INTRODUCTION
Red beet (Beta vulgaris) has abundant of betalain (Esatbeyoglu, Wagner, Schini-Kerth, &
Rimbach, 2015) and nitrate contents (Carreón- Hidalgo, Franco-Vásquez, Gómez-Linton, &
Pérez-Flores, 2022). Betacyanins, the purplish red pigments of betalain, are phytochemical alkaloid found in red beets. The compunds are heterocyclic nitrogen-based, water-soluble pigments and occured only in the order Caryophyllales (Bangar, Sharma, Sanwal, Lorenzo, & Sahu, 2022). These pigments are used in the food and cosmetic industries as natural dyes and in the pharmaceutical industry as antioxidants (Neelwarne, 2012). Red beet betacyanin is the only source of betalain which is permitted as a food additive (Polturak & Aharoni, 2018) with code Natural Red 33 in USA or E162 in european countries (da Silva et al., 2019)the antioxidant power and health-promoting properties of this pigment have been disregarded, perhaps due to the difficulty in obtaining a stable chemical
compound, which impairs its absorption and metabolism evaluation. Herein, betanin was purified by semi-preparative HPLC-LC/MS and identified by LC-ESI(+ and for its broad spectrum of biological activity (Sokolova, Shvachko, Mikhailova, & Popov, 2022).
The increasing use and demand of betacyanin has made the commercial values of red beets become more important, thus the improvement of production and productivity especially on the production centres should be realizable. One of the red beet production centres in Indonesia is Batu City, East Java with average production of 37-51 t/
ha. These volume was lower than those of red beet producing countries that reach 60-70 t/ha (Markoski et al., 2015). Efforts to increase production has faced several problems and one of them is the decreasing betacyanin level called Growth–Differentiation Balance Theory (GDB) phenomenon. Betalain as secondary metabolite would be accumulated as the defense plant response to certain circumstannces.
These physiological mechanism involves regulation ARTICLE INFO
Keywords:
Betacyanin Growth Model Nitrogen Dosages Nitrogen Supply Article History:
Received: July 21, 2022 Accepted: September 9, 2022
*) Corresponding author:
E-mail: [email protected]
ABSTRACT
One aim of productivity improvement on red beet was to restrain over-reducing the betacyanin as a secondary metabolite. Different nitrogen (N) sources were projected to affect growth, yield, and both primary and secondary plant metabolites. The research was carried out to find the effect of different sources and dosages of N on the production improvement and betacyanin. The N sources consisted of 5Ca(NO3)2NH4NO310H2O, NH4NO3NH4H2P04KCI, NO3H2P04KCI, CO(NH2)2, and (NH4)2SO4 with the N dosages of 75, 150, 225 kg N/
ha and without N treatment as a control. The results showed that the enhancement of N dosages increased higher beet root fresh weight (BRFW) for about 42-50% than control. The 5Ca(NO3)2NH4NO310H2O and CO(NH2)2 increased BRFW and were higher than other N sources.
The said N sources also contributed to 36% and 25% higher total betacyanin (BT) than those of (NH4)2SO4 and control, respectively. BRFW had the similar with BT, yet opposite patterns with betacyanin content (BC). The increase N dosages from any source diminish 20-33% BC, which was correlated with the increase of BRFW and BT following the sigmoidal pattern based on the logistics model during the growing period.
ISSN: 0126-0537Accredited First Grade by Ministry of Research, Technology and Higher Education of The Republic of Indonesia, Decree No: 30/E/KPT/2018
Cite this as: Roviq, M., Nihayati, E., Sitawati., & Soemarno. (2022). Nitrogen sources take roles on different growth balance of red beet (Beta vulgaris). AGRIVITA Journal of Agricultural Science, 44(3), 447–458. http://doi.org/10.17503/
agrivita.v41i0.3870
Nitrogen Sources Take Roles on Different Growth Balance of Red Beet (Beta vulgaris)
Mochammad Roviq1*), Ellis Nihayati1), Sitawati1) and Soemarno2)
1) Department of Agronomy, Faculty of Agriculture, Universitas Brawijaya, Malang, East Java, Indonesia
2) Department of Soil Science, Faculty of Agriculture, Universitas Brawijaya, Malang, East Java, Indonesia
growth and defense (Matyssek et al., 2012).
Consequently, these two different physiological mechanisms directed the opposite biochemical pathways in wihich improving the beet yield would reduce betalain levels per unit weight.
Red beets are responsive to nutrients where fertilization can increase plant biomass, but it is not always followed by an increase in betacyanin content. High N application increases plant biomass but decreases betacyanin (Mampa, Maboko, Soundy, & Sivakumar, 2017). In B. vulgaris L.
subsp. vulgaris var. altissima Döll, a member of the B. vulgaris species with different sub-species, N fertilization greatly affects the growth partitioning.
Excessive N fertilization tends to produce more leaves and shoots, yet delays root maturity and reduces sugar content in excess roots (Varga et al., 2022). Phenotypics characterization of ryegrass such as the plant height, root length, fresh weight and number of tillers showed that application of half recommended nitrogen doses resulted in better growth than standard and higher doses. On the other hands, excessive N application also induces stress on plants (Li et al., 2022). Likewise, the addition of high nitrogen inhibits growth as in the case of pakcoy plants (Chen et al., 2022). In contrast to the excessive condition that reduces quality, N deficiency actually induces secondary metabolites as an indication of high quality. N deficiency favored higher total polyphenol and flavonoid contents in ripened tomato (Stagnari et al., 2022).
