2023 Volume 3
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Effects of the configurations with different organic and inorganic fertilizers on grain yield and its related physiological traits of the main and its ratoon rice crops

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  • The determination for properly combined application rate of organic and inorganic fertilizers is the key to coordinating soil nutrient supplies and rice growth demands, thereby obtaining improved economic and ecological returns in rice ratooning. A complete randomized block design (RCBD) trial with different configurations of organic and inorganic nitrogen fertilizers was conducted to determine the optimal substitution rate of organic nitrogen for inorganic nitrogen and its effect on the related physiological attributes and yield performance of the main and ratoon rice crops in 2018−2019. The results showed that 30% organic and 70% inorganic nitrogen in the mixture fertilizer (GM2) could produce the best effect on root activity, nitrogen nutrient uptake and its utilization, as well as the dry matter accumulation and its partitioning. This in turn resulted in improved productive panicles and harvest index, and hence increased grain yield by 10.36% and 15.48% in GM2 regime relative to that in CF treatment. Moreover, the uptake and utilization efficiency, agronomic utilization efficiency and partial productivity of nitrogen fertilizer were 44.37%, 23.82 kg/kg and 48.97 kg/kg, respectively under the optimal GM2 treatment, which were much higher than those in CF treatment by 12.66%, 5.05% and 13.4%, respectively. The results suggested that properly combined application of organic and chemical nitrogen fertilizer could improve soil nutrient supply to guarantee an efficient plant growth and rational dry matter allocation, thereby increasing the harvest index and grain yield in rice ratooning.
  • Columnar cacti are plants of the Cactaceae family distributed across arid and semi-arid regions of America, with ecological, economic, and cultural value[1]. One trait that makes it possible for the columnar cactus to survive in the desert ecosystem is its thick epidermis covered by a hydrophobic cuticle, which limits water loss in dry conditions[1]. The cuticle is the external layer that covers the non-woody aerial organs of land plants. The careful control of cuticle biosynthesis could produce drought stress tolerance in relevant crop plants[2]. In fleshy fruits, the cuticle maintains adequate water content during fruit development on the plant and reduces water loss in fruit during postharvest[3]. Efforts to elucidate the molecular pathway of cuticle biosynthesis have been carried out for fleshy fruits such as tomato (Solanum lycopersicum)[4], apple (Malus domestica)[5], sweet cherry (Prunus avium)[6], mango (Mangifera indica)[7], and pear (Pyrus 'Yuluxiang')[8].

    The plant cuticle is formed by the two main layers cutin and cuticular waxes[3]. Cutin is composed mainly of oxygenated long-chain (LC) fatty acids (FA), which are synthesized by cytochrome p450 (CYP) enzymes. CYP family 86 subfamily A (CYP86A) enzymes carry out the terminal (ω) oxidation of LC-FA[9]. Then, CYP77A carries out the mid-chain oxidation to synthesize the main cutin monomers. In Arabidopsis, AtCYP77A4 and AtCYP77A6 carry out the synthesis of mid-chain epoxy and mid-chain dihydroxy LC-FA, respectively[10,11]. AtCYP77A6 is required for the cutin biosynthesis and the correct formation of floral surfaces[10]. The expression of CYP77A19 (KF410855) and CYP77A20 (KF410856) from potato (Solanum tuberosum) restored the petal cuticular impermeability in Arabidopsis null mutant cyp77a6-1, tentatively by the synthesis of cutin monomers[12]. In eggplant (Solanum torvum), the over-expression of StoCYP77A2 leads to resistance to Verticillium dahlia infection in tobacco plants[13]. Although the function of CYP77A2 in cutin biosynthesis has not yet been tested, gene expression analysis suggests that CaCYP77A2 (A0A1U8GYB0) could play a role in cutin biosynthesis during pepper fruit development[14].

    It has been hypothesized that the export of cuticle precursors is carried out by ATP binding cassette subfamily G (ABCG) transporters. ABCG11/WBC11, ABCG12, and ABCG13 are required for the load of cuticle lipids in Arabidopsis[1517], but ABCG13 function appears to be specific to the flower epidermis[18]. The overexpression of TsABCG11 (JQ389853) from Thellungiella salsugineum increases cuticle amounts and promotes tolerance to different abiotic stresses in Arabidopsis[19].

    Once exported, the cutin monomers are polymerized on the surface of epidermal cells. CD1 code for a Gly-Asp-Ser-Leu motif lipase/esterase (GDSL) from tomato required for the cutin formation through 2-mono(10,16-dihydroxyhexadecanoyl)glycerol esterification[20]. GDSL1 from tomato carries out the ester bond cross-links of cutin monomers located at the cuticle layers and is required for cuticle deposition in tomato fruits[21]. It has been shown that the transcription factor MIXTA-like reduces water loss in tomato fruits through the positive regulation of the expression of CYP77A2, ABCG11, and GDSL1[22]. Despite the relevant role of cuticles in maintaining cactus homeostasis in desert environments[1], the molecular mechanism of cuticle biosynthesis has yet to be described for cactus fruits.

    Stenocereus thurberi is a columnar cactus endemic from the Sonoran desert (Mexico), which produces an ovoid-globose fleshy fruit named sweet pitaya[23]. In its mature state, the pulp of sweet pitaya contains around 86% water with a high content of antioxidants and natural pigments such as betalains and phenolic compounds, which have nutraceutical and industrial relevance[23]. Due to the arid environment in which pitaya fruit grows, studying its molecular mechanism of cuticle biosynthesis can generate new insights into understanding species' adaptation mechanisms to arid environments. Nevertheless, sequences of transcripts from S. thurberi in public databases are scarce.

    RNA-sequencing technology (RNA-seq) allows the massive generation of almost all the transcripts from non-model plants, even if no complete assembled genome is available[24]. Recent advances in bioinformatic tools has improved our capacity to identify long non-coding RNA (lncRNA), which have been showed to play regulatory roles in relevant biological processes, such as the regulation of drought stress tolerance in plants[25], fruit development, and ripening[2629].

    In this study, RNA-seq data were obtained for the de novo assembly and characterization of the S. thurberi fruit peel transcriptome. As a first approach, three transcripts, StCYP77A, StABCG11, and StGDSL1, tentatively involved in cuticle biosynthesis, were identified and quantified during sweet pitaya fruit development. Due to no gene expression analysis having been carried out yet for S. thurberi, stably expressed constitutive genes were identified for the first time.

    Sweet pitaya fruits (S. thurberi) without physical damage were hand harvested from plants in a native conditions field located at Carbó, Sonora, México. They were collocated in a cooler containing dry ice and transported immediately to the laboratory. The superficial part of the peels (~1 mm deep) was removed carefully from the fruits using a scalpel. Peel samples from three fruits were pooled according to their tentative stage of development defined by their visual characteristics, frozen in liquid nitrogen, and pulverized to create a single biological replicate. Four samples belonging to four different plants were analyzed. All fruits harvested were close to the ripening stage. Samples named M1 and M2 were turning from green to ripe [~35−40 Days After Flowering (DAF)], whereas samples M3 and M4 were turning from ripe to overripe (~40−45 DAF).

    Total RNA was isolated from the peels through the Hot Borate method[30]. The concentration and purity of RNA were determined in a spectrophotometer Nanodrop 2000 (Thermo Fisher) by measuring the 260/280 and 260/230 absorbance ratios. RNA integrity was evaluated through electrophoresis in agarose gel 1% and a Bioanalyzer 2100 (Agilent). Pure RNA was sequenced in the paired-end mode in an Illumina NextSeq 500 platform at the University of Arizona Genetics Core Facility. Four RNA-seq libraries, each of them from each sample, were obtained, which include a total of 288,199,704 short reads with a length of 150 base pairs (bp). The resulting sequence data can be accessed at the Sequence Read Archive (SRA) repository of the NCBI through the BioProject ID PRJNA1030439. Libraries are named corresponding to the names of samples M1, M2, M3, and M4.

    FastQC software (www.bioinformatics.babraham.ac.uk/projects/fastqc) was used for short reads quality analysis. Short reads with poor quality were trimmed or eliminated by Trimmomatic (www.usadellab.org/cms/?page=trimmomatic) with a trailing and leading of 25, a sliding window of 4:25, and a minimum read length of 80 bp. A total of 243,194,888 reads with at least a 25 quality score on the Phred scale were used to carry out the de novo assembly by Trinity (https://github.com/trinityrnaseq/trinityrnaseq/wiki) with the following parameters: minimal k-mer coverage of 1, normalization of 50, and minimal transcript length of 200 bp.

    Removal of contaminating sequences and ribosomal RNA (rRNA) was carried out through SeqClean. To remove redundancy, transcripts with equal or more than 90% of identity were merged through CD-hit (www.bioinformatics.org/cd-hit/). Alignment and quantification in terms of transcripts per million (TPM) were carried out through Bowtie (https://bowtie-bio.sourceforge.net/index.shtml) and RSEM (https://github.com/deweylab/RSEM), respectively. Transcripts showing a low expression (TPM < 0.01) were discarded. Assembly quality was evaluated by calculating the parameters N50 value, mean transcript length, TransRate score, and completeness. The statistics of the transcriptome were determined by TrinityStats and TransRate (https://hibberdlab.com/transrate/). The transcriptome completeness was determined through a BLASTn alignment (E value < 1 × 10−3) by BUSCO (https://busco.ezlab.org/) against the database of conserved orthologous genes from Embryophyte.

    To predict the proteins tentatively coded in the S. thurberi transcriptome, the best homology match of the assembled transcripts was found by alignment to the Swiss-Prot, RefSeq, nr-NCBI, PlantTFDB, iTAK, TAIR, and ITAG databases using the BLAST algorithm with an E value threshold of 1 × 10−10 for the nr-NCBI database and of 1 × 10−5 for the others[3134]. An additional alignment was carried out to the protein databases of commercial fruits Persea americana, Prunus persica, Fragaria vesca, Citrus cinensis, and Vitis vinifera to proteins of the cactus Opuntia streptacantha, and the transcriptomes of the cactus Hylocereus polyrhizus, Pachycereus pringlei, and Selenicereus undatus. The list of all databases and the database websites of commercial fruits and cactus are provided in Supplementary Tables S1 & S2. The open reading frame (ORF) of the transcripts and the protein sequences tentative coded from the sweet pitaya transcriptome was predicted by TransDecoder (https://github.com/TransDecoder/TransDecoder/wiki), considering a minimal ORF length of 75 amino acids (aa). The search for protein domains was carried out by the InterPro database (www.ebi.ac.uk/interpro). Functional categorization was carried out by Blast2GO based on GO terms and KEGG metabolic pathways[35].

    LncRNA were identified based on the methods reported in previous studies[25,29,36]. Transcripts without homology to any protein from Swiss-Prot, RefSeq, nr-NCBI, PlantTFDB, iTAK, TAIR, ITAG, P. americana, P. persica, F. vesca, C. cinensis, V. vinifera, and O. streptacantha databases, without a predicted ORF longer than 75 aa, and without protein domains in the InterPro database were selected to identify tentative lncRNA.

    Transcripts coding for signal peptide or transmembrane helices were identified by SignalP (https://services.healthtech.dtu.dk/services/SignalP-6.0/) and TMHMM (https://services.healthtech.dtu.dk/services/TMHMM-2.0/), respectively, and discarded. Further, transcripts corresponding to other non-coding RNAs (ribosomal RNA and transfer RNA) were identified through Infernal by using the Rfam database[37] and discarded. The remaining transcripts were analyzed by CPC[38], and CPC2[39] to calculate their coding potential. Transcripts with a coding potential score lower than −1 for CPC and a coding probability lower than 0.1 for CPC2 were considered lncRNA. To characterize the identified lncRNA, the length and abundance of coding and lncRNA were calculated. Bowtie and RSEM were used to align and quantify raw counts, respectively. The edgeR package[40] was used to normalize raw count data in terms of counts per million (CPM) for both coding and lncRNA.

