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Evaluation of different lignocellulosic-wastes and their combinations on growth and yield of Oyster mushroom (Pleurotus ostreatus)

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  • Lignocellulose wastes are generated in huge amounts by various sectors like agriculture, forestry, and industry but only a small portion of these wastes are utilized and a major portion is left unused. In this study, seven different lignocellulosic wastes and their combinations in different percentages were determined for the growth and yield of Pleurotus ostreatus. The maximum growth and yield of P. ostreatus were observed on a substrate made of rice straw, with a total yield of 399.70 gm per kg of substrate. The least growth and yield were recorded on a substrate made of wood flakes and sugarcane bagasse (80% + 20%), with a total yield of 13.54 gm per kg of substrate. Rice straw showed the highest biological efficiency (B.E) of 39.40, whereas wood flakes and sugarcane bagasse (80% + 20%) had the lowest B.E. of 1.35. Other substrates had a moderate effect, and citronella bagasse (Cymbopogon nardus), which was used as a substrate for the first time, gave a biological efficiency of 39.39 gm per kg substrate. The results showed a significant effect of substrates on mean yield and biological efficiency. Our study revealed that lignocellulosic waste can be profitably utilized for mushroom cultivation and could be one of the most economical and eco-friendly techniques.
  • As a major staple crop, today maize accounts for approximately 40% of total worldwide cereal production (http://faostat.fao.org/). Since its domestication ~9,000 years ago from a subgroup of teosinte (Zea mays ssp. parviglumis) in the tropical lowlands of southwest Mexico[1], its cultivating area has greatly expanded, covering most of the world[2]. Human's breeding and utilization of maize have gone through several stages, from landraces, open-pollinated varieties (OPVs), double-cross hybrids (1930s-1950s) and since the middle 1950s, single-cross hybrids. Nowadays, global maize production is mostly provided by single-cross hybrids, which exhibit higher-yielding and better stress tolerance than OPVs and double-cross hybrids[3].

    Besides its agronomic importance, maize has also been used as a model plant species for genetic studies due to its out-crossing habit, large quantities of seeds produced and the availability of diverse germplasm. The abundant mutants of maize facilitated the development of the first genetic and cytogenetic maps of plants, and made it an ideal plant species to identify regulators of developmental processes[46]. Although initially lagging behind other model plant species (such as Arabidopsis and rice) in multi-omics research, the recent rapid development in sequencing and transformation technologies, and various new tools (such as CRISPR technologies, double haploids etc.) are repositioning maize research at the frontiers of plant research, and surely, it will continue to reveal fundamental insights into plant biology, as well as to accelerate molecular breeding for this vitally important crop[7, 8].

    During domestication from teosinte to maize, a number of distinguishing morphological and physiological changes occurred, including increased apical dominance, reduced glumes, suppression of ear prolificacy, increase in kernel row number, loss of seed shattering, nutritional changes etc.[9] (Fig. 1). At the genomic level, genome-wide genetic diversity was reduced due to a population bottleneck effect, accompanied by directional selection at specific genomic regions underlying agronomically important traits. Over a century ago, Beadle initially proposed that four or five genes or blocks of genes might be responsible for much of the phenotypic changes between maize and teosinte[10,11]. Later studies by Doebley et al. used teosinte–maize F2 populations to dissect several quantitative trait loci (QTL) to the responsible genes (such as tb1 and tga1)[12,13]. On the other hand, based on analysis of single-nucleotide polymorphisms (SNPs) in 774 genes, Wright et al.[14] estimated that 2%−4% of maize genes (~800−1,700 genes genome-wide) were selected during maize domestication and subsequent improvement. Taking advantage of the next-generation sequencing (NGS) technologies, Hufford et al.[15] conducted resequencing analysis of a set of wild relatives, landraces and improved maize varieties, and identified ~500 selective genomic regions during maize domestication. In a recent study, Xu et al.[16] conducted a genome-wide survey of 982 maize inbred lines and 190 teosinte accession. They identified 394 domestication sweeps and 360 adaptation sweeps. Collectively, these studies suggest that maize domestication likely involved hundreds of genomic regions. Nevertheless, much fewer domestication genes have been functionally studied so far.

    Figure 1.  Main traits of maize involved in domestication and improvement.

    During maize domestication, a most profound morphological change is an increase in apical dominance, transforming a multi-branched plant architecture in teosinte to a single stalked plant (terminated by a tassel) in maize. The tillers and long branches of teosinte are terminated by tassels and bear many small ears. Similarly, the single maize stalk bears few ears and is terminated by a tassel[9,12,17]. A series of landmark studies by Doebley et al. elegantly demonstrated that tb1, which encodes a TCP transcription factor, is responsible for this transformation[18, 19]. Later studies showed that insertion of a Hopscotch transposon located ~60 kb upstream of tb1 enhances the expression of tb1 in maize, thereby repressing branch outgrowth[20, 21]. Through ChIP-seq and RNA-seq analyses, Dong et al.[22] demonstrated that tb1 acts to regulate multiple phytohormone signaling pathways (gibberellins, abscisic acid and jasmonic acid) and sugar sensing. Moreover, several other domestication loci, including teosinte glume architecture1 (tga1), prol1.1/grassy tillers1, were identified as its putative targets. Elucidating the precise regulatory mechanisms of these loci and signaling pathways will be an interesting and rewarding area of future research. Also worth noting, studies showed that tb1 and its homologous genes in Arabidopsis (Branched1 or BRC1) and rice (FINE CULM1 or FC1) play a conserved role in repressing the outgrowth of axillary branches in both dicotyledon and monocotyledon plants[23, 24].

    Teosinte ears possess two ranks of fruitcase-enclosed kernels, while maize produces hundreds of naked kernels on the ear[13]. tga1, which encodes a squamosa-promoter binding protein (SBP) transcription factor, underlies this transformation[25]. It has been shown that a de novo mutation occurred during maize domestication, causing a single amino acid substitution (Lys to Asn) in the TGA1 protein, altering its binding activity to its target genes, including a group of MADS-box genes that regulate glume identity[26].

    Prolificacy, the number of ears per plants, is also a domestication trait. It has been shown that grassy tillers 1 (gt1), which encodes an HD-ZIP I transcription factor, suppresses prolificacy by promoting lateral bud dormancy and suppressing elongation of the later ear branches[27]. The expression of gt1 is induced by shading and requires the activity of tb1, suggesting that gt1 acts downstream of tb1 to mediate the suppressed branching activity in response to shade. Later studies mapped a large effect QTL for prolificacy (prol1.1) to a 2.7 kb 'causative region' upstream of the gt1gene[28]. In addition, a recent study identified a new QTL, qEN7 (for ear number on chromosome 7). Zm00001d020683, which encodes a putative INDETERMINATE DOMAIN (IDD) transcription factor, was identified as the likely candidate gene based on its expression pattern and signature of selection during maize improvement[29]. However, its functionality and regulatory relationship with tb1 and gt1 remain to be elucidated.

