Metabolic adjustments in forage oat (Avena sativa L.) genotypes under different sowing windows

Abstract

The current investigation was aimed to study the influence of planting dates (9 Oct, 24 Oct, 13 Nov, 26 Nov, 11 Dec, and 26 Dec) and maturity stages (40, 50, and 60 DAS) on nitrogen metabolic enzymes and biochemical constituents. The experiment was laid out in randomized block design during the winter season of 2019 by using two oat genotypes (OL-10 and OL-11) with variable growing ability. The activity pattern of studied enzymes revealed upregulated nitrate assimilation during optimum (Oct 24) and sub-optimum (Oct 9 and Nov 13) planting dates but a reverse trend was observed during late planting dates; LPD (Nov 26, Dec 11, and Dec 26). The environmental constraints during LPD resulted in the accrual of nitrate above toxic levels (> 2000 ppm). The regression analysis depicted a significant relationship of nitrate assimilating enzymes and nitrate–N with temperature and sunshine hour. Examination of ammonia assimilation and transaminases suggested that the enzyme activities got uplifted during late planting dates but were stable or slightly low at optimum and sub-optimum ones. Additionally, OL-10 proved to be an N-efficient genotype in comparison with the OL-11 genotype because of its high N assimilation potential. Therefore, gaining a better understanding of planting time is crucial for sustainable livestock production.

Introduction

Specimens of the same plant species growing under different environmental conditions display substantial variation in the production and accumulation of primary metabolites. These primary metabolites exert their biological role by serving as a chemical interface between the plant and the environment. Physiological and biochemical adjustments are needed to preserve functional integrity in plants facing adverse environmental conditions. Response of the crop towards climatic fluctuation can be elucidated with the help of staggering planting dates. Staggered planting is a common practice among dairy farmers to ensure the continuous production of fodder for a longer period of time.

The plant takes up nitrogen mostly in the form of nitrate or ammonium and then converts it to various amino acids (Giagnoni et al. 2016). Nitrogen metabolism is the well-regulated multilevel pathway that maintains the growth and productivity of the crop (Ashaf et al. 2018). Nitrate reductase (NR), nitrite reductase (NiR), glutamine synthetase (GS), glutamate synthase (GOGAT), glutamate dehydrogenase (GDH), glutamate pyruvate transaminase (GPT), and glutamate oxaloacetate transaminase (GOT) are all crucial enzymes participating in N metabolism and their activities help in evaluating plant N status (Rachana et al. 2018). Nitrate reductase is regarded as the rate-limiting enzyme and reduces nitrate to nitrite by using NADH as an electron donor (Setif et al. 2009). The nitrite is further reduced to ammonia by nitrite reductase which is then converted into organic form glutamine by the rate limiting step catalyzed by glutamine synthetase. The level of α-ketoglutarate in turn is balanced by GDH and GOGAT activity (Marcondes and Lernos 2012). The transaminases serve as markers of nitrogen use efficiency and act in the direction of deamination to provide amino acids especially glutamic acid for the common N-pool (Asthir et al. 2018).

The study of biochemical pathways serves as an important tool for predicting crops' response to climate variations. The nitrate assimilating enzyme, NiR is highly dependent upon photosynthetic process because the photosystem 1 (PS-1) provides six electrons required to convert nitrate into ammonia. The PS-1 converts six molecules of oxidized ferredoxin (Fd) to reduced Fd which are the carrier of electrons (Shu et al. 2016). This ensures efficient nitrate assimilation under favourable temperature and sunlight (Bian et al. 2020). The constant nitrate assimilating enzyme activities are essential to maintain optimal nitrate levels in plants (Sidhu et al. 2011). An imbalance between absorption and assimilation of nitrate is responsible for nitrate accumulation in plants (Du et al. 2008). Nitrate is present in almost all plants but it becomes toxic in forages grown in the presence of extreme stress (Basso and Ritchie 2005). A sudden decline in plant growth under any stressful condition viz. nitrogen fertilizers, detrimental weather, herbicides, diseases, imbalance of soil nutrients, low temperature and inadequate exposure to sunlight causes nitrate accumulation in plants. Low temperature and less sunshine hour are the most severe weather events that result in reduced photosynthesis leading to downregulation of NiR followed by covalent inhibition of NR (Kaur and Goyal 2016). In polygastric animals, nitrate is utilized by rumen microflora to convert it into microbial protein (Kozloski 2009). But when ingested in higher amounts, nitrate gets converted into nitrite and further excess of nitrite gets absorbed in the bloodstream. In blood, nitrite converts haemoglobin to methaemoglobin and this complex has less capability to carry oxygen to various tissues thereby resulting in tissue asphyxiation and death of the animal (Benjamin 2006). Nitrate–N level higher than 0.2% (2000 ppm) in animal feed is potentially disastrous to the health and productivity of ruminants (Kaur and Goyal 2016). With a continuous increase in demand for milk and milk products, there is a great necessity to improve nutritional quality along with forage productivity. The ammonia formed by NiR is incorporated by ammonia assimilating enzymes for the formation of amino acids and proteins. The GS activity got uplifted in rice under low temperature stress for the production of proline (Lu et al. 2005). Liu et al. (2017) well demonstrated that GDH activity remained stable or slightly increased in mosses under low temperature stress.

Oat (Avena sativa L.) is a winter cereal crop native to northern, central and eastern regions of India and gradually becoming one of the most popular forage crops worldwide. It is a widely accepted crop due to its excellent growth patterns, rapid regrowth and better yield potential (Kumari et al. 2014). It can perform well on a wide range of soils and requires a cool and moist climate for its growth. Oat substantially supports livestock production and also serves as a functional food for human consumption. Oat is valued for its highly nutritious and palatable fodder and consumed in large quantities by the livestock.

