Sex ratio, floral traits, and genetic variation of androdioecious <i>Osmanthus fragrans</i> L. (Oleaceae) and the implications for maintenance of high male frequency

Lihua Zhou; Liyuan Yang; Qiu Fang; Bin Dong; Yiguang Wang; Shiwei Zhong; Zheng Xiao; Hongbo Zhao; Lihua Zhou; Liyuan Yang; Qiu Fang; Bin Dong; Yiguang Wang; Shiwei Zhong; Zheng Xiao; Hongbo Zhao

doi:10.48130/OPR-2022-0022

2022 Volume 2

Article Contents

Abstract

Introduction

Methodology

Estimating the total carbon content (tcc) accumulation and its distribution above ground (shoots) and below ground (roots) parts in cereals

Results

Examples for validation of the model

Rights and permissions

References

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ARTICLE Open Access

Sex ratio, floral traits, and genetic variation of androdioecious Osmanthus fragrans L. (Oleaceae) and the implications for maintenance of high male frequency

Zhejiang Provincial Key Laboratory of Germplasm Innovation and Utilization for Garden Plants, School of Landscape and Architecture, Zhejiang A & F University, Hangzhou 311300, Zhejiang, China

More Information

Corresponding author: E-mail: zhaohb@zafu.edu.cn

Received: 28 July 2022
Accepted: 13 October 2022
Published online: 28 December 2022
Ornamental Plant Research 2, Article number: 22 (2022) | Cite this article

Abstract

Several examples of androdioecy appear to have evolved from dioecy and have low male frequency (< 0.5). However, the evolutionary pathway to androdioecy in Oleaceae may come from hermaphroditism. Osmanthus fragrans L. has a 1:1 sex ratio in nature populations. Significant differences are observed not only in flowering phenology but also in some floral traits between males and hermaphrodites. The protandry in the same population and the protogyny in the same plant may promote the xenogamy between genders. The majority of flower traits related with the pollen production are different between males and hermaphrodites. Males bear more flowering nodes, and more flowers per node, and larger anther in all three populations. This characteristic demonstrated that males have more male advantage than hermaphrodites. Population genetic structure of O. fragrans is genetically homogeneous at the species level, and most variations exist within a population. The percentage of variation among populations (13%) and between males and hermaphrodites (0%) is low. Moreover, genetic differentiation was very low between genders not only among populations but also in the same population. This genetic variation could be attributed to the occurrence of high levels of xenogamy between genders. Therefore, high male frequency and more male fitness advantage in males are the essential conditions for this mating system, which plays an important role during population reproduction and regeneration. The 1:1 sex ratio could be the result of integrative effects of sexual system, mating system, and reproductive success.
- Androdioecy,
- Osmanthus fragrans,
- Sex ratio,
- Floral trait,
- Genetic variation,
- Mating system

Introduction

Crop growth and yield depends on the fixed carbon content (CC) and their distribution to plant parts (e.g. roots and shoots). Understanding and calculating the total CC (TCC) accumulated in field crops is very important due to the current issues of food security and global warming. However, there is a lack of research that shows how much CC is partitioned into the roots and shoots. Our previous research work confirmed that the increase in CC in plant tissues (roots and shoots) depends on total dry matter accumulation and partitioning^{[1−4 ]}, because plant growth and yield correlates with net carbon gain on a whole plant basis^{[5, 6 ]}.

The IPCC report^[7] revealed that about 1.2 billion tonnes of carbon at annual rate of 4‰ could be stocked every year for the sustainability of agricultural systems. As the crop growth and yield depends on the total CC (TCC) of plants, therefore, any agronomic practices that help the plants to sequester more atmospheric CO₂ (photosynthesis) increases the TCC accumulation in plants in its distribution to below ground CC to roots (BCC) and above ground CC to shoots (ACC). Therefore, crop production may be defined as: maximizing photosynthetic efficiency of crops to store more carbon above (ACC) and below (BCC) ground parts. The increase in CC above ground part (ACC) depends on: number and size of leaves, number of tillers, reproductive tillers, stems, seed size and number, grain yield and harvest index etc., increase partitioning of CC for better roots development depends on: roots number and length, root proliferation and total root biomass. The increase in CC of roots (BCC) will help the plants to uptake more water and nutrients from the soil and transfers it for better shoot development (ACC) e.g. increase in grain yield, yield components and harvest index.

The TCC accumulation and its partitioning in plant bodies depends on three major factors: (1) plant genotypes e.g. plant species, varieties, hybrids, exhaustive (grasses) and restorative (legumes) crops, growth habit, growth stages, photosynthesis, C₄ crops (e.g. maize, sorghum, millets, etc.) and C₃ crops (e.g. wheat, rice, and barley etc.); (2) agronomic practices (chemical fertilizers, organic fertilizers, biofertilizers, plant nutrition, irrigation, tillage practices, soil types, SOC, plant density, seed rates, sowing time, etc. ); and (3) environmental condition viz. biotic stresses (plant competition, weeds, diseases, insects, pests, etc.) and abiotic stresses (low and high temperature stress, low and high water stress, light quality and duration, wind, chemicals, gases, soil pollution, water pollution, etc.).

In recent years, many international organizations^[8−18] and researchers^[19−43] reported about the importance of carbon footprints in plants, animals, soil, water, environment, ecosystems etc. There is still a lack of comprehensive research and reports on the precise amount of carbon sequestered or stored by individual plants. Because carbon estimation in field crops, especially in roots, is very difficult, costly and time consuming.

The primary objective of this paper is to create a simplified model for estimating the allocation of above-ground carbon content (ACC) and below-ground carbon content (BCC) in field crops, specifically cereals. The purpose of developing a simplified model is to facilitate the estimation of carbon content partitioning in field crops, such as winter crops (e.g., wheat and barley) and summer crops (e.g., maize and rice), specifically targeting students and researchers. This model aims to simplify the process and make it more accessible for those interested in studying carbon content allocation in field crops.

Methodology

Estimating the total carbon content (TCC) accumulation and its distribution above ground (shoots) and below ground (roots) parts in cereals

For the estimation of the total carbon content (TCC) accumulated by plants and its distribution (partitioning) into above ground (shoots) and below ground (roots) parts, efforts were made to calculate the two important factors for above ground parts (ACC) and below ground parts (BCC), 0.42 (% CC in shoot biomass) and 0.38 (% CC in root biomass), respectively. Then three simple equations (1, 2 and 3) were developed to easily estimate the CC in above ground biomass (AGB), below ground biomass (BGB) and total biomass (TBM):

$ACC = AGB \times 0.42$

(1)

$BCC = BGB \times 0.38$

(2)

$TCC = ACC + BCC$

(3)

The total CC (TCC) per plant or per unit area can be easily calculated in field crops by just adding ACC with BCC as shown in Eqn (3).

Based on the model developed in this study: Out of the 100% total carbon content (TCC) fixed/accumulated by field crops, 82% is partitioned into shoots or above ground carbon content (ACC) and 18% into roots or below ground carbon content (BCC). On the other hand, out of the 100% total biomass (shoots biomass + roots biomass on dry basis) (TBM) accumulation by field crops, 80% is partitioned into above ground biomass (AGB) and 20% into below ground biomass (BGB).

Note: Multiplying the amount of CO₂ by 12/44 or 0.27 is equal to the amount of carbon, means that 1 kg of CO₂ is equal to 0.27 kg of carbon. On other hand, multiplying the amount of carbon by 44/12 or 3.67 (44/12) is equal to the amount of CO₂, means that 1 kg of carbon is equal to 3.67 kg of CO₂ (where 12 is the molecular weight of carbon, and 44 is the molecular weight of CO₂ (C + O₂ = 12 + 16 × 2 = 44).

Results

Examples for validation of the model

Example 1 (wheat)

In 2018, wheat produced in metric tonnes (MT) and the total carbon content (TCC) accumulated and partitioned into roots or below ground CC (BCC) and shoots, or above ground CC (ACC) for the world leading countries was calculated through this model (Table 1). For example, in 2018, the total wheat produced in Pakistan was 25.4 MT^{[2, 44]}.

Table 1. Approximate estimation of carbon content (CC) fixed by wheat crop in metric tons (MT) for the leading countries in the world during 2018−2019.