    Smaller leaf angle and thus more compact plant architecture is a desired trait for modern maize varieties. Tian et al.[30] used a maize-teosinte BC2S3 population and cloned two QTLs (Upright Plant Architecture1 and 2 [UPA1 and UPA2]) that regulate leaf angle. Interestingly, the authors showed that the functional variant of UPA2 is a 2-bp InDel located 9.5 kb upstream of ZmRAVL1, which encodes a B3 domain transcription factor. The 2-bp Indel flanks the binding site of the transcription factor Drooping Leaf1 (DRL1)[31], which represses ZmRAVL1 expression through interacting with Liguleless1 (LG1), a SBP-box transcription factor essential for leaf ligule and auricle development[32]. UPA1 encodes brassinosteroid C-6 oxidase1 (brd1), a key enzyme for biosynthesis of active brassinolide (BR). The teosinte-derived allele of UPA2 binds DRL1 more strongly, leading to lower expression of ZmRAVL1 and thus, lower expression of brd1 and BR levels, and ultimately smaller leaf angle. Notably, the authors demonstrated that the teosinte-derived allele of UPA2 confers enhanced yields under high planting densities when introgressed into modern maize varieties[30, 33].

    Maize plants exhibit salient vegetative phase change, which marks the vegetative transition from the juvenile stage to the adult stage, characterized by several changes in maize leaves produced before and after the transition, such as production of leaf epicuticular wax and epidermal hairs. Previous studies reported that Glossy15 (Gl15), which encodes an AP2-like transcription factor, promotes juvenile leaf identity and suppressing adult leaf identity. Ectopic overexpression of Gl15 causes delayed vegetative phase change and flowering, while loss-of-function gl15 mutant displayed earlier vegetative phase change[34]. In another study, Gl15 was identified as a major QTL (qVT9-1) controlling the difference in the vegetative transition between maize and teosinte. Further, it was shown that a pre-existing low-frequency standing variation, SNP2154-G, was selected during domestication and likely represents the causal variation underlying differential expression of Gl15, and thus the difference in the vegetative transition between maize and teosinte[35].

    A number of studies documented evidence that tassels replace upper ears1 (tru1) is a key regulator of the conversion of the male terminal lateral inflorescence (tassel) in teosinte to a female terminal inflorescence (ear) in maize. tru1 encodes a BTB/POZ ankyrin repeat domain protein, and it is directly targeted by tb1, suggesting their close regulatory relationship[36]. In addition, a number of regulators of maize inflorescence morphology, were also shown as selective targets during maize domestication, including ramosa1 (ra1)[37, 38], which encodes a putative transcription factor repressing inflorescence (the ear and tassel) branching, Zea Agamous-like1 (zagl1)[39], which encodes a MADS-box transcription factor regulating flowering time and ear size, Zea floricaula leafy2 (zfl2, homologue of Arabidopsis Leafy)[40, 41], which likely regulates ear rank number, and barren inflorescence2 (bif2, ortholog of the Arabidopsis serine/threonine kinase PINOID)[42, 43], which regulates the formation of spikelet pair meristems and branch meristems on the tassel. The detailed regulatory networks of these key regulators of maize inflorescence still remain to be further elucidated.

    Kernel row number (KRN) and kernel weight are two important determinants of maize yield. A number of domestication genes modulating KRN and kernel weight have been identified and cloned, including KRN1, KRN2, KRN4 and qHKW1. KRN4 was mapped to a 3-kb regulatory region located ~60 kb downstream of Unbranched3 (UB3), which encodes a SBP transcription factor and negatively regulates KRN through imparting on multiple hormone signaling pathways (cytokinin, auxin and CLV-WUS)[44, 45]. Studies have also shown that a harbinger TE in the intergenic region and a SNP (S35) in the third exon of UB3 act in an additive fashion to regulate the expression level of UB3 and thus KRN[46].

    KRN1 encodes an AP2 transcription factor that pleiotropically affects plant height, spike density and grain size of maize[47], and is allelic to ids1/Ts6 (indeterminate spikelet 1/Tassel seed 6)[48]. Noteworthy, KRN1 is homologous to the wheat domestication gene Q, a major regulator of spike/spikelet morphology and grain threshability in wheat[49].

    KRN2 encodes a WD40 domain protein and it negatively regulates kernel row number[50]. Selection in a ~700-bp upstream region (containing the 5’UTR) of KRN2 during domestication resulted in reduced expression and thus increased kernel row number. Interestingly, its orthologous gene in rice, OsKRN2, was shown also a selected gene during rice domestication to negatively regulate secondary panicle branches and thus grain number. These observations suggest convergent selection of yield-related genes occurred during parallel domestication of cereal crops.

    qHKW1 is a major QTL for hundred-kernel weight (HKW)[51]. It encodes a CLAVATA1 (CLV1)/BARELY ANY MERISTEM (BAM)-related receptor kinase-like protein positively regulating HKW. A 8.9 Kb insertion in its promoter region was find to enhance its expression, leading to enhanced HKW[52]. In addition, Chen et al.[53] reported cloning of a major QTL for kernel morphology, qKM4.08, which encodes ZmVPS29, a retromer complex component. Sequencing and association analysis revealed that ZmVPS29 was a selective target during maize domestication. They authors also identified two significant polymorphic sites in its promoter region significantly associated with the kernel morphology. Moreover, a strong selective signature was detected in ZmSWEET4c during maize domestication. ZmSWEET4c encodes a hexose transporter protein functioning in sugar transport across the basal endosperm transfer cell layer (BETL) during seed filling[54]. The favorable alleles of these genes could serve as valuable targets for genetic improvement of maize yield.

    In a recent effort to more systematically analyze teosinte alleles that could contribute to yield potential of maize, Wang et al.[55] constructed four backcrossed maize-teosinte recombinant inbred line (RIL) populations and conducted detailed phenotyping of 26 agronomic traits under five environmental conditions. They identified 71 QTL associated with 24 plant architecture and yield related traits through inclusive composite interval mapping. Interestingly, they identified Zm00001eb352570 and Zm00001eb352580, both encode ethylene-responsive transcription factors, as two key candidate genes regulating ear height and the ratio of ear to plant height. Chen et al.[56] constructed a teosinte nested association mapping (TeoNAM) population, and performed joint-linkage mapping and GWAS analyses of 22 domestication and agronomic traits. They identified the maize homologue of PROSTRATE GROWTH1, a rice domestication gene controlling the switch from prostrate to erect growth, is also a QTL associated with tillering in teosinte and maize. Additionally, they also detected multiple QTL for days-to-anthesis (such as ZCN8 and ZmMADS69) and other traits (such as tassel branch number and tillering) that could be exploited for maize improvement. These lines of work highlight again the value of mining the vast amounts of superior alleles hidden in teosinte for future maize genetic improvement.

    Loss of seed shattering was also a key trait of maize domestication, like in other cereals. shattering1 (sh1), which encodes a zinc finger and YABBY domain protein regulating seed shattering. Interesting, sh1 was demonstrated to undergo parallel domestication in several cereals, including rice, maize, sorghum, and foxtail millet[57]. Later studies showed that the foxtail millet sh1 gene represses lignin biosynthesis in the abscission layer, and that an 855-bp Harbinger transposable element insertion in sh1 causes loss of seed shattering in foxtail millet[58].

    In addition to morphological traits, a number of physiological and nutritional related traits have also been selected during maize domestication. Based on survey of the nucleotide diversity, Whitt et al.[59] reported that six genes involved in starch metabolism (ae1, bt2, sh1, sh2, su1 and wx1) are selective targets during maize domestication. Palaisa et al.[60] reported selection of the Y1 gene (encoding a phytoene synthase) for increased nutritional value. Karn et al.[61] identified two, three, and six QTLs for starch, protein and oil respectively and showed that teosinte alleles can be exploited for the improvement of kernel composition traits in modern maize germplasm. Fan et at.[62] reported a strong selection imposed on waxy (wx) in the Chinese waxy maize population. Moreover, a recent exciting study reported the identification of a teosinte-derived allele of teosinte high protein 9 (Thp9) conferring increased protein level and nitrogen utilization efficiency (NUE). It was further shown that Thp9 encodes an asparagine synthetase 4 and that incorrect splicing of Thp9-B73 transcripts in temperate maize varieties is responsible for its diminished expression, and thus reduced NUE and protein content[63].

