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Article

Assessing Growth Performance and Agrometeorological Indices of Matricaria chamomilla L. Governed by Growing Season Length and Salicylic Acid in the Western Himalaya

by
Shalika Rathore
1,2 and
Rakesh Kumar
1,2,*
1
Agrotechnology Division, Council of Scientific and Industrial Research (CSIR)-Institute of Himalayan Bioresource Technology (IHBT), Palampur 176061, Himachal Pradesh, India
2
Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India
*
Author to whom correspondence should be addressed.
Horticulturae 2025, 11(5), 485; https://doi.org/10.3390/horticulturae11050485
Submission received: 21 February 2025 / Revised: 25 March 2025 / Accepted: 28 March 2025 / Published: 30 April 2025
(This article belongs to the Special Issue Breeding, Cultivation, and Metabolic Regulation of Medicinal Plants)
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Abstract

German chamomile (Matricaria chamomilla L.) is a suitable medicinal and aromatic crop to cultivate in diverse regions, but its relationship with weather is a major concern in evaluating the development and crop production in the Western Himalayan region. Thus, a field experiment was executed for two years (2018–2019 and 2019–2020) at CSIR-Institute of Himalayan Bioresource Technology, Palampur, India, to evaluate the crop weather relationship studies and different phenological phases of German chamomile under acidic soil conditions of mid hills of Western Himalaya. Agrometeorological indices were worked out for four different sowing times from 20 November to 20 January with foliar application of elicitor, i.e., salicylic acid at three levels (viz., SA0: 0 mg/L, SA1: 25 mg/L, SA2: 50 mg/L). The results revealed that the number of days required for attaining each phenological stage decreased with a delay in sowing time. Higher growing degree days (GDDs), photothermal units (PTUs) and heliothermal units (HTUs) were accumulated for early sowing of 20 November and showed a gradual decrease with delayed sowing. Salicylic acid application produced a significant effect on the accumulation of agrometeorological indices, irrespective of the applied doses, and showed irregularity. Higher accumulation of GDDs, PTUs, and HTUs is associated with higher flower and essential oil yield; thus, the results showed that agrometeorological indices are associated with the production of German chamomile.

Graphical Abstract

1. Introduction

Medicinal and aromatic plants (MAPs) have aided traditional medicine across the World for ages [1]. These plants have extensive utilization in food, perfumery, and cosmetics in addition to the herbal medicine sector [2]. German chamomile (Matricaria chamomilla L.) is one of the major MAPs (family: Asteraceae) in the world and has been used for hundreds of years in Greece, Egypt, and Rome in a variety of dietary/cosmetic applications and herbal medicine [3] and indigenous to the Mediterranean county, Africa, the United States, and Europe. German chamomile is now grown in various nations owing to the stipulating herbal industry’s demands. As a result, the plant is highly sought-after among herbal plants in the global market. Globally, Egypt is the largest producer, while Germany is the largest importer. In India, the cultivation of chamomile is not commercial, but the importance and elevated industrial potential have led the interest of growers towards the crop.
The earlier studies reported that the change in sowing times shifted the harvest timings, but essential oil and chamazulene content remained unchanged [4]; however, increased production of oxygenated compounds is associated with high temperatures, with delayed planting in comparison to earlier planting dates [5]. Additionally, increased temperature during blooming in later planting produced increased trans-β-farnesene content than normal conditions [6]. The highest flower weight, number of flowers, and essential oil content were obtained from 25 October sowing with a spacing of 40 × 10 cm in the dry region of Jodhpur, Rajasthan, India. Additionally, different sowing dates for chamomile produced diverse yield results [7]. Moreover, temperature increase over the recent decades has caused heat stress and reduced a few crops’ yields [8]; thus, adjustment of sowing time can mitigate adverse weather effects and is reported to increase cereal crop grain number and yield [9]. Sowing at the proper time in the region needs to be investigated for the inaugural growers, as the crop is non-native to the region. Moreover, a definite temperature is needed for the achievement of various phenophases, which can be determined through accumulated heat units [10].
German chamomile can withstand cold temperatures of 2 °C to 20 °C and is suitably grown in diverse soil types; only heavy and damp soils need to be avoided. The crop has been produced with great success on both poor (loamy sand) and alkaline soils (pH of 9.0) of Northern India. Additionally, it thrives extensively in clayey lime soils of the Hungarian region, which are considered poor for any other crop. Moreover, soil type has less impact on essential oils and azulene concentration than temperature and light conditions, such as sunlight hours [11]. The cooler conditions in Finland reported lower oxides in the essential oil when compared with Hungarian conditions [12,13]. The best temperature range for successful seed germination is between 10 °C and 20 °C, and transplanting of the crop from 10 October to 18 October was found to be the optimal time for higher yields and seed-setting at a 33 °C to 39 °C temperature range [13].
The previous findings indicated that temperature ranges throughout the blooming stage, including 25, 10, 5, and 0 °C, had an impact on a number of significant morpho-physiological properties [14]. Sowing time optimization with agrometeorological indices evaluation is requisite for successful crop cultivation [15,16]. Additionally, crop performance suitably be improved with the application of certain elicitors, which regulate different physiological processes by altering gene expression [17], eventually influencing plant growth and development. Thus, among a diversity of elicitors, a naturally occurring endogenous growth modulator in plants (minute quantities), i.e., salicylic acid (SA), known to aid in seed germination, flowering, fruit yield, and glycolysis in plants [18], plays role in stress tolerance (biological/abiological). The elicitation with SA has improved performance and yield of diverse herbs, viz., basil (Ocimum basilicum L.), marjoram (Majorana hortensis L.) [19], peppermint (Mentha piperita L.), and cornmint (Mentha arvensis L.) [20]. Temperature and photoperiod differences are documented to affect the occurrence of growth phases, growth, and biomass partitioning in wheat and soybean [21,22]. Thus, research was designed to ascertain German chamomile’s temperature regime and how it affects growth rates, agrometeorological indices accumulation in various environments through altered sowing times along with different elicitor levels.

2. Materials and Methods

2.1. Experimental Site

An experiment was executed under field conditions at Chandpur farm of the Agrotechnology Division of IHBT, Palampur during 2018–2019 and 2019–2020. This location is situated in the sub-humid region at 32°11′39″ N latitude and 76°56′51″ E longitude with an elevation of 1390 m above mean sea level. The soil was silty clay, acidic (pH 5.36) in reaction, and low in organic carbon (0.27%) with an electrical conductivity of 0.06 m mhos/cm. Soil showed medium available nitrogen (270.39 kg/ha), low phosphorus (5.45 kg/ha), and high potassium (234.31 kg/ha). Earlier to the seed sowing, farmyard manure (FYM) was directly added and mixed into the soil during ploughing at 15 t/ha two months before each sowing date during 2018 and 2019. FYM contained 0.32–0.41% N, 0.13–0.22% P2O5, and 0.21–0.29% K2O on a dry weight basis. Fertilizers were directly applied and mixed in the soil before the seed sowing during ploughing at 100 kg N/ha through urea (N: 46%), 60 kg P2O5/ha through single super phosphate (P2O5: 16%), and 40 kg K2O/ha through muriate of potash (K2O: 60%). Pre-sowing fertilization was applied with one-third of the nitrogen and full dosages of potassium and phosphorus during both crop growing years. The top dressing of the remaining nitrogen was given in equal quantities during the elongation and flower initiation stages.

2.2. Treatment Details

A factorial randomized block design (FRBD) was designed with 12 treatment combinations, replicated three times with a plot size of 3.6 m × 3.0 m (10.8 m2). The treatment combinations consisted of four sowing dates, viz., 20 November, S2: 10 December, S3: 30 December, and S4: 20 January, and foliar elicitor salicylic acid application at three levels, viz., SA0: 0 mg/L, SA1: 25 mg/L, SA2: 50 mg/L. Seed sowing was completed in lines with 40 cm spacing, and intercultural operations and weeding management were carried out as and when required through hand weeding. The seed rate of German chamomile at 1 kg/ha was calculated according to the plot size to maintain homogeneity in the experiment and mixed with sand (1:100 ratio) to make the seed distribution even and uniform. The salicylic acid (Sigma-Aldrich Germany, Darmstadt, Germany) was dissolved in ethanol and distilled water to prepare concentrations of 25 mg/L and 50 mg/L. SA and distilled water were foliar-sprayed before the bud formation stage. The spraying of the treatments was carried out from February to May, fortnightly after the first spraying. Harvesting was carried out manually, plucking the flowers at the full bloom stage at every 12–15 days’ intervals to minimize yield losses.

