ARTICLE   Open Access    

Effect of biochar-compost amendment on soilless media properties and cucumber seedling establishment

More Information
  • The interest in replacing peat with biochar in soilless substrate media is increasing however, the proportion of biochar inclusion in the media which could improve the media properties as well as the seedling performance of vegetables is still unknown. Therefore, the aim of the current study was to test different biochar types at different proportions with cotton burr-compost in the growing media on hydro-physicochemical properties of media, germination, and shoot and root growth of cucumber seedlings. Two trials were conducted in 2022 using cv 'Picolino' in Randomized Complete Block Design with three replications. Control included peat:perlite:vermiculite at 50:25:25 %v/v. Other treatments were prepared to replace peat either partially [12.5% (v/v) biochar and 12.5% (v/v) compost (Partial hardwood: PHW, Partial softwood: PSW, and Partial hemp: PH)] or completely [25% (v/v) biochar and 25% (v/v) compost (Full hardwood: FHW, Full softwood: FSW, and Full hemp: FH)]. Biochar-compost inclusion increased the pH and EC of the medium. Water retention capacity and thermal conductivity of the medium were found to be improved in hemp biochar-compost treatment. FSW increased fresh shoot weight, the number of leaves, leaf area, and shoot:root ratio by 83%, 33%, 84%, and 46%, respectively compared to control. Root length density and root surface area density increased by 40% and 47%, respectively in FSW compared to control. Most of the biochar-compost amended media performed better for the cucumber seedling production compared to control showing a possibility of replacing the peat in the media for sustainable transplant production.
  • The competition for consumer preference for fresh apples (Malus domestica) from exotic and tropical fruits is intense. Red-fleshed (RF) apple may not only provide a novel point of differentiation and enhanced visual quality, but also a source of increased concentration of potentially health-benefiting compounds within both the fresh fruit and snack/juice markets[1]. Two different types of RF apples have been characterised: Type 1 RF apple has red colouration not only in the fruit core and cortex, but also in vegetative tissues, including stems and leaves; Type 2 RF apples display red pigment only in the fruit cortex[1, 2]. To facilitate trade and lengthen the supply-window, harvested fruit are usually cold stored, which can induce a series of disorders, including physiological breakdown manifesting as a flesh browning disorder (FBD) in RF apples[3, 4]. FBD in RF apples can be caused by senescence, and there is also some evidence to suggest that a large proportion of RF apples are chilling-sensitive (Jason Johnston, Plant & Food Research Hawke's Bay, personal communication).

    Earlier studies suggested that Type 1 RF colour was determined by a promoter mutation of MdMYB10 that has a tandem replication of a myeloblastosis (MYB) binding cis-element (R6) within the promoter, resulting in autoregulation of MdMYB10[5]. However, van Nocker et al.[6] observed a large variation in the degree and pattern of red pigmentation within the cortex among the accessions carrying MdMYB10, and concluded that the presence of this gene alone was not sufficient to ensure the RF colour. A genome-wide association study (GWAS)[3], reported that, in addition to the MdMYB10 gene, other genetic factors (e.g. MdLAR1, a key enzyme in the flavonoid biosynthetic pathway) were associated with RF colour, too. Wang et al.[7] reported that many of the up-regulated genes in RF apples were associated with flavonoid biosynthesis (e.g., chalcone synthase (CHS), chalcone isomerase (CHI), dihydroflavonol 4-reductase (DFR), anthocyanin synthase (ANS), UDP-glucosyltransferase (UGT) and MYB transcription factors). Recently, MdNAC42 was shown to share similar expression patterns in RF fruit with MdMYB10 and MdTTG1, and it interacts with MdMYB10 to participate in the regulation of anthocyanin synthesis in the RF apple Redlove®[8].

    Several transcription factor genes (e.g., MYB, WRKY, bHLH, NAC, ERF, bZIP and HSF) were reported to be differentially expressed during cold-induced morphological and physiological changes in 'Golden Delicious' apples[9]. A study by Zhang et al.[10] showed that ERF1B was capable of interacting with the promotors of anthocyanin and proanthocyanidin (PA) MYB transcription factors, and suggested that ethylene regulation and anthocyanin regulation might be linked in either direction. It was reported that ethylene signal transduction pathway genes or response genes, such as ERS (ethylene response sensor), EIN3 (ethylene-insensitive3) and ERFs (ethylene response factors), may play an important role in the regulatory network of PA biosynthesis[11].

    Espley et al.[12] observed no incidence of FBD in cold-stored fruit of 'Royal Gala', but over-expression of MdMYB10 in 'Royal Gala' resulted in a high rate of FBD in RF fruit, which was hypothesised to be caused by elevated fruit ethylene concentrations before harvest and more anthocyanin, chlorogenic acid (CGA) and pro-cyanidins in RF fruit. In addition, the MYB10 transcription factor was shown to elevate the expression levels of MdACS, MdACO, and MdERF106 ethylene-regulating genes[12]. To elucidate the mechanism regulating the FBD of RF apples, Zuo et al.[13] analysed the transcriptome of tree-ripe apples at 0, 0.5 and 4 h after cutting, and reported that the differentially expressed genes at different sampling points were mainly related to plant–pathogen interactions.

    GWAS is a powerful technique for mining novel functional variants. One of the limitations of GWAS, using SNP arrays, is that they require genotyping of large numbers of individuals, which may be expensive for large populations. DNA pooling-based designs (i.e., bulk segregant analysis) test differential allele distributions in pools of individuals that exhibit extreme phenotypes (XP) in bi-parental populations, large germplasm collections or random mating populations[1416]. In addition to reducing the number of samples to be genotyped, the use of whole genome sequencing (WGS)-based XP-GWAS has the potential to identify small-effect loci and rare alleles via extreme phenotypic selection.

    In this WGS-based XP-GWAS, we investigated the genetic basis of RF and FBD by sequencing the pools of individuals that exhibited extreme phenotypes for these two traits, and analysed the differences in allele frequencies between phenotypic classes. This method combines the simplicity of genotyping pools with superior mapping resolution. We also examined the transcriptome from transgenic apple fruit harbouring the R6:MYB10 promoter as a model for red flesh in apple. Differences in gene expression of a highly pigmented line were compared with expression in control fruit and these genes were then used for comparison with the seedling population. Understanding the genetic basis of the link between RF and FBD will help in design of strategies for selection against FBD in high-quality Type 1 RF apple cultivars.

    A snapshot of visual variation in FBD and WCI is presented in Fig. 1. The average WCI and FBD across all ~900 seedlings ranged from 0 to 7, and from 0% to 58%, respectively. Based on the MLM analysis, the estimated narrow-sense heritability (h2) of WCI and FBD was 0.57 (standard error = 0.18) and 0.09 (standard error = 0.05), respectively. The estimated genetic correlation between WCI and FBD was 0.58, and several fruit quality traits displayed unfavourable correlations with WCI (Supplemental Fig. S1). Seedlings with higher WCI scores were generally characterised by poor firmness and crispness, plus higher astringency and sourness. Estimated phenotypic and genetic correlations between all pairs of traits are listed in Supplemental Table S1.

    Figure 1.  Transverse cross-sections of apple slices showing range in (a) flesh colouration, and (b) flesh browning disorder for Type 1 red-fleshed apple. The weighted cortical intensity (WCI) scores (0−9 scale) and the proportion of the cortex area showing symptoms of flesh browning disorder are also displayed.

    A few seedlings had no red pigment in the cortex, but the average WCI score across all seedlings was 2.25. About two-thirds of the seedlings did not display any FBD symptoms, but among the remaining seedlings, FBD ranged between 1% and 58% (Fig. 2). The average WCI score for the 'low' and 'high' WCI pool was 0.45 and 5.2, respectively, while the average FBD was 0% and 20.6% for the 'low' and 'high' FBD pool, respectively (Supplemental Table S2). The average WCI score of the seedlings in the FBD pools was similar (Low: 5.5; High: 4.6), while the average FBD of the high- and low-WCI pools was 4.5% and 0.1%, respectively.

    Figure 2.  The distribution of weighted cortex intensity (WCI) scores (a) and the internal flesh browning disorder IFBD%; (b) in the population of ~900 apple seedlings. The green and red circles highlight the individuals used to form the 'low' and 'high' pools of samples.

    After filtering, about 204,000 SNPs were used and the average sequencing depth of SNP loci was similar for the two pools (42 vs 44). There was a near-perfect correlation between the Z-test statistics and G-statistics, so only the latter are discussed hereafter. A plot of the G' values, smoothed over 2 Mb windows, is shown for all 17 chromosomes (Chrs) in Supplemental Fig. S2. XP-GWAS identified genomic regions significantly associated with FBD on 12 out of the 17 Chrs (Fig. 3), and putative candidate genes within ±1.0 Mb distanceof the significant G' peaks were identified (Table 1). Additional genomic regions, which did not meet the significance threshold but displayed distinguished G' peaks, were also identified across all chromosomes (Supplemental Table S3).

    Figure 3.  G'-statistics across the linkage groups (LG) showing significant association with the flesh browning disorder (FBD) in apple The horizontal red lines indicate the significance threshold. The putative candidate genes (refer to Table 1) underpinning various G' peaks are also shown.
    Table 1.  A list of the genomic regions associated with internal flesh browning disorder (FBD) in apples. Putative candidate genes residing within these regions are also listed using GDDH13v1.1 reference genome assembly.
    ChrGenomic region (Mb)Putative genes functions
    221.9–23.5Ethylene-responsive element binding factor 13 (MdERF13: MD02G1213600);
    33.1–4.9cinnamate 4-hydroxylase (C4H) enzyme (MD03G1051100, MD03G1050900 and MD03G1051000); MdWRKY2: MD03G1044400; MdWRKY33 (MD03G1057400)
    38.2–9.6ascorbate peroxidase 1 (MdAPX1: MD03G1108200, MD03G1108300)
    314.7–16.6senescence-related MdNAC90 (MD03G1148500)
    336.5–37.5Ethylene response sensor 1 (MdERS1: MD03G1292200); flavonoid biosynthesis protein MdMYB12 (MD03G1297100); Heat shock protein DnaJ (MD03G1296600, MD03G1297000); pectin methylesterase (MdPME) inhibitor protein (MD03G1290800, MD03G1290900, MD03G1291000).
    411.0–13.0phenylalanine and lignin biosynthesis protein MdMYB85 (MD04G1080600)
    422.1–24.4MYB domain protein 1 (MD04G1142200); HSP20-like protein (MD04G1140600); UDP-glucosyltransferase (UGT) proteins UGT85A7 (MD04G1140700, MD04G1140900); UGT85A3 (MD04G1140800); UGT (MD04G1141000, MD04G1141300); UGT85A2 (MD04G1141400); UGT85A4 (MD04G1141500); DNAJ heat shock protein (MD04G1153800, MD04G1153900, MD04G1154100)
    427.6–29.6HCT/HQT regulatory genes MD04G1188000 and MD04G1188400
    629.8–31.7Volz et al. (2013) QTL for IFBD; anthocyanin regulatory proteins MdMYB86 (MD06G1167200); triterpene biosynthesis transcription factor MdMYB66 (MD06G1174200); Cytochrome P450 (MD06G1162600; MD06G1162700, MD06G1162800, MD06G1163100, MD06G1163300, MD06G1163400, MD06G1163500, MD06G1163600, MD06G1163800, MD06G1164000, MD06G1164100, MD06G1164300, MD06G1164400, MD06G1164500, MD06G1164700)
    713.9–15.9heat shock protein 70B (MD07G1116300)
    717.4–19.0Drought-stress WRKY DNA-binding proteins (MdWRKY56: MD07G1131000, MD07G1131400)
    723.6–25.2MdPAL2 (MD07G1172700); drought-stress gene NGA1 (MD07G1162400); DNAJ heat shock family protein (MD07G1162300, MD07G1162200), stress-response protein (MdNAC69: MD07G1163700, MD07G1164000)
    97.9–11.8MD09G1110500 involved in ascorbate oxidase (AO); MdUGT proteins (MD09G1141200, MD09G1141300, MD09G1141500, MD09G1141600, MD09G1141700, MD09G1141800, MD09G1142000, MD09G1142500, MD09G1142600, MD09G1142800, MD09G1142900, MD09G1143000, MD09G1143200, MD09G1143400) involved in flavonoids biosynthesis; heat shock proteins 89.1 (MD09G1122200) and HSP70 (MD09G1137300); Ethylene-forming enzyme MD09G1114800; Anthocyanin regulatory protein MdNAC42 (MD09G1147500, MD09G1147600)
    914.9–16.5triterpene biosynthesis transcription factor protein MdMYB66 (MD09G1183800);
    111.7–3.0ethylene response factor proteins (MdEIN-like 3: MD11G1022400)
    1138.6–40.6Senescence-related gene 1 (MD11G1271400, MD11G1272300, MD11G1272000, MD11G1272100, MD11G1272300, MD11G1272400, and MD11G1272500); Chalcone-flavanone isomerase (CHI) protein (MD11G1273600) and MdbHLH3 (MD11G1286900, MDP0000225680); cytochrome P450 enzyme (MD11G1274000, MD11G1274100, MD11G1274200, MD11G1274300, MD11G1274500, and MD11G1274600); heat shock transcription factor A6B (MdHSFA6B: MD11G1278900) – involved in ABA-mediated heat response and flavonoid biosynthesis.
    122.2–3.5heat shock protein 70-1 ((MD12G1025600, MD12G1025700 and MD12G1026300) and heat shock protein 70 (MD12G1025800 and MD12G1025900 and MD12G1026000); ethylene (MD12G1032000) and auxin-responsive (MD12G1027600) proteins.
    127.2–8.4DNAJ heat shock domain-containing protein (MD12G1065200 and MD12G1067400)
    1327.5–30.5MD13G1257800 involved in polyphenol 4-coumarate:CoA ligase (4CL)
    1337.5–39.5pectin methyl esterase inhibitor superfamily protein MdPMEI (MD13G1278600)
    1425.4–27.5Drought-stress response gene MdWRKY45 (MD14G1154500); chalcone synthase (CHS) family proteins (MD14G1160800 and MD14G1160900); triterpene biosynthesis transcription factor MdMYB66 (MD14G1180700, MD14G1181000, MD14G1180900); anthocyanin biosynthesis protein (MdMYB86: MD14G1172900); cytochrome P450 proteins (MD14G1169000, MD14G1169200, MD14G1169600, MD14G1169700)
    150–1.5Ethylene synthesis proteins (MD15G1020100, MD15G1020300 and MD15G1020500); dihydroflavonol reductase (DFR) gene (MD15G1024100)
    154.6–6.8MdMYB73 (MD15G1076600, MD15G1088000) modulates malate transportation/accumulation via interaction with MdMYB1/10; MdNAC52: MD15G1079400) regulates anthocyanin/PA; heat shock transcription factor B4 (MD15G1080700); stress-response protein MdWRKY7 (MD15G1078200)
    1553.5–54.9MdC3H (MD15G1436500) involved in chlorogenic acid biosynthesis; MdEIN3 (MD15G1441000) involved in regulating ethylene synthesis and anthocyanin accumulation
    168.9–10.9SAUR-like auxin-responsive protein (MD16G1124300) and MdNAC83: (MD16G1125800) associated with fruit ripening; MD16G1140800 regulates proanthocyanidin; MdPAE genes (MD16G1132100, MD16G1140500) regulates ethylene production.
     | Show Table
    DownLoad: CSV

