A simple model for estimation of above and below ground carbon in cereal crops

Amanullah; Amanullah

doi:10.48130/TIA-2023-0008

2023 Volume 3

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Abstract

References

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METHOD Open Access

A simple model for estimation of above and below ground carbon in cereal crops

Amanullah^,

Department of Agronomy, Faculty of Crop Production Sciences, The University of Agriculture Peshawar-Pakistan, 25130, Pakistan

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Corresponding author: amanullah@aup.edu.pk

Received: 07 April 2023
Accepted: 29 May 2023
Published online: 19 July 2023
Technology in Agronomy 3, Article number: 8 (2023) | Cite this article

Abstract

Carbon (C) is an essential part of healthy soil. Healthy soils play an important role in improving the life of all living organisms on earth (plants, humans, animals, birds, insects, microbes etc.). Best agronomic practices for field crop production sequester more carbon (due to higher photosynthesis) below and above the ground that makes the soils healthy and sustainable. Healthy soils increase yield per unit area and so reduce the problem of food insecurity. Higher photosynthetic efficiency (higher CO₂ uptake by the plants) reduces the problem of global warming and climate change. According to an estimate, plants capture about 860 gigatons of CO₂ each year from the atmosphere, storing it in their shoots, and roots (1 kg of carbon is equal to 3.67 kg of CO₂). The aim of this study was to develop a simple calculation (model) for researchers to easily estimate the carbon content (CC) capture by plants in below (roots) and above ground (shoots) parts. Considerable variation in total CC (TCC) accumulation and its partitioning into above ground parts (ACC) and below ground parts (BCC) exists which depends on crop species and genotypes, crop nutrition, crop competitions and intercropping, fertilizers application, irrigation, tillage, biotic and abiotic stresses, soil types and environment etc. The CC estimation is explained in detail with four examples on major cereal crops (wheat, rice, maize and barley) for the world leading countries in 2018−2019. In the first example using wheat, the TCC estimated for wheat crop in Pakistan was 37.4 metric tons (MT) of which 30.5 MT was allocated into ACC (shoots) and 6.9 MT into BCC (roots). The highest value of TCC accumulation for wheat crop was estimated for the European Union which was 216.3 MT (176.4 ACC + 39.9 BCC). In the second example using rice crop, TCC for the world leading countries was estimated and the leading country was China with TCC of 161.7 MT (131.9 ACC + 29.8 BCC). Example three is about the CC estimation for maize crop, and the leading country was USA having the highest TCC value of 505.3 MT (ACC = 412.1 MT, BCC = 93.2 MT). The Russian Federation ranked first for barley crop and the highest TCC value of 29.2 MT was recorded (23.8 MT ACC + 5.4 MT BCC). It was confirmed while using this model that out of the 100% (TCC) fixed, about 82% CC is partitioned into above ground parts (ACC) and the remaining 18% CC is allocated into below ground parts (BCC). Due to this model, we can easily calculate the TCC accumulation and its partitioning into ACC and BCC per unit area (kg·ha⁻¹). For example, the TCC was easily calculated for the 40 world leading countries for wheat, rice, maize and barley during 2019. The results revealed that the TCC ranged from 4,414 to 13,243 kg·ha⁻¹ for wheat, 4,578 to 11,444 kg·ha⁻¹ for rice, 5,150 to 15,450 kg·ha⁻¹ for maize, and 3,443 to 13,733 kg·ha⁻¹ for barley among the top 40 countries. This is the most simplified approach for estimating carbon content in the below-ground (roots) and above-ground (shoots) parts of field crops.
- Carbon content (CC),
- Roots or below ground CC (BCC),
- Shoots or above ground CC (ACC),
- Total CC (TCC),
- Cereals,
- Wheat,
- Barley,
- Maize,
- Rice