    To obtain the transcript's expression, the aligning of short reads and quantifying of transcripts were carried out through Bowtie and RSEM software, respectively. A differential expression analysis was carried out between the four libraries by edgeR package in R Studio. Only the transcripts with a count equal to or higher than 0.5 in at least one sample were retained for the analysis. Transcripts with log2 Fold Change (log2FC) between +1 and −1 and with a False Discovery Rate (FDR) lower than 0.05 were taken as not differentially expressed (NDE).

    For the identification of the tentative reference genes two strategies were carried out as described below: i) The NDE transcripts were aligned by BLASTn (E value < 1 × 10−5) to 43 constitutive genes previously reported in fruits from the cactus H. polyrhizus, S. monacanthus, and S. undatus[4143] to identify possible homologous constitutive genes in S. thurberi. Then, the homologous transcripts with the minimal coefficient of variation (CV) were selected; ii) For all the NDE transcripts, the percentile 95 value of the mean CPM and the percentile 5 value of the CV were used as filters to recover the most stably expressed transcripts, based on previous studies[44]. Finally, transcripts to be tested by quantitative reverse transcription polymerase chain reaction (qRT-PCR) were selected based on their homology and tentative biological function.

    The fruit harvesting was carried out as described above. Sweet pitaya fruit takes about 43 d to ripen, therefore, open flowers were tagged, and fruits with 10, 20, 30, 35, and 40 DAF were collected to cover the pitaya fruit development process (Supplementary Fig. S1). The superficial part of the peels (~1 mm deep) was removed carefully from the fruits using a scalpel. Peel samples from three fruits were pooled according to their stage of development defined by their DAF, frozen in liquid nitrogen, and pulverized to create a single biological replicate. One biological replicate consisted of peels from three fruits belonging to the same plant. Two to three biological replicates were evaluated for each developmental stage. Two technical replicates were analyzed for each biological replicate. RNA extraction, quantification, RNA purity, and RNA integrity analysis were carried out as described above.

    cDNA was synthesized from 100 ng of RNA by QuantiTect Reverse Transcription Kit (QIAGEN). Primers were designed using the PrimerQuest™, UNAFold, and OligoAnalyzer™ tools from Integrated DNA Technologies (www.idtdna.com/pages) and following the method proposed by Thornton & Basu[45]. Transcripts quantification was carried out in a QIAquant 96 5 plex according to the PowerUp™ SYBR™ Green Master Mix protocol (Applied Biosystems), with a first denaturation step for 2 min at 95 °C, followed by 40 cycles of denaturation step at 95 °C for 15 s, annealing and extension steps for 30 s at 60 °C.

    The Cycle threshold (Ct) values obtained from the qRT-PCR were analyzed through the algorithms BestKeeper, geNorm, NormFinder, and the delta Ct method[46]. RefFinder (www.ciidirsinaloa.com.mx/RefFinder-master/) was used to integrate the stability results and to find the most stable expressed transcripts in sweet pitaya fruit peel during development. The pairwise variation value (Vn/Vn + 1) was calculated through the geNorm algorithm in R Studio software[47].

    An alignment of 17 reported cuticle biosynthesis genes from model plants were carried out by BLASTx against the predicted proteins from sweet pitaya. Two additional alignments of 17 charaterized cuticle biosynthesis proteins from model plants against the transcripts and predicted proteins of sweet pitaya were carried out by tBLASTn and BLASTp, respectively. An E value threshold of 1 × 10−5 was used, and the unique best hits were recovered for all three alignments. The sequences of the 17 characterized cuticle biosynthesis genes and proteins from model plants are showed in Supplementary Table S3. The specific parameters and the unique best hits for all the alignments carried out are shown in Supplementary Tables S4S8.

    Cuticle biosynthesis-related transcripts tentatively coding for a cytochrome p450 family 77 subfamily A (CYP77A), a Gly-Asp-Ser-Leu motif lipase/esterase 1 (GDSL1), and an ATP binding cassette transporter subfamily G member 11 (ABCG11) were identified by best bi-directional hit according to the functional annotation described above. Protein-conserved domains, signal peptide, and transmembrane helix were predicted through InterProScan, SignalP 6.0, and TMHMM, respectively. Alignment of the protein sequences to tentative orthologous of other plant species was carried out by the MUSCLE algorithm[48]. A neighbor-joining (NJ) phylogenetic tree with a bootstrap of 1,000 replications was constructed by MEGA11[49].

    Fruit sampling, primer design, RNA extraction, cDNA synthesis, and transcript quantification were performed as described above. Relative expression was calculated according to the 2−ΔΔCᴛ method[50]. The sample corresponding to 10 DAF was used as the calibrator. The transcripts StEF1a, StTUA, StUBQ3, and StEF1a + StTUA were used as normalizer genes.

    Normality was assessed according to the Shapiro-Wilk test. Significant differences in the expression of the cuticle biosynthesis-related transcripts between fruit developmental stages were determined by one-way ANOVA based on a completely randomized sampling design and a Tukey honestly significant difference (HSD) test, considering a p-value < 0.05 as significant. Statistical analysis was carried out through the stats package in R Studio.

    RNA was extracted from the peels of ripe sweet pitaya fruits (S. thurberi) from plants located in the Sonoran Desert, Mexico. Four cDNA libraries were sequenced in an Illumina NextSeq 500 platform at the University of Arizona Genetics Core Facility. A total of 288,199,704 reads with 150 base pairs (bp) in length were sequenced in paired-end mode. After trimming, 243,194,888 (84.38%) cleaned short reads with at least 29 mean quality scores per read in the Phred scale and between 80 to 150 bp in length were obtained to carry out the assembly. After removing contaminating sequences, redundancy, and low-expressed transcripts, the assembly included 174,449 transcripts with an N50 value of 2,110 bp. Table 1 shows the different quality variables of the S. thurberi fruit peel transcriptome. BUSCO score showed that 85.4% are completed transcripts, although out of these, 37.2% were found to be duplicated. The resulting sequence data can be accessed at the SRA repository of the NCBI through the BioProject ID PRJNA1030439.

    Table 1.  Quality metrics of the Stenocereus thurberi fruit peel transcriptome.
    Metric Data
    Total transcripts 174,449
    N50 2,110
    Smallest transcript length (bp) 200
    Largest transcript length (bp) 19,114
    Mean transcript length (bp) 1,198.69
    GC (%) 41.33
    Total assembled bases 209,110,524
    TransRate score 0.05
    BUSCO score (%) C: 85.38 (S:48.22, D:37.16),
    F: 10.69, M: 3.93.
    Values were calculated through the TrinityStats function of Trinity and TransRate software. Completeness analysis was carried out through BUSCO by aligning the transcriptome to the Embryophyte database through BLAST with an E value threshold of 1 × 10−3. Complete (C), single (S), duplicated (D), fragmented (F), missing (M).
     | Show Table
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    A summary of the homology search in the main public protein database for the S. thurberi transcriptome is shown in Supplementary Table S1. From these databases, the higher homologous transcripts were found in RefSeq with 93,993 (53.87 %). Based on the E value distribution, for 41,685 (44%) and 68,853 (49%) of the hits, it was found a strong homology (E value lower than 1 × 10−50) to proteins in the Swiss-Prot and RefSeq databases, respectively (Supplementary Fig. S2a & b). On the other hand, 56,539 (52.34%) and 99,599 (71.11%) of the matches showed a percentage of identity higher than 60% in the Swiss-Prot and RefSeq databases, respectively (Supplementary Fig. S2c & d).

    Figure 1 shows the homology between transcripts from S. thurberi and proteins of commercial fruits, as well as proteins and transcripts of cacti. Transcripts from S. thurberi homologous to proteins from fruits of commercial interest avocado (P. americana), peach (P. persica), strawberry (F. vesca), orange (C. sinensis), and grapefruit (V. vinifera) ranged from 77,285 (44.30%) to 85,421 (48.96%), with 70,802 transcripts homologous to all the five fruit protein databases (Fig. 1a).

    Transcripts homologous to transcripts or proteins from the cactus dragon fruit (H. polyrhizus), prickly pear cactus (O. streptacantha), Mexican giant cardon (P. pringlei), and pitahaya (S. undatus) ranged from 76,238 (43.70%) to 114,933 (65.88%), with 64,009 transcripts homologous to all the four cactus databases (Fig. 1b). Further, out of the total of transcripts, 44,040 transcripts (25.25%) showed homology only to sequences from cactus, but not for model plants Arabidopsis, tomato, or the commercial fruits included in this study (Fig. 1c).

    Figure 1.  Venn diagram of the homology search results against model plants databases, commercial fruits, and cactus. The number in the diagram corresponds to the number of transcripts from S. thurberi homologous to sequences from that plant species. (a) Homologous to sequences from Fragaria vesca (Fa), Persea americana (Pa), Prunus persica (Pp), Vitis vinifera (Vv), and Citrus sinensis (Cs). (b) Homologous to sequences from Opuntia streptacantha (Of), Selenicereus undatus (Su), Hylocereus polyrhizus (Hp), and Pachycereus pringlei (Pap). (c) Homologous to sequences from Solanum lycopersicum (Sl), Arabidopsis thaliana (At), from the commercial fruits (Fa, Pa, Pp, Vv, and Cs), or the cactus included in this study (Of, Su, Hp, and Pap). Homologous searching was carried out by BLAST alignment (E value < 1 × 10−5). The Venn diagrams were drawn by ggVennDiagram in R Studio.

    A total of 45,970 (26.35%), 58,704 (33.65%), and 48,186 (27.65%) transcripts showed homology to transcription factors, transcriptional regulators, and protein kinases in the PlantTFDB, iTAK-TR, and iTAK-PK databases, respectively (Supplementary Tables S1, S9S11). For the PlantTFDB, the homologous transcripts belong to 57 transcriptional factors (TF) families (Fig. 2 & Supplementary Table S9), from which, the most frequent were the basic-helix-loop-helix (bHLH), myeloblastosis-related (MYB-related), NAM, ATAF, and CUC (NAC), ethylene responsive factor (ERF), and the WRKY domain families (WRKY) (Fig. 2).

    Figure 2.  Transcription factor (TF) families distribution of S. thurberi fruit peel transcriptome. The X-axis indicates the number of transcripts with hits to each TF family. Alignment to the PlantTFDB database by BLASTx was carried out with an E value threshold of 1 × 10−5. The bar graph was drawn by ggplot2 in R Studio.

    Based on the homology found and the functional domain searches, gene ontology terms (GO) were assigned to 68,559 transcripts (Supplementary Table S12). Figure 3 shows the top 20 GO terms assigned to the S. thurberi transcriptome, corresponding to the Biological Processes (BP) and Molecular Function (MF) categories. For BP, organic substance metabolic processes, primary metabolic processes, and cellular metabolic processes showed a higher number of transcripts (Supplementary Table S13). Further, for MF, organic cyclic compound binding, heterocyclic compound binding, and ion binding were the processes with the higher number of transcripts. S. thurberi transcripts were classified into 142 metabolic pathways from the KEGG database (Supplementary Table S14). The pathways with the higher number of transcripts recorded were pyruvate metabolism, glycerophospholipid metabolism, glycolysis/gluconeogenesis, and citrate cycle. Further, among the top 20 KEEG pathways, the cutin, suberin, and wax biosynthesis include more than 30 transcripts (Fig. 4).