Various nitrogen sources were commonly used as fertilizer in agriculture today, but most of them were given in excessive doses and have resulted in negative impacts to the product and environment. Decreased product quality due to excessive nitrogen application has been widely reported (Albornoz, 2016). Therefore management of N in soil-plant systems is very challenging to ensure sufficiency and avoid loss or excess (Saha, Rose, Wong, Cavagnaro, & Patti, 2019). In spring, nitrogen sources such as urea and UAN (urea + NH4NO3) should be avoided for sugar beet.
Application of calcium ammonium nitrate promotes higher yield and value of specific yield weight of sugar beet than urea (Varga et al., 2022). Compared to urea, ammonium sulfate nitrate followed by nitrophoska and ammonium sulfate gives higher fresh weight and dry weight on corn. Urea also
other N sources (El-Murtada Hassan Amin, 2011).
The difference in the source of N fertilizer also affects the secondary metabolite activity in red beet. The application of calcium ammonium nitrate produces higher flavonoids, while ammonium nitrate induces the plant to have higher total phenolic than urea and ammonium sulfate (Babagil, Tasgin, Nadaroglu, &
Kaymak, 2018).
The enlargement of the beet root may be caused by the expansion of the supernumerary cambium which allows it to enlarge and resemble a ball or cone shape (Goldman & Janick, 2021).
Betacyanin biosynthesis in red beet is dynamic, changes during ontogenesis and depends on genetic and environmental factors (Sokolova, Shvachko, Mikhailova, & Popov, 2022). Laufer, Nielsen, Wilting, Koch, & Märländer (2016) summarized that for beet crops of the Beta genus, N is needed to form the canopy and grow taproot cells. In Graminear plants, the form of nitrogen increases the synthesis of amino acids (Barunawati, Giehl, Bauer, & von Wirén, 2013), such as glutamine, asparagine, and total N also increases due to the application of NH4. It was also possible that the form of N will also affect the biosynthesis of tyrosine as a precursor of betacyanin and betaxanthin in red beet plants.
This was related to the initial pathway of betacyanin synthesis that starts from the shikimate pathway, a nitrogen-containing compound. Meanwhile, the shikimic acid pathway can be derived from the primary metabolism of the erythrose-4-phosphate or phosphoenolpyruvate pathway (Carreón- Hidalgo, Franco-Vásquez, Gómez-Linton, & Pérez- Flores, 2022). Based on the substantial character of nitrogen source and rate on crop yield and quality, this research was designed to assess the dynamics of changes of primary and secondary metabolites balancing of red beet.
The aim of this research was to explore the correlation between growth variables, beetroot yield and betacyanin, and to investigate the rate of increase in fresh tuber weight and rate of change in red beet betacyanin levels and determine total betacyanin resulting from differences in nitrogen source and rate. The hypothesis concerned that increasing nitrogen rate from different sources would increase growth and yield followed by a decrease in betacyanin levels.
MATERIALS AND METHODS
This study was conducted in a greenhouse located at the Experimental Station of Universitas Brawijaya, Jatimulyo village, Malang city, East Java (7° 56’ 22” S, 112° 37’ 23” E) with the elevation of 460 m.a.s.l. The research started from June to September 2019. Red Beet ‘Ayumi 04’ hybrid F1 beet was used. The seeds were placed in a plastic bag (2 cm in diameter and 7 cm high). The uniform and healthy 10-day-old seedlings were transplanted in plastic 20 cm-plastic pots containing mixture of topsoil (Andosol), compost (3:1), and added with husk (4:1). The young plants were then maintained for 7 days before treatments.
The researach was carried out using a factorial randomized block design with three replications to facilitate the combination of two factors. The first factor was source of nitrogen (N) fertilizer, i.e.
5Ca(NO3)2NH4NO310H2O, NH4NO3NH4H2P04KCI, NO3H2P04KCI, CO(NH2)2 and (NH4)2SO4 and the second factor was the dosage of N fertilizer, i.e.. 75, 150, 225 kg N/ha. The control treatment composed of the plant without N treatment was also included within the experiment. Except control treatment, phosphorus (SP36) and potassium (KCl) were added in the same amount in all N treatments to avoid the bias effect of the ratio of N, P and K (1:1:1).
The parameters included leaf area (LA), total plant fresh weight (PFW), total plant dry weight (PWD), number of leaves (LN), root diameter (BRD), BRFW, root dry weight (BRFD) and betacyanin content (BC). The observation of all the stated parameters were carried out at 28, 35, 42, 49 and 56 days after transplanting (DAT).
LA, PFW and PDW were measured using destructive samples. BC was measured based on the betacyanin determination method developed by Sitompul & Zulfati (2019) and Stintzing, Schieber,
& Carle (2003). The absorbance of betacyanin was measured at 540 nm and the absorbance of the diluted sample at 600 nm was also measured for the correction of impurities including browning agent.