Literature is available related to nitrate toxicity in forage oat (Kaur and Goyal 2016) but limited documentation exists about nitrate accumulation in forage oat under natural environmental conditions. Many research studies were done with respect to nitrogen metabolism but no information exists regarding the influence of staggered planting dates on nitrogen metabolizing enzymes and its biochemical constituents. In this regard, the present study was done to investigate the effect of six planting dates on nitrogen metabolizing enzymes as well as on biochemical constituents in two oat genotypes.

Materials and methods

Experimental site and weather data

The current investigation was carried out at the experimental field of Forage Research farm, Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana (30°54′ N, 75°48′ E, 247 m asl) during the Rabi season of 2019. The soil of the field was characterized by sandy loam, with a slightly alkaline pH (7.8), low in organic carbon (0.36%), low in available N (208 kg/ha), high in available P (39.8 kg/ha) and medium in available K (127.5 kg/ha). The average maximum temperature, minimum temperature and sunshine hour during the planting time are presented in Fig. 1A. The weather data during the experimental period is presented in Fig. 1B. The average maximum temperature, minimum temperature and sunshine hour ranged from 11.1–25.3 °C, 3.4–14.3 °C and 0–10.4 h.

Fig. 1.

A Meteorological data during planting time. B Meteorological data during the experimental period (sampling day and two day before the sampling time)

Genotypes and planting time

The oat (Avena sativa L.) genotypes, OL-10 and OL-11 were planted in a randomized block design and replicated three times. The treatments comprised of six planting dates and were categorized as optimum (24 Oct), sub-optimum (9 Oct, 13 Nov) and late (26 Nov, 11 Dec and 26 Dec) planting dates. Optimum planting date represents the time suitable for oat growth as recommended in the package of practices of Rabi crops, PAU. Sub-optimum planting date represents the time at which climatic fluctuation was less and late planting time represents the time at which environmental stress was high. The plot size was 4.0 × 2.5 m with the row to row distance of 25.0 cm. The seed rate and fertilizers were used as per the package of practices of Rabi crops, PAU. The full dose of urea fertilizer was applied at the time of sowing.

Sample collection

Fresh leaf tissue was used for enzyme assay and chlorophyll content. The second leaf from the top was excised during morning time (about 9:00 AM) at respective growth stages (40, 50, and 60 DAS). The samples were carried to the laboratory in an icebox and mid portion of leaf tissue was used for enzyme assay.

Nitrate reductase activity

Nitrate Reductase (NR) was assayed according to Hageman and Hucklesby (1971). 0.2 g of leaf sample was cut into small pieces and added to the test tube containing 5 ml of sodium phosphate buffer, 0.25 ml of potassium nitrate (0.25 M) and 0.05 ml of n-propanol. The vials were sealed and incubated in a water bath for 90 min in dark at 30 °C. The nitrite ions (NO₂⁻) released into the medium were estimated by taking 1 ml of aliquot and treating it with 1 ml of sulphanilamide (1%) in 1 N hydrochloric acid and 1 ml of NEDD (0.02%). The tubes were kept for 20 min and the final volume was made to 10 ml using distilled water. The OD was noted using Spectrophotometer at 540 nm. The standard curve was made with NaNO₂ (0–10 μg) and enzyme activity was expressed as μmol NO₂⁻ formed h⁻¹ g⁻¹ FW tissue.

Extraction of nitrite reductase, ammonia assimilating enzymes and transaminases

The enzymes nitrite reductase (NiR), glutamine synthetase (GS), glutamate synthase (GOGAT), glutamate dehydrogenase (GDH), glutamate oxaloacetate transaminase (GOT) and glutamate pyruvate transaminase (GPT) were extracted in 5 ml of Tris buffer (25 mM, pH 7.5) containing 2-mercaptoethanol (2–3 drops), MgSO₄ (4.0 mM), EDTA (0.25 mM) by using 0.5 g of fresh leaf sample. The contents were centrifuged at 4 °C for about ten minutes at 10,000 rpm and the supernatant so obtained was used for enzyme assays.

Estimation of nitrite reductase, ammonia assimilating enzymes and transaminases

NiR was assayed as described by Ramirez et al. (1966) with slight modifications. 0.1 ml of enzyme extract was treated with 0.5 ml of Tris HCl (200 mM, pH 8.0) buffer containing methyl viologen (20 mM) and NaNO₂ (6 mM) and diluted up to 0.9 ml using distilled water. Thereafter, 0.1 ml of sodium dithionite was added to the tubes and incubated at 40 °C for 1 h. After incubation, vortexed the contents vigorously to oxidize the excess dithionite. The volume was made up to 3.0 ml with distilled water after taking an appropriate amount of aliquot. The quantity of NO₂⁻ ions in the reaction mixture was determined by adding 1.0 ml of both sulphanilamide (1%) in 1 N hydrochloric acid and NEDD (0.02%). The tubes were kept for 10 min at room temperature and the absorbance was read at 540 nm. The amount of nitrite formed was estimated with NaNO₂ (0–10 μg) as the standard and the enzyme activity was expressed as μg of NO₂⁻ removed h⁻¹ g⁻¹ FW tissue.

GS activity was measured using the assay described by Lea et al. (1990). An appropriate enzyme extract was added to 1 ml of Tris buffer, 1 ml of sodium glutamate (0.5 M), 0.5 ml of adenosine triphosphate (0.05 M, pH 7.0), 0.1 ml of hydroxylamine hydrochloride (1 M, pH 7.5) (freshly prepared) and 0.1 ml of MgSO₄ (1.5 M).‘The volume of the reaction mixture was made up to 3 ml using distilled water. A blank tube was run simultaneously in which the substrate was omitted. The reaction mixture was incubated for 30 min at 30 °C and the γ-glutamyl hydroxamate formed was estimated with the addition of 0.5 ml of ferric chloride reagent. The tubes were centrifuged for seven minutes at 12,000 rpm and OD was noted using spectrophotometer at 540 nm. The standard curve was made with 0.4–2.0 µM of γ-glutamyl hydroxamate as the standard and the enzyme activity was expressed as μmol γ-glutamyl hydroxamate formed min⁻¹ g⁻¹ FW.