    Figure 3.  Top 20 Gene Ontology (GO) terms assigned to the S. thurberi fruit peel transcriptome. Bars indicate the number of transcripts assigned to each GO term. Assignment of GO terms was carried out by Blast2GO with default parameters. BP and MF mean Biological Processes and Molecular Functions GO categories, respectively. The graph was drawn by ggplot2 in R Studio.
    Figure 4.  Top 20 KEGG metabolic pathways distribution in the S. thurberi fruit peel transcriptome. Bars indicate the number of transcripts assigned to each KEGG pathway. Assignment of KEGG pathways was carried out in the Blast2GO suite. The bar graph was drawn by ggplot2 in R Studio.

    Out of the total of transcripts, 43,391 (24.87%) were classified as lncRNA (Supplementary Tables S15 & S16). Figure 5 shows a comparison of the length (Fig. 5a) and expression (Fig. 5b) of lncRNA and coding RNA. Both length and expression values were higher in coding RNA than in lncRNA. In general, coding RNA ranged from 201 to 18,629 bp with a mean length of 1,507.18, whereas lncRNA ranged from 200 to 5,198 bp with a mean length of 481.51 (Fig. 5a). The higher expression values recorded from coding RNA and lncRNA were 12.83 and 9.45 log2(CPM), respectively (Fig. 5b).

    Figure 5.  Comparison of coding RNA and long non-coding RNA (lncRNA) from S. thurberi transcriptome. (a) Box plot of transcript length distribution. The Y-axis indicates the length of each transcript in base pairs. (b) Box plot of expression levels. The Y-axis indicates the log2 of the count per million of reads (log2(CPM)) recorded for each transcript. Expression levels were calculated by the edgeR package in R studio. (a), (b) The lines inside the boxes indicate the median. The higher and lower box limits represent the 75th and 25th percentiles, respectively. The box plots were drawn by ggplot2 in R Studio.

    To identify the transcripts without significant changes in expression between the four RNA-seq libraries, a differential expression analysis was carried out. Of the total of transcripts, 4,980 were not differentially expressed (NDE) at least in one paired comparison between the libraries (Supplementary Tables S17S20). Mean counts per million of reads (CPM) and coefficient of variation (CV)[44] were calculated for these NDE transcripts. Transcripts with a CV value lower than 0.113, corresponding with the percentile 5 of the CV, and a mean CPM higher than 1,138.06, corresponding with the percentile 95 of the mean CPM were used as filters to identify the most stably expressed transcripts (Supplementary Table S21). Based on its homology and its tentative biological function, five transcripts were selected to be tested as tentative reference genes. Besides, three NDE transcripts homologous to previously identified stable expressed reference genes in other species of cactus fruit[4143] were selected (Supplementary Table S22). Homology metrics for the eight tentative reference genes selected are shown in Supplementary Table S23. The primer sequences used to amplify the transcripts by qRT-PCR and their nucleotide sequence are shown in Supplementary Tables S24 & S25, respectively.

    The amplification specificity of the eight candidate reference genes determined by melting curves analysis is shown in Supplementary Fig. S3. For the eight tentative reference transcripts selected, the cycle threshold (Ct) values were recorded during sweet pitaya fruit development by qRT-PCR (Supplementary Table S26). The Ct values obtained ranged from 16.85 to 30.26 (Fig. 6a). Plastidic ATP/ADP-transporter (StTLC1) showed the highest Ct values with a mean of 27.34 (Supplementary Table S26). Polyubiquitin 3 (StUBQ3) showed the lowest Ct values in all five sweet pitaya fruit developmental stages (Fig. 6a).

    Figure 6.  Expression stability analysis of tentative reference genes. (a) Box plot of cycle threshold (Ct) distribution of candidate reference genes during sweet pitaya fruit development (10, 20, 30, 35, and 40 d after flowering). The black line inside the box indicates the median. The higher and lower box limits represent the 75th and 25th percentiles, respectively. (b) Bar chart of the geometric mean (geomean) of ranking values calculated by RefFinder for each tentative reference gene (X-axis). The lowest values indicate the best reference genes. (c) Bar chart of the pairwise variation analysis and determination of the optimal number of reference genes by the geNorm algorithm. A pairwise variation value lower than 0.15 indicates that the use of Vn/Vn + 1 reference genes is reliable for the accurate normalization of qRT-PCR data. The Ct data used in the analysis were calculated by qRT-PCR in a QIAquant 96 5 plex (QIAGEN) according to the manufacturer's protocol. The box plot and the bar graphs were drawn by ggplot2 and Excel programs, respectively. Abbreviations: Actin 7 (StACT7), alpha-tubulin (StTUA), elongation factor 1-alpha (StEF1a), COP1-interactive protein 1 (StCIP1), plasma membrane ATPase 4 (StPMA4), BEL1-like homeodomain protein 1 (StBLH1), polyubiquitin 3 (StUBQ3), and plastidic ATP/ADP-transporter (StTLC1).

    The best stability values calculated by NormFinder were 0.45, 0.51, 0.97, and 0.99, corresponding to the transcripts elongation factor 1-alpha (StEF1a), alpha-tubulin (StTUA), plastidic ATP/ADP-transporter (StTLC1), and actin 7 (StACT7), respectively (Supplementary Table S27). For BestKeeper, the most stable expressed transcripts were StUBQ3, StTUA, and StEF1a, with values of 0.72, 0.75, and 0.87, respectively. In the case of the delta Ct method[51], the transcripts StEF1a, StTUA, and StTLC1 showed the best stability.

    According to geNorm analysis, the most stable expressed transcripts were StTUA, StEF1a, StUBQ3, and StACT7, with values of 0.74, 0.74, 0.82, and 0.96, respectively. All the pairwise variation values (Vn/Vn + 1) were lower than 0.15, ranging from 0.019 for V2/V3 to 0.01 for V6/V7 (Fig. 6c). The V value of 0.019 obtained for V2/V3 indicates that the use of the best two reference genes StTUA and StEF1a is reliable enough for the accurate normalization of qRT-PCR data, therefore no third reference gene is required[47]. Except for BestKeeper analysis, StEF1a and StTUA were the most stable transcripts for all of the methods carried out in this study (Supplementary Table S27). The comprehensive ranking analysis indicates that StEF1a, followed by StTUA and StUBQ3, are the most stable expressed genes and are stable enough to be used as reference genes in qRT-PCR analysis during sweet pitaya fruit development (Fig. 6b).

    Three cuticle biosynthesis-related transcripts TRINITY_DN17030_c0_g1_i2, TRINITY_DN15394_c0_g1_i1, and TRINITY_DN23528_c1_g1_i1 tentatively coding for the enzymes cytochrome p450 family 77 subfamily A (CYP77A), Gly-Asp-Ser-Leu motif lipase/esterase 1 (GDSL1), and an ATP binding cassette transporter subfamily G member 11 (ABCG11/WBC11), respectively, were identified and quantified. The nucleotide sequence and predicted amino acid sequences of the three transcripts are shown in Supplementary File 1. The best homology match for StCYP77A (TRINITY_DN17030_c0_g1_i2) was for AtCYP77A4 (AT5G04660) from Arabidopsis and SmCYP77A2 (P37124) from eggplant (Solanum melongena) in the TAIR and Swiss-Prot databases, respectively (Supplementary Table S23).

    TransDecoder, InterPro, and TMHMM analysis showed that StCYP77A codes a polypeptide of 518 amino acids (aa) in length that comprises a cytochrome P450 E-class domain (IPR002401) and a transmembrane region (residues 10 to 32). The phylogenetic tree constructed showed that StCYP77A is grouped in a cluster with all the CYP77A2 proteins included in this analysis, being closer to CYP77A2 (XP_010694692) from B. vulgaris and Cgig2_012892 (KAJ8441854) from Carnegiea gigantean (Supplementary Fig. S4).

    StGDSL1 (TRINITY_DN15394_c0_g1_i1) alignment showed that it is homologous to a GDSL esterase/lipase from Arabidopsis (Q9LU14) and tomato (Solyc03g121180) (Supplementary Table S23). TransDecoder, InterPro, and SignalP analysis showed that StGDSL1 codes a polypeptide of 354 aa in length that comprises a GDSL lipase/esterase domain IPR001087 and a signal peptide with a cleavage site between position 25 and 26 (Supplementary Fig. S5).

    Supplementary Figure S6 shows the analysis carried out on the predicted amino acid sequence of StABCG11 (TRINITY_DN23528_c1_g1_i1). The phylogenetic tree constructed shows three clades corresponding to the ABCG13, ABCG12, and ABCG11 protein classes with bootstrap support ranging from 40% to 100% (Supplementary Fig. S6a). StABCG11 is grouped with all the ABCG11 transporters included in this study in a well-separated clade, being closely related to its tentative ortholog from C. gigantean Cgig2_004465 (KAJ8441854). InterPro and TMHMM results showed that the StABCG11 sequence contains an ABC-2 type transporter transmembrane domain (IPR013525; PF01061.27) with six transmembrane helices (Supplementary Fig. S6b).

    The predicted protein sequence of StABCG11 is 710 aa in length, holding the ATP binding domain (IPR003439; PF00005.30) and the P-loop containing nucleoside triphosphate hydrolase domain (IPR043926; PF19055.3) of the ABC transporters of the G family. Multiple sequence alignment shows that the Walker A and B motif sequence and the ABC signature[15] are conserved between the ABCG11 transporters from Arabidopsis, tomato, S. thurberi, and C. gigantean (Supplementary Fig. S6c).

    According to the results of the expression stability analysis (Fig. 6), four normalization strategies were tested to quantify the three cuticle biosynthesis-related transcripts during sweet pitaya fruit development. The four strategies consist of normalizing by StEF1a, StTUA, StUBQ3, or StEF1a+StTUA. Primer sequences used to quantify the transcripts StCYP77A (TRINITY_DN17030_c0_g1_i2), StGDSL1 (TRINITY_DN15394_c0_g1_i1), and StABCG11 (TRINITY_DN23528_c1_g1_i1) by qRT-PCR during sweet pitaya fruit development are shown in Supplementary Table S24.

    The three cuticle biosynthesis-related transcripts showed differences in expression during sweet pitaya fruit development (Supplementary Table S28). The same expression pattern was recorded for the three cuticle biosynthesis transcripts when normalization was carried out by StEF1a, StTUA, StUBQ3, or StEF1a + StTUA (Fig. 7). A higher expression of StCYP77A and StGDSL1 are shown at the 10 and 20 DAF, showing a decrease at 30, 35, and 40 DAF. StABCG11 showed a similar behavior, with a higher expression at 10 and 20 DAF and a reduction at 30 and 35 DAF. Nevertheless, unlike StCYP77A and StGDSL1, a significant increase at 40 DAF, reaching the same expression as compared with 10 DAF, is shown for StABCG11 (Fig. 7).

    Figure 7.  Expression analysis of cuticle biosynthesis-related transcripts StCYP77A, StGDSL1, and StABCG11 during sweet pitaya (Stenocereus thurberi) fruit development. Relative expression was calculated through the 2−ΔΔCᴛ method using elongation factor 1-alpha (StEF1a), alpha-tubulin (StTUA), polyubiquitin 3 (StUBQ3), or StEF1a + StTUA as normalizing genes at 10, 20, 30, 35, and 40 d after flowering (DAF). The Y-axis and error bars represent the mean of the relative expression ± standard error (n = 4−6) for each developmental stage in DAF. The Ct data for the analysis was recorded by qRT-PCR in a QIAquant 96 5 plex (QIAGEN) according to the manufacturer's protocol. The graph line was drawn by ggplot2 in R Studio. Abbreviations: cytochrome p450 family 77 subfamily A (StCYP77A), Gly-Asp-Ser-Leu motif lipase/esterase 1 (StGDSL1), and ATP binding cassette transporter subfamily G member 11 (StABCG11).