    Teosintes is known to confer superior disease resistance and adaptation to extreme environments (such as low phosphorus and high salinity). de Lange et al. and Lennon et al.[6466] reported the identification of teosinte-derived QTLs for resistance to gray leaf spot and southern leaf blight in maize. Mano & Omori reported that teosinte-derived QTLs could confer flooding tolerance[67]. Feng et al.[68] identified four teosinte-derived QTL that could improve resistance to Fusarium ear rot (FER) caused by Fusarium verticillioides. Recently, Wang et al.[69] reported a MYB transcription repressor of teosinte origin (ZmMM1) that confers resistance to northern leaf blight (NLB), southern corn rust (SCR) and gray leaf spot (GLS) in maize, while Zhang et al.[70] reported the identification of an elite allele of SNP947-G ZmHKT1 (encoding a sodium transporter) derived from teosinte can effectively improve salt tolerance via exporting Na+ from the above-ground plant parts. Gao et al.[71] reported that ZmSRO1d-R can regulate the balance between crop yield and drought resistance by increasing the guard cells' ROS level, and it underwent selection during maize domestication and breeding. These studies argue for the need of putting more efforts to tapping into the genetic resources hidden in the maize’s wild relatives. The so far cloned genes involved in maize domestication are summarized in Table 1. Notably, the enrichment of transcription factors in the cloned domestication genes highlights a crucial role of transcriptional re-wiring in maize domestication.

    Table 1.  Key domestication genes cloned in maize.
    GenePhenotypeFunctional annotationSelection typeCausative changeReferences
    tb1Plant architectureTCP transcription factorIncreased expression~60 kb upstream of tb1 enhancing expression[1822]
    tga1Hardened fruitcaseSBP-domain transcription factorProtein functionA SNP in exon (K-N)[25, 26]
    gt1Plant architectureHomeodomain leucine zipperIncreased expressionprol1.1 in 2.7 kb upstream of the promoter region increasing expression[27, 28]
    Zm00001d020683Plant architectureINDETERMINATE DOMAIN transcription factorProtein functionUnknown[29]
    UPA1Leaf angleBrassinosteroid C-6 oxidase1Protein functionUnknown[30]
    UPA2Leaf angleB3 domain transcription factorIncreased expressionA 2 bp indel in 9.5 kb upstream of ZmRALV1[30]
    Gl15Vegetative phase changeAP2-like transcription factorAltered expressionSNP2154: a stop codon (G-A)[34, 35]
    tru1Plant architectureBTB/POZ ankyrin repeat proteinIncreased expressionUnknown[36]
    ra1Inflorescence architectureTranscription factorAltered expressionUnknown[37, 38]
    zflPlant architectureTranscription factorAltered expressionUnknown[40, 41]
    UB3Kernel row numberSBP-box transcription factorAltered expressionA TE in the intergenic region;[4446]
    SNP (S35): third exon of UB3
    (A-G) increasing expression of UB3 and KRN
    KRN1/ids1/Ts6Kernel row numberAP2 Transcription factorIncreased expressionUnknown[47, 48]
    KRN2Kernel row numberWD40 domainDecreased expressionUnknown[50]
    qHKW1Kernel row weightCLV1/BAM-related receptor kinase-like proteinIncreased expression8.9 kb insertion upstream of HKW[51, 52]
    ZmVPS29Kernel morphologyA retromer complex componentProtein functionTwo SNPs (S-1830 and S-1558) in the promoter of ZmVPS29[53]
    ZmSWEET4cSeed fillingHexose transporterProtein functionUnknown[54]
    ZmSh1ShatteringA zinc finger and YABBY transcription factorProtein functionUnknown[57, 58]
    Thp9Nutrition qualityAsparagine synthetase 4 enzymeProtein functionA deletion in 10th intron of Thp9 reducing NUE and protein content[63]
    ZmMM1Biotic stressMYB Transcription repressorProtein functionUnknown[69]
    ZmHKT1Abiotic stressA sodium transporterProtein functionSNP947-G: a nonsynonymous variation increasing salt tolerance[70]
    ZmSRO1d-RDrought resistance and productionPolyADP-ribose polymerase and C-terminal RST domainProtein functionThree non-synonymous variants: SNP131 (A44G), SNP134 (V45A) and InDel433[71]
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    After its domestication from its wild progenitor teosinte in southwestern Mexico in the tropics, maize has now become the mostly cultivated crop worldwide owing to its extensive range expansion and adaptation to diverse environmental conditions (such as temperature and day length). A key prerequisite for the spread of maize from tropical to temperate regions is reduced photoperiod sensitivity[72]. It was recently shown that CENTRORADIALIS 8 (ZCN8), an Flowering Locus T (FT) homologue, underlies a major quantitative trait locus (qDTA8) for flowering time[73]. Interestingly, it has been shown that step-wise cis-regulatory changes occurred in ZCN8 during maize domestication and post-domestication expansion. SNP-1245 is a target of selection during early maize domestication for latitudinal adaptation, and after its fixation, selection of InDel-2339 (most likely introgressed from Zea mays ssp. Mexicana) likely contributed to the spread of maize from tropical to temperate regions[74].

    ZCN8 interacts with the basic leucine zipper transcription factor DLF1 (Delayed flowering 1) to form the florigen activation complex (FAC) in maize. Interestingly, DFL1 was found to underlie qLB7-1, a flowering time QTL identified in a BC2S3 population of maize-teosinte. Moreover, it was shown that DLF1 directly activates ZmMADS4 and ZmMADS67 in the shoot apex to promote floral transition[75]. In addition, ZmMADS69 underlies the flowering time QTL qDTA3-2 and encodes a MADS-box transcription factor. It acts to inhibit the expression of ZmRap2.7, thereby relieving its repression on ZCN8 expression and causing earlier flowering. Population genetic analyses showed that DLF1, ZmMADS67 and ZmMADS69 are all targets of artificial selection and likely contributed to the spread of maize from the tropics to temperate zones[75, 76].

    In addition, a few genes regulating the photoperiod pathway and contributing to the acclimation of maize to higher latitudes in North America have been cloned, including Vgt1, ZmCCT (also named ZmCCT10), ZmCCT9 and ZmELF3.1. Vgt1 was shown to act as a cis-regulatory element of ZmRap2.7, and a MITE TE located ~70 kb upstream of Vgt1 was found to be significantly associated with flowering time and was a major target for selection during the expansion of maize to the temperate and high-latitude regions[7779]. ZmCCT is another major flowering-time QTL and it encodes a CCT-domain protein homologous to rice Ghd7[80]. Its causal variation is a 5122-bp CACTA-like TE inserted ~2.5 kb upstream of ZmCCT10[72, 81]. ZmCCT9 was identified a QTL for days to anthesis (qDTA9). A Harbinger-like TE located ~57 kb upstream of ZmCCT9 showed the most significant association with DTA and thus believed to be the causal variation[82]. Notably, the CATCA-like TE of ZmCCT10 and the Harbinger-like TE of ZmCCT9 are not observed in surveyed teosinte accessions, hinting that they are de novo mutations occurred after the initial domestication of maize[72, 82]. ZmELF3.1 was shown to underlie the flowering time QTL qFT3_218. It was demonstrated that ZmELF3.1 and its homolog ZmELF3.2 can form the maize Evening Complex (EC) through physically interacting with ZmELF4.1/ZmELF4.2, and ZmLUX1/ZmLUX2. Knockout mutants of Zmelf3.1 and Zmelf3.1/3.2 double mutant presented delayed flowering under both long-day and short-day conditions. It was further shown that the maize EC promote flowering through repressing the expression of several known flowering suppressor genes (e.g., ZmCCT9, ZmCCT10, ZmCOL3, ZmPRR37a and ZmPRR73), and consequently alleviating their inhibition on several maize florigen genes (ZCN8, ZCN7 and ZCN12). Insertion of two closely linked retrotransposon elements upstream of the ZmELF3.1 coding region increases the expression of ZmELF3.1, thus promoting flowering[83]. The increase frequencies of the causal TEs in Vgt1, ZmCCT10, ZmCCT9 and ZmELF3.1 in temperate maize compared to tropical maize highlight a critical role of these genes during the spread and adaptation of maize to higher latitudinal temperate regions through promoting flowering under long-day conditions[72,8183].

