Search
Submit Manuscript
1.8CiteScore
Advanced Search
MAP  >  Vegetable Research
2023 Volume 3
Article Contents
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
Conflict of interest
Rights and permissions
References
About this article
Next Previous
ARTICLE   Open Access    

Comparison of SPAD-based leaf greenness and paralleled petiole sap nitrate concentrations for monitoring potato vine nitrogen status

  • Horticultural Sciences Department, University of Florida/IFAS, Gainesville, FL 32611, USA

More Information
  • Nitrogen status in potato vines plays an important role in potato production. Leaf greenness meters (SPAD-502) and portable petiole sap Cardy meters are two types of convenient and affordable handheld meters for nitrate-N testing to monitor nitrogen status. Two years of field trials were conducted to compare the feasibility and reliability of the two methods with either meter. 'Atlantic' Potato was grown at nine nitrogen rates from 0 (control) to 360 kg/ha with a 45 kg/ha increment. The nitrogen status was measured at 40, 54, 68, and 82 d after planting by using a SPAD-502 for leaf greenness and a Cardy meter for petiole sap nitrate nitrogen concentrations. Potato yield increased quadratically with the increasing of N fertilizer rate from 0 to 360 kg/ha. The result of this study shows both SPAD readings and petiole sap nitrate N concentrations had positive relationships with the N rates. The SPAD reading was able to distinguish the N status difference only in later growth stages. Petiole sap nitrate N concentration was more sensitive and started differentiating the plant growth with different N rates in early growth stage. Dynamic N fertilization guidance is imperative for optimizing yields with specific cultivars in different growth stages; more studies are needed to establish a dynamic threshold of SPAD reading for leaf greenness and petiole sap nitrate N concentration.

  • 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.
    StructuresSpeciesAccessions or cultivarsContent of trans-resveratrol (μg g−1 FW)
    Green hardVéraisonRipe
    SkinV. davidiiTangweindnd2.415 ± 0.220
    V. amurensisTonghua-30.216 ± 0.0410.656 ± 0.0432.988 ± 0.221
    Shuangyou0.233 ± 0.0620.313 ± 0.0172.882 ± 0.052
    V. amurensis × V. ViniferaBeibinghong0.336 ± 0.0761.486 ± 0.1771.665 ± 0.100
    V. viniferaRed Global0.252 ± 0.0510.458 ± 0.0571.050 ± 0.129
    Thompson seedless0.120 ± 0.0251.770 ± 0.0320.431 ± 0.006
    V. vinifera × V. labruscaJumeigui0.122 ± 0.0160.170 ± 0.0210.708 ± 0.135
    PulpV. davidiiTangwei0.062 ± 0.0060.088 ± 0.009nd
    V. amurensisTonghua-30.151 ± 0.0660.324 ±0.1040.032 ± 0.004
    Shuangyou0.053 ± 0.0080.126 ± 0.0440.041 ± 0.017
    V. amurensis × V. ViniferaBeibinghong0.057 ± 0.0140.495 ± 0.0680.087 ± 0.021
    V. viniferaRed Global0.059 ± 0.0180.159 ± 0.0130.027 ± 0.004
    Thompson seedless0.112 ± 0.0160.059 ± 0.020nd
    V. vinifera × V. labruscaJumeigui0.072 ± 0.0100.063 ± 0.0170.359 ± 0.023
    SeedV. davidiiTangwei0.096 ± 0.0140.169 ± 0.0280.049 ± 0.006
    V. amurensisTonghua-30.044 ± 0.0040.221 ± 0.0240.113 ± 0.027
    Shuangyound0.063 ± 0.0210.116 ± 0.017
    V. amurensis × V. ViniferaBeibinghongnd0.077 ± 0.0030.400 ± 0.098
    V. viniferaRed Global0.035 ± 0.0230.142 ± 0.0360.199 ± 0.009
    Thompson seedless−−−
    V. vinifera × V. labruscaJumeigui0.077 ± 0.0250.017 ± 0.0040.284 ± 0.021
    'nd' indicates not detected in samples, and '−' shows no samples are collected due to abortion.
     | Show Table
    DownLoad: CSV

    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.
    DownLoad: Full-Size Img PowerPoint