    Characteristics of a well-assembled transcriptome include an N50 value closer to 2,000 bp, a high percentage of conserved transcripts completely assembled (> 80%), and a high proportion of reads mapping back to the assembled transcripts[52]. In the present study, the first collection of 174,449 transcripts from S. thurberi fruit peel are reported. The generated transcriptome showed an N50 value of 2,110 bp, a TransRate score of 0.05, and a GC percentage of 41.33 (Table 1), similar to that reported for other de novo plant transcriptome assemblies[53]. According to BUSCO, 85.4% of the orthologous genes from the Embryophyta databases completely matched the S. thurberi transcriptome, and only 3.9% were missing (Table 1). These results show that the S. thurberi transcriptome generated is not fragmented, and it is helpful in predicting the sequence of almost all the transcripts expressed in sweet pitaya fruit peel[24].

    The percentage of transcripts homologous found, E values, and identity distribution (Supplementary Tables S1 & S2; Supplementary Fig. S2) were similar to that reported in the de novo transcriptome assembly for non-model plants and other cactus fruits[4143,54] and further suggests that the transcriptome assembled of S. thurberi peel is robust[52]. Of the total of transcripts, 70,802 were common to all the five commercial fruit protein databases included in this study, which is helpful for the search for conserved orthologous involved in fruit development and ripening (Fig. 2a). A total of 34,513 transcripts (20%) show homology only to sequences in the cactus's databases, but not in the others (Supplementary Tables S1 & S2; Fig. 1c). This could suggest that a significant conservation of sequences among plants of the Cactaceae family exists which most likely are to have a function in this species adaptation to desert ecosystems.

    To infer the biological functionality represented by the S. thurberi fruit peel transcriptome, gene ontology (GO) terms and KEGG pathways were assigned. Of the main metabolic pathways assigned, 'glycerolipid metabolism' and 'cutin, suberine, and wax biosynthesis' suggests an active cuticle biosynthesis in pitaya fruit peel (Fig. 4). In agreement with the above, the main GO terms assigned for the molecular function (MF) category were 'organic cyclic compound binding', 'transmembrane transporter activity', and 'lipid binding' (Fig. 3). For the biological processes (BP) category, the critical GO terms for the present research are 'cellular response to stimulus', 'response to stress', 'anatomical structure development', and 'transmembrane transport', which could suggest the active development of the fruit epidermis and cuticle biosynthesis for protection to stress.

    The most frequent transcription factors (TF) families found in S. thurberi transcriptome were NAC, WRKY, bHLH, ERF, and MYB-related (Fig. 2), which had been reported to play a function in the tolerance to abiotic stress in plants[55,56]. Although the role of NAC, WRKY, bHLH, ERF, and MYB TF in improving drought tolerance in relevant crop plants has been widely documented[57,58], their contribution to the adaptation of cactus to arid ecosystems has not yet been elucidated and further experimental pieces of evidence are needed.

    It has been reported that the heterologous expression of ERF TF from Medicago truncatula induces drought tolerance and cuticle wax biosynthesis in Arabidopsis leaf[59]. In tomato fruits, the gene SlMIXTA-like which encodes a MYB transcription factor avoids water loss through the positive regulation of genes related to the biosynthesis and transport of cuticle compounds[22]. Despite the relevant role of cuticles in maintaining cactus physiology in desert environments, experimental evidence showing the role of the different TF-inducing cuticle biosynthesis has yet to be reported for cactus fruits.

    Out of the transcripts, 43,391 were classified as lncRNA (Supplementary Tables S15 & S16). This is the first report of lncRNA identification for the species S. thurberi. In fruits, 3,679 lncRNA has been identified from tomato[26], 3,330 from peach (P. persica)[29], 3,857 from melon (Cucumis melo)[28], 2,505 from hot pepper (Capsicum annuum)[27], and 3,194 from pomegranate (Punica granatum)[36]. Despite the stringent criteria to classify the lncRNA of sweet pitaya fruit (S. thurberi), a higher number of lncRNAs are shown when compared with previous reports. This finding is most likely due to the higher level of redundancy found during the transcriptome analysis. To reduce this redundancy, further efforts to achieve the complete genome assembly of S. thurberi are needed.

    Previous studies showed that lncRNA is shorter and has lower expression levels than coding RNA[6062]. In agreement with those findings, both the length and expression values of lncRNA from S. thurberi were lower than coding RNA (Fig. 5). It has been suggested that lncRNA could be involved in the biosynthesis of cuticle components in cabbage[61] and pomegranate[36] and that they could be involved in the tolerance to water deficit through the regulation of cuticle biosynthesis in wild banana[60]. Nevertheless, the molecular mechanism by which lncRNA may regulate the cuticle biosynthesis in S. thurberi fruits has not yet been elucidated.

    A relatively constant level of expression characterizes housekeeping genes because they are involved in essential cellular functions. These genes are not induced under specific conditions such as biotic or abiotic stress. Because of this, they are very useful as internal reference genes for qRT-PCR data normalization[63]. Nevertheless, their expression could change depending on plant species, developmental stages, and experimental conditions[64]. Reliable reference genes for a specific experiment in a given species must be identified to carry out an accurate qRT-PCR data normalization[63]. An initial screening of the transcript expression pattern through RNA-seq improves the identification of stably expressed transcripts by qRT-PCR[44,64].

    Identification of stable expressed reference transcripts during fruit development has been carried out in blueberry (Vaccinium bracteatum)[65], kiwifruit (Actinidia chinensis)[66], peach (P. persica)[67], apple (Malus domestica)[68], and soursop (Annona muricata)[69]. These studies include the expression stability analysis through geNorm, NormFinder, and BestKeeper algorithms[68,69], some of which are supported in RNA-seq data[65,66]. Improvement of expression stability analysis by RNA-seq had led to the identification of non-previously reported reference genes with a more stable expression during fruit development than commonly known housekeeping genes in grapevine (V. vinifera)[44], pear (Pyrus pyrifolia and P. calleryana)[64], and pepper (C. annuum)[70].

    For fruits of the Cactaceae family, only a few studies identifying reliable reference genes have been reported[4143]. Mainly because gene expression analysis has not been carried out previously for sweet pitaya (S. thurberi), the RNA-seq data generated in this work along with geNorm, NormFinder, BestKeeper, and RefFinder algorithms were used to identify reliable reference genes. The comprehensive ranking analysis showed that out of the eight candidate genes tested, StEF1a followed by StTUA and StUBQ3 were the most stable (Fig. 6b). All the pairwise variation values (Vn/Vn + 1) were lower than 0.15 (Fig. 6c), which indicates that StEF1a, StTUA, and StUBQ3 alone or the use of StEF1a and StTUA together are reliable enough to normalize the gene expression data generated by qRT-PCR.

    The genes StEF1a, StTUA, and StUBQ3 are homologous to transcripts found in the cactus species known as dragonfruit (Hylocereus monacanthus and H. undatus)[41], which have been tested as tentative reference genes during fruit development. EF1a has been proposed as a reliable reference gene in the analysis of changes in gene expression of dragon fruit (H. monacanthus and H. undatus)[41], peach (P. persica)[67], apple (M. domestica)[68], and soursop (A. muricata)[69]. According to the expression stability analysis carried out in the present study (Fig. 6) four normalization strategies were designed. The same gene expression pattern was recorded for the three target transcripts evaluated when normalization was carried out by the genes StEF1a, StTUA, StUBQ3, or StEF1a + StTUA (Fig. 7). Further, these data indicates that these reference genes are reliable enough to be used in qRT-PCR experiments during fruit development of S. thurberi.

    The plant cuticle is formed by two main layers: the cutin, composed mainly of mid-chain oxygenated LC fatty acids, and the cuticular wax, composed mainly of very long-chain (VLC) fatty acids, and their derivates VLC alkanes, VLC primary alcohols, VLC ketones, VLC aldehydes, and VLC esters[3]. In Arabidopsis CYP77A4 and CYP77A6 catalyze the synthesis of midchain epoxy and hydroxy ω-OH long-chain fatty acids, respectively[10,11], which are the main components of fleshy fruit cuticle[3].

    The functional domain search carried out in the present study showed that StCYP77A comprises a cytochrome P450 E-class domain (IPR002401) and a membrane-spanning region from residues 10 to 32 (Supplementary Fig. S4). This membrane-spanning region has been previously characterized in CYP77A enzymes from A. thaliana and Brassica napus[11,71]. It suggests that the protein coded by StCYP77A could catalyze the oxidation of fatty acids embedded in the endoplasmic reticulum membrane of the epidermal cells of S. thurberi fruit. Phylogenetic analysis showed that StCYP77A was closer to proteins from its phylogenetic-related species B. vulgaris (BvCYP772; XP_010694692) and C. gigantea (Cgig2_012892) (Supplementary Fig. S4). StCYP77A, BvCYP77A2, and Cgig2_012892 were closer to SlCYP77A2 and SmCYP77A2 than to CYP77A4 and CYP77A6 proteins, suggesting that StCYP77A (TRINITY_DN17030_c0_g1_i2) could correspond to a CYP77A2 protein.

    Five CYP77A are present in the Arabidopsis genome, named CYP77A4, CYP77A5, CYP77A6, CYP77A7, and CYP77A9, but their role in cuticle biosynthesis has only been reported for CYP77A4 and CYP77A6[72]. It has been suggested that CYP77A2 from eggplant (S. torvum) could contribute to the defense against fungal phytopathogen infection by the synthesis of specific compounds[13]. In pepper fruit (C. annuum), the expression pattern of CYP77A2 (A0A1U8GYB0) and ABCG11 (LOC107862760) suggests a role of CYP77A2 and ABCG11 in cutin biosynthesis at the early stages of pepper fruit development[14].

    In the case of the protein encoded by StGDSL1 (354 aa), the length found in this work is similar to the length of its homologous from Arabidopsis (AT3G16370) and tomato (Solyc03g121180) (Supplementary Fig. S5). A GDSL1 protein named CD1 polymerizes midchain oxygenated ω-OH long-chain fatty acids to form the cutin polyester in the extracellular space of tomato fruit peel[20,21]. It has been suggested that the 25-amino acid N-signal peptide found in StGDSL1 (Supplementary Fig. S5), previously reported in GDSL1 from Arabidopsis, B. napus, and tomato, plays a role during the protein exportation to the extracellular space[21,73].

    A higher expression of StCYP77A, StGDSL1, and StABCG11 is shown at the 10 and 20 DAF of sweet pitaya fruit development (Fig. 7), suggesting the active cuticle biosynthesis at the early stages of sweet pitaya fruit development. In agreement with that, two genes coding for GDSL lipase/hydrolases from tomato named SGN-U583101 and SGN-U579520 are highly expressed in the early stages and during the expansion stages of tomato fruit development, but their expression decreases in later stages[74]. It has been shown that the expression of GDSL genes, like CD1 from tomato, is higher in growing fruit[20,21]. Like tomato, the increase in expression of StCYP77A and StGDSL1 shown in pitaya fruit development could be due to an increase in cuticle deposition during the expansion of the fruit epidermis[20].

    The phylogenetic analysis, the functional domains, and the six transmembrane helices found in the StABCG11 predicted protein (Supplementary Fig. S6), suggests that it is an ABCG plasma membrane transporter of sweet pitaya[15]. Indeed, an increased expression of StABCG11 at 40 DAF was recorded in the present study (Fig. 7). Further, this data strongly suggests that it could be playing a relevant role in the transport of cuticle components at the beginning and during sweet pitaya fruit ripening.