GOGAT was assayed using the method of Hecht et al. (1988) with slight modifications. An appropriate enzyme extract was added to 2.5 ml of Tris buffer, 0.1 ml of 2-oxoglutarate (300 mM), 0.1 ml of glutamine (300 mM) and 0.1 ml of NADH (4.2 mM). The GOGAT activity was noted at 340 nm for 3 min with the decrease in OD after every 15 s and was expressed as μmol NADH oxidized min⁻¹ g⁻¹ FW tissue.

GDH was assayed as described by Lea et al. (1990). An appropriate enzyme extract was added to 2.5 ml of Tris buffer, 0.1 ml of 2-oxoglutarate (300 mM), 0.1 ml of NH₄OH (300 mM) and 0.1 ml of NADH (4.2 mM). The GDH activity was noted at 340 nm for 3 min with the decrease in OD after every 15 s and was expressed as μmol NADH oxidized min⁻¹ g⁻¹ FW tissue.

GOT was assayed according to Tonhazy (1960a). An appropriate enzyme extract was added to 0.2 ml 2-oxaloglutarate solution, 0.5 ml buffered aspartate solution and 0.1 ml pyridoxal phosphate and the mixture was incubated for 30 min at 37ºC. The reaction was terminated with the addition of 0.1 ml TCA solution and the contents of the tubes were vortexed vigorously. After the addition of 0.2 ml aniline-citrate, the tubes were shaken and incubated for ten minutes at room temperature. Pyruvate formed, along with 2-oxaloglutarate was converted to hydrazine after adding 1 ml of 2,4-dinitrophenylhydrazine solution. Tubes were again thoroughly mixed and allowed to stand for five minutes. After the addition of 2 ml of water saturated toluene, centrifuged the contents for five minutes at 3000×g. About 1 ml of the upper layer developed was drawn into another test tube and to this 5 ml of alcoholic potassium hydroxide was added. The absorbance was noted at 520 nm and the standard curve was made using oxaloacetate (0.3–1.8 μmol) as standard and GOT activity was expressed as µmol min⁻¹ g⁻¹ FW.’

The assay of GPT activity was similar to that of GOT except aniline-citrate solution step was omitted (Tonhazy 1960b). The intensity of colour developed was read at 520 nm and the standard curve was made using pyruvate (0.3–1.8 μmol) as standard. The GPT activity was expressed as µmol min⁻¹ g⁻¹ FW.

Estimation of biochemical constituents

Chlorophyll content was estimated according to Barnes et al. (1992). 0.1 g fresh leaf sample was taken and dipped in 3 ml dimethyl sulphoxide solution. The solution was kept in water bath for 1 h at 60–70 °C for colour development. Absorbance was read at 645 nm and 663 nm.

Total Chlorophyll = $\frac{20\text{.2}\times {\text{A}}_{645}+8\text{.02}\times {\text{A}}_{663}\times \text{V}}{a\times 1000\times W}$.

Where,

W = Fresh weight of sample in gram.

V = Volume of extract.

a = Path length of light in the cell (1 cm).

A₆₄₅ and A₆₆₃ are optical densities.

Chlorophyll concentration was expressed as mg g⁻¹ fresh weight of tissue.

For the estimation of nitrate–N, nitrite–N and free amino acids, plant samples (leaves and stem) were collected at certain growth stages (40, 50 and 60 DAS) that were ovendried at 100 °C till a constant weight was reached. The dried samples were ground and used for the estimation.

Nitrate–N and nitrite–N was extracted in about 10 ml of distilled water by taking 0.1 g of dried sample and the tubes were incubated at 60–70 °C for 1 h. The samples were filtered and the supernatant so obtained was used for the estimation.

Nitrate–N content was estimated by taking about 0.2 ml of supernatant in the test tubes (Cotaldo et al. 1975). To this, added 0.8 ml of salicylic acid (5%)-H₂SO₄ (w/v). The contents were thoroughly mixed and placed for 20 min at room temp. About 19 ml of sodium hydroxide (2 N) was added to raise the pH (> 12) and then samples were cooled at room temperature. The OD was taken at 410 nm and the quantity of nitrate–N was estimated using 0–50 µg of KNO₃ as standard.

Nitrite–N content was estimated by taking a diluted 1 ml sample (Guevera et al. 1998). To this added, 1 ml of sulphanilamide (1%) and 1 ml of NEDD (0.02%). The tubes were incubated at 30 °C for 30 min and thereafter volume was made 5.0 ml with distilled water. The OD was taken at 540 nm and the quantity of nitrite–N was estimated prepared using NaNO₂ (0–10 µg) as standard.

Free amino acids (FAA) were extracted using the method of Singh et al. (1978). The FAA content was estimated according to Lee and Takahashi (1966). The reaction mixture consisted of 0.2 ml of the extract and 5 ml of ninhydrin reagent. The tubes were placed in a boiling water bath for 12 min and were cooled at room temperature. The intensity of colour developed was noted at 570 nm and the standard curve was prepared by using 0.03–0.24 µM of L-glycine to determine the amount of free amino acids.

Statistical analysis

The data obtained were statistically analysed by analysis of variance (ANOVA) using SAS software Version 9.3. The mean separation was carried out by applying Tukey’s least significant difference test. A regression model was built to study the relationship of biochemical traits with weather components. The multivariate regression model was developed to study relationship between enzymes and biochemical constituents. Cluster analysis was performed using Minitab 18 to measure similarity among variables by separating them into different clusters.