    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.
    DownLoad: Full-Size Img PowerPoint
    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.
    DownLoad: Full-Size Img PowerPoint

    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.
    DownLoad: Full-Size Img PowerPoint

    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.
    DownLoad: Full-Size Img PowerPoint

    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.
    DownLoad: Full-Size Img PowerPoint

    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:
    FPKM =cDNA FragmentsMapped Fragments(Millions)× 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).
  • The authors declare that they have no conflict of interest.
  • [1]

    Rens LR, Zotarelli L, Cantliffe DJ, Stoffella PJ, Gergela D, et al. 2015. Biomass accumulation, marketable yield, and quality of Atlantic potato in response to nitrogen. Agronomy Journal 107:931−42

    doi: 10.2134/agronj14.0408

    CrossRef   Google Scholar

    [2]

    Van Zeghbroeck J, Liu G, Mylavarapu RS, Li YC. 2021. Phosphorus management strategies for potato production in Florida: a review. American Journal of Potato Research 98:347−60

    doi: 10.1007/s12230-021-09851-2

    CrossRef   Google Scholar

    [3]

    Nurmanov YT, Chernenok VG, Kuzdanova RS. 2019. Potato in response to nitrogen nutrition regime and nitrogen fertilization. Field Crops Research 213:115−21

    doi: 10.1016/J.FCR.2018.11.014

    CrossRef   Google Scholar

    [4]

    Rens LR, Zotarelli L, Cantliffe DJ, Stoffella PJ, Gergela D, et al. 2016. Commercial evaluation of seasonal distribution of nitrogen fertilizer for potato. Potato Research 59:1−20

    doi: 10.1007/s11540-015-9304-6

    CrossRef   Google Scholar

    [5]

    Rens L, Zotarelli L, Alva A, Rowland D, Liu G, et al. 2016. Fertilizer nitrogen uptake efficiencies for potato as influenced by application timing. Nutrient Cycling in Agroecosystems 104:175−85

    doi: 10.1007/s10705-016-9765-2

    CrossRef   Google Scholar

    [6]

    Goffart JP, Olivier M, Frankinet M. 2008. Potato crop nitrogen status assessment to improve n fertilization management and efficiency: past-present-future. Potato Research 51:355−83

    doi: 10.1007/s11540-008-9118-x

    CrossRef   Google Scholar

    [7]

    Fernandes FM, Soratto RP, Fernandes AM, Souza EFC. 2021. Chlorophyll meter-based leaf nitrogen status to manage nitrogen in tropical potato production. Agronomy Journal 113:1733−46

    doi: 10.1002/agj2.20589

    CrossRef   Google Scholar

    [8]

    Wu J, Wang D, Rosen CJ, Bauer ME. 2007. Comparison of petiole nitrate concentrations, SPAD chlorophyll readings, and QuickBird satellite imagery in detecting nitrogen status of potato canopies. Field Crops Research 101:96−103

    doi: 10.1016/J.FCR.2006.09.014

    CrossRef   Google Scholar

    [9]

    Busato C, Fontes PCR, Braun H, Cecon PR. 2010. Seasonal variation and threshold values for chlorophyll meter readings on leaves of potato cultivars. Journal of Plant Nutrtion 33:2148−56

    doi: 10.1080/01904167.2010.519087

    CrossRef   Google Scholar

    [10]

    Zheng H, Liu Y, Qin Y, Chen Y, Fan M. 2015. Establishing dynamic thresholds for potato nitrogen status diagnosis with the SPAD chlorophyll meter. Journal of Integretive Agriculture 14:190−95

    doi: 10.1016/S2095-3119(14)60925-4

    CrossRef   Google Scholar

    [11]

    Aguilera J, Motavalli P, Gonzales M, Valdivia C. 2014. Evaluation of a rapid field test method for assessing nitrogen status in potato plant tissue in rural communities in the Bolivian Andean Highlands. Communications in Soil Science and Plant Analysis 45:347−61

    doi: 10.1080/00103624.2013.857680

    CrossRef   Google Scholar

    [12]

    Mackerron DKL, Young MW, Davies HV. 1995. A critical assessment of the value of petiole sap analysis in optimizing the nitrogen nutrition of the potato crop. Plant and Soil 172:247−60

    doi: 10.1007/BF00011327

    CrossRef   Google Scholar

    [13]