    The ethylene-responsive factor 13 (MdERF13: MD02G1213600) resided within the significant region (21.9–23.5 Mb) on Chr2, while the ascorbate peroxidase 3 (MdAPX3: MD02G1127800, MD02G1132200) resided in the prominent region between 9.45 and 11.28 Mb) (Fig. 3, Table 1). There were several genomic regions showing association with FBD on Chr3. The first region (8.2–9.5 Mb) flanked MdAPX1 (MD03G1108200, MD03G1108300), while MdERF3 (MD03G1194300) and heat shock protein 70 (HSP70: MD03G1201800, D03G1201700) resided within the prominent peak region (25.5–27.5 Mb). Another significant region (36.5–37.5 Mb) at the bottom on Chr3 harboured ethylene response sensor 1 (MdERS1: MD03G1292200), a flavonoid-biosynthesis related protein MdMYB12 (MD03G1297100), HSP DnaJ and pectin methyl esterase (PME) inhibitor proteins (Fig. 3, Table 1).

    The significant G' region (27.6–29.6 Mb) on Chr4 flanked the genes MD04G1188000 and MD04G1188400 involved in the biosynthesis of hydroxycinnamoyl CoA shikimate/quinate hydroxycinnamoyl transferase (HCT/HQT), while the adjacent region (22.1–24.3 Mb) harboured a cluster of UDP-glucosyltransferase (UGT) proteins and HSP (Table 1). Another region between 11.0 and 13.0 Mb encompassed phenylalanine and lignin biosynthesis gene MYB85 (MD04G1080600)[17]. The only significant region associated with FBD on Chr6 spanned between 29.8 and 31.7 Mb, which included a SNP earlier reported associated with FBD[4]. This region also harboured several MYB proteins (MdMYB86: MD06G1167200; MdMYB98: MD06G1172900; MdMYB66: MD06G1174200) and a large cluster of cytochrome P450 proteins (Table 1).

    A significant FBD-associated region (13.9–15.9 Mb) on Chr7 encompassed the HSP 70B (MD07G1116300), while another significant region between 23.6 Mb and 25.2 Mb flanked the gene coding for phenylalanine ammonia lyase 2 (MdPAL2: MD07G1172700), a drought stress gene NGA1 (MD07G1162400), DNAJ HSP, and stress-response protein (MdNAC69: MD07G1163700, MD07G1164000) (Fig. 3, Table 1). A large significant region spanning between 7.9 and 11.8 Mb on Chr9 encompassed the gene MD09G1110500 putatively involved in ascorbate oxidase (AO), HSP (HSP70: MD09G1137300; HSP89.1: MD09G1122200), an ethylene-forming enzyme (MD09G1114800), and MdNAC42 (MD09G1147500, MD09G1147600). Another significant genomic region on Chr9 was between 14.9 and 16.5 Mb, which harbours the MYB domain protein MdMYB66 (MD09G1183800) (Table 1).

    A sharp G' peak region (1.7–3.0 Mb) on Chr11 associated with FBD encompassed ethylene insensitive 3 (MdEIN3: MD11G1022400) along with a cluster of UGT proteins, WD-40, and bHLHL proteins (Table 1). A significant region between 38.6 and 40.6 Mb at the bottom of Chr11 was dominated by clusters of senescence-related genes and cytochrome P450 enzymes. This genomic region also flanked a chalcone-flavanone isomerase (CHI) family protein (MD11G1273600) and a bHLH protein (MdbHLH3: MD11G1286900, MDP0000225680), along with the heat shock transcription factor A6B (HSFA6B: MD11G1278900) (Table 1, Supplemental Table S3).

    The region (2.2–3.3 Mb) associated with FBD on Chr12 flanked genes for the HSP 70 and 70-1, NAC proteins, ethylene and auxin-responsive proteins (Table 1). An adjacent significant region (7.2–8.4 Mb) flanked DNAJ HSP, along with bZIP, bHLH and WD-40 repeat-like proteins (Table 1; Supplemental Table S3). There was a large genomic region on Chr13 showing a significant association with FBD. In this region, the first G' peak (27.5–30.5 Mb) harboured MD13G1257800, which regulates polyphenol 4-coumarate: CoA ligase (4CL) synthesis. The second G' peak region (32.6–34.7 Mb) corresponded to an earlier mapped QTL for flesh browning in white-fleshed apples[18].

    The significant region (25.4–27.5 Mb) on Chr14 harboured various genes for proteins with different putative functions, such as AP2 proteins, WD-40 repeat family proteins, bHLH proteins, MdNAC83 (MD14G1150900), MdWRKY45 (MD14G1154500), chalcone synthase (CHS) family proteins (MD14G1160800 and MD14G1160900), cytochrome P450 proteins, and several MYB domain proteins (MdMYB86: MD14G1172900; MdMYB98: MD14G1179000; MdMYB66: MD14G1180700, MD14G1181000, MD14G1180900) (Fig. 3, Table 1, Supplemental Table S3). A sharp G' peak (15.2–17.2 Mb) on Chr14 did not meet the significance threshold corresponding to the FBD QTL in white-fleshed apples[18].

    The upper 1.5 Mb region on Chr15 associated with FBD encompassed several transcription factor families, including WD-40 repeats, bHLH, bZIP, ethylene synthesis proteins, and dihydroflavonol reductase (DFR) protein (MD15G1024100) (Fig. 3, Table 1; Supplemental Table S3). Another significant region on Chr15 (4.6−6.8 Mb) harboured HSF B4, MdMYB73 (which interacts with MdMYB1/10 to modulate malate transportation) and MdNAC52 (MD15G1079400), which regulates anthocyanin and PA synthesis by directly regulating MdLAR[19]. A significantly associated region at the bottom of Chr15 (53.5–54.9 Mb) harboured MdNAC35 (MD15G1444700), MdC3H (MD15G1436500) involved in the production of p-coumarate 3-hydroxylase (C3H) enzyme, which plays a role in chlorogenic acid biosynthesis, and MdEIN3 (MD15G1441000).

    The significant region between 8.9 and 10.9 Mb on Chr16 harboured a gene for SAUR-like auxin-responsive protein (MD16G1124300) and MdNAC83 (MD16G1125800), both of which have been reportedly associated with apple fruit ripening[20]. This region also encompassed MD16G1140800, which regulates PA[11], and a gene for the pectin acetyl esterase protein MdPAE10 (MD16G1132100) involved in ethylene production and shelf-life[21]. A sharp G' peak region (20.4–22.4 Mb) in the middle of Chr16 flanked MdERF1B (MD16G1216900) and MdMYB15 (MD16G1218000 and MD16G1218900), involved in altering anthocyanin and PA concentrations[10] (Fig. 3, Table 1, Supplemental Table S3).

    The average sequencing depth of the SNP loci (~160,000) retained for marker-trait association was similar for the two WCI pools (41 vs 44). The significant regions were located on Chrs 2, 4, 6, 7, 10, 15 and 16 (Fig. 4). The genomic intervals within ±1.0 Mb of the significant G' peaks, and the putative candidate genes within those intervals, are listed in Table 2. Additional genomic regions, which did not meet the significance threshold but displayed distinct G' peaks, were also identified across most chromosomes ( Supplemental Fig. S2, Supplemental Table S3). A significant region on Chr4 encompassed chalcone synthase (CHS) genes, along with an ERF (MD04G1009000) involved in regulating PA biosynthesis[11].

    Figure 4.  G’-statistics across the linkage groups (LG) showing significant association with the weighted cortex intensity (WCI) in apple. The horizontal red lines indicate the significance threshold. The putative candidate genes (refer to Table 2) underpinning various G' peaks are also shown.
    Table 2.  A list of the genomic regions significantly associated with the weighted cortex intensity (WCI) in apples. Putative candidate genes residing within these regions are also listed using GDDH13v1.1 reference genome assembly.
    ChrGenomic region (Mb)Putative genes functions
    211.2–13.2UDP-glucosyltransferase (UGT) proteins (MD02G1153000, MD02G1153100, MD02G1153200, MD02G1153300; MD02G1153400; MD02G1153500; MD02G1153700; MD02G1153800; MD02G1153900);
    40–1.2Chalcone synthase (CHS) genes (MD04G1003000; MD04G1003300 and MD04G1003400); DNAJ heat shock protein (MD04G1003500); MdERF (MD04G1009000) involved in regulating PA biosynthesis.
    69.5–11.0Ubiquitin protein (MD06G1061100); stress-response WRKY protein MdWRKY21 (MD06G1062800);
    612.1–13.6pectin methylesterase inhibitor superfamily protein (MdPME: MD06G1064700); phenylalanine and lignin biosynthesis gene (MdMYB85)
    616.0–17.6MD06G1071600 (MDP0000360447) involved in leucoanthocyanidin dioxygenase (LDOX) synthesis
    624.0–26.0UDP-glycosyltransferase proteins (MD06G1103300, MD06G1103400, MD06G1103500 and MD06G1103600); auxin response factor 9 (MdARF9: MD06G1111100) and heat shock protein 70 (Hsp 70; MD06G1113000).
    74.1–5.6Ethylene insensitive 3 protein (MdEIN3: MD07G1053500 and MD07G1053800) involved in proanthocyanidins (PA) biosynthesis; ubiquitin-specific protease (MD07G1051000, MD07G1051100, MD07G1051200, MD07G1051500, MD07G1051300, MD07G1051700 and MD07G1051800).
    1015.9–18.3bHLH proteins (MD10G1098900, MD10G1104300, and MD10G1104600); UGT protein (MD10G1101200); UGT 74D1 (MD10G1110800), UGT 74F1 (MD10G1111100), UGT 74F2 (MD10G1111000).
    1035.6–38.6Stress-response WRKY proteins (MdWRKY28: MD10G1266400; MdWRKY65: MD10G1275800). ethylene responsive factors (MdERF2: MD10G1286300; MdERF4: MD10G1290400, and MdERF12: MD10G1290900) and a NAC domain protein (MdNAC73: MD10G1288300); polyphenol oxidase (PPO) genes ((MD10G1298200; MD10G1298300; MD10G1298400; MD10G1298500; MD10G1298700; MD10G1299100; MD10G1299300; MD10G1299400).
    1524.8–26.8heat shock factor 4 (MD15G1283700), drought-stress response WRKY protein 7 (MdWRKY7: MD15G1287300), MdMYB73 (MD15G1288600) involved in ubiquitination and malate synthesis
    1531.7–34.2MYB domain protein 93 (MdMYB93: MD15G1323500) regulates flavonoids and suberin accumulation (Legay et al. 2016); ubiquitin -specific protease 3 (MD15G1318500),
    161.5–3.4MYB domain proteins (MD16G1029400) regulates anthocyanin; senescence-associated gene 12 (MD16G1031600); ethylene response factor proteins (MdERF118: MD16G1043500, and MD16G1047700 (MdRAV1); MdMYB62 ( MD16G1040800) flavonol regulation; malate transporter MdMa2 (MD16G1045000: MDP0000244249), Ubiquitin-like superfamily protein (MD16G1036000),
    165.4–7.4MdMYB88 (MD16G1076100) regulates phenylpropanoid synthesis and ABA-mediated anthocyanin biosynthesis; MdMYB66 (MD16G1093200) regulates triterpene biosynthesis, MdWRKY72 (MD16G1077700) mediates ultraviolet B-induced anthocyanin synthesis.
     | Show Table
    DownLoad: CSV

    There were distinct G' peaks within a large genomic region (spanning between 9.0 and 18.0 Mb) significantly associated with WCI on Chr6 (Fig. 4). The G' peak region 12.1–13.6 Mb encompassed MdMYB85 (MD06G1064300), and a gene for a pectin methyl esterase

    (PME) inhibitor protein (MD06G1064700); while the region between 16.0 and 17.6 Mb flanked the genes involved in leucoanthocyanidin dioxygenase (LDOX) synthesis (Table 2). The auxin response factor 9 (MdARF9: MD06G1111100) and the HSP70 (MD06G1113000) resided in the significant region between 24.0 and 26.0 Mb on Chr6 (Table 2).

    The genomic region (4.1–5.6 Mb) with significant association with WCI on Chr7 flanked MdEIN3 (MD07G1053500, MD07G1053800), which plays an important role in the regulatory network of PA biosynthesis[11]. A significant region (35.6–38.7 Mb) on Chr10 encompassed gene clusters for bHLH and WRKY proteins along with an ethylene repressor factor (MdERF2: MD10G1286300) (Fig. 4, Table 2; Supplemental Table S3). This region also harboured MdERF4 (MD10G1290400), MdERF12 (MD10G1290900) and NAC domain genes (MdNAC73: MD10G1288300), which have been reported to be associated with fruit ripening[20]. In addition, there was a cluster of polyphenol oxidase (PPO) genes residing in this region (Table 2).

