data available. ‡Average over PCC from 4 sugarbeet processing factories. §Average over PCC from 3 sugarbeet processing factories.

Table 2. Concentrations of plant available nutrients in precipitated calcium carbonate (PCC) produced at the Paul, ID sugarbeet processing plant.
Pile Location	Depth	Total P	Total K	Inorg. N†	Total Zn	Total P2O5	Total K2O	Inorg. N	Total Zn
	m (ft)	———————–mg kg-1———————–				————————- kg Mg-1 (lbs ton-1) ————————-
1	0-0.3 (0-1)	5409	3109	86.4	32.5	12.4 (24.8)	3.8 (7.5)	0.087 (0.173)	0.033 (0.065)
	0.3-0.6 (1-2)	4612	2535	59.9	22.8	10.6 (21.1)	3.1 (6.1)	0.060 (0.120)	0.023 (0.046)
	0.6-0.9 (2-3)	5080	2289	28.5	23.5	11.7 (23.3)	2.8 (5.5)	0.029 (0.057)	0.024 (0.047)
	0.9-1.2 (3-4)	5701	1557	39.0	22.9	13.1 (26.1)	1.9 (3.7)	0.039 (0.078)	0.023 (0.046)
	1.2-1.6 (4-6)	5086	1540	93.3	23.4	11.7 (23.3)	1.9 (3.7)	0.094 (0.187)	0.024 (0.047)
	Mean	5178	2206	61.4	25.0	11.9 (23.7)	2.7 (5.3)	0.062 (0.123)	0.025 (0.050)
2	0-0.3 (0-1)	7215	3012	196.6	24.0	16.5 (33.0)	3.6 (7.2)	0.197 (0.393)	0.024 (0.048)
	0.3-0.6 (1-2)	5517	1479	41.5	22.6	12.7 (25.3)	1.8 (3.5)	0.042 (0.083)	0.023 (0.045)
	0.6-0.9 (2-3)	4052	1549	18.7	20.4	9.3 (18.6)	1.9 (3.7)	0.019 (0.037)	0.021 (0.041)
	0.9-1.2 (3-4)	5190	1394	34.2	21.0	11.9 (23.8)	1.7 (3.3)	0.034 (0.068)	0.021 (0.042)
	1.2-1.6 (4-6)	5750	1407	34.9	19.7	13.2 (26.3)	1.7 (3.4)	0.035 (0.070)	0.020 (0.039)
	Mean	5545	1768	65.2	21.5	12.7 (25.4)	2.1 (4.2)	0.065 (0.130)	0.022 (0.043)
3	0-0.3 (0-1)	5319	1562	34.4	34.0	12.2 (24.4)	1.9 (3.7)	0.035 (0.069)	0.034 (0.068)
	0.3-0.6 (1-2)	5207	1405	11.6	35.3	11.9 (23.8)	1.7 (3.4)	0.012 (0.023)	0.036 (0.071)
	0.6-0.9 (2-3)	5494	1228	12.6	34.6	12.6 (25.2)	1.5 (2.9)	0.013 (0.025)	0.035 (0.069)
	Mean	5340	1398	19.5	34.6	12.3 (24.5)	1.7 (3.3)	0.020 (0.039)	0.029 (0.058)
4	0-0.3 (0-1)	5241	1130	16.9	32.2	12.0 (24.0)	1.4 (2.7)	0.017 (0.034)	0.032 (0.064)
	0.3-0.6 (1-2)	5262	1170	7.6	33.9	12.1 (24.1)	1.4 (2.8)	0.008 (0.015)	0.034 (0.068)
	0.6-0.9 (2-3)	5869	1181	7.5	33.3	13.5 (26.9)	1.4 (2.8)	0.008 (0.015)	0.034 (0.067)
	0.9-1.2 (3-4)	5970	1339	9.9	37.6	13.7 (27.3)	1.6 (3.2)	0.010 (0.020)	0.038 (0.075)
	Mean	5586	1205	10.5	34.3	12.8 (25.6)	1.5 (2.9)	0.011 (0.021)	0.033 (0.066)
All Site/Depth	Mean	5410	1699	43.1	27.9	12.4 (24.8)	2.1 (4.1)	0.043 (0.086)	0.028 (0.056)
† Inorganic N = NO3-N + NH4-N

Scenario Calculations

Values in Tables 3, 4, 5, and 6 are averaged across years for each crop from field level data. The scenario calculations were also conducted at the field level, thus, when calculations are done using table averages will result in variation in the results for: P and K Fertilizer Savings from PCC and Total Area P and K Fertilizer Savings from PCC (Eqs. 1d and 1e, Scenario 1), and K Fertilizer Savings from PCC and Total Area K Fertilizer Savings from PCC (Eqs. 2d and 2e, Scenario 2).

Scenario 1 (Table 3 and Table 4):

Eq. 1a: P Fertilizer Value of PCC = P Fertilizer Price × P Concentration in PCC

Where, P Fertilizer value of PCC = $ Mg^-1 PCC ($ ton^-1 PCC), Fertilizer P Price = $ kg^-1 P₂O₅ ($ lb^-1 P₂O₅), P Concentration in PCC= 12.4 kg P₂O₅ Mg^-1 (24.8 lbs P₂O₅ ton^-1).

Eq. 1b: PCC Rate Needed to Meet Crop P Fertilizer Requirement = P Recommendation / P Concentration in PCC

Where, PCC Needed to Meet P Requirement = Mg PCC ha^-1 (tons PCC ac^-1), P Recommendation = $ kg^-1 P₂O₅ ($ lbs^-1 P₂O₅), P Concentration in PCC = 12.4 kg P₂O₅ Mg^-1 (24.8 lbs P₂O₅ ton^-1) (Table 2).

Eq. 1c: K Fertilizer Value of PCC = K Fertilizer Price × K Concentration in PCC

Where, K Fertilizer value of PCC = $ Mg^-1 PCC ($ ton^-1 PCC), Fertilizer K Price = $ kg^-1 K₂O ($ lb^-1 K₂O), K Concentration in PCC = 2.1 kg P₂O₅ Mg^-1 (4.1 lbs K₂O ton^-1).

Eq. 1d: P and K Fertilizer Savings from PCC =  A + B

1. For fields where the rate of PCC needed to meet K requirements is higher than the rate of PCC to meet the P requirement = (PCC Needed to Meet Crop P Requirement × P Fertilizer Value of PCC) + (PCC Needed to Meet Crop P Requirement × K Fertilizer Value of PCC).
2. For fields where the rate where the PCC application rate applied sufficient K to meet the crop K requirement = (PCC Needed to Meet Crop P Requirement × P Fertilizer Value of PCC) + (PCC Needed to Meet Crop K Requirement from PCC Needed to Meet Crop P Requirement × K Fertilizer Value of PCC).

Where, P and K Fertilizer Savings from PCC = $ ha-1 ($ ac-1), PCC Needed to Meet Crop P Fertilizer Requirement = Mg ha^-1 (tons ac^-1), PCC Needed to Meet Crop K Fertilizer Requirement = Mg ha^-1 (tons ac^-1), P Fertilizer Value of PCC = $ Mg^-1 PCC ($ ton^-1 PCC), K Fertilizer Value of PCC = $ Mg^-1 PCC ($ ton^-1 PCC).

Eq. 1e: Total Area P and K Fertilizer Savings from PCC = P and K Fertilizer Savings from PCC × Area Requiring P and K

Where, Total Area P and K Fertilizer Savings from PCC = $, P and K Fertilizer Savings from PCC = $ ha^-1 ($ ac^-1), Area Requiring P = ha (ac). Values rounded to the nearest 1,000 ac.

Table 3. Scenario 1 (Fields with a crop P requirement, and with or without a K requirement) agronomic data. Land area and soil test data are from the ASCO AgriData database. For each year, values are derived from the assumption the entire area will grow the listed crop. Crop P recommendations are based on published sources (Amalgamated Sugar Company, 2020; Walsh et al., 2019; Brown et al., 2020; Robertson et al., 2003; Stark et al., 2004).
Year	Total Area	Area Requiring P	Area Requiring P and K	Percent of Total Area Requiring P and K	Percent of Total Area Requiring K	Average STP† of Area Requiring P	Average P2O5 Recommendation of Area Requiring P	Average STK‡ of Area Requiring K	Average K2O Recommendation of Area Requiring K	K2O Applied in PPC§ Credited to K Recommendation
	—————————-ha (ac)—————————-			%		mg kg-1	kg ha-1 (lbs ac-1)	mg kg-1	———– kg ha-1 (lbs ac-1) ———–
Sugarbeet
2018	63052 (155804)	17762 (43891)	8791 (21723)	28.2	13.9	15.5	100 (89)	126.7	146 (130)	36 (32)
2019	64975 (160557)	20465 (50571)	10525 (26009)	31.5	16.2	16.1	94 (84)	138.7	113 (101)	28 (25)
2020	66859 (165211)	23986 (59271)	15003 (37073)	35.9	22.4	15.7	100 (89)	137.9	115 (103)	28 (25)
2021	67471 (166724)	24378 (60240)	15124 (37373)	36.1	22.4	13.7	122 (109)	124.9	150 (134)	37 (33)
Mean	65589 (162074)	21648 (53493)	12361 (30545)	33.0	18.8	15.3	104 (93)	132.1	131 (117)	32 (29)
Corn
2018	63052 (155804)	5970 (14753)	3450 (8525)	9.5	5.5	11.5	31 (28)	113.6	100 (89)	25 (22)
2019	64975 (160557)	5459 (13490)	2634 (6508)	8.4	4.1	11.7	30 (27)	129.1	76 (68)	19 (17)
2020	66859 (165211)	7782 (19230)	5282 (13052)	11.6	7.9	11.8	28 (25)	131.4	73 (65)	18 (16)
2021	67471 (166724)	9589 (23696)	6072 (15005)	14.2	9.0	9.1	53 (47)	106.4	110 (98)	27 (24)
Mean	65589 (162074)	7200 (17792)	4306 (10773)	11.0	6.6	11.0	36 (32)	120.1	90 (80)	22 (20)
Spring Malt Barley
2018	63052 (155804)	11469 (28340)	877 (2166)	18.2	1.4	13.6	78 (70)	14.1	218 (195)	29 (26)
2019	64975 (160557)	12569 (31059)	142 (350)	19.3	0.2	14.2	74 (66)	47.8	97 (87)	17 (15)
2020	66859 (165211)	15696 (38786)	303 (749)	23.5	0.5	13.9	75 (67)	40.0	125 (112)	28 (25)
2021	67471 (166724)	17197 (42494)	2114 (5224)	25.5	3.1	11.7	102 (91)	14.0	218 (195)	24 (21)
Mean	65589 (162074)	14233 (35170)	859 (2122)	21.7	1.3	13.4	83 (74)	29.0	165 (147)	25 (22)
Potato (Russet Burbank)
2018	63052 (155804)	23605 (58328)	10224 (25265)	18.2	16.2	16.9	116 (104)	124.6	225 (201)	55 (49)
2019	64975 (160557)	26009 (64270)	11691 (28889)	19.3	18.0	17.3	112 (100)	136.8	171 (153)	41 (37)
2020	66859 (165211)	30978 (76547)	17586 (43455)	23.5	26.3	16.8	120 (107)	134.9	179 (160)	44 (39)
2021	67471 (166724)	29288 (72373)	17102 (42260)	25.5	25.3	14.7	148 (132)	122.5	235 (210)	57 (51)
Mean	65589 (162074)	27470 (67879)	14151 (34967)	21.7	21.6	16.4	124 (111)	129.7	203 (181)	49 (44)
† STP = Soil test P ‡ STK = Soil test K § PCC = Precipitated calcium carbonate