Countries	Metric tons (MT)
Countries	GY	AGB	ACC	BGB	BCC	TBM	TCC
European Union	147.0	420.0	176.4	105.0	39.9	525.0	216.3
China (mainland)	126.7	362.0	152.0	90.5	34.4	452.5	186.4
India	98.6	281.7	118.3	70.4	26.8	352.1	145.1
Russian Federation	72.0	205.7	86.4	51.4	19.5	257.1	105.9
United States of America	49.7	142.0	59.6	35.5	13.5	177.5	73.1
Canada	31.3	89.4	37.6	22.4	8.5	111.8	46.1
Pakistan	25.4	72.6	30.5	18.1	6.9	90.7	37.4
Ukraine	23.4	66.9	28.1	16.7	6.4	83.6	34.4
Australia	21.9	62.6	26.3	15.6	5.9	78.2	32.2
Turkey	21.0	60.0	25.2	15.0	5.7	75.0	30.9
Argentina	20.0	57.1	24.0	14.3	5.4	71.4	29.4
Kazakhstan	14.0	40.0	16.8	10.0	3.8	50.0	20.6
Iran Islamic Rep.	13.4	38.3	16.1	9.6	3.6	47.9	19.7
Other countries	71.7	204.9	86.0	51.2	19.5	256.1	105.5
World	736.1	2103.1	883.3	525.8	199.8	2628.9	1083.1
Source: FAO outlook^[11] and Amanullah et al.^[64]. Where: CC = Carbon content, MT = Metric tons, GY = Grain yield, AGB = Above ground biomass, ACC = Above ground CC, BGB = Below ground biomass, BCC = Below ground CC, TBM = Total biomass (AGB + BGB), TCC = Total CC (ACC + BCC).

| Show Table

DownLoad: CSV

The above ground biomass (AGB) or shoot dry weight (shoot biomass) was calculated using Eqn 4:

$\begin{split}{\boldsymbol{AGB}} =\;&{\boldsymbol{ Grain}}\; {\boldsymbol{Production}}\; {\bf\div}{\bf{ 0.35}} ({\boldsymbol{Factor}}) \\=\;& 25.4 \div 0.35 \\=\;& 72.6 \;(MT)\end{split}$

(4)

The carbon content of the AGB or shoot CC (ACC) was calculated using the following equation:

$\begin{split} {\boldsymbol{ACC}} =\;&{\boldsymbol{ AGB}} \times {\boldsymbol{factor}}\; ({\bf{0.42}})\\ =\;& 72.6 \times 0.42 \\=\;& 30.5 MT \;({\bf{82}}{ {\%}})\end{split}\tag{1}$

The total biomass (TBM) (shoots + roots) was calculated using Eqn 5:

$\begin{split} {\boldsymbol{TBM}} =\;&{\boldsymbol{ AGB}} \times {\bf{1.25}} \;({\boldsymbol{Factor}})\\ =\;& {\bf{72.6}} {\bf\times}{\bf{ 1.25}}\\ =\;&{\bf{ 90.7}}{\boldsymbol{ MT}}\end{split}$

(5)

The below ground biomass (BGB) or root biomass was calculated using Eqn 6:

$\begin{split}{\boldsymbol{ BGB}} =\;&{\boldsymbol{ TBM}}{\bf \times} {\bf{0.20}}\; ({\boldsymbol{Factor}})\\ =\;& 90.7 \times 0.20 \\=\;& 18.1 MT \end{split}$

(6)

The CC of the BGB or root biomass (BCC) was calculated using Eqn 2:

$\begin{split}{\boldsymbol{BCC}} =\;&{\boldsymbol{ BGB}} {\bf\times} {\boldsymbol{factor}}\; ({\bf{0.38}}) \\=\;& 18.1 \times 0.38\\ =\;& 6.9 MT\; ({\bf{18}}{ {\%}})\end{split} \tag{2}$

The total CC (TCC) fixed by wheat in Pakistan during 2018 was calculated using Eqn 3:

$\begin{split}{\boldsymbol{ TCC}} = \;&{\boldsymbol{ACC}} + {\boldsymbol{BCC}}\\ =\;&\bf 30.5 \;(82{ {\%}}) + 6.9 \;(18{ {\%}}) \\=\;&{\bf{ 37.4}}{\boldsymbol{ MT}}\; (\bf100{ {\%}})\end{split}\tag{3}$

Example 2 (rice)

In 2018, rice produced in metric tonnes (MT) and the total carbon content (CC) fixed in rice below (BCC) and above (ACC) ground parts for the world leading countries was calculated (Table 2). For example, in 2018 the total rice produced in China was 141.3 MT^{[2, 44]}.

Table 2. Approximate estimation of carbon content (CC) fixed by rice crop in metric tons (MT) for the leading countries in the world during 2018.

Countries	Metric tons (MT)
Countries	GY	AGB	ACC	BGB	BCC	TBM	TCC
China (mainland)	141.3	314.0	131.9	78.5	29.8	392.5	161.7
India	113.5	252.2	105.9	63.1	24.0	315.3	129.9
Indonesia	46.7	103.8	43.6	25.9	9.9	129.7	53.4
Bangladesh	35.3	78.4	32.9	19.6	7.5	98.1	40.4
Viet Nam	28.7	63.8	26.8	15.9	6.1	79.7	32.8
Thailand	22.8	50.7	21.3	12.7	4.8	63.3	26.1
Myanmar	18.2	40.4	17.0	10.1	3.8	50.6	20.8
Philippines	12.9	28.7	12.0	7.2	2.7	35.8	14.8
Brazil	8	17.8	7.5	4.4	1.7	22.2	9.2
Japan	7.5	16.7	7.0	4.2	1.6	20.8	8.6
Pakistan	7.6	16.9	7.1	4.2	1.6	21.1	8.7
United States of America	6.5	14.4	6.1	3.6	1.4	18.1	7.4
Cambodia	6.4	14.2	6.0	3.6	1.4	17.8	7.3
Egypt	4.2	9.3	3.9	2.3	0.9	11.7	4.8
Nigeria	4.3	9.6	4.0	2.4	0.9	11.9	4.9
World	511.4	1136.4	477.3	284.1	108.0	1420.6	585.3
Source: FAO outlook ^[11] and Amanullah et al.^[64]. Where: CC = Carbon content, MT = Metric tons, GY = Grain yield, AGB = Above ground biomass, ACC = Above ground CC, BGB = Below ground biomass, BCC = Below ground CC, TBM = Total biomass (AGB + BGB), TCC = Total CC (ACC + BCC).

| Show Table

DownLoad: CSV

The above ground biomass (AGB) or shoots dry weight (shoot biomass) was calculated using Eqn 4:

$\begin{split}{\boldsymbol{ AGB}} =\;&{\boldsymbol{ Grain}}\; {\boldsymbol{Production}} \bf\div 0.45\; ({\boldsymbol{Factor}}) \\=\;& 141.3 \div 0.45\\ =\;& 314.0\; (MT)\end{split}\tag{4}$

The carbon content of the AGB or shoots (ACC) was calculated using the following Eqn 1:

$\begin{split}{\boldsymbol{ ACC}} =\;&{\boldsymbol{ AGB}} {\bf\times} {\boldsymbol{factor}}\; ({\bf{0.42}}) \\=\;& 314.0 \times 0.42 \\=\;& 131.9 MT\; (82{\text{%}})\end{split}\tag{1}$

The total biomass (TBM) (shoots + roots) was calculated using Eqn 5:

$\begin{split}{\boldsymbol{ TBM}} = \;&{\boldsymbol{AGB}} {\bf\times}{\bf{ 1.25}} \\=\;&\bf 314 \times 1.25\\ =\;&{\bf {392.5}} {\boldsymbol{MT}}\end{split}\tag{5}$

The below ground biomass (BGB) or root biomass was calculated using Eqn 6:

$\begin{split}{\boldsymbol{BGB}} =\;&{\boldsymbol{ TBM}} {\bf\times} {\bf{0.20}}\; ({\boldsymbol{Factor}})\\ =\;& 392.5 \times 0.20 \\=\;& 78.5\; MT\end{split}\tag{6}$

The CC of the BGB or root biomass (BCC) was calculated using Eqn 2:

$\begin{split}{\boldsymbol{ BCC}} =\;&{\boldsymbol{ BGB}} {\bf\times} {\boldsymbol{factor}}\; ({\bf{0.38}})\\ =\;& 78.5 \times 0.38 \\=\;& 29.8\; MT\; ({\bf{18}}{ {\%}})\end{split}\tag{2}$

The total CC (TCC) fixed by rice crop in china during 2018 was calculated using Eqn 3:

$\begin{split}{\boldsymbol{ TCC}} =\;&{\boldsymbol{ ACC}} + {\boldsymbol{BCC}}\\ =\;&{\bf{ 131.9}}\; ({\bf{82}}{ {\%}}) + {\bf{29.8}}\; ({\bf{18}}{ {\%}})\\ =\;&{\bf {161.7}}\;{\boldsymbol{ MT}}\; ({\bf{100}}{ {\%}})\end{split}\tag{3}$

Example 3 (maize)

In 2018, maize produced in metric tonnes (MT) and the TCC, BCC and ACC for maize in world leading countries was calculated (Table 3). For example, in 2018 the total maize produced in USA was 392.5 MT^{[2, 44]}.

