Optimizing Oat (Avena sativa L.) Production Strategies to Mitigate Nitrate Toxicity Without Compromising Fodder Quality

Abstract

Oat or Jai (Avena sativa L.), a vital winter forage crop in India and several other countries, is frequently affected by nitrate toxicity, compromising animal health and fodder quality. This study aimed to identify optimal agronomic management practices that enhance fodder quality and reduce nitrate accumulation in the Trans-Gangetic plains of India. Using a pooled split-plot design, this experiment evaluated the effects of different oat varieties, nutrient management regimes, and seeding rates. The experimental results revealed variability in fodder quality across varieties. Increasing the nitrogen, phosphorus, and potassium (NPK) fertilizer application by 25% above the recommended rate, coupled with 95 kg ha⁻¹ of seed rate, significantly improved fodder yield, nutritional energy status, and reduced fiber fractions ($p<0.05$). Conversely, reducing fertilizer application to 75% of the recommended dose and supplementing with manure and plant growth-promoting rhizobacteria (PGPR) markedly decreased the risk of nitrate accumulation. Specifically, the combination of 100% recommended dose of fertilizer (RDF) or 75% RDF with PGPR and farmyard manure (FYM) at a seed rate of 90 kg ha⁻¹ proved most effective for the JHO-851 and Kent varieties in single-cut systems and JHO-851 and HJ-8 varieties in multicut systems, achieving the highest dry matter yield and quality with minimal nitrate levels ($p<0.05$). Future research should explore shade management, a potentially significant factor in nitrate accumulation, and its interaction with other management strategies.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

1. Introduction

According to the 20th Livestock Census, India has the largest livestock population in the world, with 535.82 million. This requires a consistent supply of high-quality feed all year round to support the substantial portion of the country’s population that depends entirely on livestock [1]. Unfortunately, the land area dedicated to forage crops has progressively diminished over the years, compelling farmers to rely on less nutritious alternatives such as crop residues and dry fodder [2]. Suboptimal feed quality adversely affects livestock nutritional intake, manifesting in diminished milk yields; thus, the cultivation of high-quality forage crops emerges as a pivotal strategy to enhance milk production [3–5]. Moreover, as the prediction indicates, the demand will increase up to 1012.70 million tonnes by 2050 [6, 7]. As demand grows, there is an increasing need for more land to cultivate fodder. However, the area dedicated to fodder crops is shrinking annually, facing significant competition from other crops, primarily food crops [1]. In such situations, agronomic management, including selecting crops with higher biomass production, faster growth rates, and adequate nutrient and crop management, is a promising approach to increase production.

Oat (Avena sativa L.), predominantly cultivated in India’s northern and northwestern regions, is a crucial winter cereal crop even as a nonleguminous fodder. It is particularly noted for its high protein, vitamin B, phosphorus, energy, and iron content, ranking second only to berseem in nutrient density [8]. A spectrum of agronomic practices significantly influences the productivity and quality of fodder oat, including parameters such as crude protein (CP), crude fat, mineral content, and fiber fractions. These include the selection of oat varieties, the management of nutrients, the density of planting, and the timing of sowing [9]. Each factor is critical in optimizing fodder oats’ growth conditions and nutritional outcomes, underpinning the importance of strategic agronomic planning in pursuing superior fodder quality [10].

However, feed quality is frequently compromised by antinutritional factors such as oxalates, nitrates, and prussic acid. These compounds are inherently present in various forages and get elevated when the plant faces any biotic or abiotic stress, which can severely impair animal health. Nitrate toxicity is a significant problem in fodder oats. Several agronomic factors influence nitrate accumulation in oat plants [11]. The process begins with nitrate absorption through the roots, and it is heavily dependent on the soil’s nitrate availability and the plant’s metabolic demand for nitrogen [12, 13]. Studies have shown that higher concentrations of available nitrate in the soil can lead to greater uptake by oat plants. However, if not properly managed through crop rotation or controlled fertilizer application, this can also increase the risk of nitrate toxicity [11, 14, 15].

The method and timing of nitrogen fertilizers’ application significantly affect how much nitrate accumulates within the plant tissues. Split applications of nitrogen can help reduce peak concentrations, thereby minimizing potential toxicity [16, 17].

Furthermore, environmental conditions such as light and temperature play critical roles in nitrate metabolism within plants [12]. Light enhances nitrate uptake and nitrate reductase activity, an enzyme crucial for converting nitrate to organic forms within the plant [18]. However, colder temperatures can slow the metabolic processes, leading to higher nitrate accumulation, as less is converted to amino acids and proteins [12, 19].

In addition, plant age and developmental stage significantly impact nitrate levels, with younger plants typically exhibiting higher concentrations [20]. This accumulation pattern is crucial for understanding how to manage grazing or cutting schedules for forage to avoid nitrate poisoning in livestock. Lastly, genetic factors also play a role, as certain oat cultivars may inherently accumulate more or less nitrate depending on their physiological makeup and breeding [21].

Nitrates are pervasive in the environment, primarily sourced from agricultural fertilizers, industrial discharges, and urban runoff. The application of synthetic fertilizers and manure in farming can significantly elevate nitrate levels in nearby water bodies if not managed correctly [22].

Nitrate toxicity in animals, particularly livestock, presents significant health challenges, primarily through the induction of methemoglobinemia, inhibiting the blood’s ability to carry oxygen effectively [23, 24]. This condition can lead to respiratory distress and, in severe cases, death [25]. Ruminants are especially susceptible because their gut flora converts nitrates more readily to the more toxic nitrites [26]. In addition, chronic nitrate exposure can interfere with the metabolism of key nutrients such as vitamins A and E, negatively affecting overall animal health and productivity [27]. Further effects of nitrate toxicity include alterations in thyroid function and hematologic parameters, such as an increase in red blood cells to compensate for oxygen transport inefficiency and inflammatory responses indicated by elevated neutrophils and eosinophils [9, 28].

Climate and crop management are two major contributing factors to nitrate accumulation; thus, suitable management through agronomic intervention is one key approach to preventing nitrate toxicity. The agronomic approach becomes even more crucial as quantitative herbage production and quality are also linked to plant nutrition, especially nitrogen management [29]. Among agronomic management practices, proper planting densities/seed rates, selection of cultivars, and fertilizer application are essential practices affecting nitrate accumulation [30].

The incorporation of organic manure and plant growth-promoting rhizobacteria (PGPR) plays a crucial part in mitigating nitrate accumulation in plants. Organic manure improves soil structure and increases microbial activity, which enhances the soil’s capacity to retain nitrates, thus reducing leaching [31]. Furthermore, the microbial degradation of organic matter in manure can lead to the immobilization of nitrates, making them marginally available for plant uptake or leaching [32, 33]. Moreover, certain PGPR strains can convert nitrates into less harmful nitrogen forms within the soil, further preventing nitrate buildup [34].

Considering all these factors, it is necessary to understand the effect of varieties, nutrient management strategies, seed rates, and their interactions on fodder quality and nitrate accumulation. To date, only a few studies have thoroughly explored these interactions. Acknowledging this gap in research, this study was designed to investigate these dynamics and provide insights into how these factors collectively impact fodder quality and nitrate levels in oats.