INTRODUCTION

Grapevines are among the most widely grown and economically important fruit crops globally. Grapes are used not only for wine making and juice, but also are consumed fresh and as dried fruit^[1]. Additionally, grapes have been increasingly recognized as an important source of resveratrol (trans-3, 5,4'-trihydroxystilbene), a non-flavonoid stilbenoid polyphenol that in grapevine may act as a phytoalexin. In humans, it has been widely reported that dietary resveratrol has beneficial impacts on various aspects of health^{[2, 3]}. Because of the potential value of resveratrol both to grapevine physiology and human medicine, resveratrol biosynthesis and its regulation has become an important avenue of research. Similar to other stilbenoids, resveratrol synthesis utilizes key enzymes of the phenylpropanoid pathway including phenylalanine ammonia lyase (PAL), cinnamate 4-hydroxylase (C4H), and 4-coumarate-CoA ligase (4CL). In the final steps, stilbene synthase (STS), a type II polyketide synthase, produces trans-resveratrol from p-coumaroyl-CoA and malonyl-CoA, while chalcone synthase (CHS) synthesizes flavonoids from the same substrates^{[4, 5]}. Moreover, trans-resveratrol is a precursor for other stilbenoids such as cis-resveratrol, trans-piceid, cis-piceid, ε-viniferin and δ-viniferin^[6]. It has been reported that stilbenoid biosynthesis pathways are targets of artificial selection during grapevine domestication^[7] and resveratrol accumulates in various structures in response to both biotic and abiotic stresses^[8−12]. This stress-related resveratrol synthesis is mediated, at least partialy, through the regulation of members of the STS gene family. Various transcription factors (TFs) participating in regulating STS genes in grapevine have been reported. For instance, MYB14 and MYB15^{[13, 14]} and WRKY24^[15] directly bind to the promoters of specific STS genes to activate transcription. VvWRKY8 physically interacts with VvMYB14 to repress VvSTS15/21 expression^[16], whereas VqERF114 from Vitis quinquangularis accession 'Danfeng-2' promotes expression of four STS genes by interacting with VqMYB35 and binding directly to cis-elements in their promoters^[17]. Aided by the release of the first V. vinifera reference genome assembly^[18], genomic and transcriptional studies have revealed some of the main molecular mechanisms involved in fruit ripening^[19−24] and stilbenoid accumulation^{[8, 25]} in various grapevine cultivars. Recently, it has been reported that a root restriction treatment greatly promoted the accumulation of trans-resveratrol, phenolic acid, flavonol and anthocyanin in 'Summer Black' (Vitis vinifera × Vitis labrusca) berry development during ripening^[12]. However, most of studies mainly focus on a certain grape variety, not to investigate potential distinctions in resveratrol biosynthesis among different Vitis genotypes. In this study, we analyzed the resveratrol content in seven grapevine accessions and three berry structures, at three stages of fruit development. We found that the fruits of two wild, Chinese grapevines, Vitis amurensis 'Tonghua-3' and Vitis davidii 'Tangwei' showed significant difference in resveratrol content during development. These were targeted for transcriptional profiling to gain insight into the molecular aspects underlying this difference. This work provides a theoretical basis for subsequent systematic studies of genes participating in resveratrol biosynthesis and their regulation. Further, the results should be useful in the development of grapevine cultivars exploiting the genetic resources of wild grapevines.

RESULTS

Extractable trans-resveratrol content in developing grape berries

For each of the seven cultivars, we analyzed resveratrol content in the skin, pulp, and seed at three stages of development: Green hard (G), véraison (V), and ripe (R) (Table 1). In general, we observed the highest accumulation in skins at the R stage (0.43−2.99 µg g⁻¹ FW). Lesser amounts were found in the pulp (0.03−0.36 µg g⁻¹ FW) and seed (0.05−0.40 µg g⁻¹ FW) at R, and in the skin at the G (0.12−0.34 µg g⁻¹ FW) or V stages (0.17−1.49 µg g⁻¹ FW). In all three fruit structures, trans-resveratrol showed an increasing trend with development, and this was most obvious in the skin. It is worth noting that trans-resveratrol was not detectable in the skin of 'Tangwei' at the G or V stage, but had accumulated to 2.42 µg g⁻¹ FW by the R stage. The highest amount of extractable trans-resveratrol (2.99 µg g⁻¹ FW) was found in 'Tonghua-3' skin at the R stage.

Table 1. Resveratrol concentrations in the skin, pulp and seed of berries from different grapevine genotypes at green hard, véraison and ripe stages.