    Goffart JP, Olivier M, Frankinet M. 2011. Crop nitrogen status assessment tools in a decision support system for nitrogen fertilization management of potato crops. HortTechnology 21:282−86

    doi: 10.21273/HORTTECH.21.3.282

    CrossRef   Google Scholar

    [14]

    Fontes PCR, Braun H, Busato C, Cecon PR. 2010. Economic optimum nitrogen fertilization rates and nitrogen fertilization rate effects on tuber characteristics of potato cultivars. Potato Research 53:167−79

    doi: 10.1007/s11540-010-9160-3

    CrossRef   Google Scholar

    [15]

    Minotti PL, Halseth DE, Sieczka JB. 1994. Field chlorophyll measurements to assess the nitrogen status of potato varieties. HortScience 29:1497−500

    doi: 10.21273/HORTSCI.29.12.1497

    CrossRef   Google Scholar

    [16]

    Rodrigues MÂ. 2004. Establishment of continuous critical levels for indices of plant and presidedress soil nitrogen status in the potato crop. Communications in Soil Science and Plant Analysis 35:2067−85

    Google Scholar

    [17]

    Vos J, Bom M. 1993. Hand-held chlorophyll meter: a promising tool to assess the nitrogen status of potato foliage. Potato Research 36:301−8

    doi: 10.1007/BF02361796

    CrossRef   Google Scholar

    [18]

    Li L, Qin Y, Liu Y, Hu Y, Fan M. 2012. Leaf positions of potato suitable for determination of nitrogen content with a SPAD meter. Plant Production Science 15:317−22

    doi: 10.1626/PPS.15.317

    CrossRef   Google Scholar

  • Cite this article

    Li Q, Denison J, Gluck M, Liu G. 2023. Comparison of SPAD-based leaf greenness and paralleled petiole sap nitrate concentrations for monitoring potato vine nitrogen status. Vegetable Research 3:30 doi: 10.48130/VR-2023-0030
    Li Q, Denison J, Gluck M, Liu G. 2023. Comparison of SPAD-based leaf greenness and paralleled petiole sap nitrate concentrations for monitoring potato vine nitrogen status. Vegetable Research 3:30 doi: 10.48130/VR-2023-0030
    shu

Figures(7)  /  Tables(2)

Download PDF

Article Metrics

Article views(2765) PDF downloads(404)

Other Articles By Authors

ARTICLE   Open Access    

Comparison of SPAD-based leaf greenness and paralleled petiole sap nitrate concentrations for monitoring potato vine nitrogen status

  • Horticultural Sciences Department, University of Florida/IFAS, Gainesville, FL 32611, USA

  • Received: 03 June 2023
  • Accepted: 01 September 2023
  • Published online: 09 November 2023
Vegetable Research  3,  Article number: 30  (2023)  |  Cite this article

Abstract: Nitrogen status in potato vines plays an important role in potato production. Leaf greenness meters (SPAD-502) and portable petiole sap Cardy meters are two types of convenient and affordable handheld meters for nitrate-N testing to monitor nitrogen status. Two years of field trials were conducted to compare the feasibility and reliability of the two methods with either meter. 'Atlantic' Potato was grown at nine nitrogen rates from 0 (control) to 360 kg/ha with a 45 kg/ha increment. The nitrogen status was measured at 40, 54, 68, and 82 d after planting by using a SPAD-502 for leaf greenness and a Cardy meter for petiole sap nitrate nitrogen concentrations. Potato yield increased quadratically with the increasing of N fertilizer rate from 0 to 360 kg/ha. The result of this study shows both SPAD readings and petiole sap nitrate N concentrations had positive relationships with the N rates. The SPAD reading was able to distinguish the N status difference only in later growth stages. Petiole sap nitrate N concentration was more sensitive and started differentiating the plant growth with different N rates in early growth stage. Dynamic N fertilization guidance is imperative for optimizing yields with specific cultivars in different growth stages; more studies are needed to establish a dynamic threshold of SPAD reading for leaf greenness and petiole sap nitrate N concentration.