2. Materials and Methods

2.1. Site Characteristics

A two-year field experiment took place in 2018-19 and 2019-20 at the Forage Research and Management Center, ICAR-National Dairy Research Institute, Karnal, Haryana, India. The research farm is situated at 29.68° N latitude and 76.99° E longitude, with an elevation of 245 m above sea level. The soil in the experimental field was identified as clay loam, possessing low organic carbon content (0.50%), low nitrogen availability (190.5 kg ha⁻¹), medium phosphorus availability (19.2 kg ha⁻¹), and high potassium availability (270.8 kg ha⁻¹). The soil had a pH of 7.2 and a bulk density of 1.5 Mg m³⁻¹.

2.2. Weather Condition

The meteorological phenomenon during the crop growing season (2018-19 and 2019-20) was logged from the automatic weather station installed at the ICAR-CSSRI, Karnal, and exhibited in Figures 1(a) and 1(b). During the 2018-19 study year, the maximum and minimum temperatures of 29.54°C and 3.37°C were recorded in the 44^th and 52^nd standard week, respectively. The maximum and minimum sunshine hours of 2.56 and 0.96 were recorded in the 45^th week of 2018 and the 1^st week of 2019, respectively. Maximum relative humidity of 100% was noted in the 52^nd week of 2018 and the 1^st week of 2019. The maximum evaporation rate of 11.54 mm was in the 5^th week of 2019, whereas the highest rainfall (8.14 mm) was in the 51^st week of 2018. In the 2019-20 study year, the maximum and minimum temperatures of 28.33°C and 4.23°C were recorded in the 45^th week of 2019 and the 1^st week of 2020, respectively. The 5^th and 6^th standard weeks of 2020 were higher for relative humidity (100%). A maximum sunshine hour and a minimum sunshine hour (2.93 and 0.76 h) were recorded in the 47^th and 52^nd week of 2019, respectively. The maximum evaporation rate (6 mm) was in the 2^nd week of 2019. At the same time, the highest rainfall (7.31 mm) was recorded in the 47^th week of 2019.

[figure(s) omitted; refer to PDF]

2.3. Experimental Setup and Treatments

The experimental block was tilled multiple times with a disc plow and harrow to make the plots clod-free and suitable for sowing. The experiment was laid under a split-plot design with two main plots and two subplot factors. The main plot factors were the study years (2018-2019 and 2019-2020) and varieties (Kent, HJ-8, HFO-114, and JHO-851). The subplot factor constituted nutrient management with four levels and seed rate with three levels. The four levels of nutrient management were (1) 125% of RDF (150 kg N ha⁻¹: 50 kg P₂O₅ ha⁻¹: 50 kg K₂O ha⁻¹), (2) 100% RDF (120 kg N ha⁻¹: 40 kg P₂O₅ ha⁻¹: 40 kg K₂O ha⁻¹), (3) 75% RDF (90 kg N ha⁻¹: 30 kg P₂O₅ ha⁻¹: 30 kg K₂O ha⁻¹) + PGPR, and (4) 75% RDF + PGPR + farmyard manure (FYM). The PGPR was applied as a liquid consortium containing nitrogen-fixing strains of Rhizobium, Azotobacter, Acetobacter, phosphorus-solubilizing Pseudomonas, and potassium-solubilizing Bacillus. The PGPR liquid consortium was procured from ICAR- Indian Agricultural Research Institute and had an effective microorganism count of 1 × 10⁷ CFU mL⁻¹. The FYM used in this experiment had 0.87% N, 0.47% P₂O_5, and 0.65% K₂O and was used at 5 tons ha⁻¹. The three seed rates used in the subplot were 75, 90, and 105 kg ha⁻¹. All the treatment combinations were replicated three times, while the gross plot size was kept at 5 × 4 m.

Four varieties are allocated according to the treatments. As per the treatments, the half dose of nitrogen, full dose of phosphorus, and potassium were applied as basal doses in the form of urea, diammonium phosphate, and muriate of potassium, respectively, while the remaining half of nitrogen was applied in two splits at 30 days after sowing (DAS) and the first cut after irrigation.

2.4. Sowing and Cultural Operations

The four varieties were sown in the line sowing method with a row spacing of 30 cm on 28^th October 2018 and 7^th November 2019 for the first and second study years, respectively. Pendimethalin was sprayed 2 DAS as pre-emergence at a rate of 0.75 kg a.i. ha⁻¹. Besides cultural weeding, manual weeding was used on the water channel and bunds. The plots were kept sufficiently moist as the moisture content ranged from field capacity to 50% available soil moisture.

The first harvesting/first cut was taken 60 DAS from 8 to 10 cm above the ground, and the second cut was taken 45 days after the first cut.

2.5. Recording of Observation and Fodder Quality Assessment

After each cut, 500 g of fresh samples were collected in paper bags and subsequently dried in a hot air oven at 82°C to assess the dry matter yield (DMY) and nutritional quality attributes. Oven-dried samples were finely grounded, sieved, and analyzed for CP, ether extract (EE), ash yield, neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), cellulose, hemicellulose content, the net energy of lactation (NEL), digestible energy (DE), metabolizable energy (ME), nitrate (NO₃), and NO₃–nitrogen (NO₃–N) content.

Nitrogen content was estimated using the Kjeldahl method [35]. CP content was estimated by multiplying nitrogen content by 6.25. CP yield (CPY) is estimated by multiplying CP content with DM yield, and the same goes for EE and total ash yield (TAY).

The fraction of cell wall constituents such as ADF and hemicellulose was estimated [36], while NDF content was estimated as per [37].$$\begin{array}{c}\text{(1)}& \text{NDF}\%=\frac{\text{weight}\text{of}\text{the}\text{crucible}\text{with}\text{residue}-\text{weight}\text{of}\text{the}\text{crucible}\text{with}\text{residual}\text{ash}}{\text{weight}\text{of}\text{the}\text{dried}\text{sample}}\times 100,\text{ADF}\%=\frac{\text{weight}\text{of\hspace{0.17em}the}\text{crucible}\text{with}\text{residue}\mathrm{g}-\text{weight}\text{of}\text{the}\text{empty}\text{crucible}\mathrm{g}}{\text{weight}\text{of}\text{the}\text{dried}\text{sample}\mathrm{g}}\times 100,\\ \text{ADL\hspace{0.17em}}\%=\frac{\text{weight}\text{of}\text{the}\text{crucible}+\text{lignin}\mathrm{g}-\text{weight}\text{of}\text{the}\text{crucible}+\text{ash}\mathrm{g}}{\text{weight}\text{of}\text{the}\text{total}\text{GFY}\text{was}\text{considerably}\text{dried}\text{sample}\mathrm{g}}\times 100.\end{array}$$

Cellulose, hemicellulose content, the net energy for lactation, DE, and ME were estimated by the following [38, 39] formulas:$$\begin{array}{c}\text{(2)}& \text{cellulose\hspace{0.17em}}\%=\text{ADF}\%-\text{ADL\hspace{0.17em}}\%,\text{hemicellulose\hspace{0.17em}}\%=\text{NDF}\%-\text{ADF}\%,\\ \text{NEL\hspace{0.17em}}\frac{\text{Mcal}}{\text{kg}}=1.044-0.0119\times \text{ADF}\times 2.205,\\ \text{NEL}\frac{\text{MJ}}{\text{kg}}=\text{NEL}\frac{\text{Mcal}}{\text{kg}}\times 4.184,\\ \text{DE\hspace{0.17em}}\frac{\text{Mcal}}{\text{kg}}=0.27+0.0428\times \text{DMD}\%,\\ \text{DE}\frac{\text{MJ}}{\text{kg}}=\text{DE}\frac{\text{Mcal}}{\text{kg}}\times 4.184,\\ \text{ME}\frac{\text{MJ}}{\text{kg}}=\text{DE}\frac{\text{MJ}}{\text{kg}}\times 0.821.\end{array}$$

NO₃–N in plant tissues was determined by using the method described by Woolley, Hicks, and Hageman (1960) [40]. Plant material was homogenized, and nitrate was extracted using distilled water. The extract was then subjected to colorimetric analysis, where nitrate ions reacted with specific reagents to form a colored complex, which was measured spectrophotometrically at a defined wavelength. The nitrate concentration was calculated using a standard curve, and the NO₃–N content was derived by using the [40] following formula:$$\begin{array}{}\text{(3)}& {{NO}_3-N=NO}_3\times 0.226,\end{array}$$where 0.226 is the conversion factor based on the molecular weight ratio of nitrogen to nitrate. The results were expressed as mg NO₃–N per g of plant tissue.