    On Chr15, the significant WCI-associated region spanning between 31.7 Mb and 34.2 Mb encompassed several TF families, including MdMYB93 (MD15G1323500) (Fig. 4, Table 2, Supplemental Table S3). A distinguished G' peak region (24.9–26.9 Mb) on Chr15 harboured genes for WD-40 repeat-like proteins, bHLH proteins, redox responsive transcription factor 1 (MD15G1283200), cytochrome P450 proteins, HSF4 (MD15G1283700), and MdWRKY7 (MD15G1287300). The MdMYB73 (MD15G1288600) residing in this region has been shown to interact with MdMYB1/10 and regulates several functions, including cold-stress response, ubiquitination and malate synthesis[22, 23].

    The significant 1.5–3.4 Mb region on Chr16 flanked an anthocyanin repressor MYB protein (MD16G1029400), senescence-associated gene 12 (MD16G1031600), malate transporter MdMa2 (MD16G1045000: MDP0000244249), and two ethylene response factor genes (MdERF118: MD16G1043500; MdRAV1: MD16G1047700), which interact in retaining flesh firmness[24]. The MdMYB62 (MD16G1040800) gene residing in this region is phylogenetically linked to MdMYB8 (MD06G1217200), which plays a major role in flavonoid biosynthesis[25]. Another significant region (5.4–7.4 Mb) on Chr16 harboured several bHLH genes, including MdMYB88 (MD16G1076100) involved in phenylpropanoid synthesis resulting in drought resistance[26] and ABA-mediated anthocyanin production[27]. The MdMYB66 (MD16G1093200) gene, which is involved in triterpene biosynthesis, and the MdWRKY72 (MD16G1077700) gene involved in anthocyanin synthesis, were also present in this region (Table 2, Fig. 4).

    The genomic regions tagged by XP-GWAS were enriched for regulatory functions, and several of these have a paralog (Table 3). For example, paralogues for a trio of genes (MdMYB86, MdMYB98 and Cytochrome 450) resided in the FBD-associated genomic regions on Chr6 and Chr14. Several pairs of genes resided together in the FBD-associated paralog regions; for example, MdNAC90 and germin-like protein 10 on Chr3 and Chr11; WRKY55 and WRKY70 on Chr1 and Chr7; and ERF1 and ERF5 on Chr4 and Chr6. The ethylene response factor MdERF1B had a paralog in the FBD-associated region on Chr13 and Chr16. Interestingly, three copies of MdNAC83 (Chrs 14, 16 17) and four copies of MdMYB66 (Chrs 4, 6, 9 and 14) resided in the FBD-associated regions. A pair of genes (RAV1 and MYB62) resided together in the WCI-associated paralog regions on Chrs13 and 16 (Table 3).

    Table 3.  A list of homologues genes/genomic regions associated with the internal flesh browning disorder (IFBD) and red flesh (WCI) in apples. Putative candidate genes residing within these regions are also listed using GDDH13v1.1 reference genome assembly.
    TraitChr (genomic region: Mb)Gene namePredicted gene IDPutative function
    IFBDChr6 (29.8–31.7 Mb)MdMYB66MD06G1174200Suberin/triterpene deposition
    IFBDChr14 (25.4–27.5 Mb)MdMYB66MD14G1180700, MD14G1181000, MD14G1180900Suberin/triterpene deposition
    IFBDChr4 (7.0–8.1 Mb)MdMYB66MD04G1060200Suberin/triterpene deposition
    IFBDChr9 (14.9–16.5 Mb)MdMYB66MD09G1183800Suberin/triterpene deposition
    IFBDChr6 (29.8–31.7 Mb)MdMYB86MD06G1167200Anthocyanin regulation
    IFBDChr14 (25.4–27.5 Mb)MdMYB86MD14G1172900Anthocyanin regulation
    IFBDChr6 (29.8–31.7 Mb)MdMYB98MD06G1172900Drought stress response
    IFBDChr14 (25.4–27.5 Mb)MdMYB98MD14G1179000Drought stress response
    IFBDChr6 (29.8–31.7 Mb)Cytochrome P450MD06G1162600, MD06G1162700, MD06G1162800, MD06G1163100, MD06G1163300, MD06G1163400, MD06G1163500, MD06G1163600, MD06G1163800, MD06G1164000, MD06G1164100, MD06G1164300, MD06G1164400, MD06G1164500, MD06G1164700Flavonoid and triterpenic metabolism
    IFBDChr14 (25.4–27.5 Mb)Cytochrome P450MD14G1169000, MD14G1169200, MD14G1169600, MD14G1169700Flavonoid and triterpenic metabolism
    IFBDChr3 (14.7–16.6 Mb)MdNAC90MD03G1148500Senescence-related
    IFBDChr11 (16.5–18.5 Mb)MdNAC90MD11G11679000Senescence-related
    IFBDChr14 (25.4–27.5 Mb)MdNAC83MD14G1150900Senescence/ripening-related
    IFBDChr16 (8.9–10.9 Mb)MdNAC83MD16G1125800Senescence/ripening-related
    IFBDChr17 (0–0. 7 Mb)MdNAC83MD17G1010300Senescence/ripening-related
    IFBDChr3 (14.7–16.6 Mb)Germin-like protein 10MD03G1148000Polyphenol oxidase
    IFBDChr11 (16.5–18.5 Mb)Germin-like protein 10MD11G1167000, MD11G1167100, MD11G1167400, MD11G1169200Polyphenol oxidase
    IFBDChr1 (26.9–28.5 Mb)MdWRKY55MD01G1168500Drought stress response
    IFBDChr7 (30.3–31.9 Mb)MdWRKY55MD07G1234600Drought stress response
    IFBDChr1 (26.9–28.5 Mb)MdWRKY70MD01G1168600Drought stress response
    IFBDChr7 (30.3–31.9 Mb)MdWRKY70MD07G1234700Drought stress response
    IFBDChr4 (7.0–8.1 Mb)MdERF1MD04G1058000Ethylene responsive factor
    IFBDChr6 (5.5–7.3 Mb)MdERF1MD06G1051800Ethylene responsive factor
    IFBDChr4 (7.0–8.1 Mb)MdERF5MD04G1058200Ethylene responsive factor
    IFBDChr6 (5.5–7.3 Mb)MdERF5MD06G1051900Ethylene responsive factor
    IFBDChr13 (18.0–20.0 Mb)MdERF1BMD13G1213100Ethylene response factor 1
    IFBDChr16 (20.4–22.4 Mb)MdERF1BMD16G1216900Ethylene response factor 1
    WCIChr13 (2.6–4.4 Mb)MdRAV1MD13G1046100Ethylene responsive factor
    WCIChr16 (1.5–3.4 Mb)MdRAV1MD16G1047700Ethylene responsive factor
    WCIChr13 (2.6–4.4 Mb)MdMYB62MD13G1039900Flavonol biosynthesis
    WCIChr16 (1.5–3.4 Mb)MdMYB62MD16G1040800Flavonol biosynthesis
    IFBDChr9 (7.9–11.8 Mb)MdNAC42MD09G1147500, MD09G1147600Anthocyanin accumulation
    WCIChr17 (11.4–12.4 Mb)MdNAC42MD17G1134400Anthocyanin accumulation
    IFBDChr9 (7.9–11.8 Mb)HSP 70MD09G1137300Heat stress response
    WCIChr17 (11.4–12.4 Mb)HSP 70MD17G1127600Heat stress response
    IFBDChr4 (11.0–13.0 Mb)MdMYB85MD04G1080600Phenylalanine and lignin biosynthesis
    WCIChr6 (12.1–13.6 Mb)MdMYB85MD06G1064300Phenylalanine and lignin biosynthesis
    IFBDChr15 (13.2–14.2 Mb)MdEBF1MD15G1171800Ethylene inhibition
    WCIChr8 (15.2–17.2 Mb)MdEBF1MD08G1150200Ethylene inhibition
    IFBDChr15 (53.5–54.9 Mb)MdEIN3MD15G1441000Ethylene insensitive 3 protein
    WCIChr8 (30.3–31.6 Mb)MdEIN3MD08G1245800Ethylene insensitive 3 protein
    IFBDChr11 (1.7–3.0 Mb)MdEIN3MD11G1022400Ethylene insensitive 3 protein
    WCIChr7 (4.1–5.6 Mb)MdEIN3MD07G1053500, MD07G1053800Ethylene insensitive 3 protein
    IFBDChr15 (4.6–6.8 Mb)MdMYB73MD15G1076600, MD15G1088000Cold-stress response & malate accumulation
    WCIChr15 (24.8–26.8 Mb)MdMYB73MD15G1288600Cold-stress response & malate accumulation
    IFBDChr15 (4.6–6.8M b)MdWRKY7MD15G1078200Anthocyanin accumulation
    WCIChr15 (24.8–26.8 Mb)MdWRKY7MD15G1287300Anthocyanin accumulation
    IFBDChr15 (43.5–45.5 Mb)MdMYB93MD15G1369700Flavonoid & suberin accumulation
    WCIChr15 (31.7–34.2 Mb)MdMYB93MD15G1323500Flavonoid & suberin accumulation
     | Show Table
    DownLoad: CSV

    Paralogs of several regulatory functions were also found in the regions associated with either FBD or WCI; for example, a significant region (7.9–11.8 Mb) harbouring a gene trio (MdNAC42, HSP70 and HSP89.1) on Chr9 was associated with FBD, but the paralogs of this trio also resided in a distinct G' region associated with WCI on Chr17 (Table 3, Supplemental Table S3). Some other examples included MdMYB85 (Chr4 for FBD, and Chr6 for WCI), MdEBF1 (Chr8 for WCI, and Chr15 for FBD), MdEIN3 (Chr8 for WCI, and Chr15 for FBD), and EIN3 (Chr7 for WCI, and Chr11 for FBD). A pair of genes (MdMYB73 and MdWRKY7) resided together in the separate regions associated with FBD (4.6–6.8 Mb) and WCI (24.8–26.8 Mb) within Chr15 (Table 3).

    The red pigmentation in fruit flesh differed amongst transgenic lines, with fruit from line A10 presenting the most deeply pigmented tissues (Supplemental Fig. S3), while those from lines A2 and A4 were similar in having a lower intensity of pigmentation. No pigmentation was observed in the flesh of control 'Royal Gala' (RG) fruit. RNAseq analysis of a representative transgenic line (A2) compared with RG revealed that a total of 1,379 genes were differentially expressed (log 2-fold), with 658 genes upregulated and 721 genes downregulated. This list was then assessed for commonality with the genomic regions and candidate genes from the XP-GWAS.

    Genes that contained mis-sense SNPs (which would result in a change in predicted protein) and that were also differentially expressed between A2 red-fleshed transgenic line and white-fleshed 'Royal Gala' (control) apples included anthocyanin-related flavanone 3-hydroxylase, chalcone synthase and dihydroflavonol 4-reductase (Table 2 & 4). Many more upstream DNA variants (e.g. in potential promoter-controlling elements) were seen in this group of differentially expressed genes (DEGs) that were also in regions underlying WCI or FBD. Genes encoding enzymes that may be involved in FBD, such as a Rho GTPase activating protein, peroxidase, lipoxygenase 1, and ethylene-forming enzyme (ACO4), were DEGs and showed mis-sense SNPs, including a potential stop codon in the Rho GTPase-activating protein (Table 4). This intersection between DNA change and differential expression warrants further research to evaluate the functions of these genes.

    Table 4.  List of candidate genes associated with WCI or FBD in the XP-GWA and R6:MdMYB10 apple datasets.
    Mutations in GWAS apple populationPredicted gene functionExpression in R6 and ‘Royal Gala’ apples
    GeneMissense
    SNPs
    SNP
    Stop
    Upstream
    variants
    DatasetLocusAnnotation and TAIR IDAverage
    RPKM
    R6 flesh
    Average RPKM
    WT flesh
    log2Fold
    Change
    MD02G11322001FBD Supplemental Table S3Chr02:9450615-11289014flavanone 3-hydroxylase
    (F3H, TT6, F3'H) AT3G51240
    387752.47
    MD02G11336003FBD Supplemental Table S3Chr02:9450615-11289014fatty acid desaturase 5
    (FAD5) AT3G15850
    18109.98
    MD02G11537001WCI Table 2Chr02:11278254-13234556UDP-Glycosyltransferase, lignin related AT2G1856010385341.03
    MD03G10592002FBD Supplemental Table S3Chr03:3177994-4910866Peroxidase AT5G053402514.21
    MD03G1143300422FBD Table 1Chr03:14797661-16611554bZIP transcription factor (DPBF2, AtbZIP67) AT3G44460114852-2.80
    MD03G1147700121FBD Table 1Chr03:14797661-16611554Rho GTPase activating protein AT5G22400205891.29
    MD04G1003400331WCI Table 2Chr04:1-1284894Chalcone synthase (CHS, TT4) AT5G1393014433122.34
    MD04G120410063FBD Table 1Chr04:27629536-29612282lipoxygenase 1 (LOX1, ATLOX1) AT1G550205462681.09
    MD06G1160700129FBD Table 1Chr06:29862341-31737341peptide met sulfoxide reductase AT4G251302121254562.04
    MD06G116140014FBD Table 1Chr06:29862341-31737341Pectin lyase-like protein AT5G63180614129530-2.22
    MD07G1240700126FBD Supplemental Table S3Chr07:30357332-31979025Fe superoxide dismutase 2 AT5G511001051826-3.99
    MD07G13069006FBD Supplemental Table S3Chr07:34607570-36531467UDP-glucosyl transferase 78D2 AT5G170506771112.78
    MD08G12491002WCI Supplemental Table S3Chr08:30486569-31607516HSP20-like chaperone (ATHSP22.0) AT4G102503186456.12
    MD09G111400032FBD Table 1Chr09:7999212-11883202fatty acid desaturase 5 (FAD5) AT3G158507608.68
    MD09G11468001FBD Table 1Chr09:7999212-11883202PHYTOENE SYNTHASE (PSY) AT5G1723038456160901.32
    MD10G132810045FBD Supplemental Table S3Chr10:40235258-41736791ethylene-forming enzyme (ACO4) AT1G050108769033910331.21
    MD15G10236001FBD Table 1Chr15:1-1487288jasmonic acid carboxyl methyltransferase (JMT) AT1G19640472111552.07
    MD15G10241008FBD Table 1Chr15:1-1487288dihydroflavonol 4-reductase (DFR, TT3, M318) AT5G428008723091.63
    MD17G11334004WCI Supplemental Table S3Chr17:11422073-12433463PHYTOENE SYNTHASE (PSY) AT5G172306001721.87
    MD17G12606002FBD Supplemental Table S3Chr17:31098955-32776972dehydroascorbate reductase 1 (DHAR3) AT5G167101867−1.78
     | Show Table
    DownLoad: CSV

    Several gene families (e.g. chalcone synthase, UGT, anthocyanin synthase, HSP, PAL, ERF, WRKY proteins, ABA, and bZIP transcription factors) residing in WCI-associated genomic regions have been reported to be associated with RF in apple[7, 12, 28]. Several of these genes are implicated in stress responses, suggesting that flavonoids and anthocyanin biosynthesis could also be associated with stress (e.g., drought, water loss) tolerance of RF apples[7].