    HTML

    Introduction

    • Florida produces spring potatoes mainly in the tri-county (St. Johns, Putnam, and Flagler Counties, FL, USA) agricultural area (TCAA), where the soil is mostly sandy[1,2]. Supplementing initial soil nitrates with nitrogen (N) fertilization remarkedly affect potato growth and tuber yield[3]. N-fertilizer application rate and timing influence uptake efficiency and tuber yield in this area[4,5]. However, accurate site-specific N recommendations have never been established for potato production due to variable weather, soil, and cultural practices[6]. It is critical to detect nitrogen deficiency as early as possible to estimate supplemental N requirements during the rest of the growing season. Leaf greenness meters (SPAD-502) and portable quick petiole sap nitrate-N meters are two convenient methods to monitor the nitrogen status for potato and other crops[6].

      SPAD meters can be used to estimate leaf greenness content and detect N status under conducive environmental conditions and have optimized N management by reducing application rates for potato production[7]. SPAD readings were able to detect nitrogen deficiency one month after its emergence; petiole sap nitrate-N concentrations were able to be measured two weeks earlier[8] than SPAD readings. This can be explained by the time from nitrate nitrogen assimilation to chlorophyll biosynthesis. The threshold SPAD reading decreased within days after emergence but varied with cultivar and plant site[9,10]. It seems difficult to standardize SPAD threshold values for diagnosing the N status of a specific cultivar[9].

      Petiole sap nitrate concentration is well correlated to potato N status between 20 to 60 d after N deficiency occurrence and has been widely investigated for potato production[6]. Low-cost, portable petiole sap nitrate meters such as Horiba Cardy meter have been used as a rapid field test method to improve nitrogen management[11]. However, the influences of cultivar and field condition on the petiole sap nitrate concentration make it difficult to propose standard sap concentration thresholds[6]. Solar radiation and wind may also affect field measurement accuracy[11]. Twenty petioles are required to increase the accuracy due to this variation, which is time- and labor-consuming for sampling and handling procedures[6]. These causations should be considered when using these measurements as guidance for N fertilization[12].

      Though ground-based, airborne, and space-based remote sensing technologies are developing[13], hand-held greenness meters and portable petiole sap nitrate N meters are still two feasible methods to measure potato N status, particularly for small farms. However, the accuracy of both types of portable meters is influenced by the meter's hardware, as well as cultivar, growth stage, growing season, cultivation practices, and weather. Both methods have demonstrated merits and limitations. The objective of this study is to compare the suitability of the pocketable SPAD meter and portable Horiba Cardy nitrate meter to monitor the nitrogen status of potato grown in the tri-county agricultural area in Northeast Florida.

    Materials and methods

    • The experiments were carried out in 2021 and 2022 at the University of Florida/IFAS Hastings Agriculture Extension Center, Hastings, FL (USA), in the Northeast Florida potato production area. The soil properties of the trial field are shown in Table 1. The trials were completed with hilled rows. The hilled rows (0.35 m in height) were formed with 1-m distance between row centers. At planting, granular fertilizer was banded on the soil surface of each row and subsequently incorporated. The potassium and phosphorus fertilizer were applied two days before planting according to IFAS potato production guidelines. Nine nitrogen fertilizer levels were prepared and supplied as granular calcium nitrate (15-0-0) from 0 (control) to 360 kg/ha with a 45 kg/ha increment. Nitrogen fertilizer was distributed in three applications: 30% two days before planting, 30% at emergence, 40% 41 d after planting (DAP). The experiments were arranged in a randomized complete block design with four blocks each with nine plots, four rows in each block, and 12.2 m in length for each plot. There was a 1.5-m skip between plots, and the total area of each block was 198.1 m2. Commercial chipping potato (Solanum tuberosum 'Atlantic') seed pieces were planted on Feb 5, 2021, and Jan 26, 2022, respectively.