Structures	Species	Accessions or cultivars	Content of trans-resveratrol (μg g⁻¹ FW)
Structures	Species	Accessions or cultivars	Green hard	Véraison	Ripe
Skin	V. davidii	Tangwei	nd	nd	2.415 ± 0.220
	V. amurensis	Tonghua-3	0.216 ± 0.041	0.656 ± 0.043	2.988 ± 0.221
	V. amurensis	Shuangyou	0.233 ± 0.062	0.313 ± 0.017	2.882 ± 0.052
	V. amurensis × V. Vinifera	Beibinghong	0.336 ± 0.076	1.486 ± 0.177	1.665 ± 0.100
	V. vinifera	Red Global	0.252 ± 0.051	0.458 ± 0.057	1.050 ± 0.129
	V. vinifera	Thompson seedless	0.120 ± 0.025	1.770 ± 0.032	0.431 ± 0.006
	V. vinifera × V. labrusca	Jumeigui	0.122 ± 0.016	0.170 ± 0.021	0.708 ± 0.135
Pulp	V. davidii	Tangwei	0.062 ± 0.006	0.088 ± 0.009	nd
	V. amurensis	Tonghua-3	0.151 ± 0.066	0.324 ±0.104	0.032 ± 0.004
	V. amurensis	Shuangyou	0.053 ± 0.008	0.126 ± 0.044	0.041 ± 0.017
	V. amurensis × V. Vinifera	Beibinghong	0.057 ± 0.014	0.495 ± 0.068	0.087 ± 0.021
	V. vinifera	Red Global	0.059 ± 0.018	0.159 ± 0.013	0.027 ± 0.004
	V. vinifera	Thompson seedless	0.112 ± 0.016	0.059 ± 0.020	nd
	V. vinifera × V. labrusca	Jumeigui	0.072 ± 0.010	0.063 ± 0.017	0.359 ± 0.023
Seed	V. davidii	Tangwei	0.096 ± 0.014	0.169 ± 0.028	0.049 ± 0.006
	V. amurensis	Tonghua-3	0.044 ± 0.004	0.221 ± 0.024	0.113 ± 0.027
	V. amurensis	Shuangyou	nd	0.063 ± 0.021	0.116 ± 0.017
	V. amurensis × V. Vinifera	Beibinghong	nd	0.077 ± 0.003	0.400 ± 0.098
	V. vinifera	Red Global	0.035 ± 0.023	0.142 ± 0.036	0.199 ± 0.009
	V. vinifera	Thompson seedless	−	−	−
	V. vinifera × V. labrusca	Jumeigui	0.077 ± 0.025	0.017 ± 0.004	0.284 ± 0.021
'nd' indicates not detected in samples, and '−' shows no samples are collected due to abortion.

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Analysis of RNA-Seq data

To gain insight into gene expression patterns influencing resveratrol biosynthesis in 'Tangwei' and 'Tonghua-3', we profiled the transcriptomes of developing berries at G, V, and R stages, using sequencing libraries representing three biological replicates from each cultivar and stage. A total of 142.49 Gb clean data were obtained with an average of 7.92 Gb per replicate, with average base Q30 > 92.5%. Depending on the sample, between 80.47%−88.86% of reads aligned to the V. vinifera reference genome (Supplemental Table S1), and of these, 78.18%−86.66% mapped to unique positions. After transcript assembly, a total of 23,649 and 23,557 unigenes were identified as expressed in 'Tangwei' and 'Tonghua-3', respectively. Additionally, 1,751 novel transcripts were identified (Supplemental Table S2), and among these, 1,443 could be assigned a potential function by homology. Interestingly, the total number of expressed genes gradually decreased from the G to R stage in 'Tangwei', but increased in 'Tonghua-3'. About 80% of the annotated genes showed fragments per kilobase of transcript per million fragments mapped (FPKM) values > 0.5 in all samples, and of these genes, about 40% showed FPKM values between 10 and 100 (Fig. 1a). Correlation coefficients and principal component analysis of the samples based on FPKM indicated that the biological replicates for each cultivar and stage showed similar properties, indicating that the transcriptome data was reliable for further analyses (Fig. 1b & c).

Figure 1. Properties of transcriptome data of 'Tangwei' (TW) and 'Tonghua-3' (TH) berry at green hard (G), véraison (V), and ripe (R) stages. (a) Total numbers of expressed genes with fragments per kilobase of transcript per million fragments mapped (FPKM) values; (b) Heatmap of the sample correlation analysis; (c) Principal component analysis (PCA) showing clustering pattern among TW and TH at G, V and R samples based on global gene expression profiles.

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GO and KEGG pathway enrichment analysis of DEGs

By comparing the transcriptomes of 'Tangwei' and 'Tonghua-3' at the G, V and R stages, we identified 6,770, 3,353 and 6,699 differentially expressed genes (DEGs), respectively (Fig. 2a). Of these genes, 1,134 were differentially expressed between the two cultivars at all three stages (Fig. 2b). We also compared transcriptional profiles between two adjacent developmental stages (G vs V; V vs R) for each cultivar. Between G and V, we identified 1,761 DEGs that were up-regulated and 2,691 DEGs that were down-regulated in 'Tangwei', and 1,836 and 1,154 DEGs that were up-regulated or down-regulated, respectively, in 'Tonghua-3'. Between V and R, a total of 1,761 DEGs were up-regulated and 1,122 DEGs were down-regulated in 'Tangwei', whereas 2,774 DEGs and 1,287 were up-regulated or down-regulated, respectively, in 'Tonghua-3' (Fig. 2c). Among the 16,822 DEGs between the two cultivars at G, V, and R (Fig. 2a), a total of 4,570, 2,284 and 4,597 had gene ontology (GO) annotations and could be further classified to over 60 functional subcategories. The most significantly represented GO terms between the two cultivars at all three stages were response to metabolic process, catalytic activity, binding, cellular process, single-organism process, cell, cell part and biological regulation (Fig. 2d).