    In addition, Barnes et al.[84] recently showed that the High Phosphatidyl Choline 1 (HPC1) gene, which encodes a phospholipase A1 enzyme, contributed to the spread of the initially domesticated maize from the warm Mexican southwest to the highlands of Mexico and South America by modulating phosphatidylcholine levels. The Mexicana-derived allele harbors a polymorphism and impaired protein function, leading to accelerated flowering and better fitness in highlands.

    Besides the above characterized QTLs and genes, additional genetic elements likely also contributed to the pre-Columbia spreading of maize. Hufford et al.[85] proposed that incorporation of mexicana alleles into maize may helped the expansion of maize to the highlands of central Mexico based on detection of bi-directional gene flow between maize and Mexicana. This proposal was supported by a recent study showing evidence of introgression for over 10% of the maize genome from the mexicana genome[86]. Consistently, Calfee et al.[87] found that sequences of mexicana ancestry increases in high-elevation maize populations, supporting the notion that introgression from mexicana facilitating adaptation of maize to the highland environment. Moreover, a recent study examined the genome-wide genetic diversity of the Zea genus and showed that dozens of flowering-related genes (such as GI, BAS1 and PRR7) are associated with high-latitude adaptation[88]. These studies together demonstrate unequivocally that introgression of genes from Mexicana and selection of genes in the photoperiod pathway contributed to the spread of maize to the temperate regions.

    The so far cloned genes involved in pre-Columbia spread of maize are summarized in Fig. 2 and Table 2.

    Figure 2.  Genes involved in Pre-Columbia spread of maize to higher latitudes and the temperate regions. The production of world maize in 2020 is presented by the green bar in the map from Ritchie et al. (2023). Ritchie H, Rosado P, and Roser M. 2023. "Agricultural Production". Published online at OurWorldInData.org. Retrieved from: 'https:ourowrldindata.org/agricultural-production' [online Resource].
    Table 2.  Flowering time related genes contributing to Pre-Columbia spread of maize.
    GeneFunctional annotationCausative changeReferences
    ZCN8Florigen proteinSNP-1245 and Indel-2339 in promoter[73, 74]
    DLF1Basic leucine zipper transcription factorUnknown[75]
    ZmMADS69MADS-box transcription factorUnknown[76]
    ZmRap2.7AP2-like transcription factorMITE TE inserted ~70 kb upstream[7779]
    ZmCCTCCT-domain protein5122-bp CACTA-like TE inserted ~2.5 kb upstream[72,81]
    ZmCCT9CCT transcription factorA harbinger-like element at 57 kb upstream[82]
    ZmELF3.1Unknownwo retrotransposons in the promote[84]
    HPC1Phospholipase A1 enzymUnknown[83]
    ZmPRR7UnknownUnknown[88]
    ZmCOL9CO-like-transcription factorUnknown[88]
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    Subsequent to domestication ~9,000 years ago, maize has been continuously subject to human selection during the post-domestication breeding process. Through re-sequencing analysis of 35 improved maize lines, 23 traditional landraces and 17 wild relatives, Hufford et al.[15] identified 484 and 695 selective sweeps during maize domestication and improvement, respectively. Moreover, they found that about a quarter (23%) of domestication sweeps (107) were also selected during improvement, indicating that a substantial portion of the domestication loci underwent continuous selection during post-domestication breeding.

    Genetic improvement of maize culminated in the development of high planting density tolerant hybrid maize to increase grain yield per unit land area[89, 90]. To investigate the key morphological traits that have been selected during modern maize breeding, we recently conducted sequencing and phenotypic analyses of 350 elite maize inbred lines widely used in the US and China over the past few decades. We identified four convergently improved morphological traits related to adapting to increased planting density, i.e., reduced leaf angle, reduced tassel branch number (TBN), reduced relative plant height (EH/PH) and accelerated flowering. Genome-wide Association Study (GWAS) identified a total of 166 loci associated with the four selected traits, and found evidence of convergent increases in allele frequency at putatively favorable alleles for the identified loci. Moreover, genome scan using the cross-population composite likelihood ratio approach (XP-CLR) identified a total of 1,888 selective sweeps during modern maize breeding in the US and China. Gene ontology analysis of the 5,356 genes encompassed in the selective sweeps revealed enrichment of genes related to biosynthesis or signaling processes of auxin and other phytohormones, and in responses to light, biotic and abiotic stresses. This study provides a valuable resource for mining genes regulating morphological and physiological traits underlying adaptation to high-density planting[91].

    In another study, Li et al.[92] identified ZmPGP1 (ABCB1 or Br2) as a selected target gene during maize domestication and genetic improvement. ZmPGP1 is involved in auxin polar transport, and has been shown to have a pleiotropic effect on plant height, stalk diameter, leaf length, leaf angle, root development and yield. Sequence and phenotypic analyses of ZmPGP1 identified SNP1473 as the most significant variant for kernel length and ear grain weight and that the SNP1473T allele is selected during both the domestication and improvement processes. Moreover, the authors identified a rare allele of ZmPGP1 carrying a 241-bp deletion in the last exon, which results in significantly reduced plant height and ear height and increased stalk diameter and erected leaves, yet no negative effect on yield[93], highlighting a potential utility in breeding high-density tolerant maize cultivars.

    Shade avoidance syndrome (SAS) is a set of adaptive responses triggered when plants sense a reduction in the red to far-red light (R:FR) ratio under high planting density conditions, commonly manifested by increased plant height (and thus more prone to lodging), suppressed branching, accelerated flowering and reduced resistance to pathogens and pests[94, 95]. High-density planting could also cause extended anthesis-silking interval (ASI), reduced tassel size and smaller ear, and even barrenness[96, 97]. Thus, breeding of maize cultivars of attenuated SAS is a priority for adaptation to increased planting density.

    Extensive studies have been performed in Arabidopsis to dissect the regulatory mechanism of SAS and this topic has been recently extensively reviewed[98]. We recently showed that a major signaling mechanism regulating SAS in Arabidopsis is the phytochrome-PIFs module regulates the miR156-SPL module-mediated aging pathway[99]. We proposed that in maize there might be a similar phytochrome-PIFs-miR156-SPL regulatory pathway regulating SAS and that the maize SPL genes could be exploited as valuable targets for genetic improvement of plant architecture tailored for high-density planting[100].