2.6. Statistical Analysis

All the recorded data under different parameters were analyzed with the help of the analysis of variance (ANOVA) technique for a pooled split-plot design using the “Doebioresearch” package in the R statistical program [41]. The varieties were taken as the main plot factor while the nutrient management and seed rate were taken as subplot factors. The Duncan multiple range test (DMRT) was employed to interpret the main and interaction effects of treatments at a 5% significance level. Pearson’s correlation coefficient was calculated and visualized in a correlogram using R, and principal component analysis (PCA) was performed with the “FactoMineR” package in the R statistical program [42].

3. Result and Discussion

3.1. Yield and Quality Attributes

Yearly variation, varieties, nutrient management, and seed rates significantly ($p<0.05$) influenced oat yield and quality attributes in both the 1^st and 2^nd cut; however, interactions among these factors were significant in a few instances (Figures 2 and 3, Table 1).

[figure(s) omitted; refer to PDF]

Table 1

The effect of different experimental years, varieties, nutrient management, and seed rates on dry matter content (%), crude protein content (%), ether extract content (%), and total ash content (%) of oats under two different cuts.

Treatments	Dry matter content (%)		Crude protein content (%)		Ether extract content (%)		Total ash content (%)
	Cut I	Cut II	Cut I	Cut II	Cut I	Cut II	Cut I	Cut II
Years
2018-19	18.19 ± 0.04	17.87 ± 0.07	12.59a ± 0.02	11.37b ± 0.03	2.56a ± 0.03	2.22a ± 0.03	8.54a ± 0.07	7.72 ± 0.07
2019-20	18.12 ± 0.06	17.91 ± 0.03	12.33b ± 0.03	11.63a ± 0.01	2.19b ± 0.06	1.99b ± 0.02	8.75a ± 0.03	7.70 ± 0.04
Varieties
V1: Kent	18.17 ± 0.02	17.72 ± 0.02	12.57a ± 0.04	11.38c ± 0.02	2.43 ± 0.02	2.08a ± 0.04	8.68b ± 0.02	7.66b ± 0.02
V2: HJ-8	18.15 ± 0.03	17.93 ± 0.03	12.53a ± 0.02	11.60b ± 0.03	2.36 ± 0.01	2.12b ± 0.02	8.65b ± 0.05	7.78a ± 0.05
V3: HFO-114	18.04 ± 0.06	17.84 ± 0.01	12.13a ± 0.02	11.25c ± 0.05	2.32 ± 0.02	2.05c ± 0.04	8.43c ± 0.05	7.58b ± 0.07
V4: JHO-851	18.26 ± 0.03	18.08 ± 0.05	12.62a ± 0.03	11.78a ± 0.04	2.39 ± 0.03	2.17a ± 0.02	8.82a ± 0.03	7.82a ± 0.03
Nutrient management practices
N1: 125% RDF	18.30a ± 0.07	18.07a ± 0.05	12.57 ± 0.04	12.68a ± 0.08	2.42a ± 0.01	2.15a ± 0.02	8.71a ± 0.02	7.79a ± 0.03
N2: 100% RDF	18.17a ± 0.02	17.96a ± 0.04	12.51 ± 0.03	11.65a ± 0.02	2.39a ± 0.03	2.12a ± 0.08	8.70a ± 0.04	7.79a ± 0.04
N3: 75% RDF + PGPR	17.98b ± 0.02	17.68b ± 0.02	12.33 ± 0.07	11.07b ± 0.038	2.32b ± 0.04	2.03b ± 0.05	8.54b ± 0.05	7.49b ± 0.03
N4: 75% RDF + PGPR + FYM	18.18a ± 0.05	17.86ab ± 0.03	12.42 ± 0.05	11.60a ± 0.05	2.37ab ± 0.05	2.11a ± 0.03	8.65a ± 0.01	7.77a ± 0.03
Seed rates
S1: 75 kg ha−1	18.07 ± 0.02	17.77 ± 0.02	12.25b ± 0.01	12.28b ± 0.03	2.30b ± 0.04	2.02b ± 0.05	8.54b ± 0.02	7.58b ± 0.06
S2: 90 kg ha−1	18.15 ± 0.05	17.95 ± 0.01	12.62a ± 0.05	11.66a ± 0.04	2.43a ± 0.02	2.15a ± 0.02	8.72a ± 0.03	7.81a ± 0.08
S3: 105 kg ha−1	18.24 ± 0.04	17.96 ± 0.03	12.51a ± 0.04	11.57a ± 0.06	2.39a ± 0.03	2.13a ± 0.04	8.68a ± 0.04	7.74a ± 0.07

Note: Values are represented as mean ± standard error of the mean; 125% RDF, 150 kg N ha⁻¹: 50 kg P₂O₅ ha⁻¹: 50 kg K₂O ha⁻¹; 100% RDF, 120 kg N ha⁻¹: 40 kg P₂O₅ ha⁻¹: 40 kg K₂O ha⁻¹; 75% RDF, 90 kg N ha⁻¹: 30 kg P₂O₅ ha⁻¹: 30 kg K₂O ha⁻¹. Treatments having the same letter in superscript are not significantly different ($p<0.05$) as per the least significant difference test.

Abbreviations: FYM, farmyard manure; PGPR, plant growth-promoting rhizobacteria; RDF, recommended dose of fertilizer.

The difference in green fodder yield (GFY) was nonsignificant in 1^st cut; in 2^nd cut, the total GFY was considerably ($p<0.05$) higher in the first year of the experiment (2018-19) (Figure 2(a)). Similarly, the first year of the experiment also recorded a maximum DMY during the 2^nd cut and cumulative total yield, respectively (Figure 2(b)). Similarly, the higher CP content (CP content in percent) in 1^st cut was recorded during the 1^st year of the experiment; however; on the contrary, the second year of the experiment recorded a higher CP content in the 2^nd cut (Table 1). The CPY, however, followed the trend of DMY, where the first year of the experiment recorded significantly higher CPY, which were 3.88%, 3.39%, and 3.68% higher than the second year of the experiment (Figure 2(c). The CP content and yield were higher in 1^st cut than in the 2^nd cut. A higher EE content (EE content in percent) and EE yield (EEY) were also recorded during the first year of the experiment both in 1^st and 2^nd cut which resulted in an overall 18.45% higher total EEY than the second year of the experiment (Figure 2(d) and Table 1). The TA content (TA content in percent) was maximum in the second year of the experiment during the 1^st cut; however, it recorded a nonsignificant result during the second year of the experiment (Table 1). The variation in overall TA yield (TAY) did not reach a significant level; however, in 2^nd cut, TAY was significantly higher in the first year than in the second year of the experiment (Figure 2(e)).