Figure 2. Analysis of differentially expressed genes (DEGs) at the green hard (G), véraison (V), and ripe (R) stages in 'Tangwei' (TW) and 'Tonghua-3' (TH). (a) Number of DEGs and (b) numbers of overlapping DEGs between 'Tangwei' and 'Tonghua-3' at G, V and R; (c) Overlap among DEGs between G and V, and V and R, for 'Tangwei' and 'Tonghua-3'; (d) Gene ontology (GO) functional categorization of DEGs.

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We also identified 57 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that were enriched, of which 32, 28, and 31 were enriched at the G, V, and R stages, respectively. Seven of the KEGG pathways were enriched at all three developmental stages: photosynthesis-antenna proteins (ko00196); glycine, serine and threonine metabolism (ko00260); glycolysis/gluconeogenesis (ko00010); carbon metabolism (ko01200); fatty acid degradation (ko00071); cysteine and methionine metabolism (ko00270); and valine, leucine and isoleucine degradation (ko00280) (Supplemental Tables S3−S5). Furthermore, we found that the predominant KEGG pathways were distinct for each developmental stage. For example, phenylpropanoid biosynthesis (ko00940) was enriched only at the R stage. Overall, the GO and KEGG pathway enrichment analysis showed that the DEGs in 'Tangwei' and 'Tonghua-3' were enriched for multiple biological processes during the three stages of fruit development.

DEGs related to phenylalanine metabolism

We then analyzed the expression of genes with potential functions in resveratrol and flavonoid biosynthesis between the two cultivars and three developmental stages (Fig. 3 and Supplemental Table S6). We identified 30 STSs, 13 PALs, two C4Hs and nine 4CLs that were differentially expressed during at least one of the stages of fruit development between 'Tangwei' and 'Tonghua-3'. Interestingly, all of the STS genes showed increasing expression with development in both 'Tangwei' and 'Tonghua-3'. In addition, the expression levels of STS, C4H and 4CL genes at V and R were significantly higher in 'Tonghua-3' than in 'Tangwei'. Moreover, 25 RESVERATROL GLUCOSYLTRANSFERASE (RSGT), 27 LACCASE (LAC) and 21 O-METHYLTRANSFERASE (OMT) DEGs were identified, and most of these showed relatively high expression at the G and V stages in 'Tangwei' or R in 'Tonghua-3'. It is worth noting that the expression of the DEGs related to flavonoid biosynthesis, including CHS, FLAVONOL SYNTHASE (FLS), FLAVONOID 3′-HYDROXYLASE (F3'H), DIHYDROFLAVONOL 4-REDUCTASE (DFR), ANTHOCYANIDIN REDUCTASE (ANR) and LEUCOANTHOCYANIDIN REDUCTASE (LAR) were generally higher in 'Tangwei' than in 'Tonghua-3'at G stage.

Figure 3. Expression of differentially expressed genes (DEGs) associated with phenylalanine metabolism. TW, 'Tangwei'; TH, 'Tonghua-3'. PAL, PHENYLALANINE AMMONIA LYASE; C4H, CINNAMATE 4-HYDROXYLASE; 4CL, 4-COUMARATE-COA LIGASE; STS, STILBENE SYNTHASE; RSGT, RESVERATROL GLUCOSYLTRANSFERASE; OMT, O-METHYLTRANSFERASE; LAC, LACCASE; CHS, CHALCONE SYNTHASE; CHI, CHALCONE ISOMERASE; F3H, FLAVANONE 3-HYDROXYLASE; FLS, FLAVONOL SYNTHASE; F3'H, FLAVONOID 3′-HYDROXYLASE; DFR, DIHYDROFLAVONOL 4-REDUCTASE; LAR, LEUCOANTHOCYANIDIN REDUCTASE; ANR, ANTHOCYANIDIN REDUCTASE; LDOX, LEUCOANTHOCYANIDIN DIOXYGENASE; UFGT, UDP-GLUCOSE: FLAVONOID 3-O-GLUCOSYLTRANSFERASE.