Results

Variation in nitrate assimilation with environmental fluctuations

Nitrate reductase (NR) activity depicted a significant relationship with staggered planting dates; PD (F = 508, P < 0.01), growth stages; GS (F = 22.1, P < 0.01) and genotypes; G (F = 752, P < 0.01). The three-way interaction of PD × GS × G was found to be significant and ranged from 4.18–25.2 µmol h⁻¹ g⁻¹ FW (Fig. 2). Highest NR activity was exhibited at 40 DAS of sub-optimum planting date (Oct 9) in OL-10 genotype whereas the lowest NR activity was exhibited at 40 DAS of late planting date (Dec 11) in OL-11 genotype. All the interactions except growth stage × genotype were significant (Table 1). The regression equation showed that the R² value is 0.866 depicting 86.6% relationship between NR and other variables (enzymes and biochemical constituents). The schematic representation of nitrogen metabolic enzyme activities during the experimentation period is depicted in Fig. 3. NR showed a positive relationship with NiR, chlorophyll content, and a negative relationship with GDH, nitrite–N and free amino acids (Table 2). In addition, NR is positively associated with maximum temperature, minimum temperature and sunshine hour (Table 3).

Fig. 2.

Nitrogen metabolic enzymes and biochemical constituents as influenced by planting dates and growth stages in OL-10 and OL-11 genotypes (Error bars represents standard error, mean with same letter(s) are non-significant while different letters showed significant difference at 0.05 level of significance after Tukey’s test)

Table 1.

Variation in mean squares values of nitrogen metabolic enzymes and biochemical constituents with staggered planting, growth stages and genotypes in oat

Treatments	Df	NR	NiR	GS	GOGAT	GDH	GOT	GPT	TChl	Nitrate–N	Nitrite–N	FAA
PD	5	195**	24,408**	0.535**	3.1**	0.392**	1.05**	1.02**	0.264**	8,399,984**	710**	9.5**
GS	2	8.5**	16,856**	0.006**	0.176**	0.043**	0.434**	1.07**	0.101**	16,321,630**	2559**	16.1**
G	1	289**	856**	0.448**	0.359**	1.01**	0.380**	0.598**	0.078**	238,008**	233**	NS
PD x GS	10	60.5**	17,717**	0.700**	0.236**	0.433**	0.215**	0.170**	0.118**	2,825,101**	14.3**	0.567
PD x G	5	15.9**	483**	0.026**	0.027**	0.321**	0.059**	0.091**	0.024*	1,028,279**	NS	0.419
GS x G	2	NS	2143**	0.018**	0.011**	0.056**	NS	NS	0.001**	39,335**	NS	NS
PD x GS x G	10	5.7**	982**	0.012**	0.029**	0.047**	0.020**	0.017**	0.001**	35,085**	2.08**	NS

*, **Significant at 0.05 and 0.01 levels of significance

Df: degrees of freedom; NR: Nitrate reductase; NiR: Nitrite reductase; GS: Glutamine synthetase; GOGAT: Glutamate synthase; GDH: Glutamate dehydrogenase; GOT: Glutamate oxaloacetate transaminase; GPT: Glutamate pyruvate transaminase; TChl: total chlorophyll; FAA: free amino acids PD: planting dates; GS: growth stages; G: genotypes; NS: non-significant

Fig. 3.

Schematic representation of nitrogen metabolic enzymes during the experimental period. NR: Nitrate reductase; NiR: Nitrite reductase; GS: Glutamine synthetase; GOGAT: Glutamte synthase; GDH: Glutamate dehydrogenase; GOT: Glutamate oxaloacetate transaminase; GPT: Glutamate pyruvate transaminase; IDH: Isocitrate dehydrogenase; Glu: glutamate; Gln: glutamine; NO₃⁻: nitrate; NO₂⁻: nitrite; Fe²⁺: ferrous ion; Fe³⁺: ferric ion; Hb: haemoglobin; LTS: Low temperature stress

Table 2.

Multivariate linear regression among nitrogen metabolic enzymes and biochemical constituents in oat (Df = 10)

Fitted model	R2	SOS	MS	F-value	P value
NR = 4.327 + 0.039**NiR + 0.767 GS + 0.336 GOGAT − 2.161**GDH − 0.088 GPT + 1.718 GOT + 7.203**TChl + 0.000 Nitrate–N − 0.149** Nitrite–N − 1.125**FAA	0.866	1779	178	62.9	**
NiR = − 34.077 + 10.975**NR + 35.669*GS − 20.472 GOGAT + 21.927 GDH + 28.347 GPT − 22.142GOT + 4.025 TChl − 0.012* Nitrate–N + 1.815**Nitrite–N − 5.762 FAA	0.808	322,191	32,219	40.7	**
GS = − 0.230 + 0.01NR + 0.002*NiR + 0.072 GOGAT + 0.406**GDH − 0.445** GPT + 0.407**GOT + 0.202 TChl − 3.71 Nitrate–N + 0.019** Nitrite–N + 0.197** FAA	0.668	7.0	0.701	19.5	**
GOGAT = − 0.798 + 0.011 NR − 0.002*NiR + 0.187 GS + 0.644**GDH − 0.087 GPT + 0.023 GOT + 0.628 TChl − 8.301 Nitrate–N + 0.031** Nitrite–N + 0.079 FAA	0.525	10.1	1.01	10.7	**
GDH = 0.510 − 0.031**NR + 0.001 NiR + 0.466**GS + 0.283** GOGAT + 0.189 GPT + 0.005 GOT + 0.096 TChl + 0.000** Nitrate–N − 0.026** Nitrite–N − 0.129**FAA	0.590	5.8	0.576	14.0	**
GPT = − 0.261 − 0.001 NR + 0.001 NiR − 0.399**GS − 0.030 GOGAT + 0.147 GDH + 0.795** GOT + 0.389*TChl + 3.81 Nitrate–N + 0.013**Nitrite–N + 0.095**FAA	0.696	7.1	0.713	22.2	**
GOT = 0.015 + 0.017 NR − 0.001 NiR + 0.321**GS + 0.007 GOGAT + 0.003 GDH + 0.700**GPT-0.418*TChl − 1.64 Nitrate–N − 0.006 Nitrite–N + 0.005 FAA	0.701	6.4	0.644	22.8	**
TChl = 0.572 + 0.022**NR + 4.30 NiR + 0.048 GS + 0.057 GOGAT + 0.020 GDH + 0.103* GPT − 0.125* GOT − 3.60* Nitrate–N − 0.005* Nitrite–N − 0.015 FAA	0.720	2.1	0.211	24.9	**
Nitrate–N = 791.0 + 46.273 NR − 4.576*NiR-317.5 GS − 270.6 GOGAT + 1147.3** GDH + 363.3 GPT − 178.3 GOT − 1304.0*TChl + 76.2**Nitrite–N − 1.056 FAA	0.728	79,495,369	7,949,537	25.8	**
Nitrite-N = 23.956 − 0.872**NR + 0.038**NiR + 8.861**GS + 5.478**GOGAT − 10.501**GDH + 6.501**GPT − 3.618GOT − 9.047*TChl + 0.004**Nitrate–N-4.121**FAA	0.823	7509	751	45.2	**
FAA = 3.646 − 0.112**NR − 0.002 NiR + 1.556**GS + 0.239 GOGAT − 0.886** GDH + 0.835 ** GPT + 0.052 GOT − 0.491 TChl − 9.74 Nitrate–N − 0.070**Nitrite-N	0.679	62.9	6.29	22.3	**