Table 3. Approximate estimation of carbon content (CC) fixed by maize crop in metric tons (MT) for the leading countries in the world during 2018.

Countries	Metric tons (MT)
Countries	GY	AGB	ACC	BGB	BCC	TBM	TCC
United States of America	392.5	981.1	412.1	245.3	93.2	1226.4	505.3
China (mainland)	257.2	642.9	270.0	160.7	61.1	803.7	331.1
Brazil	82.3	205.7	86.4	51.4	19.5	257.2	105.9
Argentina	43.5	108.7	45.6	27.2	10.3	135.8	56.0
Ukraine	35.8	89.5	37.6	22.4	8.5	111.9	46.1
Indonesia	30.3	75.6	31.8	18.9	7.2	94.5	39.0
India	27.8	69.6	29.2	17.4	6.6	86.9	35.8
Mexico	27.2	67.9	28.5	17.0	6.5	84.9	35.0
Romania	18.7	46.7	19.6	11.7	4.4	58.3	24.0
Canada	13.9	34.7	14.6	8.7	3.3	43.4	17.9
France	12.7	31.7	13.3	7.9	3.0	39.6	16.3
South Africa	12.5	31.3	13.1	7.8	3.0	39.1	16.1
Russian Federation	11.4	28.5	12.0	7.1	2.7	35.7	14.7
Nigeria	10.2	25.4	10.7	6.3	2.4	31.7	13.1
Hungary	8.0	19.9	8.4	5.0	1.9	24.9	10.3
Philippines	7.8	19.4	8.2	4.9	1.8	24.3	10.0
Ethiopia	7.4	18.4	7.7	4.6	1.7	23.0	9.5
Egypt	7.3	18.3	7.7	4.6	1.7	22.8	9.4
Serbia	7.0	17.4	7.3	4.4	1.7	21.8	9.0
Pakistan	6.3	15.8	6.6	3.9	1.5	19.7	8.1
World	−	−	−	−	−		−
Source: FAO outlook ^[11] (Accessed on 5-5-2020). Where: CC = Carbon content, MT = Metric tons, GY = Grain yield, AGB = Above ground biomass, ACC = Above ground CC, BGB = Below ground biomass, BCC = Below ground CC, TBM = Total biomass (AGB + BGB), TCC = Total CC (ACC + BCC).

| Show Table

DownLoad: CSV

The above ground biomass (AGB) or shoot biomass was calculated using Eqn 4:

$\begin{split}{\boldsymbol{AGB}} =\;&{\boldsymbol{ Grain}}\; {\boldsymbol{Production}} {\bf\div} {\bf{0.40}}\; ({\boldsymbol{Factor}}) \\=\;& 392.5 \div 0.40\\ =\;& 981.1\; (MT)\end{split}\tag{4}$

The carbon content of the AGB or shoot CC (ACC) was calculated using the following Eqn 1:

$\begin{split}{\boldsymbol{ ACC}} =\;&{\boldsymbol{ AGB}} {\bf\times} {\boldsymbol{factor}}\; ({\bf{0.42}}) \\=\;& 981.1 \times 0.42 \\=\;& 412.1 \;MT\; ({\bf{82}}{ {\%}})\end{split} \tag{1}$

The total biomass (TBM) (shoots + roots dry weights) was calculated using Eqn 5:

$\begin{split}{\boldsymbol{ TBM}} =\;& {\boldsymbol{AGB}} {\bf\times} {\bf{1.25}}\\ =\;&{\bf{ 981.1}} {\bf\times} {\bf{1.25}} \\=\;&{\bf{ 1226.4}}\;{\boldsymbol{ MT}}\end{split}\tag{5}$

The below ground biomass (BGB) or root dry weight (root biomass) was calculated using equation-6:

$\begin{split}{\boldsymbol{ BGB}} =\;&{\boldsymbol{ TBM}} {\bf\times} {\bf{0.20}}\; ({\boldsymbol{Factor}})\\ =\;& 1226.4 \times 0.20 \\=\;& 245.3\; MT\end{split}\tag{6}$

The CC of the BGB or root biomass (BCC) was calculated using Eqn 2:

$\begin{split} {\boldsymbol{BCC}} =\;&{\boldsymbol{ BGB}} {\bf\times} {\boldsymbol{factor}}\; ({\bf{0.38}}) \\=\;& 245.3 \times 0.38 \\=\;& 93.2 \;MT\; ({\bf{18}}{ {\%}})\end{split}\tag{2}$

The total CC (TCC) accumulated by maize crop in the United States of America (USA) during 2018 was calculated using Eqn 3:

$\begin{split}{\boldsymbol{ TCC}} =\;&{\boldsymbol{ ACC}} + {\boldsymbol{BCC}}\\ =\;&{\bf{ 412.1}}\; ({\bf{82}}{ {\%}}) + {\bf{93.2}}\; ({\bf{18}}{ {\%}})\\ =\;&{\bf{ 505.3}}\;{\boldsymbol{ MT}}\; ({\bf{100}}{ {\%}})\end{split}\tag{3}$

Example 4 (barley)

In 2018, barley produced in MT and the TCC fixed in the barley growing area in the world leading countries was estimated (Table 4). For example, in 2018 the total barley produced in the Russian Federation was 17.0 MT^{[2, 44]}.

Table 4. Approximate estimation of carbon content (CC) fixed by barley crop in metric tons (MT) for the leading countries in the world during 2018−2019.

Countries	Metric tons (MT)
Countries	GY	AGB	ACC	BGB	BCC	TBM	TCC
Russian Federation	17.0	56.6	23.8	14.2	5.4	70.8	29.2
France	11.2	37.3	15.7	9.3	3.5	46.6	19.2
Germany	9.6	31.9	13.4	8.0	3.0	39.9	16.5
Australia	9.3	30.8	13.0	7.7	2.9	38.6	15.9
Spain	9.1	30.4	12.8	7.6	2.9	38.0	15.7
Canada	8.4	27.9	11.7	7.0	2.7	34.9	14.4
Ukraine	7.3	24.5	10.3	6.1	2.3	30.6	12.6
Turkey	7.0	23.3	9.8	5.8	2.2	29.2	12.0
United Kingdom	6.5	21.7	9.1	5.4	2.1	27.1	11.2
Argentina	5.1	16.9	7.1	4.2	1.6	21.1	8.7
Kazakhstan	4.0	13.2	5.6	3.3	1.3	16.5	6.8
Denmark	3.5	11.6	4.9	2.9	1.1	14.5	6.0
United States of America	3.3	11.1	4.7	2.8	1.1	13.9	5.7
Poland	3.0	10.2	4.3	2.5	1.0	12.7	5.2
Morocco	2.9	9.5	4.0	2.4	0.9	11.9	4.9
Iran	2.8	9.3	3.9	2.3	0.9	11.7	4.8
Ethiopia	2.1	7.0	2.9	1.8	0.7	8.8	3.6
Algeria	2.0	6.5	2.7	1.6	0.6	8.2	3.4
Romania	1.9	6.2	2.6	1.6	0.6	7.8	3.2
India	1.8	5.9	2.5	1.5	0.6	7.4	3.1
World	−	−	−	−	−	−	−
Source: FAO outlook^[11] (Accessed on 5-5-2020). Where: CC = Carbon content, MT = Metric tons, GY = Grain yield, AGB = Above ground biomass, ACC = Above ground CC, BGB = Below ground biomass, BCC = Below ground CC, TBM = Total biomass (AGB + BGB), TCC = Total CC (ACC + BCC).

| Show Table

DownLoad: CSV

The above ground biomass (AGB) or shoot biomass was calculated using Eqn 4:

$\begin{split} {\boldsymbol{AGB}} =\;&{\boldsymbol{ Grain}} \;{\boldsymbol{Production}} {\bf\div} {\bf{0.30}}\; ({\boldsymbol{Factor}}) \\=\;& 17.0 \div 0.30 \\=\;& 56.6\; (MT)\end{split} \tag{4}$

The carbon content of the AGB or shoot CC (ACC) was calculated using the following Eqn 1:

$\begin{split}{\boldsymbol{ ACC}} =\;&{\boldsymbol{ AGB}} {\bf\times} {\boldsymbol{factor}}\; ({\bf{0.42}})\\ =\;& 56.6 \times 0.42 \\=\;& 23.8\; MT\; ({\bf{82}}{ {\%}})\end{split}\tag{1}$