    MdMYB73, which may play a role in the cold-stress response[23, 29], resided in the WCI-associated regions. Ethylene is among the various modulators of environmental stresses induced by factors such as drought and cold temperatures[9, 28]. Several ERF proteins (e.g. MdERF1B, MdERF3) interact with the promoters of MYB domain proteins to regulate anthocyanin and proanthocyanidin (PA) accumulation in apple[10, 11, 30]. The WCI-associated genomic regions in our study flanked several ERFs previously reported to have roles in anthocyanin and PA regulation. MdERF4 (MD10G1290400), one of the three ERFs residing in the WCI-associated region on Chr10, has high phylogenetic similarity with MdERF38, which interacts with MdMYB1 to regulate drought-related anthocyanin biosynthesis[31]. Some ERFs (e.g. MdERF2: MD10G1286300; MdERF4: MD10G1290400, and MdERF12: MD10G1290900) and MdNAC73 residing in the WCI-associated regions have been reported to be associated with fruit maturation[20] – suggesting an interaction between ethylene production and RF colour[12].

    The WCI-associated region on Chr15 included a gene MdMYB73 (MD15G1288600) involved in malate acid synthesis. Previous studies have reported that MdMYB1 regulates both anthocyanin and malate accumulation, and perhaps MdBT2 regulates MdMYB73-mediated anthocyanin accumulation[22, 23]. The malate transporter gene MdMa2 and the MdMYB7 gene, involved in the regulation of anthocyanin and flavanols in RF apple[32], resided together in the WCI-associated upper region of LG16. Taken together, these results support the hypothesis that there is interplay between anthocyanin and malate accumulation in the RF apple. There was a cluster of ubiquitin-specific proteases underpinning the WCI-associated regions on Chrs 7 and 15, which is supported by earlier reports suggesting that ubiquitin-specific proteases respond to auxin and might suppress anthocyanin biosynthesis proteins[33]. The auxin response factor 9 (MdARF9: MD06G1111100), which has also been shown to suppress anthocyanin biosynthesis in RF callus samples[33], also resided in the WCI-associated genomic region on LG7.

    Seedlings in both the low- and high-FBD pools carried the MdMYB10 gene, which suggests that MdMYB10 itself is not the causal factor of FBD in RF apples. Long-term cold storage generally results in senescence-related flesh breakdown, and several transcription factor genes (e.g. MYB, WRKY, NAC, ERF, cytochrome P450, and HSP) have been shown to express differentially during long-term cold storage[9]. The FBD-associated genomic regions in our study harboured several ERFs, suggesting that ethylene synthesis proteins may be contributing to cell wall disassembly, allowing PPO enzymes to come into contact with phenolic compounds and potentially leading to FBD symptoms[18].

    Pectin methyl esterase (PME) genes resided in the FBD-associated significant regions on Chrs 3, 13 and 17. Volatile generation and senescence degradation have been suggested to be bio-markers of FBD, and the expression levels of methyl esters were found to be associated with FBD and senescence in 'Fuji' apples after cold storage[34]. The MdPME2 gene has also been reported to be associated with apple flesh firmness and mealiness[21, 35]. The co-location of genes encoding cell wall-degrading enzymes (MdPME) and QTLs for FBD has been reported in apple[18, 36].

    The clusters of cytochrome P450 enzymes and senescence-related genes resided in some of the FBD-associated regions in this study. There are no earlier reports of the involvement of P450 enzymes in apple FBD expression, but some genes related to cytochrome P450 were found to be upregulated during litchi fruit senescence[37]. Pericarp browning in litchi is mainly attributable to the degradation of anthocyanin, and the ABA-initiated oxidation of phenolic compounds by PPO[37]. Flavonoids are among the major polyphenols in RF apples[5], and cytochrome P450 is part of the regulatory mechanism for flavonoid metabolism[38]. Association of FBD with the genomic region harbouring P450 would suggest its role in enhanced polyphenol synthesis causing FBD. The co-occurrence of a senescence-related gene MdNAC90 (MD03G1148500; MD11G11679000) and the PPO regulator germin-like proteins (in the FBD-associated paralog regions on Chrs 3 and 11) lends support to an interplay between senescence and oxidation of phenolics and anthocyanins.

    A significant region on Chr9 encompassed a cluster of UGT proteins, which play a role in the regulation of flavonoids and phenolic compounds, as well as converting phloretin to phloridzin[39]. Cytochrome P450, which resided within several FBD-associated regions, is reportedly involved in flavonoid metabolism, such as chlorogenic acid (CGA) acid and phloridzin, which have also been positively associated with suberin production and cell wall disassembly[40, 41].

    Reactive oxygen species (ROS) play an important role in regulating physiological processes in plants, such as senescence[37]. Legay et al.[42] suggested that MdMYB93 (MD15G1369700), found residing in the FBD-associated regions in this study, plays a critical role in remobilisation of flavonoid/phenolic compounds, which can be utilised for detoxification of ROS in the case of oxidative stress. However, flavonoid biosynthesis has also been linked with suberin production causing cuticle cracks in apples[42, 43], and the development of cuticular cracks could accelerate flesh browning as a result of an enhanced oxidative process[44].

    Several HSP (e.g. HSP70, HSP70-1, HSP60, HSP89.1, and HSP DNAJ) and HSF (e.g. HSF4, HSFB4, HSFA6B) resided in the FBD-associated genomic regions. Ferguson et al.[45] showed that, during summer, apple flesh temperature could reach as high as 43 °C, and that an increase in the expression of HSP in apples was associated with high daily flesh temperatures, suggesting a role of HSP to counter heat stress. HSPs have been reported to interact with AP2/ERFs and to play a role in flavonoid biosynthesis and drought tolerance in apple[46]. Heat stress affects lignin accumulation and its substrate, O-phenols, and has been reported to play role in enzymatic browning[47]. Additionally, HSF that regulate HSP expression have also been reported to be regulated by cold stress to generate heat-induced cold tolerance in banana[48]. HSF1 was shown to transcriptionally regulate the promoters of HSP to enhance chilling tolerance in loquat fruit[49]. Activity of the enzymes (PAL, C4H, 4CL) of the phenylpropanoid pathway was positively correlated with loquat fruit lignification, whilst suppression of their expression by heat shock treatment and low-temperature conditioning significantly reduced fruit lignification[50].

    Wang et al.[7] showed that ascorbate peroxidase (APX) was among the genes that were upregulated in RF apple compared with in white-fleshed apples. Several genes (e.g. MdAPX1, MdAPX3, MdAPX4, and MdDHAR1) involved in ascorbate synthesis resided in the genomic regions associated with FBD. Co-localisation of MdDHAR and ascorbic acid (AsA) synthesis genes in the FBD-linked genomic regions have been reported[51], suggesting that the low AsA content increases fruit susceptibility to FBD[52]. It has been shown that the expression of MdMYB1 and MdDHAR genes was strongly correlated in RF apples, and that AO and APX were upregulated by anthocyanin regulatory genes[31].

    There were several genes associated with CGA biosynthesis residing in the FBD-associated regions on various linkage groups, including the genes MYB19 (MD07G1268000) and MdC3H (MD15G1436500). Higher concentrations of CGA were reported in transgenic apple lines carrying MdMYB10[12], suggesting a role of CGA metabolism in the expression of FBD[53]. Interestingly, some of the FBD-associated regions (e.g. Chrs 9, 11, 13, 14 and 17) reported here in RF apples coincide with those reported earlier for FBD in white-fleshed apples[18, 36, 53], suggesting some common underlying genetic mechanisms.

    As discussed above, ERFs have been reported to be involved in the accumulation of anthocyanin and PA biosynthesis, while ethylene synthesis proteins also contribute to cell membrane breakdown, allowing the PPO enzyme to come into contact with phenolic compounds, potentially leading to FBD symptoms[12, 18]. We observed clusters of anthocyanin biosynthesis proteins (bHLH), ERFs (MdERF2, MdERF4, MdERF12) and PPO genes together in the genomic region associated with WCI on Chr10. The co-occurrence of these gene families perhaps facilitates potential interactions that contribute to the genetic correlation between WCI and FBD. We noted that genes involved in flavonoid regulation and ethylene synthesis occurred together in the FBD-associated regions on several chromosomes (e.g. MD16G1140800 and MdPAE10: MD16G1132100; MdERF1B: MD16G1216900 and MdMYB15: MD16G1218000, MD16G1218900) – suggesting these genes could be in linkage disequilibrium and this would contribute to the expression of WCI and FBD.

    MYB7 (MD16G1029400) resided in the WCI-associated upper region of Chr16, and the expression level of MYB7 was shown to be correlated with that of LAR1 in peach fruit[54]. The MdLAR1 protein (MDP0000376284), which is located about 1.2 Mb upstream of MYB7 (MD16G1029400), was reported to be associated with WCI and FBD in apple[3]. Mellidou et al.[36] reported that 4CL (MD13G1257800 in the FBD-linked region on Chr13), which catalyses the last step of the phenylpropanoid pathway, leading either to lignin or to flavonoids, was upregulated in browning-affected flesh tissues. The gene MdMYB85, involved in the regulation of flavonoid and lignin biosynthesis, resided in the WCI-associated region on Chr6 and FBD-associated paralogous region on Chr4. Metabolic interactions between anthocyanin and lignin biosynthesis have been reported for apple[55] and strawberry[56], while flesh lignification and internal browning during low-temperature storage in a red-fleshed loquat cultivar was shown to be modulated by the interplay between ERF39 and MYB8[57].

    The co-localisation of MdMYB66 and cytochrome P450 proteins, along with the anthocyanin regulatory protein MdMYB86, in paralogous FBD-associated genomic regions on Chr6 and Chr14 suggests that these genes interact as a 'hub' contributing to the WCI-FBD genetic link. The paralogs of some other genes were found to be residing in the regions associated with either WCI or FBD. For example, MdNAC42 and HSP70 co-localised in the FBD-associated region on Chr9, but this same pair of genes also resided in the most prominent region associated with WCI on Chr17. Similarly, paralogs of MdEIN3 resided in the WCI-associated region on Chr7 (MD07G1053500, MD07G1053800) and FBD-associated region on Chr11 (MD11G1022400). Interestingly, the paralogs of MdMYB73 and MdWRKY7 co-localised in the WCI- (24.6–26.8 Mb) and FBD-associated (4.6–6.8 Mb) regions on Chr15. The WCI-associated region at the bottom of Chr11 hosted a cluster of senescence-related genes, along with the anthocyanin biosynthesis gene MdbHLH3 (MD11G1286900), suggesting they might interact in the genetic nexus between FBD and WCI.

    FBD in RF apples can be caused by senescence, injury via extreme temperature exposure (chilling or heat), or enzymatic (cut fruit) reaction. Genes reported to be connected to all three factors were located in various FBD-associated genomic regions in this study. Postharvest strategies that both delay senescence and limit exposure to low temperatures may be needed to manage FBD. We also hypothesise that high ascorbic acid content could help to minimise expression of FBD in Type-1 RF cultivars. The adverse genetic correlation between WCI and FBD appears to arise from dual and/or interactive roles of several transcription factors, which would pose challenges for designing a conventional marker-assisted selection strategy. The use of bivariate genomic BLUP to estimate breeding values to simultaneously improve adversely correlated polygenic traits (e.g. WCI and FBD), could be an alternative approach[58, 59].

    A population of 900 apple seedlings composed of 24 full-sib families was generated in 2011 by selected crossings between six red-leaved pollen parents and six white-fleshed female parents. All six pollen parents inherited their red-leaf phenotype from the same great-grandparent 'Redfield'[1]. Each pollen parent was involved in four crosses, and the female parents were involved in three to six crosses each. Foliage colour of young seedlings is a phenotypic marker for Type 1 RF apple. The main purpose of this trial was to understand the flesh colour variation and FBD in the Type 1 RF seedlings, so only the seedlings with red foliage (i.e. carrying MdMYB10) were kept for this trial. The number of seedlings per family varied from 10 to 95. The seedlings were grafted onto 'M9' rootstock and were planted in duplicate at the Plant & Food Research orchard in Hawke's Bay, New Zealand (39°39′ S, 176°53′ E) in 2015.

    Phenotyping for RF and FBD was conducted over two consecutive fruiting seasons (2017 and 2018). Fruit were harvested once, when judged mature, based on a change in skin background colour from green to yellow, and when the starch pattern index (SPI) was between 1.0 and 2.0 (on a scale of 0 to 7). In each season, six fruit were harvested from each plant and stored for 70 d at 0.5 °C, followed by 7 d at 20 °C before fruit evaluation. Fruit were cut in half across the equator and the proportion of the cortex area (PRA) that was red in colour, and the intensity of the red colour (RI) (= 1 (low) to 9 (high)) was scored. A weighted cortical intensity (WCI) was then calculated (PRA × RI) as an estimation of the amount of red pigment in the fruit. The proportion of the cortex area showing symptoms of FBD was also recorded. WCI and FBD were averaged over all fruit for a particular seedling. Fruit were also assessed for the following eating quality traits on a 1 (lowest) to 9 (highest) scale: firmness, crispness, juiciness, sweetness, sourness and astringency, to understand the genetic correlations of eating-quality traits with WCI and FBD.