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Differentially expressed TF genes

Among all DEGs identified in this study, 757 encoded potential TFs, and these represented 57 TF families. The most highly represented of these were the AP2/ERF, bHLH, NAC, WRKY, bZIP, HB-HD-ZIP and MYB families with a total of 76 DEGs (Fig. 4a). We found that the number of downregulated TF genes was greater than upregulated TF genes at G and V, and 48 were differentially expressed between the two cultivars at all three stages (Fig. 4b). Several members of the ERF, MYB, WRKY and bHLH families showed a strong increase in expression at the R stage (Fig. 4c). In addition, most of the TF genes showed > 2-fold higher expression in 'Tonghua-3' than in 'Tangwei' at the R stage. In particular, a few members, such as ERF11 (VIT_07s0141g00690), MYB105 (VIT_01s0026g02600), and WRKY70 (VIT_13s0067g03140), showed > 100-fold higher expression in 'Tonghua-3' (Fig. 4d).

Figure 4. Differentially expressed transcription factor (TF) genes. (a) The number of differentially expressed genes (DEGs) in different TF families; (b) Number of differentially expressed TF genes, numbers of overlapping differentially expressed TF genes, and (c) categorization of expression fold change (FC) for members of eight TF families between 'Tangwei' and 'Tonghua-3' at green hard (G), véraison (V), and ripe (R) stages; (d) Heatmap expression profiles of the three most strongly differentially expressed TF genes from each of eight TF families.

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Weighted gene co-expression network construction and identification of TF genes co-expressed with STSs

We constructed a gene co-expression network using the weighted gene co-expression network analysis (WGCNA) package, which uses a systems biology approach focused on understanding networks rather than individual genes. In the network, 17 distinct modules (hereafter referred to by color as portrayed in Fig. 5a), with module sizes ranging from 91 (antiquewhite4) to 1,917 (magenta) were identified (Supplemental Table S7). Of these, three modules (ivory, orange and blue) were significantly correlated with resveratrol content, cultivar ('Tonghua-3'), and developmental stage (R). The blue module showed the strongest correlation with resveratrol content (cor = 0.6, p-value = 0.008) (Fig. 5b). KEGG enrichment analysis was carried out to further analyze the genes in these three modules. Genes in the ivory module were significantly enriched for phenylalanine metabolism (ko00360), stilbenoid, diarylheptanoid and gingerol biosynthesis (ko00945), and flavonoid biosynthesis (ko00941), whereas the most highly enriched terms of the blue and orange modules were plant-pathogen interaction (ko04626), plant hormone signal transduction (ko04075) and circadian rhythm-plant (ko04712) (Supplemental Fig. S1). Additionally, a total of 36 genes encoding TFs including in 15 ERFs, 10 WRKYs, six bHLHs, two MYBs, one MADs-box, one HSF and one TRY were identified as co-expressed with one or more STSs in these three modules (Fig. 5c and Supplemental Table S8), suggesting that these TFs may participate in the STS regulatory network.

Figure 5. Results of weighted gene co-expression network analysis (WGCNA). (a) Hierarchical clustering tree indicating co-expression modules; (b) Module-trait relationship. Each row represents a module eigengene, and each column represents a trait. The corresponding correlation and p-value are indicated within each module. Res, resveratrol; TW, 'Tangwei'; TH, 'Tonghua-3'; (c) Transcription factors and stilbene synthase gene co-expression networks in the orange, blue and ivory modules.

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Validation of RNA-seq data by RT-qPCR

To assess the reliability of the RNA-seq data, 12 genes determined to be differentially expressed by RNA-seq were randomly selected for analysis of expression via real-time quantitative PCR (RT-qPCR). This set comprised two PALs, two 4CLs, two STSs, two WRKYs, two LACs, one OMT, and MYB14. In general, these RT-qPCR results strongly confirmed the RNA-seq-derived expression patterns during fruit development in the two cultivars. The correlation coefficients between RT-qPCR and RNA-seq were > 0.6, except for LAC (VIT_02s0154g00080) (Fig. 6).

Figure 6. Comparison of the expression patterns of 12 randomly selected differentially expressed genes by RT-qPCR (real-time quantitative PCR) and RNA-seq. R-values are correlation coefficients between RT-qPCR and RNA-seq. FPKM, fragments per kilobase of transcript per million fragments mapped; TW, 'Tangwei'; TH, 'Tonghua-3'; G, green hard; V, véraison; R, ripe.