While all the treatments and cultural operations were kept the same, the origin of the variation might be attributed to the variation in weather conditions such as temperature and sunshine hours. The second experiment year faced higher temperature fluctuations and sunshine hours (Figure 1). Critical physiological processes, including nitrogen assimilation and photosynthate translocation, largely depend on the optimum temperature range and regular sunshine hours [11, 43]. Moreover, this physiological process is also significantly influenced by the fluctuation of plant water uptake, which is also indirectly affected by weather attributes [44]. Therefore, the variation in CPY over the years might be due to the better transformation of nitrate to CP, in which optimum temperature and sunshine hour play an essential role. Research has shown that the efficiency of nitrate reductase, an enzyme crucial for nitrate reduction, is significantly influenced by temperature and light conditions [45]. Similarly, research on rice varieties has highlighted that both temperature and sunshine hours during the grain-filling stage are critical for optimal protein content in the grains [46].

The experiment also recorded statistically significant variation ($p<0.05$) among different varieties in GFY and DMY; the maximum was obtained from the JHO-851 which recorded significantly higher GFY and DMY in the 1^st cut, 2^nd cut, and total respect. DM content, however, recorded a nonsignificant difference among the four selected oat varieties (Table 1). Among three primary fodder quality attributes, i.e., CP content, EE content, and TA content, only TA content recorded a significant difference among oat varieties during the 1^st cut, which was maximum in JHO-851, while the minimum was in HFO-114. However, the varieties differ considerably during the 2^nd cut; maximum CP, EE, and TA content were recorded in JHO-851, while the minimum was recorded with HFO-114 (Table 1).

The CPY, EEY, and TAY exhibited a similar pattern to the CP, EE, and TA content, as they are simply the products of CP, EE, and TA multiplied by the yield. Among the varieties, JHO-851 showed the highest CPY, EEY, and TAY in both the 1st and 2nd cuts, as well as on a cumulative basis (Figures 2(c), 2(d), and 2(e)). Overall, the 1^st cut recorded higher CP, EE, and TA content and yield compared to the 2^nd cut.

The significant variation in GFY and DMY among the oat varieties ($p<0.05$) underscores the critical role of genetic factors in oat fodder productivity. JHO-851, which recorded the highest GFY and DMY in both cuts, likely possesses superior genetic traits that enhance its growth and biomass accumulation. This observation is consistent with studies indicating that genetic variations among oat cultivars significantly influence biomass production due to differences in photosynthetic efficiency and resource use [47, 48].

The nonsignificant difference in DM content among the varieties suggests that while genetic factors influence biomass production, the DM content is primarily determined by the stage of physiological maturity and environmental conditions at harvest. When fodder crops are harvested at similar growth stages, particularly at a critical stage of maturity (e.g., booting or flowering), the water content and nutrient distribution stabilize, leading to comparable DM accumulation across varieties. This indicates that DM content may be less influenced by inherent genetic variation and more by uniform harvest timing and similar environmental growing conditions, which limit variability in moisture loss and plant desiccation rates. Therefore, despite genetic differences, the similar physiological and environmental factors at the time of harvest result in nonsignificant differences in DM content among the varieties [49, 50].

The reason behind higher TA content in JHO-851 might be attributed to genetic differences influencing mineral uptake efficiency and accumulation. Varietal disparities in root system architecture and nutrient assimilation pathways might lead to differential absorption of key minerals, contributing to higher ash content in certain varieties. However, most certain reasoning can only be drawn from comparative studies among selected varieties based on root morphology and genetic level analysis [51].

The general trend of higher CP, EE, and TA contents and yields during the 1^st cut compared to the 2^nd cut, can be attributed to the physiological state of the plants. Initially, plants tend to accumulate higher levels of nutrients as they are in a vigorous growth phase, with ample access to soil nutrients. In subsequent regrowth periods, nutrient reserves are depleted, and metabolic activities slow down, leading to reduced nutrient content in the fodder [52].

The different nutrient management regimes also recorded statistically significant differences ($p<0.05$) in GFY, DMY, and other fodder quality attributes (Table 1 and Figure 2). Increasing the fertilizer application rate by 25% (125% recommended dose of fertilizer [RDF]) has resulted in maximum GFY and DMY in 1^st cut, 2^nd cut, and total basis, respectively, although its difference with 100% RDF was statistically nonsignificant. This suggests that the nutrient uptake by the plants reached a saturation point where additional nutrients did not significantly increase yields [53].

The same trend has also been observed in the case of DM, CP, EE, and TA content as well as CPY, EEY, and TAY, where 125% RDF remained at par with 100% RDF. This indicates that beyond a certain threshold, additional fertilizer did not further enhance nutrient content and yields, likely due to the plants’ limited capacity to utilize extra nutrients efficiently [54]. In the treatment where 25% fertilizer was reduced and replaced with PGPR (75% RDF + PGPR), the outcome was significantly lower yield and quality attributes than 100% RDF. This reduction can be attributed to the insufficient nutrient supply from PGPR alone, which could not fully compensate for the reduced chemical fertilizer, leading to suboptimal growth conditions [55]. Conversely, adding PGPR and 75% RDF with FYM significantly improved yield and quality attributes. Combined application of PGPR with chemical fertilizer presents numerous benefits including enhanced soil enzyme activity, improved nutrient uptake, and increased efficiency of fertilizer especially of nitrogen by significantly reducing the leaching. However, the effectiveness of PGPR is reduced when chemical fertilizer is applied in ample amounts because of the reduced need for plants to engage in microbial symbiosis. Excessive fertilizer use decreases root exudation, which PGPR rely on for sustenance, leading to lower microbial colonization and activity [56, 57].

The effect of different seed rates on yield and quality attributes was significant ($p<0.05$). A higher seed rate of 105 kg ha⁻¹ resulted in higher GFY, DMY, CPY, EEY, and TAY during the 1^st cut, 2^nd cut, and cumulative total yield (Table 1 and Figure 2). However, the seed rate of 90 kg ha⁻¹ reported a statistically similar result, suggesting that the increase from 90 kg ha⁻¹ to 105 kg ha⁻¹ may be approaching the optimal seed rate for maximizing yield without further significant gains [58].

The trend of these yield parameters was apparent in different content attributes too; a seed rate of 105 kg ha⁻¹ resulted in maximum CP, EE, and TA content during 1^st and 2^nd cut which were statistically at par with a seed rate of 90 kg ha⁻¹. While DM content on both cuttings showed no statistically significant difference between different seed rates, CP, EE, and TA content were higher in the 1^st cut than in the 2^nd, regardless of the different seed rates. This suggests that the higher seed rate enhances nutrient availability and uptake due to better plant density and competition, leading to improved quality attributes [59]. A particularly interesting insight related to the impact of seed rate on CP content is that increased seed rate resulted in increased CP content up to a certain extent. This is primarily due to increased competition for resources such as light, water, and nutrients. In denser plant populations, individual plants tend to produce smaller leaves and thinner stems, which can result in a higher leaf-to-stem ratio. Since leaves generally contain more protein than stems, this shift in biomass allocation can lead to higher overall CP content. In addition, increased competition may prompt plants to allocate more resources toward nitrogen assimilation and protein synthesis to support growth under competitive conditions. This results in elevated CP levels, as nitrogen is a key component of amino acids and proteins [60]. The decline in quality attributes in the 2^nd cut can be attributed to the depletion of soil nutrients and reduced metabolic activity as the plants mature and regrow [61].