    The binary vector pSA277-R6:MYB10 was transferred into Agrobacterium tumefaciens strain LAB4404 by electroporation. Transgenic 'Royal Gala' plants were generated by Agrobacterium-mediated transformation of leaf pieces, using a method previously reported[5]. Wild-type 'Royal Gala' and three independent transgenic lines (A2, A4 and A10) of R6:MdMYB10 were grown under glasshouse conditions in full potting mix with natural light. The resulting fruit were assessed for flesh colour phenotypes at harvest (around 135 d after full bloom). Fruit peel and cortex from three biological replicates were collected and frozen in liquid nitrogen, with each replicate compiled from five pooled mature fruit for each transgenic line or wild-type control.

    Total RNA of 36 samples (3 R6:MYB10 lines and 1 wild type control, 3 time points, 3 biological replicates) was extracted, using Spectrum Plant Total RNA Kit (SIGMA). Removal of genomic DNA contamination and first-strand cDNA synthesis were carried out using the mixture of oligo (dT) and random primers according to the manufacturer's instructions (QuantiTect Reverse Transcription Kit, Qiagen). Real-time qPCR DNA amplification and analysis was carried out using the LightCycler 480 Real-Time PCR System (Roche), with LightCycler 480 software version 1.5. The LightCycler 480 SYBR Green I Master Mix (Roche) was used following the manufacturer's method. The qPCR conditions were 5 min at 95 °C, followed by 45 cycles of 5 s at 95 °C, 5 s at 60°C, and 10 s at 72 °C, followed by 65 °C to 95 °C melting curve detection. The qPCR efficiency of each gene was obtained by analyzing the standard curve of a cDNA serial dilution of that gene. The expression was normalized to Malus × domestica elongation factor 1-alpha MdEF1α (XM_008367439) due to its consistent transcript levels throughout samples, with crossing threshold values changing by less than 2.

    Individual fruit measurements were first averaged for each seedling. As the phenotyping was repeated over two years, we used a mixed linear model (MLM) accounting for this 'permanent environmental effect', as previously described[58]. Pedigree-based additive genetic relationships among seedlings were taken into account for estimation of genetic parameters using ASReml software[60]. Product-moment correlations between best linear unbiased predictions (BLUP) of breeding values of all seedlings for different traits were used as estimates of genetic correlation among traits.

    A selective DNA pooling procedure was adopted to construct DNA pools. A high-pool and a low-pool were constructed separately for the two traits (WCI and FBD). Genomic DNA was extracted from the leaves of selected seedlings, and quantified by fluorimetry using the picogreen reagent (Cat#P11496, Thermo). The low and high pools consisted of 35 seedlings each, and normalised amounts (~300 ng) of DNA from individuals were pooled. The pools were dried down with DNA Stable reagent (Cat#93021001, Biomatrica) in a centrifugal evaporator and shipped for sequencing. Each DNA pool was sequenced using paired-end 125 bp reads on the Illumina HiSeq 2500 platform. The quality of raw sequence reads was checked with FastQC/0.11.2 and MultiQC/1.2. Based on the quality control reports, the reads were aligned to the published apple reference genome GDDH13 v1.1[61] using the program bowtie2/2.3.4.3[62] with trimming from both ends before alignments and aligning in full read length ("-5 6 -3 5 –end-to-end"). The mapping results were marked for duplicate alignments, sorted, compressed and indexed with samtools/1.12[63]. Based on the alignment of binary alignment map (BAM) files of the high and low pools, single nucleotide polymorphism (SNP) identification was performed using samtools/1.12 ('samtools mpileup') and bcftools/1.12 ('bcftools call –mv')[63, 64]. Variant sites with missing genotype in any of the pools, or having the same genotype between the pools, were discarded. To minimise the influence of sequencing quality on association analysis, the identified SNPs were further filtered according to the following criteria: 1) a Phred-scaled quality score > 20; and 2) the read depth in each pool was neither < 35, nor > 500.

    The allele frequencies between each pair of bulk DNAs (low versus high WCI; low versus high FBD) were compared at each SNP locus. Differences in the allele frequencies between the low and high pool were expected to be negligible for unlinked SNP markers, but allele frequency differences would be larger for SNPs closely linked to the underlying quantitative trait loci (QTLs) contributing to the extreme phenotypes. A nonparametric test (G-statistic = 2 × Σni ln(ni/nexp), where ni (i = 1 to 4) represented counts of reference and alternate alleles at a particular SNP generated from sequencing of the low and high pool, and nexp was the expected allele count assuming no allele frequency divergence between the two DNA pools[65].

    We then calculated a modified statistic (G'), which took into account read count variation caused by sampling of segregants as well as variability inherent in short-read sequencing of pooled samples[65]. Using R package QTLseqr[66], firstly a G-statistic was calculated for each SNP marker, and then a weighted average using Nadaraya-Watson kernel was obtained to yield a G' statistic for a sliding genomic window of 2 Mb size. The Nadaraya-Watson method weights neighbouring markers' G-values by their distance from the focal SNP so that closer SNPs receive higher weights. The 95th percentile value of G' was used as a threshold to identify significant hotspots and to identify the putative candidate genes residing within the ±1 Mb region around the G' peak. For comparison purposes, a standard two-sided Z-test[14] was also performed to determine the significance of allele frequency differences at SNP loci between the pools for each trait.

    The GDDH gene models intersecting with the XP-GWA hotspots were pulled out with bedtools/2.30.0 ("bedtools intersect -wo -nonamecheck"). The selected genes were further blasted to TAIR10 ("-evalue 1e-5") and the annotated functions from Arabidopsis genes with the best blast score, the highest % identity, and the longest aligned length, were used. Then the expressions of genes located in the GWA hotspots were extracted from the RNAseq analysis of the R6:MdMYB10 representative transgenic line, and the log 2-fold change between the R6:MdMYB10 and 'Royal Gala' apples were calculated.

    This research was funded in 2017/18 by the Strategic Science Investment Fund of the New Zealand Ministry of Business, Innovation and Employment (MBIE) and from 2019 by the Plant & Food Research Technology Development – Pipfruit programme. We thank our colleague Jason Johnston for providing some pictures of the flesh browning disorder in red-fleshed apples. Richard Volz and Jason Johnston provided constructive comments and suggestions on the manuscript.

  • The authors declare that they have no conflict of interest.

  • [1]

    Marcelis LFM, Netherlands WUT, Costa JM, Heuvelink E, de Lisboa Portugal U, et al. 2019. Achieving sustainable greenhouse production: present status, recent advances and future developments, eds. Marcelis LFM, Netherlands WUT. London: Burleigh Dodds Science Publishing. pp. 1−14. https://doi.org/10.19103/as.2019.0052.01

    [2]

    Gruda NS. 2019. Increasing sustainability of growing media constituents and stand-alone substrates in soilless culture systems. Agronomy 9:298

    doi: 10.3390/agronomy9060298

    CrossRef   Google Scholar

    [3]

    Álvarez JM, Pasian C, Lal R, López R, Díaz MJ, et al. 2018. Morpho-physiological plant quality when biochar and vermicompost are used as growing media replacement in urban horticulture. Urban Forestry & Urban Greening 34:175−80

    doi: 10.1016/j.ufug.2018.06.021

    CrossRef   Google Scholar

    [4]

    Eulenstein F, Schindler U, Saljnikov E, Klemm M, Lühmann T, et al. 2021. Development of alternative growing media with hydrochar from extensive grassland biomass and evaluation of their soil-chemical quality. Acta Horticulturae 1305:263−70

    doi: 10.17660/ActaHortic.2021.1305.36

    CrossRef   Google Scholar

    [5]

    Fryda L, Visser R, Schmidt J. 2019. Biochar replaces peat in horticulture: environmental impact assessment of combined biochar & bioenergy production. Detritus 5:132−49

    doi: 10.31025/2611-4135/2019.13778

    CrossRef   Google Scholar

    [6]

    Regmi A, Singh S, Moustaid-Moussa N, Coldren C, Simpson C. 2022. The negative effects of high rates of biochar on violas can be counteracted with fertilizer. Plants 11:491

    doi: 10.3390/plants11040491

    CrossRef   Google Scholar

    [7]

    Luo X, Liu G, Xia Y, Chen L, Jiang Z, et al. 2017. Use of biochar-compost to improve properties and productivity of the degraded coastal soil in the Yellow River Delta, China. Journal of Soils and Sediments 17:780−89

    doi: 10.1007/s11368-016-1361-1

    CrossRef   Google Scholar

    [8]

    Venkataramani S, Kafle A, Singh M, Singh S, Simpson C, et al. 2023. Greenhouse cultivation of cucumber (Cucumis sativus L.) in standard soilless media amended with biochar and compost. HortScience 58:1035−44

    doi: 10.21273/HORTSCI17257-23

    CrossRef   Google Scholar

    [9]

    Zheng H, Wang X, Chen L, Wang Z, Xia Y, et al. 2018. Enhanced growth of halophyte plants in biochar-amended coastal soil: roles of nutrient availability and rhizosphere microbial modulation. Plant, Cell & Environment 41:517−32

    doi: 10.1111/pce.12944

    CrossRef   Google Scholar

    [10]

    Vista SP, Khadka A. 2017. Determining appropriate dose of biochar for vegetables. Journal of Pharmacognosy and Phytochemistry 6:673−77

    Google Scholar

    [11]

    Luo X, Wang Z, Meki K, Wang X, Liu B, et al. 2019. Effect of co-application of wood vinegar and biochar on seed germination and seedling growth. Journal of Soils and Sediments 19:3934−44

    doi: 10.1007/s11368-019-02365-9

    CrossRef   Google Scholar

    [12]

    Dumroese RK, Heiskanen J, Englund K, Tervahauta A. 2011. Pelleted biochar: chemical and physical properties show potential use as a substrate in container nurseries. Biomass and Bioenergy 35:2018−27

    doi: 10.1016/j.biombioe.2011.01.053

    CrossRef   Google Scholar

    [13]

    Steiner C, Harttung T. 2014. Biochar as a growing media additive and peat substitute. Solid Earth 5:995−99

    doi: 10.5194/se-5-995-2014

    CrossRef   Google Scholar

    [14]

    Leskovar DI, Stoffella PJ. 1995. Vegetable seedling root systems: morphology, development, and importance. HortScience 30:1153−59

    doi: 10.21273/HORTSCI.30.6.1153

    CrossRef   Google Scholar

    [15]

    Nair A, Carpenter B. 2016. Biochar rate and transplant tray cell number have implications on pepper growth during transplant production. HortTechnology 26:713−19

    doi: 10.21273/HORTTECH03490-16

    CrossRef   Google Scholar

    [16]

    Bu X, Ji H, Ma W, Mu C, Xian T, et al. 2022. Effects of biochar as a peat-based substrate component on morphological, photosynthetic and biochemical characteristics of Rhododendron delavayi Franch. Scientia Horticulturae 302:111148

    doi: 10.1016/j.scienta.2022.111148

    CrossRef   Google Scholar

    [17]

    USDA. 2021. Fresh cucumber imports capture nearly 90 percent of the U.S. market. Economic Research Service. https://www.ers.usda.gov/data-products/chart-gallery/gallery/chart-detail/?chartId=101346

    [18]

    Singh M, Singh S, Parkash V, Ritchie G, Wallace RW, et al. 2022. Biochar implications under limited irrigation for sweet corn production in a semi-arid environment. Frontiers in Plant Science 13:853746

    doi: 10.3389/fpls.2022.853746

    CrossRef   Google Scholar

    [19]

    Agathokleous E, Belz RG, Kitao M, Koike T, Calabrese EJ. 2019. Does the root to shoot ratio show a hormetic response to stress? An ecological and environmental perspective Journal of Forestry Research 30:1569−80

    doi: 10.1007/s11676-018-0863-7

    CrossRef   Google Scholar

    [20]

    Parkash V, Singh S, Singh M, Deb SK, Ritchie GL, et al. 2021. Effect of deficit irrigation on root growth, soil water depletion, and water use efficiency of cucumber. HortScience 56:1278−86

    doi: 10.21273/HORTSCI16052-21

    CrossRef   Google Scholar

    [21]

    Martins TC, Machado RMA, Alves-Pereira I, Ferreira R, Gruda NS. 2023. Coir-based growing media with municipal compost and biochar and their impacts on growth and some quality parameters in lettuce seedlings. Horticulturae 9:105

    doi: 10.3390/horticulturae9010105

    CrossRef   Google Scholar

    [22]

    Dispenza V, De Pasquale C, Fascella G, Mammano MM, Alonzo G. 2016. Use of biochar as peat substitute for growing substrates of Euphorbia × lomi potted plants. Spanish Journal of Agricultural Research 14:e0908

    doi: 10.5424/sjar/2016144-9082

    CrossRef   Google Scholar

    [23]

    Park JH, Choppala GK, Bolan NS, Chung JW, Chuasavathi T. 2011. Biochar reduces the bioavailability and phytotoxicity of heavy metals. Plant and Soil 348:439−51

    doi: 10.1007/s11104-011-0948-y

    CrossRef   Google Scholar

    [24]

    Zhang L, Sun X, Tian Y, Gong X. 2014. Biochar and humic acid amendments improve the quality of composted green waste as a growth medium for the ornamental plant Calathea insignis. Scientia Horticulturae 176:70−78

    doi: 10.1016/j.scienta.2014.06.021

    CrossRef   Google Scholar

    [25]

    Huang L, Gu M. 2019. Effects of biochar on container substrate properties and growth of plants—a review. Horticulturae 5:14

    doi: 10.3390/horticulturae5010014

    CrossRef   Google Scholar

    [26]

    Fan R, Luo J, Yan S, Zhou Y, Zhang Z. 2015. Effects of biochar and super absorbent polymer on substrate properties and water spinach growth. Pedosphere 25:737−48

    doi: 10.1016/S1002-0160(15)30055-2

    CrossRef   Google Scholar

    [27]

    Chartzoulakis KS. 1992. Effects of NaCl salinity on germination, growth and yield of greenhouse cucumber. Journal of Horticultural Science 67:115−19

    doi: 10.1080/00221589.1992.11516227

    CrossRef   Google Scholar

    [28]

    Kim HS, Kim KR, Yang JE, Ok YS, Kim WI, et al. 2017. Amelioration of horticultural growing media properties through rice hull biochar incorporation. Waste and Biomass Valorization 8:483−92

    doi: 10.1007/s12649-016-9588-z

    CrossRef   Google Scholar

    [29]

    Nieto A, Gascó G, Paz-Ferreiro J, Fernández JM, Plaza C, et al. 2016. The effect of pruning waste and biochar addition on brown peat based growing media properties. Scientia Horticulturae 199:142−48

    doi: 10.1016/j.scienta.2015.12.012

    CrossRef   Google Scholar

    [30]

    Ghanbarian B, Daigle H. 2016. Thermal conductivity in porous media: percolation-based effective-medium approximation. Water Resources Research 52:295−314

    doi: 10.1002/2015WR017236

    CrossRef   Google Scholar

    [31]

    Javid F. 2015. Analysis of heat transfer through porous perlite with varying pore size and moisture content. Doctoral dissertation, Southern Illinois University, Edwardsville.