    In Arabidopsis, ABCG11 (WBC11) exports cuticular wax and cutin compounds from the plasma membrane[15,75]. It has been reported that a high expression of the ABC plasma membrane transporter from mango MiWBC11 correlates with a higher cuticle deposition during fruit development[7]. The expression pattern for StABCG11, StCYP77A, and StGDSL1 suggests a role of StABCG11 as a cutin compound transporter in the earlier stages of sweet pitaya fruit development (Fig. 7). Further, its increase at 40 DAF suggests that it could be transporting cuticle compounds other than oxygenated long-chain fatty acids, or long-chain fatty acids that are not synthesized by StCYP77A and StGDSL1 in the later stages of fruit development.

    Like sweet pitaya, during sweet cherry fruit (Prunus avium) development, the expression of PaWCB11, homologous to AtABCG11 (AT1G17840), increases at the earlier stages of fruit development decreases at the intermediate stages, and increases again at the later stages[76]. PaWCB11 expression correlated with cuticle membrane deposition at the earlier and intermediate stages of sweet cherry fruit development but not at the later[76]. The increased expression of StABCG11 found in the present study could be due to the increased transport of cuticular wax compounds, such as VLC fatty acids and their derivates, in the later stages of sweet pitaya development[15,75].

    Cuticular waxes make up the smallest amount of the fruit cuticle. Even so, they mainly contribute to the impermeability of the fruit's epidermis[3]. An increase in the transport of cuticular waxes at the beginning of the ripening stage carried out by ABCG transporters could be due to a greater need to avoid water loss and to maintain an adequate amount of water during the ripening of the sweet pitaya fruit. Nevertheless, further expression analysis of cuticular wax biosynthesis-related genes, complemented with chemical composition analysis of cuticles could contribute to elucidating the molecular mechanism of cuticle biosynthesis in cacti and their physiological contribution during fruit development.

    In this study, the transcriptome of the sweet pitaya (S. thurberi) fruit peel was assembled for the first time. The reference genes found here are a helpful tool for further gene expression analysis in sweet pitaya fruit. Transcripts tentatively involved in cuticle compound biosynthesis and transport are reported for the first time in sweet pitaya. The results suggest a relevant role of cuticle compound biosynthesis and transport at the early and later stages of fruit development. The information generated will help to improve the elucidation of the molecular mechanism of cuticle biosynthesis in S. thurberi and other cactus species in the future. Understanding the cuticle's physiological function in the adaptation of the Cactaceae family to harsh environmental conditions could help design strategies to increase the resistance of other species to face the increase in water scarcity for agricultural production predicted for the following years.

    The authors confirm contribution to the paper as follows: study conception and design: Tiznado-Hernández ME, Tafolla-Arellano JC, García-Coronado H, Hernández-Oñate MÁ; data collection: Tiznado-Hernández ME, Tafolla-Arellano JC, García-Coronado H, Hernández-Oñate MÁ; analysis and interpretation of results: Tiznado-Hernández ME, García-Coronado H, Hernández-Oñate MÁ, Burgara-Estrella AJ; draft manuscript preparation: Tiznado-Hernández ME, García-Coronado H. All authors reviewed the results and approved the final version of the manuscript.

    All data generated or analyzed during this study are included in this published article and its supplementary information files. The sequence data can be accessed at the Sequence Read Archive (SRA) repository of the NCBI through the BioProject ID PRJNA1030439.

    The authors wish to acknowledge the financial support of Consejo Nacional de Humanidades, Ciencias y Tecnologías de México (CONAHCYT) through project number 579: Elucidación del Mecanismo Molecular de Biosíntesis de Cutícula Utilizando como Modelo Frutas Tropicales. We appreciate the University of Arizona Genetics Core and Illumina for providing reagents and equipment for library sequencing. The author, Heriberto García-Coronado (CVU 490952), thanks the CONAHCYT (acronym in Spanish) for the Ph.D. scholarship assigned (749341). The author, Heriberto García-Coronado, thanks Dr. Edmundo Domínguez-Rosas for the technical support in bioinformatics for identifying long non-coding RNA.

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

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    Weng P, Yang S, Pang Z, Huang J, Shen L, et al. 2023. Effects of the configurations with different organic and inorganic fertilizers on grain yield and its related physiological traits of the main and its ratoon rice crops. Technology in Agronomy 3:2 doi: 10.48130/TIA-2023-0002
    Weng P, Yang S, Pang Z, Huang J, Shen L, et al. 2023. Effects of the configurations with different organic and inorganic fertilizers on grain yield and its related physiological traits of the main and its ratoon rice crops. Technology in Agronomy 3:2 doi: 10.48130/TIA-2023-0002

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Effects of the configurations with different organic and inorganic fertilizers on grain yield and its related physiological traits of the main and its ratoon rice crops

Technology in Agronomy  3 Article number: 2  (2023)  |  Cite this article

Abstract: The determination for properly combined application rate of organic and inorganic fertilizers is the key to coordinating soil nutrient supplies and rice growth demands, thereby obtaining improved economic and ecological returns in rice ratooning. A complete randomized block design (RCBD) trial with different configurations of organic and inorganic nitrogen fertilizers was conducted to determine the optimal substitution rate of organic nitrogen for inorganic nitrogen and its effect on the related physiological attributes and yield performance of the main and ratoon rice crops in 2018−2019. The results showed that 30% organic and 70% inorganic nitrogen in the mixture fertilizer (GM2) could produce the best effect on root activity, nitrogen nutrient uptake and its utilization, as well as the dry matter accumulation and its partitioning. This in turn resulted in improved productive panicles and harvest index, and hence increased grain yield by 10.36% and 15.48% in GM2 regime relative to that in CF treatment. Moreover, the uptake and utilization efficiency, agronomic utilization efficiency and partial productivity of nitrogen fertilizer were 44.37%, 23.82 kg/kg and 48.97 kg/kg, respectively under the optimal GM2 treatment, which were much higher than those in CF treatment by 12.66%, 5.05% and 13.4%, respectively. The results suggested that properly combined application of organic and chemical nitrogen fertilizer could improve soil nutrient supply to guarantee an efficient plant growth and rational dry matter allocation, thereby increasing the harvest index and grain yield in rice ratooning.

    • In contemporary China, accelerated urbanization has prompted a large number of rural populations to migrate to cities, resulting in a shortage of agricultural labor. This conflict inevitably leads to increased agricultural labor cost. Objectively, it has also accelerated the transition of double-cropping rice to single-cropping rice or mid-season rice production, especially in southeastern China. In addition, the poor quality of early rice, weak market competitiveness and low comparative efficiency result in a reduction in rice planting area, which incurs a threat to regional food security[1]. Therefore, the local governments in southeastern China are adopting a low-stubble mechanized rice ratooning technology in an attempt to deal with this passive situation. The technology for rice ratooning can be used to increase the rice multiple cropping index for coping with insufficient arable land, which is therefore considered to be environmentally friendly, simplified, convenient, labor-saving and highly efficient. Furthermore, ratooning rice can produce a higher grain quality compared with its main crop, and the same is true in the case when it was compared with its counterpart synchronizing in heading time (late season)[2]. Therefore, in recent years, rice ratooning has been rekindled to develop rapidly in southeast China[3, 4], and the low left stubble cultivation technology for rice ratooning has been developed, by which the planting areas of ratoon rice are continuously expanding, and the mean yields of ratooning rice was more than 4,500 kg·hm−2, of which the highest output was 6,750 kg·hm−2, the total grain yield of the main and ratoon rice crops was more than 15 t·hm−2, which slightly corresponds with the total yield of double cropping rice[5]. In recent years, a large number of new rice varieties with high regenerative ability have been developed and released, making the technology scale up quickly[2]. Noteworthy, the fertilization pattern of low cut stubble was different from that of high cut stubble in rice ratooning[1]. In the case of rice ratooning under the low stubble height regime, a proper application rate of fertilizer was used to boost root vigor before harvesting the main crop. However, if a high dose of fertilizer application was used, it would inevitably accelerate the spout and elongation of axillary buds in this time. This in turn results in cut damage to the axillary buds after mechanically harvesting the main crop, especially under low cut stubbles. We also found that inappropriate application of bud-promoting fertilizer could also lead to significantly increased greenhouse gases, especially N2O emission, decreased nitrogen utilization efficiency, and affected rice quality[6]. Therefore, suitable fertilization management, especially at the late growing stage of the main crop, is the key to determining high yield and good quality of the main crop and ratooning rice from mechanically low cut stubble[1,4]. Several scholars have documented that under appropriate total nitrogen supplies, a reasonable nitrogen application method for the main crop not only could increase its output, but also has a good carry-over effect on subsequent ratooning rice[2]. However, the long-run large-scale application of chemical fertilizers often causes soil compaction and acidification, and hence decreases available nutrients and organic matter content, in turn declines nitrogen fertilizer utilization and increases farmland non-point source pollution. Moreover, long-run uses of sole inorganic nitrogen fertilizer not only increase the concern about agricultural environmental problems, but also often boost overgrowth of the main crop in the early growth stage, thus increasing ineffective tillers. Meanwhile, it often results in insufficient nutrient supplies in the later growth period due to the rapid release characteristics of chemical fertilizer, consequently triggering premature senescence and hence declining yield of the main crop, thereby producing negative carry-over effect on the regeneration rate and yield of the regenerated rice. Previous studies mostly focused on farmland productivity, nutrient absorption and soil fertility, but ignored the effects of combined organic-inorganic fertilizer with an appropriate proportion on plant nutrient absorption and utilization efficiency at the grain-filling and ripening stage in the main crop and its carrying-over effect on ratooning rice from low cut stubbles. It therefore has been suggested that properly combined application of organic and inorganic fertilizers is a fertilization system for rationally using natural resources to improve soil fertility, and maintain high and stable crop yields based on the quick-acting properties of chemical fertilizers and the better durability of organic fertilizers in the nitrogen mixture supplies[713]. The current research focal point was how to apply this principle as mentioned above to make a reasonable configuration of organic and inorganic fertilizers to improve soil fertility for balancing the physiological demands of the main and its ratooning rice crops in early and late growth stages, and hence to increase nutrient utilization and realize the targets for one planting with two higher harvests in rice ratooning practice. This experiment of combined organic/inorganic fertilizers was conducted using different substitutions of chemical nitrogen with organic nitrogen according to equivalent nitrogen amounts to further study the effects of different combined organic and inorganic fertilizer on the physiological trails and yield performance of the main and ratooning rice crops. This was to clarify the physio-ecological action mode of the organic nitrogen in replacement of inorganic nitrogen, and hence to recommend the most optimal configuration with organic and inorganic nitrogen fertilizers for providing a scientific basis to establish a cost effective fertilization system with best nutrient efficiency, uniform population, stable and high grain yield for rice ratooning in the southeast China.

    • A three-line indica hybrid rice cultivar Yongyou 1540, widely popularized in southeast China, was used as the experimental material. The topsoil (0–20 cm layer) of the experimental field had a clay loam texture with the following properties: 5.22 pH, 25.38 g∙kg−1 organic matter, 1.67 g∙kg−1 total nitrogen, 0.73 g∙kg−1 total phosphorus, 7.37 g∙kg−1 alkalin hydrolysis nitrogen, 167.91 mg∙kg−1 total potassium, 19.83 mg∙kg−1 available phosphorus and 101.64 mg∙kg−1 available potassium, respectively. The nutrient content of commercial organic fertilizer was 53.51 g∙kg−1 organic matter, 8.01 g∙kg−1 total nitrogen, 1.57 g∙kg−1 total phosphorus and 1.94 g∙kg−1 total potassium.