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DISCUSSION

Grapevines are among the most important horticultural crops worldwide^[26], and recently have been the focus of studies on the biosynthesis of resveratrol. Resveratrol content has previously been found to vary depending on cultivar as well as environmental stresses^[27]. In a study of 120 grape germplasm cultivars during two consecutive years, the extractable amounts of resveratrol in berry skin were significantly higher in seeded cultivars than in seedless ones, and were higher in both berry skin and seeds in wine grapes relative to table grapes^[28]. Moreover, it was reported that total resveratrol content constantly increased from véraison to complete maturity, and ultraviolet-C (UV-C) irradiation significantly stimulated the accumulation of resveratrol of berry during six different development stages in 'Beihong' (V. vinifera × V. amurensis)^[9]. Intriguingly, a recent study reported that bud sport could lead to earlier accumulation of trans-resveratrol in the grape berries of 'Summer Black' and its bud sport 'Nantaihutezao' from the véraison to ripe stages^[29]. In the present study, resveratrol concentrations in seven accessions were determined by high performance liquid chromatography (HPLC) in the seed, pulp and skin at three developmental stages (G, V and R). Resveratrol content was higher in berry skins than in pulp or seeds, and were higher in the wild Chinese accessions compared with the domesticated cultivars. The highest resveratrol content (2.99 µg g⁻¹ FW) was found in berry skins of 'Tonghua-3' at the R stage (Table 1). This is consistent with a recent study of 50 wild Chinese accessions and 45 cultivars, which reported that resveratrol was significantly higher in berry skins than in leaves^[30]. However, we did not detect trans-resveratrol in the skins of 'Tangwei' during the G or V stages (Table 1). To explore the reason for the difference in resveratrol content between 'Tangwei' and 'Tonghua-3', as well as the regulation mechanism of resveratrol synthesis and accumulation during berry development, we used transcriptional profiling to compare gene expression between these two accessions at the G, V, and R stages. After sequence read alignment and transcript assembly, 23,649 and 23,557 unigenes were documented in 'Tangwei' and 'Tonghua-3', respectively. As anticipated, due to the small number of structures sampled, this was less than that (26,346) annotated in the V. vinifera reference genome^[18]. Depending on the sample, 80.47%−88.86% of sequence reads aligned to a single genomic location (Supplemental Table S1); this is similar to the alignment rate of 85% observed in a previous study of berry development in Vitis vinifera^[19]. Additionally, 1751 novel transcripts were excavated (Supplemental Table S2) after being compared with the V. vinifera reference genome annotation information^{[18, 31]}. A similar result was also reported in a previous study when transcriptome analysis was performed to explore the underlying mechanism of cold stress between Chinese wild Vitis amurensis and Vitis vinifera^[32]. We speculate that these novel transcripts are potentially attributable to unfinished V. vinifera reference genome sequence (For example: quality and depth of sequencing) or species-specific difference between Vitis vinifera and other Vitis. In our study, the distribution of genes based on expression level revealed an inverse trend from G, V to R between 'Tangwei' and 'Tonghua-3' (Fig. 1). Furthermore, analysis of DEGs suggested that various cellular processes including metabolic process and catalytic activity were altered between the two cultivars at all three stages (Fig. 2 and Supplemental Table S3−S5). These results are consistent with a previous report that a large number of DEGs and 100 functional subcategories were identified in 'Tonghua-3' grape berries after exposure to UV-C radiation^[8]. Resveratrol biosynthesis in grapevine is dependent on the function of STSs, which compete with the flavonoid branch in the phenylalanine metabolic pathway. Among the DEGs detected in this investigation, genes directly involved in the resveratrol synthesis pathway, STSs, C4Hs and 4CLs, were expressed to significantly higher levels in 'Tonghua-3' than in 'Tangwei' during V and R. On the other hand, DEGs representing the flavonoid biosynthesis pathway were upregulated in 'Tangwei', but downregulated in 'Tonghua-3' (Fig. 3 and Supplemental Table S6). These expression differences may contribute to the difference in resveratrol content between the two cultivars at these stages. We note that 'Tangwei' and 'Tonghua-3' are from two highly diverged species with different genetic backgrounds. There might be some unknown genetic differences between the two genomes, resulting in more than 60 functional subcategories being enriched (Fig. 2d) and the expression levels of genes with putative roles in resveratrol biosynthesis being significantly higher in 'Tonghua-3' than in 'Tangwei' during V and R (Fig. 3). A previous proteomic study also reported that the expression profiles of several enzymes in the phenylalanine metabolism pathway showed significant differences between V. quinquangularis accession 'Danfeng-2' and V. vinifera cv. 'Cabernet Sauvignon' at the véraison and ripening stages^[33]. In addition, genes such as RSGT, OMT and LAC involved in the production of derivatized products of resveratrol were mostly present at the G and V stages of 'Tangwei', potentially resulting in limited resveratrol accumulation. However, we found that most of these also revealed relatively high expression at R in 'Tonghua-3' (Fig. 3). Despite this situation, which does not seem to be conducive for the accumulation of resveratrol, it still showed the highest content (Table 1). It has been reported that overexpression of two grapevine peroxidase VlPRX21 and VlPRX35 genes from Vitis labruscana in Arabidopsis may be involved in regulating stilbene synthesis^[34], and a VqBGH40a belonging to β-glycoside hydrolase family 1 in Chinese wild Vitis quinquangularis can hydrolyze trans-piceid to enhance trans-resveratrol content^[35]. However, most studies mainly focus on several TFs that participate in regulation of STS gene expression, including ERFs, MYBs and WRKYs^{[13, 15, 17]}. For example, VvWRKY18 activated the transcription of VvSTS1 and VvSTS2 by directly binding the W-box elements within the specific promoters and resulting in the enhancement of stilbene phytoalexin biosynthesis^[36]. VqWRKY53 promotes expression of VqSTS32 and VqSTS41 through participation in a transcriptional regulatory complex with the R2R3-MYB TFs VqMYB14 and VqMYB15^[37]. VqMYB154 can activate VqSTS9/32/42 expression by directly binding to the L5-box and AC-box motifs in their promoters to improve the accumulation of stilbenes^[38]. In this study, we found a total of 757 TF-encoding genes among the DEGs, including representatives of the MYB, AP2/ERF, bHLH, NAC, WRKY, bZIP and HB-HD-ZIP families. The most populous family was MYB, representing 76 DEGs at G, V and R between 'Tangwei' and 'Tonghua-3' (Fig. 4). A recent report indicated that MYB14, MYB15 and MYB13, a third uncharacterized member of Subgroup 2 (S2), could bind to 30 out of 47 STS family genes. Moreover, all three MYBs could also bind to several PAL, C4H and 4CL genes, in addition to shikimate pathway genes, the WRKY03 stilbenoid co-regulator and resveratrol-modifying gene^[39]. VqbZIP1 from Vitis quinquangularis has been shown to promote the expression of VqSTS6, VqSTS16 and VqSTS20 by interacting with VqSnRK2.4 and VqSnRK2.6^[40]. In the present study, we found that a gene encoding a bZIP-type TF (VIT_12s0034g00110) was down-regulated in 'Tangwei', but up-regulated in 'Tonghua-3', at G, V and R (Fig. 4). We also identified 36 TFs that were co-expressed with 17 STSs using WGCNA analysis, suggesting that these TFs may regulate STS gene expression (Fig. 5 and Supplemental Table S8). Among these, a STS (VIT_16s0100g00880) was together co-expressed with MYB14 (VIT_07s0005g03340) and WRKY24 (VIT_06s0004g07500) that had been identified as regulators of STS gene expression^{[13, 15]}. A previous report also indicated that a bHLH TF (VIT_11s0016g02070) had a high level of co-expression with STSs and MYB14/15^[15]. In the current study, six bHLH TFs were identified as being co-expressed with one or more STSs and MYB14 (Fig. 5 and Supplemental Table S8). However, further work needs to be done to determine the potential role of these TFs that could directly target STS genes or indirectly regulate stilbene biosynthesis by formation protein complexes with MYB or others. Taken together, these results identify a small group of TFs that may play important roles in resveratrol biosynthesis in grapevine.