Table 4. Scenario 1 (Fields with a crop P requirement, and with or without a K requirement) economic data. Determination of precipitated calcium carbonate (PCC) fertilizer value based on P and K content for the sugarbeet growing area in 2018, 2019, 2020, and 2021 for the Northwest U.S. For each year, analysis assumes all area will grow sugarbeet, corn, spring malt barley, or potato (Russet Burbank). Fertilizer P and K and (MAP, DAP, and Potash) values are the means across each year (DTN, 2022). The P fertilizer value in PCC is based on average annual MAP and DAP prices and average P2O5 content of lime (12.4 kg P2O5 Mg-1 PCC [24.8 lbs P2O5 ton-1 PCC], Table 2). The K fertilizer value in PCC is based on average annual Potash prices and average K2O content of lime (2.1 kg K2O Mg-1 PCC [4.1 lbs K2O ton-1 PCC], Table 2). Data for 2022 is based on mean values across all years (2018-2021) in Table 3.
Year†	Fertilizer P Price‡	P Fertilizer Value of PCC	PCC Rate Needed to Meet Crop P Fertilizer Requirement	Fertilizer K Price	K Fertilizer Value of PCC	P and K Fertilizer Savings from PCC	Total Area P and K Fertilizer Savings from PCC
		Eq. 1a	Eq. 1b		Eq. 1c	Eq. 1d	Eq. 1e
	$ kg-1 P2O5 ($ lb-1 P2O5)	$ Mg-1 PCC  ($ ton-1 PCC)	Mg ha-1 (ton ac-1)	$ kg-1 K2O ($ lb-1 K2O)	$ Mg-1 PCC ($ ton-1 PCC)	$ ha-1 ($ ac-1)	$
Sugarbeet
2018	0.23 (0.51)	13.94 (12.65)	8.09 (3.61)	0.13 (0.29)	1.31 (1.19)	118.51 (47.96)	2,106,000
2019	0.23 (0.51)	13.94 (12.65)	7.60 (3.39)	0.14 (0.31)	1.40 (1.27)	111.37 (45.07)	2,201,000
2020	0.20 (0.45)	12.30 (11.16)	8.03 (3.58)	0.13 (0.29)	1.31 (1.19)	104.72 (42.38)	2,603,000
2021	0.34 (0.74)	20.23 (18.35)	9.89 (4.41)	0.20 (0.44)	1.98 (1.80)	210.21 (85.07)	4,689,000
2022	0.47 (1.03)	28.15 (25.54)	8.41 (3.75)	0.32 (0.70)	3.16 (2.87)	236.68 (95.78)	5,123,000
Corn
2018	0.23 (0.51)	13.94 (12.65)	2.53 (1.13)	0.13 (0.29)	1.31 (1.19)	37.49 (15.17)	223,000
2019	0.23 (0.51)	13.94 (12.65)	2.42 (1.08)	0.14 (0.31)	1.40 (1.27)	35.58 (14.40)	199,000
2020	0.20 (0.45)	12.30 (11.16)	2.29 (1.02)	0.13 (0.29)	1.31 (1.19)	30.12 (12.19)	256,000
2021	0.34 (0.74)	20.23 (18.35)	4.28 (1.91)	0.20 (0.44)	1.98 (1.80)	90.32 (36.55)	765,000
2022	0.47 (1.03)	28.15 (25.54)	2.89 (1.29)	0.32 (0.70)	3.16 (2.87)	81.42 (32.95)	586,000
Spring Malt Barley
2018	0.23 (0.51)	13.94 (12.65)	6.34 (2.83)	0.13 (0.29)	1.31 (1.19)	89.90 (36.38)	1,035,000
2019	0.23 (0.51)	13.94 (12.65)	5.94 (2.65)	0.14 (0.31)	1.40 (1.27)	82.93 (33.56)	990,000
2020	0.20 (0.45)	12.30 (11.16)	6.08 (2.71)	0.13 (0.29)	1.31 (1.19)	74.92 (30.32)	1,243,000
2021	0.34 (0.74)	20.23 (18.35)	8.20 (3.66)	0.20 (0.44)	1.98 (1.80)	168.53 (68.20)	2,511,000
2022	0.47 (1.03)	28.15 (25.54)	6.68 (2.98)	0.32 (0.70)	3.16 (2.87)	188.32 (76.21)	2,680,000
Potato (Russet Burbank)
2018	0.23 (0.51)	13.94 (12.65)	9.44 (4.21)	0.13 (0.29)	1.31 (1.19)	137.69 (55.72)	3,197,000
2019	0.23 (0.51)	13.94 (12.65)	9.06 (4.04)	0.14 (0.31)	1.40 (1.27)	132.18 (53.49)	3,422,000
2020	0.20 (0.45)	12.30 (11.16)	9.68 (4.32)	0.13 (0.29)	1.31 (1.19)	125.80 (50.91)	3,898,000
2021	0.34 (0.74)	20.23 (18.35)	11.90 (5.31)	0.20 (0.44)	1.98 (1.80)	252.57 (102.21)	6,743,000
2022	0.47 (1.03)	28.15 (25.54)	10.04 (4.48)	0.32 (0.70)	3.16 (2.87)	282.47 (114.31)	7,759,000
† 2022 calculations are based on annual mean values in Table 3. Fertilizer price used are the mean for 2022 (Figure 1). ‡ Yearly mean fertilizer P and K (MAP, DAP and Potash) prices for 2018, 2019, 2020, and 2021 (Figure 1).

Scenario 2 (Table 5 and Table 6):

Eq. 2a: K Fertilizer Value of PCC = K Fertilizer Price × K Concentration in PCC

Where, K Fertilizer value of PCC = $ ha^-1 PCC ($ ton^-1 PCC), Fertilizer K Price = $ kg^-1 K₂O ($ lb^-1 K₂O), K Concentration in PCC = 2.1 kg P₂O₅ Mg^-1 (4.1 lbs K₂O ton^-1).

Eq. 2b: PCC Rate Needed to Meet Crop K Fertilizer Requirement = K Recommendation / K Concentration in PCC

Where, PCC Needed to Meet K Requirement = tons PCC ac^-1, K Recommendation = kg K₂O ha^-1 (lbs K₂O ac^-1), K Concentration in PCC = 2.1 kg P₂O₅ Mg^-1 (4.1 lbs K₂O ton^-1) (Table 2).

Eq. 2c: K Fertilizer Savings from PCC = PCC Needed to Meet Crop K Requirement × K Fertilizer Value of PCC

Where, K Fertilizer Savings from PCC = $ ha^-1 ($ ac^-1), PCC Needed to Meet Crop K Fertilizer Requirement = tons/ac, PCC K Fertilizer Value = $ Mg^-1 PCC ($ ton^-1 PCC).

Eq. 2d: Total Area K Fertilizer Savings from PCC = K Fertilizer Savings from PCC × Area Requiring K

Where, Total Area K Fertilizer Savings from PCC = $, K Fertilizer Savings from PCC = $ ha^-1 ($ ac^-1), Area Requiring K = ha (ac). Values rounded to the nearest 1,000 ac.

Table 5. Scenario 2 (Fields with a crop K requirement but no crop P requirement) agronomic data. Land area and soil test data are from the ASCO AgriData database. For each year, values are derived from the assumption the entire area will grow the listed crop. Crop K recommendations are based on published sources (Amalgamated Sugar Company, 2020; Walsh et al., 2019; Brown et al., 2020; Robertson et al., 2003; Stark et al., 2004).
Year	Total Area	Area Requiring K	Percent of Total Area Requiring K	Average STK†	K2O Recommendation
	————–ha (ac)————-		%	mg kg-1	kg ha-1 (lbs ac-1)
Sugarbeet
2018	63052 (155804)	10274 (25388)	16.3	141	105 (94)
2019	64975 (160557)	9749 (24091)	15.0	149	84 (75)
2020	66859 (165211)	11961 (29555)	17.9	143	102 (91)
2021	67471 (166724)	14565 (35990)	21.6	142	104 (93)
Mean	65589 (162074)	11637 (28756)	17.7	144	99 (88)
Corn
2018	63052 (155804)	15615 (38586)	24.8	140	60 (54)
2019	64975 (160557)	17641 (43592)	27.2	147	49 (44)
2020	66859 (165211)	21617 (53575)	32.4	143	56 (50)
2021	67471 (166724)	23617 (58358)	35.0	142	57 (51)
Mean	65589 (162074)	19639 (48528)	29.9	143	56 (50)
Spring Malt Barley
2018	63052 (155804)	612 (1512)	1.0	23	185 (165)
2019	64975 (160557)	127 (315)	0.2	34	149 (133)
2020	66859 (165211)	458 (1131)	0.7	23	186 (166)
2021	67471 (166724)	416 (1029)	0.6	18	205 (183)
Mean	65589 (162074)	403 (997)	0.6	24	181 (162)
Potato (Russet Burbank)
2018	63052 (155804)	6599 (16307)	10.5	137	172 (154)
2019	64975 (160557)	6403 (15821)	9.9	146	131 (117)
2020	66859 (165211)	7191 (17769)	10.8	141	152 (136)
2021	67471 (166724)	9761 (24121)	14.5	139	164 (146)
Mean	65589 (162074)	7488 (18504)	11.4	140	155 (138)
† STK = Soil test K

Table 6. Scenario 2 (Fields with a crop K requirement but no crop P requirement) economic data. Determination of precipitated calcium carbonate (PCC) fertilizer value based on K content for the sugarbeet growing area in 2018, 2019, 2020, and 2021 for the Northwest U.S. For each year, analysis assumes all area will grow sugarbeet, corn, spring malt barley, or potato (Russet Burbank). Fertilizer K (Potash) values are the means across each year (DTN, 2022). The K fertilizer value in PCC is based on average annual Potash prices and average K2O content of lime (2.1 kg K2O Mg-1 PCC [4.1 lbs K2O ton-1 PCC], Table 2). Data for 2022 is based on mean values across all years (2018-2021) in Table 4.
Year†	Fertilizer K Price‡	K Fertilizer Value of PCC	PCC Rate Needed to Meet K Fertilizer Requirement	K Fertilizer Savings from PCC	Total Area PCC K Fertilizer Value K Fertilizer Savings from PCC
		Eq. 2a	Eq. 2b	Eq. 2c	Eq. 2d
	$ kg-1 K2O ($ lb-1 K2O)	$ Mg-1 PCC ($ ton-1 PCC)	Mg ha-1 (ton ac-1)	$ ha-1 ($ ac-1)	$
Sugarbeet
2018	0.13 (0.29)	1.31 (1.19)	51.6 (23.0)	67.63 (27.37)	674,000
2019	0.14 (0.31)	1.40 (1.27)	41.0 (18.3)	57.62 (23.32)	532,000
2020	0.13 (0.29)	1.31 (1.19)	49.8 (22.2)	65.33 (26.44)	743,000
2021	0.20 (0.44)	1.98 (1.80)	50.9 (22.7)	101.31 (41.00)	1,341,000
2022	0.32 (0.70)	3.16 (2.87)	48.4 (21.6)	152.83 (61.85)	1,779,000
Corn
2018	0.13 (0.29)	1.31 (1.19)	29.4 (13.1)	38.60 (15.62)	601,000
2019	0.14 (0.31)	1.40 (1.27)	24.2 (10.8)	33.98 (13.75)	582,000
2020	0.13 (0.29)	1.31 (1.19)	27.1 (12.1)	35.66 (14.43)	761,000
2021	0.20 (0.44)	1.98 (1.80)	28.0 (12.5)	55.52 (22.47)	1,288,000
2022	0.32 (0.70)	3.16 (2.87)	27.1 (12.1)	85.99 (34.80)	1,689,000
Spring Malt Barley
2018	0.13 (0.29)	1.31 (1.19)	90.3 (40.3)	118.54 (47.97)	69,000
2019	0.14 (0.31)	1.40 (1.27)	72.6 (32.4)	101.73 (41.17)	17,000
2020	0.13 (0.29)	1.31 (1.19)	90.6 (40.4)	118.61 (48.00)	58,000
2021	0.20 (0.44)	1.98 (1.80)	100.0 (44.6)	198.77 (80.44)	90,000
2022‡	0.32 (0.70)	3.16 (2.87)	88.3 (39.4)	279.60 (113.15)	113,000
Potato (Russet Burbank)
2018	0.13 (0.29)	1.31 (1.19)	84.1 (37.5)	110.28 (44.63)	716,000
2019	0.14 (0.31)	1.40 (1.27)	64.1 (28.6)	89.75 (36.32)	561,000
2020	0.13 (0.29)	1.31 (1.19)	74.6 (33.3)	97.75 (39.56)	677,000
2021	0.20 (0.44)	1.98 (1.80)	79.6 (35.5)	158.34 (64.08)	1,401,000
2022	0.32 (0.70)	3.16 (2.87)	75.5 (33.7)	239.17 (96.79)	1,791,000
† 2022 calculations are based on annual mean values in Table 3. Fertilizer price used are the mean for 2022 (Figure 1). ‡ Yearly mean fertilizer P and K [diammonium phosphate (DAP), monoammonium phosphate (MAP), and Potash] prices for 2018, 2019, 2020, and 2021 (Figure 1).

3 RESULTS AND DISCUSSION

3.1 ASCO AgriData

Of the total acres in the ASCO AgriData, 91% were used in this analysis. The 9% of data removed represented outliers caused by possible soil test analysis errors or data entry errors. However, the deleted data was not confirmed as erroneous, it was removed simply to increase confidence in the remaining data. Outliers were removed when they met the following criteria: soil free lime > 15% (1,732 ha [4,280 ac]), soil pH < 5 (18,261 ha [45,125 ac]), soil pH > 8.5 (2,887ha [7,134 ac]), STP = 0 mg kg^-1 (1,594 ha [3,940 ac]), soil test K (SPK) = 0 mg kg^-1 (252 ha [622 ac]) and fields < 10 acres (44,644 ha [110,318 ac]). After filtering, the cropland area used in the analysis in 2018, 2019, 2020, and 2021 was 63,052 ha (155,804 ac), 64,975 ha (160,557 ac), 66,859 ha (165,211 ac) and 67,471 ha (166,724 ac), respectively. The filtered cropland area is 91% of the total area across years. The average number of fields per year was 2,085. Averaged across years, the area of fields requiring P and K (scenario 1) for proposed crops of sugarbeet, corn, spring malt barley, and potato (Russet Burbank) totaled 21,648 ha (53,493 ac), 7,200 ha (17,792 ac), 14,233 ha (35,170 ac), 27,470 ha (67,879 ac), respectively (Table 3). Averaged across years, area requiring only P (scenario 1) for proposed crops of sugarbeet, corn, spring malt barley, and potato (Russet Burbank) totaled 9,287 ha (22,949 ac), 2,841 ha (7,020 ac), 13,374 ha (33,047 ac), 13,319 ha (32,912 ac), respectively (Table 3). Averaged across years, area with a K but not a P requirement (scenario 2) for proposed crop of sugarbeet, corn, spring malt barley, and potato (Russet Burbank) totaled 11,637 ha (28,756 ac), 19,639 ha (48,528 ac), 403 ha (997 ac), 7,488 ha (18,504 ac), respectively (Table 5). Averaged across years, area growing sugarbeet, corn, spring malt barley, and potato (Russet Burbank) with no P or K requirement totaled 32,304 ha (79,825 ac), 38,750 ha (95,754 ac), 50,953 ha (125,908 ac), 30,631 ha (75,691 ac), respectively (Table 3).

Across all crops and years, an average of 27% of total cropped area required P or K fertilizer, indicating that there is significant potential market for a PCC as a P and K source. These acres had an average STP and STK levels of 13.5 mg P/kg and 122.7 mg K/kg (Table 3). Depending on free lime content of the soil, the threshold for STP adequacy was 15 to 25 mg/kg for crops assessed in this paper (ASCO, 2020; Walsh et al., 2019; Brown et al., 2020; Robertson et al., 2003; Stark et al., 2004). The threshold for STK adequacy was 75 to 187 mg/kg for crops assessed in this paper (ASCO, 2020; Walsh et al., 2019; Brown et al., 2020; Robertson et al., 2003; Stark et al., 2004). The P and K recommendations for these acres are dependent on crop, however, an annual average of 95 kg P₂O₅ ha^-1 (85 lbs P₂O₅ acre^-1) and 119 kg K₂O ha^-1 (106 lbs K₂O acre^-1) was required (Table 3). The average annual total P requirement crop ranking was potato (Russet Burbank) > sugarbeet > spring malt barley > corn. The average annual total K requirement crop ranking was potato (Russet Burbank) > sugarbeet > corn > spring malt barley.