    • The experiment was carried out in the ratooning rice base in Chongluo Township, Jianyang District, N 27°35′4.68″, E 118°21′50.88, Nanping City, Fujian Province, Southeast China from March to November in 2018 and 2019, respectively. In the design of the combination treatment with different proportions of inorganic and organic nitrogen, we applied total nitrogen 225 kg∙hm−2, and based on actual nitrogen contents in inorganic and organic fertilizers, eight combinations of the organic and inorganic fertilizers were set, namely: no fertilizer (CK) used in the whole season, chemical fertilizer application only (CF), 85% inorganic nitrogen + 15% organic nitrogen (GM1), 70% inorganic nitrogen + 30% organic nitrogen (GM2), 55% inorganic nitrogen + 45% organic nitrogen (GM3), 40% inorganic nitrogen + 60% organic nitrogen (GM4), 25% inorganic nitrogen + 75% organic nitrogen (GM5), and 100% organic fertilizer (GM6). Moreover, organic nitrogen was applied as basal fertilizer based on the design in each combined nitrogen fertilizer treatment, and the chemical nitrogen fertilizer was applied as topdressing at the ratios of 3:1:1:4 at transplanting, early tillering, middle tillering and panicle formation stages of the main crop. Fertilizer application at tillering stage of the main crop was split into two time points, 7 d after transplanting (early tillering stage) and 15 d after transplanting (middle tillering stage). The same application rate of fertilizers for ratooning rice was applied in the second cropping season. The amounts of total nitrogen applied in chemical fertilizers were 187.5 kg∙hm−2, of which 10% nitrogen fertilizer for root-vigor preserving and bud survival promoting was applied at 25 d after the fully heading stage of the main crop (Yongyou154, hybrid rice), and the rest of the nitrogen fertilizer was used twice for bud and tillering promotion at 3 and 9 d after the main crop was harvested. The application ratio of the two time fertilizer amounts was 1:1. The ratios of N : P5O2 : K2O were kept at 1:0.8:1 in the second season for ratooning rice. The area of each experimental plot was 100 m2, and the plots were arranged in completely randomized block design (RCBD) with three repeats. Each plot was isolated by a ridge covered with black waterproof mulch film. The height of the ridge was 40 cm and the width is 50 cm, and irrigation and drainage of each plot were independent. In 2018, the seedlings of the main season rice crop were raised on March 12, transplanted on April 14, fully headed on July 21, and matured on August 15; the ratooning season rice fully headed on September 23 and matured on October 26. In addition, in 2019, the same experiment and tests were carried out in the same plot as that in the previous year, i.e., the main season rice crop was sown on March 15, transplanted on April 10, fully headed on July 24, and matured on August 21, and its ratooning season rice fully headed on October 1 and matured on November 3, 2019. Rice transplanting and its harvest were conducted by combining the planting density of 30 cm × 17 cm, and 2−4 plants per hill. The cutting height of the main crop was 25 cm.

    • The sap amounts of rice plants were collected at the active tillering stage (AT),booting (BS), fully heading stage (HS), the day close to the stage fertilizing for root-vigor preserving and bud promoting (CS), and ripening stages (RS) of the main season crop, as well as fully heading (RHS) and ripening stages (RRS) of ratooning season rice by using the cotton absorption method described by Huang et al.[5]. Briefly, approximately 15 g absorbent cottons were placed in a ziplock bag, then marked and weighed and recorded as W1. Three representative uniform plants with the same number of tillers or effective panicles were randomly selected and sampled in each treatment, and the number of tillers per hill were recorded as A. The rice plant was then cut off at 10 cm from the ground and the wound stubbles were covered completely with the weighed cotton and wrapped tightly with the corresponding ziplock bag. Sap collection was performed from 6 pm to 6 am the following day. Afterwards, the cotton and the ziplock bags were weighed and recorded as W2. The bleeding sap intensity (SI) was calculated in a single stem based on the following formula:

      (SI)(mgh1)=[W2W1]A1121
    • The sampled plant parts were weighed, ground into powder and passed through a 0.25 mm sieve, then the powder samples were put into the ziplock bag, then marked and saved at room temperature until used. The concentrated sulfuric acid-hydrogen peroxide digestion method was used to prepare the test solution of total nitrogen, phosphorus and potassium of the rice plants. 0.25 g of the rice plant sample powders were added into the cleaned and dried digestion tube, and marked, 5 ml of concentrated sulfuric acid was then added to each tube, the digestion tube was then put into the digestion furnace and the temperature adjusted to 265 °C. It was then heated for 15 min; after preheating, the temperature was adjusted to 365 °C, the digestion tube was then removed and cooled to room temperature after digestion for 45 min, 4 ml of hydrogen peroxide solution was then added for the first time. The digestion tube was then returned to the digestion furnace to digest after 30 min, in turn the tube was cooled, thereafter, 2 ml of hydrogen peroxide solution was added for the second time and left to digest for 30 min, it was then removed again for cooling. 1 ml of hydrogen peroxide solution was then added for the third time, digested for 45−60 min, if the solution is still not clear and transparent, we continued to add 1 ml of hydrogen peroxide solution and digest for another 30 min. This step was repeated until the solution became clear and transparent, then the digestion was stopped. The solution was then cooled to room temperature, distilled water was used to increase the volume to 100 ml, and then it was poured into a clean centrifuge tube, labeled and stored for testing. The total nitrogen, phosphorus, and potassium content of rice plants were measured using a German automatic chemical discontinuous analyzer (Smartchem, 2000).

      Nitrogen utilization efficiency was then calculated based on the formula:

      Plant nitrogen content (%) = [(Leaf dry weight per unit area × Leaf nitrogen content + Dry weight of stem and sheath per unit area × Nitrogen content in stem and sheath + Dry weight per panicle per unit area × Nitrogen content of panicle) / Aboveground parts of whole plant (Dry weights of stems, sheaths, leaves and panicles) per unit area] × 100

      Nitrogen accumulation in leaves (stem, sheath and spikelets) (kg·hm−2) = Dry weight of leaf (stem sheath, spikelets) per unit area at each stage × Nitrogen content in leaves (stem sheath and spikelets)

      Nitrogen physiological efficiency (%) = [(Grain yield in nitrogen treatment area − Grain yield in the area without nitrogen treatment) / Total nitrogen uptake by plants in nitrogen treatment area − Total nitrogen uptake by plants in the area without nitrogen treatment)] × 100

      Nitrogen agronomic efficiency (kg/kg) = (Grain yield in nitrogen treatment area − Grain yield in the area without nitrogen treatment) / Nitrogen application rate

      Nitrogen uptake efficiency (%) = [(Nitrogen uptake by plants in nitrogen treatment area − Nitrogen uptake by plants in the area without nitrogen treatment) / Nitrogen application rate] × 100

      Partial productivity of nitrogen fertilizer (kg/kg) = Grain yield of nitrogen treatment area / Nitrogen application rate

      Fertilizer contribution rate (%) = (Yield of nitrogen treatment area − Yield in the area without nitrogen treatment) / Yield of nitrogen application area

    • Rice plants were randomly sampled from different plots at AT, BS, HS, CS, RS of the main crop and RHS, RRS of the ratooning season rice, respectively. Five representative plants were selected and sampled based on the average of plants at different stages of main and ratooning rice crops. The sampled plants were quickly cleaned with water, and then separated into roots, stems, sheaths, leaves, panicles, and all these different parts of the main crop stubbles were placed into dry-oven at 105 °C to be deactivated for 30 min. Subsequently these were dried at 75 °C to a constant weight, then weighed and cooled at room temperature. Simultaneously, exportation rate of stem and sheath dry matters (ERSSDM), translocation rate of stem and sheath dry matters (TRSSDM) were calculated based on the formula:

      ERSSDM(%)=[(A)(B)/A]100
      TRSSDM(%)=[(AB)/GW]100

      Where A refers to dry matter weight at the heading stage of rice, B stands for dry matter weight at the mature stage of rice (B), and GW refers to fully filled grain weight in the formula.

    • The effective panicles of more than 50 rice plants were randomly sampled and recorded in each plot on the day that the main and its ratoon rice crops were harvested. Then, the averages of effective panicles were calculated. According to the average of effective panicles, six representative rice plants were selected from each plot to investigate the number of grains per panicle, empty grains per panicle and filled grains per panicle, seed setting percentage, 1000 grain weight and theoretical yield. In addition, in order to calculate plant height and harvest index of the main crop (HI) and ratooning rice (RHI), the rice was cut from the stem base close to the field surface at the mature stage of the main crop and ratooning rice (including old rice stubbles). Where HI or RHI = Grain yield (g) / Total dry matter weight including grain yield and residual stubble per plant (g).

    • Office 2016 software for data processing was used for making the tables and figure drawings. DPS 7.05 statistical software was used to analyze the experiment results.

    • Figure 1 displays that the bleeding sap intensity of all fertilization treatments was greater than that of the zero applied fertilizer regime in the whole growth period of the main and ratoon rice crops. The main and ratoon rice crops in all treatments performed the highest intensity of the root bleeding sap at both heading stages of the main crop and its ratoon rice (Fig. 1). At BS stage, the bleeding sap intensity of the single stem in the main crop was the highest under the treatment with chemical fertilizer (CF), followed by that under GM2 regime, but both were not significantly different. Furtherthermore, the obviously decreasing tendency in the bleeding sap intensity was determined under the other combined rates of organic and inorganic fertilizers. However, there was no significant difference in bleeding sap intensity under GM1, GM3 and GM4 treatments, it was significantly higher than that under GM5, GM6 and CK regimes. At the heading stage of the main crop, insignificant difference in the bleeding sap intensity was detected under GM1 and GM2 treatments, and the same was true in the cases under GM4, GM5 and GM6 treatments, but all fertilization treatments were significantly higher than CK without nitrogen treatment in terms of the sap intensity. At the CS stage of the main crop, except for GM6, the other treatments displayed insignificant difference in the sap intensity. After the fertilization treatment for preserving vigor roots and promoting bud sprout, the sap intensity in the GM2 regime increased to the maximum at the ripening stage of the main crop. Although there was no significant difference in the sap intensity in GM1 and GM2 treatments, they were significantly higher than those in other treatments. At the same time, the sap intensity in CF treatment decreased to significantly lower than that in the treatments with GM1 and GM2, but the difference was not obvious compared with those under GM3, GM4, GM5 and GM6 regimes. In the period of ratooning season, the sap intensity increased to the peak at the heading stage of ratooning season rice under fertilization treatments with the same amounts of chemical nitrogen for tillering-promotion applied on day 5 after the main crop was harvested, and the largest value of the sap intensity was found in the CF regime. Further, the second place was found in GM1 and GM2 treatments, but they were not significantly different in three fertilization treatments. Insignificant difference was also detected in GM4, GM5 and GM6 treatments except for that in CK. At RRS stage, the sap intensity of the ratoon plants was much higher in GM2 treatment, but insignificantly different in all fertilization treatments (except for that in CK). Therefore, an appropriate organic nitrogen proportion in the fertilizer mixture (such as GM2 treatment) could improve the bleeding sap intensity, thereby increasing root-vigor at earlier and late growth stages of the main and ratoon rice crops, which lays an important physiological foundation of nutrient absorption and utilization as well as high-yielding formation in rice ratooning.

      Figure 1. 

      Effects of treatment with different ratios of organic and inorganic fertilizers on the root sap intensity of main and ratoon rice crop plants in single stem level. Note: Different lower case letters indicate significant differences in p < 0.05 level between different treatments in the same part and the same period. BS: booting; HS: fully heading stage, CS: the day close to the stage fertilizing for root-vigor preserving and bud promoting, RS: ripening stages of the main season crop, RHS: fully heading of ratooning season rice, RRS: ripening stages of ratooning season rice.