The interaction between nutrient management practices and seed rates was significant in the case of GFY, DMY, CPY, EEY, and TAY (Figure 3). The total GFY was maximum with the application of 125% RDF with 90 kg ha⁻¹ seed rate and was at par with the application of 100% RDF with 90 kg ha⁻¹ seed rate, 100% RDF, and 105 kg ha⁻¹ seed rate and 75% RDF + PGPR + FYM with 105 kg ha⁻¹ seed rate. This indicates that the combined effect of optimal nutrient availability and appropriate seed rate enhances plant growth and biomass production, as confirmed by studies showing that balanced fertilization and optimal seeding density synergistically improve forage yield and quality [61].

Further increase in seed rate to 105 kg ha⁻¹ with 125% RDF application decreased the GFY suggesting potential intraplant competition for resources at higher densities, which can reduce yield efficiency [56]. The maximum total DMY, CPY, EEY, and TAY were also obtained when 125% RDF was applied with 90 kg ha⁻¹ of seed rate. Conversely, minimum GFY, DMY, CPY, EEY, and TAY were recorded when 75% RDF + PGPR was applied with a 75 kg ha⁻¹ seed rate indicating that reduced fertilization coupled with lower seed rates cannot meet the nutrient demands of the crop, resulting in lower productivity. This finding aligns with previous research that highlights the inadequacy of reduced fertilization and lower seeding rates in achieving optimal forage yield and quality [53].

3.2. Fiber Fractions

The four fiber fraction attributes, i.e., NDF, ADF, ADL, cellulose, and hemicellulose, have been significantly ($p<0.05$) influenced by experimental years, different varieties, nutrient management practices, and seed rates in both 1^st and 2^nd cut and depicted in Table 2.

Table 2

Neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), cellulose, and hemicellulose content during 1^st and 2^nd cut in fodder oat as influenced by different study years, varieties, nutrient management practices, and seed rates.

Treatments	Neutral detergent fiber (%)		Acid detergent fiber (%)		Acid detergent lignin (%)		Cellulose (%)		Hemicellulose (%)
	Cut I	Cut II	Cut I	Cut II	Cut I	Cut II	Cut I	Cut II	Cut I	Cut II
Years
2018-19	55.97 ± 0.03	57.58a ± 0.06	33.69 ± 0.07	34.67 ± 0.03	4.23a ± 0.06	4.87b ± 0.03	29.46 ± 0.05	29.81a ± 0.04	22.29 ± 0.03	22.91 ± 0.05
2019-20	55.55 ± 0.02	56.46b ± 0.03	33.65 ± 0.04	34.36 ± 0.02	4.11b ± 0.03	4.98a ± 0.02	29.53 ± 0.02	29.38b ± 0.02	21.9 ± 0.02	22.1 ± 0.06
Varieties
V1: Kent	55.79b ± 0.3	57.47b ± 0.04	33.64b ± 0.02	34.6 ± 0.06	4.19 ± 0.02	4.96b ± 0.05	29.45b ± 0.03	29.64 ± 0.03	22.15 ± 0.05	22.87b ± 0.02
V2: HJ-8	55.22c ± 0.05	56.32c ± 0.06	33.63b ± 0.05	34.4 ± 0.04	4.17 ± 0.03	4.90c ± 0.07	29.46b ± 0.05	29.51 ± 0.04	21.59 ± 0.06	21.91c ± 0.03
V3: HFO-114	57.12a ± 0.02	58.75a ± 0.03	34.23a ± 0.07	34.87 ± 0.02	4.24 ± 0.04	5.06a ± 0.04	29.99a ± 0.06	29.81 ± 0.05	22.89 ± 0.02	23.88a ± 0.05
V4: JHO-851	54.91c ± 0.04	55.56d ± 0.02	33.17c ± 0.06	34.2 ± 0.05	4.10 ± 0.07	4.76d ± 0.05	29.07c ± 0.03	29.44 ± 0.05	21.74 ± 0.06	21.36c ± 0.06
Nutrient management practices
N1: 125% RDF	54.64b ± 0.02	56.07c ± 0.05	33.03b ± 0.04	34.06c ± 0.04	4.05b ± 0.05	4.84c ± 0.02	28.98b ± 0.04	29.23c ± 0.04	21.61b ± 0.04	22.00b ± 0.03
N2: 100% RDF	55.05b ± 0.04	56.58bc ± 0.02	33.30b ± 0.02	34.27bc ± 0.02	4.10b ± 0.06	4.87c ± 0.04	29.20b ± 0.05	29.40bc ± 0.01	21.75b ± 0.05	22.31b ± 0.04
N3: 75% RDF + PGPR	56.82a ± 0.07	58.48a ± 0.03	34.23a ± 0.07	35.23a ± 0.07	4.28a ± 0.03	5.06a ± 0.05	29.94a ± 0.02	30.17a ± 0.08	22.60a ± 0.07	23.25a ± 0.03
N4: 75% RDF + PGPR + FYM	56.53a ± 0.03	56.96b ± 0.01	34.11a ± 0.06	34.51b ± 0.03	4.25a ± 0.04	4.92b ± 0.06	29.86a ± 0.05	29.59b ± 0.04	22.43a ± 0.02	22.45b ± 0.04
Seed rates
S1: 75 kg ha−1	54.98b ± 0.04	56.32b ± 0.05	33.27c ± 0.07	34.24b ± 0.02	4.11b ± 0.04	4.86b ± 0.04	29.16b ± 0.07	29.38b ± 0.04	21.71b ± 0.05	22.08b ± 0.03
S2: 90 kg ha−1	55.26b ± 0.03	56.79b ± 0.02	33.58b ± 0.03	34.44b ± 0.07	4.14b ± 0.03	4.89b ± 0.03	29.44b ± 0.04	29.55b ± 0.02	21.67b ± 0.06	22.35b ± 0.06
S3: 105 kg ha−1	57.04a ± 0.07	57.96a ± 0.04	34.15a ± 0.05	34.87a ± 0.05	4.27a ± 0.05	5.01a ± 0.05	29.87a ± 0.04	29.86a ± 0.05	22.90a ± 0.07	23.08a ± 0.05

Note: Values are represented as mean ± standard error of the mean. 125% RDF, 150 kg N ha⁻¹: 50 kg P₂O₅ ha⁻¹: 50 kg K₂O ha⁻¹; 100% RDF, 120 kg N ha⁻¹: 40 kg P₂O₅ ha⁻¹: 40 kg K₂O ha⁻¹; 75% RDF, 90 kg N ha⁻¹: 30 kg P₂O₅ ha⁻¹: 30 kg K₂O ha⁻¹. Treatments having the same letter in superscript are not significantly different ($p<0.05$) as per the least significant difference test.

Abbreviations: ADF, acid detergent fiber; ADL, acid detergent lignin; FYM, farmyard manure; NDF, neutral detergent fiber; PGPR, plant growth-promoting rhizobacteria; RDF, recommended dose of fertilizer.