*, ** Significant at 0.05 and 0.01 levels of significance

SOS: sum of squares; Df: degrees of freedom; MS: mean sum of squares; NR: Nitrate reductase; NiR: Nitrite reductase; GS: Glutamine synthetase; GOGAT: Glutamate synthase; GDH: Glutamate dehydrogenase; GOT: Glutamate oxaloacetate transaminase; GPT: Glutamate pyruvate transaminase; TChl: total chlorophyll; FAA: free amino acids

Table 3.

Linear regression of nitrogen metabolic enzymes and biochemical constituents with weather parameters in oat (Df = 1)

Variables	Fitted Model	R2	SOS	MS	F-Ratio	P value
Maximum Temperature	14.336 + 0.355 NR	0.128	259	259	15.6	**
	12.399 + 0.038 NiR	0.528	564	564	40.9	**
	8.772 + 12.46 TChl	0.225	456	456	30.9	**
	20.819 − 0.001 Nitrate–N	0.119	241	241	14.4	**
	21.832 – 0.168 Nitrite-N	0.127	256	256	15.4	**
Minimum Temperature	5.097 + 0.274 NR	0.179	154	154	23.1	**
	5.110 + 0.019 NiR	0.173	149	149	22.1	**
	10.6–3.329 GOT	0.118	102	102	14.2	**
	2.493 + 7.399 TChl	0.186	160	160	24.3	**
	12.394 – 1.528 FAA	0.245	211	211	34.3	**
Sunshine hour	0.756 + 0.289 NR	0.125	172	172	15.1	**
	− 0.351 + 0.028 NiR	0.220	304	304	30.0	**
	− 3.626 + 9.960 TChl	0.211	291	291	28.3	**
	5.765 – 0.001 Nitrate–N	0.088	121	121	10.2	**
	7.498 – 0.166 Nitrite-N	0.183	253	253	23.8	**

SOS sum of squares, Df degrees of freedom, MS mean sum of squares, NR nitrate reductase, NiR nitrite reductase, TChl total chlorophyll, GOT glutamate oxaloacetate transaminase, FAA free amino acids

*, **Significant at 0.05 and 0.01 levels of significance

Significant variation was observed for nitrite reductase (NiR) activity among six planting dates (F = 457, P < 0.01) and three maturity stages (F = 315, P < 0.01). The NiR activity ranged from 74.0 to 302.9 µmol h¹ g⁻¹ FW with significant three way interaction (Fig. 2). Highest NiR activity was observed at 40 DAS of sub-optimum planting date (Oct 9) in OL-10 genotype and lowest NiR activity was observed at 60 DAS of optimum planting date (Oct 24) in OL-11 genotype. OL-10 exhibited significantly (F = 856, P < 0.01) higher NiR activity as compared to OL-11 genotype. All the two way interactions were also significant (Table 1). The R² value for regression equation is 0.808 depicting 80.8% relationship between NiR and other variables. The NiR activity showed synergistic relationship with NR (Table 2), maximum temperature, minimum temperature and sunshine hour (Table 3).

Variation in ammonia assimilation with agronomic variables

The present study reported significant differences in glutamine synthetase (GS) activity at different planting intervals (F = 666, P < 0.01), stages of harvest (F = 7.9, P < 0.01) and varieties (F = 557, P < 0.01). The GS activity varied from 0.92–2.02 mmol min⁻¹ g⁻¹ FW having significant PD × GS × G interaction (Fig. 2). Highest GS activity was reported at 60 DAS of late planting date (Dec 11) in both the genotypes and lowest at 50 DAS of sub-optimum planting date (Oct 9) and 60 DAS of optimum planting date (Oct 24) in OL-11 genotype. The two-way interactions among treatments were also observed significant (Table 1). According to regression equation, 66.8% relationship was observed between GS and other variables. GS activity had synergistic relationship with GDH, GOT, nitrite–N and free amino acids (Table 2).