    In support of this, it has been shown that the ZmphyBs (ZmphyB1 and ZmphyB2), ZmphyCs (ZmphyC1 and ZmphyC2) and ZmPIFs are involved in regulating SAS in maize[101103]. In addition, earlier studies have shown that as direct targets of miR156s, three homologous SPL transcription factors, UB2, UB3 and TSH4, regulate multiple agronomic traits including vegetative tillering, plant height, tassel branch number and kernel row number[44, 104]. Moreover, it has been shown that ZmphyBs[101, 105] and ZmPIF3.1[91], ZmPIF4.1[102] and TSH4[91] are selective targets during modern maize breeding (Table 3).

    Table 3.  Selective genes underpinning genetic improvement during modern maize breeding.
    GenePhenotypeFunctional annotationSelection typeCausative changeReferences
    ZmPIF3.1Plant heightBasic helix-loop-helix transcription factorIncreased expressionUnknown[91]
    TSH4Tassel branch numberTranscription factorAltered expressionUnknown[91]
    ZmPGP1Plant architectureATP binding cassette transporterAltered expressionA 241 bp deletion in the last exon of ZmPGP1[92, 93]
    PhyB2Light signalPhytochrome BAltered expressionA 10 bp deletion in the translation start site[101]
    ZmPIF4.1Light signalBasic helix-loop-helix transcription factorAltered expressionUnknown[102]
    ZmKOB1Grain yieldGlycotransferase-like proteinProtein functionUnknown[121]
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    In a recent study to dissect the signaling process regulating inflorescence development in response to the shade signal, Kong et al.[106] compared the gene expression changes along the male and female inflorescence development under simulated shade treatments and normal light conditions, and identified a large set of genes that are co-regulated by developmental progression and simulated shade treatments. They found that these co-regulated genes are enriched in plant hormone signaling pathways and transcription factors. By network analyses, they found that UB2, UB3 and TSH4 act as a central regulatory node controlling maize inflorescence development in response to shade signal, and their loss-of-function mutants exhibit reduced sensitivity to simulated shade treatments. This study provides a valuable genetic source for mining and manipulating key shading-responsive genes for improved tassel and ear traits under high density planting conditions.

    Nowadays, global maize production is mostly provided by hybrid maize, which exhibits heterosis (or hybrid vigor) in yields and stress tolerance over open-pollinated varieties[3]. Hybrid maize breeding has gone through several stages, from the 'inbred-hybrid method' stage by Shull[107] and East[108] in the early twentieth century, to the 'double-cross hybrids' stage (1930s−1950s) by Jones[109], and then the 'single-cross hybrids' stage since the 1960s. Since its development, single-cross hybrid was quickly adopted globally due to its superior heterosis and easiness of production[3].

    Single-cross maize hybrids are produced from crossing two unrelated parental inbred lines (female × male) belonging to genetically distinct pools of germplasm, called heterotic groups. Heterotic groups allow better exploitation of heterosis, since inter-group hybrids display a higher level of heterosis than intra-group hybrids. A specific pair of female and male heterotic groups expressing pronounced heterosis is termed as a heterotic pattern[110, 111]. Initially, the parental lines were derived from a limited number of key founder inbred lines and empirically classified into different heterotic groups (such as SSS and NSS)[112]. Over time, they have expanded dramatically, accompanied by formation of new 'heterotic groups' (such as Iodent, PA and PB). Nowadays, Stiff Stalk Synthetics (SSS) and PA are generally used as FHGs (female heterotic groups), while Non Stiff Stalk (NSS), PB and Sipingtou (SPT) are generally used as the MHGs (male heterotic groups) in temperate hybrid maize breeding[113].

    With the development of molecular biology, various molecular markers, ranging from RFLPs, SSRs, and more recently high-density genome-wide SNP data have been utilized to assign newly developed inbred lines into various heterotic groups, and to guide crosses between heterotic pools to produce the most productive hybrids[114116]. Multiple studies with molecular markers have suggested that heterotic groups have diverged genetically over time for better heterosis[117120]. However, there has been a lack of a systematic assessment of the effect and contribution of breeding selection on phenotypic improvement and the underlying genomic changes of FHGs and MHGs for different heterotic patterns on a population scale during modern hybrid maize breeding.

    To systematically assess the phenotypic improvement and the underlying genomic changes of FHGs and MHGs during modern hybrid maize breeding, we recently conducted re-sequencing and phenotypic analyses of 21 agronomic traits for a panel of 1,604 modern elite maize lines[121]. Several interesting observations were made: (1) The MHGs experienced more intensive selection than the FMGs during the progression from era I (before the year 2000) to era II (after the year 2000). Significant changes were observed for 18 out of 21 traits in the MHGs, but only 10 of the 21 traits showed significant changes in the FHGs; (2) The MHGs and FHGs experienced both convergent and divergent selection towards different sets of agronomic traits. Both the MHGs and FHGs experienced a decrease in flowering time and an increase in yield and plant architecture related traits, but three traits potentially related to seed dehydration rate were selected in opposite direction in the MHGs and FHGs. GWAS analysis identified 4,329 genes associated with the 21 traits. Consistent with the observed convergent and divergent changes of different traits, we observed convergent increase for the frequencies of favorable alleles for the convergently selected traits in both the MHGs and FHGs, and anti-directional changes for the frequencies of favorable alleles for the oppositely selected traits. These observations highlight a critical contribution of accumulation of favorable alleles to agronomic trait improvement of the parental lines of both FHGs and MHGs during modern maize breeding.

    Moreover, FST statistics showed increased genetic differentiation between the respective MHGs and FHGs of the US_SS × US_NSS and PA × SPT heterotic patterns from era I to era II. Further, we detected significant positive correlations between the number of accumulated heterozygous superior alleles of the differentiated genes with increased grain yield per plant and better parent heterosis, supporting a role of the differentiated genes in promoting maize heterosis. Further, mutational and overexpressional studies demonstrated a role of ZmKOB1, which encodes a putative glycotransferase, in promoting grain yield[121]. While this study complemented earlier studies on maize domestication and variation maps in maize, a pitfall of this study is that variation is limited to SNP polymorphisms. Further exploitation of more variants (Indels, PAVs, CNVs etc.) in the historical maize panel will greatly deepen our understanding of the impact of artificial selection on the maize genome, and identify valuable new targets for genetic improvement of maize.

    The ever-increasing worldwide population and anticipated climate deterioration pose a great challenge to global food security and call for more effective and precise breeding methods for crops. To accommodate the projected population increase in the next 30 years, it is estimated that cereal production needs to increase at least 70% by 2050 (FAO). As a staple cereal crop, breeding of maize cultivars that are not only high-yielding and with superior quality, but also resilient to environmental stresses, is essential to meet this demand. The recent advances in genome sequencing, genotyping and phenotyping technologies, generation of multi-omics data (including genomic, phenomic, epigenomic, transcriptomic, proteomic, and metabolomic data), creation of novel superior alleles by genome editing, development of more efficient double haploid technologies, integrating with machine learning and artificial intelligence are ushering the transition of maize breeding from the Breeding 3.0 stage (biological breeding) into the Breeding 4.0 stage (intelligent breeding)[122, 123]. However, several major challenges remain to be effectively tackled before such a transition could be implemented. First, most agronomic traits of maize are controlled by numerous small-effect QTL and complex genotype-environment interactions (G × E). Thus, elucidating the contribution of the abundant genetic variation in the maize population to phenotypic plasticity remains a major challenge in the post-genomic era of maize genetics and breeding. Secondly, most maize cultivars cultivated nowadays are hybrids that exhibit superior heterosis than their parental lines. Hybrid maize breeding involves the development of elite inbred lines with high general combining ability (GCA) and specific combining ability (SCA) that allows maximal exploitation of heterosis. Despite much effort to dissect the mechanisms of maize heterosis, the molecular basis of maize heterosis is still a debated topic[124126]. Thirdly, only limited maize germplasm is amenable to genetic manipulation (genetic transformation, genome editing etc.), which significantly hinders the efficiency of genetic improvement. Development of efficient genotype-independent transformation procedure will greatly boost maize functional genomic research and breeding. Noteworthy, the Smart Corn System recently launched by Bayer is promised to revolutionize global corn production in the coming years. At the heart of the new system is short stature hybrid corn (~30%−40% shorter than traditional hybrids), which offers several advantages: sturdier stems and exceptional lodging resistance under higher planting densities (grow 20%−30% more plants per hectare), higher and more stable yield production per unit land area, easier management and application of plant protection products, better use of solar energy, water and other natural resources, and improved greenhouse gas footprint[127]. Indeed, a new age of maize green revolution is yet to come!