2.3. Observation Recorded

The meteorological data recorded by the Agrometeorology Observatory of CSKHPKV, Palampur, were collected from “Crop weather outlook” to compute agrometeorological indices using crop phenology, biomass, and yield measurements. The meteorological data illustrated in a previous publication from this research work [15] and not detailed here to avoid repetition, and only illustrated in Supplementary Figure S1. The agrometeorological indices, viz., GDD, PTU, and HTU, can be utilized successfully to describe the behavior of crop phenology and growth, such as leaf area, biomass, and oil production [23], and computed by using the formulas [24].

2.4. Agrometeorological Indices

Growing degree day (GDD °C day) = Σ[{(Tmax + Tmin)/2} − Tb]
where Tmax and Tmin are the daily maximum and minimum temperatures (°C), respectively.
Tb is the base temperature (2 °C), the temperature below which no plant physiological activity takes place; 2 °C for German chamomile is considered as base temperature [13].
Helio thermal unit (HTU °C day hour) = Σ(GDD × actual bright sunshine hours)
Photothermal unit (PTU °C day hour) = Σ(GDD × day length)

2.5. Growth Analysis Parameters

Using the above observations and leaf area, crop growth rate (CGR), relative growth rate (RGR), absolute growth rate (AGR), net assimilation rate (NAR), and leaf area duration (LAD) were calculated according to the classical formulas of growth analysis [25,26,27]. The complete details are given in the formulas in the Supplementary File.
  • Crop growth rate
CGR (g/m2/day) = (W2 − W1)/(t2 − t1)
  • Absolute growth rate (AGR)
  • The enhancement in plant height on more than one occasion for a time “t”, here, the increase in size over a given time period, can be determined and calculated by the following formula.
AGR (cm/day) = (H2 − H1)/(t2 − t1)
  • Relative growth rate (RGR)
  • The increase in plant dry weight per unit of material present per unit of time “t” was calculated by the following formula [28] (Radford, 1967):
RGR (g/g/day) = (LogeW2 − LogeW1)/(T2 − T1)
  • Net assimilation rate (NAR)
  • The increase in plant material, specifically, unit leaf area per unit time, calculated from the expression [28]:
NAR (g/m2/day) = [(Loge A2 − Loge A1)/(A2 − A1)] × (W2 − W1)/t2 − t1)
  • Leaf area duration (LAD)
  • LAD is an opportune and most suitable measure of approximating the photosynthetic efficiency of leaves and is calculated as per the formula suggested by [29].
LAD (days) = A0 + A1/2 (t1 − t0)

2.6. Yield and Yield Components and Essential Oil Components

The blooming started in late March and lasted until May; plucking was carried out from the core region at 12–15 days’ interval and then dried according to the mentioned procedure in German chamomile [15]. The thorough data related to flower yield, essential oil content (v/w%), and identification of essential oil constituents were detailed in an earlier publication [15] from the research and excluded from the present paper.

2.7. Statistical Analysis

Analysis of variance (ANOVA) for factorial randomized block design was performed on the data at a confidence level of 5% to observe if there were any significant differences among the sowing dates and elicitor level for the characters under study [30]. Additionally, correlation analysis was performed to determine the growth response of German chamomile to varying sowing dates and its interdependence with agrometeorological variables.

3. Results

3.1. Agrometeorological Indices

3.1.1. Growing Degree Days (GDDs)

The GDDs differ significantly among different sowing dates and phenological stages during both years (Table 1 and Supplementary Table S1). In the case of early sowing time, the crop acquired more accumulated GDDs to reach every phenological stage as compared to the late sowing of German chamomile during both years of study. From seed sowing to 100% flowering stage, accumulated GDDs ranged from 48.92 °C days to 1187.53 °C days during 2018–2019 and 38.67 °C days to 1246.67 °C days during 2019–2020 (Table 1). The GDD accumulation was highest in the 20 November sowing time and showed a decline with postponement in sowing times during both years of experimentation from sowing to flower bud formation and seed sowing to 100% flowering phenological stages, while other stages showed an irregular trend. During 2018–2019, significantly higher GDDs were accumulated on 20 November sowing time from seed sowing to 100% flowering stage (1187.53 °C days) compared to other sowing times. During 2019–2020, a similar trend was observed as in the previous year; significantly higher GDDs were accumulated in early sowing on 20 November from seed sowing to 100% flowering stage (1246.67 °C days) compared to delayed sowing times. The SA application significantly affected the accumulation of GDDs at a few of the phenological stages during both years (Table 1) and recorded higher GDD accumulation in the control compared to the SA application during both years from seed sowing to the 100% flowering stage.

3.1.2. Photothermal Units (PTUs)

The accumulated PTUs were evaluated for German chamomile during the seasons 2018–2019 and 2019–2020, and the results are detailed in Table 2 and Supplementary Table S1. During 2018–2019 and 2019–2020, from seed sowing to the 100% flowering stage, accumulated PTUs were significantly higher in early sowing time, i.e., 20 November (12,635.67 and 13,215.11 °C days h), respectively, compared to other sowing times. The SA produced a significant effect on PTU accumulation during both years, except at three stages during 2019–2020 (Table 2). Accumulated PTUs ranged from 708.32 °C days h to 10,255.58 °C days h during 2018–2019 and 604.17 °C days h to 13,215.11 °C days h during 2019–2020, respectively. During both years, from seed sowing to the 100% flowering stage, significantly higher PTUs were accumulated in the control compared to other SA application levels. For seed sowing to 100% flowering, PTUs decreased with delay in sowing and accumulated the highest at early sowing.

3.1.3. Heliothermal Units (HTUs)

HTU accumulation by different phenophases of German chamomile is detailed in Table 3 and Supplementary Table S1. Accumulation of HTUs for different phenophases was significantly higher under early sowing than late sowing time, except for flower bud formation to 50% budding, 50% budding to flower initiation, and flower initiation to 50% flowering. During both years, accumulated HTUs were highest in early sowing time, i.e., from 20 November at seed sowing to the 100% flowering stage, compared to other sowing times. Longer phenophases resulted in higher HTU accumulation by timely sowing of the crop than late sowing. Similar to GDDs, fewer HTUs were accumulated in late-sown crops with shorter phenophases. The SA application produced a significant effect on HTU accumulation at different phenophases in an irregular manner. During 2018–2019, significantly higher HTUs were accumulated in the control, while during the second growth year, SA at 25 mg/L (6964.00 °C days h) recorded higher HTUs compared to SA at 50 mg/L and the control from seed sowing to the 100% flowering stage.

3.2. Growth Analysis Parameters

3.2.1. Crop Growth Rate (CGR)

The CGR of German chamomile was significantly influenced by the date of sowing at different phenological stages during both years (Table 4). During 2018–2019, in general, CGR showed a considerable decrease in value at bud formation to the 50% flowering stage and from seed emergence to the bud formation stage, but showed a substantial increase at the 50 to 100% flowering stage. However, from bud formation to the 50% flowering stage, the earliest sowing time on 20 November produced the highest CGR (1.16 g/m2/day) and thereafter, considerably declined with delayed sowing, i.e., 10 December to 20 January. Conversely, penultimate sowing on 30 December (27.22 g/m2/day) obtained the highest CGR from the 50 to 100% flowering stage compared to other sowing times. During the second growth year, CGR showed a declining trend with a delay in sowing time from seed emergence to the bud formation stage. CGR on early sowing on 20 November (5.10 g/m2/day) was highest and trailed by the following sowing times. The CGR was significantly affected by SA levels only at the 50 to 100% flowering stage during 2018–2019 and the seed emergence to bud formation stage during 2019–2020. During 2018–2019, CGR was highest with a higher dose of SA at 50 mg/L (20.70 g/m2/day) compared to the control, but behaved statistically alike with a lower dose, i.e., SA at 25 mg/L (20.23 g/m2/day). Likewise, during 2019–2020, SA at 50 mg/L produced statistically highest CGR (4.82 g/m2/day) compared to the control (4.40 g/m2/day) and SA at 25 mg/L (4.55 g/m2/day), but latter two remained at par with each other at seed emergence to bud formation stage.