Glutamate synthase (GOGAT) activity varied significantly (F = 2196, P < 0.01) among planting dates and harvest stages (F = 125, P < 0.01). It was observed that the three way interaction was significant and lied between 0.447–1.913 mmol min⁻¹ g⁻¹ FW (Fig. 2). Highest GOGAT activity was observed at 50 DAS of late planting date (Nov 26) in OL-11 genotype and lowest activity was observed at 60 DAS of optimum planting date (Oct 24) in OL-11 genotype. Moreover, higher significant (F = 254, P < 0.01) GOGAT activity was observed in OL-10 as compared to OL-11 at almost all the planting dates and growth stages. The R² equation signifies 52.5% relationship between GOGAT and other variables. GOGAT activity showed synergistic association with GDH and nitrite–N and antagonistic association with NiR (Table 2).

In the current investigation, the glutamate dehydrogenase (GDH) activity differed significantly at six planting dates (F = 161, P < 0.01), three growth stages (F = 416, P < 0.01) and two genotypes (F = 416, P < 0.01). The PD × GS × G interaction for GDH activity lied in the range between 0.433 and 1.994 mmol min⁻¹ g⁻¹ FW (Fig. 2). Highest GDH activity was observed at 40 DAS of late planting date (Nov 26) in OL-10 genotype and lowest activity was observed at 60 DAS of optimum planting date (Oct 24) in OL-11 genotype. The regression equation showed 59% relationship between GDH and other variables. Meanwhile, GDH was positively related to GS, GOGAT, nitrate–N and negatively related with NR activity (Table 2).

Variation in transaminases with agronomic variables

Significant effect was observed for glutamate oxaloacetate transaminase (GOT) activity with staggered planting dates (F = 1503, P < 0.01), stages of harvest (F = 624, P < 0.01) and varieties (F = 546, P < 0.01). The two-way and three-way interactions were found to be significant and the GOT activity lied in the range from 0.139 to 1.400 µmol min⁻¹ g⁻¹ FW (Fig. 2). Highest GOT activity was observed at 50 DAS of late planting date (Dec 11) in OL-10 genotype and lowest GOT activity was exhibited at 60 DAS of optimum planting date (Oct 24) in OL-11 genotype. The R² value for regression equation is 0.701 depicting 70.1% relationship between GOT and other variables. GOT showed positive relationship with GS, GPT and negative relationship with chlorophyll content (Table 2) and minimum temperature (Table 3).

Significant influence was observed for glutamate pyruvate transaminase (GPT) activity planting time (F = 771, P < 0.01), maturity of the crop (F = 807, P < 0.01) and genotypes (F = 453, P < 0.01). GPT activity ranged from 0.177–1.391 µmol min⁻¹ g⁻¹ FW having significant three way interaction (Fig. 2). Highest GPT activity was observed at 50 DAS of Dec 26 planting date (late) in OL-11 genotype and lowest GPT activity was observed at 60 DAS of Oct 24 (optimum) planting date in OL-11 genotype. According to regression equation, 69.6% relationship was observed between GPT and other variables. GPT showed synergistic association with GOT, nitrite–N and free amino acids (Table 2).

Variation in biochemical constituents with environmental fluctuations

Chlorophyll content differed significantly with variable planting dates (F = 1360, P < 0.01), growth stages (F = 521, P < 0.01) and varieties (F = 405, P < 0.01). The interactive effect of PD × GS × G was observed significant and ranged from 0.367–1.063 mg g⁻¹ FW (Fig. 2). Highest chlorophyll content was observed at 40 DAS of sub-optimum planting date (Oct 9) in OL-10 genotype and lowest was observed at 40 DAS of late planting date (Dec 11) in both the genotypes. The R² equation signifies 72% relationship between chlorophyll content and other variables. Meanwhile, chlorophyll content is positively related with NR and negatively related with nitrate–N and nitrite–N content (Table 2) and also with maximum temperature, minimum temperature and sunshine hour (Table 3).

The significant variation in nitrate–N content was observed with staggered planting (F = 1286, P < 0.01) and genotypes (F = 36.4, P < 0.01). Nitrate–N content ranged between 680–4788 ppm with significant three way interaction (Fig. 2). Highest nitrate–N content was observed at 40 DAS of late planting date (Nov 26) in both the genotypes and lowest nitrate–N content was observed at 60 DAS of sub-optimum planting dates (Oct 9) in OL-10 genotype. Nitrate–N decreased significantly (F = 2498, P < 0.01) with the maturity of the plant. The R² value for regression equation is 0.728 depicting 72.8% relationship between nitrate–N and other variables. Nitrate–N was positively associated with GDH, nitrite–N and negatively associated with NiR and chlorophyll content (Table 2). In addition to this, nitrate–N content showed antagonistic relationship with maximum temperature and sunshine hour (Table 3).

Nitrite–N content differed significantly with variation in planting time (F = 1138, P < 0.01) and varieties (F = 374, P < 0.01). The nitrite–N content with significant three way interaction ranged between 6.8 and 40.6 ppm (Fig. 2). Highest nitrite–N content was observed at 40 DAS of late planting date (Nov 26) in OL-11 genotype and lowest nitrite–N content was observed at 60 DAS of sub-optimum planting date (Oct 9) in OL-10 genotype. Nitrite–N content significantly decreased with the maturity of the plant (F = 4101, P < 0.01). According to regression equation, 82.3% relationship was observed between nitrite–N and other variables. Nitrite–N was positively related with GS, GOGAT and GPT, nitrate–N and negatively related with NR, NiR and chlorophyll content (Table 2). A negative relationship was observed among nitrite–N, maximum temperature and sunshine hour (Table 3).

Significant effect (F = 305, P < 0.01) of staggered planting dates was observed for free amino acid (FAA) content. The FAA content ranged from 0.91–4.51 mg g⁻¹ DW (Fig. 2). Highest FAA content was observed at 60 DAS of late planting dates (Nov 26 and Dec 26) and lowest at 40 DAS of sub-optimum planting date (Oct 9). The FAA content increased significantly (F = 518, P < 0.01) from 40 to 60 DAS. The FAA content varied non-significantly among two genotypes. The R² equation signifies 67.9% relationship between FAA and other variables. The FAA was positively related to GS, GPT but negatively related to NR (Table 2). The FAA was negatively associated with minimum temperature (Table 3).