BC was calculated with the following equation:
BC (mg/l) = [(A×DF×MW×1000) /(ε×L)] ...1) Where: A = the absorbance, DF = the dilution factor, ε = molar extinction coefficient (60,000 l/mol cm in H2O), L = the path length (1 cm) of the cuvette, MW
= molecular weight (550 g/mol).
We used SPSS 1.6 software for data analyses. The data were analyzed using ANOVA.
The difference test, logistic modeling and polynomial order 3 as well as calculating the inflection point, maximum and minimum points were performed using MS excel. The least significant difference (LSD) at P 0.05 (α 0.05) was used to compare the mean values among treatments. Pearson correlation coefficients (R) was used to determine the correlation between the dependent variables.
The polynomial is linear trend combination (chain) relationship between the response variable and the nitrogen variable coefficient. Polynomial models in this data estimated in order 3 (unknown parameters:
a, b, c, d), each with the following equation:
...2) Where: Y = the value of the dependent variable; d
= the Y-intercept of the regression surface; a, b, c= the slope of the regression surface with respect to variable X;x1, x2, x3 = the value of the independent variable.
The application of the logistic model (Verhulst model) on plant growth is based on the combined assumption. The rate of biomass production (∂W/∂t) of plants is determined by the quantity of the growth machine which is proportional to the plant biomass (W) and the quantity of available substrate with the following equation:
...3) Where: W = the plant biomass or others value of the dependent variable; Wm = asymptotic max; b and µ= shape parameters; e = exponential equation; t = time of observation.
RESULTS AND DISCUSSION
Number of leaves (LN) and leave area (LA) were not significant under nitrogen sources at 56 days after transplanting (Fig. 1A). The N source of 5Ca(NO3)2NH4NO310H2O; NH4NO3NH4H2P04KCI, NO3H2P04KCI, CO(NH2)2 and (NH4)2SO4 were able to increase LN for about 21-32% and LA for about 43-57%, respectively compared to control.
On the other hands, N rate treatment significanlty promoted only on LA. The N rates at 75 and 150 kg N increased LA about 38% and 57% compared to control. The N rate at 225 kg N/ha, however, gave only 6% increase of LN from 75 kg/ha and decrease for about 3% from 150 kg/ha.
The BRD was not significantly affected by source nitrogen and rate of nitrogen at 66 DAT.
Application of nitrogen of 5Ca(NO3)NH4NO310H2O, NH4NO3NH4H2P04KCI, NO3H2P04KCI, CO(NH2)2, and (NH4)2SO4 increased BRD for about 32-40% compared to control. The supply of 5Ca(NO3)2NH4NO310H2O, CO(NH2)2, NH4NO3NH4H2P04KCI and (NH4)2SO4 also increased BRFW by 120-130 g (58-64%), 50-60 g (17-23%), 45, and 55 g (17-21%), respectively compared to control as shown in Fig.
Fig. 1. LN and LA under different N source (A) and rates (B) at 56 DAT. Both mean values as shaded bar with st-dev value as line on the top of bar marked with different letters are significantly different at P<0.05 according to the ANOVA using LSD test.
Fig. 2. BRD, BRFW and BRDW of red beet under different N source (A) and rates (B) at 66 DAT. Both of mean value as shaded bar with st-dev value as line on the top of bar marked with different letter are significantly different at P<0.05 according to the ANOVA using LSD test.
2A. The application of 5Ca(NO3)2NH4NO310H2O and CO(NH2)2 was observed to promote 38-40% higher BRDW compared to the control. At the same age 66 DAT, the values of BRDW were negligible among the nitrogen rate treatments (Fig. 2B), though in average the values were 19-32% higher compared to the control. Meanwhile, the treatment rates of 150 and 225 kg N could rise BRFW by 50% (100 g) and 8% (20 g) compared to control and 75 kg N.
Fig. 3. BC (mg.100g/BRFW) and BT (mg/BRFW(g)) under different N source (A) and rate (B) treatments at 66 DAT. Both of mean value as shaded bar with st-dev value as line on the top of bar marked with different letter are significantly different at P<0.05 according to the ANOVA using LSD test.
The treatment of nitrogen source and rate gave insignificant effect on BC at 66 DAT. However, in general, the application of N fertilizer reduced BC by 20-33% (41-69 mg.100g/BRFW) compared to control. Similar phenomenon was also observed on the effect of nitrogen rate on BT at 66 DAT (Fig. 3B).
On the other hands, 5Ca(NO3)2NH4NO310H2O and CO(NH2)2 induced higher total BT for about 36%
(145 mg/BRFW (g)) and 25% (110 mg/BRFW (g)) than (NH4)2SO4 and control (Fig. 3A).
In general, the treatment of the nitrogen source and rate increased red beet yield. Application of nitrogen at 75 kg/ha gave 48%, while 150 and 225 kg/ha promoted 50% higher yield than control.
These condition was in accordance with the findings of Mampa, Maboko, Soundy, & Sivakumar (2017), that stated the supply of N rate at 120 and 150 kg/ha increase fresh weight and number of leaves, fresh and dry weight of roots, diameter, and length of roots on red beet. On the other hands, the applicatiion of nitrogen less than 90 kg/ha decreased red beet production for 4.2 t/ha or 8.2%.