3.2.2. Relative Growth Rate (RGR)

During 2018–2019, data indicated that RGR was highest at early sowing time, i.e., 20 November and then declined continuously with delay in sowing times (Table 5). From seed emergence to bud formation and from 50% flowering to 100% flowering stage, the RGR was significantly higher on 20 November (5.25 and 5.28 g/g/day) compared to other delayed sowing times. Likewise, during 2019–2020, from seed emergence to bud formation and from bud formation to the 50% flowering stage, RGR was highest on early sowing time on 20 November (5.17 and 5.34 g/g/day), respectively, but showed a subsequent decrease with delay in sowing time. The SA application significantly affected the RGR during both years, except from 50% flowering to the 100% flowering stage during 2019–2020 (Table 6). During the first growth year, higher application of SA at 50 mg/L recorded the highest RGR (5.22 and 5.34 g/g/day) compared to other SA levels at bud formation to 50% flowering and 50 to 100% flowering stage, respectively. During the second growth year, from seed emergence to the bud formation stage and bud formation to the 50% flowering stage, RGR was significantly higher with a maximum dose of SA application at 50 mg/L (5.07 and 5.30 g/g/day) but declined with decreasing dose at 25 mg/L SA (5.02 and 5.27 g/g/day) and control (5.01 and 5.25 g/g/day).

3.2.3. Absolute Growth Rate (AGR)

During 2018–2019, from bud formation to the 50% flowering stage, 20 November (1.88 cm/day) recorded a significant increase in AGR and evidenced a decline with delay in sowing (Table 6). However, from 50 to 100% flowering, 30 December (3.63 cm/day) recorded significantly higher AGR compared to other sowing times. Also, during 2019–2020, from seed emergence to bud formation stage and bud formation to the 50% flowering stage, earliest sowing on 20 November recorded the highest AGR (0.76 and 2.10 cm/day), while 30 December (2.65 cm/day) recorded significantly higher AGR compared to other sowing times. The SA application significantly affected the AGR during both years, only from bud formation to the 50% flowering stage. In general, an increment in AGR was observed with the enhancement of SA application and recorded maximum values. SA application at 50 mg/L observed significantly higher AGR (1.55 and 1.64 cm/day) compared to control (0.83 and 1.07 cm/day) but evidenced no statistical dissimilarity with lower SA dose at 25 mg/L (1.38 and 1.50 cm/day), during 2018–2019 and 2019–2020, respectively.

3.2.4. Net Assimilation Rate (NAR)

During 2018–2019, from seed emergence to bud formation and 50 to 100% flowering stage, the highest NAR was recorded on 30 December (2.91 and 27.22 g/m2/day) compared to other sowing times (Table 7). Conversely, at bud formation to the 50% flowering stage, 20 November (3.33 g/m2/day) recorded the highest NAR and showed a considerable decline with delay in sowing time. During 2019–2020, early sowing increased NAR, thereafter, showing a subsequent decrease with late sowings. From seed emergence to the bud formation stage, a significantly higher NAR was recorded on 20 November (5.10 g/m2/day), while from the 50 to 100% flowering stage, the NAR was significantly higher on 30 December (20.65 g/m2/day) compared to other sowing times. The SA application had a significant effect on the NAR only from the 50 to 100% flowering stage during 2018–2019 and from seed emergence to the bud formation stage during 2019–2020. SA application at 50 mg/L recorded the highest NAR during 2018–2019 at the 50 to 100% flowering stage and at seed emergence to the bud formation stage during 2019–2020.

3.2.5. Leaf Area Duration (LAD)

The collected data demonstrated a significant effect on LAD by varied sowing dates at different phenological stages during both years (Table 8). The maximum LAD was achieved by early sowing on 20 November, and the minimum LAD was observed by late sowing on 20 January during both years till the 50% flowering stage. During the first growth year, from seed emergence to bud formation and bud formation to 50% flowering stage, LAD was significantly higher at 20 November (147.53 and 198.57 days) sowing time, while from the 50 to 100% flowering stage, LAD was highest on 10 December (132.78 days) sowing time. During 2019–2020, from seed emergence to the bud formation stage and bud formation to the 50% flowering stage, LAD declined with a delay in sowing from 20 November to 20 January. Furthermore, 10 December (145.66 days) detailed a significant increase in LAD trailed by 20 November, 20 January, and 30 December sowing from 50 to 100% flowering stage. SA application produced a significant effect on LAD, and the maximum LAD was achieved by a higher dose of SA (50 mg/L), followed by SA at 25 mg/L and control during both years.

3.3. Yield and Essential Oil Components

The yield components and essential oil composition are comprehensively detailed and discussed in an earlier published research paper from this experiment [15] (Rathore and Kumar, 2021) but not extensively detailed in this study. However, the data on phenological stages, dry flower yield, interaction effect, essential oil content, and essential oil constituents during the two years of the experiment are tabulated in Supplementary Tables S1–S5.

3.4. Correlation and Regression Equations

The weather factors, particularly temperature, impact crop phenology and several different agrometeorological indices. The agrometeorological indices had a significant positive and negative correlation with essential oil yield at different phenological stages during both years (Table 9). Essential oil yield had a highly significant positive correlation at p < 0.01 with GDDs during sowing to bud formation (0.847 and 0.773) and sowing to full flowering (0.834 and 0.768) phenophases of crop development during 2018–2019 and 2019–2020, respectively. The thermal duration is a helpful indicator of crop yield potential in a given environment. Photothermal units displayed a significant positive correlation (p < 0.01) with essential oil yield (0.839) during sowing to bud formation and a significant positive correlation (p < 0.05) with essential oil yield (0.661) during sowing to full flowering in the first year. Similarly, accumulated HTUs also exhibited significant positive correlation (p < 0.01) with essential oil yield at sowing to bud formation, flower opening to full flowering, and sowing to full flowering (with correlation coefficient 0.812, 0.770, and 0.784) during 2018–2019. However, during 2019–2020, essential oil yield exhibited a significant positive correlation (p < 0.05) during sowing to bud formation (0.702) and sowing to full flowering (0.690). The thermal indices detail the amount of heat energy and sunshine hours consumed at different phenological stages. All the agrometeorological indices showed significant positive correlation at the 0.01 and 0.05 level of significance (Figure 1). GDD, PTU, HTU, DL, and BSS showed significant positive correlation at the 0.01 level of significance with each other, except for HTUs with RTD, where correlation was significant at the 0.05 level of significance (Figure 1). The relationship of GDD, PTU, and HTU with essential oil yield during 2018–2019 and 2019–2020 is presented through a regression equation in Figure 2. The equation revealed that essential oil yield showed a positive linear relationship with GDD, PTU, and HTU, which disclosed that increased accumulation of thermal and heat units directly increased the yield.

4. Discussion

4.1. Agrometeorological Indices

4.1.1. Growing Degree Days (GDDs)

The GDD concept explains the relationship between the growth period and temperature and assumes a direct relationship between growth and temperature [31]. During both years, GDDs were significantly higher in early sowing time (20 November) compared to late sowing times (10 December, 30 December, and 20 January). This might be due to the longer time period taken to develop each phenological stage in earlier sowing times compared to late sowings. The late sowing times subjected the plants to short crop growth duration, which might have led to lesser heat unit accumulation and eventually lower GDD. The results coincide with the earlier findings on Trifolium repens [24] and Tagetes minuta [32] with decreased GDD accumulation on delayed sowing. A continuing delay in sowing shortened the phenophases, reduced GDD accumulation across different phenophases, and forced the crop to mature early [33]; this might be because of low and high temperatures prevailing during the vegetative and reproductive phases, respectively [34]. Similar results with a decreased GDD in late sowing were also demonstrated by [35] and [36] in brassica and amaranth, respectively. Additionally, the late planting experienced higher temperatures for a short period during the later stage of crop growth, which eventually resulted in GDD reduction [37]. Accumulation of GDDs significantly reduced with an increase in SA application at the majority of the stages and at complete flowering. The days taken to accomplish physiological maturity in the present study were significantly higher in control compared to applied SA levels, while significantly higher GDDs and HUE in brassica species were recorded by [37] at 100 ppm SA application compared to control. Accumulation of GDDs significantly reduced with an increase in SA application at seed sowing to 100% flowering stage, while showing an irregular trend at other stages. Furthermore, it is well-known that crop phenology is strongly influenced by environmental and genetic variables, such as temperature, relative humidity, sunshine hours, and rainfall [38]. However, the SA application might have a function in early flowering in plants, which eventually results in less GDD accumulation by reducing the time period.

4.1.2. Photothermal Units (PTUs)

The photothermal unit is a consistent marker of crop progress aimed at predicting the crop yield. Accumulation of PTUs was significantly higher in early sowing time (20 November) compared to late sowing times at the 100% flowering stage, during both years. The higher accumulated PTUs might be because the crop took a longer duration to enter each phenological stage. Similar findings with higher PTU accumulation were reported at October 10 sowing time in mustard than at later sowing times [39]. A steady reduction in PTUs was observed with delayed sowing of German chamomile from 20 November to 20 January and corroborating the findings in wheat [40]. Likewise, the early-sown black gram in the first week of March recorded more GDDs, PTUs, and HTUs than the later-sown crop [41]. Delays in sowing may prevent appropriate vegetative growth, and the crop might be subjected to higher temperatures during later stages of growth, resulting in early maturity and low production. Significant reduction in PTU accumulation was recorded with an increase in SA levels at seed sowing to 100% flowering, while it showed an irregular trend in other phenological stages. The results implicated negligible relevance of SA application in temperature regulation of the crop, thus showing an irregular trend in PTU accumulation. However, reported enhancement of antioxidative enzyme activity with SA application had direct as well as indirect effects in improving the photosynthetic efficiency, metabolism, and growth of plants, which might have altered PTU accumulation [42].