      Table 1.  Soil properties of the trial field. All the essential elements (PPM) listed were extracted with Mehlich-III.

      pHCECPKMgCaMnFe
      5.25.937.537.575.5475.5826

      Soil samples from each plot were collected 23, 44, and 82 DAP in 2022 and nitrate-N concentration was analyzed by Waters Agricultural Laboratories, Inc. (Camilla, GA, USA) SPAD readings were measured by the SPAD 502 C (Konica Minolta, Inc., Osaka, Japan) on the 4th leaf with 30 measurements on different plants, on Mar 10, Mar 23, Apr 6, and Apr 21 in 2021; and on Mar 7, Mar 21, Apr 4, and Apr 18 (40, 54, 68, and 82 DAP). Petiole sap nitrate-N concentrations were measured with a Horiba LAQUAT Cardy nitrate meter on the same day. At least, 10 petioles were sampled for each measurement, depending on sap availability per petiole.

      Potato tubers were harvested on May 10, 2021, and May 6, 2022. The tubers from the middle 6.1 m of the two central rows in each plot were weighed and calculated for total and marketable yields. Specific gravity was measured with a specific gravity scale. Twenty marketable tubers were randomly picked from each plot and weighed in air and in water. The specific gravity was calculated as shown below:

      Specific Gravity = weight in air ÷ (weight in air − weight in water)

      Ten tubers from each plot were cut into quarters and the incidence of tuber hollow heart, corky ring spot, internal heat, and brown center were counted.

      Differences in data between N-rates or growth stages were analyzed with one way ANOVA. Means were separated by Tukey HSD at 0.05 level for significant differences. Regression analyses between N-rate on yield, SPAD readings, petiole sap nitrate-N concentrations; SPAD readings and petiole sap nitrate-N concentrations on yield; Both of SPAD and petiole sap nitrate-N readings were conducted by using JMP pro 16.1 (SAS Institute, 2020). Figures were plotted in Excel.

    Results

      Tuber yield and quality responses to nitrogen application rate

    • Both the total yield and marketable yield were increased with nitrogen fertilizer application, and the relationship between yield and N-rate significantly fitted with quadratic regressions (Fig. 1). The tuber yields of 2022 at different N-rates were generally greater than that of 2021. The respective maximum total and marketable yields of 2021 reached 33,306 kg/ha with 326 kg/ha N and 23,659 kg/ha with 360 kg/ha N based on the calculation vertex of the quadratic regressions. The corresponding maximum total yield and marketable yields of 2022 achieved 38,885 kg/ha with 332 kg/ha and 33,286 kg/ha with 333 kg/ha, respectively. The maximum total and marketable yields were more consistent in 2022 than in 2021. Basically, nitrogen applications did not significantly affect the tuber quality indices such as incidence of hollow heart, corky ring spot, and internal heat, except that the brown center rate was significantly decreased with nitrogen fertilization (Table 2).

      Figure 1. 

      Potato marketable tuber yield and total tuber yield response to nitrogen fertilizer application rate.

      Table 2.  The tuber yield and quality of potato grown under different nitrogen fertilizer rates.

      N rate
      (kg/ha)      		Marketable yield
      (kg/ha)      		Total yield
      (kg/ha)      		Specific gravity
      (g/cm3)      		Tuber hollow heart
      (%)      		Corky ring spot
      (%)      		Internal heat
      (%)      		Brown center
      (%)
      0 14,061 ± 1,363d18,949 ± 1,204d1.08 ± 0.00a2.50 ± 1.64a0.00 ± 0.00a0.00 ± 0.00a3.75 ± 1.83a
      45 16,586 ± 755d22,302 ± 776d1.08 ± 0.00a1.25 ± 1.25a0.00 ± 0.00a0.00 ± 0.00a1.25 ± 1.25ab
      90 25,957 ± 1,684c31,594 ± 1,569bc1.08 ± 0.00a1.25 ± 1.25a0.00 ± 0.00a1.25 ± 1.25a0.00 ± 0.00b
      13526,521 ± 1,404bc31,168 ± 1,769c1.08 ± 0.00a2.50 ± 2.50a0.00 ± 0.00a0.00 ± 0.00a0.00 ± 0.00b
      18029,841 ± 1,471abc35,773 ± 1,499abc1.09 ± 0.00a2.50 ± 1.64a0.00 ± 0.00a1.25 ± 1.25a0.00 ± 0.00b
      22529,956 ± 1,016abc35,951 ± 1,401abc1.09 ± 0.00a0.00 ± 0.00a0.00 ± 0.00a0.00 ± 0.00a0.00 ± 0.00b
      27031,784 ± 919abc36,934 ± 751abc1.08 ± 0.00a5.00 ± 1.89a1.25 ± 1.25a0.00 ± 0.00a0.00 ± 0.00b
      31532,680 ± 2,171ab38,270 ± 2,440ab1.08 ± 0.00a6.25 ± 3.75a0.00 ± 0.00a0.00 ± 0.00a0.00 ± 0.00b
      36034,181 ± 1,209a39,875 ± 1,336a1.08 ± 0.00a3.75 ± 2.63a0.00 ± 0.00a0.00 ± 0.00a0.00 ± 0.00b
      Data (mean ± SE, n = 4) followed with same letter in the same column were not significant different according to Tukey HSD at 0.05 level.