3.2 PCC Nutrient Content

Total P and K concentrations in PCC sampled from the Paul, ID sugarbeet processing factory ranged from 9.3 to 16.5 kg P₂O₅ Mg^-1 (18.6 to 33 lbs P₂O₅ ton^-1) and 1.35 to 3.75 kg K₂O Mg^-1 (2.7 to 7.5 lbs K₂O ton^-1) across sample locations and depths (Table 2). The mean P and K concentrations were 12.4 kg P₂O₅ Mg^-1 (24.8 lbs P₂O₅ ton^-1) and 2.1 kg K₂O Mg^-1 (4.1 lbs K₂O ton^-1). These concentrations were within the ranges measured in PCC at other sugarbeet production areas of the US (Table 1). The P concentration in the PCC from the Paul, ID facility was slightly lower than PCC P from the Twin Falls, ID facility (15 kg P₂O₅ Mg^-1 [30 lbs P₂O₅ ton^-1]) (Tarkalson et al, 2022). However, the PCC from both locations contained significant concentrations of P, making both viable alternatives to fertilizer P.

In addition to P, PCC also contains small amounts of plant available nitrogen (N) (NH₄-N + NO₃-N) and Zn (0.045 kg N Mg^-1 [0.09 lbs N ton^-1] and 0.03 kg Zn Mg^-1 [0.06 lbs Zn ton^-1]) (Table 2). Although both these nutrients have fertilizer value, the application rate of PCC required to address any crop requirement for these nutrients would be so large as to make it impractical. The amount of plant available N was insignificant with respect to common crop N requirements. In this paper we only discuss PCC P and K data because the ASCO AgriData includes STP and STK data, not soil test Zn data. From 2018 to 2020, the average annual land area that had Zn applied was 1,288 ha (3,183 ac), with an average rate 20 kg Zn ha^-1 (18 lbs Zn ac^-1). A PCC application rate of 673 Mg ha^-1 (300 tons ac^-1) would be required to meet a rate of 20 kg Zn ha^-1 (18 lbs Zn ac^-1). Using PCC to meet Zn requirements would be impractical due to the high PCC application rates. Research has only evaluated the effect of PCC on crops and soils at rates up to 90 Mg ha^-1 (40 tons ac^-1) (Tarkalson et al., 2022). It is possible that at PCC application rates to meet P and K fertilizer requirements, useful crop nutrition amounts of Zn could be applied. The average amount of Zn and plant available N in the PCC applied at the rates under scenario 1 (fields requiring only P, and fields requiring P and K) and scenario 2 (fields requiring only K) across all years and crops would be 0.22 and 1.23 kg Zn ha^-1 (0.2 and 1.1 lbs Zn ac^-1) and 0.34 and 1.9 kg NO₃-N+NH₄-N ha^-1 (0.3 and 1.7 lbs NO₃-N+NH₄-N ac^-1). For this reason, only PCC P and K are considered for the economic analysis in this paper.

Demand for K fertilizer is not as great as for P fertilizer because most western U.S. soils have adequate native K levels for crop production. Data from a commercial soil analysis lab of over 8,800 soil samples from Idaho, Nevada, Utah, and Wyoming showed an average STK (ammonium acetate method) of 355 mg/kg (commercial lab director, per. comm.). For sugarbeet, corn, spring malt barley, and potato (Russet Burbank) in Idaho, K inputs are not recommended when soil test concentrations exceed 187, 180, 75, and 175 mg/kg, respectively. Despite this, the ASCO AgriData data set showed that 17,724 ha (43,798 ac) (27% of the land area), averaged across all years and crops in this study, required K additions (Table 5).

3.3 PCC Value

To determine the economic value of PCC, we assumed that PCC P and K have equivalent plant P and K availability as that of commercial fertilizer. Past research has supported this assumption (Sailsbery and Hill, 1987; Sims, 2010). Sailsbery and Hill (1987) found that sugarbeet production responded equally to P fertilizer and PCC when P was applied at the same rate of 134 kg P₂O₅ ha^-1 (120 lbs P₂O₅ ac^-1). The PCC P concentration in their study was kg 28 P₂O₅ ha^-1 (25 lbs P₂O₅ ton^-1) (Table 1). The fertilizer P and PCC was applied in the spring before sugarbeet planting and incorporated with tillage, providing evidence that the PCC P is readily available after application and incorporation. Sims (2010) showed that increases in STP after PCC application was a result of a significant portion of the PCC P becoming available soon after application. Although release dynamics were not measured directly in both Sailsbery and Hill (1987) and Sims (2010), increased plant available P was observed following PCC additions in both studies. Increased STP from PCC application were also observed by Tarkalson et al. (2022) and in continuing research at our research location (Figure 2). Across the three studies and three PCC rates, they found that PCC increased STP concentrations by an average of 0.6 mg kg^-1 per ton of applied PCC from the fall application to the pre-plant soil test in the spring (Figure 2).

From 2020 to 2022 the annual mean P and K fertilizer prices increased 230% and 240%, respectively (Figure 1). This had a significant impact on the cost of agricultural production. These high costs have made alternative lower cost P sources, such as PCC, more attractive to growers. Across all scenarios the value of PCC as a P fertilizer ranged from $13.94 Mg^-1 ($12.65 ton^-1) in 2018 to $28.15 Mg^-1 ($25.54 ton^-1) in 2022 because of its direct relationship with the increase of P fertilizer price from $0.23 kg^-1 P₂O₅ ($0.51 lb^-1 P₂O₅) to $0.47 kg^-1 P₂O₅ ($1.03 lb^-1 P₂O₅) over the same time (Table 3 and Table 5). Across all scenarios the value of PCC as a K fertilizer ranged from $1.31 Mg^-1 ($1.19 ton^-1) in 2018 to $3.16 Mg^-1 ($2.87 ton^-1) in 2022 because of its direct relationship with the increase of K fertilizer price from $0.13 kg^-1 K₂O ($0.29 lb^-1 K₂O) to $0.32 kg^-1 K₂O ($0.70 lb^-1 K₂O) over the same time (Table 3 and Table 5). As a result, P fertilizer savings arising from substituting PCC for P fertilizer also increased over time.

Before applying these results to real-world situations, we note that there may be additional site-specific costs and/or logistical issues that may need to be considered in to understand the true economic value of PCC more fully for a particular farm or field. It is also noteworthy that PCC has economic value in addition to its benefits as a nutrient source. For example, the economic value of reducing stockpile P accumulation could be a viable economic rationale for application of PCC to agricultural fields, particularly because ASCO is a grower owned cooperative and PCC utilization strategies are economically important for sugarbeet growers. Additionally, PCC has economic value when used for Aphanomyces disease suppression in sugarbeet production. In some areas this is the primary reason for PCC application.

This paper relates the price of P and K in fertilizer to PCC. Due to the lower concentration of P in PCC compared to P fertilizer, greater PCC amounts need to be applied, so transportation and application costs of PCC solely as a P source will be higher than P fertilizer. The final price of nutrients needs to account for these costs. These costs will vary based on distance from stockpiles to fields, and transportation and application equipment costs. Transportation and application costs are highly variable. For example, in the ASCO growing area during the harvest season (September and October), ASCO can use sugarbeet haul trucks to back haul the PCC to fields near sugarbeet collection piles that the trucks are traveling to collect sugarbeets. However, this method is only viable during the harvest season. An additional economic factor to consider is that ASCO currently offers a payment incentive to growers of $3.31 Mg^-1 PCC ($3 ton^-1 PCC) for PCC removed from stockpiles for agricultural use (minimum of 13.6 Mg [15 tons] PCC required for payment).

3.3.1 Scenario 1: Fields with both a crop P and a K requirement

Assuming PCC replaced P and K fertilizer as a nutrient source, the total quantity of PCC required to meet the P crop recommendations for all acres under scenario 1 was 183,191 Mg (201,933 tons), 21,799 Mg (24,029 tons), 95,968 Mg (105,787 tons), and 276,736 Mg (305,049 tons) for sugarbeet, corn, spring malt barley, and potato (Russet Burbank), respectively (averaged across years). Assuming the annual production of PCC from all ASCO factories remains at around 351,081 dry Mg yr^-1 (387,000 dry tons yr^-1), and PCC will be used as the sole P fertilizer source for all ASCO AgriData acres going into sugarbeet, 52% of the annually produced PCC would be utilized. This PCC utilization does not account for PCC applications that are possible for other acres with sugarbeet in rotation that are growing other crops (approximately 153,376 ha [379,000 ac] annually). Additionally, other crop land without sugarbeets in rotation could also utilize PCC as a P source.

If PCC is applied for other reasons beyond meeting P recommendations (Aphanomyces cochlioides related disease suppression in sugarbeet production, bulk land application to reduce stockpiles, etc.) more PCC from stockpiles would be needed, reducing the overall stockpiled quantity over time. Current stockpiles are estimated to contain over 5.1 million dry Mg (12.6 million dry tons).

Averaged across years for sugarbeet, corn, spring malt barley, and potato (Russet Burbank), the amount of K applied in PCC after meeting the P requirement was on average 75% 75% 84%, and 76% less than the K crop requirement (131, 90, 165, 203 kg K₂O ha^-1 [117, 80, 147, 181 lbs K₂O ac^-1]), respectively (Table 3).

Under this scenario, the P and K fertilizer savings from using PCC as an alternative P and K source, increased from $95.90 ha^-1 ($38.81 ac^-1) in 2018 to $197.21 ha^-1 ($79.81 ac^-1) in 2022, an increase of 206% (Table 4). From 2018 to 2022 summed across all acres, the total fertilizer P and K value of PCC when applied to meet P crop requirements ranged from $2,106,000 to $5,123,000 for sugarbeet, $199,000 to $765,00 for corn, $990,000 to $2,680,000 for spring malt barley, and $3,197,000 to $7,759,000 for potato (Russet Brubank) (Table 4).

3.3.2 Scenario 2: Fields with a crop K requirement but no crop P requirement

Assuming PCC replaced K fertilizer as a nutrient source, the total quantity of PCC required to meet the K recommendations for all acres under this scenario was 566,511 Mg (624,471 tons), 533,879 Mg (588,501 tons), 36,905 Mg (40,681 tons), and 569,711 Mg (627,999 tons) for sugarbeet, corn, spring malt barley, and potato (Russet Burbank), respectively (Table 2 and Table 5).

Assuming the annual production of PCC from all ASCO factories remains at around 351,081 dry Mg yr^-1 (387,000 dry tons yr^-1), and PCC will be used as the sole K fertilizer source for all ASCO AgriData acres going into sugarbeet, 161% of the annually produced PCC would be utilized. However, current PCC storage amounts are available. This PCC utilization does not account for PCC applications that are possible for other acres with sugarbeet in rotation that are growing other crops (approximately 153,376 ha [379,000 ac] annually). Additionally, other crop land without sugarbeets in rotation could also utilize PCC as a K source.

Under this scenario, the K fertilizer savings from using PCC as an alternative K source, increased from $83.77 ha^-1 ($33.90 ac^-1) in 2018 to $189.41 ha^-1 ($76.65 ac^-1) in 2022, an increase of 226% (Table 6). From 2018 to 2022, the total fertilizer K value of PCC if it was used for all acres requiring K would have ranged from $523,000 to $1,779,000 for sugarbeet, $582,000 to $1,689,000 for corn, $17,000 to $113,000 for spring malt barley, and $561,000 to $1,791,000 for potato (Russet Burbank) (Table 6).

On average, the application of PCC to meet K crop requirements, results in excess P being applied based on nutrient recommendations for all crops (ASCO, 2020; Walsh et al., 2019; Brown et al., 2020; Robertson et al., 2003; Stark et al., 2004). This paper did not account for excess P application value, however, as P is used and removed by crops over time, the excess P will have value.

4 CONCLUSIONS

The PCC produced by ASCO in the Northwest U.S. has fertilizer value and can reduce stockpile accumulations. The PCC in this study had average P and K concentrations of 12.4 kg P₂O₅ Mg^-1 (24.8 lbs P₂O₅ ton^-1) and 2.05 kg K₂O Mg^-1 (4.1 lbs K₂O ton^-1). The PCC is an alternative P and K source, data from this and other research studies suggests PCC P and fertilizer P likely have equivalent plant P availability. Across all crops assessed in this study (sugarbeet, corn, spring malt barley, and potato (Russet Burbank), as P and K fertilizer prices increased the value of P and K in PCC increased from $13.94 Mg^-1 ($12.65 ton^-1) to $28.15 Mg^-1 ($25.54 ton^-1), and $1.31 Mg^-1 ($1.19 ton^-1) to $3.16 Mg^-1 ($2.87 ton^-1) from 2018 and 2022. Additional agronomic value from non-nutrient uses as well as additional costs incurred in transport and application of PCC need to be accounted for to fully understand the value of PCC compared to commercial fertilizers. As costs of commercial P and K fertilizers increase, the value of PCC increases.

 

REFERENCES

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Introduction

Sugarbeet production in Minnesota is concentrated on flat, fine-textured soils in the Western and Northwestern part of the state, where wind erosion rates are estimated at between 10 and 11.6 Mg/ha/yr (Soil Survey Staff, NRCS, 2015, USDA National Agricultural Statistics Service, 2018, Erosion by State NRI 2017). Standard sugarbeet management practices in Western Minnesota include full-width fall tillage, which can exacerbate erosivity by reducing soil structure and reducing plant residue cover (den Biggelaar et al., 2003). However, many growers use spring-planted cover crops, which provide a small amount of living residue to slow soil movement and protect sugarbeet seedlings (Wilson et al., 2001).