The experiment recorded a statistically significant less (1.98%) NDF content in the second experimental year during the 2^nd cut; however, the difference at the 1^st cut was nonsignificant. This decrease in NDF content could be attributed to variations in environmental conditions such as temperature and rainfall, which can influence the plant’s fiber composition [62]. For ADL, the variation due to the experimental year was interesting as the first experimental year had higher ADL content in the 1^st cut, which was reversed in the 2^nd cut. This might be due to changes in lignification patterns influenced by environmental stresses and plant maturity stages during different years [63]. The difference in cellulose content in the two experimental years was nonsignificant in 1^st cut; in 2^nd cut, higher cellulose content was recorded in the first year of the experiment. This variation may result from the differential growth rates and physiological maturity achieved under varying annual climatic conditions [63]. Yearly variations were nonsignificant in ADF and hemicellulose content indicating that these fiber components might be more stable across different environmental conditions and less affected by annual variations compared to other fiber fractions. This stability could be due to the inherent genetic makeup of the varieties used, which determines the fundamental fiber composition [64].

While comparing the oat varieties, JHO-851 and HJ-8 varieties had statistically similar (at par) NDF content in the 1^st cut, and both were significantly lower than Kent and HFO-114. In the 2^nd cut, JHO-851 contained statistically lower NDF content than Kent, HJ-8, and HFO-114. This could be attributed to the genetic differences among the varieties, which influence the plant’s structural composition and fiber development [65]. ADF contents did not vary significantly between the oat varieties at the 2^nd cut, but at the 1^st cut, JHO-851 had significantly lower ADF content than the other varieties. The lower ADF content in JHO-851 at the 1^st cut may be due to its genetic propensity for lower lignification early in the growth cycle [66]. ADL, although nonsignificant during the 1^st cut, was significantly lower at the 2^nd cut with JHO-851 over other varieties. The lower ADL content in JHO-851 suggests better digestibility, as lignin is a major indigestible component of fiber [67]. The lower cellulose content was recorded with the JHO-851 compared to the other tested varieties at 1^st cut, while it was nonsignificant during the 2^nd cut. This indicates that JHO-851 has a lower initial cellulose accumulation, which might be beneficial for early-stage digestibility [67]. The difference in hemicellulose content was significant only during the 2^nd cut, as lower hemicellulose content was recorded with JHO-851 compared to Kent and HFO-114 and was at par with HJ-8. Lower hemicellulose content in JHO-851 indicates a potential advantage in terms of digestibility and nutrient availability [64].

Among different nutrient management practices, the application of 125% RDF and 100% RDF were at par with each other and recorded significantly lower NDF content than 75% RDF + PGPR and 75% RDF + PGPR + FYM at both the 1^st and 2^nd cuts. This indicates that higher rates of RDF can effectively reduce the NDF content, likely due to improved nutrient availability and uptake efficiency, which enhances cell wall degradation and reduces the accumulation of structural carbohydrates [54]. Furthermore, at the 2^nd cut, 100% RDF was similar to 75% RDF + PGPR + FYM, indicating that the addition of organic amendments such as PGPR and FYM can partially compensate for the reduced chemical fertilizer, enhancing nutrient efficiency and sustaining lower NDF content [56] This outcome is in line with similar findings and indicates the potential of integrated nutrient management practices in optimizing fodder quality by balancing the use of chemical fertilizers with organic amendments, ensuring sustained nutrient availability and improving forage digestibility [55].

The application of 125% RDF and 100% RDF resulted in significantly lower ADF content compared to 75% RDF + PGPR and 75% RDF + PGPR + FYM during both the 1^st and 2^nd cuts. This outcome can be attributed to the sufficient nutrient supply provided by the higher RDF rates, which promote more efficient plant growth and fiber synthesis, reducing the proportion of indigestible fibers such as ADF [67]. In contrast, 75% RDF + PGPR + FYM showed similar ADF content to 100% RDF in the 2nd cut, indicating that organic amendments such s PGPR and FYM can partially compensate for reduced chemical fertilizer but may not match the efficiency of full RDF application.

For ADL content, 100% RDF recorded significantly lower values compared to 75% RDF + PGPR and 75% RDF + PGPR + FYM. Increasing RDF to 125% further decreased ADL content at both cuts. The reduction in ADL content with higher RDF applications is likely due to enhanced plant growth and development, leading to less lignification [5, 55]. Nutrient management practices also influenced cellulose content. Both 100% RDF and 125% RDF recorded lower cellulose content compared to 75% RDF + PGPR at both cuts. This suggests that adequate nutrient supply through higher RDF promotes better cellulose synthesis, enhancing overall forage quality [57].

Interestingly, 100% RDF showed comparable cellulose content to 75% RDF + PGPR + FYM in the 2^nd cut, highlighting the potential of combined organic and inorganic nutrient management to sustain cellulose levels. In terms of hemicellulose content, both 125% RDF and 100% RDF recorded similar values and were lower than 75% RDF + PGPR and 75% RDF + PGPR + FYM during the 1st cut. In the 2nd cut, 125% RDF, 100% RDF, and 75% RDF + PGPR + FYM were similar and lower than 75% RDF + PGPR. These results underscore the role of adequate nutrient supply in reducing hemicellulose content, likely through improved plant metabolic processes and nutrient assimilation [68].

Increasing the seed rate resulted in an increased plant population, which in turn increased the fiber fraction, as evident from the results (Table 2). Specifically, the maximum seed rate of 105 kg ha⁻¹ recorded the highest NDF, ADF, ADL, cellulose, and hemicellulose content, while the 75 kg ha⁻¹ seed rate recorded the lowest values. This increase in fiber content can be attributed to the higher plant population leading to greater competition for light, nutrients, and water. Consequently, plants produced more structural carbohydrates to support the increased density, which is consistent with the findings in maize and sorghum where higher seeding densities led to greater plant height and fiber content [69, 70].

Across varying seed rates, the second harvest consistently exhibited elevated fiber fractions compared to the first harvest, indicating a persistent trend. This observation suggests that plant maturation is associated with increased fiber accumulation due to enhanced lignin and structural carbohydrate synthesis during later growth stages. Such patterns have been documented in multiple forage crops, where successive cuts show increased fiber content as plants transition from vegetative to more mature stages [71]. This can be explained by the plants’ response to higher density, prioritizing structural growth over storage or soluble carbohydrates [71]. Enhanced competition among plants for resources such as light and nutrients drives this shift, which aligns with findings in fodder crops where higher seed rates led to changes in nutrient allocation favoring fiber accumulation [72]. While the increased fiber content at higher seed rates can enhance the structural integrity of the fodder, it may also impact its digestibility and nutritional value. The elevated levels of NDF and ADF associated with higher seed rates may reduce the overall digestibility of the forage, affecting its suitability for livestock feed. Balancing seed rates to optimize both yield and quality is thus crucial for achieving the desired nutritional outcomes in fodder production [73].

3.3. Energy Values and Nitrate Accumulation

The impact of variation in experimental years, varieties, nutrient management regimes, and seed rates on the energy values and nitrate accumulation in oat fodder is presented in Table 3. The variations due to experimental years on three estimated energy content attributes, namely, NEL, DE, and ME, as well as nitrate and NO₃–N content, were nonsignificant during both the 1^st and 2^nd cuts.

Table 3

Net energy for lactation (NEL) (MJ kg⁻¹), digestible energy (DE) (MJ kg⁻¹), metabolizable energy (ME) (MJ kg⁻¹), nitrate and nitrate–nitrogen content in oat during 1^st and 2^nd cut in fodder oat as influenced by different study years, varieties, nutrient management practices, and seed rates.