    • The results revealed that the nitrogen content in stem and sheath was the highest at heading stage of the main crop and the lowest at RRS stage of ratoon rice. In terms of the changing trend of different treatments in the main and ratoon rice crops, the nitrogen content in stem and sheath of conventional fertilization treatment (CF) was the largest at HS stage of the main crop, and decreased with the increase of organic fertilizer proportion in the organic/inorganic mixture at the same stage. In the CS stage as shown in Fig 2, the nitrogen content in stem and sheath of the main and ratoon rice crops decreased rapidly in all treatments, and the largest declining rate was observed in CF treatment. During this period, the nitrogen content of each fertilization treatment was significantly higher than that of CK (without fertilization treatment). The nitrogen content of sole chemical fertilizer (CF) treatment showed the highest in stem and sheath among all fertilization treatments, and insignificant difference was observed among other treatments of organic/inorganic fertilizer mixtures. Furthermore, the nitrogen content of all fertilization treatments increased from CS to RS stage of the main crop, the reverse was true in the case of CK treatment. In terms of ratooning season rice, the nitrogen content in stem and sheath of CF was higher at the RHS stage of ratoon rice. However, at the RRS stage of ratooning season rice, the nitrogen content in the stem and sheath of all treatments decreased rapidly in a way that it ended up with no significant difference in nitrogen content among fertilization treatments except for CK treatment, suggesting that compared with other combined application of organic and inorganic nitrogen, sole chemical nitrogen fertilizer treatment (CF) increased the amount of allocated nitrogen in the stem sheath of the main crop rice, but there was no significant difference in the RRS stage of ratooning season rice.

      Figure 2. 

      Effect of treatment with different ratios of organic and inorganic fertilizers on nitrogen content in various parts in different periods of main and ratoon rice crops at various phases of grain-filling. Note: Different lower case letters indicate significant differences in p < 0.05 level between different treatments in the same part and the same period. HS: fully heading stage, CS: the day close to the stage fertilizing for root-vigor preserving and bud promoting, RS: ripening stages of the main season crop, RHS: fully heading of ratooning season rice, RRS: ripening stages of ratooning season rice.

      The results displayed a similar trend change of nitrogen content in leaves as those in the stem, and sheath of the main and ratoon rice crop in all the treatments as shown in stem and sheath, which indicated a decreasing trend at first then increased and decreased again. The leaf nitrogen content reached the maximum at the heading stage, and then decreased rapidly until maturity of the main crop in all treatments. From the HS to CS stage of the main crop, the nitrogen content of leaves in the CF treatment was higher than that in other treatments. Meanwhile, a reduction of nitrogen content in leaves was also accompanied by the increasing proportion of organic fertilizer in the fertilizer mixture treatment, and at maturity stage of the main crop the more organic nitrogen in the fertilizer applied, the greater the leaf nitrogen content of the corresponding treatment. In fully heading and mature stages of ratoon rice, the nitrogen content of leaves was GM6 > GM5 > GM3 > GM4 > GM2 > GM1 > CF > CK in order. It implies that the excessive proportion of organic fertilizer in the mixture fertilizer will lead to the retention of nitrogen in rice leaves at the late stage and eventually lead to the undesirable consequences of stay-green.

      Figure 2 also reveals that the changing trend of nitrogen content in the panicle was different from that in stems, sheaths and leaves under different treatments from the HS stage of the main crop to the RRS stage of ratoon rice. At the BS stage of the main crop, the nitrogen content in the panicle was the highest in the GM2 regime, but the variation trend was irregular among different treatments. Compared with that at the HS stage, the nitrogen content in the panicle decreased to a certain extent until the CS stage of the main crop. The nitrogen content in panicles of the main crop was GM1 > GM2 > CF > GM3 > GM4 > GM5 > GM6 > CK. At the RHS stage of ratooning rice, the changes of nitrogen content in the panicles were different under different fertilization treatments. Furthermore, compared with that at the HS stage of the main crop, the nitrogen content decreased in the panicles of ratoon rice under CF, GM1, GM2 and GM3 treatments, while the reverse was true in the case of the other four treatments GM4, GM5, GM6 and CK. At the RRS stage, the panicle nitrogen content of ratoon rice under the treatment with more than 70% organic fertilizer in the replacement was significantly higher than that in the other treatments. Therefore, it is suggested that the higher proportions of chemical nitrogen in the combined fertilizer had the earlier and faster physiological effect, the opposite was true in the case under treatment with the higher proportions of organic nitrogen in the combined fertilizer. It is therefore assumed that a reasonable ratio of organic and inorganic nitrogen (such as GM2) was able to coordinate the contradiction between supply and demand of nutrients, hence making the rice plant absorb nitrogen and allocate it reasonably in various rice plant parts, and ultimately improve the nutrient utilization efficiency and economic returns.

      The characteristics of dry matter accumulation and distribution were further analyzed as shown in Fig. 3. At the booting stage of the main crop, the dry matter accumulation in stem, sheath and leaves was higher in GM2 treatment, but it was lowest in leaves under CF treatment compared with that in the other treatments, while the lowest dry matter weight of stem and sheath was detected in the GM6 treatment. At the fully heading stage of the main crop, the dry matter accumulation in different plant parts was increased under all treatments, of which GM2 showed the highest dry matter accumulation in stem and sheath, followed by the GM1 regime. The results further displayed a decreased dry matter accumulation in the stem and sheath under the treatments with the increases of the combined application proportions of organic nitrogen in the fertilizer mixture. In terms of the leaf dry matter weights, all treatments of combined application with different proportions of organic fertilizer were significantly lower than that of the chemical fertilizer treatment (CF), but the GM2 treatment effect on dry matter weight of panicles was enhanced, indicating the highest value of dry matter accumulation in panicles, and significantly higher than that in the other treatments. In the pre stage of fertilizer application for root-vigor and bud preserving, GM1 and GM2 showed significantly higher dry matter weight of stem and sheath than the other treatments, but both of them indicated insignificant difference. In terms of dry matter weights of leaves, insignificant was found in CF, GM1 and GM2 treatments, the same was true in the case of GM3, GM4 and GM5 treatments, but all of them showed higher values than that in CK (without fertilizer). All treatments displayed significantly different dry matter weight of panicles, indicating the following order: GM2 > GM1 > CF > GM3 > GM4 > GM5 > GM6 > CK. At the maturity stage of the main crop, both GM1 and GM2 treatments showed higher dry matter weights of panicle, stem and sheath compared with the other treatments. While in terms of leaf dry matter weight, it was lower in GM2 treatment than that in the CF regime. At the fully heading stage of ratooning rice, the dry matter weights of stem, sheath, leaves and panicles as well as old stubbles were higher in GM1, GM2 and GM3 regimes than that in CF treatment. Meanwhile, at the maturity stage of ratooning rice, the dry matter weights of stem, sheath and leaves, as well as the old stubbles of the main crop, were significantly decreased, the reverse was true in the case of the panicle dry weight. Additionally, a positive performance in terms of panicle weight was noted in GM2, GM1, GM3, and GM4 treatments compared to other treatments. These results further confirmed that suitable combination of organic and inorganic fertilizer was beneficial to increase dry matter production and accumulation in late stage of main crop.

      Figure 3. 

      Effect of combined application treatment with different ratios of organic and inorganic fertilizers on dry matter accumulation of ground rice plants. Note: Different lower case letters indicate significant differences in p < 0.05 level between different treatments in the same part and the same period. BS: booting, HS: fully heading stage, CS: the day close to the stage fertilizing for root-vigor preserving and bud promoting, RS: ripening stages of the main season crop, RHS: fully heading of ratooning season rice, RRS: ripening stages of ratooning season rice.

      Furthermore, GM2 treatment exhibited the best effect on exportation and translocation rates of the stem and sheath dry matter, displaying the highest promoting effect on the two fertilizer rates compared with that in the other treatments, followed by those in GM1 and CF, whereas all other treatments showed lower comparative grades with increasing proportion of organic nitrogen in the mixed fertilizer treatments (Table 1). Therefore, this result further confirmed that good soil fertilizer supply guarantees an efficient plant growth as well as dry matter production and allocation in late growth stage, which greatly contributes to the higher harvest index and grain yield of main and ratooning rice crops.

      Table 1.  Effects of different ratios of organic fertilizers and chemical fertilizers on dry matter accumulation and transport during filling stage.

      StageTreatmentSSWF (kg·hm−2)SSWM (kg·hm−2)PWM (kg·hm−2)DMAF (kg·hm−2)EPMSS (%)TPMSS (%)
      MaincropCF7025.43c5342.27b9903.15b4026.62b23.96c16.99d
      GM17258.68b5397.21ab10250.41ab3844.83c25.64b18.16b
      GM27518.68a5436.57a10380.56a4778.16a27.69a20.05a
      GM36791.52d5190.93c9634.42c3945.44b23.57d16.61d
      GM46575.77e4997.32d9108.53d3772.75d24.00c17.33cd
      GM56471.57e4915.14d8583.96e3740.11d24.05c18.13b
      GM66244.54f4821.60e8059.98f3137.99e22.78e17.65c
      CK5137.59g4144.89f6261.79g2660.41f19.32f15.85e
      RatoonriceCF5340.86cd3890.75c6149.61d2483.25cd27.15bc23.58b
      GM15567.44b3971.67b6544.57bc2408.53e28.66b24.38ab
      GM25889.77a4051.99a7348.64a3219.63a31.20a25.01a
      GM35440.62c4009.24ab6604.73b2626.66b26.3cd21.67c
      GM45022.85d3827.70d6293.14c2539.09c23.79d18.99d
      GM54861.61e3789.66d5783.45e2435.55de22.05e18.53d
      GM64329.81f3417.23e5169.39f1916.51f21.07f17.65de
      CK3047.31g2649.84f3539.34g1441.24g13.04g11.23f
      SSWH: Stem-sheath dry matter weight in full-heading stage; SSWM: Stem-sheath dry matter weight in mature stage; PWM: Panicle dry matter weight in mature stage; DMAH: Dry-matter accumulation after full-heading stage; DMAH: Dry-matter accumulation after full-heading stage; EPMSS: exportation rate of stem-sheath dry matter; TPMSS: Translocation rate of stem-sheath dry matter.

      Based on the statistical analysis results, an insignificant difference was found in two years for grain yield and its components of the main crop and ratooning rice under different combined application treatments with different proportions of organic and inorganic fertilizers in the main crop season. The reverse was true in fertilization treatments and the interaction between years and fertilization treatments (Table 1). Therefore, we here use the average of the two year results for the comparison analysis. The results showed that the gain yield of main and ratooning rice crops under GM2 treatment was significantly higher than that in other treatments, which resulted from the higher productive tillers and harvest index, especially in the case of ratooning rice under the optimal fertilizer configuration (GM2). The total yield of the main and ratoon rice crops under GM1, GM2 and GM3 showed an increased effect compared with the conventional fertilization treatment (CF), among which GM2 had the largest increase effect, with an average increase of 10.36% in two years. The yield under GM1 and GM3 treatments increased by 3.37% and 1.9%, respectively, however, a more obvious decrease in rice yield and grain yield was recorded with the increase in the proportion of organic nitrogen in the combined application treatments, in which GM5 and GM6 decreased by 5.83% and 10.31% on average in two years, respectively, which might be attributed to lower number of effective panicles and poor harvest index (Table 1).