4.1.3. Heliothermal Units (HTUs)

Accumulated HTUs were significantly higher in early sowing (20 November) compared to late sowings. Early-sown crops resulted in the accumulation of more sunshine hours during crop development, owing to prolonged growth duration, which resulted in higher HTU accumulation as compared to delayed sowing times. Sowing on 20 January accumulated less HTUs because of delayed sowing, thus requiring fewer days to reach the 100% flowering stage from sowing. Likewise, early-sown white clover accumulated higher heat units from sowing to maturity than the late-sown crop [24]. The postponed sowing was reported to reduce HTUs at various phenological stages in wheat [43], barley [44], and rapeseed [45]. Furthermore, the variation in bright sunshine in varied sowing times led to a discrepancy in HTUs because it is essentially the product of GDDs and the bright sunshine period (average) for a particular spell. Significantly reduced HTU accumulation was recorded with an increase in SA application at seed sowing to 100% flowering, while showing irregular trends in other phenological stages. In contrast, SA at 100 ppm increased HTU accumulation in wheat over no SA application [38]. Similarly, comparable results were reported in wheat with SA application [46].

4.2. Growth Analysis Parameters

Growth analysis parameters like CGR, RGR, AGR, NAR, and LAD were influenced by different sowing times at different growth stages. The delayed sowing time of 30 December recorded 50.88 and 44.74% higher CGR at 50% to 100% flowering stage compared to 10 December during 2018–2019 and 2019–2020, respectively. CGR measured the mass increase in crop biomass per unit of ground area per unit time. The sowing time of 30 December resulted in greater CGR during the 50% to 100% flowering stage of growth in both years, possibly due to a speedy growth in later sowing time than early-sown crop that resulted in an increase in biomass accumulation in lesser time, consequently increased the CGR in 30 December sowing time. RGR is the rate of growth or accumulation of new dry mass per unit of existing dry mass and is recorded higher during early sowing in 20 November and 10 December, probably because of more conducive weather conditions like sunshine hours, rainfall, and temperature. The discrepancy in growth attributes of German chamomile owing to diverse environmental conditions at different sowing times contributed to the overall variation in crop duration. Corroborating the present findings, higher LAI, PAR accumulation, and root growth attributes contributed to increased and faster cell division/enlargement, eventually leading to more biomass accumulation and increased RGR [47]. AGR, measurement of total growth per unit time, was higher in 20 November sowing at bud formation to 50% flowering stage, whereas the sowing time of 30 December recorded higher AGR at 50% to 100% flowering stage, which might be due to relatively higher temperature during the later phases of cropping cycle, which accelerated crop growth in late-sown crop than early sown. NAR is the rate of increase in dry weight per unit of leaf area and unit time, while LAD measures the volume of ground covered relative to upper leaf surface area against time. NAR and LAD were recorded highest on 20 November at bud formation to 50% flowering stage, whereas 30 December recorded the highest NAR and 10 December recorded the highest LAD at 50% to 100% flowering stage. The increase in NAR in late-sown crops during the 50% to 100% flowering stage is owing to longer day length during the crop growth period, which contributed to rapid growth and increased biomass in this particular stage. The enhancement in LAD (integrated increase in leaf area and longevity) was associated positively with an upsurge in dry matter at bud formation to 50% flowering and 50% to 100% flowering stage in early sowing times, i.e., 20 November and 10 December. The dry matter accumulation increase was achieved by a reasonable increase in LAI and collective above ground plant growth to promote LAD. The increase in LAD with an increase in dry matter accumulation is also detailed by [48] in maize and [49] in wheat, barley, and oats. Similarly, LAD and CGR were earlier studied in soybean [50,51], and reports are also available on CGR, RGR, and NAR in rice [52]. The significant increase in CGR, RGR, AGR, NAR, and LAD with SA application is similar to earlier findings in wheat [53,54] and maize [55]. The increase in growth analysis parameters might be because of the role of SA in plant metabolism, increasing the photosynthetic efficiency, and resource allocation to growth processes. Moreover, foliar application of SA under stressed conditions improved LAI, CGR, and NAR in wheat [56] and CGR, RGR, and NAR in cotton [57]. The enhancement in growth analysis parameters may be due to triggering the antioxidant machinery and its effect on membrane permeability and lipid peroxidation. Furthermore, it also prevents the lowering of cytokinin and indole acetic acid content, which consequently reduces growth inhibition in plants [58,59].

4.3. Correlation and Regression Analysis

All the agrometeorological indices showed significant positive correlation with essential oil yield at the 0.01 and 0.05 level of significance from sowing to the full flowering stage (Table 4). The finding showed that the agrometeorological indices accumulation is directly associated and positively enhances the essential oil yield. Similar positive correlation of agrometeorological indices with seed and biological yield was reported in Indian mustard [16] and cotton [60]. A significant positive correlation between agrometeorological indices and yield is regarded as suitable for a crop corresponding to the best yield response. GDD, PTU, HTU, DL, and BSS showed significant positive correlation at the 0.01 level of significance (Figure 1), which indicated that the indices are associated directly with one another and affect the accumulation of heat units. The regression relationship of GDD, PTU, and HTU with essential oil yield in Figure 2 showed an increase in yield with an increase in heat unit accumulation. Agrometeorological indices and regression relationship assessment were earlier detailed in corn [61] and wheat [62] through models. It is important for crop yield estimation in diverse crops to analyze and explain the factors affecting crop yield. The earlier findings on cotton [63], wheat [64], and rice [65] reported a relationship of agrometeorological indices and regression analysis with crop yield.

5. Conclusions

The understanding of agrometeorological indices and their correlation with growth and yield is crucial for evaluating crop performance and maximizing yield. The early sowing in 20 November exposes the crop to utilize the external environmental conditions to its maximum and leads to maximum GDD, PTU, and HTU accumulation at the majority of the phenophases, thus, had a significant positive effect on yield. Additionally, the studied growth analysis revealed that early sowing of German chamomile recorded improved CGR, RGR, and AGR compared to the late-sown crop, and these growth parameters are associated with enhanced plant performance. Early sowing of German chamomile coupled with salicylic acid application is a suitable preference to achieve increased crop productivity in the Western Himalayan region of India. The potential advantage of agrometeorological indices evaluation led to the prediction of German chamomile performance and yield prior to crop cultivation in other non-native regions and helpful for the farmers.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/horticulturae11050485/s1. The supporting information, supplementary Tables S1–S5 and Figure S1, is also added. Detailed formulas have also been added in the supplementary section.

Author Contributions

S.R.: formal analysis, data observation, data compilation, statistical analysis, data curation, data presentation, validation, literature search, and writing—review and editing; R.K.: conceptualization, methodology, project administration, supervision, data curation, validation, and review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data can be obtained by contacting the corresponding author.

