Philippa Kaur

In line with the results of present study, we suggest that the exploitation of forage resources by agro-pastoralists occurs in a non-random manner. The use of forage resources is guided by a series of functional characters related to palatability and nutritional value, which determine preferential use due to the better quality of resource. At the same time, we understand that forage uses are complex and multifactorial in nature, and regulated in a substantial way by seasonality and ecological factors (Fig. 5), such as the availability of plant resources and local diversity.Figure 5Diagrammatic representation for the effects of vegetation cover and seasonality on forage resource selection in Dry Forests. Image created with Microsoft Office 2019 PowerPoint (www.office.com).Full size imageThe differences of plant species cited between areas reveal the positive effect of vegetation cover on the use and knowledge of plants by agro-pastoralists. Our findings reveal that the greater number of plant species mentioned by agro-pastoralists in Area II is directly associated with greater availability of resources in this area, as long as we consider vegetation cover as availability of resources, which allows different species to be used throughout the year. On the other hand, in regions with low vegetation cover (Area I), the low availability of resources limits the use and knowledge of plants by residents, which can lead to greater pressure on a small set of available species. Such findings reinforce the importance of vegetation cover for ecosystem provision of goods and services to human populations that depend directly or indirectly on these services.The most represented families found in the present study have also been reported in several other ethnobotanical studies6,16,17,29, with emphasis on Fabaceae and Poaceae, which are recognized for their high forage potential, which derives, above all, from high palatability and nutritional value30. Simultaneously, citations mostly for native species reflect the importance and potential of Caatinga resources as important components of the ruminant diet11, both for the woody and herbaceous strata, corroborating the estimate in the literature that 70% of vegetation has potential use as forage31.The characteristic seasonality of vegetation, on the other hand, represents a limiting factor for forage productivity, culminating in high fluctuations in quality and availability, as well as changes in the dominance of different strata and composition of forage species throughout the seasons11,32. The seasonal distribution of species explains the similarity of seasons between areas, with a higher similarity percentage for the dry seasons, since there is less availability of resources to be exploited compared to the rainy season. In this context, the potentially used species are commonly accessible woody species in both areas. However, during the rainy season, the high availability of herbaceous plants regulates different uses (Fig. 4), but even so, they also exhibit relatively similar patterns, mainly due to the woody component that denotes the common demand by ruminants at the beginning of this season.The effect of climatic variables on vegetation use patterns was documented by16,17, both of which showed greater richness in the use of herbaceous forage during the rainy season, a finding that reflects the seasonal distribution—restriction to that season—and decrease in the qualitative character of annual species33. At the same time, it also reflects the greater number of unique species for the rainy season. However, when compared to woody strata, significant differences in terms of richness are not found because although the diversity of herbaceous species in the Caatinga is greater24, it is much less known than that of the tree-shrub stratum11.Agro-pastoralists even characterize animal preferences for herbaceous stratum, but as its diversity is immense and ephemeral, they claim to have limited ability to identify the species. The high abundance of resources in the rainy season also reduces the concern with forage use, which implies less attention to the species that are consumed. In contrast, woody species, due to multiple uses and greater availability over time, tend to be better known10,34, with a different effect in the dry season making the optimal foraging pattern in this period inherent to the knowledge of agro-pastoralists35.In addition, according to the ecological appearance hypothesis, there is a general tendency for less apparent species to be neglected by populations36. Some studies have corroborated the hypothesis within the context of forage use, with woody species being cited more and having more uses6,15. In addition, people tend to focus on resources whose supply is given continuously10, which may explain why woody species are well represented in both seasons.Security in the provisioning of ecosystem services is an essential component for local populations, and thus woody species are highly valued because they reflect predictability of use15,35. This can be a particularly influential criterion because perennial or late leaf deciduous species, such as Cynophalla flexuosa and Myracrodruon urundeva, had significant amounts of citations and perceptions employing high valuation, as represented by some statements by some interviewees: “É um refrigero na seca” (it is savage in the dry season), “É uma ração boa na seca” (it is a good food in the dry season).In turn, differences in richness of the species cited by the two areas corroborate our first hypothesis that populations inserted in environments with greater vegetation cover tend to cite more species. In line with these findings, considerable floristic dissimilarity was also found between the two areas, given the exclusivity of species. Such dissimilarity may suggest particularities in the vegetation attributes of each area, such as greater floristic diversity7,37,38.Since anthropic processes are irregularly distributed in space, variation in the provisioning of ecosystem services by vegetation also occurs, and influences different collection profiles39. On the other hand, areas with greater species richness have been shown to have greater use patterns6,7. The larger number of species cited as woody and native for Area II is, therefore, associated with greater general richness, as well as herbaceous species present in the rainy season. In contrast, common species are reflected in trends of similar foraging patterns, as well as the presence of common species between areas38. In addition to different levels of disturbance, differences in floristic composition between areas may also be due to edaphic variation40.Our second hypothesis was refuted because the difference in the richness of exotic species between the areas. Plausible explanations for this finding are that, in general, exotic herbaceous species are commonly used for forage in the semi-arid region of Brazil41. Herbaceous species comprise the primary component of the ruminant diet. However, in the midst of their occurrence restricted to the short rainy period, exotic species, mainly of Fabaceae and Poaceae, have been introduced to increase the forage availability, which currently represents an important attribute of forage resources in the Caatinga41,42,43. At the same time, and to also increase the availability of forage resources, the cultivation of species by agro-pastoralists may be common in their properties44, mainly exotics, such as Prosopis juliflora, that have high adaptive potential and governmental incentives45.Regarding use patterns, according to the data presented here it is possible to state that agro-pastoralists ’ experiences with herding activities provide an accumulation of a vast knowledge about forage resources15. This knowledge allows forage resources to be characterized by their potential according to a variety of criteria associated with seasonal variation and qualitative attributes, as commonly found by other studies14,15,16,17,37. Such criteria are often revealed by qualitative approaches that define the valuation perception of resources. Thus, nutritional value and palatability can be implicitly associated with the definitions of “É uma ração boa” (it is a good food), “o bicho gosta muito” (the animals like it very much) and “Rico em proteínas” (rich in protein).It should be added that the establishment of intrinsic relationships with resources allows a particular understanding at a high level of detail15,35, such as changes in palatability throughout development with descriptions including chemical17 and structural changes. Studies confirm that some Caatinga species vary in their chemical composition during leaf maturation, which influences nutritional quality17,46.In addition to revealing the domain of information, this body of knowledge allows maximizing forage use based on nutritional properties weighted by availability14,37. Nunes37 confirmed that the forage species selected by informants and the criteria they adopted coincided with nutritional values measured by the literature, and that, as also found in the present study, younger plants were recognized as highly appreciated by animals. This appreciation is due to the greater palatability of plant organs at this stage47. This is a matter of concern for the sustainability of the Caatinga, since direct or indirect grazing has compromised the regeneration process12 since younger individuals are clearly more sensitive to damage48.Also, considering the potential of Caatinga, we suggest that investment through government actions encourage the cultivation of native species to ensure the production of forage and, consequently, guarantee the sustainability of livestock activity and the ecosystem in question. More