Principal component analysis was performed among six planting dates. The biplot of N metabolic enzymes and biochemical constituents in relation to different planting dates is shown in Fig. 4. The first and second principal components comprised 66.5 and 16.1% variance respectively. The activities of NR, NiR and TChl content were found to be occupied at the left side of the plot and the remaining parameters (GS, GOGAT, GDH, GPT, GOT, nitrate–N, nitrite–N, FAA) were found to be occupied at right side of the plot. This indicates that the activities of NR, NiR and TChl content had a relationship among themselves. NR and NiR were negatively associated with nitrate–N and nitrite–N. Further, cluster analysis was performed to find out similarities among staggered planting dates. The planting dates were separated into five clusters (Fig. 5). The first cluster having 92% similarity consists of Oct 9 and Oct 24 planting dates, the second cluster having 89% similarity consists of 11 Dec and 26 Dec planting dates, the third cluster having 81% similarity consists of initial three planting dates, and the fourth cluster having 54% similarity consists of last three planting dates.

Fig. 4.

Biplot of nitrogen metabolic enzymes and biochemical traits in forage oat. NR: Nitrate reductase; NiR: Nitrite reductase; GS: Glutamine synthetase; GOGAT: Glutamate synthase; GDH: Glutamate dehydrogenase; GOT: Glutamate oxaloacetate transaminase; GPT: Glutamate pyruvate transaminase; FAA: Free amino acids; TChl: Total chlorophyll

Fig. 5.

Cluster analysis of nitrogen metabolic enzymes and biochemical constituents in forage oat

Discussion

Variation in nitrate assimilation with environmental fluctuations

Depending upon the environmental conditions that prevailed during the experimental period, planting dates were divided into three categories; optimum (Oct 24), sub-optimum (Oct 9 and Nov 13) and late (Nov 26, Dec 11, and Dec 26) planting dates. During optimum and sub-optimum sowing dates, plant experiences favourable or near to favourable temperature and sunlight conditions but during the late planting dates, the temperature and sunshine hour dips down. Our findings observed differential nitrogen metabolic enzymes activities with staggered planting dates. Light is a positive inducer of NR activity as it enhances the NR gene expression and also makes available the reductants (ATP and NADPH) required for nitrate reduction (Iglesias-Bartolome et al. 2004). In plants, 25% of the ATP and NADPH produced during photosynthetic electron transport are used for nitrate assimilation (Walker et al. 2014). The nitrate reductase (NR) and nitrite reductase (NiR) activities were observed maximum during optimum and sub-optimum planting dates. But as the photosynthetic machinery slowed down with low light conditions, the NR and NiR activities also drops down in late planting dates. Nitrite reductase moves in coordination with nitrate reductase since the product of nitrate reductase is used as a substrate for nitrite reductase and the gene expression of both enzymes is also regulated in a similar manner (Pathak et al. 2011). Our findings also observed that the NiR is synergistically associated with NR as presented in the regression model. The assimilation of nitrite should be much higher than nitrate because the excess nitrite can form diazo compounds with nucleobases leading to mutations in nucleic acids (Liao et al. 2019). The NiR enzyme activity predominantly depends on reduced ferredoxin for electrons which is supplied by photosystem I in the process of photosynthesis (Ali et al. 2020). Previous study reported that NiR activity slowed down under low intensity of light because of the lack of available reduced ferredoxin which is partially compensated by pentose phosphate pathway (Kaiser et al. 2011). With low NiR activity, the NR activity and its de novo synthesis also inhibited (Heldt and Piechulla 2011). Similar observations were recorded in our study. Further, temperature also influences nitrate assimilation. Previous studies documented that low temperature stress prevailing during the plant growth resulted in disturbed electron transfer throughout the NR channel at the interdomain site between heme and MoCo domains (Aydin and Nalbantoglu 2010; Gao et al. 2000). The thermal conditions were comparatively low during late planting dates that might have resulted in lower NR and NiR activities in our study. In addition, the nitrate assimilating enzymes were observed higher in the OL-10 genotype as compared to the OL-11 genotype. OL-10 being a fast growing genotype requires more nutrients from the soil, so its uptake and assimilation potential of nitrate may be higher in comparison with the relatively slow growing OL-11 genotype. Genotypic differences for NR and NiR activities were also reported by earlier workers (Kaur et al. 2015; Kaur and Goyal 2016; Shah et al. 2017).