      Soil nitrate-N concentration response to N-rate

    • In general, soil nitrate-N concentrations decreased within the growth stage, especially before harvest. However, due to the great variances within group, there was no statistical significance between the N-rates, or between soil sampling dates (Fig. 2).

      Figure 2. 

      Soil nitrate-N concentration response to N-rate in different growth stages. (Feb 18, Mar 11, Apr 18, 2022, that were 23, 44, and 82 d after planting).

      SPAD and petiole sap nitrate-N response to N-rate

    • SPAD value and petiole sap nitrate-N concentration increased with the N-rate in all growth stages in both years (Figs 3 & 4); all the data fitted well with quadratic or linear regression.

      Figure 3. 

      SPAD value response to N-rate in different growth stages (Mar 7, Mar 21, Apr 4, and Apr 18, that were 40, 54, 68, and 82 d after planting).

      Figure 4. 

      Petiole sap nitrate-N response to N-rate in different growth stages. (Mar 7, Mar 21, Apr 4 that were 40, 54, 68 d after planting).

      However, petiole sap Nitrate-N concentration showed more range of variation with a greater slope (Fig. 4). The SPAD values at early growth stage showed a smaller range of variation. On Mar 10, 2021, SPAD did not indicate any significant difference between all the N-rates except for the zero-N control. On Mar 7, 2022, only the SPAD reading was significantly lower with the control (0 kg/ha) than with all the other nitrogen levels, but all the SPAD readings at the N-rates from 45 to 360 kg/ha ranged narrowly from 53.8 to 56.5 without significant difference (Fig. 3). Thus, the SPAD value on the 40 DAP was not able to distinguish the difference in leaf nitrogen status according to the N-rate in this stage. For the second measurement in 2021, the SPAD readings ranged from 43 to 50, but there was no significant difference between N-rates from 45 kg/ha to 315 kg/ha. In 2022, the SPAD readings of the second measurements on Mar 21 ranged from 38 to 47 and showed significant differences between N-rates below 135 kg/ha and above 270 kg/ha. On Apr 6, the third measurements showed significant difference between the N-rate below 90 kg/ha and above 180 kg/ha and ranged from 43 to 54. In 2022, the SPAD values were lower with the range from 33 to 45 and showed significant differences between N-rates below 135 kg/ha and above 225 kg/ha. The regression between SPAD and N-rate in this growth stage also showed best fitting with a greater R2. It indicated that SPAD on the 68th DAP was more sensitive to distinguishing the differences of leaf N status caused by different N-rates. The SPAD values of last measurement near harvest also increased linearly with N-rate, but with much smaller values, it indicated that the soil N were used up and leaf N were transferred to the tubers.

      In contrast, petiole sap nitrate-N concentration in all growth stages had a wider range and showed clear quadratic or linear relations to N-rate (Fig. 5). The wider range made it easier to separate the N status from different N-rates. However, there were still no significant differences between the N-rates. For example, the petiole sap nitrate-N of Mar 10, 2021, at N-rates from 45 kg/ha to 360 kg/ha had no significant difference. In the 2021 trial, only the petiole sap nitrate-N concentrations at N-rates of 360, 90, and 0 kg/ha had significant differences on 54 DAP. In 2022, when the N-rates were 135 kg/ha, 180 kg/ha, and 225 kg/ha, the petiole sap nitrate-N of 68 DAP were 427, 717, and 990 ppm respectively (Fig. 5). There was also no significant difference between the petiole sap nitrate-N concentrations from 180 kg/ha to 360 kg/ha, though the petiole sap nitrate-N concentrations ranged from 717 to 1,300 ppm.