Growers may be able to increase protection by planting fall-seeded cover crops, which produce more biomass and more effectively slow wind erosion during vulnerable early spring periods (de Baets et al., 2001), and by switching to strip-tillage, cultivating only the strips where crops will be planted. Strip tillage is known to reduce water erosion (Ryken et al, 2018). This effect is presumably due to both changes in surface roughness (Wagner & Fox, 2013) and increases in soil organic matter (Fernández et al., 2015) and biological activity (Jaskulska et al., 2020), although aggregate stability is not always increased in strip-till systems (Al-Kaisi et al., 2014, Fernández et al., 2015). While rates of wind erosion in strip-till systems had not been studied in the field, the Wind Erosion Prediction model (WEPS) predicted that the erosion rate decreased 94% in strip till relative to conventional till (Ruffin & Tallman, 2017), as tilled strips break up the surface and slow the movement of dislodged particles.

In order for environmental benefits to be realized on agricultural lands, they must fit into a profitable farm system, and evidence is mixed on how fall-seeded cover crops and strip-tillage may affect sugarbeet yield, quality, and profit. Winter camelina and winter wheat cover crops decreased sugarbeet stand establishment and root yield in four site-years of a North Dakota study, coincident with lower soil water content under winter cover crops (Cabello-Leiva, 2022). In Germany, a winter-hardy cover crop decreased beet emergence due to increased residue, but researchers thought that better planting equipment could overcome the challenges of planting through residue, and yield was comparable as long as weeds were controlled (Peterson & Rover, 2005). Living mulch terminated at sugarbeet stage V2 had no impact on beet yield in a 4-year Montana field experiment (Keshavarz Afshar et al., 2018). These mixed results show that cover crops must be applied carefully, in the context of a complete growing system, in order to mitigate potential yield losses (Marcillo & Miguez, 2017).

Strip-till has been used in sugarbeet in relatively few locations (Evans et al., 2009; Overstreet, 2009), so few growers are willing to risk high-value crop yield to experiment with new practices. Satellite data suggests that no more than 32% of acres in MN and ND beet-growing regions along the Red River of the North used conservation tillage between 2005 and 2020 (OpTIS, 2020). In recent years, Vice President of Agriculture and Research with Minn-Dak Farmers’ Cooperative, Mike Metzger, reported sugar content was similar with strip till and conventional tillage plots with and without cover crops.  However, root yield was 5.1 Mg/ha less with strip till than with conventional tillage (Metzger, 2019, personal communication).  In contrast, Overstreet (2009) found the sugar content was lower with strip till than with conventional tillage but had similar tonnage when the berm was leveled and the planter could plant the seed at the proper depth.

A large-field study in Montana investigated the economics of strip till versus conventional tillage at 6 locations (Ruffin & Tallman, 2017). The researchers reported that, overall, farmers saved 40% per acre when using strip till in sugarbeet. This is due to using less fuel, less wear and tear on equipment, less irrigation, and fewer passes across the field. Farmers also saved an average of one hour of labor per acre. Cover crops and strip-till can be a risk mitigation strategy as they help the soil be more resilient to the effects of wheel traffic by reducing wheel rutting and soil compaction during periods of excess moisture. Cover crop residue also conserves moisture later in the growing season, which has been shown to reduce yield variability in maize (Leuthold et al., 2021). The soil resilience to wheel traffic may also allow sugarbeet producers to be more precise in critically timed pesticide applications allowing for a healthier crop and potentially higher yields due to more field working days (Fletcher and Featherstone 1987).

Strip till and cover crops could potentially reduce environmental impacts, and strip-tillage can increase efficiency by combining field passes to till and fertilize, as well as use less fuel. However, the systems need to be tested in the upper Midwest climate and soils. Here, we tested the effects of strip till and fall-seeded cover crops on sugarbeet yield and quality in farmers’ fields, using field-scale equipment.

Materials and Methods

This field experiment was conducted at three fields near Winthrop (Field 1), Danvers (Field 2), and Granite Falls (Field 3), Minnesota (Figure 1). Treatments included: strip-till (ST), strip-till with cover crops interseeded early in the corn growing season (ST + Early CC), strip-till with cover crops seeded late in the corn growing season (ST + Late CC), and chisel plow (CP). The field experiment was a randomized complete block design with three replications, although different treatments were present in different locations (Table 1). The timeline of crop management and data collection is given in Table 2.

This area is characterized by cold winters and hot, dry, summers, with mean annual precipitation of 1,817 mm (515 mm May-Oct) and mean annual temperature of 6 °C (16°C May-Oct, -4.4°C in Nov-April) (Minnesota DNR, no date). Both growing seasons were drier than normal, with 372 mm of precipitation May-Oct in 2020, and 447 mm May-Oct in 2021, which mostly fell late in the growing season (Minnesota DNR, no date). We observed that the cover crop establishment in 2020 was not very successful: there was minimal emergence of clover and annual ryegrass in the ST + Early CC treatment, which soon died back as corn canopy closed. Late-planted cereal rye in the ST + Late CC also produced small amounts biomass. Based on these field conditions, cover crop treatments were relatively minimal.

Table 1: Tillage and cover crop treatment descriptions applied at three Minnesota farm locations between 2020 and 2021

Treatment	Tillage	Cover crop	Fields
ST	Fall strip till (Field 3 had a second additional pass in the spring on the strip till treatments)	None	1,2,3
ST + Early CC	Fall strip till	Crimson clover (5.6 kg/ha) & annual rye (14.5 kg/ha) interseeded at V2-V4 corn	1
ST + Late CC	Fall strip till	Cereal rye (67 kg/ha) applied aerially by drone as corn was reaching maturity	1, 2, 3
CP	Fall chisel plow, spring field cultivation	None	2, 3

Figure 1: Field locations in Minnesota. The pink shaded area on the US map represents approximate range of beet production in the region.

Figure 2: Representative image of poor growth of cereal rye cover crops at Field 3 in ST+ Late CC (Table 1), planted Sept 9 2020 and pictured here April 26 2021.

Table 2: Timeline of field activities

Year	Month	Activity
2020	May 10-20	Soil sampling for fertility, corn planting (all treatments)
	June 5-10	Cover crop treatments drilled (ST+ Early CC)
	September 9-10	Cover crop treatments aerially planted (ST+ Late CC)
	October 15-21	Harvest corn
	Oct 21-Nov 6	Fall strip till and chisel plow tillage treatments
2021	April 30	Field cultivation (CP)
	April 22- April 30	Plant sugarbeets (Field 1: Crystal M837; Field 2: SESVanderHave 862; Field 3: SESVanderHave 863)
	June 9	Assess beet stands
	September 28 and October 4	Hand-harvest beets

All tillage and planting equipment used was field scale. Corn and sugarbeets were planted in 0.56-m rows, in 13.6-m wide plots. Plots were the length of the fields, 0.8 km. Sugarbeet stand count was assessed by counting seedlings in .914 m sections at eight randomly distributed locations per plot (avoiding wheel-trafficked areas). At harvest three meters of row were hand-harvested at six locations per plot (three evenly distributed transects at each long end of the plot) and weighed for Mg per hectare yield calculation.

Each sample was analyzed for percent sucrose, percent extractable sucrose, and percent purity by the Southern Minnesota Beet Sugar Cooperative (SMBSC). The SMBSC used a near-infrared (NIR) system to assess % sucrose (DA 7250 NIR Analyzer, Perten Instruments, Springfield IL) based on a calibration curve comparing NIR to the GS6-4 ICUMSA (ICUMSA Method GS6-3, 1994). Percent purity was also assessed using NIR, based on the quantity of sucrose relative to the total dissolved solids in the beet, reported as a percent. Percent extractable sucrose was calculated using a SMBSC proprietary formula estimating the percent of the sucrose in the beet that the factory will be able to extract and granulate, based on percent sugar, percent purity, factory operation assumptions and constants.

Statistical Methods

Since treatments were unbalanced among fields, we used two separate models to assess 1) the effect of late-planted cover crops in strip till at all three sites (ST vs ST + Late CC at Fields 1, 2, and 3) and 2) the effect of three tillage/cover crop treatments at two sites (ST vs ST+ Late CC vs CP at Fields 2 and 3). The same models were used to evaluate the response variables of stand counts, yield, % sucrose, % purity, and % extractable sucrose. We used a linear mixed model with treatment, field and field x treatment as fixed effects, and replicate as a random effect (lmer, R package lmerTest (Kuznetsova et al., 2020), analyses conducted in R v 4.0.4 (R Core Team, 2016)). Pairwise contrasts between treatments or fields were assessed using estimated marginal means (emmeans R package) (Searle et al. 2022).

Results

We evaluated beet response to cover crop and tillage treatments, and overall, we found that fields varied from each other in beet yield and quality, but there was no effect of tillage and cover crop treatment. Comparing ST and ST + Late CC at all three fields, we found no treatment differences in stand counts, yield, % sucrose, % extractable sucrose, or % purity (Table 3). We did find a significant main effect of field in all metrics except stand count (Table 4). The yield (Figure 1) was significantly lower at Field 1 than at Field 3. Field 1 had lower % sucrose (Figure 2) and % extractable sucrose (13.3% compared to Field 2, 14.0% and Field 3, 14.1%). Percent purity was greater at Field 2 (91.0%) than at Fields 1 (90.3%) and 3 (90.3%).

Evaluating three treatments (CP, ST, ST + Late CC) at Fields 2 and 3, we also found no treatment effects, and similar trends by field as the assessment across three locations (Table 3). Fields 2 and 3 differed in stand counts, yield and extractable sucrose. Yield and extractable sucrose were greater in Field 3, while stand count was higher in Field 2.

Table 3: Analysis of variance results for beet yield and quality, based on the three treatments present in two fields or two treatments present in three fields (see Tables 1  and 2 for treatment details).

	Sum Squares	Mean Squared Error	Numerator degrees of freedom	Denominator degrees of freedom	F value	Pr(>F)
	2 treatments, 3 fields
Stand Counts
Treatment	3.0E+08	3.0E+08	1	186	2.37	0.125
Field	2.7E+08	1.4E+08	2	186	1.09	0.339
Field x Treatment	3.1E+08	1.5E+08	2	186	1.25	0.288
Yield
Treatment	0.05	0.05	1	102	0.0283	0.867
Field	16.89	8.44	2	102	5.15	<0.01
Field x Treatment	0.73	0.37	2	102	0.223	0.800
Percent Sucrose
Treatment	0.17	0.17	1	100	0.301	0.584
Field	16.28	8.14	2	100	14.4	<0.0001
Field x Treatment	0.14	0.07	2	100	0.125	0.882
Percent Extractable Sucrose
Treatment	1.7E+05	1.7E+05	1	102	0.175	0.676
Field	5.0E+07	2.5E+07	2	102	24.9	<0.0001
Field x Treatment	6.8E+05	3.4E+05	2	102	0.342	0.711
Purity
Treatment	0.0085	0.0085	1	102	0.0217	0.883
Field	13.05	6.52	2	102	16.6	<0.0001
Field x Treatment	0.49	0.24	2	102	0.616	0.542
	3 treatments, 2 fields
Stand Counts
Treatment	3.2E+07	1.6E+07	2	210	0.144	0.866
Field	6.1E+08	6.1E+08	1	210	5.43	0.021
Field x Treatment	1.7E+08	8.6E+07	2	210	0.762	0.468
Yield
Treatment	3.08	1.54	2	101	0.926	0.399
Field	11.88	11.88	1	101	7.150	<0.01
Field x Treatment	1.14	0.57	2	101	0.343	0.710
Percent Sucrose
Treatment	1.238	0.619	2	101	0.983	0.378
Field	0.043	0.043	1	101	0.068	0.795
Field x Treatment	0.336	0.168	2	101	0.267	0.766
Percent Extractable Sucrose
Treatment	7.9E+05	3.9E+05	2	101	0.449	0.639
Field	2.6E+07	2.6E+07	1	101	29.957	<0.0001
Field x Treatment	2.3E+06	1.1E+06	2	101	1.304	0.276
Purity
Treatment	0.0139	0.0070	2	99	0.015	0.985
Field	1.080	1.080	1	99	2.358	0.128
Field x Treatment	0.472	0.236	2	99	0.515	0.599

Table 4: Stand count (plants per hectare).

Treatment	Field 1	Field 2	Field 3
ST	102,666	107,963	98,592
ST + Late CC	116,519	107,148	104,296
CP	NA	112,037	97,778
ST + Early CC	101,116	NA	NA

Figure 3: 2021 sugarbeet yields by strip-till and cover crop treatments at 3 locations in Minnesota. In the box-and-whisker plots, the colored portion represents data between the 25^th and 75^th percentile, the horizontal line in the middle represents the median, and the vertical lines extend to the maximum and minimum of the data. See Table 3 for statistical analysis, and Tables 1 and 2 for full treatment details. CP = chisel plow, ST = strip-till, CC = cover crop.

Figure 4: Sugarbeet 2021 extractable sucrose yield (%) by strip-till and cover crop treatments at 3 locations in Minnesota. In the box-and-whisker plots, the colored portion represents data between the 25^th and 75^th percentile, the horizontal line in the middle represents the median, and the vertical lines extend to the maximum and minimum of the data. See Table 3 for statistical analysis, and Tables 1 and 2 for full treatment details. CP = chisel plow, ST = strip-till, CC = cover crop.

Discussion

We found that beets grown with strip-tillage, with or without cover crops, yielded similar to beets grown using conventional tillage at two field locations (P=0.866). The sugarbeet quality measured in our study was similar to SMBSC 2021 averages: 16.37% sucrose, 13.79% extractable sucrose, and 90.6% purity (Mark Bloomquist, SMBSC, personal communication). Our beet yields are also in the range of other recent research in Minnesota (Chaterjee et al. 2019, Lystad et al. 2020), and average yield for SMBSC in 2021 were 82 Mg/ha (Mark Bloomquist, SMBSC, personal communication), so we are confident that the treatments imposed here are relevant for competitive regional beet production. This is similar to regional data showing competitive yields in corn and soybean with strip-till (Daigh et al. 2019). Beet yields in strip till in were similar to conventional till Montana (Keshavarz et al. 2019), lower in Germany (Laufer and Koch, 2017), and greater in Poland (Gorski et al. 2022). Others have found variation by site-year in strip-till (Wenninger et al. 2019, Overstreet 2009) and Evans et al. (2009) suggests that clay soils may require fall strip-tillage while sandier soils may do better with a spring pass, so the technology will need to be tailored to individual conditions. Another consideration will be the interaction of tillage system with disease severity, especially for Cercospora beticola. This has not been studied specifically in strip-till systems. Tillage is recommended after harvest to speed leaf decay in Montana (Jacobsen et al. 2010), and whether tillage in the strip would be sufficient for this purpose has not been studied.

