Using Time-to-Event Model in Seed Germination Test to Evaluate Maturity during Cow Dung Composting

Abstract

Maturity is a matter of concern for the utilization of livestock manures after composting because of the phytotoxicity of immature compost. The seed germination test is widely used for evaluating the maturity of compost. However, the process of seed germination was not studied by establishing a model for evaluating the maturity. Here, we established a time-to-event model for the data of germination proportion over time in a seed germination test with cow dung compost at different composting times. Results show that the profile of the seed germination proportion over time for Chinese cabbage (Brassica rapa L.) and garden cress (Lepidium sativum L.) were both well described by the model. Seed germination was delayed in composts at the early stage of composting from parameter t₅₀ (half germination time) of the model. Parameter t₅₀ was significantly negatively related to radicle length (RL), which indicated that there is an organic relationship between seed germination (i.e., radicle emergence) and radicle elongation. In conclusion, the immature compost can hinder seed radicle elongation by delaying seed germination.

1. Introduction

Composting is usually adopted for treating and utilizing the solid fraction of livestock manures [1]. However, there is a serious concern for the maturity of compost before soil application [2]. Immature compost has a negative effect on plant growth. If compost is directly used in seedling substrate, it will be especially important for evaluating the effect of compost on seed germination [3]. Seed germination test for evaluating the maturity of compost is widely used and recognized as an effective method for making the final decision on the evaluation result [4]. The results of compost on seed germination (i.e., radicle emergence) and radicle elongation are included in the test. For a seed lot, different individuals have different abilities to germinate. Not only germination proportion is needed to record after incubating in compost water extract for several days [5], but also the model for the data of germination proportion over time to study germination process is necessary to establish [6]. The time-to-event model is quite appropriate for analyzing the germination data [7,8]. This model can be applied to analyze the effect of compost on seed germination from germination time and proportion. In addition, the radicle emergence of a seed is operationally defined as germination, but there is an organic relationship between the processes of seed germination and radicle elongation physiologically [9]. In an actual situation, it is not difficult to find that the faster the seed germinates, the longer the seed radicle elongates. However, there is a lack of evidence for this aspect up to now.

Therefore, in this study, we established a time-to-event model for seed germination data of composts at different composting time and revealed the relationship between the processes of seed germination and radicle elongation.

2. Materials and Methods

2.1. Composting and Compost

The fresh composts used in this study were from Liu et al. [10]. Briefly, cow dung was used as the feed stock for composting. Corn straw and spent mushroom substrate were added as bulking agents for composting. The initial moisture content and C/N of composting material was around 67% and 23, respectively. A treatment T1 was added with a microbial agent (Bacillus), while a treatment T2 was not. The composting was run in a 60 L plastic foam box for 70 days. The compost pile was turned once a week, while the compost on days 7, 14, 21, 35, and 70 was taken as the samples at different maturity levels for further analysis.

2.2. Seed Germination Test with Compost Water Extract

A fresh compost sample and deionized water were mixed at the ratio of 1:20 (w/v, on dry weight basis), shaken at 150 rpm for 1 h, and centrifuged at 5000 rpm for 10 min to take the supernatant as the compost water extract. Chinese cabbage seeds and garden cress seeds were selected in this study, which were commonly used in the test [4] (Luo et al., 2018). Seeds were placed on a filter paper in a 90 mm Petri dish, which was added with the compost water extract (and deionized water as the control) of 5 mL. Next, the dishes were put in an incubator under 25 °C in darkness for 48 h. Firstly, a total of 160 seeds were arranged in a dish. When the seeds began to germinate, the number of germinated seeds was recorded every hour until the germination proportion reached more than 80%. Subsequently, the number of 20 germinated seeds with similar sizes were transferred to a new dish added with the same substance and incubated until the end of the test for measuring seed radicle length. The method for establishing a time-to-event model to analyze the data of germination proportion over time was according to Ritz et al. [11] and Luo et al. [8].

2.3. Statistical Analysis

The establishment of the time-to-event model for seed germination and the estimation of parameters in the model were completed with drm function in the drc package by R software (https://www.R-project.org/ (accessed on 28 November 2022)). The correlation analysis between the indicators in the seed germination test was performed by R software 4.2.1.

3. Results

3.1. Effect of Compost on Germination of Chinese Cabbage Seeds

It can be seen from Figure 1 and Figure 2 that Chinese cabbage seeds began to germinate after 10 h and the germination proportion reached more than 80% after 24 h. The profile of seed germination proportion over time was well described by the time-to-event model from the width of confidence intervals. In the treatment T1, with addition of composting microbial agent (Table 1), compared with CK, the steepness of the curve (−b) in the compost at day 7 significantly increased; the maximum germination proportion (d) in the compost at day 70 significantly increased; half the germination time (t₅₀) did not change in the compost at any day. In the treatment T2 without addition of composting microbial agent, compared with CK, the parameter t₅₀ significantly increased in composts at all days except for day 35.