      Figure 5. 

      Relation of petiole sap nitrate concentration to SPAD value.

      Relation between the petiole sap nitrate-N concentration and SPAD value

    • In 2021, petiole sap nitrate-N and SPAD values of Mar 10 did not have any significant relationship. The data of 40 DAP and 68 DAP showed significant relations, but the linear regressions were not well-fitted and had low R2 (Fig. 5, left). Data of 2022 showed better linear relationships between petiole sap nitrate-N concentrations and SPAD value at 40, 54, 68 DAP, with R2 of 0.5084, 0.6246, and 0.7155, respectively (Fig. 5, right). The low R2 values indicated that it was not sufficient to predict petiole sap nitrate-N concentrations from the SPAD values.

      Yield response to SPAD or petiole sap nitrate-N concentrations

    • The marketable yield had significant linearity relative to both the SPAD value and petiole sap nitrate-N in different growth stages of both years. However, the regressions of the two years were vastly different. The regressions between yields and SPAD readings on 68 DAP in 2022 were best fitting (Fig. 6). All the linear equations had large slopes, which means the yield changed significantly with a slight change of SPAD value. The SPAD value did not change significantly when the N-rate increased from 45 to 360 kg/ha in the early growth stage, consistent with the data of Fig. 3. On the other hand, the linear equations between yield and petiole sap nitrate-N showed smaller slopes (Fig. 7), meaning that yield increased gradually with the increase of petiole sap nitrate-N. The R2 were greater on 40 and 54 DAP. The petiole sap nitrate-N was more sensitive in distinguishing the yield difference at different N-rates than SPAD reading.

      Figure 6. 

      Yield responses to SPAD value at different growth stages.

      Figure 7. 

      Marketable yield response to petiole sap nitrate-N in 2021 and 2022 trials.

      Potato tuber yield increased by increasing the N fertilization rate. In Brazil, the N fertilization rate was 175 kg/ha to achieve maximum marketable yield of potato 'Atlantic'[14]. In our two years trials, the N fertilization rates for maximum yield were 360 kg/ha and 333 kg/ha, which was approximately doubled as compared with the N rate in Brazil. This difference could be caused by differing soil types, weather, soil N, or cultivation practices. However, considering the potato tuber price and fertilizer price, the economic optimum N fertilization rates were 92%−95% of estimated N rates for maximum yield at low N fertilizer price and high potato price, or 86%−92% of the estimated N rates at high N fertilizer price and low potato price[14].

      In the two years of trials, both SPAD readings and petiole sap nitrate-N concentrations had clear relations to N-rates. However, petiole sap nitrate-N concentration showed wider ranges and was therefore more sensitive for determining the plant N status difference between different N-rates. The SPAD readings were within narrower ranges regardless of N-rates, and besides the control values were not significantly different, especially in the early growth stage. SPAD readings could detect more separation between different N-rates in later growth stage, but that may be too late to side-dress the N fertilizer. It is difficult to define the threshold of SPAD values for N deficiency of specific potato cultivars due to differences in soil, weather, growth stage, growing season, and management practices[9]. Especially, when temperature, solar radiation, and intensive rainfall were less favorable for potato production, following N application by SPAD values did not guide proper N fertilization and resulted in reduced yield[7]. Minotti et al. reported that SPAD readings could identify severe N deficiency in potatoes but had limited value for identifying situations of marginal N deficiency[15]. Wu et al. also reported that petiole sap NO3-N concentrations were more sensitive than SPAD readings to N fertilization throughout the growing season[8]. Rodrigues in Portugal found that it was possible to know the N requirement by potato plants in the early growth stages[16]. The Portugal scientist emphasized that pre-side dress soil NO3-N and inorganic N were the best N indicators of the need of N application. Petiole sap nitrate-N concentration was more closely related to plant N-status between 20 to 60 d after emergence[6].