Cover crop growth in this study was minimal and had a correspondingly nil effect on the beet yield and quality. In Idaho, oilseed radish planted before beets was found to increase mesopores in the soil, leading to increased soil saturation (Wenninger et al. 2019). This could be a concern in the beet growing areas of North Dakota and Minnesota, where spring water saturation can delay planting or cause a need for replanting, which usually reduces yields (Bloomquist et al., 2019). However, under wet conditions, Cabello-Leiva (2022) found no difference in soil water content, beet yield or quality with a large number of cover crop treatments in North Dakota. We need more studies on effects of cover crops on soil moisture under different conditions, to better predict whether the increased soil water retention due to residue or water use of growing cover crops dominates the annual water balance.

Conclusions

We found no response in beet yield or quality to tillage or cover crop treatments at three site-years in Western Minnesota. There is a need to test these technologies in more locations and conditions, and future research should explore cover crop treatments with more robust growth, as that could change the cover crop’s effect on beet outcomes. Farmer adoption of strip-till technology can be hampered by high equipment costs and pressure to conform to production norms in their area (Grover and Gruver, 2017) as well as fear of risk to production, so addressing these other barriers will be critical to increase adoption of strip-till from current low levels (<32% between 2005 and 2020 in Western Minnesota (OpTIS, Conservation Technology Information Center)). In addition, future work should more precisely quantify the expected environmental benefits of reduced till and cover crop systems in sugarbeets.

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Precipitated calcium carbonate (PCC) is a byproduct of sucrose extraction from sugarbeet (Beta vulgaris L.). Other commonly used terms for PCC are beet lime and spend lime. The PCC is the by-product formed as a result of impurity removal during the purification of the sucrose. Impurities that need to be removed include organic molecules, phosphorus, magnesium, calcium, potassium and sodium (Hergert et al. 2017). To remove impurities from the sugarbeet sucrose liquid juice stream, calcium oxide and carbon dioxide are added to the stream to form calcium carbonate (CaCO₃) that precipitates out of the liquid juice stream with the impurities. The combination of the CaCO₃ and impurities form the PCC which is removed from the juice stream as a solid material.

Lime materials (PCC, calcium oxide, calcium hydroxide, calcium and magnesium carbonates, marl, blast-furnace slag, fly ash, and wastewater treatment sludge) are often used in agriculture to ameliorate the negative effects of soil acidification on crop production (Havlin et. al, 1999). These effects include Al and/or Mn toxicity, H ion toxicity, decreased bioavailability of some plant nutrients (Mg, Ca, K, P, and Mo), and inhibition of root growth (Marschner, 1995).  An estimated 25 to 30% of world soils are acidic (Havlin et. al, 1999). In 1999, over 6.7 million Mg of agricultural lime was applied to acid soils in the U.S. (USGS, 2022). In agroecosystems, soil acidification is mainly attributed to the nitrification process (Tarkalson et al., 2006) and is enhanced by leaching of basic cations and conjugate bases such as nitrate ions and the removal of bases in harvested crops (Barak et al., 1997; Bouman et al., 1995; Dick, 1983; Heenan and Taylor, 1995; Juo et al., 1995; Lilienfein et al., 2000; Tarkalson et al., 2006).  The incomplete return of neutralizing anions when nitrates are taken up by plants also contributes to soil acidification (Tarkalson et al., 2006). Soil acidification is common in areas with excess water leaching through soils due to higher rainfall amounts (typically >500 mm/yr) and lower soil base content (Miller and Gardiner, 2001). Periodic application of liming materials is often used on these soils to increase or maintain their productivity.

In the North Central U.S. sugarbeet producing area soils are often acidic and PCC is used to raise soil pH as well as to suppress Aphanomyces cochliodes, a pathogenic oomycete that causes sugarbeet root damage (dampening off and rot) (Olsson et al., 2019; Lien et al., 2016; Brantner et al., 2015; Windels et al., 2008). Although, PCC has been applied to alkaline soils in the region without negative effects on crop production (Christenson et al., 2000). In Michigan, sugar beet growers apply approximately 220,000 tons of PCC annually (Clark et. al, 2015). Because PCC has value as an ameliorator of low pH soils, it is widely used (Barber, 1984). This prevents the kind of accumulation of PCC at North Central U.S. sugarbeet factories that is so common, and problematic, in the Pacific Northwest growing area (Clark et. al, 2015). In one study in Minnesota, PCC applied at rates ranging from 6 to 23.8 Mg ha^-1 increased soil pH from 6.5 to 7.5 and sugar beet sucrose yield from 4,400 to 10,300 kg ha^-1, respectively. This increase in yield was attributed to ameliorating negative effects associated with low soil pH. In soils with high Aphanomyces cochliodes disease pressure, PCC applications have been shown to ameliorate root damage and yield losses (Lien et al., 2016; Brantner et al., 2015).

In the Amalgamated Sugar Company growing area in Idaho, Oregon and Washington calcareous soils prevail. High in base cations, these soils typically have pH’s in the range 7.5-8.5. These soils do not cause the same negative effects on crop production as those associated with acidic soils and therefore do not require lime applications to adjust soil pH. The soil pathogen Aphanomyces cochliodes is also present in this growing region and PCC is often applied to reduce its damaging effects, however this accounts for a very small proportion of overall PCC production each year and is not in itself a solution for reducing the ever-growing stockpiles of PCC at the factories. Additional uses for PCC are required.

The simplest way to dispose of the PCC is to apply it each year to the agricultural soils within an economically feasible proximity to the sugarbeet factories. This could only be considered if there was confidence that the PCC caused no harm either to the soil chemical /physical properties, to sugarbeet productivity, or to the other crops commonly grown in rotation with sugarbeet. Additional questions regarding lime source applications to soils are potential negative effects from added salts and metals. The main soluble salts in the soil are composed of the combinations of the cations sodium (Na⁺), calcium (Ca⁺²), magnesium (Mg⁺²), ammonium (NH₄⁺), and potassium (K⁺), and the anions chloride (Cl^–), sulfate (SO₄^-2), bicarbonate (HCO₃^–), carbonate (CO₂^-2), and nitrate (NO₃^–) (Miller and Gardiner, 2001). High soluble salts concentrations lower the osmotic water potential in soil resulting in plants being unable to draw water into the roots, resulting in water deficiencies in plants. Additionally, high soluble salts in the root zone can compromise sugarbeet seed germination and emergence resulting in poor stand counts (Walter et al., 1951). Preliminary research on the effects of PCC applied to arid alkaline soils (Scottsbluff NE, Ft. Morgan CO, and Torrington WY) showed no negative effects on the emergence of sugarbeet (Hergert et al., 2017).  Hergert et al. (2017) stated that additional research was needed to evaluate the effects of PCC on soil characteristics and plant growth under field conditions. In addition, when land applying amendments, concentrations of potentially toxic metals need to be considered. Some common metals that can be toxic to plants if soluble concentrations in soils are high enough are Al, Cu, Zn, Cd, and Pb (Angulo-Bejarano et al., 2021).

The Amalgamated Sugar Company LLC’s major sugarbeet processing factories (Paul, ID; Twin Falls, ID; and Nampa, ID) produce approximately 351,000 Mg of PCC annually (Amalgamated Sugar Company LLC, personal conversation). In 2018, PCC stockpiles at these factories totaled approximately 11.4 million Mg. Without an offsite beneficial use or disposal method for the PCC, the stockpiles will continue to grow. The difficulty in finding more land to stockpile PCC due to availability issues and high land prices, and potential environmental issues have resulted in the need for Amalgamated Sugar Company LLC to find more offsite beneficial use or disposal methods

The objective of the study was to assess the effects of added PCC to a common alkaline soil on a sugarbeet-dry bean-barley rotation yields and soil chemical properties.  The data will be used to determine if PCC can be land applied on high pH soils.

Materials and Methods

This study was conducted from 2014 to 2020 at the USDA-ARS Northwest Irrigation & Soils Research Lab in Kimberly, ID on a Portneuf silt loam (coarse-silty mixed superactive, mesic Durixerollic Xeric Haplocalcids). The treatments included four PCC (obtained from the Twin Falls Idaho factory) application rate/timings. Table 1 outlines the treatments application details. The treatments included:

1. 0 Mg PCC ha^-1 (control)
2. 7 Mg PCC ha^-1 fall applied in 2014, 2015, 2016, and 2017
3. 4 3 Mg PCC ha^-1 fall applied in 2014, 2015, 2016, and 2017
4. 7 Mg ha^-1 applied in the fall of 2014.

Treatments 3 and 4 contained the same cumulative rate of 89.7 Mg ha^-1 (Table 1).  The treatments were arranged in a randomized block design and each treatment was replicated four times. Each plot was 6.7 m wide and 18.3 m long.  Soils were sampled in the spring and fall of each year. Samples were collected from 0 to 0.3 m depth.  In the fall of each year the soil sampling was done before PCC application. Soil sampling dates are in Table 1. The soil samples were analyzed for pH (Kalra, 1995), electrical conductivity (EC) (Rhoades, 1996), bicarbonate extractable P (Olsen et al., 1954), NO₃-N and NH₄-N (Mulvaney, 1996), Total C and N using a FlashEA1112 CN analyzer (CE, Elantech, Lakewood, NJ), and total elements (P, K, Ca, Na, Al, Cu, Zn, Cd, Pb) with ICP-OES detection (U.S. Environmental Protection Agency, 1996).  Due to the significant concentration of P in the PCC (Tables 2 and 3) and the marginal crop requirement concentrations in the soil over the study area (site bicarbonate extractable P average = 18.1 mg kg^-1), to eliminate the crop productivity responses to P, in spring 2015, 450 kg P₂O₅ ha^-1 (mono ammonium phosphate fertilizer) was applied over the entire study area. Soil fertilizer recommendations were determined each year based on University of Idaho recommendations for sugarbeet (Walsh et al., 2019; 168 kg N ha^-1 and 224 kg K₂O ha^-1 in 2015, and 78 kg N ha^-1 in 2018), dry bean (Moore et al., 2012; no fertilizer recommended), and barley (Robertson and Stark, 2003; 168 kg N ha^-1).

 

Table 1. For each year of the study, precipitated calcium carbonate (PCC) treatment annual rates and cumulative total amounts applied (in parentheses), crop grown, soil sample date, and lime application date in Idaho.

Year	2014	2015	2016	2017	2018	2019	2020
Crop	—	Sugarbeet	Dry Bean	Barley	Sugarbeet	Dry Bean	Barley
	——————————————–Mg ha-1——————————————–
Control	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
6.7A	6.7 (6.7)	6.7 (13.5)	6.7 (20.2)	6.7 (26.9)	0 (26.9)	0 (26.9)	0 (26.9)
22.4A	22.4 (22.4)	22.4 (44.8)	22.4 (67.3)	22.4 (89.7)	0 (89.7)	0 (89.7)	0 (89.7)
89.7T	89.7 (89.7)	0 (89.7)	0 (89.7)	0 (89.7)	0 (89.7)	0 (89.7)	0 (89.7)
Soil Sample Date	Oct. 29	Nov. 17	Nov. 15	Oct. 25	Nov. 14	Oct. 24	Oct. 16
Lime Application Date	Oct. 30	Nov. 18	Nov. 30	Oct. 31	—	—	—

 

Table 2.  Selected average chemical characteristics and constituent contents of the PCC used in this study.

CCE (%)	81
pH	8.4
EC  (μS cm-1)	2280
NO3-N (mg kg-1)	183.8
NH4-N (mg kg-1)	8.5
P (mg kg-1)	6559
K (mg kg-1)	1008
Ca (mg kg-1)	289069
Na (mg kg-1)	453.2
Al (mg kg-1)	3636
Cu (mg kg-1)	16.3
Zn (mg kg-1)	36.2
Cd (mg kg-1)	0.40
Pb (mg kg-1)	0.92

 

Table 3. Total rates of selected constituents applied from the PCC treatments. Rates are based on total lime applied for each treatment: 26.9, 89.7, and 89.7 Mg ha^-1 for the 6.7A, 22.4A, 89.7T treatments, respectively.