CONCLUSIONS

In summary, we documented the trans-resveratrol content of seven grapevine accessions by HPLC and performed transcriptional analysis of the grape berry in two accessions with distinct patterns of resveratrol accumulation during berry development. We found that the expression levels of genes with putative roles in resveratrol biosynthesis were significantly higher in 'Tonghua-3' than in 'Tangwei' during V and R, consistent with the difference in resveratrol accumulation between these accessions. Moreover, several genes encoding TFs including MYBs, WRKYs, ERFs, bHLHs and bZIPs were implicated as regulators of resveratrol biosynthesis. The results from this study provide insights into the mechanism of different resveratrol accumulation in various grapevine accessions.

MATERIALS AND METHODS

Plant material

V. davidii 'Tangwei', V. amurensis × V. Vinifera 'Beibinghong'; V. amurensis 'Tonghua-3' and 'Shuangyou'; V. vinifera × V. labrusca 'Jumeigui'; V. vinifera 'Red Globe' and 'Thompson Seedless' were maintained in the grapevine germplasm resource at Northwest A&F University, Yangling, Shaanxi, China (34°20' N, 108°24' E). Fruit was collected at the G, V, and R stages, as judged by skin and seed color and soluble solid content. Each biological replicate comprised three fruit clusters randomly chosen from three plants at each stage. About 40−50 representative berries were separated into skin, pulp, and seed, and immediately frozen in liquid nitrogen and stored at −80 °C.

Extraction and analysis of resveratrol content

Resveratrol extraction was carried out as previously reported^[8]. Quantitative analysis of resveratrol content was done using a Waters 600E-2487 HPLC system (Waters Corporation, Milford, MA, USA) equipped with an Agilent ZORBAX SB-C18 column (5 µm, 4.6 × 250 mm). Resveratrol was identified by co-elution with a resveratrol standard, and quantified using a standard curve. Each sample was performed with three biological replicates.