Sample collection and metagenomic sequencingWritten informed consent was obtained prior to participation in the project. The study protocol for the Japanese (Disease, Drug, Diet, Daily life) microbiome project was approved by the medical ethics committees of the Tokyo Medical University (Approval No: T2019-0119), National Center for Global Health and Medicine (Approval No: 1690), the University of Tokyo (Approval No: 2019185NI), Waseda University (Approval No: 2018-318), and the RIKEN Center for Integrative Medical Sciences (Approval No: H30-7). We conducted a prospective cross-sectional study of 4198 individuals participating in the Japanese 4D microbiome project, which commenced in January 2015 and is ongoing20.Participants registered in the project were those who visited hospitals in the area for disease diagnosis or a health checkup. Faecal samples are collected from both healthy and diseased participants. The eligibility criteria for participants are as follows: (1) born and raised in Japan; (2) age >15 years; (3) written informed consent provided; and (4) having an endoscopic diagnosis on colonoscopy; either having undergone a colonoscopy within the last 3 years or planning to undergo colonoscopy for colorectal cancer screening, surveillance, and diagnosis of various gastrointestinal symptoms. The exclusion criteria were as follows: (1) suspected acute infectious disease based on clinical findings (e.g., acute enterocolitis, pneumonia, tuberculosis etc.); (2) acute bleeding; (3) hearing loss; (4) unable to understand written documents; (5) unable to write and (6) limited ability to perform activities of daily living. No compensation was paid to participants.Participants collected faecal samples using a Cary–Blair medium-containing tube60 at home, and the samples were refrigerated for up to 2 days before the hospital visit. Immediately after participants arrived at the hospital, their faecal samples were frozen at −80 °C until DNA extraction. We avoided collecting samples within 1 month of administering bowel preparation for colonoscopy because it has a profound effect on the gut microbiome and metabolome61. Health professionals checked that the amount of stool was sufficient for analysis. Shotgun metagenomic sequencing was performed for 4241 faecal samples and quality controls were conducted20, from which 43 samples were excluded from further analyses due to the low number of high-quality reads (130 bp. Encoded genes in the contigs were predicted by MetaGeneMark (3.38)70. Assembled contigs were defined as phages if they passed all of the following six criteria.