Constituent	6.7A	22.4A	89.7T
	——————Total kg ha-1——————
NO3-N	4.9	16.5	16.5
NH4-N	0.23	0.76	0.76
P	176	588	588
P2O5	404	1347	1347
K	27.1	90.4	90.4
K2O	32.5	108	108
Ca	7776	25930	25930
Na	12.2	40.7	40.7
Al	98	326	326
Cu	0.4	1.5	1.5
Zn	1.0	3.2	3.2
Cd	0.011	0.036	0.036
Pb	0.025	0.083	0.083

 

The PCC was uniformly surface broadcast using a manure spreader. Following PCC applications each fall the entire study area was disked, moldboard plowed, and roller harrowed.  The study area was planted to sugarbeet (BTS 21RR25) in 2015 and 2018, dry beans (Ruby Small Red) in 2016 and 2019, and barley (Moravian 69) in 2017 and 2020. The crops were furrow irrigated to meet estimated crop evapotranspiration (ET_c) rates (Wright, 1982).  The harvest areas within each plot for each crop were 18.7, 25.5, and 25.5 m² for sugarbeet, dry bean, and barley, respectively. Sugarbeets were harvested using a custom 2 row (1.1176 m) research harvester attached to a New Holland (Turin, Italy) TM90 tractor. Sugarbeets from the plots harvest areas were removed from the soil and placed onto a load cell platform where each plot weights were measured, and two 8 beet subsamples were collected.  Subsamples were sent to the Amalgamated Sugar Company tare lab for analysis of percent sugar and quality parameters (conductivity and nitrates).  Percent sugar was determined using an Autopol 880 polarimeter (Rudolph Research Analytical, Hackettstown, NJ), a half-normal weight sample dilution, and aluminum sulfate clarification method [ICUMSA Method GS6-3 1994] (Bartens, 2005). Conductivity was measured using a Foxboro conductivity meter Model 871EC (Foxboro, Foxboro, MA) and nitrate was measured using a Denver Instruments Model 250 multimeter (Denver Instruments, Denver, CO) with Orion probes 900200 and 9300 BNWP (Krackler Scientific, Inc., Albany, NY). Recoverable sucrose yield per ton of roots was estimated by: [(extraction)(0.01)(gross sucrose/ha)]/(t/ha), where extraction = 250 + [[(1255.2)(conductivity) – (15000)(percent sucrose – 6185)]/[(percent sucrose)(98.66 – [(7.845)(conductivity)])] ] and gross sucrose = (t/ha)(percent sucrose)(0.01)(1000 kg/t). Dry bean and barley were harvested with an Almaco (Nevada, Iowa, U.S.) PMC20 Plot Master Combine with a 1.524 m wide cutting head, The harvested grain and beans were collected in sacks, weighed, and yield determined.

Analysis of variance was determined for treatment main effects for production factors (sugarbeet root yield, sugarbeet ERS yield, sugarbeet root sucrose concentration, sugarbeet root brei nitrate concentration, barley grain yield, and dry bean yield) using a randomized block design model in Statistix 8.2 (Analytical Software, Tallahassee, FL).  For significant (0.05 probability level) main effects, the LSD mean separation method were used to determine treatment differences.

Results and Discussion

There were no significant impacts of PCC on sugarbeet, dry bean, and barley crop yields in years 2015, 2016, 2017, 2019, and 2020. (Table 4). However, in 2018, sugarbeet root yield was lower for the control treatment compared to the 22.4A treatment (Table 4) but there was no difference between the control and the remaining two PCC treatments (6.7A and 89.7T) or between the 22.4A treatment and the other two PCC treatments. This significant difference was not easily interpreted according to PCC application rates and timings, thus any negative or positive effects associated with PCC could not be determined. In 2018, both the 22.4A and 89.7T treatments had the same total lime application rate of 89.7 kg ha^-1 (Table 1). In both sugarbeet crop years (2015 and 2018) PCC had no significant effect on sucrose concentration, sugar quality indicators (conductivity and nitrates) (Table 4), or seed germination (data not shown). The average sugarbeet populations at harvest in 2015 and 2018 were 110,00 and 122,000 plants ha^-1, respectively. Plant populations were not determined for barley and dry beans.

The calcium carbonate equivalency (CCE) is the acid neutralizing value of PCC compared to 100% calcium carbonate. The average CCE of PCC used in this study was 81%. This PCC is a good lime source compared to other by-product related lime sources. For example, Class C fly ash (by-product of subbituminous coal combustion) utilized in Nebraska as an agricultural lime source has an average CCE of 40-45% (Tarkalson et al., 2005; Yunusa et al., 2012).  Despite PCC’s acid neutralizing value and at the high rates applied in this study, none of the PCC treatments caused significant increases in soil pH in any of the years measured (Table 5). The PCC pH (8.4) was not much higher than many alkaline soils in the arid western U.S. The research area for this study had control treatment (no PCC) pH levels ranging from 7.8 to 8.1 across sampling times (Table 5). The average EC value of the PCC was 2280 µS cm^-1 (Table 2). Although this was much higher than the control soil (average 569 µS cm^-1) it did not result in any significant increase in soil EC even at the highest applied rate (Table 5). This could explain why sugarbeet sugar quality, which is negatively influenced by high salts, remained unaffected by any PCC treatment.

The PCC contained a significant amount of crop nutrients P and K (Table 2). The PCC additions increased soil bicarbonate extractable and total P concentrations (Table 5). Across all crops and PCC treatments, PCC applied between 1.6 and 5.3 times more P₂O₅ than the highest recommended rates for sugarbeet, barley and dry bean (Walsh et al., 2019; Moore et al., 2012; Robertson and Stark, 2003) (Table 3). Across all crops and PCC treatments, PCC applied between 0.07 and 0.42 times more K₂O than the highest recommended rate (Table 3). The PCC was not a significant source of available N (Table 2 and 3).

Comparisons between the soil in 2014 prior to PCC applications and the PCC material showed that PCC contained 6.6, 5.0, 1.8, and 1.2 times higher concentrations of P, Ca, Na, and Cu than the soil, respectively.  At the rates of PCC applied in the study, the masses of Na and Cu added to the soil were minimal. Precipitated calcium carbonate applied at a cumulative amount of 26.9 kg ha^-1 (6.7A treatment) increased total soil Na and Cu masses by 1.2% and 0.82% in the top 0.3 m of soil, respectively. Precipitated calcium carbonate applied at a cumulative amount of 89.7 kg ha^-1 increased total soil Na and Cu masses by 3.9% and 2.7% in the top 0.3 m of soil, respectively. The only constituent that increased in concentration in the soil over time compared to the control was P (Table 5). All other measurements and constituent concentrations did not increase in the soil after lime applications across time. The soil (0-0.3 m) contains 3.5, 5.0, 1.8, 1.4, and 12.4-times higher concentrations of K, Al, Zn, Cd, and Pb than the PCC, respectively.  Because the PCC was incorporated into the top 0.3 m layer, the addition of PCC cannot increase the total concentrations of K, Al, Zn, Cd and Pb in the soil. Overall, PCC additions at rates in this study only increased soil P concentrations thus serving as a P source.  Compared to the control, the bicarbonate soil P concentrations increased by 25% and 73% for the final PCC application amounts of 26.9 kg ha^-1 (6.7A treatment) and 89.7 kg ha^-1 (6.7A and 89.7T treatments), respectively. The applied PCC at all rates did not negatively impact soil properties.  Christenson et al. (2000) showed that PCC application rates up to 5.6 Mg ha^-1 increased the concentrations of Mn and Zn in sugarbeet and soybean leaves but did not affect yields compared to no PCC. The concentrations of Mn and Zn in the PCC was not reported in the study (Christenson et al., 2000).

The elements Al, Cu, Zn, Cd and Pb when in sufficient plant available concentrations can be toxic to plants (Angulo-Bejarano et al., 2021). However, there were no negative impacts on crop production from these elements.

Table 4. Sugarbeet production factors and analysis of variance (ANOVA) for production factors (significance at p>f = 0.05). Bolded p>f values were significant at the 0.05 probability level. Within each production factor, study, and year values with the same letters are not different at the 0.05 probability level. Sugarbeet root yields are reported at approximately 77% water content. Barley and dry bean yields are reported based on dry matter.

Year	Crop	Treatment	Cumulative Lime Applied Prior to Listed Year Crop (Mg ha-1)	——————–Production Measurements——————-
				Root Yield	Sucrose Yield	Sucrose	Root Nitrate	Root Conductivity
				Mg ha-1	kg ha-1	g kg-1	mg kg-1	mmhos
2015	Sugarbeet	Control	0	92.2	14024	17.8	140	0.70
		6.7A	6.7	87.8	13383	17.8	139	0.69
		22.4A	22.4	88.0	13310	17.7	140	0.70
		89.7T	89.7	91.8	13940	17.7	136	0.68
		Mean		89.9	13664.4	17.7	138.9	0.70
		p>f		0.444	0.300	0.991	0.699	0.969
2016	Dry Bean			No treatment yields measured due to significant crop damage from hailstorm in early June.
				Yield
				kg ha-1
2017	Barley	Control	0	5879
		6.7A	20.2	5527
		22.4A	67.3	5600
		89.7T	89.7	5168
		Mean		5543
		p>f		0.306
				Root Yield	Sucrose Yield	Sucrose	Root Nitrate	Root Conductivity
				Mg ha-1	kg ha-1	g kg-1	mg kg-1	mmhos
2018	Sugarbeet	Control	0	64.0 b	10697	19.3	84.0	0.64
		6.7A	26.9	73.5 ab	11871	18.9	90.2	0.75
		22.4A	89.7	83.6 a	13154	18.4	129.3	0.73
		89.7T	89.7	71.5 ab	11514	18.8	78.8	0.71
		Mean		73.2	11809	18.8	95.6	0.70
		p>f		0.042	0.082	0.253	0.456	0.256
				Yield
				kg ha-1
2019	Dry Bean	Control	0	3635
		6.7A	26.9	4079
		22.4A	89.7	4041
		89.7T	89.7	4130
		Mean		3971
		p>f		0.317
				Yield
				kg ha-1
2020	Barley	Control	0	7341
		6.7A	26.9	7359
		22.4A	89.7	7309
		89.7T	89.7	7108
		Mean		7279
		p>f		0.905

Conclusions

The PCC used in this study can safely be applied (at rates up to 89.7 kg ha^-1) to heavier textured alkaline soils in the local growing area. The application of PCC did not negatively affect sugarbeet, dry bean and barley yields in a silt loam soil. The PCC applied at rates up to 89.7 kg ha^-1 was not a significant source of toxic elements to plants. Although the pH of PCC was higher than the soil, PCC rates application rates up to 89.7 kg ha^-1 did not increase soil pH. The sugarbeet PCC used in this study could be used as a P fertilizer. In soils that have high soil P, PCC can potentially increase negative surface water impacts. The extent of the environmental impacts will vary based on management practices that affects the amount of runoff that enters off-site water streams. Practices that reduce runoff will reduce risks.

 

 

 

 

 

Table 5. Fall soil sample analysis and analysis of variance (significance at p>f = 0.05) for selected variables for treatments across years of the study. Bolded p>f values were significant at the 0.05 probability level.

Year	Treatment	Cumulative Lime Applied Prior to Soil Sample	pH	EC	Bicarbonate P	Total Inorganic N	Total P	Total K	Total Ca	Total Na	Total Al	Total Cu	Total Zn	Total Cd	Total Pb
		Mg ha-1		μS cm-1	—————————————————————mg kg-1—————————————————————
2014	Control	0	7.9	409	20.0	12.6	975	3421	64734	290.6	17766	12.6	64.3	0.54	11.8
	6.7A	0	7.8	412	22.3	11.3	1004	3552	55531	243.1	18356	13.5	66.5	0.55	11.7
	22.4A	0	7.8	393	14.2	10.6	968	3593	55675	249.3	18521	13.6	65.2	0.54	11.2
	89.7T	0	7.8	425	17.4	12.3	987	3532	54459	249.6	18355	13.6	64.7	0.54	11.1
	p>f		0.903	0.693	0.381	0.412	0.717	0.575	0.662	0.314	0.404	0.662	0.850	0.804	0.611
2015	Control	0	7.8	708	23.5b	25.5	1018b	3362	62567	261.0	17836	12.5	67.6	0.62	11.3
	6.7A	6.7	7.8	475	28.1b	29.7	1035b	3709	53458	272.0	18992	13.6	71.4	0.64	11.8
	22.4A	22.4	7.9	668	29.3b	21.9	1067b	3534	58949	263.5	18549	13.0	68.6	0.64	11.5
	89.7T	89.7	7.9	732	45.8a	23.4	1139a	3582	57703	278.3	18599	13.5	70.4	0.64	11.5
	p>f		0.434	0.070	0.020	0.801	0.008	0.267	0.836	0.515	0.220	0.645	0.693	0.599	0.756
2016	Control	0	7.8	498	27.7b	19.8	1082b	3812	60310	268.0	19010	12.8	76.4	0.61	12.4
	6.7A	20.2	7.8	533	37.5ab	22.6	1117ab	4182	51918	269.5	20397	13.5	69.6	0.58	11.9
	22.4A	67.3	7.8	547	43.3a	20.5	1090b	4071	56805	267.8	19754	13.0	66.7	0.60	11.8
	89.7T	89.7	7.9	590	48.7a	24.6	1158a	3942	54630	270.7	19326	13.4	66.7	0.60	11.8
	p>f		0.152	0.074	0.015	0.161	0.044	0.362	0.839	0.998	0.322	0.869	0.135	0.756	0.851
2017	Control	0	8.1	578	26.1c	35.4	1055	3639	60024	252.6	18385	12.9	403.0	0.66	11.4
	6.7A	26.9	8.1	543	35.6b	25.4	1067	4075	51641	230.9	19864	13.8	106.1	0.63	12.4
	22.4A	89.7	8.1	557	49.5a	32.3	1131	3909	55584	250.7	19252	13.7	270.4	0.65	9.5
	89.7T	89.7	8.1	454	45.6a	33.3	1123	3880	56154	308.8	19044	13.5	388.9	0.63	9.5
	p>f		0.711	0.721	0.001	0.527	0.098	0.195	0.815	0.185	0.231	0.816	0.801	0.679	0.062
2018	Control	0	8.1	596	29.7c	28.5	1016	3615	57880	246.1	18334	13.3	374.5	0.56	10.3
	6.7A	26.9	8.1	636	40.2b	46.0	997	3358	51561	294.1	16895	12.9	345.7	0.53	9.6
	22.4A	89.7	8.2	627	53.3a	30.8	1129	3755	64397	285.9	18822	13.6	115.0	0.59	11.8
	89.7T	89.7	8.1	621	44.0ab	27.7	1142	3791	62528	353.6	19044	14.4	127.5	0.59	11.1
	p>f		0.384	0.819	0.005	0.307	0.092	0.517	0.626	0.334	0.366	0.578	0.261	0.289	0.370
2019	Control	0	8.0	697	28.1c	50.3	1053	3625	62728	285.8	18455	13.5	63.0	0.60	12.8
	6.7A	26.9	8.1	594	35.8b	37.3	1053	3798	53345	263.1	19026	13.9	64.0	0.60	12.9
	22.4A	89.7	8.2	596	47.0a	34.6	1136	3625	60103	280.7	18407	13.9	63.6	0.62	12.4
	89.7T	89.7	8.1	706	43.3a	48.5	1141	3502	57258	272.7	17903	13.9	63.4	0.58	12.8
	p>f		0.236	0.583	0.001	0.596	0.061	0.780	0.776	0.525	0.783	0.952	0.991	0.530	0.788
2020	Control	0	8.1	502	22.0c	18.8	1030c	3454	60787	258.2	17730	12.9	63.3	0.63	12.4
	6.7A	26.9	8.1	467	30.8b	16.9	1060bc	3663	55792	260.1	18561	13.5	64.9	0.65	12.7
	22.4A	89.7	8.1	494	44.1a	13.8	1128a	3634	61948	277.9	18549	13.1	62.4	0.63	12.6
	89.7T	89.7	8.1	458	37.9a	15.2	1104ab	3615	54092	249.6	18470	13.7	65.6	0.64	12.9
	p>f		0.617	0.743	<0.001	0.5237	0.019	0.442	0.802	0.501	0.321	0.802	0.602	0.771	0.861

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Introduction

Nitrogen (N) management is important in sugarbeet production because it can affect both yields and quality (Stout, 1960; Tarkalson et al., 2016). Under supplying N can reduce yields while over supplying N often results in decreased root sucrose content and increased root impurities which decreases sucrose extraction efficiency (Carter and Traveller, 1981; James et al. 1971). In addition, over supplying N can lead to increased N losses to the environment and represents an unnecessary cost to the grower. Because of the importance of optimizing N supply for sugarbeet production and due to the continuing trend for increasing yields, research studies have been conducted periodically in the Northwest U.S. sugarbeet growing area to determine sugarbeet N requirement.