The total biomass (TBM) (shoots + roots dry weights) was calculated using Eqn 5:

$\begin{split} {\boldsymbol{TBM}} =\;&{\boldsymbol{ AGB}} {\bf\times} {\bf{1.25}}\\ =\;&{\bf{ 56.6}} {\bf\times} {\bf{1.25}}\\ =\;&{\bf{ 70.8}}\;{\boldsymbol{ MT}}\end{split}\tag{5}$

The below ground biomass (BGB) or root dry weight (root biomass) was calculated using Eqn 6:

$\begin{split}{\boldsymbol{ BGB}} =\;&{\boldsymbol{ TBM}} {\bf\times} {\bf{0.20}}\; ({\boldsymbol{Factor}})\\ =\;& 70.8 \times 0.20 \\=\;& 14.2\; MT\end{split}\tag{6}$

The CC of the BGB or root biomass (BCC) was calculated using Eqn 2:

$\begin{split}{\boldsymbol{BCC}} =\;&{\boldsymbol{ BGB}} {\bf\times} {\boldsymbol{factor}}\; ({\bf{0.38}})\\ =\;& 14.2 \times 0.38\\ =\;& 5.4\; MT\; ({\bf{18}}{ {\%}})\end{split}\tag{2}$

The total CC (TCC) fixed by wheat in Pakistan during 2018 was calculated using Eqn 3:

$\begin{split}{\boldsymbol{ TCC}} =\;&{\boldsymbol{ ACC}} +{\boldsymbol{ BCC}}\\ =\;&{\bf{ 23.8}}\; ({\bf{82}}{ {\%}}) + {\bf{5.4}} \;({\bf{18}}{ {\%}})\\ =\;& {\bf{29.2}}\;{\boldsymbol{ MT}}\; ({\bf{100}}{ {\%}})\end{split}\tag{3}$

The TCC (shoots + roots), ACC (shoots) and BCC (roots) for cereal crops can be easily calculated:

Parameter	Equal to	Parameter	Symbol	Factor	Equation
ACC	=	AGB	×	0.42	1
BCC	=	BGB	×	0.38	2
TCC	=	ACC	+	BCC	3
AGB	=	GY	÷	0.35*	4
TBM	=	AGB	×	1.25	5
BGB	=	TBM	×	0.20	6
TBM	=	AGB	+	BGB	7
Where: W, R, M and B stands for wheat, rice, maize and barley, respectively. Factor: * 0.35 for wheat, 0.45 for rice, 0.40 for maize, and 0.30 for barley. GY = Grain yield; AGB = Above ground biomass; ACC = Above ground CC; BGB = Below ground biomass; BCC = Below ground CC; TBM = Total biomass (AGB + BGB); TCC = Total CC (ACC + BCC).

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Discussion

This model was used to calculate the TCC (kg·ha⁻¹) accumulated by the four major cereal crops (wheat, rice, maize and barley) and its distribution into ACC (kg·ha⁻¹) and BCC (kg·ha⁻¹) for the 40 leading countries during 2019 (USDA). The grain yield data (t ha⁻¹) was accessed on the USDA website on 8th May 2020 and converted into kg·ha¹ (t·ha⁻¹ × 1,000 = kg·ha⁻¹). The results revealed that remarkable variations were observed in TCC, ACC and BCC among the 40 countries for each crop. For example, the TCC for wheat crop ranged from 4,414 for Pakistan to 13,243 kg·ha⁻¹ for New Zealand (Table 5). For the rice crop, the TCC ranged from 4578 (Philippines) to 11,444 kg·ha⁻¹ (Australia) as shown in Table 6. The lowest TCC (5,150 kg·ha⁻¹) in the case of maize crop was calculated for Korea Democratic and the highest (15,450 kg·ha⁻¹) for Chile (Table 7). In the case of barley, the TCC ranged from 3,443 (Algeria) to 13,733 kg·ha⁻¹ (Chile), respectively (Table 8). These results revealed that out of the TCC (100%) accumulated by the crops (wheat, rice, maize and barley), 82% was partitioned into ACC (shoots) and 18% to BCC (roots). The higher TCC partitioning into shoots (ACC) than roots (BCC) in different crop species was the possible cause of higher shoot dry matter (shoot biomass) than total root dry matter (roots biomass) production^[45−47]. The differences in the ACC and BCC among the four crop species was attributed to the genetic differences among the crops species^{[4, 46,47]}. Variation in C content among plant organs are also reported in previous research^[48,49].

Table 5. Carbon content (kg·ha⁻¹) calculation for wheat crop in 40 leading countries in the world during 2019−2020.

Countries	Kilograms per hectare (kg·ha⁻¹)
Countries	GY	ACC	BCC	TCC
New Zealand	9000	10800	2443	13243
Namibia	6000	7200	1629	8829
Saudi Arabia	6000	7200	1629	8829
Switzerland	6000	7200	1629	8829
Chile	6000	7200	1629	8829
China	6000	7200	1629	8829
EU-27	6000	7200	1629	8829
Egypt	6000	7200	1629	8829
Zambia	6000	7200	1629	8829
Uzbekistan	5000	6000	1357	7357
Japan	5000	6000	1357	7357
Mexico	5000	6000	1357	7357
Norway	5000	6000	1357	7357
Macedonia	4000	4800	1086	5886
Mali	4000	4800	1086	5886
Serbia	4000	4800	1086	5886
Ukraine	4000	4800	1086	5886
Korea, Republic	4000	4800	1086	5886
Belarus	4000	4800	1086	5886
Albania	4000	4800	1086	5886
Bosnia-Herzegovina	4000	4800	1086	5886
Bangladesh	4000	4800	1086	5886
Zimbabwe	4000	4800	1086	5886
South Africa	3000	3600	814	4414
Azerbaijan	3000	3600	814	4414
Armenia	3000	3600	814	4414
Argentina	3000	3600	814	4414
Canada	3000	3600	814	4414
Brazil	3000	3600	814	4414
Iran	3000	3600	814	4414
India	3000	3600	814	4414
United States of America	3000	3600	814	4414
Uruguay	3000	3600	814	4414
Tajikistan	3000	3600	814	4414
Turkey	3000	3600	814	4414
Russian Federation	3000	3600	814	4414
Pakistan	3000	3600	814	4414
Sudan	3000	3600	814	4414
Syrian Arab Rep.	3000	3600	814	4414
Lebanon	3000	3600	814	4414
Grain yield data of 2019 taken from USDA website accessed on 8 May 2020. *GY (grain yield), CC (carbon content), ACC (above ground CC), BCC (below ground CC) and TCC (total below and above ground CC).

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Table 6. Carbon content (kg·ha⁻¹) calculation for rice crop in 40 leading countries in the world during 2019.

Countries	Kilograms per hectare (kg ha⁻¹)
Countries	GY	ACC	BCC	TCC
Australia	10000	9333	2111	11444
Turkey	9000	8400	1900	10300
Peru	8000	7467	1689	9156
Morocco	8000	7467	1689	9156
Egypt	8000	7467	1689	9156
United States of America	8000	7467	1689	9156
Uruguay	8000	7467	1689	9156
EU-27	7000	6533	1478	8011
Japan	7000	6533	1478	8011
Argentina	7000	6533	1478	8011
Chile	7000	6533	1478	8011
China	7000	6533	1478	8011
Korea	7000	6533	1478	8011
Mexico	6000	5600	1267	6867
Paraguay	6000	5600	1267	6867
Russian Federation	6000	5600	1267	6867
El Salvador	6000	5600	1267	6867
Taiwan	6000	5600	1267	6867
Brazil	6000	5600	1267	6867
Guyana	6000	5600	1267	6867
Viet Nam	6000	5600	1267	6867
Ukraine	5000	4667	1056	5722
Indonesia	5000	4667	1056	5722
Iraq	5000	4667	1056	5722
Iran	5000	4667	1056	5722
Dominican Republic	5000	4667	1056	5722
Bangladesh	5000	4667	1056	5722
Colombia	5000	4667	1056	5722
Suriname	5000	4667	1056	5722
Mauritania	5000	4667	1056	5722
Niger	5000	4667	1056	5722
Kazakhstan	5000	4667	1056	5722
Sri Lanka	4000	3733	844	4578
Nicaragua	4000	3733	844	4578
Nepal	4000	3733	844	4578
Panama	4000	3733	844	4578
Korea, Democratic	4000	3733	844	4578
Malaysia	4000	3733	844	4578
Senegal	4000	3733	844	4578
Philippines	4000	3733	844	4578
Grain yield data of 2019 taken from USDA website accessed on 8 May 2020. * GY (grain yield), CC (carbon content), ACC (above ground CC), BCC (below ground CC) and TCC (total below and above ground CC).