RNA extraction, transcriptome library construction and sequencing

Three biological replicates of each stage (G, V and R) from whole berries of 'Tangwei' and 'Tonghua-3' were used for all RNA-Seq experiments. Total RNA was extracted from 18 samples using the E.Z.N.A. Plant RNA Kit (Omega Bio-tek, Norcross, GA, USA). For each sample, sequencing libraries were constructed from 1 μg RNA using the NEBNext UltraTM RNA Library Prep Kit for Illumina (New England Biolabs, Ipswich, MA, USA). The library preparations were sequenced on an Illumina HiSeq2500 platform (Illumina, San Diego, CA, USA) at Biomarker Technologies Co., Ltd. (Beijing, China).

RNA-seq data analysis

Raw sequence reads were filtered to remove low-quality reads, and then mapped to the V. vinifera 12X reference genome^{[18, 31]} using TopHat v.2.1.0^[41]. The mapped reads were assembled into transcript models using Stringtie v2.0.4^[42]. Transcript abundance and gene expression levels were estimated as FPKM^[43]. The formula is as follows:

$\text{FPKM =}\frac{\text{cDNA Fragments}}{\text{Mapped Fragments}\left(\text{Millions}\right)\text{× Transcript Length (kb)}}$

Biological replicates were evaluated using Pearson's Correlation Coefficient^[44] and principal component analysis. DEGs were identified using the DEGSeq R package v1.12.0^[45]. A false discovery rate (FDR) threshold was used to adjust the raw P values for multiple testing^[46]. Genes with a fold change of ≥ 2 and FDR < 0.05 were assigned as DEGs. GO and KEGG enrichment analyses of DEGs were performed using GOseq R packages v1.24.0^[47] and KOBAS v2.0.12^[48], respectively. Co-expression networks were constructed based on FPKM values ≥ 1 and coefficient of variation ≥ 0.5 using the WGCNA R package v1.47^[49]. The adjacency matrix was generated with a soft thresholding power of 16. Then, a topological overlap matrix (TOM) was constructed using the adjacency matrix, and the dissimilarity TOM was used to construct the hierarchy dendrogram. Modules containing at least 30 genes were detected and merged using the Dynamic Tree Cut algorithm with a cutoff value of 0.25^[50]. The co-expression networks were visualized using Cytoscape v3.7.2^[51].

Validation of RNA-Seq data by RT-qPCR

RT-qPCR was carried out using the SYBR Green Kit (Takara Biotechnology, Beijing, China) and the Step OnePlus Real-Time PCR System (Applied Biosystems, Foster, CA, USA). Gene-specific primers were designed using Primer Premier 5.0 software (PREMIER Biosoft International, Palo Alto, CA, USA). Cycling parameters were 95 °C for 30 s, 42 cycles of 95 °C for 5 s, and 60 °C for 30 s. The grapevine ACTIN1 (GenBank Accession no. AY680701) gene was used as an internal control. Each reaction was performed in triplicate for each of the three biological replicates. Relative expression levels of the selected genes were calculated using the 2^−ΔΔCᴛ method^[52]. Primer sequences are listed in Supplemental Table S9.

ACKNOWLEDGMENTS

This research was supported by the National Key Research and Development Program of China (2019YFD1001401) and the National Natural Science Foundation of China (31872071 and U1903107).

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions
Copyright: © 2023 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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Amanullah. 2023. A simple model for estimation of above and below ground carbon in cereal crops. Technology in Agronomy 3:8 doi: 10.48130/TIA-2023-0008

Amanullah. 2023. A simple model for estimation of above and below ground carbon in cereal crops. Technology in Agronomy 3:8 doi: 10.48130/TIA-2023-0008

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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).

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).

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).

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).

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).

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).

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).

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).

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).

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].

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A simple model for estimation of above and below ground carbon in cereal crops

Abstract

INTRODUCTION

RESULTS

Extractable trans-resveratrol content in developing grape berries

Analysis of RNA-Seq data

GO and KEGG pathway enrichment analysis of DEGs

DEGs related to phenylalanine metabolism

Differentially expressed TF genes

Weighted gene co-expression network construction and identification of TF genes co-expressed with STSs

Validation of RNA-seq data by RT-qPCR

DISCUSSION

CONCLUSIONS

MATERIALS AND METHODS

Plant material

Extraction and analysis of resveratrol content

RNA extraction, transcriptome library construction and sequencing

RNA-seq data analysis

Validation of RNA-Seq data by RT-qPCR

ACKNOWLEDGMENTS

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