The sugarbeet production in the Pacific Northwest is located primarily from south central Idaho to southeastern Oregon. Beets are produced by growers who are part of TASCO, a grower-owned cooperative. From 2011 to 2020 an average of 73,700 ha year^-1 of sugarbeets were harvested in this growing area (NASS, 2022).

Historically in the Northwest U.S. sugarbeet growing area, a yield goal N management (YGNM) approach has been utilized to determine fertilizer N requirement. The basis of YGNM was to determine the total available soil N supply [soil (0-0.9 m) NO₃-N and NH₄-N + fertilizer N] needed to optimize sucrose and root yields. However, recent data analysis and research in the Northwest U.S. growing area has indicated that a YGNM approach can lead to over supplying N over time and a static N range approach may better match sugarbeet N supply needs (Tarkalson et al., 2016a; Tarkalson et al. 2018; Tarkalson, Olsen, Bjorneberg, in submission, this issue). Improvements in N management practices that better match N supply with crop need will improve production economics and reduce negative environmental impacts. The YGNM approach is based on research derived N requirement factors (Nr) multiplied by site-specific root yield goals. The Nr factor represent the Mg of N needed to grow a Mg of sugarbeet roots (kg N Mg^-1 roots). Research from Tarkalson et al. (2016a) showed that the Nr factor (2.75-3.0 kg N Mg^-1 roots) was lower than previously research derived Nr factors (3.5 to 4.0 kg N Mg^-1 roots) (Tarkalson et al., 2018). It was determined that the decrease in Nr was likely due to increasing yields over time (Figure 1) and a relatively stable crop N supply requirement. This observation was highlighted in Tarkalson et al. (2018) and led to research in Tarkalson, Olsen, Bjorneberg (in submission, this issue). Tarkalson, Olsen, Bjorneberg (in submission, this issue) found that the N supply required to maximize yields in research plots in 2018 and 2019 (209 kg N ha^-1) were similar to N supplies required in research studies conducted from 2005 to 2010 (202 kg N ha^-1), even when the root yields increased by 22% over the time between the research studies. Prior to major adjustments in the Northwest U.S. sugarbeet growing area, additional research was needed fine-tune comparisons of the SRNM with other N management approaches.

The objective of this study was to compare surgarbeet yield and quality factors using a SRNM, YGNM, and a commonly used agricultural consultant business (ACB) N management approach in the Northwest U.S.

Materials and Methods

Site Characteristics

The studies in this paper were conducted in 2020 and 2021 at 6 research sites (Table 1). The sites ranged across the southern Idaho sugarbeet production area. All sites had the same tillage practices (conventional), spring soil sample depth (0-0.9 m), row spacing (0.56 m), N source (urea), and timing of N application (4 to 6 leaf stage) (Table 1). Research has shown that the 4 to 6 leaf stage is the start of significant crop N uptake (TASCO, 2020). Other cultural and experimental practices varied across sites (plot size, N application rate, treatment replications, irrigation system, planting date, glyphosate application timings, planting date, and harvest date) (Table 1).

Table 1. Site information for the Idaho study sites.

Site-Year	City, County	Year	Soil Texture	Plot Size	Irrigation System	Variety	No. Treatment Replications	Plant Date	Harvest Date
1	Jerome, Jerome	2020	loam	3.4m ×11.1m	wheel line	BTS 251N	6	Apr 17	Oct 5
2	Nampa, Canyon	2020	silt loam	3.4m × 7.3m	drip	BTS 251N	6	Mar 24	Oct 5
3	Fruitland, Payette	2020	silt loam	3.4m × 7.3m	furrow	BTS 251N	6	Apr 13	Sept 30
4	Burley, Minidoka	2021	silt loam	3.4m × 10.5m	wheel line	Crystal A702NT	8	Apr 7	Oct 7
5	Jerome, Jerome	2021	silt loam	3.4m × 10.2m	wheel line	Crystal A702NT	8	Apr 3	Sep 28
6	Nampa, Canyon	2021	silt loam	3.4m × 7.3m	drip	Crystal A702NT	8	Apr 1	Sept 27

N Application

Prior to N fertilizer treatment applications in spring, 4 cores were taken in each rep to a depth of 0.9 m in 0.03 m depth increments. Soil samples were analyzed for nitrate-N (NO₃-N) and ammonium-N (NH₄-N) after extraction in 2M KCl (Mulvaney, 1996) using a flow injection analyzer (Lachat Instruments, Loveland, CO). At each site, the 0-0.9 m NO₃-N and NH₄-N in was averaged across all cores to determine site N supply.

Six N supply treatments (fertilizer N + residual inorganic soil N [NO₃-N + NH₄N]) were applied and replicated 6 times in 2020 and 8 times in 2021 and arranged in a randomized block design. The treatments were a control (no fertilizer N), SRNM low range rate, SRNM medium range rate, SRNM high range rate, YGNM rate, and a ACB N supply recommendation. Treatment N supply specifics are detailed in Table 2. The SRNM treatments were based on past research studies (Tarkalson et al, 2016; Tarkalson, Olsen, Bjorneberg, in submission, this issue). For the YGNM treatment, the N supply recommendation are based on current published recommendations (TASCO, 2020; Tarkalson et al., 2016b). The site YGNM calculations were:

YGNM N Supply (kg N ha^-1) = Nr (kg N supply Mg^-1 root) × Yield Goal (Mg roots ha^-1)

Site 1 YGNM N supply (kg N ha^-1) = 3 kg N supply Mg^-1 root × 92 Mg roots ha^-1

Site 2 YGNM N supply (kg N ha^-1) = 2.75 kg N supply Mg^-1 root × 114 Mg roots ha^-1

Site 3 YGNM N supply (kg N ha^-1) = 2.75 kg N supply Mg^-1 root × 119 Mg roots ha^-1

Site 4 YGNM N supply (kg N ha^-1) = 3 kg N supply Mg^-1 root × 101 Mg roots ha^-1

Site 5 YGNM N supply (kg N ha^-1) = 3 kg N supply Mg^-1 root × 92 Mg roots ha^-1

Site 6 YGNM N supply (kg N ha^-1) = 2.75 kg N supply Mg^-1 root × 112 Mg roots ha^-1

Yield Goals are based on actual root yields over the previous 5 years (2015-2019 average) from the site area. Any slight variations in calculations above and values in Table 2 are due to rounding errors.

A subsample of the spring soil samples and yield goals (same as for YGNM treatments) from each site were sent to a ACB for analysis and a N fertilizer recommendation. The ACB N recommendations are proprietary, so we have no information on the calculations. However, when multiplying the ACB recommended N supplies by the yield goals, the Nr factors range from 6.5 to 6.8 kg N supply Mg^-1 root. For all sites, N treatments were applied as urea fertilizer and immediately incorporated using conventional tillage.

Harvest and Analysis

Root yield was measured in each plot using a load cell scale mounted to a plot harvester. Three samples per plot (each at least 12 kg of harvested roots) were collected and analyzed at the TASCO tare lab for percent sugar, nitrate concentration, and electrical conductivity. Percent sugar was determined using an Autopol 880 polarimeter (Rudolph Research Analytical, Hackettstown, NJ), a half-normal weight sample dilution, and aluminum sulfate clarification method [ICUMSA Method GS6-3 1994] (Bartens, 2005). Conductivity was measured using a Foxboro conductivity meter Model 871EC (Foxboro, Foxboro, MA) and nitrate was measured using a Model 250 multimeter (Denver Instruments, Denver, CO) with Orion probes 900200 and 9300 BNWP (Krackler Scientific, Inc., Albany, NY). Recoverable sucrose yield per ton of roots was estimated by: [(percent extraction)(0.01)(gross sucrose/ha)]/(t/ha), where percent extraction = 250 + [[(1255.2)(conductivity) – (15000)(percent sucrose – 6185)]/[(percent sucrose)(98.66 – [(7.845)(conductivity)])] ] and gross sucrose (t/ha) = (gross root yield, t/ha)(percent sucrose)(0.01)(1000 kg/t).

Statistical Analysis and Calculations

Statistical analyses were conducted separately for each site. Analysis of variance was conducted for N supply treatment main effects on the selected production factors of sucrose yield, root yield, N use efficiency, N requirement, root sucrose percent, and root brei nitrate concentration using a randomized block design model in Statistix 8.2 (Analytical Software, Tallahassee, FL). Nitrogen use efficiency was defined as the quantity of sucrose produced per kg N supply (fertilizer N + spring soil residual inorganic N). Nitrogen requirement was defined as the kg N supply per Mg of harvested sugarbeet root.

For site-years with significant N supply main effects on root and sucrose yield, the maximum yield was identified by comparing adjacent numerically ordered means using the least significant difference method (LSD) at the 0.05 probability level. For each site-year with no significant N supply main effect on yields, the yield for the control treatment was considered the maximum yield.

For fertilizer economic calculations, mean Urea prices over the past 5 years (2018-2022) were used (DTN, 2022).

Table 2. Site-year yield and N treatment information for sugarbeet at the Idaho study sites.

Site-Year	No. Treatment Replications	Field 5-Year Root Yield Average	Idaho 5-Year Root Yield Average	Control	SRNM†  Low	SRNM Med	SRNM High	YGNM‡	ACB⁋
		———–Mg ha-1———–		———————kg N Supply ha-1 (kg N Fertilizer ha-1) ———————
1	6	92	90	132(0)	202(69)	230(97)	258(125)	276(143)	298(166)
2	6	114	90	196(0)	202(6)	230(34)	258(62)	314(118)	372(176)
3	6	119	90	137(0)	202(65)	230(93)	258(121)	328(192)	394(258)
4	8	101	90	148(0)	202(54)	230(82)	258(110)	302(155)	336(188)
5	8	92	90	174(0)	202(28)	230(56)	258(84)	274(101)	314(140)
6	8	112	90	218(0)	218(0)	230(11)	258(39)	308(90)	381(162)
† SRNM = Static Range N Management ‡ YGNM = Yield Goal N Management. Nr = 2.75 to 3 kg N Mg-1 root yield goal (TASCO, 2020; Tarkalson et al. 2016a; Tarkalson et al. 2016b). ⁋ACB = Agricultural Consultant Business.

Table 3. Site-year root yield, sucrose yield, nitrogen use efficiency (NUE), root sucrose percent, root nitrate concentration, root conductivity, and root juice purity (PJTP) concentration. Analysis of variance (ANOVA) for relationships between N supply and the listed measurements is shown. For significant ANOVA main effects (p< 0.05), the least significant difference (LSD) method was used to compare numerically adjacent measurements. For maximum root or sucrose yields, treatment ID’s and maximum yield N rates (MYNR) are bolded. For studies with no significant relationships between N supply and yields, the control is considered the maximum treatment.