The accumulation rate of BRFW followed a sigmodial curve based on logistic model (Fig.
4A and B) and from the general reliability range of 0.991 to 1,000, it reached the value of 0.9955 at N source and 0.9956 at nitrogen rate. The accumulation rate of BRFW was divided into 3 phases, namely the exponential with a slowed increase phase, an exponential phase with rising
growth, and a stationary phase with a steady rate. The first phase was detected up to 35 DAT with the accumulation BRFW of 19-33 g/plant and the accumulation rate of 0.5-0.9 g/day. While the exponential phase was detected from 35 to 56 DAT with BRFW levels ranged from 175 g/plant at the control treatment to 250 g/plant at the treatments of 5Ca(NO3)2NH4NO310H2O and CO(NH2)2. The accumulation rate in this period ranged from 7.5 to 10.5 g/day or increased at 150-225 g/plant. The stationary phase was observed after 56 DAT. In this phae BRFW began to decrease at 3.0 – 8.8 g/day, but the accumulation still increased.
BC showed an opposite pattern from the yield, in which the increase of yield due to nitrogen treatment resulted in 20-33% decrease of BC. Similar phenomemena was also reported Sitompul & Zulfati (2019), in that an increase of biomass by rate of nitrogen reduced the BC by 25% at around 351.5 to 257.6 ml/l. Mampa, Maboko, Soundy, & Sivakumar (2017) also stated that the supply of nitrogen rate as much as 90 kg N/ha was able to produce higher antioxidants and betalain content than that by the treatment without Nitrogen sources and N application rate of 60, 120, and 150 kg N/ha. In particular on environmental effect, N deficiency might induce betacyanin biosynthesis. This was supported by the results of Salahas et al. (2011)which are desired by consumers due to their considerable free radical scavenger and antioxidant properties.
The aim of the present research was to evaluate the effect of N starvation on accumulation of betacyanins and total phenolics in leaves and roots, as well as biomass production, tissue N concentrations, total chlorophyll content and leaf gas exchange. Leaves of hydroponically cultivated red beet plants subjected to nitrogen deprivation (22.4 mg l(-1 that all levels of nitrogen supply reduced betacyanin levels.
Supply of different nitrogen sources, such as 5Ca(NO3)2NH4NO310H2O and CO(NH2)2 can increase BRFW at around 58-64% higher than
control. Babagil, Tasgin, Nadaroglu, & Kaymak (2018) stated that different types of N fertilizer might affect the activity of secondary metabolites.
In this study, the application of nitrogen sources was not the only nutrient sources, since manure containment within the growing media also supplied the nutrient to the plant. Sapkota, Sharma, Giri, Shrestha, & Panday (2021) study on beetroot also supported that the mixture of 50% N in the form of poultry manure and 50% N in the form of urea resulted in higher yields than single application of the said fertilizers.
Fig. 4. (A) BRFW accumulation as function of days after transplanting with the source of N fertilizer and (B) with the rate of N fertilizer. Each point in the figure represents an average of tree replicates with two sample plants of each treatment. Graphs in the figure were derived from Logistic model to describe the trend of BRFW with time for red beet. The dotted line connecting the x and y axes indicated the value of the inflection point. (C) R2 = Coefficient determination was plotted between observed and estimated BRFW.
Supplied with 100 kg/ha calcium ammonium nitrate accumulated the highest flavonoids and the application of 100 kg/ha ammonium nitrate supplemeted with urea and ammonium sulfate produced the highest total phenolic content. The supply of Ca2+ promotes the accumulation of betacyanin, while the reduction of Ca2+ reduces the accumulation of betacyanin. The Ca2+ channel blocker was LaCl3 which acts to inhibit betacyanin accumulation. These results suggest that Ca2+ and CaM-regulated ion channels play an important role in betacyanin accumulation (Wang & Wang, (2007). The findings of Fenn & Taylor (1991) showed that calcium stimulated ammonium absorption. The increasing Ca concentration was in line with the uptake of 15NH4 as shown by all tested plant species. Environment factors such as greenhouse and field cultivation or altitude and temperature also affected yield and BC in red beet. This was similar to the results of previous studies where a rate of 150 kg N/ha produces the highest yield in total dry weight when planted at a height of more than 1000 masl, but at lower altitudes, a higher nitrogen dose as much as 1.2 g N/ha or equivalent to 300 kg N/ha was required (Sitompul et al., 2020).
The inflected-point the BRFW accumulation rate was divided into 2 phases. The rate increased and reached a peak at the age of 47-51 DAT, respectively. After that point, the rate of accumulation begins to slowly decrease even though the accumulation continues to increase. The reliability (R23) of the logistic model for each nitrogen source was listed in Table 1. The accumulation rate of BRFW on 5Ca(NO2)2NH4NO310H2O and CO(NH2)2 was 56-63% and 6-23% higher than control and other N sources, respectively. The nitrogen rates and nitrogen sources caused variations on BRFW at 35 DAT. In this period, 75, 150, and 225 kg N/
ha gave higher BRFW higher at was 6, 10, and 6 g/plant, respectively than that of control, while at 42 DAT, it was 17, 47, and 29 kg N/ha. The BRFW distance of the inflected point between the control and the stated rates reached the widest point at the end of the observation, namely 87, 106, and 105 kg N/ha, respectively. The same pattern occurs at rate 75 where BRFW was consistently lower than BRFW at rates of 150 and 225 kg N/ha since 42 DAT. On the other hands, the rates from 150 and 225 kg N/
ha, at 42 DAT, the distance started to closer at the age of 66 DAT. The reliability of the model on the difference N sources was more than 0.99 with the increase patern of BRFW (Table 1).