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Table 7. Carbon content (kg·ha⁻¹) calculation for maize crop in 40 leading countries in the world during 2019.

Countries	Kilograms per hectare (kg·ha⁻¹)
Countries	GY	ACC	BCC	TCC
Chile	12000	12600	2850	15450
Turkey	12000	12600	2850	15450
United States	11000	11550	2613	14163
New Zealand	11000	11550	2613	14163
Uzbekistan	10000	10500	2375	12875
Jordan	10000	10500	2375	12875
Switzerland	9000	9450	2138	11588
Canada	9000	9450	2138	11588
Argentina	8000	8400	1900	10300
Bangladesh	8000	8400	1900	10300
Egypt	8000	8400	1900	10300
EU-27	7000	7350	1663	9013
Albania	7000	7350	1663	9013
Australia	7000	7350	1663	9013
Iran	7000	7350	1663	9013
Kyrgyzstan	7000	7350	1663	9013
Uruguay	7000	7350	1663	9013
Ukraine	7000	7350	1663	9013
Serbia	7000	7350	1663	9013
Russian Federation	6000	6300	1425	7725
Saudi Arabia	6000	6300	1425	7725
Paraguay	6000	6300	1425	7725
Taiwan	6000	6300	1425	7725
Tajikistan	6000	6300	1425	7725
Iraq	6000	6300	1425	7725
Kazakhstan	6000	6300	1425	7725
Lao	6000	6300	1425	7725
Malaysia	6000	6300	1425	7725
Azerbaijan	6000	6300	1425	7725
Bosnia-Herzegovina	6000	6300	1425	7725
Brazil	6000	6300	1425	7725
Belarus	6000	6300	1425	7725
China	6000	6300	1425	7725
Korea, Republic	5000	5250	1188	6438
Cambodia	5000	5250	1188	6438
Viet Nam	5000	5250	1188	6438
South Africa	5000	5250	1188	6438
Pakistan	5000	5250	1188	6438
Thailand	4000	4200	950	5150
Korea, Democratic	4000	4200	950	5150
Grain yield data of 2019 taken from USDA website accessed on 8 May 2020. * GY (grain yield), CC (carbon content), ACC (above ground CC), BCC (below ground CC) and TCC (total below and above ground CC).

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Table 8. Carbon content (kg·ha⁻¹) calculation for barley crop in 40 leading countries in the world during 2019−2020.

Countries	Kilograms per hectare (kg·ha⁻¹)
Countries	GY	ACC	BCC	TCC
Chile	8000	11200	2533	13733
New Zealand	7000	9800	2217	12017
Zimbabwe	6000	8400	1900	10300
Switzerland	6000	8400	1900	10300
EU-27	5000	7000	1583	8583
Saudi Arabia	5000	7000	1583	8583
Ukraine	4000	5600	1267	6867
United States	4000	5600	1267	6867
Uruguay	4000	5600	1267	6867
Serbia	4000	5600	1267	6867
Norway	4000	5600	1267	6867
Korea, Republic	4000	5600	1267	6867
Brazil	4000	5600	1267	6867
Belarus	4000	5600	1267	6867
Canada	4000	5600	1267	6867
Argentina	4000	5600	1267	6867
Azerbaijan	3000	4200	950	5150
Bosnia- Herzegovina	3000	4200	950	5150
China	3000	4200	950	5150
Japan	3000	4200	950	5150
Kenya	3000	4200	950	5150
India	3000	4200	950	5150
Mexico	3000	4200	950	5150
South Africa	3000	4200	950	5150
Tunisia	2000	2800	633	3433
Turkey	2000	2800	633	3433
Ethiopia	2000	2800	633	3433
Russian Federation	2000	2800	633	3433
Peru	2000	2800	633	3433
Uzbekistan	2000	2800	633	3433
Tajikistan	2000	2800	633	3433
Iran	2000	2800	633	3433
Georgia	2000	2800	633	3433
Kyrgyzstan	2000	2800	633	3433
Lebanon	2000	2800	633	3433
Moldova	2000	2800	633	3433
Macedonia	2000	2800	633	3433
Colombia	2000	2800	633	3433
Algeria	2000	2800	633	3433
Grain yield data of 2019 taken from USDA website accessed on 8 May 2020. * GY (grain yield), CC (carbon content), ACC (above ground CC), BCC (below ground CC) and TCC (total below and above ground CC).

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The TCC accumulation in crop plants and its partitioning into above ground parts (ACC) and below ground parts (BCC) depends on these three major factors: (1) plant genotypes (species, varieties, hybrids, growth habit, growth stages); (2) agronomic practices (chemical fertilizers, organic fertilizers, biofertilizers, plant nutrition, irrigation, tillage practices, soil types, SOC, plant density, seed rates, sowing time, etc. ); and (3) environmental condition viz. biotic stresses (plant competition, weeds, diseases, insects, pests, etc.) and abiotic stresses (low and high temperature stress, low and high water stress, light quality and duration, wind, chemicals, gases, soil pollution, water pollution etc.). Total dry matter accumulation and its partitioning into roots and shoots depends on plant nutrition^{[46, 49]}, light availability^[50], soil types^[45], plant competitions^[51], organic sources^[52], beneficial microbes^[52,53], plant species^{[4, 45,54]}, plants genotypes^{[2, 55]}, plant tissues^{[48, 56−58]}, and plant growth stages^{[46, 59,60]}, etc.

The differences in the TCC accumulation and its partitioning into ACC and BCC in different crop species under study may be attributed to the differences in genetic makeup and differences in plant heights, leaf area, leaf area index and crop growth rate, water and nutrients use efficiency^{[45,46, 61]}. Bagrintseva & Nosov^[62] and Mut et al.^[63] reported changes in the total biomass accumulation in different crops. Therefore, crops which could sequester more carbon above (shoots) and below ground (roots) indicating more carbon dioxide sequestration from the atmosphere and therefore the cultivation of these crops could help reduce global warming. Therefore, plant breeder's efforts to produce crop species and ideotypes with higher TCC accumulation could be useful. As the cereals and grasses are executive crops^[64], so the use of sustainable soil management practices^{[8, 11, 65]}, could also increase the TCC accumulation and reduce CO₂ in the atmosphere.

In our previous experiments^{[45, 66]}, the NPK source which was associated with higher total biomass (TCC) also increases both root biomass (BCC) and shoot biomass (ACC). The increase and decrease in the CC accumulation in both roots (BCC) and shoots (ACC) in this study showed positive relationship with increase in biomass production and partitioning^[1] and better growth^[45,46]. Bagrintseva & Nosov^[62] reported more DM partitioning in wheat and barley with combined application of N + P + K than N + P. Amanullah & Stewart^{[67 ]} reported that N toxicity had reduced total biomass formation and partitioning into shoots (ACC) and roots (BCC).

In other studies^[59] where both organic and inorganic soils were compared. The results revealed that the higher BCC under three organic soils (S₃, S₄ and S₅) at different growth stages was attributed to the longer root lengths and formation of a greater number of roots per plant^[59], that increased water use efficiency that allocated more total biomass (TCC) into below ground biomass (BCC)^[45,46]. In contrast, the lesser BCC obtained under inorganic soils (S₁ and S₂) was attributed the shorter root lengths and less number of roots per plant produced^[59], and low WUE with lesser allocation of dry matter into roots^[45]. Likewise, the higher ACC under three organic soils (S₃, S₄ and S₅) at different growth stages was attributed to formation of taller plants with more number of leaves and larger leaf area, and formation of a greater number of tillers per plant^[59] and high WUE with greater allocation of dry matter into shoots^[45]. In contrast, the lesser ACC obtained under inorganic soils (S₁ and S₂) was attributed to development of shorter plants with less number of leaves and less leaf area and formation of a less number of tillers per plant^[59] and low WUE with and so lesser allocation of DM into shoots^[45].

Integrated nutrients management in field crop production, especially plant residue incorporation improve soil fertility that improves crop growth and total biomass^{[2−4, 68]} and reduces the problem of food security. Amanullah,^[69] in a FAO global conference (Rome, Italy) reported that integrated use of organic carbon source (animal manures and plant residues), plant nutrients (macro and micro nutrients) and bio-fertilizers (beneficial microbes) is key to improve soil organic carbon and field crops productivity. The best management practices that increase SOC could reduce soil pollution and improve the health of all on the earth^[70]. According to Lal,^[71] field crop production in Africa, Asia and South America could be increased by millions every year, by increasing soil organic matter by one ton per hectare.