3.2. Effect of Compost on Germination of Garden Cress Seeds

For garden cress seeds, it could be seen from Figure 3 and Figure 4 that the seeds began to germinate after 5 h and the germination proportion approached 80% after 24 h. The profile of seed germination proportion over time was described by the time-to-event model with the large width of confidence intervals. In the treatment T1 with the addition of composting microbial agent (Table 2), compared with CK, the parameter −b in the compost at day 7 and day 70 significantly increased; the parameter d in the compost at day 7 significantly decreased; and the parameter t₅₀ significantly increased in the compost at day 7. In the treatment T2 without addition of composting microbial agent, compared with CK, the parameter d significantly decreased in composts at all days; and the parameter t₅₀ significantly increased in the compost at day 7.

3.3. Relation between the Indicators in Seed Germination Test

There were strong relationships between several indicators in the seed germination test (Figure 5). The indicator GP was significantly positively related to the parameter d (P < 0.01). The parameter −b was significantly positively related to the parameter t₅₀ (P < 0.01) and significantly negatively related to the indicator RL (P < 0.01). The parameter d was significantly positively related to the parameter t₅₀ (P < 0.01) and significantly negatively related to the indicator RL (P < 0.01). The parameter t₅₀ was significantly negatively related to the indicator RL (P < 0.01).

4. Discussion

The fitting results of the germination data of Chinese cabbage seeds to the time-to-event model were better than that of garden cress seeds from the confidence intervals of germination curves. They can be attributed to the difference in the germination uniformity of the two seeds [12]. The higher the steepness of the germination curve is, the better the germination uniformity of the seeds is [7]. The steepness of the germination curve of Chinese cabbage seeds was higher than that of garden cress seeds. The parameter t₅₀ reflects the germination speed of seeds. At the early stage of composting, two composting treatments both showed a delayed effect on germination of different seeds. This indicated that the livestock manures at the composting early stage had high phytotoxicity and were at low maturity levels [13,14]. From the germination speed of Chinese cabbage seeds in different composting treatments, it can be concluded that the microbial agent promotes the compost to mature. However, the parameters from the model did not decrease or increase significantly with time of composting. This was due to the fact that the compost has high phytotoxicity to radicle elongation rather than seed germination [15]. From the results of the seed germination test at the end, it can be concluded that germination proportion is less sensitive than radicle length to compost maturity. So the latter result can be directly used to evaluate the maturity without considering the former result [10]. However, we focused on the germination process through the time-to-event model and found that the immature compost delayed seed germination. Recently, transcriptomic and proteomic analysis revealed that the inhibition of seed germination by the raw livestock manure was attributed to the disturbance of the phenylpropanoid synthesis pathway involved in lignin synthesis [16]. Interestingly, the profile of seed germination speed was just opposite to that of seed radicle length in this study, which indicated that there was an organic relationship between seed germination (i.e., radicle emergence) and radicle elongation. The radicle length in the seed germination test is easy to measure, but the incubation time is relatively long. Although the test duration for establishing the seed germination model is relatively short, a lot of seeds and intensive manual operation are needed. With the development of artificial intelligence technology, it is expected to quickly establish a seed germination model to evaluate compost maturity by collecting germination data with machines [17].

5. Conclusions

The profile of seed germination proportion over time is well described by the time-to-event model. From the parameter t₅₀ of the model, the compost at early stages delays seed germination. There is an organic relationship between seed germination (i.e., radicle emergence) and radicle elongation. The immature compost can hinder seed radicle elongation by delaying seed germination. The maturity of compost can be judged by the parameters from the time-to-event model of seed germination. It is necessary to adopt the method for evaluating the maturity carefully when the compost is directly used in seedling substrate.

Author Contributions

Y.L. (Yuan Luo) conceived the idea of this study. Y.L. (Yuan Luo), X.M. and Y.L. (Yuan Liu) performed laboratory experiments. Y.L. (Yuan Luo), K.O. and H.C. analyzed the data and prepared the manuscript. All authors contributed substantially to revisions. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets used and analyzed during the current study available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare that they have no known competing financial interest or personal relationship that could have appeared to influence the work reported in this paper.

Footnotes

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Figures and Tables

Figure 1. Seed germination proportion of Chinese cabbage over time in composts at different composting times in the treatment T1. (a) CK (deionized water), (b) composts at day 7, (c) day 14, (d) day 21, (e) day 35, and (f) day 70. Gray curves represent 95% confidence intervals.

Figure 2. Seed germination proportion of Chinese cabbage over time in composts at different composting time in the treatment T2. (a) CK (deionized water), (b) composts at day 7, (c) day 14, (d) day 21, (e) day 35, and (f) day 70. Gray curves represent 95% confidence intervals.