The accumulation of BRFW was followed by the decrease of BC. The trend of BC data would be proccessed using polynomial model or a 3rd order linear regression. This poly-regression model produces higher realibilty than other empirical models, namely linear, exponential, logarithmic, and power (data not shown). This model achieved a reliability of 0.8401 for the nitrogen source and 0.81 for the nitrogen rate (Fig. 5A and B). Like the logistic model, the order 3 polynomial model has an inflected-point, a minimum point, and a maximum point. The maximum point of BC occurred at the beginning of the observation, at 28 DAT and possibly earlier. The inflection point begins to occur at around the age of 42 to 63 DAT. After the inflected-point, BC was expected to continue to decline and reach a minimum point, however, the minimum point was unknown until 66 DAT. BC was extremely decreased at 28-42 DAT, particularly in control treatment, 5Ca(NO3)2NH4NO310H2O, NH4NO3NH4H2P04KCI, NO3H2P04KCI, CO(NH2)2, and (NH4)2SO4 were for about 54, 36, 45, 37, 30 and 47%, respectively.
While, at the age of 49-66 DAT the declination was more slowly at around 7, 18, 31, 16, 20 and 33%.
Nitrogen source of 5Ca(NO3)2NH4NO310H2O and CO(NH2)2 are two types of fertilizers that gave faster BC inflected-point which reach at 42 and 41 DAT than other fertilizers. Likewise, the BC decreasing rate of these N source was also higher than other fertilizers, which was around 4.9 mg.100g/BRFW/day (Table 1). The trend was in contrast with the turning point and BRFW rate. The reality of the order 3 polynomial model for these two fertilizer types was around at 0.75. Watari, Ikeura, Tsuge, & Motoki (2017) stated that the decrease in betacyanin levels occured by the time. Compared to the N supply of 75, 150, and 225 N fertilizers, the decrease in BC based on polynomial order 3 model was observed (figure not shown). The high level of BC in the control at the end of the observation (66 DAT) might indicate the nutrient stress affect that stimulate the increase betacyanin as a secondary metabolite.
BT that was reached between BC and BRFW indicates the potential of betacyanin per plant. BT was calculated based on the tap root organ by ignoring BC and BRFW of other organs, such as leaves and petiole. While the BC decreases with time, hence the BT increases due to the BRFW factor. BT continued to increase following the logistic model as BRFW with R2=0.9474.
d of poli linier regression model (Y= ax3+bx2+cx+d) describing BC.
Component of
an Equation Control
--- Source --- --- Supply (kgN/ha) --- (NO3)5Ca2NH4NO3
10H2O
NH4NO3NH4 H2P04 KCI
NO3 H2P04
KCI
(NHCO2)2 (NH4)2
SO4 75 150 225
Beetroot Fresh Weight:
Constant
A 216.0 355.2 340.8 292.5 371.0 293.2 318.0 332.1 340.8
β 8854 6806 3468 4009 3509 7208 4186 5068 1574
µ -0.191 -0.176 -0.16 -0.171 -0.163 -0.186 -0.167 -0.175 -0.148
Functions
Tinf 47.58 50.14 50.95 48.52 50.08 47.76 49.94 48.75 49.74
BRFWi 108.0 177.6 170.4 146.2 185.5 146.6 159.0 166.0 170.4
BRFWr 2.270 3.542 3.344 3.014 3.705 3.070 3.184 3.406 3.427
Reliability
R2 1.000 0.999 0.992 0.991 0.996 0.997 0.998 0.992 0.999
Betacyanin Content:
Contant
a -0.017 0.007 -0.014 -0.010 0.003 -0.002 -0.017 -0.005 0.001
b 2.662 -0.851 2.115 1.539 -0.431 0.470 2.662 0.822 -0.021
c -137.0 30.29 -103.9 -78.36 13.89 -31.14 -137.0 -44.67 -5.211
d 2482 -61.72 1850 1494 115.5 850.9 2482 997.9 401.3
Functions
Tinf 52.81 42.13 50.01 51.54 41.10 63.85 52.81 53.10 10.21
BCi 196.8 207.3 182.3 181.8 201.5 141.2 196.8 170.4 346.7
BCr 3.727 4.921 3.645 3.527 4.904 2.211 3.727 3.209 33.95
Reliability
R2 0.870 0.760 0.644 0.869 0.752 0.797 0.870 0.832 0.612
Total Betacyanin:
Contant
A 449.4 606.0 513.1 478.6 610.4 410.6 450.8 533.6 574.2
β 2346 2023 14321 6054 1448 23749 6347 6882 1574
µ -0.156 -0.151 -0.209 -0.191 -0.146 -0.233 -0.192 -0.192 -0.148
Functions
Tinf 49.75 50.41 45.79 45.59 49.85 43.24 45.60 46.02 49.74
BTi 224.7 303.0 256.6 239.3 305.2 205.3 225.4 266.8 287.1
BTr 4.517 6.010 5.603 5.248 6.123 4.748 4.943 5.797 5.772
Reliability
R2 0.974 0.960 0.921 0.978 0.968 0.909 0.963 0.937 0.955
Remarks: A = Asymptote, b = integral constant describing the relationship between Y0 (initial) and time (T), µ = parameter indicating the growth rate towards the final fresh weight, Tinf = point of inflection which includes time of inflection (Tinf), i = inflection, r = rate.