Acknowledgments

The authors are grateful to the Director, CSIR-IHBT, Palampur, India, for providing necessary facilities during the study. The authors are also thankful to Kuldip Singh for providing technical support in carrying out the research work. Financial grants from the Council of Scientific and Industrial Research, New Delhi, India, under the CSIR Aroma mission project (HCP-0007), are also acknowledged. This is the IHBT communication number 5718.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:
TLAThree-letter acronym
LDLinear dichroism
CSIRCouncil of Scientific and Industrial Research
IHBTInstitute of Himalayan Bioresource Technology
GDDsGrowing degree days
PTUsPhotothermal units
HTUsHeliothermal units
MAPsMedicinal and aromatic plants
cmCentimeter
°CDegree Celsius
pHPotential of hydrogen
%Percentage
kg/haKilogram per hectare
t/haTons per hectare
FYMFarmyard manure
NNitrogen
P2O5Phosphorus pentoxide
K2OPotassium oxide
FRBDFactorial randomized block design
m Meter
m2Square meter
SASalicylic acid
mg/LMilligram per liter
CSKHPKVChaudhary Sarwan Kumar Himachal Pradesh Krishi Vishvavidyalaya
HUEHeat use efficiency
CGRCrop growth rate
RGRRelative growth rate
AGRAbsolute growth rate
NARNet assimilation rate
LADLeaf area duration
v/w%Volume/weight
SEm±Standard error of mean
LSDLeast significant difference
NSNot significant
°C days hDegree Celsius days hour
gGram
cmCentimeter
DLDay length
BSSBright sunshine
RHRelative humidity
ppmParts per million