Treatments	NEL (MJ kg−1)		DE (MJ kg−1)		ME (MJ kg−1)		NO3 (ppm)		NO3–N (ppm)
	1st cut	2nd cut	1st cut	2nd cut	1st cut	2nd cut	1st cut	2nd cut	1st cut	2nd cut
Years
2018-19	5.93 ± 0.02	5.82 ± 0.03	12.35 ± 0.04	12.21 ± 0.05	10.14 ± 0.03	10.03 ± 0.08	4114 ± 12.5	4230 ± 12.13	935 ± 14.78	956 ± 15.43
2019-20	5.94 ± 0.03	5.86 ± 0.06	12.36 ± 0.05	12.26 ± 0.02	10.14 ± 0.06	10.06 ± 0.04	4207 ± 8.71	4272 ± 9.05	963 ± 16.32	965 ± 8.97
Varieties
V1: Kent	5.94b ± 0.04	5.83 ± 0.02	12.36b ± 0.07	12.22 ± 0.04	10.14b ± 0.05	10.03 ± 0.03	4131 ± 15.23	4211 ± 11.98	942 ± 14.46	951 ± 8.54
V2: HJ-8	5.94b ± 0.05	5.85 ± 0.04	12.36b ± 0.03	12.25 ± 0.03	10.15b ± 0.04	10.06 ± 0.04	4014 ± 11.87	4112 ± 8.76	916 ± 13.07	929 ± 7.32
V3: HFO-114	5.87c ± 0.03	5.8 ± 0.02	12.28c ± 0.03	12.19 ± 0.07	10.08c ± 0.07	10.00 ± 0.02	4184 ± 9.37	4274 ± 13.76	954 ± 11.65	966 ± 9.45
V4: JHO-851	5.99a ± 0.05	5.88 ± 0.05	12.42a ± 0.05	12.28 ± 0.05	10.20a ± 0.03	10.08 ± 0.08	4313 ± 21.43	4409 ± 15.06	983 ± 13.54	996 ± 12.32
Nutrient management practices
N1: 125% RDF	6.01a ± 0.07	5.89a ± 0.02	12.44a ± 0.03	12.30a ± 0.05	10.21a ± 0.04	10.10a ± 0.07	4702a ± 17.88	4821a ± 16.87	1062a ± 15.23	1089a ± 9.87
N2: 100% RDF	5.98a ± 0.04	5.87ab ± 0.04	12.40a ± 0.6	12.27ab ± 0.04	10.18a ± 0.07	10.07ab ± 0.03	4212b ± 14.32	4306b ± 11.87	952b ± 12.23	973b ± 6.54
N3: 75% RDF + PGPR	5.87b ± 0.08	5.76c ± 0.05	12.27b ± 0.07	12.13c ± 0.02	10.08b ± 0.06	9.96c ± 0.02	3736d ± 12.11	3770c ± 13.23	880c ± 14.32	852c ± 14.32
N4: 75% RDF + PGPR + FYM	5.89b ± 0.03	5.84b ± 0.05	12.29b ± 0.05	12.24b ± 0.05	10.09b ± 0.06	10.05b ± 0.05	3992c ± 11.54	4109b ± 15.10	902c ± 11.45	928b ± 11.21
Seed rates
S1: 75 kg ha−1	5.98a ± 0.05	5.87a ± 0.04	12.41a ± 0.03	12.27a ± 0.05	10.19a ± 0.05	10.08a ± 0.04	3923c ± 10.21	3994b ± 12.09	895c ± 16.34	902b ± 13.21
S2: 90 kg ha−1	5.94b ± 0.07	5.85a ± 0.04	12.36b ± 0.04	12.24a ± 0.06	10.15b ± 0.07	10.05a ± 0.02	4106b ± 13.32	4192b ± 0.0611.88	937b ± 11.87	947b ± 11.13
S3: 105 kg ha−1	5.88c ± 0.05	5.80b ± 0.02	12.29c ± 0.06	12.18b ± 0.07	10.09c ± 0.04	10.00b ± 0.03	4452a ± 11.76	4569a ± 16.02	1015a ± 13.44	1032a ± 9.87

Note: Values are represented as mean ± standard error of the mean. NO₃, nitrate content; NO₃–N, nitrate–nitrogen content; 125% RDF, 150 kg N ha⁻¹: 50 kg P₂O₅ ha⁻¹: 50 kg K₂O ha⁻¹; 100% RDF, 120 kg N ha⁻¹: 40 kg P₂O₅ ha⁻¹: 40 kg K₂O ha⁻¹; 75% RDF, 90 kg N ha⁻¹: 30 kg P₂O₅ ha⁻¹: 30 kg K₂O ha⁻¹. Treatments having the same letter in superscript are not significantly different ($p<0.05$) as per the least significant difference test.

Abbreviations: DE, digestible energy; FYM, farmyard manure; ME, metabolizable energy; NEL, net energy for lactation; PGPR, plant growth-promoting rhizobacteria; RDF, recommended dose of fertilizer.

However, the difference in NEL was significant only in the 1st cut, with JHO-851 recording the maximum values, followed by Kent, while HFO-114 recorded the lowest value. This trend was also observed in the 1^st cut for DE and ME. The results indicate that NEL, DE, and ME were higher in the 1^st cut than in the 2^nd cut regardless of oat varieties, and there was no significant difference in nitrate and NO₃–N accumulation among the oat varieties.

The nutrient management practices significantly affected NEL, DE, and ME of oat varieties. The 125% RDF recorded the highest NEL, DE, and ME during both the 1^st and 2^nd cuts, with statistically similar results observed from 100% RDF, while the lowest energy values were recorded with 75% RDF plus PGPR during both cuts. The higher energy values with increased fertilizer application can be ascribed to greater nutrient availability and uptake, which supports higher energy content in the plant biomass [74, 75].

In addition, the maximum NO₃ and NO₃–N content were recorded from the plots where 125% RDF was applied, while the lowest values were observed with 75% RDF + PGPR nutrient management. This pattern aligns with findings that higher fertilizer rates often lead to increased nitrate accumulation in crops due to excessive nitrogen availability [76].

Among the different seed rates, the lowest seed rate of 75 kg ha⁻¹ recorded the maximum NEL, DE, and ME during both cuts. In contrast, the higher seed rate of 90 kg ha⁻¹ produced significantly lower NEL, DE, and ME during the 1st cut, while in the 2nd cut, it was at par with 75 kg ha⁻¹. The highest seed rate used in this experiment, 105 kg ha⁻¹, resulted in the lowest NEL, DE, and ME during both cuts. This inverse relationship between seed rate and energy values could be due to increased plant density leading to higher competition for resources, thereby reducing individual plant energy content [77]. Moreover, the maximum NO₃ and NO₃–N were recorded with the highest seed rate of 105 kg ha⁻¹ during both cuts, while the minimum values were observed with the seed rate of 75 kg ha⁻¹. Increased seed rates likely enhance nitrate accumulation due to greater nitrogen uptake and reduced nitrogen use efficiency per plant [78].

3.4. Correlation and PCA

The correlation analysis elaborated on the interrelationship between yield, quality, fiber fractions, nitrate, and NO₃–N content of oats during the 1^st cut, 2^nd cut, and on a total basis (Figure 4). GFY demonstrated a strong correlation with other quality-yield factors, exhibiting a robust positive correlation with DMY (r = 0.996, 0.991, and 0.978), CPY (r = 0.992, 0.971, and 0.967), EEY (r = 0.988, 0.953, and 0.940), and TAY (r = 0.992, 0.958, and 0.971) on an overall basis, 1^st cut, and 2^nd cut, respectively. In addition, DM, CP, EE, and TA contents also showed a strong positive correlation with GFY, suggesting that higher yields are associated with improved quality attributes. Similar findings were observed in studies on forage sorghum, where yield and quality traits such as CP and EE were positively correlated with overall fodder yield [71, 79].