Fig. 5. (A) BC rate as function of days after transplanting with the source of N fertilizer. Each point in the figure represents an average of tree replicates with two sample plants of each treatment. Graphs in the figure were derived from Logistic model to describe the trend of BC with time for red beet. The dotted line connecting the x and y axes indicated the value of the inflection point (B) R2 = Coefficient determination was plotted between observed and estimated BC.
Fig. 6. (A) BT rate as function of days after transplanting with the source of N fertilizer. Each point in the figure represents an average of tree replicates with two sample plants of each treatment. Graphs in the figure were derived from Logistic model to describe the trend of BT with time for red beet. The dotted line connecting the x and y axes indicated the value of the inflection point (B) R2 = Coefficient determinationwas plotted between observed and estimated BT.
This shows that BRFW was a fairly dominant factor in determining BT. BT was slightly decelerated at 56 DAT. This was because at that age of decreasing BC and the increase of BRFW did not sufficiently compensate for the decrease, especially after 56 DAT (Fig. 6A and B). The inflection point between 5Ca(NO3)2NH4NO310H2O and CO(NH2)2 coincided each other at 50 DAT, as well as the control treatment. The differences among them were the estimation of maximum BT. The maximum BT estimation for 5Ca(NO3)2NH4NO310H2O and CO(NH2)2 was estimated at 303-305 mg/BRFW (g) while at control, it was estimated at 224 mg per BRFW (Table 1).
The different level of BT ranking was not stable until the age of 49 DAT, where the highest BT at 28 DAT was achieved by (NH4)2SO4, while at 35, 42, 49 and 56-66 DAT, the highest was CO(NH2)2, NO3H2P04KCI, NH4NO3NH4H2P04KCI and CO(NH2)2, consecutively. Control treatment consistently produced lower BT up to 49 DAT due to the lower BRFW factor which was not compensated by the high BC. Meanwhile (NH4)2SO4 showed low BT values in two observation periods (56 and 66 DAT). At 66 DAT, BT from the application of 5Ca(NO3)2NH4NO310H2O and CO(NH2)2 was 25%
higher than control and 36% higher than (NH4)2SO4. During these period, the margin increase achived
by 5Ca(NO3)2NH4NO310H2O was 13-20% higher than control and 25-33% higher than (NH4)2SO4 at 56 DAT.
In regards to Growth–Differentiation Balance Theory (GDB) in clarifying primary and secondary metabolites, the dependent variables were analyzed to examine the level of correlation between growth, yield, and quality variables (Table 2). LA was positively correlated with PFW and PDW as growth variables and also with BRD, BRFW, and BRDW as outcome variables. However, LA was less correlated with BC and BT as secondary metabolites. As growth variables, PFW and PDW are positively correlated with yield and BT as secondary metabolite variables.
BRD, BRFW, and BRDW as primary metabolite variables were positively correlated with among others and all these three variables were positively correlated with BT as a quality variable. BC was the particular variable that was positively correlated with BT and less correlated with the other variables. This indicated that if BC decreased, BT would potentially decreased and vice versa.
In this study, the highest BC was detected at 28 DAT and then sharply decreased up to 42 DAT (30-54%). The BC continuously decreased steadily in the rate of 7-33% at 49-66 DAT. Watari, Ikeura, Tsuge, & Motoki (2017) stated that the highest betacyanin was produced during seedling phase secondary metabolites of red bed
Primary (P) Secondary (S)
--- Growth (G) --- --- Yield (Y) --- ---- Qualtity (Q) ----
LN LA PFW PDW BRD BRFW BRDW BC BT
LN 1 0.64** 0.17 0.28 0.32* 0.23 0.28 -0.17 0.10
G LA 1 0.31* 0.43** 0.43** 0.34* 0.43** -0.20 0.10
PFW 1 0.80** 0.86** 0.88** 0.80** -0.21 0.64**
P PDW 1 0.77** 0.86** 1.00** -0.21 0.62**
BRD 1 0.86** 0.77** -0.19 0.64**
Y BRFW 1 0.86** -0.16 0.75**
BRDW 1 -0.21 0.62**
S Q BC 1 0.33*
BT 1
Remarks: **. Correlation was significant at the 0.01 level (2-tailed) and *. at the 0.05 level (2-tailed) and blank mark indicate insignificant correlations. Primary metabolites variables: leaves number (LN), leaves area (LA), total plant dry weight (PDW), plant fresh weight (PFW), beetroot diameter (BRD), beetroot fresh weight (BRFW), beetroot dry weight (BRDW) and Metabolites variables: betacyanin content (BC), total betacyanin (BT).
that might reach 37.2 mg/100 g FW, followed by a planting phase at 31.1 mg/100 g FW, and then tuber enlargement phase and the growth late vegetative phase at 24.5 mg/100g FW. This furtherly strengthens (Sokolova, Shvachko, Mikhailova, & Popov, 2022) findings that betacyanin biosynthesis in red beet was dynamic, changes during ontogenesis and depends on genetic, environmental and cultivation management factors.