References

  1. Gurib-Fakim, A. Medicinal plants: Traditions of yesterday and drugs of tomorrow. Mol. Asp. Med. 2006, 27, 1–93. [Google Scholar] [CrossRef]
  2. Zouaoui, N.; Chenchouni, H.; Bouguerra, A.; Massouras, T.; Barkat, M. Characterization of volatile organic compounds from six aromatic and medicinal plant species growing wild in North African drylands. NFS J. 2020, 18, 19–28. [Google Scholar] [CrossRef]
  3. Mann, C.; Staba, E.J. The chemistry, pharmacology and commercial formulations of chamomile. In Herbs, Spices and Medicinal Plants- Recent Advances in Botany, Horticulture and Pharmacology; Craker, L.E., Simon, J.E., Eds.; Haworth Press Inc.: Binghamton, NY, USA, 2002; pp. 235–280. [Google Scholar]
  4. Zalecki, R. Cultivation and fertilizing of the tetraploid Matricaria chamomilla L.I. The sowing time. Herba Pol. 1971, 17, 367–375. [Google Scholar]
  5. Rafieiolhossaini, M.; Sodaeizadeh, H.; Adams, A.; De Kimpe, N.; Van Damme, P. Effects of planting date and seedling age on agro-morphological characteristics, essential oil content and composition of German chamomile (Matricaria chamomilla L.) grown in Belgium. Ind. Crops Prod. 2010, 31, 145–152. [Google Scholar]
  6. Ghasemi, M.; Jelodar, N.B.; Modarresi, M.; Bagheri, N.; Jamali, A. Increase of chamazulene and α-bisabolol contents of the essential oil of german chamomile (Matricaria chamomila L.) using salicylic acid treatments under normal and heat stress conditions. Foods 2016, 5, 56. [Google Scholar]
  7. Mehriya, M.L.; Singh, D.; Verma, A.; Saxena, S.N.; Alataway, A.; Al-Othman, A.A.; Dewidar, A.Z.; Mattar, M.A. Effect of date of sowing and spacing of plants on yield and quality of chamomile (Matricaria chamomilla L.) grown in an arid environment. Agronomy 2022, 12, 2912. [Google Scholar] [CrossRef]
  8. Xiao, D.P.; Tao, F.L. Contributions of cultivar shift, management practice and climate change to maize yield in North China Plain in 1981–2009. Int. J. Biometeorol. 2016, 60, 1111–1122. [Google Scholar] [CrossRef]
  9. Zhou, B.Y.; Yue, Y.; Sun, X.F.; Wang, X.B.; Wang, Z.M.; Ma, W. Maize grain yield and dry matter production responses to variations in weather conditions. Agron. J. 2016, 108, 196–204. [Google Scholar] [CrossRef]
  10. Gouri, V.; Reddy, D.R.; Rao, S.B.S.N.; Rao, A.Y. Thermal requirement of rabi groundnut in southern Telangana zone of Andhra Pradesh. J. Agrometeorol. 2005, 7, 90–94. [Google Scholar]
  11. Kerches, J. Experiments with the cultivation of chamomile (Matricaria chamomilla). Herba Hung. 1966, 5, 141–147. [Google Scholar]
  12. Galambosi, B.; Holm, Y.; Szebeni Galambosi, Z.S.; Repcak, M.; Cernaj, P. The effect of spring sowing times and spacing on the yield and essential oil of chamomile (Chamomilla recutita L.) cv. ’Bona’ grown in Finland. Herba Hung. 1991, 10, 47–53. [Google Scholar]
  13. Singh, O.; Khanam, Z.; Misra, N.; Srivastava, M.K. Chamomile (Matricaria chamomilla L.). An overview. Pharmacognocy Rev. 2011, 5, 82–95. [Google Scholar] [CrossRef]
  14. Bagheri, R.; Dehdari, M.; Salehi, A. Effect of cold stress at flowering stage on some important characters of five German chamomile (Matricaria chamomilla L.) genotypes in a pot experiment. J. Appl. Res. Med. Aromat. Plants 2019, 16, 100228. [Google Scholar] [CrossRef]
  15. Rathore, S.; Kumar, R. Agronomic interventions affect the growth, yield, and essential oil composition of German chamomile (Matricaria chamomilla L.) in the western Himalaya. Ind. Crops Prod. 2021, 171, 113873. [Google Scholar] [CrossRef]
  16. Kumar, P.; Kumar, S.; Hooda, V.S.; Neelam; Kumar, A.; Thakral, S.K.; Kumar, S.; Kumar, P. Agrometeorological indices influenced by different sowing dates, irrigation and fertilizer levels under late sown Indian mustard in western Haryana, India. J. Agrometeorol. 2022, 24, 172–178. [Google Scholar]
  17. Broeckling, C.D.; Huhman, D.V.; Farag, M.A.; Smith, J.T.; May, G.D.; Mendes, P.; Dixon, R.A.; Sumner, L.W. Metabolic profiling of Medicago truncatula cell cultures reveals the effects of biotic and abiotic elicitors on metabolism. J. Exp. Bot. 2005, 56, 323–336. [Google Scholar] [CrossRef]
  18. Es-sbihi, F.Z.; Hazzoumi, Z.; Joutei, K.A. Effect of salicylic acid foliar application on growth, glandular hairs and essential oil yield in Salvia offcinalis L. grown under zinc stress. Chem. Biol. Technol. Agric. 2020, 7, 26. [Google Scholar] [CrossRef]
  19. Gharib, F.A.L. Effect of salicylic acid on the growth, metabolic activities and oil content of basil and marjoram. Int. J. Agric. Biol. 2007, 9, 294–301. [Google Scholar]
  20. Haydari, M.; Maresca, V.; Rigano, D.; Taleei, A.; Shahnejat-Bushehri, A.A.; Hadian, J.; Sorbo, S.; Guida, M.; Manna, C.; Piscopo, M.; et al. Salicylic acid and melatonin alleviate the effects of heat stress on essential oil composition and antioxidant enzyme activity in Mentha piperita and Mentha arvensis L. Antioxidants 2019, 8, 547. [Google Scholar] [CrossRef]
  21. Patra, B.K.; Sahu, D.D. Use of agrometeorological indices for suitable sowing time of wheat under South Saurashtra Agroclimatic Zone of Gujarat. J. Agrometeorol. 2007, 9, 74–80. [Google Scholar]
  22. Singh, A.; Rao, V.U.M.; Singh, D.; Singh, R. Study on agrometeorological indices for soybean crop under different growing environments. J. Agrometeorol. 2007, 9, 81–85. [Google Scholar] [CrossRef]
  23. Neog, P.; Chakravarty, N.V.K.; Srivastava, A.K.; Bhagavati, G.; Katiyar, R.K.; Singh, H.B. Thermal time and its relationship with seed yield and oil productivity in Brassica cultivars. Brassica 2005, 7, 63–70. [Google Scholar]
  24. Kumar, R.; Kaundal, M.; Vats, S.K.; Kumar, S. Agrometeorological indices of white clover (Trifolium repens) in western Himalayas. J. Agrometeorol. 2012, 14, 138–142. [Google Scholar] [CrossRef]
  25. Gregory, F.G. Physiological Conditions in Cucumber Houses; 3rd Annual Report; Experimental and Research Station: Cheshunt, UK, 1917; pp. 19–28. [Google Scholar]
  26. Blackman, V.H. The compound interest law and plant growth. Ann. Bot. 1919, 33, 353–360. [Google Scholar] [CrossRef]
  27. Briggs, G.E.; Kidd, R.; West, C. A quantitative analysis of plant growth. Part I. Ann. Appl. Biol. 1920, 7, 103–123. [Google Scholar] [CrossRef]
  28. Radford, P.J. Growth Analysis Formulae: Their Use and Abuse. Crop Sci. 1967, 7, 171–175. [Google Scholar] [CrossRef]
  29. Power, J.F.; Willis, W.O.; Gunes, D.L.; Peichman, G.A. Effect of soil temperature, phosphorus and plant age on growth analysis of barley. Agron. J. 1967, 59, 231–234. [Google Scholar] [CrossRef]
  30. Gomez, K.A.; Gomez, A.A. Analysis of data from a series of experiments. In Statistical Procedures for Agricultural Research, 2nd ed.; John Wiley & Sons: New York, NY, USA, 1984; pp. 316–356. [Google Scholar]
  31. Nuttonson, M.Y. Wheat-Climate Relationships and the Use of Phenology in Ascertaining the Thermal and Photo-Thermal Requirements of Wheat; American Institute of Crop Ecology: Washington DC, USA, 1955; Volume vii, 388p. [Google Scholar]
  32. Kumar, R.; Ramesh, K.; Pathania, V.; Singh, B. Effect of transplanting date on growth, yield and oil quality of Tagetes minuta L. in mid hill of North–Western Himalaya. J. Essent. Oil Bear. Plants 2012, 15, 405–414. [Google Scholar] [CrossRef]
  33. Prajapat, A.L.; Saxena, R. Thermal requirements of wheat (Triticum aestivum L.) cultivars under different growing environments. Int. J. Chem. Stud. 2018, 6, 17–22. [Google Scholar]
  34. Khichar, M.L.; Niwas, R. Thermal effect on growth and yield of wheat under different sowing environments and planting systems. Indian J. Agric. Res. 2007, 41, 92–96. [Google Scholar]
  35. Roy, S.; Meena, R.L.; Sharma, K.C.; Kumar, V.; Chattopadhyay, C.; Khan, S.A.; Chakravarthy, N.V.K. Thermal requirement of oilseed Brassica cultivars at different phenological stages under varying environmental conditions. Indian J. Agric. Sci. 2005, 75, 717–721. [Google Scholar]
  36. Murty, N.S.; Singh, R.K.; Roy, S. Influence of weather parameters on growth and yield of Amaranth in Uttarakhand region. J. Agrometeorol. 2008, 10, 384–387. [Google Scholar]
  37. Muhal, S.; Solanki, N.S. Effect of seeding dates and salicylic acid foliar spray on growth, yield, phenology and agrometeorological indices of Brassica species. J. Oilseed Brassica 2015, 6, 183–190. [Google Scholar]
  38. Choudhary, R.N.; Suthar, K.J.; Mevada, K.D.; Singh, S.; Mahariya, V.D.; Doba, S.D. Agro-meteorological indices, phenological stages and productivity of durum wheat (Triticum durum Desf.) influenced by seed soaking and foliar spray of stress mitigating bio-regulators under conserved moisture condition. Pharma Innov. J. 2021, 10, 1138–1146. [Google Scholar] [CrossRef]
  39. Choudhary, D.; Singh, R.; Dagar, C.C.; Kumar, A.; Singh, S. Temperature based agrometeorological indices for Indian mustard under different growing environments in western Haryana, India. Int. J. Curr. Microbiol. Appl. Sci. 2018, 7, 1025–1035. [Google Scholar] [CrossRef]
  40. Pathania, R.; Prasad, R.; Singh, R.; Mishra, S.K. Heat unit requirement and yield of wheat (Triticum aestivum L.) varieties under different growing environment in mid hill conditions of Himachal Pradesh. J. Agrometeorol. 2019, 21, 282–287. [Google Scholar]
  41. Banerjee, P.; Mukherjee, B.; Venugopalan, V.K.; Nath, R.; Chandran, M.A.S.; Dessoky, E.S.; Ismail, I.A.; El-Hallous, E.I.; Hossain, A. Thermal Response of Spring–Summer-Grown Black Gram (Vigna mungo L. Hepper) in Indian Subtropics. Atmosphere 2021, 12, 1489. [Google Scholar] [CrossRef]