SiteYear	Treatment ID	N Supply	Root Yield	Sucrose Yield	NUE	Root Sucrose	Root Nitrate	Root Conductivity	PJTP
	kg ha-1	kg ha-1	Mg ha-1	Mg ha-1	kg sucrose kg-1 N	%	mg kg-1	mmhos cm-1	%
1	Control	132	96.6 b	16.6 b	125.6 a	19.7	35.2	0.63	94.9
	SRNM Low	202	111.2 a	18.9 a	93.6 b	19.6	30.4	0.64	94.9
	SRNM Med	230	115.8 a	19.7 a	85.9 c	19.5	26.5	0.60	95.0
	SRNM High	258	114.8 a	19.5 a	76.6 d	19.4	29.5	0.59	95.1
	YGNM	276	115.3 a	19.5 a	70.8 de	19.4	28.4	0.60	95.0
	ACB	298	116.4 a	19.5 a	65.6 e	19.3	32.7	0.61	94.9
	p>f		0.001	<0.001	<0.001	0.092	0.647	0.657	0.698
2	Control	196	136.5	19.1	97.5 a	17.2	365.7	0.99	91.4
	SRNM Low	202	133.0	18.2	90.5 b	16.8	433.3	0.99	91.0
	SRNM Med	230	136.0	18.6	81.2 c	16.8	430.0	1.00	91.0
	SRNM High	258	136.5	19.2	74.5 d	17.0	412.0	0.92	91.4
	YGNM	314	136.6	19.1	60.9 e	16.9	386.9	0.93	91.5
	ACB	372	144.8	19.7	53.0 f	16.9	449.8	1.08	90.5
	p>f		0.214	0.348	<0.001	0.616	0.779	0.059	0.0391
3	Control	137	123.6	18.2	133.0 a	16.9	130.8	0.61	94.3
	SRNM Low	202	123.7	18.6	92.1 b	17.1	118.6	0.54	94.7
	SRNM Med	230	122.7	18.2	79.4 c	17.0	126.2	0.57	94.6
	SRNM High	258	118.0	17.3	68.2 d	16.8	119.0	0.58	94.6
	YGNM	328	118.3	17.7	53.9 e	17.1	135.2	0.55	94.6
	ACB	394	119.3	17.8	45.1 f	17.0	124.7	0.55	94.7
	p>f		0.910	0.905	<0.001	0.148	0.400	0.842	0.793
4	Control	146	91.3 c	16.2	111.7 a	20.2 a	17.8 b	0.57	95.2
	SRNM Low	202	95.5 bc	16.9	84.2 b	20.1 a	20.4 b	0.57	95.2
	SRNM Med	230	105.5 ab	18.6	80.4 b	20.1 a	20.2 b	0.56	95.2
	SRNM High	258	103.9 ab	18.4	71.2 c	20.1 a	22.9 b	0.57	95.2
	YGNM	302	104.1 ab	17.9	59.2 d	19.7 b	30.1 a	0.59	95.0
	ACB	336	105.6 a	18.2	54.2 d	19.7 b	30.3 a	0.61	95.0
	p>f		0.014	0.054	<0.001	<0.001	<0.001	0.177	0.082
5	Control	174	100.3	16.6	95.5 a	19.1	26.3 b	0.68	94.7
	SRNM Low	202	103.6	16.8	83.3 b	19.0	26.6 b	0.69	94.7
	SRNM Med	230	103.0	16.8	73.1 c	19.1	25.7 b	0.70	94.6
	SRNM High	258	103.6	17.0	65.9 d	19.0	27.7 ab	0.68	94.7
	YGNM	274	102.9	16.8	61.2 e	19.0	29.9 ab	0.67	94.7
	ACB	314	103.5	16.6	53.0 f	18.7	32.0 a	0.71	94.5
	p>f		0.840	0.947	<0.001	0.312	0.049	0.756	0.490
6	Control/ SRNM Low	218	115.7	18.5	84.5 a	18.8 ab	47.6	0.88	93.8
	SRNM Med	230	121.8	19.4	84.6 a	18.8 ab	41.8	0.83	94.0
	SRNM High	258	121.5	19.2	74.4 b	19.0 a	43.8	0.92	93.6
	YGNM	308	128.1	19.9	64.6 c	18.7 b	53.5	0.89	93.7
	ACB	381	123.8	19.3	50.7 d	18.7 b	55.6	0.88	93.7
	p>f		0.181	0.373	<0.001	0.018	0.340	0.117	0.128

Results and Discussion

There were significant effects of N supply treatments on root and/or sucrose yields for site years 1 (Jerome 2020) and 4 (Burley 2021) (Table 3). The N supply for the maximum sucrose and root or sucrose yields were highlighted in bold lettering in Table 3. For the remaining site-years there was no significant relationships between N supply treatments and yields (Table 3). The N supplies treatments that maximized yields for site-years 1 and 4 were the SRNM low and SRNM med rates (average N supply = 216 kg N ha^-1). For site years 2, 3, 5 and 6, the control treatment average N supply was 181 kg N ha^-1. The maximum sucrose and root yields for site-years 1 and 2 were similar, 18.9 and 18.6 Mg sucrose ha^-1 and 111.2 and 105.5 Mg roots ha^-1, respectively. Across site-years 2, 3, 5 and 6, the sucrose and root yields for the control treatments ranged from 16.6 to 19.1 Mg sucrose ha^-1 and 100.3 and 136.5 Mg roots ha^-1. The average root yield across all sites and N supply treatments was 115.8 Mg roots ha^-1, 22.5% greater than the average yields in Idaho during 2020 and 2021 (89.7 Mg roots ha^-1) (Figure 1).

The SRNM low N supply treatment met or was closest to the N supply required to maximize yields than either the YGNM or ACB N supply treatments for 5 of the 6 site years. For the remaining site-year, the SNRN med N supply treatment resulted in maximizing yield. This data agrees with past research showing that the SRNM approach to determining N supply requirement better matches sugarbeet N supply needs than the YGNM approach (Tarkalson, Olsen, Bjorneberg, in submission, this issue; Tarkalson et al., 2018). In this study, the ACB supply treatment used a Nr multiplier ranging from 3.2 to 3.4 kg N Mg^-1 roots, which is higher than the range of Nr values used in the current industry recommended YGNM approach (2.75-3.0 kg N Mg^-1 roots) (Tarkalson et al., 2016a; Tarkalson et al., 2016b; TASCO, 2020). By comparison, the calculated Nr factor from site-year 1 and 2 of this study was 2.0 kg N Mg^-1 roots. Tarkalson, Olsen, Bjorneberg (in submission, this issue) showed that research derived Nr factors have been decreasing over time due to increasing yields (Figure 1) over time and a stable sugarbeet N supply requirement to reach maximum yields over time, supporting past research conclusions that a SRNM approach better meets sugarbeet N supply requirements than a YGNM approach (Tarkalson, Olsen, Bjorneberg, in submission, this issue; Tarkalson et al., 2018).

The YGNM and ACB N supply recommendations over supplied N at all site-years (Table 4). For site years 1 and 2, the amount of excess N was determined by subtracting the YGNM and ACB N supply recommendations from the N supply needed to reach maximum yields (site-year 1 = 202 kg N ha^-1, site-year 4 = 230 kg N ha^-1) (Table 3). For site years 2, 3, 5 and 6 the amount of excess N was determined by subtracting the YGNM and S ACB N supply recommendations from the N supply of the SRNM low treatment even though there were no differences in yields across all treatments. Although the yields were maximized at the N supply of the control treatment, in a real-world production setting, a recommendation of supplemental N would have been made based on established recommendations. Across all site-years, the YGNM approach recommended an average of 91 kg N ha^-1 of excess N fertilizer. Depending on the price of urea in a given year, this represented unnecessary cost to the grower of between $79 to $200 ha^-1 (Table 4). The ACB N recommendation was even less accurate, averaging an excess of 140 kg N ha^-1 across all site-years, and adding unnecessary cost to the grower of between $122 to $308 ha^-1 (Table 4).

Table 4. Excess fertilizer N amount and cost from 2018 to 2022 for the YGNM and ACB recommended N supplies relative to the optimal SNMR N supply. Fertilizer N cost is calculated using the average cost of urea for each year in the U.S. (DTN, 2022). For site-years 1 and 4, the optimal SRNM supply was the N supply that had the maximum yield (Table 3). For site-years 2, 3, 4, and 6, the optimal N supply was considered the SRNM Low treatment (Table 3). We assumed that in a production setting, a SRNM Low N supply was recommended.

Excess N Calculation	Site Year	Excess Fertilizer N	————-Cost of Excess N————-
			2018	2019	2020	2021	2022
		kg N ha-1	——————$ ha-1 ——————-
			$0.88	$0.97	$0.87	$1.57	$2.20
YGNM-SRNM Low	1	74	$65	$72	$65	$117	$163
YGNM-SRNM Low	2	112	$99	$109	$97	$176	$246
YGNM-SRNM Low	3	126	$111	$122	$110	$198	$277
YGNM-SRNM Med	4	72	$63	$70	$63	$113	$158
YGNM-SRNM Low	5	72	$63	$70	$63	$113	$158
YGNM-SRNM Low	6	90	$79	$87	$78	$141	$198
	Mean	91	$80	$88	$79	$143	$200
ACB – SRNM Low	1	96	$84	$93	$84	$151	$211
ACB – SRNM Low	2	170	$150	$165	$148	$267	$374
ACB – SRNM Low	3	192	$169	$186	$167	$301	$422
ACB – SRNM Med	4	106	$93	$103	$92	$166	$233
ACB – SRNM Low	5	112	$99	$109	$97	$176	$246
ACB – SRNM Low	6	163	$143	$158	$142	$256	$359
	Mean	140	$123	$136	$122	$220	$308

For all site-years, N supply treatments had a significant effect on NUE, and NUE was highly correlated to N supply (Table 3, Figure 2). As N supply increased, NUE decreased. The NUE at the mean N supply required for maximum yield for the two responsive site years (216 kg N ha^-1) was 85.7 kg sucrose kg^-1 N. This NUE was higher than previous studies conducted in southern Idaho. In research conducted in 2018 and 2019 the NUE was 75.2 kg sucrose kg^-1 N, and in research conducted from 2005 to 2010 the NUE was 60.3 kg sucrose kg^-1 N (Tarkalson, Olsen, Bjorneberg, in submission, this issue; Tarkalson et al., 2016). As root yields increase over time and N supply required to reach maximum root yields remains relatively stable, the NUE increases. The increase in NUE over time indicates that a SRNM approach is valid.

For site-years 1, 2, 3, and 4, N supply did not affect the quality factors (root sucrose percentage, nitrate, conductivity, and juice purity) (Table 3). Some past research has indicated that sugarbeet root sucrose is negatively correlated with increasing N supply (Tarkalson et al, 2016a; Tarkalson et al, 2016c). However, other research has shown no relationship between N supply and sugarbeet root sucrose (Tarkalson et al., 2012; Tarkalson et al, 2016a). In our study, only site-year 4 had a significant relationship between N supply and sugarbeet root sucrose percent, with the highest rates of N supply showing the lowest sucrose percent (Table 3). Although statistically significant, even these lowest sucrose percentages were still high compared to historic industry averages The highest root sucrose percentage for site year 4 was 20.2%. Nitrogen supply treatments significantly affected root nitrate concentrations for site-years 4 and 5 only. Across all sites root nitrates concentrations ranged from 450 to 18 mg kg^-1, with most concentrations lower than the critical threshold considered harmful for sucrose % or sugar quality. Past research has indicated that root nitrate concentrations greater than 200 mg nitrate kg^-1, can cause reductions in root sucrose percent (Tarkalson et al., 2016). Only site 2  had root nitrate greater than 200 mg kg^-1 (Table 3). At sites 4 and 5 higher rates of N supply caused significantly, though not agronomically important, higher root nitrate concentrations. Across all site-years, there was no significant effect of N supply treatments on root conductivity. Conductivity ranged from 0.54 to 1.0 mmhos cm^-1, values considered normal for sugarbeet production. Likewise, PTJP was unaffected by N supply and within range considered typical for sugarbeet production. Across all site years and N supply treatments, the average root conductivity and PJTP was 0.71 mmhos cm^-1 and 94.0%, respectively (Table 3).

CONCLUSIONS

This research demonstrated that high yields can be achieved at N supply levels much lower than those recommended by the YGNM or ACB methods, without negative quality issues. This will result in improved N use efficiency and reduced fertilizer N costs for growers. For 5 of the 6 site-years in this study, the SRNM low N supply treatment met or was closer to the N supply required to maximize yields than either the YGNM or ACB N supply treatments. For the sixth site, the SRNM med N supply treatment maximized yield. Following the current recommended YGNM approach, an average of 91 kg N ha^-1 in excess fertilizer N was applied, costing from $79 to $200 ha^-1 depending on N price variations from 2018 to 2022. Following the current recommended ACB N recommendations, an average of 140 kg N ha^-1 in excess fertilizer N was applied costing from $122 to $308 ha^-1 depending on N price variations. Of the three SRNM levels tested, the low and mid rates were superior in maximizing yields than the SRNM high treatment. The SRNM approach better matches N supply with crop need compared to the YGNM and ACB N recommendations over time.

References

TASCO. 2020. Sugar beet Growers Guide Book. TASCO, LLC. Boise, ID.

DTN, 2022. www.dtn.com.

Bartens, A., 2005. International Commission for Uniform Methods of Sugar Analysis Methods Book 2005. p. 431. Dr. Albert Bartens KG, Berlin, Germany.

Carter, J.N. and D.J. Traveller. 1981. Effect of time and amount of nitrogen uptake on sugarbeet growth and yield. Agron. J. 73:665-671.

James, D.W., A.W. Richards, W.H. Weaver, and R.L. Reeder. 1971. Residual soil nitrate measurement as a basis for managing nitrogen fertilizer practices for sugarbeets. J. Amer. Soc. Sug. Beet Tech. 16:313-322.

Mulvaney, R.L. 1996. Nitrogen-inorganic forms. p. 1123-1184. In D.L. Sparks (ed.) Methods of soil analysis: Part 3. SSSA Book Ser. 5. SSSA, Madison, WI.

Tarkalson, D.D., D. Bjorneberg, and A.  Moore. 2012. Effects of tillage system and nitrogen supply on sugarbeet production. Journal of Sugar Beet Research. 49:79-102.

Tarkalson, D.D., and Bjorneberg, D.L. 2018. Is static nitrogen management in Northwest U.S. sugar production appropriate? Agriculture & Environmental Letters. doi: 10.2134/ael2018.01.0001.

Tarkalson, D.D., D.L. Bjorneberg, S. Camp, G. Dean, D. Elison and R. Foote. 2016a.  Improving nitrogen management in Pacific Northwest sugarbeet production. J of Sugar Beet Res. 53:14-36. 2016.

Tarkalson, D.D., D.L. Bjorneberg, and A. Moore, 2016c. Fall and spring tillage effects on sugarbeet production. Journal of Sugar Beet Research. 52:30-38.

USDA-NASS, 2022 Online: https://www.nass.usda.gov/ (Accessed 10 October 2022).

Tarkalson, D.D., Bjorneberg, D.L., Dean, G., Camp, S., Elison, D., and Foote, P. 2016b. Nitrogen: How much is needed? Sugar Guide. USDA-ARS, Kimberly, ID.

ACKNOWLEDGEMENTS

This research was supported by the U.S. Department of Agriculture, Agricultural Research Service. Mention of trade names or commercial products in this article is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. The USDA is an equal opportunity employer.