    This work was supported by grants from the Key Research and Development Program of Guangdong Province (2022B0202060005), National Natural Science Foundation of China (32130077) and Hainan Yazhou Bay Seed Lab (B21HJ8101). We thank Professors Hai Wang (China Agricultural University) and Jinshun Zhong (South China Agricultural University) for valuable comments and helpful discussion on the manuscript. We apologize to authors whose excellent work could not be cited due to space limitations.

  • The authors declare that they have no conflict of interest. Haiyang Wang is an Editorial Board member of Seed Biology who was blinded from reviewing or making decisions on the manuscript. The article was subject to the journal's standard procedures, with peer-review handled independently of this Editorial Board member and his research groups.

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    Biswas PR, Boro H, Doley SN, Dutta AK, Tayung K. 2023. Evaluation of different lignocellulosic-wastes and their combinations on growth and yield of Oyster mushroom (Pleurotus ostreatus). Studies in Fungi 8:7 doi: 10.48130/SIF-2023-0007
    Biswas PR, Boro H, Doley SN, Dutta AK, Tayung K. 2023. Evaluation of different lignocellulosic-wastes and their combinations on growth and yield of Oyster mushroom (Pleurotus ostreatus). Studies in Fungi 8:7 doi: 10.48130/SIF-2023-0007

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ARTICLE   Open Access    

Evaluation of different lignocellulosic-wastes and their combinations on growth and yield of Oyster mushroom (Pleurotus ostreatus)

Studies in Fungi  8 Article number: 7  (2023)  |  Cite this article

Abstract: Lignocellulose wastes are generated in huge amounts by various sectors like agriculture, forestry, and industry but only a small portion of these wastes are utilized and a major portion is left unused. In this study, seven different lignocellulosic wastes and their combinations in different percentages were determined for the growth and yield of Pleurotus ostreatus. The maximum growth and yield of P. ostreatus were observed on a substrate made of rice straw, with a total yield of 399.70 gm per kg of substrate. The least growth and yield were recorded on a substrate made of wood flakes and sugarcane bagasse (80% + 20%), with a total yield of 13.54 gm per kg of substrate. Rice straw showed the highest biological efficiency (B.E) of 39.40, whereas wood flakes and sugarcane bagasse (80% + 20%) had the lowest B.E. of 1.35. Other substrates had a moderate effect, and citronella bagasse (Cymbopogon nardus), which was used as a substrate for the first time, gave a biological efficiency of 39.39 gm per kg substrate. The results showed a significant effect of substrates on mean yield and biological efficiency. Our study revealed that lignocellulosic waste can be profitably utilized for mushroom cultivation and could be one of the most economical and eco-friendly techniques.

    • Lignocellulosic wastes constitute a major portion of plant biomass and are generated in huge amounts annually in various sectors like agriculture, forestry and the food industry. These wastes consist of rich organic compounds and are worthy of being recovered and transformed[1]. Despite their usability, only a small fraction of the total waste is utilized and a major portion is left unused. Some of them are disposed in open dumps or burnt, resulting in emission of black carbon causing serious environmental pollution. Therefore, the utilization of these lignocellulosic wastes into profitable products has become one of the major objectives at present. Mushroom cultivation using lignocellulosic wastes could be one of the most economical and eco-friendly techniques for the conversion of these wastes into profitable products. Mushrooms are an excellent source of protein that can be a substitute for meat for vegetarians. Mushrooms contain about 85%–95% water, 3% protein, 4% carbohydrate, 0.1% fat, 1% minerals and vitamins[2].

      Amongst various mushrooms, Pleurotus spp. (Oyster mushroom) can be cultivated on a wide range of lignocellulosic substrates. Therefore, cultivation of this mushroom species needs to be popularized so that unused lignocellulosic waste can be properly utilized for mushroom production. Oyster mushrooms are widely consumed worldwide and are regarded as a nutritious food option due to both its nutritional and medicinal properties. Over the past few decades, there has been a global trend toward the cultivation of significantly greater numbers of oyster mushrooms[3,4]. After button mushrooms, oyster mushrooms are the most common type of mushroom consumed[5]. The cultivation process of oyster mushrooms is cost-effective because of its easy cultivation techniques using substrates that are locally available[6,7]. Various agro-wastes have been utilized to cultivate the edible mushroom out of which paddy straw and wheat straw are the most common. The other substrates include sawdust, sugarcane, corn cob, corn stalks, leaves, and the pseudo stem of banana[810].

      This study aims to investigate the yield and biological efficiency of the selected substrates and their combination on the productivity of Pleurotus ostreatus (Jacq.) P. Kumm through the usage of various locally accessible and unused lignocellulosic waste for its cultivation.

    • Seven agricultural and plant-based lignocellulosic wastes were collected from agro-based and paper and pulp industries in Guwahati, Assam, India. The seven individual substrates and their combinations were T1 = rice straw, T2 = sugarcane bagasse, T3 = wood chips, T4 = wood flakes, T5 = citronella bagasse [Cymbopogon nardus (L.) Rendle], T6 = sawdust, T7 = leaf litter [Monoon longifolium (Sonn.) B.Xue & R.M.K. Saunders], and the combinations includes: T8 = rice straw + sugarcane bagasse (50% + 50%), T9 = Rice straw + wood chips (60% + 40%), T10 = wood flakes + sawdust (50% + 50%), T11 = wood flakes + sugarcane bagasse (80% + 20%), T12 = wood flakes + sugarcane bagasse + woodchips (35% + 35% + 30%), T13 = rice straw + wood flakes + sawdust (50% + 40% + 10%) and T14 = citronella bagasse + sugarcane bagasse + wood flakes + wood chips (25% each).

    • Each bag contained 1 kg of the collected materials as substrates. Firstly, the collected materials were cut into small pieces of 2–3 cm and thoroughly washed with normal water followed by surface sterilization with hot water treatment. After rinsing, the substrates were separately packed in polypropylene bags of 45 cm × 30 cm, autoclaved and then allowed to cool.

    • The spawn of Pleurotus ostreatus was collected from Assam Agriculture University of Kahikuchi campus, Guwahati, India. After cooling, the substrates were mixed with gram flour (8 g/kg substrate) and stacked in three layers in a separate clean polypropylene bag. Between each stacking layer, spawning was done on the entire surface of the beds. A number of holes (measuring ca. 2 mm in diam.) were created to maintain an aerobic condition.

    • The bags were incubated by hanging them in a closed room with ventilation kept open throughout along with an exhaust fan, at a temperature ranging from 25–28.5 °C. Water was sprinkled regularly to maintain moisture.