Figure 3. Seed germination proportion of garden cress over time in composts at different composting times in the treatment T1. (a) CK (deionized water), (b) composts at day 7, (c) day 14, (d) day 21, (e) day 35, and (f) day 70. Gray curves represent 95% confidence intervals.

Figure 4. Seed germination proportion of garden cress over time in composts at different composting times in the treatment T2. (a) CK (deionized water), (b) composts at day 7, (c) day 14, (d) day 21, (e) day 35, and (f) day 70. Gray curves represent 95% confidence intervals.

Figure 5. Correlation matrix among the parameters from the seed germination model and the indicators from seed germination and radicle elongation. Red circles and blue circles represent the indicators from Chinese cabbage seeds and garden cress seeds, respectively. The matrix at the diagonal line is the histogram of each parameter or indicator; d, the maximum proportion of seed germination that can be achieved in test; t50, the germination time of half the seeds; −b, a constant and its value directly proportional to the slope of the curve of germination proportion over time at the point t50; GP, the seed germination proportion at the end of test; and RL, radicle length.

Table 1

Parameters obtained by fitting the time-to-event model for the germination process of Chinese cabbage seeds in composts at different composting times.

Treatment	Time (d)	−b a	d	t 50
T1	CK	5.52 (4.35–6.68) b	1.00 (0.85–1.15)	18.40 (16.86–19.94)
	7	7.60 (5.92–9.28) *	0.87 (0.75–0.99)	18.83 (17.75–19.91)
	14	6.10 (4.63–7.57)	1.24 (0.89–1.58)	21.24 (18.81–23.66)
	21	6.35 (4.96–7.74)	1.02 (0.85–1.18)	19.37 (17.88–20.86)
	35	6.14 (4.68–7.61)	0.92 (0.75–1.09)	19.18 (17.53–20.82)
	70	6.29 (4.93–7.64)	0.78 (0.68–0.89) *	17.49 (16.29–18.69)
T2	CK	5.66 (4.55–6.77)	1.04 (0.92–1.16)	17.60 (16.31–18.88)
	7	6.60 (4.98–8.23)	1.08 (0.80–1.36)	20.98 (18.87–23.08) **
	14	5.74 (4.47–7.02)	1.19 (0.94–1.43)	20.03 (18.02–22.05) *
	21	6.24 (4.60–7.88)	1.20 (0.77–1.64)	22.01 (19.05–24.98) **
	35	6.57 (5.03–8.11)	0.99 (0.80–1.18)	19.66 (18.00–21.32)
	70	5.46 (4.01–6.92)	1.38 (0.75–2.02)	22.83 (18.72–26.95) *

^a d, the maximum proportion of seed germination that can be achieved in test; t₅₀, the germination time of half the seeds; −b, a constant and its value directly proportional to the slope of the curve of germination proportion over time at the point t₅₀. ^b 95% confidence interval in parentheses. * P < 0.05, and ** P < 0.01 (compared to CK in the same treatment).

Table 2

Parameters obtained by fitting the time-to-event model for the germination process of garden cress seeds in composts at different composting times.

Treatment	Time (d)	−b a	d	t 50
T1	CK	4.18 (3.41–4.96) b	0.81 (0.74–0.89)	11.81 (10.81–12.81)
	7	5.68 (4.47–6.89) *	0.60 (0.51–0.68) **	14.66 (13.60–15.72) **
	14	3.73 (2.93–4.52)	0.72 (0.62–0.81)	13.07 (11.56–14.57)
	21	3.38 (2.66–4.10)	0.82 (0.71–0.93)	13.24 (11.49–15.00)
	35	3.29 (2.60–3.98)	0.91 (0.79–1.03)	13.53 (11.68–15.37)
	70	6.29 (2.48–3.79) *	0.95 (0.82–1.08)	13.83 (11.80–15.86)
T2	CK	3.65 (3.06–4.24)	1.03 (0.98–1.07)	10.89 (9.93–11.84)
	7	4.63 (3.50–5.76)	0.73 (0.59–0.87) **	17.22 (15.27–19.17) **
	14	4.00 (3.26–4.74)	0.82 (0.74–0.89) **	11.69 (10.64–12.73)
	21	3.70 (3.01–4.38)	0.84 (0.76–0.92) **	11.79 (10.61–12.96)
	35	4.14 (3.41–4.88)	0.82 (0.75–0.90) **	11.36 (10.42–12.29)
	70	3.75 (3.08–4.42)	0.87 (0.80–0.94) **	11.45 (10.38–12.53)

^a d, the maximum proportion of seed germination that can be achieved in test; t₅₀, the germination time of half the seeds; and −b, a constant and its value directly proportional to the slope of the curve of germination proportion over time at the point t₅₀. ^b 95% confidence interval in parentheses. * P < 0.05, ** P < 0.01 (compared to CK in the same treatment).

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