[figure(s) omitted; refer to PDF]

Conversely, all three fiber fractions, i.e., NDF (r = −0.408, −0.082, −0.345), ADF (r = −0.526, −0.084, −0.252), and ADL (r = −0.849, −0.076, −0.338), had negative correlations with GFY on a total basis, 1st cut, and 2nd cut, respectively. This inverse relationship suggests that increased yield is typically associated with lower fiber content, enhancing the digestibility of the fodder. This phenomenon is supported by research on maize and other forage crops, where higher yields often correspond with reduced fiber fractions, leading to better-quality forage [79, 80]. The nitrate and NO₃–N content recorded a positive correlation with GFY, indicating that higher-yielding crops may also accumulate more nitrates, a common observation in intensively managed agricultural systems [76].

The PCA was conducted using all 15 variables, with 14 being quantitative and cutting management as the sole qualitative variable (Table 4 and Figure 5). PCA accounted for 82.6% of the total dataset inertia, with the first principal component (PC1) explaining 73.35% of the total variability, the second (PC2) describing 9.2%, and the third (PC3) describing 8.13% of the variability (Figure 5(a) and Table 4). High cos2 values, which indicate a good representation of the variable on the principal component, were noted in yield-linked quality attributes such as TAY, followed by EEY, CPY, GFY, and DM.

Table 4

Factor loading/eigenvectors of significant fodder quality attributes, fiber fractions, and nitrate accumulation of oat from PCA.

	Principal component.1	Principal component.2	Principal component.3
Variance	10.269	1.289	1.139
Percentage of variance explained	73.352	9.204	8.136
Cumulative percentage	73.352	82.556	90.692
Variables	Eigenvectors
GFY	0.966	0.219	−0.004
DM%	0.355	−0.194	0.617
DMY	0.973	0.186	0.044
CP%	0.961	0.012	−0.012
CPY	0.982	0.147	0.025
EE%	0.957	0.097	0.047
EEY	0.982	0.160	0.041
TA%	0.979	0.078	−0.112
TAY	0.984	0.150	−0.004
NDF%	−0.583	0.767	0.146
ADF%	−0.696	0.667	0.133
ADL%	−0.921	0.120	0.244
NO3	0.922	0.107	−0.003
NO3-N	0.205	−0.153	0.800

Note: NO₃, accumulated nitrate content in the plant (ppm); NO₃–N, nitrate–nitrogen in the plant (ppm).

Abbreviations: ADF%, acid detergent fiber (%); ADL%, acid detergent lignin (%); CP%, crude protein (%); CPY, crude protein yield (q ha⁻¹); DM%, dry matter (%); DMY, dry matter yield (q ha⁻¹); EE%, ether extract (%); EEY, ether extract yield (q ha⁻¹); GFY, green fodder yield (q ha⁻¹); NDF%, neutral detergent fiber (%); TA%, total ash (%); TAY, total ash yield (q ha⁻¹).

[figure(s) omitted; refer to PDF]

These findings suggest that these variables are well-represented in PC1, aligning with studies that emphasize the significance of these traits in determining overall fodder quality and yield [69, 77]. The quality content attributes, including TA%, ADF%, NDF%, EE%, and CP%, formed a second group with moderate cos2 values, indicating their moderate contribution to the variability explained by PC1. The third group consisted of ADL and NO₃, with the lowest cos2 values recorded for DM% and NO₃–N (Figure 5(b)). Positively correlated variables (GFY, DMY, CP%, CPY, EE%, EEY, TA%, and TAY) clustered together showed a positive correlation with PC1, whereas fiber fractions (ADF%, NDF%, and ADL%) had a negative correlation with quality attributes and PC1 but contributed more to PC2. These findings are consistent with the understanding that higher quality attributes are inversely related to fiber fractions, which tend to increase as plants mature and accumulate more structural carbohydrates (Figure 5(c)) [81, 82].

The TAY (TAY, 0.984), NDF (NDF%, 0.767), and DM (DM%, 0.617) acquired the highest loading factors under PC1, PC2, and PC3, respectively (Table 4). When cutting management was introduced into the plot, it was evident that the quality and yield attributes of the 1st cut formed a distinct group opposite to the fiber fraction group of the 2^nd cut. This observation highlights the differential impact of cutting stages on fodder quality and yield, with the first cut typically associated with higher quality and lower fiber content, while subsequent cuts often show increased fiber accumulation [83–85].

4. Conclusion and Limitation

Oat is a crucial winter forage crop valued for its high nutritional quality, though its usability is influenced by both optimal forage quality and nitrate accumulation. This study demonstrates that agronomic practices, including variety selection, nutrient management, and planting densities, play a significant role in enhancing forage quality while mitigating the risks of nitrate toxicity. Environmental variation between cropping years had a notable impact on results, with the first year yielding better outcomes due to more favorable conditions.

The experiment revealed that increasing the RDF by 25% improved yield, nutritional quality, and energy content, though it also led to a slight increase in nitrate levels. While NO₃–N levels below 1000 ppm are generally safe for all cattle, and levels between 1000 and 1500 ppm are considered safe for nonpregnant animals, the highest recorded NO₃–N content in this study (1089 ppm) occurred at the second cut under 125% RDF. Therefore, for multicut harvesting, 100% RDF is recommended for general use, while 125% RDF can be applied for higher biomass production intended for nonpregnant livestock. For a more sustainable approach with improved soil health, nutrient management involving 75% RDF + PGPR + FYM is advisable.

In addition, a seeding rate of 90 kg ha⁻¹ is recommended for achieving balanced biomass production with good nutritional quality and minimized nitrate accumulation. While this study provides insights into the interactions between study years, oat varieties, nutrient management, and seeding rates, long-term research over multiple growing seasons is necessary to validate these findings, and multilocation trials will further enhance the broader applicability of the recommendations.

Author Contributions

Prasanna S. Pyati, Phool Singh Hindorya, and Kanika Bhakuni have conducted the field and lab experiments. Rakesh Kumar, Hardev Ram, and Rajesh Kumar Meena have conceptualized the experiment. Subhradip Bhattacharjee has written the manuscript. Suryakanta Kashyap and Bisworanjita Biswal have conducted the statistical analysis. The rest of the authors coordinated the study, supervised the field and laboratory work, and contributed to the writing and editing of the manuscript.

Funding

This project was carried out under inhouse research project funded by ICAR-National Dairy Research Institute.

Acknowledgments

The authors express their gratitude to the Director of ICAR-National Dairy Research Institute, Karnal, Haryana, India, for providing the field and laboratory facilities essential for this research.

Glossary

Nomenclature

ADFAcid detergent fiber

ADLAcid detergent lignin

ANOVAAnalysis of variance

CPCrude protein

CPYCrude protein yield

DEDigestible energy

DMRTDuncan’s multiple range test

DMYDry matter yield

EEEther extract

EEYEther extract yield

FYMFarmyard manure

GFYGreen fodder yield

MEMetabolizable energy

NDFNeutral detergent fiber

NELNet energy for lactation

PGPRPlant growth-promoting rhizobacteria

RDFRecommended dose of fertilizer

TATotal ash

TAYTotal ash yield

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