    • After complete colonization, longitudinal slits were made to facilitate the proper development of fruiting bodies. Harvesting of the fruiting bodies was done on the fourth day after the appearance of pinheads. The mycelial growth, complete colonization, primordial initiation, and yield in terms of biological efficiency were recorded. Biological Efficiency (B.E) was calculated as the percentage of yield of fresh mushrooms in relation to the dry weight of the substrate as given by Chang & Miles[11].

      Biological efficiency (B.E.) in % = yield of fresh mushroom (in gm)/ total weight of the dry substrate (i.e., 1,000 gm) × 100

    • Statistical analyses were performed for the comparison of treatment means of the first mycelial growth, complete mycelial colonization, pin-head initiation, the time required for first harvesting, yield and biological efficiency. The Shapiro-Wilk Normality Test was pre-performed to check for the goodness of fit normality of the data. Accordingly, after the log transformation of the original data, it eventually follows the assumption of normality. After that, the transformed data are implemented in a Completely Randomized Design with fourteen different substrates with three replications each and the analysis of variance (one-way ANOVA) along with a multiple comparison test viz. Least Significant Difference for comparison of the pairs of treatments using the RStudio version 1.2.1335. The primary software packages used in the analyses are agricolae, DescTools, ggplot2, tidyverse and dplyr.

    • In this study, Pleurotus ostreatus growth and yield were determined using seven lignocellulosic wastes and their combinations in varied proportions. The result showed that first mycelial growth in different substrates and their combinations ranged from 1.00–2.67 d (Table 1). The lowest day of first mycelial growth was observed on T5 and T14 substrates. Among all the substrates, T6 showed a significantly higher time for the appearance of first mycelial growth. Further, the time required for the appearance of the first mycelial growth was similar in T2, T3, T7 and T11. Similar trends were also observed in the other substrates as well. The study indicated that there was a significant effect of substrates on mean first mycelial growth (p-value = 0.04). Analysis using the Least Significant Difference (LSD) showed that the T6 substrate took a longer mean time for first mycelial growth (2.67 d).

      Table 1.  Effect of substrates on the mycelial growth of Pleurotus ostreatus.

      SubstratesFirst mycelial growth
      in the substrate (d)
      Time required for completion
      of mycelial running (d)
      T1 = Rice straw1.67abc16.33de
      T2 = Sugarcane bagasse2.33ab15.67ef
      T3 = Wood chips2.33ab26.00b
      T4 = Wood flakes1.67abc17.33de
      T5 = Citronella bagasse1.00c16.33de
      T6 = Sawdust2.67a20.00c
      T7 = Leaf litter2.33ab24.67b
      T8 = Rice straw + sugarcane bagasse (50% each)1.33bc14.00f
      T9 = Rice straw + wood chips (60% + 40%)1.33bc17.33de
      T10 = Wood flakes + sawdust (50% each)1.67abc28.67a
      T11 = Wood flakes + sugarcane bagasse (80% + 20%)2.33ab18.00cd
      T12 = Wood flakes + sugarcane + wood chips (35% + 35% + 30%)1.33bc15.33ef
      T13 = Rice straw + wood flakes + sawdust (50% + 40% + 10%)1.33bc18.33cd
      T14 = Citronella bagasse + sugarcane bagasse + wood flakes + wood chips (25% each)1.00c17.00de
      Significance****
      CV (%)82.421.50
      Treatments followed with the same letter are not significantly different by LSD (Least Significance Difference) test at a 5% level of significance.

      From Table 1, it was observed that the completion of mycelial running in different substrates and their combinations ranged from 14.00–28.67 d. The lowest days of completion of mycelial running were observed on the T8 substrate i.e., 28.67 d. Again, among all substrates, T10 showed a significantly higher mean first completion of mycelial running (28.67 d) and this observation is also supported by the LSD analysis. The T1, T2, T4, T5, T9 and T14 substrates were not significantly different from each other and similar result was observed for the remaining substrates as well. There was a significant effect of substrates on mean complete mycelial running (p-value ≤ 2e-16).

      First pinhead initiation in different substrates and their combinations ranged from 18.67–34.00 d (Table 2). The lowest days of the first pinhead initiation were observed on T2 and T8 substrates. Substrates T3, T7, and T10 showed considerably longer mean initial pinhead initiation times than the other substrates. The remaining substrates were not significantly different from each other in terms of first pinhead initiation. However, there was significant effect of the substrates on the first pinhead initiation (p-value ≤ 2e-16). Similar to previous studies, LSD analysis showed that the T10 substrate took the longest duration for first pinhead initiation among all substrates (34.00 d).

      Table 2.  Effect of different substrates on first pin-head initiation and the time required for the first harvest.

      SubstratesTime required for first
      pin- head initiation (d)
      Time required for
      first harvesting (d)
      T1 = Rice straw23.33b26.33b
      T2 = Sugarcane bagasse18.67d21.67d
      T3 = Wood chips33.33a36.33a
      T4 = Wood flakes22.67bc24.67bc
      T5 = Citronella bagasse23.33b26.33b
      T6 = Sawdust20.00d23.00d
      T7 = Leaf litter33.00a36.00a
      T8 = Rice straw + sugarcane bagasse (50% each)18.67d21.67d
      T9 = Rice straw + wood chips (60% + 40%)22.00bc25.00bc
      T10 = Wood flakes + sawdust (50% each)34.00a37.00a
      T11 = Wood flakes + sugarcane bagasse (80% + 20%)21.67c24.67c
      T12 = Wood flakes+ sugarcane + wood chips (35% + 35% + 30%)20.00d23.00d
      T13 = Rice straw + wood flakes + sawdust (50% + 40% + 10%)22.67bc25.67bc
      T14 = Citronella bagasse + sugarcane bagasse + wood flakes + wood chips (25% each)22.00bc25.00bc
      Significance******
      CV (%)3.983.53
      Treatments followed with the same letter are not significantly different by LSD (Least Significance Difference) test at a 5% level of significance.

      The first harvest in different substrates ranged from 21.67–37.00 d (Table 2). T2 and T8 substrates needed the least time (21.67 d) for the first harvest out of all the substrates. However, T3, T7, and T10 substrates required much more time than other substrates. Similar to the previous finding, T10 had a mean first harvest of 37.00 d, which was longer than the other substrates (p-value = 2e-16). After the pin head initiation, harvesting was done within a week (Fig. 1). A total of four harvests were made depending upon the yield on different substrates. The results showed that the first harvest in different substrates and their combinations ranged from 13.50–222.43 gm (Table 3).

      Figure 1. 

      Growth of Pleurotus ostreatus on different substrates (a) T1 = rice straw, (b) T2 = sugarcane bagasse, (c) T3 = wood chips, (d) T4 = wood flakes, (e) T5 = citronella bagasse (Cymbopogon nardus), (f) T6 = sawdust, (g) T7 = leaf litter (Monoon longifolium), (h) T8 = rice straw + sugarcane bagasse (50% + 50%), (i) T9 = rice straw + wood chips (60% + 40%), (j) T10 = wood flakes + sawdust (50% + 50%), (k) T11 = wood flakes + sugarcane bagasse (80% + 20%), (l) T12 = wood flakes + sugarcane bagasse + woodchips (35% + 35% + 30%), (m−n) T13 = rice straw + wood flakes + sawdust (50% + 40% + 10%), and T14 = Citronella bagasse + sugarcane bagasse + wood flakes + wood chips (25% each).