Variation in ammonia assimilation with agronomic variables

The ammonium ions are incorporated by glutamine synthetase (GS) enzyme from variety of sources such as nitrite reduction, photorespiration, degradation of nitrogenous compounds, metabolism of phenylpropanoids and direct absorption from the soil (Hakeem et al. 2012). The present study observed that the activities of ammonia assimilating enzymes were higher during late planting dates that otherwise experiencesd low temperature and less sunshine hour. Literature documented that low temperature stress led to an increase in GS activity while glutamate dehydrogenase (GDH) activity was declined in rice (Lu et al. 2005). However, a reverse trend was observed in mosses (Liu et al. 2017). But in our study, both GS/GOGAT cycle and GDH activity showed upregulation with low temperature stress though the activity was stable or slightly low at optimum and sub-optimum planting dates. Our data corroborated the review by Ali (2020) who suggested that variability in the growing season and environmental conditions affects the GS activity. GS enzyme exists in two isoforms i.e. GS1 and GS2; GS1 isoform is present in the cytosol and is mainly responsible for reassimilation of ammonia when photosynthesis got declined and GS2 isoform assimilates the ammonia formed during photorespiration in chloroplasts (Habash et al. 2001). In our study, GS1 may be more active at late planting dates leading to more assimilation of ammonia. Further, the demand for proline was more in adverse environmental conditions and glutamate is the substrate for its synthesis (Lu et al. 2005; Yan et al. 2006). Proline is an osmolyte accumulated in the cell in response to various abiotic and biotic stresses. The increase in GS/GOGAT activity with low temperature stress may be to provide the constant glutamate pool for proline synthesis via the formation of glutamine. GOGAT moves in parallel with GS and the combination of these two enzymes leads to the rapid assimilation of ammonia into numerous N-containing compounds such as amino acids, proteins, and nucleotides (Nagy et al. 2013). The GDH is primarily known for maintaining the balance between carbon and nitrogen pools (Miflin and Habash 2002). GDH is considered as an alternative enzyme to GS/GOGAT cycle during thermal stress conditions (El-Shora et al. 2001). The generation of C-skeleton (α-ketoglutarate) is maintained during low temperature stress via the constant activity of isocitrate dehydrogenase, a TCA cycle enzyme (Liu et al. 2017). Likewise, in our study this available carbon skeleton may be utilized in the ammonium assimilation during late planting dates via the constant GDH activity. Oat being the Rabi crop experiences low temperature in its growth. The upregulation of both GS/GOGAT and GDH cycles helps it to sustain in low temperatures efficiently. A study reported that the enhanced BjGDH2 gene expression with low temperature stress was responsible for upregulated GDH activity (Goel and Singh 2015). Additionally, the activities of ammonia assimilating enzymes were observed higher in OL-10 genotype in comparison with OL-11 genotype. The assimilation potential of ammonia may be more in the genotype with high regeneration potential (OL-10) as compared to single cut variety (OL-11). Genotypic differences for GS, GOGAT and GDH activity were also reported by previous workers (Kaur et al. 2015; Kaur and Goyal 2016; Shah et al. 2017).

Variation in transaminases with agronomic variables

Glutamate Oxaloacetate Transaminase (GOT) and Glutamate Pyruvate Transaminase (GPT) are the key transaminases occupying the central position between N and C metabolism by maintaining the cellular levels of three major components i.e. glutamate, ammonium ions and α-ketoglutarate (Dubois et al. 2003). In the present investigation, the enzyme activities were slightly low during optimum and sub optimum planting dates as compared to late planting dates. The increased requirement of glutamate during late planting dates may be responsible for higher enzyme activities. Glutamate is the precursor of many amino acids and may be produced by the combined action of ammonia assimilating as well as transaminase enzymes (Asthir and Tak 2017). The present study observed a direct association of free amino acids with GPT activity as presented in the regression model (Table 2). Our findings also observed high free amino acid content at late planting dates. Indeed, the accumulation of free amino acids during cold acclimation has been demonstrated in oat (Goyal and Kaur 2018). Furthermore, these free amino acids are important for maintaining homeostasis during stress, preserving the stability of the system and compensating for environmental changes. Kaur et al. (2017) reported a significant increase of transaminases activities in wheat with increased free amino acid content. The activities of transaminases were observed higher in OL-10 genotype as compared to OL-11 genotype. Genotypic differences for transaminases activities were also reported by Kaur et al. (2017) in wheat.

Variation in biochemical constituents with environmental fluctuations

Chlorophyll content is the marker of the photosynthetic efficiency of plants. Temperature is the main factor affecting the photosynthetic capacity thereby altering chlorophyll content in plants. The present study observed a decrease in chlorophyll content with a delay in planting time. Better environmental conditions were available during optimum and sub-optimum planting dates but low temperature and less available sunshine hour during late planting dates may slow down the photosynthetic machinery of plants. Disruption in photosynthetic process and reduced chlorophyll content with low temperature is already been documented (Koc et al. 2010; Aghaee et al. 2011; Dhillon and Uppal 2019).

Nitrate is taken up by the plant roots from the soil thereafter transported via the xylem to other parts of the plant. Normally, nitrate is a non-toxic ion but the elevated level above 2000 ppm in forages causes nitrate toxicity to herbivores (Sidhu et al. 2011). The adverse climatic conditions such as low temperature, low intensity of sunlight, hail and frost cause damage to the plant by disrupting their photosynthetic machinery (Misha and Dubey 2011). These environmental extremes cause an imbalance in nitrate uptake and assimilation resulting in its accumulation at elevated levels in vegetative parts of the plant (Uwah et al. 2009). With the herbivore feeding of nitrate rich fodder, the nitrate gets converted into nitrite but excess nitrite gets absorbed in bloodstream and form a complex with haemoglobin (Hb) to form met-haemoglobin. Met-Hb is a poor transporter of oxygen with the result the animal suffers difficulty in breathing (Benjamin 2006). Some animal deaths due to consumption of nitraterich fodder are reported by several workers (Gontijo et al. 2017; Niyas et al. 2019). In our study, nitrate–N, and nitrite–N content were observed above toxic levels during the initial growth stages of late planting dates. These anti-nutritional compounds got decline with advancing maturity in vegetative parts of the plant. Under prolonged low temperature stress, oat fodder should not be fed solely to animals but should be mixed with other forages in order to avoid nitrate toxicity.

Conclusion

The extensive crosstalk between temperature and light during staggered planting dates resulted in oscillations in N assimilating enzyme activities. The nitrate assimilation was downregulated but ammonia assimilation was upregulated during late planting dates in oat but the reverse trend was observed in optimum and sub-optimum planting dates. The build-up of nitrate above toxic levels during initial growth stages of late planting dates was a consequence of unfavourable environmental conditions. In order to mitigate the effects on environment, oat should be sown at optimum time. Additionally, environmentally stressed oat fodder should be mixed with other forage crops in order to avoid toxic effect of nitrate.

Declarations

Conflict of interest

The authors have declared no conflict of interest.