    [32]

    Balliu A, Zheng Y, Sallaku G, Fernández JA, Gruda NS, et al. 2021. Environmental and cultivation factors affect the morphology, architecture and performance of root systems in soilless grown plants. Horticulturae 7:243

    doi: 10.3390/horticulturae7080243

    CrossRef   Google Scholar

    [33]

    Vaughn SF, Byars JA, Jackson MA, Peterson SC, Eller FJ. 2021. Tomato seed germination and transplant growth in a commercial potting substrate amended with nutrient-preconditioned Eastern red cedar (Juniperus virginiana L.) wood biochar. Scientia Horticulturae 280:109947

    doi: 10.1016/j.scienta.2021.109947

    CrossRef   Google Scholar

    [34]

    Liopa-Tsakalidi A, Barouchas PE. 2017. Effects of biochar on pepperoncini (Capsicum annuum L cv. Stavros) germination and seedling growth in two soil types. Australian Journal of Crop Science 11:264−70

    doi: 10.21475/ajcs.17.11.03.pne328

    CrossRef   Google Scholar

    [35]

    Ma G, Mao H, Bu Q, Han L, Shabbir A, et al. 2020. Effect of compound biochar substrate on the root growth of cucumber plug seedlings. Agronomy 10:1080

    doi: 10.3390/agronomy10081080

    CrossRef   Google Scholar

    [36]

    Revell KT, Maguire RO, Agblevor FA. 2012. Influence of poultry litter biochar on soil properties and plant growth. Soil Science 177:402−08

    doi: 10.1097/SS.0b013e3182564202

    CrossRef   Google Scholar

    [37]

    Bargmann I, Rillig MC, Buss W, Kruse A, Kuecke M. 2013. Hydrochar and biochar effects on germination of spring barley. Journal of Agronomy and Crop Science 199:360−73

    doi: 10.1111/jac.12024

    CrossRef   Google Scholar

    [38]

    Rogovska N, Laird D, Cruse RM, Trabue S, Heaton E. 2012. Germination tests for assessing biochar quality. Journal of Environmental Quality 41:1014−22

    doi: 10.2134/jeq2011.0103

    CrossRef   Google Scholar

    [39]

    Solaiman ZM, Murphy DV, Abbott LK. 2012. Biochars influence seed germination and early growth of seedlings. Plant and Soil 353:273−87

    doi: 10.1007/s11104-011-1031-4

    CrossRef   Google Scholar

    [40]

    Gaskin JW, Steiner C, Harris K, Das KC, Bibens B. 2008. Effect of low-temperature pyrolysis conditions on biochar for agricultural use. Transactions of the ASABE 51:2061−69

    doi: 10.13031/2013.25409

    CrossRef   Google Scholar

    [41]

    Mumme J, Getz J, Prasad M, Lüder U, Kern J, Mašek O, et al. 2018. Toxicity screening of biochar-mineral composites using germination tests. Chemosphere 207:91−100

    doi: 10.1016/j.chemosphere.2018.05.042

    CrossRef   Google Scholar

    [42]

    Headlee WL, Brewer CE, Hall RB. 2014. Biochar as a substitute for vermiculite in potting mix for hybrid poplar. BioEnergy Research 7:120−31

    doi: 10.1007/s12155-013-9355-y

    CrossRef   Google Scholar

    [43]

    Tian Y, Sun X, Li S, Wang H, Wang L, et al. 2012. Biochar made from green waste as peat substitute in growth media for Calathea rotundifola cv. Fasciata. Scientia Horticulturae 143:15−18

    doi: 10.1016/j.scienta.2012.05.018

    CrossRef   Google Scholar

    [44]

    Tosca A, Valagussa M, Martinetti L, Frangi P. 2021. Biochar and green compost as peat alternatives in the cultivation of photinia and olive tree. Acta Horticulturae 1305:257−62

    doi: 10.17660/ActaHortic.2021.1305.35

    CrossRef   Google Scholar

    [45]

    Zulfiqar F, Younis A, Chen J. 2019. Biochar or biochar-compost amendment to a peat-based substrate improves growth of Syngonium podophyllum. Agronomy 9:460

    doi: 10.3390/agronomy9080460

    CrossRef   Google Scholar

    [46]

    Parkash V, Singh S. 2020. Potential of biochar application to mitigate salinity stress in eggplant. HortScience 55:1946−55

    doi: 10.21273/HORTSCI15398-20

    CrossRef   Google Scholar

    [47]

    Regmi A, Poudyal S, Singh S, Coldren C, Moustaid-Moussa N, et al. 2023. Biochar influences phytochemical concentrations of Viola cornuta flowers. Sustainability 15:3882

    doi: 10.3390/su15053882

    CrossRef   Google Scholar

    [48]

    Jiménez-Arias D, García-Machado FJ, Morales-Sierra S, García-García AL, Herrera AJ, et al. 2021. A beginner's guide to osmoprotection by biostimulants. Plants 10:363

    doi: 10.3390/plants10020363

    CrossRef   Google Scholar

    [49]

    Sani MNH, Yong JWH. 2022. Harnessing synergistic biostimulatory processes: a plausible approach for enhanced crop growth and resilience in organic farming. Biology 11:41

    doi: 10.3390/biology11010041

    CrossRef   Google Scholar

    [50]

    Tüzel Y, Balliu A. 2020. Advances in liquid-and solid-medium soilless culture systems. In Advances in Horticultural Soilless Culture, eds Gruda NS. London: Burleigh Dodds Science Publishing. pp. 213−48. https://doi.org/10.1201/9781003048206-10

    [51]

    Shi K, Hu W, Dong D, Zhou Y, Yu J. 2007. Low O2 supply is involved in the poor growth in root-restricted plants of tomato (Lycopersicon esculentum Mill.). Environmental and Experimental Botany 61:181−89

    doi: 10.1016/j.envexpbot.2007.05.010

    CrossRef   Google Scholar

    [52]

    Judd LA, Jackson BE, Fonteno WC. 2015. Advancements in root growth measurement technologies and observation capabilities for container-grown plants. Plants 4:369−92

    doi: 10.3390/plants4030369

    CrossRef   Google Scholar

    [53]

    Wraith JM, Wright CK. 1998. Soil water and root growth. HortScience 33:951−59

    doi: 10.21273/HORTSCI.33.6.951

    CrossRef   Google Scholar

    [54]

    Jones JB. 1985. Growing plants hydroponically. The American Biology Teacher 47:356−58

    doi: 10.2307/4448083

    CrossRef   Google Scholar

    [55]

    Bláha L. 2019. Importance of root-shoot ratio for crops production. Journal of Agronomy & Agricultural Science 2:12

    doi: 10.24966/aas-8292/100012

    CrossRef   Google Scholar

  • Cite this article

    Kafle A, Singh S, Singh M, Venkataramani S, Saini R, et al. 2024. Effect of biochar-compost amendment on soilless media properties and cucumber seedling establishment. Technology in Horticulture 4: e001 doi: 10.48130/tihort-0023-0029
    Kafle A, Singh S, Singh M, Venkataramani S, Saini R, et al. 2024. Effect of biochar-compost amendment on soilless media properties and cucumber seedling establishment. Technology in Horticulture 4: e001 doi: 10.48130/tihort-0023-0029

Figures(4)  /  Tables(3)

Article Metrics

Article views(3540) PDF downloads(620)

ARTICLE   Open Access    

Effect of biochar-compost amendment on soilless media properties and cucumber seedling establishment

Technology in Horticulture  4 Article number: e001  (2024)  |  Cite this article

Abstract: The interest in replacing peat with biochar in soilless substrate media is increasing however, the proportion of biochar inclusion in the media which could improve the media properties as well as the seedling performance of vegetables is still unknown. Therefore, the aim of the current study was to test different biochar types at different proportions with cotton burr-compost in the growing media on hydro-physicochemical properties of media, germination, and shoot and root growth of cucumber seedlings. Two trials were conducted in 2022 using cv 'Picolino' in Randomized Complete Block Design with three replications. Control included peat:perlite:vermiculite at 50:25:25 %v/v. Other treatments were prepared to replace peat either partially [12.5% (v/v) biochar and 12.5% (v/v) compost (Partial hardwood: PHW, Partial softwood: PSW, and Partial hemp: PH)] or completely [25% (v/v) biochar and 25% (v/v) compost (Full hardwood: FHW, Full softwood: FSW, and Full hemp: FH)]. Biochar-compost inclusion increased the pH and EC of the medium. Water retention capacity and thermal conductivity of the medium were found to be improved in hemp biochar-compost treatment. FSW increased fresh shoot weight, the number of leaves, leaf area, and shoot:root ratio by 83%, 33%, 84%, and 46%, respectively compared to control. Root length density and root surface area density increased by 40% and 47%, respectively in FSW compared to control. Most of the biochar-compost amended media performed better for the cucumber seedling production compared to control showing a possibility of replacing the peat in the media for sustainable transplant production.

    • Transplant production in the greenhouse is a well-known technique for commercial high-value vegetable production[1]. The use of soilless mix for transplant production is popular among commercial greenhouse growers as it provides optimum growing conditions and promotes growth[2]. Generally, peat moss is used in soilless potting mixes in commercial greenhouses[3]. However, excessive use of peat is creating environmental issues of imbalance in carbon budgeting[4]. After the disturbance due to peat extraction, peat lands which act as carbon sinks, turn into a source of carbon dioxide, releasing it into the atmosphere. This is why the negative carbon footprint of peat mining demands the need for other alternative sustainable substrate media for the horticulture industry[46].

      Several soilless alternative media have been tested including coconut coir, sawdust, bark, compost, and biochar[3]. Among them, the combination of biochar-compost to replace peat has been given importance because of their combined effect in creating a conducive growth media environment and promoting plant growth[79]. Álvarez et al.[3] reported that biochar and compost can be used as a part of the growing medium to partially substitute peat. It is known that once applied, biochar can be effective for more than 100 years in field soil conditions[10] but short-term studies in soilless media in the greenhouse can explain the immediate effect of biochar[11]. A study suggests that the 25% pelletized biochar inclusion in sphagnum peat moss improved media hydraulic properties at high matric potentials while increasing pH in the forest seedling production system[12].

      Previous studies support the idea that biochar inclusion in a soilless production system improves the physicochemical properties of the media but full replacement of peat with a greater proportion of biochar may show negative effects[13]. Venkataramani et al.[8] reported that soilless peat media amended with different types and proportions of biochar-compost can enhance the concentration of some essential macronutrients like P, Ca, and S in the media. It is believed that these two components amend the soilless media and promote plant productivity. It is important to see the effect of biochar and compost combination on seedling productivity. The transplant productivity also depends upon the healthy root system of the transplants[14]. A healthy root system helps to establish the transplant in the growing media which is dependent upon the media properties[15]. The development of the root system directly influences the ability of transplants to obtain nutrients from the media[16]. While the interest is growing in biochar-compost combination use in soilless media, their effect on seedling establishment has been yet under-explained.

      Cucumber (Cucumis sativus L.) is one of the important vegetable crops that is largely consumed in the US. According to the report, the import of fresh cucumbers for salads or for snacking has increased between 1970−2020 reflecting that domestic production cannot fulfill the increasing demand of greenhouse-produced cucumbers[17]. One of the major problems with less cucumber production in the greenhouse could be the lack of selection of appropriate media composition and healthy transplant production in greenhouse industries. Proper selection of soilless media is the base for the successful production of cucumber or any transplanted vegetables.

      Nair & Carpenter[15] reported that studies are concentrated on the production of mature plants by transplanting into a biochar-amended medium; however, there is a knowledge gap in the direct seeding of vegetable crops in the biochar-amended with compost medium. It is equally important to understand the effect of different amendments like biochar and compost inclusion on germination[15] and the quality of seedlings produced. Hence, there is a need for studies to evaluate biochar of different types with a proper combination with other media components such as compost for healthy cucumber transplant production.

      Many studies have reported the effect of biochar-compost inclusion in soilless media on plants' performance but changes in the physical and chemical properties of the media are still overlooked. Venkataramani et al.[8] tried to expand knowledge on the successful production of cucumber in biochar-compost amended soilless media but lacked how such integration can influence the seedling phase of the plant. Also, how peat replacement with biochar-compost can bring change in the roots of the seedling plants has to be understood well before using these components in media on a large scale. Hence, the objective of this study was to compare the combinations of three biochar types at different proportions with cotton-burr-compost in the standard growing media, and quantify their effect on media physicochemical properties, cucumber germination, and shoot and root growth of cucumber seedlings.