      Table 3.  Effect of different substrates and substrate combinations on yield of Pleurotus ostreatus.

      SubstratesWeight of the fruiting bodies (in gm)Net weight
      (in gm)
      1st harvest2nd harvest3rd harvest4th harvest
      T1 = Rice straw131.67c163.33b90.00b14.70b399.70a
      T2 = Sugarcane bagasse14.86ij8.55i23.41j
      T3 = Wood chips63.17e22.84g86.01g
      T4 = Wood flakes13.50j5.63i3.33d22.45j
      T5 = Citronella bagasse222.43a123.73c47.75c393.90b
      T6 = Sawdust161.56b15.20h176.76d
      T7 = Leaf litter23.33h13.05h36.38i
      T8 = Rice straw + sugarcane bagasse (50% each)50.73f77.51d128.24f
      T9 = Rice straw + wood chips (60% + 40%)17.56i17.56k
      T10 = Wood flakes + sawdust (50% each)65.81e71.74e137.55a
      T11 = Wood flakes + sugarcane bagasse (80% + 20%)13.54j13.54l
      T12 = Wood flakes + sugarcane + wood chips (35% + 35% + 30%)53.23f25.12g78.35h
      T13 = Rice straw + wood flakes + sawdust (50% + 40% + 10%)83.55d203.97a104.97a392.49b
      T14 = Citronella bagasse + sugarcane bagasse + wood flakes + wood chips (25% each)32.34g66.09f106.80a66.67a271.90c
      Significance***************
      CV (%)3.363.305.585.661.16
      Treatments followed with the same letter are not significantly different by LSD (Least Significance Difference) test at a 5% level of significance.

      T4 and T11 substrates had the lowest first harvest yield of 13.50 gm, whereas the T5 substrate had the highest mean yield. T3 and T10 substrates yielded similarly in the first harvest. Other substrates had similar first-harvest yields. The study indicated that there was a significant effect of substrates on the mean yield of the first harvest (p-value ≤ 2e-16). Least Significant Difference (LSD), analysis showed that T5 substrates produced the highest mean yield of the first harvest (i.e., 222.43 gm).

      The yield of the second harvest ranged from 5.63–203.97 gm (Table 2). The lowest yield of the second harvest was observed on T2 and T4 substrates i.e., 5.63 and 8.55 gm, respectively. Among the substrates, T13 a showed significantly higher mean yield in the second harvest (203.97 gm). Similar to the previous observations, there was a significant effect of substrates on the mean yield of the second harvest (p-value = 1.01e-0.5).

      The yield of the third harvest ranged from 3.33–106.80 gm (Table 3). The lowest yield in the third harvest was observed on the T4 substrate i.e., 3.33 gm, while the highest yield in the third harvest was recorded in T13 and T14 substrates. The results revealed a significant effect of substrates on the mean yield of the third harvest (p-value = 6.88e-11). However, LSD analysis showed that the T14 substrate gave the highest mean yield in the third harvest (106.80 g).

      T1 and T14 substrates yielded 14.70 and 66.67 gm in the fourth harvest (Table 3). The yield of the total harvest in different substrates and their combinations ranged from 13.54–399.70 gm. T11 substrate had the lowest harvest yield of 13.54 gm. T1 and T5 substrates had higher average harvest yields, but T1 had the highest overall yield (399.70 gm).

      The effect of different substrates and their combination on the yield of Pleurotus ostreatus was determined in terms of biological efficiency. From Table 4, it was observed that biological efficiency ranged from 1.35%–39.40%. The lowest biological efficiency was observed in T11 and the highest was that on T1, respectively. The higher the total yield, the higher the biological efficiency. It was observed that there was a significant effect of substrates on the mean biological efficiency of substrates and their combinations (p-value ≤ 2e-16). Using Least Significant Difference (LSD) analysis, the additional study revealed that T1 was connected to the highest mean biological efficiency (39.40%). In the present study, the highest biological efficiency of P. ostreatus was observed on Straw (39.40%) followed by Citronella (39.39%) and the T13 substrate (rice straw 50%, wood flakes 40%, and sawdust 10%) (39.25%). The lowest B.E. of 1.35% was observed on wood flakes (80%) plus Sugarcane bagasse (20%) substrate combination.

      Table 4.  Yield of Pleurotus ostreatus in terms of biological efficiency.

      SubstratesBiological
      efficiency (%)
      T1 = Rice straw39.40a
      T2 = Sugarcane bagasse2.34j
      T3 = Wood chips8.60g
      T4 = Wood flakes2.25j
      T5 = Citronella bagasse39.39b
      T6 = Sawdust17.68d
      T7 = Leaf litter3.64i
      T8 = Rice straw + sugarcane bagasse (50% each)12.82f
      T9 = Rice straw + wood chips (60%+40%)1.76k
      T10 = Wood flakes + sawdust (50% each)13.76a
      T11 = Wood flakes + sugarcane bagasse (80% + 20%)1.35l
      T12 = Wood flakes + sugarcane + wood chips (35% + 35% + 30%)7.83h
      T13 = Rice straw + wood flakes + sawdust (50% + 40% + 10%)39.25b
      T14 = Citronella bagasse + sugarcane bagasse + wood flakes + wood chips (25% each)27.19c
      Significance***
      CV (%)1.16
      Treatments followed with the same letter are not significantly different by LSD (Least Significance Difference) test at a 5% level of significance.
    • The choice of substrate significantly influenced the yield of Pleurotus ostreatus. In our study, most of the substrates used for the cultivation were lignocellulosic wastes, and similar work was also carried out by Zadrazil[12] where several unused agro-wastes in the form of straws, leaves, stems, roots, etc. were selected for the cultivation of mushroom. Our finding showed that although T5 and T14 substrates required the fewest days for the first mycelial growth, the T8 substrate required the least days for the first mycelial running over the substrate. The substrate combination (T10), which was a combination of wood flakes and sawdust in equal amounts, dried out after the initial flushing since it had a lower water retention capacity and moisture content[13]. Similarly, supplementation of mushroom beds with gram powder provided a better yield of mushrooms as earlier reported by Bano et al.[8]. It was observed that the total yield of Oyster mushrooms on lemon grass (Cymbopogon citratus) after three flushes was 264.80 gm on 1 kg of substrate[14]. The yield of fruiting bodies on T5 substrate, or Citronella bagasse (Cymbopogon nardus) was 393.90 gm on 1 kg of the substrate after three flushes, which is significantly higher than the yield on lemon grass reported by Mumtaz et al.[14]. The biological efficiency of mushrooms varied significantly in different substrates and their combinations. In many instances, the production of mushrooms was found to be low as the substrates accounted for various changes like temperature, the activity of microbes, and aeration that affected the mushroom production.

    • We thank Mr. Ratnadeep Sharma, Department of Statistics, Gauhati University (India) for his immense effort and help in carrying out the statistical data analysis of our present study.

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

      • Copyright: © 2023 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
    Figure (1)  Table (4) References (14)
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    Biswas PR, Boro H, Doley SN, Dutta AK, Tayung K. 2023. Evaluation of different lignocellulosic-wastes and their combinations on growth and yield of Oyster mushroom (Pleurotus ostreatus). Studies in Fungi 8:7 doi: 10.48130/SIF-2023-0007
    Biswas PR, Boro H, Doley SN, Dutta AK, Tayung K. 2023. Evaluation of different lignocellulosic-wastes and their combinations on growth and yield of Oyster mushroom (Pleurotus ostreatus). Studies in Fungi 8:7 doi: 10.48130/SIF-2023-0007

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