      Our results showed that SPAD reading, and petiole sap nitrate-N concentration had weak linear relations, and there were great variations in different growth stages and between the two years. It was consistent with the report that SPAD values correlated well with the chlorophyll content and the nitrogen concentration in leaves but did not closely correlate with petiole sap nitrate concentration[17]. The error rates of N indicator by SPAD reading and petiole sap nitrate reading were greater than that of soil N and petiole nitrate by laboratory analysis[16]. The accuracy of the handheld meters also affected the results. For SPAD reading, leaf position on the plant stem affected the reliability of measurements. The 4th compound leaf is reportedly more suitable for estimating N by SPAD meter[18]. For petiole sap nitrate N concentration monitoring, 20 petioles are required for increased measurement accuracy[6].

      Yields linearly increased with the increasing of the SPAD reading and petiole sap nitrate-N concentration, so it was unlike the quadratic regression of yield to N-rate, which can calculate the maximum yield according to the vertex point. As the equations changed much in different growth stages and growing seasons in Florida, it is hard to define the threshold of SPAD reading or petiole sap nitrate-N concentration. But SPAD meter is still considered as a good tool for diagnosis of nitrogen status as it is easier to use[17]. Individual dynamic threshold SPAD values in different growth stages should be established to using the SPAD readings as potato production guidance for N fertilization[10].

    Conclusions

    • Potato yield increased quadratically with N-fertilizer rate from 0 to 360 kg/ha, without affecting the tuber quality except for the greater brown center incidence at 0 kg/ha. Both SPAD and petiole sap nitrate N readings had close relations with the N rate. However, SPAD could only distinguish the difference in plant N status in later growth stages, which can be too late to supplement the fertilizer as the shoots were too tall for a tractor to drive into the field and side-dress the rows. Petiole sap nitrate N concentration was more responsive and was able to start differentiating the plant N status between different N rates in early growth stages. It is important for sustainable N management for potato production by (1) improving the representativeness and accuracy of petiole sap nitrate nitrogen readings by testing more varieties of both chipping and table-stock potatoes and (2) establishing a dynamic threshold of petiole sap nitrate N reading for optimizing N application rates in individual growth stages for different potato cultivars.

    Author contributions

    • The authors confirm contribution to the paper as follows: funding, study conception, experimental design, manuscript finalization: Liu G; performed the experiments, data collection, analysis and interpretation of results, and draft manuscript preparation: Li Q; performed the experiments, data collection, analysis and interpretation of results, and manuscript preparation: Denison J; data collection, and manuscript preparation: Gluck M. All authors reviewed the final version of this manuscript and granted approval for its publication.

    Data availability

    • The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

      Acknowledgments

      • This work was financially supported by UF/IFA Extension Horticultural Sciences Department (Project Number: 60230065-103-3300-CRRNT). The UF/IFAS Hastings Agricultural Extension Center Crew supported and helped with the field trials conducted at HAEC, Hastings, Florida.

      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/.
    Figure (7)  Table (2) References (18)
  • About this article
    Cite this article
    Li Q, Denison J, Gluck M, Liu G. 2023. Comparison of SPAD-based leaf greenness and paralleled petiole sap nitrate concentrations for monitoring potato vine nitrogen status. Vegetable Research 3:30 doi: 10.48130/VR-2023-0030
    Li Q, Denison J, Gluck M, Liu G. 2023. Comparison of SPAD-based leaf greenness and paralleled petiole sap nitrate concentrations for monitoring potato vine nitrogen status. Vegetable Research 3:30 doi: 10.48130/VR-2023-0030
    shu

Catalog

  • HTML

  • Introduction
  • Materials and methods
  • Results

  • Tuber yield and quality responses to nitrogen application rate

  • Soil nitrate-N concentration response to N-rate

  • SPAD and petiole sap nitrate-N response to N-rate

  • Relation between the petiole sap nitrate-N concentration and SPAD value

  • Yield response to SPAD or petiole sap nitrate-N concentrations

  • Conclusions
  • Author contributions
  • Data availability
  • Acknowledgments
  • Conflict of interest
  • Rights and permissions
  • About this article
logo

Contents

Policies

Services

Partnerships

© 2025 Maximum Academic Press, except Open Access articles. MAP is a member of ALPSP, COPE, CrossRef, cOAlition S, OASPA and STM.

Privacy Policy  |  Site Map  |  Terms and Conditions

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return