The variations in the TCC estimated for different countries and it's partitioning into roots (BCC) and shoots (ACC) may be attributed to the differences in the genetic make of the crop varieties used in different countries, the variation in the environmental conditions among the countries, and different agronomic practices used in different countries. The review of global data^[72] showed that TCC transfer was highest in maize, which yielded the greatest soil C sequestration potential (1.0 Mg C ha⁻¹ yr⁻¹ or 19% total assimilation), followed by sorghum (1.0 Mg C ha⁻¹, 17%) and wheat (0.8 Mg C ha⁻¹ yr⁻¹, 23%). Variation in TCC among cereals such as maize, sorghum, wheat and rice has been earlier reported by McKendry^[73]. According to Zengeni et al.^[72] higher TCC transfer to soils occurred under clayey soils and warmer climates provided that exudation is high enough to offset respiration C losses. According to Ma et al.^[21], plant organ C content is 45.0% in reproductive organs, 47.9% in stems, 46.9% in leaves and 45.6% in roots.

For increasing TCC accumulation in plants and it's partitioning into ACC and BCC it is important to (1) select high yielding plants genotypes, (2) use best agronomic (management) practices and (3) planting of crops in suitable environmental conditions. The best agronomic practices that improve crop growth and development, increase grain and total biomass thus increase TCC in different crop species (Tables 5−8). Any biotic or abiotic stress that could reduce the productivity (yield or biomass) of field crops could reduce the TCC and its partitioning into ACC and BCC under different environments. The agronomists study various crop production problems and work for better soil and crop management practices to obtain higher yield^[74].

Conclusions

The increase in carbon sequestration (1 kg of carbon is equal to 3.67 kg of CO₂) through the process of photosynthesis in field crops is essential to combat the issue of food security and global warming. However, the lack of simplified and easy way of carbon estimation restricts researchers to estimate data on total carbon content (TCC) sequestered or captures by the field crops and it's partitioning into roots and shoots. In this study, highly simplified calculations have been developed to provide easy estimation of carbon content in below-ground parts (BCC) and above-ground parts (ACC) of various field crops, including wheat, rice, maize, barley, and more. Best agronomic practices that improve or increase the rate of photosynthesis under field conditions significantly increase the capture or sequestration carbon. Crops with higher TCC took more CO₂ (higher photosynthetic efficiency) from the atmosphere therefore increase the yield per unit area and decrease the negative impacts of global warming and food security. The simplified approach for carbon content (CC) estimation utilized in this study can be highly beneficial for researchers and students. It allows for easy estimation of the carbon content fixed in the roots and shoots of diverse field crops, including wheat, rice, maize, and barley. The total carbon content (TCC) in plants exhibited a positive correlation with total biomass. Furthermore, both below-ground carbon content (BCC) and above-ground carbon content (ACC) demonstrated a positive association with TCC. It was confirmed from the model, that out of the total 100% TCC accumulation by field crops, 82% is partitioned into ACC (shoots) and 18% into BCC (roots). The practices that increase grain yield, harvest index, and total biomass increased carbon content in roots and shoots of different crop species. The best agronomic practices that increase grain yield in field crops per unit area will also increase the TCC accumulation and it's portioning into ACC and BCC. Selecting carbon superior genotypes of crop species along with best management practices including sustainable soil management practices will significantly reduce CO₂ emission and increase soil health, productivity and sustainability with more carbon sequestration into the soils.

Conflict of interest

The author declares that there is no conflict of interest.

Rights and permissions
Copyright: © 2022 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.

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Zhou L, Yang L, Fang Q, Dong B, Wang Y, et al. 2022. Sex ratio, floral traits, and genetic variation of androdioecious Osmanthus fragrans L. (Oleaceae) and the implications for maintenance of high male frequency. Ornamental Plant Research 2:22 doi: 10.48130/OPR-2022-0022

Zhou L, Yang L, Fang Q, Dong B, Wang Y, et al. 2022. Sex ratio, floral traits, and genetic variation of androdioecious Osmanthus fragrans L. (Oleaceae) and the implications for maintenance of high male frequency. Ornamental Plant Research 2:22 doi: 10.48130/OPR-2022-0022

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INTRODUCTION

Androdioecy, the coexistence of males and hermaphrodites in the same population, is an extremely rare sexual system in both plants and animals^[1−5]. Currently, the sexual system of at least 15 plant species is reported as androdioecious. However, several species reported to be androdioecious are in fact functionally dioecious^[6]. According to the phenotypic selection model, male functionally androdioecious species should have a siring success at least double that of hermaphrodites to remain in the population^[7,8], leading to a male frequency of less than 0.5, with even lower male frequency in a metapopulation model^[5]. Males should even have greater fecundity to remain in partially selfing populations than in outcrossing populations because fewer ovules are available for outcrossing^[7−9]. Thus, functionally androdioecious species are expected to exhibit self-incompatibility or at least a low selfing rate, and a large male advantage^[10,11].

Datisca glomerata (Datiscaceae) has completely satisfied theoretical predictions^[12−15]. They have several common characteristics, such as wind pollination, relatively low (< 30%) male frequency in the populations, protogyny of hermaphrodite flowers, and larger pollen production in males than in hermaphrodites. Additionally, Fraxinus lanuginosa (Oleaceae), a wind- and insect-pollinated tree, has male frequency ranging from 10% to 50% in populations; the hermaphrodites in this species exhibit strong self-sterility, and the male advantage is 2.7-fold in terms of seed production^[16].

However, the 1:1 ratio of males and hermaphrodites and self-fertility of hermaphrodites have been described in two Oleaceae species , Phillyrea angustifolia^[17] and Fraxinus ornus^[18,19]. P. angustifolia, a wind-pollinated shrub, can maintain high male frequency in natural populations because hermaphrodites belong exclusively to one of two self-incompatibility groups, and each group can fertilize only half of all pollen recipients^[20]. F. ornus has the similar mechanism as that of P. angustifolia through the combined action of a Diallelic Selfincompatibility System (DSI) and male compatibility with both groups^[21].

Since Lepart & Dommée^[22] first reported the functional androdioecy of P. angustifolia in Oleaceae, androdioecy species were reported in at least three genera (Phillyrea, Fraxinus, and Osmanthus) in this family. Mock privet (P. latifolia) is morphologically androdioecious and functionally dioecious^[23]. At least three species are functional androdioecious in the genus Fraxinus^[18,24]. These species lack petals and are presumed wind-pollinated, whereas those species with petals are probably insect-pollinated. Another long-lived woody species, Osmanthus fragrans L., has also been reported as androdiecious^[25]. The male frequency of O. fragrans in populations is near 0.5 (males versus all)^[25,26]. O. fragrans grows in low to middle altitude on limestone mountains and naturally distributes from the Yangtze River Basin in South China to Southeast China^[27].

Gender differences associated with reproduction in sexually dimorphic plants allow intersexual comparisons of energy allocation^[28]. The intersexual comparison under androdioecy represents the most asymetrical situation (two extremes of the continuum)^[19]. The hermaphrodite individuals of Fraxinus appear to produce small and non-functional anthers^[4,24], and indicated that hermaphrodite individuals of Fraxinus already lost male function^[26]. In F. ornus, males compensate the fitness advantage of hermaphrodites with greater reproductive, but not vegetative output^[19]. In O. fragrans, the hermaphrodite flower has well-developed pistil and two stamens, and the male flower only has pistillode and two stamens. Pollen quantity per flower and their morphology between males and hermaphrodites are not significantly different. Moreover, both male and hermaphrodite individuals have high activity pollen and can germinate normally on stigmas^[25] . It is necessary to qualify the pollen production of male and hermaphrodite individual and value the fitness of each sexual individuals.

For androdioecious species, high male frequency in populations is presumed to result high outcrossing rate and maintain high genetic diversity. A significant negative correlation exists between male frequency and inbreeding coefficient in Schizopepon bryoniaefolius (Cucurbitaceae), indicating that outcrossing rate was affected by the population sex ratio^[29]. Moreover, outcrossing rate increased with increasing male frequency among sub-populations of F. lanuginose in north Japan^[30]. Male individuals of Laguncularia racemosa (Combretaceae) in androdioecious populations had significantly more visitors than hermaphroditic plants, increasing the number of vectors carrying pollen from male plants. Further, many insects visited few flowers during foraging bouts, which should increase outcrossing frequency^[31]. However, the strength of inbreeding depression does not increase with male frequency^[32]. As functional androdioecious species, the inbreeding level and genetic structure of O. fragrans may offer a clue for us to understand the sexual system.