CONCLUSION AND SUGGESTION
Beetroot fresh weight increased with time following the sigmoidal curve of the logistic model, while BC decreased forming a polynomial order trend 3. 5Ca(NO3)2NH4NO310H2O and CO(NH2)2 increased 58-64% higher BRFW and nitrogen rate induced 42-50% higher BRFW than control. On the other hands, the nitrogen rate reduced 20-33%
BC. The compensation of decreasing BC to BT was through increasing BRFW. The application of 5Ca(NO3)2NH4NO310H2O and CO(NH2)2 promote higher BT than (NH4)2SO4 and control. Further study was needed to determine the harvesting period in which produced high yield and betacyanin containment.
ACKNOWLEDGEMENT
This research was granted by the non-taxable government revenue (Penerimaan Negara Bukan Pajak-PNBP) at the fiscal year of 2019, Faculty of Agriculture, Universitas Brawijaya (UB), Malang.
The authors would like to express their gratitude to the Chairperson of Griya Santa Experimental Garden and Physiology Laboratory of the Faculty of Agriculture, UB.
REFERENCES
Albornoz, F. (2016). Crop responses to nitrogen overfertilization: A review. Scientia Horticulturae, 205, 79-83. https://doi.org/10.1016/j.
scienta.2016.04.026
Babagil, A., Tasgin, E., Nadaroglu, H., & Kaymak, H.
C. (2018). Antioxidant and antiradical activity of beetroot (Beta vulgaris L. var. conditiva Alef.) grown using different fertilizers. Journal of Chemistry, 2018, 7101605. https://doi.
org/10.1155/2018/7101605
Bangar, S. P., Sharma, N., Sanwal, N., Lorenzo, J.
M., & Sahu, J. K. (2022). Bioactive potential of beetroot (Beta vulgaris). Food Research
International, 158, 111556. https://doi.org/10.1016/j.
foodres.2022.111556
Barunawati, N., Giehl, R. F. H., Bauer, B., & von Wirén, N. (2013). The influence of inorganic nitrogen fertilizer forms on micronutrient retranslocation and accumulation in grains of winter wheat.
Frontiers in Plant Science, 4, 320. https://doi.
org/10.3389/fpls.2013.00320
Carreón-Hidalgo, J. P., Franco-Vásquez, D. C., Gómez- Linton, D. R., & Pérez-Flores, L. J. (2022).
Betalain plant sources, biosynthesis, extraction, stability enhancement methods, bioactivity, and applications. Food Research International, 151, 110821. https://doi.org/10.1016/j.
foodres.2021.110821
Chen, L., Huang, J., Liu, Q., Li, Z., Chen, X., Han, J., … Zhou X. (2022). Low R/FR ratio affects pakchoi’s growth and nitrate content under excess nitrate stress. Horticulturae, 8(3), 186. https://doi.
org/10.3390/horticulturae8030186
da Silva, D. V. T., dos Santos Baião, D., de Oliveira Silva, F., Alves, G., Perrone, D., Del Aguila, E. M., &
Paschoalin, V. M. F. (2019). Betanin, a natural food additive: stability, bioavailability, antioxidant and preservative ability assessments.
Molecules, 24(3), 458. https://doi.org/10.3390/
molecules24030458
El-Murtada Hassan Amin, M. (2011). Effect of different nitrogen sources on growth, yield and quality of fodder maize (Zea mays L.). Journal of the Saudi Society of Agricultural Sciences, 10(1), 17-23.
https://doi.org/10.1016/j.jssas.2010.06.003 Esatbeyoglu, T., Wagner, A. E., Schini-Kerth, V. B., &
Rimbach, G. (2015). Betanin—A food colorant with biological activity. Molecular Nutrition and Food Research, 59(1), 36-47. https://doi.
org/10.1002/mnfr.201400484
Fenn, L. B., & Taylor, R. M. (1991). Calcium stimulation of ammonium absorption in plants. In B. L.
McMichael & H. Persson (Eds.), Plant Roots and their Environment (Vol. 24, pp. 39–47). Elsevier.
https://doi.org/10.1016/B978-0-444-89104- 4.50010-4
Goldman, I. L., & Janick, J. (2021). Evolution of root morphology in table beet: Historical and iconographic. 12, 689926. https://doi.
org/10.3389/fpls.2021.689926
Laufer, D., Nielsen, O., Wilting, P., Koch, H.-J., &
Märländer, B. (2016). Yield and nitrogen use efficiency of fodder and sugar beet (Beta vulgaris
Europe. 73, 124–132. https://doi.org/10.1016/j.
eja.2015.11.008
Li, Y., Wang, M., Teng, K., Dong, D., Liu, Z., Zhang, T., & Han, L. (2022). Transcriptome profiling revealed candidate genes, pathways and transcription factors related to nitrogen utilization and excessive nitrogen stress in perennial ryegrass. Scientific Reports, 12, 3353. https://
doi.org/10.1038/s41598-022-07329-7
Mampa, S. S., Maboko, M. M., Soundy, P., & Sivakumar, D.