    • Two study trials were conducted at Horticulture Gardens and Greenhouse Complex, Texas Tech University, Lubbock, TX, USA (lat. 33°35'2.72'' N, long. 101°53'12.95'' W). Trial 1 was conducted from 12 February 2022 to 12 March 2022, while Trial 2 was conducted from 30 March 2022 to 30 April 2022. The greenhouse was east-west oriented, and most of the sunlight was allowed to be transmitted inside the greenhouse. Both experiments were conducted in a greenhouse where temperatures of 30 °C during the day and 25 °C at night were maintained throughout the experiment period. No additional lighting was provided.

      The slicing type of cucumber cultivar 'Picolino' (Johnny's Selected Seeds, ME, USA) was used for the trials. Seven different treatments were prepared using different volume combinations of peat, perlite, vermiculite, cotton-burr-compost, hardwood biochar, softwood biochar, and hemp biochar[8]. Control had peat:perlite:vermiculite (50:25:25, %v/v). The peat had 85% peat moss and 15% perlite (BM6 Berger, Saint-Modeste, QC, USA), which was combined with perlite and vermiculite to make standard peat media. In the remaining six treatments, peat was either partially [12.5% (v/v) biochar and 12.5% (v/v) compost (Partial hardwood: PHW, Partial softwood: PSW, and Partial hemp: PH)] or completely [25% (v/v) biochar and 25% (v/v) compost (Full hardwood: FHW, Full softwood: FSW, and Full hemp: FH)] replaced by biochar-compost mixture. Cotton-burr-compost was purchased from Back to Nature (Slaton, TX, USA). Hardwood (HW) and softwood (SW) biochars were obtained from Wakefield Agricultural Carbon LLC (Columbia, MO, USA). Hemp biochar was manually prepared by burning hemp residues in an oxygen-limited 208-liter capacity steel drum for 24 h. The physicochemical properties of hardwood and softwood biochar have already been reported in Singh et al.[18]. No fertilizers were added for seedling growth in the greenhouse. Seedlings were grown in 6-cell trays where each cell was 3.8 cm2 (length × breadth) and 5.7 cm deep (9Greenbox, CA, USA). The treatments were randomized in a Randomized Complete Block Design with three replications. Each 6-cell tray was a replication leading to a total of 18 seedlings per treatment. The seedlings were watered daily with tap water in the greenhouse, and the experiment was terminated 30 d after the sowing of seeds.

    • The pH and EC of the media were measured by a handheld Orion Star A325 pH/Conductivity Portable Multiparameter Meter (ThermoScientific, MA, USA) only at the beginning of the experiment. Four samples were collected for each treatment media which were mixed, and the measurements were done by following the protocol previously described by Singh et al.[18].

    • Four core samples were collected using 5 cm × 5 cm stainless steel cores (AMS, American Falls, ID, USA) with the help of a core sampler for each media treatment. The samples were subjected to different potential pressures (-ve) [0 (saturation), 0.1, 0.3, 1, 10, and 15 bar] in a pressure plate apparatus (Soil moisture Equipment Corp., Goleta, CA, USA) to obtain a water retention curve. Porosity and plant available water (PAW) were calculated as described by Singh et al.[18]. The thermal conductivity of the samples was recorded simultaneously at different pressure by using the KD2Pro Thermal Conductivity Meter (TR-1 sensor, Edaphic Scientific, Australia).

    • The germination count was made when the plumule structure appeared above the media surface. The germination count was recorded at 4, 5, 6, 7, and 15 Days After Planting (DAP) in Trial 1 and at 5, 7, 8, 9, 11, and 13 DAP in Trial 2. Seedling height was measured from the base of the seedling to the tip from three random seedlings from each 6-cell trays of each treatment after 11, 15, 19, 25, and 31 DAP in Trial 1 and 13, 18, 21, 26, and 30 DAP in Trial 2.

    • Two random seedlings from each replication of each treatment were selected after 32 DAP in Trial 1 and 31 DAP in Trial 2. The selected seedlings were separated into shoots and roots. The shoot length and root length were measured with the help of a measuring ruler and the elongation ratio or shoot:root ratio (cm/cm) was calculated by dividing shoot length by root length[19]. The leaves were separated from the seedling stem and the number of leaves was counted, and the leaf area was measured using a benchtop leaf area meter (LI-3100C Area Meter, Lincoln, NE, USA). The fresh shoot weight was measured using a balance scale.

    • Each root sample was washed placing them over the fine-mesh sieve strainer to avoid root escaping. Forceps were used to remove any attached soilless particles in the roots. The clean root samples were stored in 50 ml falcon tubes filled with 40 mm of deionized water. The root samples were scanned by a scanner (EPSON V800; Reagent Instruments Inc.) and analyzed for different root parameters including root length density (RLD), root surface area density (RSAD), and root fineness classification (% of total root length of each diameter class) using WinRHIZO Pro version 2016a software (Regent Instruments Inc., Canada). Four diameter classes a) 0–0.5 mm, b) 0.5–1.0 mm, and c) 1.0−1.5 mm d) > 1.5 mm were categorized for the total root length and % of the total root length for each diameter classes were calculated[20].

    • The data were analyzed using R version 3.5.2 with the Agricolae package 1.3–5 to evaluate the effect of each treatment on the measured parameters. Data for each trial were analyzed separately. The values presented in tables and graphs are the average values for each media treatment. Analysis of variance (ANOVA) was performed, and the Duncan Multiple Range Test (DMRT) test at a 5% level of significance was used for the mean separation. SigmaPlot version 14 (Systat Software, San Jose, CA, USA) was used for preparing graphs and figures.

    • The pH of control was acidic (5.2), whereas biochar-amended media increased the pH to 7.8−9.4 (Table 1). The highest pH was recorded in FH. The pH of biochar-compost amended media was increased by 50%–73% than the control and was comparatively higher in full biochar-compost compared to partial biochar-compost replacement media (FHW vs. PHW, FSW vs. PSW, FH vs. PH). This shows that the amount of biochar in the media can affect the pH of the media. Comparing different types of biochar-compost integrations, hemp biochar-compost treatment maintained the most basic nature of the media compared to hardwood and softwood biochar-compost amendments in both replacement rates. This indicates that the feedstock source of the biochar can also influence the pH of the media. The result suggests that biochar as a liming agent prevents acidity in the growing media. The results can be supported by the study of Martins et al.[21] which demonstrated that biochar pH was 8.76 whereas the compost pH was near to neutral (7.91). Similar results were obtained in previous studies which reported that the biochar itself is basic in nature which increases the pH of the substrate[2224]. The buffering capacity of the biochar can be attributed to the negative charge on the surface of the biochar which prevents rapid change in pH[25]. This indicates that biochar contributes to maintaining pH which depends upon the proportion as well as the feedstock source of the biochar in the substrate media.

      Table 1.  The pH and EC of different media treatments used in greenhouse cucumber experiments at Lubbock, TX, USA in 2022.

      Soilless media typepHEC (dS/m)
      C5.20.178
      PHW7.90.378
      PSW7.80.38
      PH8.70.74
      FHW9.00.592
      FSW8.30.612
      FH9.41.078
      C: Control, FHW: Full hardwood, FSW: Full softwood, FH: Full hemp, PHW: Partial hardwood, PSW: Partial softwood, PH: Partial hemp.

      The EC of the control was 0.178 dS/m which was very low compared to biochar-compost media types. The highest EC was reported in FH i.e., 1.078 dS/m. This shows that EC was increased 2–6 times by the biochar-compost amendment compared to the control. Comparing partial and full replacement treatments, the full replacement treatments (FHW, FSW, FH) had higher EC compared to partial replacement treatments (PHW, PSW, PH). Similar to pH, hemp biochar-compost treatment had higher EC values both in partial as well as full replacements compared to hardwood or softwood biochar-compost treatments. Huang & Gu[25] suggested that the increase in EC due to biochar inclusion in container substrate can be due to the high EC of the biochar. The high EC of the substrate can also be due to the high pH of the media component[26]. The high EC of the media except the control could be due to the combined effect of biochar and compost. Similarly, Venkataramani et al.[8] reported that the EC of the media was increased with an addition of both biochar and compost amendments compared to standard peat media. In contrast, Martins et al.[21] suggested that compost is responsible for increasing the EC of the media than biochar. This is because biochar mixes had low cation exchange capacity (CEC) and low EC because of poor extractable macronutrients than the compost. The EC varied from 0.378−1.078 dS/m in biochar-compost amendment treatments which falls under the normal range (< 1.2 dS/m) of the EC requirement for cucumber seedlings[27]. This indicates that biochar and compost combination maintain pH and EC in the normal range and encourages partial availability of nutrients for growing seedlings and, hence, ameliorating the media.

    • The effect of the biochar-compost inclusion in the media on water retention capacity and thermal conductivity over different pressure bars (-ve) is presented in Figs 1 & 2, respectively. Biochar-compost amendment improved the hydro-physical properties of soilless media. In Trial 1, PH maintained comparatively greater volumetric water content over different pressure regimes compared to other treatments although the difference was not significant (Fig. 1a). But in Trial 2, FH maintained a noticeable improvement in water retention capacity over different pressure regimes (Fig. 1b). Both FH and PH in both trials had greater water retention capacity. Kim et al.[28] reported that water content in the media was increased because of the improvement in pore space for water storage in biochar-amended growing media. We also noticed that biochar-compost amended media improved porosity and enhanced the plant’s available water. Porosity was 3% greater in PH in Trial 1, and 3% and 1.5% more in PHW and FH, respectively in Trial 2 compared to the control (data not shown). Plant available water (PAW) was 21%, 14%, 8% and 2% greater in PH, FSW, FH and PSW, respectively in Trial 1, and 16%, 12%, 9%, 7% and 1% greater in PH, FSW, PHW, FH and PSW, respectively in Trail 2 compared to the control (data not shown). However, other biochar-compost amended media did not improve porosity and PAW, which implies that change in hydro-physical properties depends on peat and biochar-compost properties as well as their proportion in the media. Similarly, Nieto et al.[29] reported that the hydro-physical properties are influenced by the types and amounts of different components of the media.

      Figure 1. 

      Water retention curve of different media in (a) Trial 1 and (b) Trial 2. The pressures applied (-ve) were 0, 0.1, 0.3, 1, 5, 10, and 15 bar. Bars in the graph indicate the standard error. * and *** indicate significant differences at a p ≤ 0.05 and p ≤ 0.001 level of significance, respectively. C: Control, FHW: Full hardwood, FSW: Full softwood, FH: Full hemp, PHW: Partial hardwood, PSW: Partial softwood, PH: Partial hemp.

      Figure 2. 

      Thermal conductivity (W/m/K) of different media in (a) Trial 1 and (b) Trial 2. The pressures applied (-ve) were 0, 0.1, 0.3, 1, 5, 10, and 15 bar. Bars in the graph indicate the standard error and *, **, *** indicate significant differences at a p ≤ 0.05, p ≤ 0.01, and p ≤ 0.001 level of significance, respectively. C: Control, FHW: Full hardwood, FSW: Full softwood, FH: Full hemp, PHW: Partial hardwood, PSW: Partial softwood, PH: Partial hemp.

      The thermal conductivity of the media depends upon the amount of perlite and water content in it[30,31]. Javid[31] reported that thermal conductivity improves substantially when perlite is present because it establishes a strong relationship with heat transfer in the medium. In this study, the difference in the thermal property (conductivity) was solely for water content in the media because perlite was present in every treatment (Fig. 2a & b). The greater the water content in the media, the higher will be the thermal conductivity because water is a better medium for heat transfer than air[30]. That is why there is a sharp increase in thermal conductivity in FH in Trial 2 (Fig. 2b) when there is a high volume of water present in the media throughout the pressure bars (Fig. 1b). Thermal conductivity can be an important factor for soilless substrate vegetable production as it maintains the media temperature and helps in root growth and development. The temperature of the media environment can be a prime factor for germination and root system development. It can be of more importance during the winter season for protected agriculture, where growers had to spend lots of money maintaining an optimum environment to promote seedling growth. The high thermal conductivity facilitates heat transfer, which in the short term improves water and nutrient uptake by reducing water viscosity and by increasing membrane permeability[32]. Considering this, most of the biochar-compost amended treatments, especially FH can be of choice that not only holds water for a longer time but also improves the thermal properties of media for optimum seedling growth.

    • In both trials, biochar-compost-amended media accelerated the germination process compared to the control (Fig. 3). In Trial 1, FH, PH, and PSW had all the germination (18 seeds) at 6 DAP whereas FHW, PH, and PSW had full germination after 9 DAP in Trial 2. Except for others, PHW had only 17 seeds germinated in Trial 1 at 11 DAP (Fig. 3a) whereas FH, FSW, and, PHW had only 17 seeds germinated in Trail 2 (Fig. 3b). It is evident that the control achieved full germination only at 11 DAP in both trials. Also, looking at the trend in both trials, FH and PH tend to have a faster germination rate than other treatments. Phosphorus (P), a macronutrient responsible for seed germination is found in higher concentrations in hemp biochar-compost amended media than in other biochar-compost treatments[8]. It may also be due to the higher water retention capacity of hemp biochar amended media (Fig. 1), which could have helped in faster germination. It has been well-reported that biochar can enhance the germination of different vegetable crops including tomato[11,33], pepper[11,15,34], cucumber[35] and lettuce[36]. However, there are some reports that show biochar did not influence or negatively influenced the germination of some crops[3739]. This difference in germination using different biochar amendments is because of the difference in concentration of nutrients present in the biochar, which is largely affected by the source of biochar[40,41].

      Figure 3. 

      Germination counts of cucumber seeds in (a) Trial 1 and (b) Trial 2. Germination count was recorded at 4, 5, 6, 7, and 11 DAP in Trial 1 and 5, 7, 9, 11, and 12 DAP in Trial 2. C: Control, FHW: Full hardwood, FSW: Full softwood, FH: Full hemp, PHW: Partial hardwood, PSW: Partial softwood, PH: Partial hemp.