The aim of this paper is to study the sexual system of O. fragrans and understand the maintenance of the 1:1 sex ratio among populations. We investigated the sex ratios in three populations. Second, to determine floral trait differences between genders and among populations, floral traits in three natural populations were investigated and the differences between genders and among populations were analyzed. Finally, genetic variations (heterozygosity and genetic differentiation) among natural populations and between genders were estimated using AFLP methods.

DISCUSSION

Sex ratio of O. fragrans

High male biased sex ratio had been described in two species of Oleaceae, P. angustifolia^[33] and F. ornus^[18]. For O. fragrans, the 1:1 sex ratio was also observed in multiple populations (Table 1). Charlesworth^[9] proposed that the 1:1 gender ratio observed in several suggested androdioecious species indicated that hermaphrodites preform female function in natural conditions, and that the populations functioned as a dioecious species such as F. ornus^[18]. Pollen quantity and quality (normal pollen germination on the stigmas and pollen tube growth) of hermaphrodites in O. fragrans was not significantly different with that of males^[25], and indicated that O. fragrans could be functional androdioecy. In P. angustifolia, Saumitou-Laprade et al.^[20] reported the existence of a diallelic sporophytic self incompatibility (SSI) system that was controlled by two alleles. The SSI system can solve the paradox of high proportion of males in the populations. There are two groups of self-incompatible hermaphrodites and only the crosses performed between two different phenotypes of hermaphrodites and between males and hermaphrodites were compatible. Contributing to this, males can potentially sire twice as many ovules as hermaphrodites. This SSI system may exist in the wild, not only driving the diversification of the Oleaceae mating systems, but also other cases of distyly^[20]. In O. fragrans, further work is necessary to clarify whether SSI systems exist or not.

Floral difference between males and hermaphrodites and male advantage
In androdioecious populations, males are expected to make more than twice the genetic contribution compared to hermaphrodites^[8,9]. This contribution can be conducted either through a higher pollen dispersal, or through a higher siring success of dispersed pollen^[4]. In F. ornus, the number of pollen gains per flower and the number of flowers per inflorescences were not significantly different between the genders, while males produced a significantly higher number of inflorescences than hermaphrodites^[19]. In all three O. fragrans populations, males produce more flowers and larger anther. The pollen production of males is about 1.7−2 times that of hermaphrodites. This indicated that male individuals advance in male fitness compared with hermaphrodites.

The flowering phenology of males and hermaphrodites correspond to sexual function. In the population, males flower bloom 1 to 2 d earlier than hermaphrodites; for hermaphrodite pistil mature and stigma get receptive period 1 to 2 d earlier before pollen dispersal. Hermaphrodite perform female function earlier than male function and during the female function period of hermaphrodite, male individuals are offering pollen.

Significant difference between males and hermaphrodites was found not only in flowering phenology but also in some floral traits. In the flower blooming process, males flowered 1 to 2 d earlier than hermaphrodites did within the population. This synchronization provides a better chance of mating between male and hermaphrodite individuals and ensure the fitness of male individuals.

In summary, males have more male fitness advantage than hermaphrodites on producing more pollen and scheduled flower blooming process. That may explain the occurrence and maintainenance of male individuals in the population and the sexual separation of O. fragrans.

Genetic variation, mating system, and the maintenance of 1:1 sex ratio
The nested AMOVA analysis indicates that the population genetic structure of O. fragrans is genetically homogeneous at the species level. The largest percentage of genetic variation existed within the population and within gender not only among populations but also between males and hermaphrodites. The percentages of variation between male and hermaphrodite groups and between male and hermaphrodite genders in the same populations were both low (0% and 2%, respectively), and the percentage of variation among populations was higher (14%). This result indicates that the three populations experienced a relatively high gene flow and low level of genetic isolation. In general, outcrossing and long-lived seed plants maintain high genetic variation within populations. Whereas predominantly selfing, short-lived species harbor comparatively higher variation among populations^[34]. The overall degree of genetic differentiation (Φpt = 0.143) among populations of O. fragrans was much lower than the average of 0.27 (genetic differentiation coefficient) for plants with a mixed breeding system, with an average of 0.25 for long-lived perennials and an average of 0.36 for dicotyledons^[35,36]. Moreover, genetic differentiation was very low (Φpt = 0.003 and 0.020) between genders not only among populations but also in the same population. This genetic structure showed high levels of heterozygotes within populations especially between the two genders. This could be greatly attributed to the occurrence of a high level of xenogamy between males and hermaphrodites. Simultaneously, high male frequency (50%) and more male fitness advantage in males are the essential condition for high outcrossing between males and hermaphrodites. Males could not produce progenies directly and transmit genetic information relaying on the females (hermaphrodites) in the population. However, those have a similar degree of genetic diversity with hermaphrodites. It attributes to very high gene flow between males and hermaphrodites. And xenogamy between males and hermaphrodites is the principal mating pattern in population reproduction and regeneration in this species. The male function of hermaphrodites chiefly plays the role for reproductive assurance. Therefore, the 1:1 sex ratio in this species could be the result of the integrative effects of sexual system, mating system, and reproductive success.

Plants benefit from high levels of genetic diversity and gene flow to adapt to the variable environmental conditions. Although the three populations are far from each other, a certain degree of gene flow among populations through mediate flow was observed. Because O. fragrans is a prolific species, one of primary species of evergreen broad-leaved forest that is widely distributed in the south of the Yangtze River Basin, high gene flow among close populations occurs, and gene flow among populations with distance occurs through gene introgression. The current populations and their distribution patterns are widely affected by the destruction of a large number of populations, particularly during the recent two decades. Habitat fragmentation and rapid decrease of population size have no obvious effects on genetic diversity of the surviving individuals but have great influence on population proliferation, regeneration, and dispersal and genetic diversity of the next generations. This condition may cause numerous species becoming endangered in the immediate future. The conservation of existing populations and their habitats, particularly for some populations that have rich morphological variations, is important in maintaining the genetic structure of O. fragrans.

CONCLUSIONS

The androdioecious mating system of Osmanthus fragrans L. (Oleaceae) may be explained by three aspects of evidence: male frequency, the floral traits, and genetic variation. In three nature O. fragrans population, male individuals maintain at about 50% in the population. Males bear more flowering nodes, and more flowers per node, and larger anther in all three populations. Males produce almost twice pollen comparing to the hermaphrodites. Genetic differentiation was very low between genders not only among populations but also in the same population. This genetic variation could be attributed to the occurrence of high levels of xenogamy between genders. Therefore, high male frequency and more male fitness advantage in males are the essential conditions for this mating system, which plays an important role during population reproduction and regeneration. The 1:1 sex ratio could be the result of integrative effects of sexual system, mating system, and reproductive success.

Population	Locality	Description	Males	Hermaph-rodites	Male frequency *	χ²
CT	Guanfang, Changting, Fujian	Isolated island of limestone mountains	100	61	0.620	9.96273
LY	Guihuaxia, Zhouluo, Liuyang, Hunan	Along a stream, mainly east-facing	35	33	0.515 ns	0.01470
QDH	Guihua island, Thousand-isle Lake, Jiande, Zhejiang	Isolated island of limestone mountains	26	21	0.553 ns	0.34042
LQ-GC	Gongcun, Longquan, Zhejiang	Along a stream, west-facing	35	48	0.422 ns	1.73494
LQ-MYC	Maoyucun, Longquan, Zhejiang	Along a stream, west-facing	19	13	0.594 ns	0.78125
Total			189	172	0.524 ns	0.89751
Sex ratio and male frequency of O. fragrans in different populations were firstly stated by Hao et al.^[25]. Here, we cite the part of data and newly add the results of QDH population and make further statistics analysis in this paper. 'ns' means sex ratio does not significantly deviate from 1:1.