(2017). Nitrogen application and leaf harvesting improves yield and nutritional quality of beetroot.
HortTechnology, 27(3), 337-343. https://doi.
org/10.21273/HORTTECH03599-16
Markoski, M., Bogevska, Z., Petrov, P., Tanaskovik, V., Davitkovska, M., & Spalevic, V. (2015). The impact of foliar nutrition on the yield of beetroot crop grown in high fertility soil. Agriculture and Forestry, 61(2), 235–242. https://doi.
org/10.17707/AgricultForest.61.2.21
Matyssek, R., Schnyder, H., Oßwald, W., Ernst, D., Munch, J. C., & Pretzsch, H. (2012). Growth and defence in plants - Resource allocation at multiple scales. In M. M. Caldwell, G. Heldmaier, R. B. Jackson, O. L. Lange, H. A. Mooney, E.- D. Schulze, & U. Sommer (Eds.). Ecological Studies, Vol. 220. Berlin: Springer. https://doi.
org/10.1007/978-3-642-30645-7
Neelwarne, B. (2012). Red beet biotechnology: Food and pharmaceutical applications. New York:
Springer. https://doi.org/10.1007/978-1-4614- 3458-0
Polturak, G., & Aharoni, A. (2018). “La Vie en Rose”:
Biosynthesis, sources, and applications of betalain pigments. Molecular Plant, 11(1), 7–22.
https://doi.org/10.1016/j.molp.2017.10.008 Saha, B. K., Rose, M. T., Wong, V. N. L., Cavagnaro, T.
R., & Patti, A. F. (2019). A slow release brown coal-urea fertiliser reduced gaseous N loss from soil and increased silver beet yield and N uptake.
Science of The Total Environment, 649, 793–800.
https://doi.org/10.1016/j.scitotenv.2018.08.145 Salahas, G., Papasavvas, A., Giannakopoulos, E.,
Tselios, T., Konstantopoulou, H., & Savvas, D.
(2011). Impact of nitrogen deficiency on biomass production, leaf gas exchange, and betacyanin and total phenol concentrations in red beet (Beta vulgaris L. ssp. vulgaris) plants. European Journal of Horticultural Science, 76(5-6), 194–
200. Retrieved from https://www.pubhort.org/
ejhs/2011/file_2934851.pdf
& Panday, D. (2021). Effect of organic and inorganic sources of nitrogen on growth, yield, and quality of beetroot varieties in Nepal.
Nitrogen, 2(3), 378-391. https://doi.org/10.3390/
nitrogen2030026
Sitompul, S. M., & Zulfati, A. P. (2019). Betacyanin and growth of beetroot (Beta vulgaris L.) in response to nitrogen fertilization in a tropical condition.
AGRIVITA Journal of Agricultural Science, 41(1), 40–47. https://doi.org/10.17503/agrivita.
v41i1.2050
Sitompul, S. M., Roviq, M., Yudha, A., Khesia, S. A., Avyneysa, N. J., & Yolanda. (2020). Plant growth of beetroots (Beta vulgaris L.) with nitrogen supply at suboptimal elevations in a tropical region.
AGRIVITA Journal of Agricultural Science, 42(2), 272–282. https://doi.org/10.17503/agrivita.
v0i0.2667
Sokolova, D. V., Shvachko, N. A., Mikhailova, A. S.,
& Popov, V. S. (2022). Betalain content and morphological characteristics of table beet accessions: Their interplay with abiotic factors.
Agronomy, 12(5), 1033. https://doi.org/10.3390/
agronomy12051033
Stagnari, F., Ficcadenti, N., Manetta, A. C., Platani, C., Dattoli, M. A., & Galieni, A. (2022). Modulation of light and nitrogen for quality-traits improvement:
A case study of altino sweet pepper.
Horticulturae, 8(6), 499. https://doi.org/10.3390/
horticulturae8060499
Stintzing, F. C., Schieber, A., & Carle, R. (2003).
Evaluation of colour properties and chemical quality parameters of cactus juices. European Food Research and Technology, 216, 303–311.
https://doi.org/10.1007/s00217-002-0657-0 Varga, I., Jovi, J., Rastija, M., Kulundžić, A. M., Zebec, V.,
Lončarić, Z., … Antunović, M. (2022). Efficiency and management of nitrogen fertilization in sugar beet as spring crop: A review. Nitrogen, 3(2), 170- 185; https://doi.org/10.3390/nitrogen3020013 Wang, C.-Q., & Wang, B.-S. (2007). Ca2+-calmodulin
is involved in betacyanin accumulation induced by dark in c3 halophyte Suaeda salsa.
Journal of Integrative Plant Biology, 49(9), 1378–1385. https://doi.org/10.1111/j.1744- 7909.2007.00489.x
Watari, M., Ikeura, H., Tsuge, K., & Motoki, S. (2017).
Betalain content of different varieties, growth stages, and parts of beet (Beta vulgaris L.).
Horticultural Research, 16(3), 301–308. https://
doi.org/10.2503/hrj.16.301