      The significant difference in height among the different media started from the very beginning (Fig. 4). FSW media constantly maintained a greater seedling height in both trials. In Trial 1, FSW had the highest seedling height of 8.5 cm after 31 DAP (Fig. 4a); whereas, in Trail 2, it had a 5.9 cm seedling height after 30 DAP (Fig. 4b). Comparing full and partial replacement treatments, FSW had significantly taller seedlings than FHW and FH, and PSW and PHW had significantly taller seedlings compared to PH in Trial 1. FSW and PHW produced significantly taller seedlings compared to PSW and FHW, respectively. In Trial 2, FSW had significantly taller seedlings compared to FHW and FH, but the height was comparable among PSW, PHW, and PH. It was only FSW that had significantly taller seedlings than PSW among full and partial replacement treatments. This finding is in accordance with the previous study of Venkataramani et al.[8], where authors reported that softwood biochar amended with compost can accelerate the height of cucumber seedlings compared to other biochar types. Control had the poorest growth and had a height of 4.3 cm and 3.16 cm in Trial 1 and Trial 2, respectively. The poor growth in the control can be attributed to low pH and EC, where these were higher in other biochar-compost amended treatments (Table 1). The low pH impacts the seedlings growth by limiting the availability of essential nutrients and increasing the toxicity of aluminum (Al) and manganese (Mn). The low EC indicates ion imbalance and less dissolved salts including essential nutrients. It may also be because the control had relatively poor water holding capacity (Fig. 1) and lower thermal conductivity (Fig. 2). This indicates that control media tends to dry up quickly which reduced the growth of cucumber seedlings. The results are supported by previous studies where seedling height was enhanced by biochar amendment compared to un-amended media[42,43]. Another reason for the increased height in cucumber seedlings in biochar-compost amended media was the cotton-bur compost present in the media. The compost might have provided some nutrients to the seedlings in a manner that stimulated growth in all treatments except control. A similar result was obtained by Tosca et al.[44] where Photinia sps growth was accelerated in biochar cum compost media compared to control. The growth of ornamental crops like Syngonium podophyllum was also improved through the inclusion of biochar or biochar-compost mixture in peat substrate[45]. The seedling height has an important role for transplant nursery growers[15], and it is better to have a vigorous seedling transplant than a poor stunted one for sustainable crop production.

      Figure 4. 

      Cucumber seedling height in (a) Trial 1 and (b) Trial 2. Bars in the graph indicate the standard error. ** and *** indicate significant differences at a p ≤ 0.01 and p ≤ 0.001 level of significance, respectively. C: Control, FHW: Full hardwood, FSW: Full softwood, FH: Full hemp, PHW: Partial hardwood, PSW: Partial softwood, PH: Partial hemp.

    • The different media showed significant effects on fresh shoot weight, the number of leaves, leaf area, and shoot:root ratio of cucumber seedlings (Table 2). Fresh shoot weight, number of leaves, and leaf area were significantly higher in FSW compared to other full, and partial replacement treatments (Table 2). In Trial 1, control had 82%, 35%, 82%, and 45 % less fresh shoot weight, number of leaves, leaf area, and shoot: root ratio, respectively compared to FSW. In Trial 2, fresh shoot weight, number of leaves, leaf area, and shoot: root ratio reduced by 84%, 31%, 85%, and 47%, respectively in control compared to FSW. FSW only had significantly greater fresh shoot weight, number of leaves and leaf area compared to other full replacement treatments (FHW and FH) as well as partial replacements treatments (PSW, PHW and PH) in both trials. Similar results were obtained by Venkataramani et al.[8] on the dry biomass of cucumber plants where softwood biochar-compost amended treatments contributed to higher vegetative growth of cucumber plants compared to other treatments. The greater fresh weight, number of leaves, leaf area, and shoot:root ratio in FSW can be due to greater seedling height compared to other treatments (Fig. 4). The results show that biochar when used as a component of the media with compost can produce a healthier and more vigorous transplant compared to control. This claim is supported by Parkash & Singh[46] and Regmi et al.[47] who reported that biochar has a larger surface area which tends to allow more water and nutrient retention leading to vigorous young growing plants. Another study by Dispenza et al.[22] reported that conifer biochar in potting mixture media increased shoot weight, leaf number, and leaf area in Euphorbia × lomi plant. Due to the presence of humic acid in the compost, seedlings are protected through osmoregulation and ion-homeostasis against potential salt stress caused by biochar and compost leading to healthy growth of the seedlings[21,48,49].

      Table 2.  Effect of different media types on fresh shoot weight (g), number of leaves, leaf area, and shoot:root ratio of cucumber seedlings in Trial 1 and Trial 2 in greenhouse experiment at Lubbock, TX, USA in 2022.

      TrialMediaFresh shoot weight (g)No. of leavesLeaf area (cm2)Shoot:Root
      Trial 1C0.82c*3.00d11.88c0.30c
      FHW1.85bc4.16ab23.26bc0.41bc
      FSW4.77a4.66a69.07a0.55a
      FH1.54bc3.66bc17.97bc0.35c
      PHW1.68bc4.00b22.48bc0.48ab
      PSW2.15b4.00b30.29b0.39bc
      PH1.38bc3.16cd16.70bc0.37bc
      Trial 2C0.56b3.00b8.36b0.19d
      FHW1.26b3.00b16.07b0.30bc
      FSW3.52a4.33a54.60a0.36ab
      FH1.55b3.16b17.08b0.41a
      PHW1.16b3.00b16.77b0.26cd
      PSW1.40b3.16b22.02b0.34abc
      PH1.20b2.83b15.85b0.33bc
      The values represent the mean value of individual treatment in each of the trials. * The different letters in a column and s indicate a significant difference at p ≤ 0.05. C: Control, FHW: Full hardwood, FSW: Full softwood, FH: Full hemp, PHW: Partial hardwood, PSW: Partial softwood, PH: Partial hemp.

      RLD significantly varied among the media treatments (Table 3). In Trial 1, RLD increased by 37%, 38%, 21%, and 8% in FSW, FH, PHW, PSW, respectively; whereas, in Trial 2, it increased by 43%, 3%, 17%, 14% in FSW, PHW, PSW, PH, respectively compared to control. RLD decreased by 7% and 12% in FHW and PH, respectively in Trial 1 whereas it decreased by 8% and 26% in FHW and FH, respectively in Trial 2 compared to control. FSW and FH had significantly higher RLD compared to PSW and PH, respectively but PHW increased RLD significantly than FHW in Trial 1. FSW had significantly higher RLD than PSW but contrarily PH increased RLD more than FH in Trial 2 while others remained comparable. RSAD was also affected by various media types (Table 3). In Trial 1, RSAD increased by 38%, 46%, and 24% in FSW, FH, and PHW, respectively, and decreased by 3%, 15%, and 1% in FHW, PSW, and PH, respectively compared to control. However, all the biochar amended media enhanced RSAD in Trial 2 by 19%, 55%, 9%, 23%, 35%, and 28% in FHW, FSW, FH, PHW, PSW, and PH respectively, compared to control. Similar to RLD, FSW and FH had significantly higher RSAD compared to PSW and PH, respectively, and PHW increased RSAD significantly than FHW in Trial 1. A significant increase in RSAD was obtained in FSW compared to PHW in Trial 2. Comparing different types of biochar-compost treatments, softwood biochar- and hemp biochar-compost amended media in full replacement increased RLD and RSAD in Trial 1, but only softwood biochar full replacement outnumbered the other two full replacements in Trial 2. PHW had significantly higher RLD than PH, and significantly greater RSAD than PSW and PH in Trial 1. No significant difference was observed among partial replacements for RLD and RSAD in Trial 2. This suggests that roots are sensitive to media components which also affect other physical properties of the media. The space for root growth is limited in soilless culture, and hence seedling tends to increase root density[50]. This increased root density indicates more oxygen and nutrient consumption per unit volume at the root zone. In such limited rooting volume, the seedling tends to utilize the available resources like space, nutrients, and water[32,51]. In this study, the greater RLD and RSAD in biochar-compost amended treatments indicate that this combination promotes root growth compared to the peat-dominated control treatment. Root growth is crucial for transplants because transplanting shock may occur with poor root structure, and later reduce the overall performance of the crop[14]. The roots are important for anchorage, water, and nutrient uptake throughout the growth stages of the plant[52,53]. The increase in roots biomass suggests that the transplants produced in the amendment treatments will help in successful acclimatization and adaptability after transplanting.

      Table 3.  Effect of different media types on root length density (RLD), root surface area density (RSAD), and root classification categories (0.0−0.5 mm, 0.5−1.0 mm, 1.0−1.5 mm, > 1.5 mm) of cucumber seedlings in Trial 1 and Trial 2 in greenhouse experiment at Lubbock, TX, USA in 2022.

      TrialMediaRLD
      (cm/
      cm3)
      RSAD
      (cm2/
      cm3)
      Root classification category (% of total root length per diameter)
      0.0−0.5
      mm
      0.5−1.0
      mm
      1−1.5
      mm
      > 1.5
      mm
      Trial 1C6.08c*0.76c77.22b18.52ab2.59d1.66cd
      FHW5.69c0.74c77.21b17.47ab2.43de2.88ab
      FSW9.69a1.22ab76.59bc18.04ab3.35bc2.00c
      FH9.88a1.41a73.87cd18.20ab4.38a3.54a
      PHW8.17ab1.00b78.09b16.60b3.13cd2.16bc
      PSW6.62bc0.66c84.82a12.23c1.73e1.20d
      PH5.38c0.75c73.35d19.79a3.98ab2.86ab
      Trial 2C6.58bc0.71d83.57a12.67d2.05d1.69ab
      FHW6.04bc0.88bcd70.04cd24.25a3.83ab1.86ab
      FSW11.48a1.58a75.58b18.04c3.96ab2.40a
      FH5.21c0.78cd69.18d24.07a4.46a2.28a
      PHW6.80bc0.92bcd73.75bc21.70ab3.09bc1.44b
      PSW7.97b1.09b75.17b19.91bc3.24bc1.65ab
      PH7.65b0.99bc75.50b20.46bc2.72cd1.30b
      The values represent the mean value of individual treatment in each of the trials. * The different letters in a column and s indicate a significant difference at p ≤ 0.05. C: Control, FHW: Full hardwood, FSW: Full softwood, FH: Full hemp, PHW: Partial hardwood, PSW: Partial softwood, PH: Partial hemp.

      The distribution of roots among different diameter classes also varied among media treatments (Table 3). Although there was some variation in diameter classes (% of total root length per diameter) in Trial 1 and Trial 2, but it is noticeable that the greatest share in root diameter classes was 73.35% to 84.82% in 0.0–0.5 mm followed by 12.23% to 19.79% in 0.5–1.0 mm, 1.73% to 4.38% in 1.0–1.5 mm, and 1.20% to 3.54% in > 1.5 mm. This indicates that cucumber seedling has more proportion of fine roots with a diameter of 0.0–0.5 mm which can penetrate the tiny pores of media for extracting water and nutrients as well as for developing a robust root system. Judd et al.[52] suggest that plants grown in soilless substrates tend to develop finer roots. A similar proportion of roots was found by Parkash et al.[20] in cucumbers in field conditions. This implies that cucumber plants tend to develop fine roots in soilless media as well as in field soil conditions, and extracts water and nutrients for developing plants. Another, interesting finding of this experiment was to record the highest root distribution in FH among all the classes (> 0.5 mm) in both trials. PH also contributed a similar proportion in Trial 1 only. This may be attributed to higher pH and EC, and greater water retaining capacity of the hemp biochar-cotton compost amendment in the media compared to other treatments. This is in agreement with the statement by Balliu et al.[32] and Jones[54] that the physical and chemical properties of the media alter nutrient and water availability which in turn affect root growth. Thermal property (temperature) can also influence root growth. Balliu et al.[32] reported the vital role of root zone temperature in enhancing the root density of lateral roots of peas. This suggests how roots interact with the complex media environment and alter root morphology depending on water and nutrient availability and thermal property. Bláha[55] even reports the importance of root growth by stating a conception that a 1% change in root system can bring a 2% change in crop yield which was further highlighted by Balliu et al.[32]. All this signifies the importance of the study of root system architecture that will explain its adaptation to the growing environment and its contribution to crop growth and performance.

    • This experiment aimed to prepare a suitable substrate combination by adding different biochar types with cotton burr compost at different proportions to see their effect on the seedling performance of cucumber. Biochar-compost amended treatments acted as a liming agent that prevented the media from acidity and improved electrical conductivity. Among the treatments, full hemp biochar-compost treatment (FH) had the largest increase in pH and EC. FH significantly increased the volumetric water content and thermal properties of the media, indicating its potential to retain more water in the media and maintain warmness around the growing roots. Germination was accelerated by the biochar-compost amendment, which can be profitable for industries that require a supply of a large number of transplants for commercial production. Full softwood biochar-compost treatment (FSW) increased shoot as well as root growth of cucumber seedlings. This study suggests that biochar-compost can replace peat either partially or even completely and improve properties for vigorous seedling production.

    • The authors confirm contribution to the paper as follows: conceptualization: Kafle A, Singh M, Singh S; methodology: Kafle A, Singh M, Venkataramani S; data collection: Kafle A, Venkataramani S; data curation: Kafle A; software and analysis: Kafle A; writing-original draft preparation: Kafle A; supervision: Singh S; review and editing: Deb S, Singh S, Saini R. All authors reviewed the results and approved the final version of the manuscript.

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

    • This project did not receive any external funding. We would like to acknowledge the Horticulture Gardens and Greenhouse Complex, Department of Plant and Soil Science, Texas Tech University for providing all the necessary facilities and support during the study period. We also thank Preetaman Bajwa for assisting in the experiment.

      • The authors declare that they have no conflict of interest.

      • Copyright: © 2024 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 (4)  Table (3) References (55)
  • About this article
    Cite this article
    Kafle A, Singh S, Singh M, Venkataramani S, Saini R, et al. 2024. Effect of biochar-compost amendment on soilless media properties and cucumber seedling establishment. Technology in Horticulture 4: e001 doi: 10.48130/tihort-0023-0029
    Kafle A, Singh S, Singh M, Venkataramani S, Saini R, et al. 2024. Effect of biochar-compost amendment on soilless media properties and cucumber seedling establishment. Technology in Horticulture 4: e001 doi: 10.48130/tihort-0023-0029

Catalog

  • About this article

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return