  42. Hayat, S.; Masood, A.; Yusuf, M.; Fariduddin, Q.; Ahmad, A. Growth of Indian mustard (Brassica juncea L.) in response to salicylic acid under high-temperature stress. Braz. Soc. Plant Physiol. 2009, 21, 187–195. [Google Scholar] [CrossRef]
  43. Haider, S.A.; Alam, M.Z.; Alam, M.F.; Paul, N.K. Influence of different sowing dates on the phenology and accumulated heat units in wheat. J. Biol. Sci. 2003, 3, 932–939. [Google Scholar] [CrossRef]
  44. Alam, M.Z.; Haider, S.A.; Paul, N.K. Influence of sowing date and nitrogen fertilizer on the phenology and accumulated heat units in barley. Plant Environ. Dev. 2007, 1, 75–81. [Google Scholar]
  45. Akhter, M.T.; Mannan, M.A.; Kundu, P.B.; Paul, N.K. Effects of different sowing dates on the phenology and accumulated heat units in three rapeseed (Brassica campestris L.) varieties. Bangladesh J. Bot. 2015, 44, 97–101. [Google Scholar] [CrossRef]
  46. Amrawat, T.; Solanki, N.S.; Sharma, S.K.; Jajoria, D.K.; Dotaniya, M.L. Phenology growth and yield of wheat in relation to agrometeorological indices under different sowing dates. Afr. J. Agric. Res. 2013, 8, 6366–6374. [Google Scholar] [CrossRef]
  47. Mahajan, N.C. Improving Wheat (Triticum aestivum L.) and Soil Productivity Through Precision Nitrogen Management Practices and Efficient Planting System. Master’s Thesis, Sardar Vallabhbhai Patel University of Agriculture & Technology, Meerut, India, 2018. [Google Scholar] [CrossRef]
  48. Li, Y.; Ming, B.; Fan, P.; Liu, Y.; Wang, K.; Hou, P.; Xue, J.; Li, S.; Xie, R. Quantifying contributions of leaf area and longevity to leaf area duration under increased planting density and nitrogen input regimens during maize yield improvement. Field Crops Res. 2022, 283, 108551. [Google Scholar] [CrossRef]
  49. Verma, D.; Gontia, A.S.; Jha, A.; Deshmukh, A. Study on leaf area index and leaf area duration of growth analytical parameters in Wheat, Barley, and Oat. Int. J. Agric. Environ. Biotechnol. 2016, 9, 827–831. [Google Scholar] [CrossRef]
  50. Monzon, J.P.; Menza, N.C.L.; Cerrudo, A.; Canepa, M.; Edreira, J.I.R.; Specht, J.; Andrade, F.H.; Grassini, P. Critical period for seed number determination in soybean as determined by crop growth rate, duration, and dry matter accumulation. Field Crops Res. 2021, 261, 108016. [Google Scholar] [CrossRef]
  51. Tandale, M.D.; Ubale, S.S. Evaluation of effect of growth parameters, leaf area index (LAI), leaf area duration (LAD), crop growth rate (CGR) on seed yield of soybean during kharif season. Int. J. Agric. Sci. 2007, 3, 119–123. [Google Scholar]
  52. Rajput, A.; Rajput, S.S.; Jha, G. Leaf area index, crop growth rate, relative growth rate and net assimilation rate of different varieties of rice grown under different planting geometries and depths. Int. J. Pure Appl. Biosci. 2017, 5, 362–367. [Google Scholar] [CrossRef]
  53. Banik, B.; Korav, S.; Sujatha, H.T.; Changade, N.; Bisarya, D. Performance of Phytohormones under Distinct Levels of Drip Irrigation on Growth and Productivity of Wheat (Triticum aestivum L.). Indian J. Agric. Res. 2024, A-6225, 1–7. [Google Scholar] [CrossRef]
  54. Singh, V.P.; Dwivedi, P.; Kashyap, S. Effect of exogenous application of salicylic acid and sodium nitroprusside in wheat (Triticum aestivum L.) cultivars subjected to heat stress under early and late sown conditions. Pharma Innov. J. 2022, 11, 151–156. [Google Scholar]
  55. Amin, A.A.; El-Kader, A.A.A.; Shalaby, M.A.F.; Gharib, F.A.E.; Rashad, E.S.M.; Teixeira da Silva, J.A. Physiological Effects of Salicylic Acid and Thiourea on Growth and Productivity of Maize Plants in Sandy Soil. Commun. Soil Sci. Plant Anal. 2013, 44, 1141–1155. [Google Scholar] [CrossRef]
  56. El-Hawary, M.M.; Hashem, O.S.M.; Hasanuzzaman, M. Seed Priming and Foliar Application with Ascorbic Acid and Salicylic Acid Mitigate Salt Stress in Wheat. Agronomy 2023, 13, 493. [Google Scholar] [CrossRef]
  57. Ashraf, M.A.; Ragavan, T.; Naziya, S.B. Influence of In-situ soil moisture conservation practices with pusa hydrogel on physiological parameters of rainfed cotton. Int. J. Bio-Resour. Stress Manag. 2020, 11, 548–557. [Google Scholar] [CrossRef]
  58. Desoky, E.-S.M.; Merwad, A.R.M. Improving the Salinity Tolerance in Wheat Plants Using Salicylic and Ascorbic Acids. J. Agric. Sci. 2015, 7, 203. [Google Scholar]
  59. EL-Hadidi, E.; El-Zehery, T.; ALWetwat, R. Response of Wheat Plant to Foliar Application of Organic Acids under Saline Conditions. J. Soil Sci. Agric. Eng. 2018, 9, 135–143. [Google Scholar]
  60. Waghmare, S.V.; Singh, M. Agrometeorological Indices and Correlation Coefficient of Bt Cotton Under Different Growing Environment. Int. J. Curr. Microbiol. Appl. Sci. 2017, 6, 551–557. [Google Scholar]
  61. Mathieua, J.A.; Aires, F. Assessment of the agro-climatic indices to improve crop yield forecasting. Agric. For. Meteorol. 2018, 253–254, 15–30. [Google Scholar] [CrossRef]
  62. Gouache, D.; Bouchon, A.S.; Jouanneau, E.; Bris, X.L. Agrometeorological analysis and prediction of wheat yield at the departmental level in France. Agric. For. Meteorol. 2015, 209–210, 1–10. [Google Scholar] [CrossRef]
  63. Brar, H.R.; Singh, P. Relationship of agro-meteorological indices with cotton yield under varied pre-sowing irrigation levels, sowing dates and time of first irrigation in North-Western India. Commun. Soil Sci. Plant Anal. 2021, 53, 170–179. [Google Scholar] [CrossRef]
  64. Bairagi, G.D.; Goswami, S.B.; Sharma, S.K. Wheat Crop Yield Prediction Using Agro-Meteorological and Space Based Indices: A Case Study of Indore District, M.P. J. Agrometeorol. 2014, 16, 219–224. [Google Scholar]
  65. Medhi, K.; Neog, P.; Goswami, P.; Deka, R.L.; Hussain, R. Agrometeorological Indices in Relation to Phenology and Yield of Rice Genotype (Oryza sativa L.) under Upper Brahmaputra Valley Zone of Assam, India. Int. J. Curr. Microbiol. Appl. Sci. 2019, 8, 1459–1471. [Google Scholar] [CrossRef]
Horticulturae 11 00485 g001
Figure 1. Correlation matrix plot with significance levels between the agrometeorological indices shows the Pearson correlation with a significance level (as asterisk). Each significance level is associated with a symbol: p-values 0.01 (**), 0.05 (*). GDDs: growing degree days; RTD: relative temperature disparity; PTUs: photothermal units; HTUs: heliothermal units; DL: day length; BSS: bright sunshine.
Figure 1. Correlation matrix plot with significance levels between the agrometeorological indices shows the Pearson correlation with a significance level (as asterisk). Each significance level is associated with a symbol: p-values 0.01 (**), 0.05 (*). GDDs: growing degree days; RTD: relative temperature disparity; PTUs: photothermal units; HTUs: heliothermal units; DL: day length; BSS: bright sunshine.
Horticulturae 11 00485 g001
Horticulturae 11 00485 g002
Figure 2. Relationship of GDD, PTU, and HTU with essential oil yield during 2018–2019 and 2019–2020. y: dependent variable; x: independent variable; value with x: slope of the line; single numeric value is intercept; R2: proportion of variance for a dependent variable that is explained by independent variable.
Figure 2. Relationship of GDD, PTU, and HTU with essential oil yield during 2018–2019 and 2019–2020. y: dependent variable; x: independent variable; value with x: slope of the line; single numeric value is intercept; R2: proportion of variance for a dependent variable that is explained by independent variable.
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Table 1. Effect of sowing time and salicylic acid application on growing degree days (°C days) accumulation at different phenological stages in German chamomile.
Table 1. Effect of sowing time and salicylic acid application on growing degree days (°C days) accumulation at different phenological stages in German chamomile.
TreatmentGDD (°C Days)
Phenological Stages
Sowing to Flower Bud FormationFlower Bud Formation to 50% Budding50% Budding to Flower InitiationFlower Initiation to 50% Flowering50% Flowering to 100% FloweringSeed Sowing to 100% Flowering
2018–20192019–20202018–20192019–20202018–20192019–20202018–20192019–20202018–20192019–20202018–20192019–2020
Sowing time
20 November 887.06912.6763.0092.8971.5674.3348.9240.33117.00126.161187.531246.67
10 December 591.79668.0078.44108.4457.9374.3372.3338.6786.33143.93886.891033.33
30 December 512.67671.5368.1761.50156.33136.6957.1155.4973.3378.09867.741003.22
20 January 425.56533.6085.2799.50111.78109.2367.6767.9377.3390.60767.41901.00
SEm(±)0.900.250.170.190.120.150.130.120.170.130.940.32
LSD (p = 0.05)2.650.740.510.570.370.450.380.340.510.372.780.94
Salicylic acid
Control605.93698.6373.9991.29100.5398.6865.8950.5589.58109.67935.971049.00
25 mg/L605.51698.6775.5088.83100.5698.5959.3250.6386.83109.67927.691046.42
50 mg/L601.37692.0571.6791.6397.1198.6859.3250.6389.08109.75918.531042.75
SEm(±)0.780.220.150.170.110.130.110.100.150.110.820.28
LSD (p = 0.05)2.300.640.440.490.32NS0.33NS0.44NS2.410.81
SEm(±): standard error of mean; LSD: least significant difference; NS: not significant.
Table 2. Effect of sowing time and salicylic acid application on photothermal unit (°C days h) accumulation at different phenological stages in German chamomile.
Table 2. Effect of sowing time and salicylic acid application on photothermal unit (°C days h) accumulation at different phenological stages in German chamomile.
TreatmentPTU (°C Days h)
Phenological Stages
Sowing to Flower Bud FormationFlower bud Formation to 50% Budding50% Budding to Flower InitiationFlower Initiation to 50% Flowering50% Flowering to 100% FloweringSeed Sowing to 100% Flowering