    • Table 2 illustrates that the combined application with different proportions of organic and inorganic fertilizers in the main crop have dissimilar effects on nitrogen use efficiency of the main and ratoon rice crops. In terms of nitrogen partial productivity, the performance values of fertilization treatment were significantly higher than CK treatment; notably, with the increase of organic fertilizer proportion, nitrogen partial productivity of the main and ratoon rice crops increased at first and then decreased gradually. Among the fertilization treatments, GM2 had the highest partial nitrogen productivity (48.97 kg/kg), which was significantly higher than other fertilization treatments with 75 kg/kg higher than that of conventional fertilization. Likewise, for nitrogen absorption and utilization efficiency, the nitrogen absorption and utilization efficiency of conventional fertilization treatment was only 33.71%. The nitrogen use efficiency of GM2 was the highest (44.37%), while those of GM1 and GM3 were more than 40%. Compared with conventional fertilization treatment (CF), GM2, GM1 and GM3 increased by 10.66%, 8.12% and 7.86% in nitrogen use efficiency, respectively. Moreover, nitrogen agronomic use efficiency for GM2 reached to 23.82 kg/kg, which was significantly higher than that of other treatments, and increased by 30.55% compared with that in conventional fertilization regime. GM1 and GM3 regimes also showed higher nitrogen agronomic utilization efficiency than the CF treatment, but the other fertilization treatments performed poorly in nitrogen agronomic utilization efficiency than the CF regime. Hence the difference in grain yield of the main and ratooning rice crops results from the different treatments with different configurations, with different proportions of inorganic/organic nitrogen in fertilization treatments (Table 2).

      Table 2.  Comparison on the grain yield components and related physiological traits of the main and ratoon rice crops under combined application treatments with different proportions of organic and inorganic fertilizers.

      TreatmentsThe main cropThe ratooning riceMTY
      (kg·hm−2)
      RTY
      (kg·hm−2)
      TTY
      (kg·hm−2)
      MPH
      (cm)
      RPH
      (cm)
      MHIRHIMGD
      (days)
      RGD
      (days)
      NFPP (kg/kg)NAUE
      (%)
      PENU
      (kg/kg)
      NAUE
      (kg/kg)
      NCRF
      (%)
      EP
      (104·hm−2)
      GNTGW
      (g)
      SSP
      (%)
      EP
      (104·hm−2)
      GNTGW
      (g)
      SSP
      (%)
      CF264.16b205.24a22.63a87.57b330.60bc108.55c21.61a89.18ab10750.68bc6901.61cd17528.21bc135a85a0.46a0.51a142.0070.0043.22c33.71d41.94c18.07c41.80c
      GM1267.49b203.49ab22.71ab88.23ab338.70bc110.50b21.65a90.06ab10914.32b7287.35bc18118.53b135a95b0.49b0.55b142.0072.0044.82b41.83b44.00b19.67b43.89b
      GM2282.64a201.69bc22.69ab89.52a356.10a115.08a21.47ab90.66a11600.46a7970.31a19342.97a140b95b0.55c0.61c142.0075.0048.97a44.37a48.79a23.82a48.64a
      GM4258.34c194.59d22.55ab89.82a328.66c110.71b21.66a90.32ab10145.36d7103.01bc17243.15c135a90c0.43c0.50a148.0078.0042.57d37.22c40.44d17.42d40.92d
      GM5247.61d195.43d22.63ab89.56a308.70d111.71b21.45a90.64ab9797.27e6681.84d16507.49d135a85a0.41c0.48d150.0078.0040.64e34.83d35.98e15.94e38.12e
      GM6240.58d194.89cd22.56ab90.04a295.53e107.62c21.51ab90.93a9486.38e6234.12e15722.10e135a85a0.41c0.45d150.0078.0038.37f32.17e30.29f13.22f24.46f
      CK186.48e182.83e22.38b86.83ab215.02f99.52d21.03b88.12b6688.71f3751.59f10715.73f
      Year(A)NSNSNSNSNSNSNSNSNSNSNS
      Fertilization
      (B)
      32.43**12.25**NS2.34*33.25**11.51**NSNS19.98**76.13**99.30**
      A×BNSNSNSNSNSNSNSNSNSNS163.80**
      The comparison of differences is conducted to the average value of single factor effect and the interaction of double factor in the same column in the average of two year since no difference was found in two year results. The same letter after each data means that the difference is not significant, otherwise the difference is significant. Lower case letters represent significant level at < 5% and upper case letters represent significant level at < 1% , EP: effective panicles; GN: grain number per panicle; TGW: 1000-grain weight; SSP: seed setting percentage; MTY: theoretical yields of the main crop; RTY: theoretical yields of the ratoon rice; TTY: total theoretical yield; MPH: Plant height of the main crop; RPH: Plant height of ratooning rice; MHI: Harvest index of the main crop; RHI: Harvest index of ratooning rice; MGD: growth duration of the main crop; RGD: growth duration of ratooning rice; NFPP: Nitrogen fertilizer partial productivity; NAUE: nitrogen absorption and utilization efficiency; PENU: Physiological efficiency of nitrogen use; NAUE: Nitrogen agronomic use efficiency; NCRF: Nitrogen contribution rate of fertilizer.
    • The results indicated that the nitrogen uptake and accumulation in stems, sheaths and leaves of the main and ratoon rice crops were the highest at the heading stage of the main crop, and gradually decreased with proceeding rice plant growth under the fertilizer configurations with different proportions of inorganic and organic nitrogen, simply because during the grain-filling and ripening stage of the main and ratoon rice crops, a large proportion of dry matter was rapidly transferred from the stem, sheath and leaf parts to the grain pools of the main and ratooning rice crops[14]. The result also revealed that the GM2 treatment (70% chemical nitrogen + 30% organic nitrogen) performed the best output compared with the other treatments, which is due to the fact that the rice plants in the early stage need much more nutrients to accumulate biomass, an appropriate proportion of inorganic nitrogen supply can meet the nutrient demands for the growth and development of rice in the early growth stage. However, the carry-over effect of the chemical nitrogen is too short to be favorable for the growth of rice at the late stage, which often leads to decreased root activity, thereby accelerated premature senescence. Therefore, a proper amount of organic nitrogen fertilizer in the combined application treatment could improve the situation since the proper amounts of organic nitrogen in the combined application have better durability of fertility, and the organic nitrogen could be effectively decomposed by rhizosphere microbes to release a large number of small molecules into rhizosphere for direct absorption by rice plants at the late growth stage to support the rice plant physiological demands, thereby preventing root premature senescence and root-vigor decreases, thereby improving the survival status of the axillary buds in the remaining stubbles, consequently increasing grain yield of the main and ratooning rice crops[1517].

      However, the present results revealed that the excessive proportion of organic fertilizer in the mixture fertilizer led to the retention of nitrogen in rice leaves at the late stage and eventually led to the undesirable consequences of stay-green, which is responsible for the good carry-over effect of the organic fertilizer in terms of its fertility. Therefore, too much organic nitrogen in the combined application regime has a stay-green effect on rice growth, consequently resulting in yield reduction in the mixture treatment containing more than 45% organic nitrogen (such as GM3, GM4 etc.). In this experiment, as overused organic fertilizer inevitably results in nitrogen retention and reduces the nitrogen release efficiency, thereby decreasing the nitrogen utilization efficiency. Our present studies suggested that the proper combined application of organic and inorganic fertilizers such as GM2 treatment could make the soil nitrogen supply timely and effective as indicated by the higher root activity and the better nitrogen accumulation and allocation in the main crop under the optimal configuration regime (see Figs 1, 2 & 3 and Tables 1 & 2), which is consistent with the previous studies on conventional rice cultivation[1,8]. Zhang et al. also documented that under an equal nitrogen usage, the substitution of organic fertilizers for chemical fertilizers improved the stability and sustainability of rice yields, reduced soil mineral nitrogen losses, and hence improved the utilization of nitrogen fertilizers[18]. Zhang also suggested that the combined application of organic and inorganic fertilizers could increase the nitrogen uptake of the plants in the later stage and increase the nitrogen use efficiency[19]. Liu et al. reported that under the condition of equal nitrogen replacement, organic and inorganic fertilizers were applied in different mixture combinations, and the soil available nitrogen content was higher in the treatment with a high proportion of chemical fertilizer application in the early stage of rice growth, the reverse was true in the case of the treatment with a high replacement ratio of organic fertilizer at the late growth stage of rice[20].

      Previous studies also suggested that with the increase in the proportion of organic fertilizers, the nitrogen fixed by microorganisms increases, and this in turn causes insufficient nutrients to the crops, especially in early plant growth stage[11,21]. Moreover, the microbial biomass carbon, nitrogen and mineral nitrogen in soil under the combined application treatment with organic and inorganic fertilizers were lower than those under sole chemical fertilizer regimes before the rice tillering stage, however, the reverse was true in the case of the heading stage to the grain filling stage. The findings attested that nitrogen supply dynamics should be in accordance with the law of nitrogen absorption and utilization by rice to the highest degree, which is conducive to preventing the delay-green and premature senescence of root and shoot in the late growth stage, in turn improving the status of rice growth and development, and hence leading to increased grain yield of rice[2227]. Therefore it is suggested that a suitable combined application of organic and inorganic fertilizers could greatly increase rice root activity and nutrient uptake capacity, improve dry matter accumulation and its allocation pattern, enhance nitrogen use efficiency, nitrogen absorption efficiency, agronomic efficiency and nitrogen partial productivity, harvest index and hence increased yield of the main and ratoon rice crops.

    • In summary, the results suggested that under the premise of the same amount of nitrogen fertilizer per hectare (225 kg·hm−2), the organic nitrogen is replaced with 30% equivalent nitrogen to inorganic nitrogen (GM2), the yield performance of the main and ratoon rice crops was the best in the two-year studies. Relative to the CF control, the total yield of the main and ratooning rice crops increased by 10.36%, of which the ratooning rice yield increased by 15.48% under GM2 treatment. The yield performance of the main and ratooning rice crops in two years was higher under GM2 > GM1 > GM3 > CF > GM4 > GM5 > GM6 > CK, and that of ratooning rice was better at GM2 > GM3 > GM1 > GM4 > CF > GM5 > GM6 > CK in a descending order. The finding suggested that the treatment with more than 30% proportion of organic nitrogen in the mixture fertilizer had a significant impact on the yield of the main crop, but the regime with less than 45% organic nitrogen in the mixture treatment had a negative carry-over effect on the yield of the ratooning rice. The optimal combination treatment (GM2) could achieve 44.37% nitrogen absorption and utilization efficiencies, this in turn led to an improvement in the agronomic utilization efficiency and the partial productivity, and hence increased economic and ecological returns.

      • This research was supported by the national research grants (2016yfd00300508; 2017YFD0301602; 2018yfd0301105), Fujian Taiwan planting resources creation and green cultivation coordination Innovation Center (Fujian 2011 project, no. 2015-75) and the Science and technology development funds of Fujian agricultural and Forestry University (kf2015043).

      • Wenxiong Lin is the Editorial Board member of the journal Technology in Agronomy. He 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 the Editorial Board member and his research group.

      • 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 (3)  Table (2) References (27)
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    Weng P, Yang S, Pang Z, Huang J, Shen L, et al. 2023. Effects of the configurations with different organic and inorganic fertilizers on grain yield and its related physiological traits of the main and its ratoon rice crops. Technology in Agronomy 3:2 doi: 10.48130/TIA-2023-0002
    Weng P, Yang S, Pang Z, Huang J, Shen L, et al. 2023. Effects of the configurations with different organic and inorganic fertilizers on grain yield and its related physiological traits of the main and its ratoon rice crops. Technology in Agronomy 3:2 doi: 10.48130/TIA-2023-0002

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