1.

A genome size threshold was applied, and contigs less than 10 Kb were excluded, as typical dsDNA phages have genomes larger than >10 Kb71.

2.

Viral-specific k-mer patterns were checked by DeepVirFinder (v1.0)22. Contigs with p-values >0.05 were excluded from further analysis.

3.

To detect viral hallmark genes (VHGs) and plasmid hallmark genes, we performed a highly sensitive HMM-HMM search against the Pfam database72. First, the encoded genes were aligned to the viral protein database, collected from complete (circular) viral genomes (n = 13,628) in the IMG/VR v2 database30 using JackHMMER. The obtained HMM profiles were searched against the Pfam database using hhblits73 with a  >95% probability cut-off. These procedures were performed using the pipeline_for_high_sensitive_domain_search script (https://github.com/yosuken/pipeline_for_high_sensitive_domain_search)74,75. Contigs with plasmid hallmark genes or those without VHGs were excluded. The hallmark genes used in this analysis are summarised in Supplementary Data 3.

4.

The presence of housekeeping marker genes of prokaryotic species was checked by fetchMG (v1.0)76, and ribosomal RNA genes (5 S, 16 S and 23 S) were identified by barrnap (0.9) (https://github.com/tseemann/barrnap). Contigs with the marker genes and ribosomal RNA genes were excluded from further analysis.

5.

The encoded genes of each contig were aligned to the viral protein database and a plasmid protein database constructed from the reference plasmids in RefSeq (n = 16,136, in April 2020) using DIAMOND (v0.9.29.130)77 with the more-sensitive option. The number of genes aligned to each database was compared, and contigs with more genes aligned to the plasmid protein database were excluded from further analysis.

6.