2018–20192019–20202018–20192019–20202018–20192019–20202018–20192019–20202018–20192019–20202018–20192019–2020
Sowing time
20 November9071.729373.78704.681036.22811.73839.93560.22460.001487.381505.3312,635.6713,215.11
10 December 6122.436863.67879.391207.00662.69857.33843.40443.001039.401712.339547.3311,083.33
30 December 5419.174383.31781.61705.891871.801627.03703.18678.33910.40960.339686.118355.00
20 January4619.545778.721002.591176.441353.441319.59835.93835.33988.821122.008800.2210,232.22
SEm(±)1.851.030.220.120.110.190.170.130.280.141.670.29
LSD (p = 0.05)5.453.030.640.360.330.160.510.380.820.414.940.84
Salicylic acid
Control6332.226623.72845.571041.421188.921160.92790.42604.171098.531325.0010,255.5810,755.25
25 mg/L6307.176623.83863.071011.421188.881161.08708.32604.171095.781325.0010,163.1710,725.50
50 mg/L6285.276552.07817.571041.331146.951160.92708.32604.171125.181325.0010,083.2510,683.50
SEm(±)1.600.890.190.110.100.550.150.110.240.121.450.25
LSD (p = 0.05)4.722.620.560.310.28NS0.44NS0.71NS4.270.73
SEm(±): standard error of mean; LSD: least significant difference; NS: not significant.
Table 3. Effect of sowing time and salicylic acid application on heliothermal unit (°C days h) accumulation at different phenological stages in German chamomile.
Table 3. Effect of sowing time and salicylic acid application on heliothermal unit (°C days h) accumulation at different phenological stages in German chamomile.
TreatmentHTU (°C Days h)
Phenological Stages
Sowing to Flower Bud FormationFlower Bud Formation to 50% Budding50% Budding to Flower InitiationFlower Initiation to 50% Flowering50% Flowering to 100% FloweringSeed Sowing to 100% Flowering
2018–20192019–20202018–20192019–20202018–20192019–20202018–20192019–20202018–20192019–20202018–20192019–2020
Sowing time
20 November5637.93 5973.11 261.00 656.98 510.87 375.33 308.78 313.89 806.56 961.60 7524.44 8281.00
10 December 3127.18 4297.22 471.56 766.89 394.67 375.33 390.53 314.00 506.46 968.93 4890.44 6722.33
30 December 2430.11 4383.13 485.50 328.45 851.33 961.60 433.13 361.80 544.46 554.84 4744.44 6589.78
20 January2006.56 3774.53 503.22 620.06 664.58 672.87 605.72 420.80 467.83 689.93 4248.00 6178.00
SEm(±)0.71 0.27 0.19 0.13 0.12 0.27 0.12 0.21 0.11 0.16 0.76 0.37
LSD (p = 0.05)2.10 0.80 0.56 0.37 0.35 0.78 0.35 0.62 0.33 0.46 2.23 1.09
Salicylic acid
Control3318.01 4628.13 438.13 593.06 617.38 596.28 477.64 352.57 577.07 793.88 5428.08 6963.83
25 mg/L3298.18 4628.13 438.33 593.14 617.38 596.28 412.95 352.65 574.43 793.80 5341.00 6964.00
50 mg/L3285.15 4564.75 414.50 593.08 581.32 596.28 413.03 352.65 592.48 793.80 5286.42 6900.50
SEm(±)0.62 0.24 0.17 0.11 0.10 0.23 0.10 0.18 0.10 0.14 0.66 0.32
LSD (p = 0.05)1.81 0.69 0.49 0.32 0.30 NS 0.30 NS 0.29 NS 1.93 0.95
SEm(±): standard error of mean; LSD: least significant difference; NS: not significant.
Table 4. Crop growth rate (CGR) of German chamomile among three growth phases.
Table 4. Crop growth rate (CGR) of German chamomile among three growth phases.
Treatment CGR (g/m2/Day)
Seed Emergence to Bud FormationBud Formation to 50% Flowering50 to 100% Flowering
2018–20192019–20202018–20192019–20202018–20192019–2020
Sowing Time
20 November2.355.101.162.8618.1216.31
10 December2.514.520.832.7813.3711.41
30 December2.914.400.692.8227.2220.65
20 January2.824.340.482.9921.6614.78
SEm(±)0.120.070.080.090.221.03
LSD (p = 0.05)0.340.190.23NS0.663.05
Salicylic acid
Control2.674.400.752.8419.3515.23
25 mg/L2.644.550.782.9520.2315.90
50 mg/L2.634.820.832.7920.7016.23
SEm(±)0.100.060.140.150.190.90
LSD (p = 0.05)NS 0.17NSNS0.57NS
SEm(±): standard error of mean; LSD: least significant difference; NS: not significant.
Table 5. Relative growth rate (RGR) of German chamomile among three growth phases.
Table 5. Relative growth rate (RGR) of German chamomile among three growth phases.
TreatmentRGR (g/g/Day)
Seed Emergence to Bud FormationBud Formation to 50% Flowering50 to 100% Flowering
2018–20192019–20202018–20192019–20202018–20192019–2020
Sowing Time
20 November5.255.175.285.345.515.52
10 December5.195.075.205.285.575.58
30 December5.145.005.195.275.065.15
20 January5.104.905.115.205.135.22
SEm(±)0.010.010.010.010.010.04
LSD (p = 0.05)0.020.030.020.020.020.11
Salicylic acid
Control5.165.015.185.255.305.35
25 mg/L5.175.025.195.275.325.36
50 mg/L5.185.075.225.305.345.40
SEm(±)0.010.010.010.010.000.03
LSD (p = 0.05)0.020.030.020.020.01NS
SEm(±): standard error of mean; LSD: least significant difference; NS: not significant.
Table 6. Absolute growth rate (AGR) of German chamomile among three growth phases during both years.
Table 6. Absolute growth rate (AGR) of German chamomile among three growth phases during both years.
TreatmentAGR (cm/Day)
Seed Emergence to Bud FormationBud Formation to 50% Flowering50 to 100% Flowering
2018–20192019–20202018–20192019–20202018–20192019–2020
Sowing Times
20 November0.560.761.882.100.811.94
10 December0.630.671.050.850.831.67
30 December0.540.461.331.773.632.65
20 January0.520.400.760.893.322.10
SEm(±)0.040.050.130.110.140.14
LSD (p = 0.05)NS0.150.390.330.410.40
Salicylic acid
Control0.630.630.831.072.282.08
25 mg/L0.530.521.381.501.952.05
50 mg/L0.540.571.551.642.222.15
SEm(±)0.040.040.120.100.120.12
LSD (p = 0.05)NSNS0.340.28NSNS
SEm(±): standard error of mean; LSD: least significant difference; NS: not significant.
Table 7. Net assimilation rate (NAR) of German chamomile among three growth phases during both years.
Table 7. Net assimilation rate (NAR) of German chamomile among three growth phases during both years.
TreatmentNAR (g/m2/Day)
Seed Emergence to Bud FormationBud Formation to 50% Flowering50 to 100% Flowering
2018–20192019–20202018–20192019–20202018–20192019–2020
Sowing Time
20 November2.355.103.332.8618.1216.31
10 December2.514.521.812.7813.3711.41
30 December2.914.402.442.8227.2220.65
20 January2.824.341.302.9921.6614.78
SEm(±)0.120.070.220.090.221.03
LSD (p = 0.05)0.340.190.66NS0.663.05
Salicylic acid
Control2.674.402.122.8419.3515.23
25 mg/L2.644.552.242.9520.2315.90
50 mg/L2.634.822.302.7920.7016.23
SEm(±)0.100.060.190.080.190.90
LSD (p = 0.05)NS0.17NSNS0.57NS
SEm(±): standard error of mean; LSD: least significant difference; NS: not significant.
Table 8. Leaf area duration (LAD) of German chamomile among three growth phases during both years.
Table 8. Leaf area duration (LAD) of German chamomile among three growth phases during both years.
TreatmentLeaf Area Duration (LAD) (Days)
Seed Emergence to Bud FormationBud Formation to 50% Flowering50 to 100% Flowering
2018–20192019–20202018–20192019–20202018–20192019–2020
Sowing Times
20 November147.53140.49198.57193.01111.46116.06
10 December111.80108.99165.83182.59132.78145.66
30 December89.74100.47165.67188.2658.3076.44
20 January78.8378.92145.85168.7865.8887.93
SEm(±)4.195.192.634.540.743.69
LSD (p = 0.05)12.3615.327.7613.392.1710.90
Salicylic acid
Control99.25102.47156.30175.1888.73106.43
25 mg/L102.92101.43166.79180.6391.11105.66
50 mg/L118.76117.76183.85193.6796.47107.48
SEm(±)3.634.502.283.930.643.20
LSD (p = 0.05)10.7113.276.7211.601.88NS
SEm(±): standard error of mean; LSD: least significant difference; NS: not significant.
Table 9. Correlation of different meteorological indices at different phenological stages with essential oil yield.
Table 9. Correlation of different meteorological indices at different phenological stages with essential oil yield.
Agrometeorological IndicesCorrelation Coefficient with Essential Oil Yield
2018–20192019–2020
GDDsSowing to bud formation 0.847 **0.773 **
Bud formation to bud opening−0.840−0.102
Bud opening to ray floret emergence−0.377−0.420
Ray floret emergence to flower opening−0.527−0.826
Flower opening to full flowering0.686 *0.512
Sowing to full flowering 0.834 **0.768 **
PTUsSowing to bud formation 0.839 **0.493
Bud formation to bud opening−0.890−0.225
Bud opening to ray floret emergence−0.407−0.458
Ray floret emergence to flower opening−0.631−0.822
Flower opening to full flowering0.661 *0.492
Sowing to full flowering 0.801 **0.411
HTUsSowing to bud formation 0.812 **0.702 *
Bud formation to bud opening−0.7740.095
Bud opening to ray floret emergence−0.337−0.379
Ray floret emergence to flower opening−0.854−0.863
Flower opening to full flowering0.770 **0.499
Sowing to full flowering 0.784 **0.690 *
* and ** indicates significant correlation coefficients at p < 0.05 and p < 0.01, respectively.
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Rathore, S.; Kumar, R. Assessing Growth Performance and Agrometeorological Indices of Matricaria chamomilla L. Governed by Growing Season Length and Salicylic Acid in the Western Himalaya. Horticulturae 2025, 11, 485. https://doi.org/10.3390/horticulturae11050485

AMA Style

Rathore S, Kumar R. Assessing Growth Performance and Agrometeorological Indices of Matricaria chamomilla L. Governed by Growing Season Length and Salicylic Acid in the Western Himalaya. Horticulturae. 2025; 11(5):485. https://doi.org/10.3390/horticulturae11050485

Chicago/Turabian Style

Rathore, Shalika, and Rakesh Kumar. 2025. "Assessing Growth Performance and Agrometeorological Indices of Matricaria chamomilla L. Governed by Growing Season Length and Salicylic Acid in the Western Himalaya" Horticulturae 11, no. 5: 485. https://doi.org/10.3390/horticulturae11050485

APA Style

Rathore, S., & Kumar, R. (2025). Assessing Growth Performance and Agrometeorological Indices of Matricaria chamomilla L. Governed by Growing Season Length and Salicylic Acid in the Western Himalaya. Horticulturae, 11(5), 485. https://doi.org/10.3390/horticulturae11050485

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