Stage	Stage description	Day
Stage	Stage description	Males	Hermaphrodites
1	Bracts dropped and florets appeared gradually.	1	2
2	All florets appeared.	2	3
3	The pedicels of florets elongated gradually.	3	4
4	Florets opened gradually while the petals have not expanded completely.	4	5
5	Florets opened completely while anthers have not dehisced. (However, if the weather was sunny and temperature was high, anthers would dehisce at several hours after flowering.)	5	6−7
6	Anthers dehisced gradually and pollen grains dispersed. (If anthers have dehisced last day, they would brown gradually.)	6	7−8
7	Anthers browned gradually.	7	8−9
8	Florets dropped gradually.	8−9	−
9	The petals of florets browned and wilted gradually but not dropped.	−	9−11

Populations	Gender	Sample size	No. of flowering node	Flower number per node	Flower diameter	Petal length	Petal width	Anther width
CT	M	34	3.73 ± 0.64A	15.47 ± 4.90a	7.00 ± 1.81A	4.05 ± 0.74A	2.49 ± 0.56a	1.28 ± 0.21A
	H	26	3.17 ± 1.18B	13.73 ± 4.69a	5.81 ± 1.01B	3.48 ± 0.48B	2.49 ± 0.42a	1.11 ± 0.11B
LY	M	31	4.00 ± 0.83A	15.93 ± 5.32A	6.13 ± 0.52B	3.01 ± 0.67a	2.05 ± 0.31a	1.19 ± 0.08A
	H	33	3.47 ± 1.07B	13.33 ± 3.62B	6.69 ± 0.61A	3.05 ± 0.54a	2.05 ± 0.28a	1.06 ± 0.10B
LQ-GC	M	33	4.30 ± 0.88A	18.77 ± 8.94A	6.52 ± 1.47a	3.48 ± 0.43B	2.60 ± 0.43A	1.38 ± 0.14A
	H	34	3.37 ± 0.81B	14.50 ± 5.97B	6.87 ± 1.86a	3.65 ± 0.63A	2.42 ± 0.43B	1.24 ± 0.19B
Males^*	M	98	4.01 ± 0.81A	16.7 ± 6.73A	6.55 ± 1.41a	3.51 ± 0.76a	2.38 ± 0.50a	1.28 ± 0.17A
Hermaphrodites	H	93	3.33 ± 1.03B	13.9 ± 4.83B	6.46 ± 1.34a	3.40 ± 0.60b	2.32 ± 0.43a	1.14 ± 0.16B
CT^**		60	3.45 ± 0.98b	14.60 ± 4.83b	6.42 ± 1.57b	3.77 ± 0.68A	2.49 ± 0.49a	1.19 ± 0.19B
LY		64	3.73 ± 0.99a	14.63 ± 4.70b	6.41 ± 0.63b	3.03 ± 0.60C	2.05 ± 0.30b	1.12 ± 0.11C
LQ-GC		67	3.83 ± 0.96Aa	16.63 ± 7.84a	6.69 ± 1.67a	3.56 ± 0.54B	2.51 ± 0.44a	1.31 ± 0.18A
M: male; H: hermaphrodite. Different upper case letters mean significant differences at p < 0.01 and different lowercase letters mean significant differences at p < 0.05. * Comparison between genders, populations pooled; ** comparison among populations, genders pooled.

Populations	Sample size	Ovary length	Ovary width	Stigma length	Stigma width
CT	26	2.01 ± 0.17a	1.21 ± 0.11a	0.99 ± 0.10a	0.84 ± 0.17a
LY	33	1.91 ± 0.26a	1.26 ± 0.11a	0.95 ± 0.10ab	0.85 ± 0.19a
LQ-GC	34	1.99 ± 0.16a	1.27 ± 0.14a	0.94 ± 0.12b	0.78 ± 0.10a
The different letters indicate significant differences at p < 0.05.

Population	No. individuals sampled	PPL (%)	Na	Ne	I	He	UHe	Br
CT	30	67.62	1.399 ± 0.023	1.287 ± 0.009	0.274 ± 0.007	0.175 ± 0.005	0.178 ± 0.005	1.293
LY	30	72.75	1.503 ± 0.022	1.266 ± 0.008	0.265 ± 0.006	0.166 ± 0.005	0.169 ± 0.005	1.290
LQ-GC	22	71.24	1.472 ± 0.022	1.289 ± 0.009	0.282 ± 0.007	0.179 ± 0.005	0.183 ± 0.005	1.313
Mean	—	70.54	1.458 ± 0.013	1.280 ± 0.005	0.274 ± 0.004	0.174 ± 0.003	0.177 ± 0.003	—
CTm	15	58.74	1.252 ± 0.024	1.273 ± 0.009	0.257 ± 0.007	0.166 ± 0.005	0.172 ± 0.005	1.290
CTh	15	56.83	1.206 ± 0.025	1.267 ± 0.009	0.250 ± 0.007	0.162 ± 0.005	0.168 ± 0.005	1.285
LYm	15	62.77	1.326 ± 0.024	1.264 ± 0.009	0.257 ± 0.007	0.164 ± 0.005	0.169 ± 0.005	1.296
LYh	15	57.31	1.219 ± 0.025	1.243 ± 0.008	0.236 ± 0.007	0.150 ± 0.005	0.156 ± 0.005	1.278
LQ-GCm	6	50.48	1.080 ± 0.025	1.265 ± 0.009	0.245 ± 0.007	0.160 ± 0.005	0.175 ± 0.005	1.336
LQ-GCh	16	64.55	1.368 ± 0.023	1.275 ± 0.009	0.265 ± 0.007	0.169 ± 0.005	0.174 ± 0.005	1.304
Mean	—	58.45	1.242 ± 0.010	1.265 ± 0.004	0.252 ± 0.003	0.162 ± 0.002	0.169 ± 0.002	—
M	36	78.83	1.606 ± 0.020	1.305 ± 0.009	0.300 ± 0.006	0.190 ± 0.005	0.192 ± 0.005	1.326
H	46	84.29	1.709 ± 0.018	1.301 ± 0.008	0.300 ± 0.006	0.188 ± 0.005	0.191 ± 0.005	1.327
Mean	—	81.56	1.362 ± 0.012	1.288 ± 0.004	0.275 ± 0.003	0.177 ± 0.002	0.190 ± 0.003	—
CTm and CTh represent male and hermaphrodite gender respectively in Changting population; LYm and LYh represent male and hermaphrodite gender respectively in Liuyang population; LQ-GCm and LQ-GCh represent male and hermaphrodite gender respectively in Longquan population; M and H represent male and hermaphrodite gender respectively from all three populations.

Source	df	MS	Est. Var.	%	Phi	p-value
AMOVA analysis 1
Among populations	2	759.002	20.962	13	0.125	0.001
Among genders/ populations	3	186.594	3.354	2	0.023	0.006
Within gender	76	143.296	143.296	85	0.145	0.001
AMOVA analysis 2
Among sex groups	1	215.540	0.000	0	0.042	1.000
Among genders/sex groups	4	465.561	24.252	14	0.145	0.001
Within gender	76	143.296	143.296	86	0.109	0.001
p-value estimates are based on 999 permutations. df = degree of freedom and MS = mean squared deviations.

Population	CT	LY	LQ-GC
CT	—	1.256	2.624
LY	0.166	—	1.536
LQ-GC	0.087	0.140	—

Sub-population	CTm	CTh	LYm	LYh	LQ-GCh	LQ-GCm
CTm	—	6.893	1.498	1.073	1.943	2.177
CTh	0.035	—	1.247	0.935	2.559	3.481
LYm	0.143	0.167	—	12.908	1.549	2.354
LYh	0.189	0.211	0.019	—	1.162	1.719
LQ-GCh	0.114	0.089	0.139	0.177	—	27.528
LQ-GCm	0.103	0.067	0.096	0.127	0.009	—

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Sex ratio, floral traits, and genetic variation of androdioecious Osmanthus fragrans L. (Oleaceae) and the implications for maintenance of high male frequency

Abstract

Introduction

Methodology

Estimating the total carbon content (TCC) accumulation and its distribution above ground (shoots) and below ground (roots) parts in cereals

Results

Examples for validation of the model

Example 1 (wheat)

Example 2 (rice)

Example 3 (maize)

Example 4 (barley)

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Sex ratio, floral traits, and genetic variation of androdioecious Osmanthus fragrans L. (Oleaceae) and the implications for maintenance of high male frequency

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Sex ratio of O. fragrans among populations

Phenology of O. fragrans

Floral morphology and correlation analysis

Genetic diversity

Genetic structure and genetic differentiation

Sex ratio of O. fragrans

Floral difference between males and hermaphrodites and male advantage

Genetic variation, mating system, and the maintenance of 1:1 sex ratio

Study sites and sampling

Sex ratio, flower blooming process, and floral trait data recording

DNA extraction and AFLP analysis

Data analysis

Statistics analysis

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Sex ratio of o. fragrans among populations

Phenology of o. fragrans

Floral morphology and correlation analysis

Genetic diversity

Genetic structure and genetic differentiation

Sex ratio of o. fragrans

Floral difference between males and hermaphrodites and male advantage

Genetic variation, mating system, and the maintenance of 1:1 sex ratio

Study sites and sampling

Sex ratio, flower blooming process, and floral trait data recording

Dna extraction and aflp analysis

Data analysis

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