The proportion of provirus regions was assessed by CheckV (v0.7)24, and contigs estimated with 70% and 10% contamination.To evaluate the performance of this custom pipeline, we applied the pipeline to reference phage genomes (n = 2609, as positive data) and plasmid sequences (n = 16,136, as negative data) in Refseq. The true positive rate was defined as the number of phages detected as phages by the pipeline divided by the number of reference phages. The false positive rate was defined as the number of plasmids detected as phages by the pipeline divided by the number of reference plasmids. DeepVirFinder22, VirSorter (v1.0.3)23 Virsorter2 (2.2.3)25, VIBRANT (v1.2.1)26, Seeker (v1.0.3)27 and ViralVerify (v1.1)28 were also applied to the same datasets with the default parameters, and the performance was compared among them.Analysis of phage genomesViral operational taxonomic units (vOTUs) were constructed by clustering phage genomes with a  > 95% identity29 using dRep (v2.2.3)78 with the default options. Representative sequences of each vOTU selected by dRep were further clustered with reference sequences in RefSeq, IMG/VR30, gut virome database (GVD)15, gut phage database (GPD)9, and metagenomic gut virus (MGV) database31 with >95% identity and >85% length coverage using aniclust.py script in the CheckV package to identify common sequences among the databases.To further construct broader viral clusters (VC), proportions of protein clusters shared between phages were assessed. First, to define protein clusters, similarity searches of all protein sequences from all the phages identified in this study were performed using DIAMOND with the more-sensitive option (e-value 20% of clusters were grouped as a VC, which corresponds approximately to family- or subfamily-level clusters7,37. Rarefaction curves of the vOTUs and VCs were estimated with the iNEXT function in the iNEXT package (v2.0.20)80. The similarity matrix of the phages based on the percentage of shared protein clusters was further projected by tSNE using the tsne function in the Rtsne package (v0.16).Taxonomy annotation of phages was performed with a voting approach described previously16 with minor modifications. First, the protein sequences of each phage were aligned to viral proteins detected from phage genomes in RefSeq (n = 2609, in April 2020) using DIAMOND with the more-sensitive option. Then, the best-hit taxonomy of each protein (family levels) was counted, and the most common taxonomy was assigned to the phage if >20% of proteins in the phage were aligned to the same taxonomy.Phage lifestyles (i.e. virulent or temperate) were predicted by BACPHLIP40 and alignments to reference bacterial genomes in the RefSeq. Phages were defined as temperate if the BACPHLIP score was >0.8 or the phage genome was aligned to any reference genomes with >1000 bp alignment length with >95% identity.Host predictionBacterial and archaeal genomes were downloaded from the RefSeq database (in April 2019). To reduce the redundancy of genomes from closely related strains in the same species (e.g. Escherichia coli), 10 genomes were selected randomly for species with more than 10 genomes, and other genomes were excluded from the dataset. The reference dataset consisted of 33,215 bacterial and 822 archaeal genomes.Host prediction of the identified phages was performed using CRISPR spacers81. CRISPR spacers were predicted from the reference microbial genomes and assembled contigs ( >10,000 bp) from the 4198 metagenomic datasets using PILER-CR (1.06)82. Short (100 bp) spacers were discarded. In total, 679,323 and 283,619 spacers were identified from the reference microbial genomes and assembled contigs, respectively. Taxonomy information was assigned to the assembled contigs if they were aligned to the microbial reference genomes with >90% identity and >70% length coverage thresholds using MiniMap283. The CRISPR spacers were mapped to the phage genomes using BLASTN with the option for short sequences: -a20 -m9 -e1 -G10 -E2 -q1 -W7 -F F81. CRISPR spacers, which were mapped with 100% identity or 1 mismatch/indel with >95% sequence alignment, were used for host assignment at the genus level. Assignments of host species were checked manually, and if any of the following non-human intestinal species were assigned, the host was excluded: Dickeya, Anaerobutyricum, Rubellimicrobium, Eisenbergiella, Harryflintia, Leucothrix, Photorhabdus, Spirosoma, Syntrophobotulus, Thermincola, Algoriphagus, Franconibacter, Kandleria, Lawsonibacter, Methylomonas, Provencibacterium, Pseudoruminoccoccus, Rhodanobacter, Romboutsia, Sharpea, Varibaculum and Thioalkalivibrio.Quantification of viral abundance and analysis of the virome profileTo quantify the viral abundances in each sample, metagenomic reads were mapped to the gene set of VHGs (Supplementary Data 3) of each representative vOTU using Bowtie2 with a  > 95% identity threshold, and reads per kilobase million (RPKM) were calculated for each vOTU. The reason for using only VHGs in the analysis was to avoid over-counting of viral reads, which could be caused by spurious mapping of reads from horizontally transferred genes of other phages or bacterial species. The α-diversity (Shannon diversity) of the vOTU-level viral profile was calculated using the diversity function in the vegan package. The β-diversity (Bray-Curtis distance) between individuals was assessed using the vegdist function, and the average distance against other individuals was calculated for each individual. The VC-level viral profile was obtained by summing all the RPKM of vOTUs for each VC.Phylogenetic analysis of novel VCsTo construct phylogenetic trees for the vOTUs and reference genomes, protein sequences of large terminases, portal proteins, and major capsid proteins (Supplementary Data 3), which are often used to construct phage phylogenetic trees7,9, were extracted from the vOTUs in the 10 most abundant VCs (VC_19, 1, 2, 24, 12, 15, 3, 44, 18, 6), and their homologues were searched for in the reference phage genomes in RefSeq using DIAMOND with the more-sensitive option (e-value 0.01% (n = 865) and genera with average relative abundance >0.5% (n = 32) were included in the analysis.Analysis of VLPs and whole metagenomes from 24 faecal samplesQuality filtering of sequenced reads from the 24 VLPs and whole metagenomes was performed using fastp (version 0.20.1)92 with the default parameters. Contamination with human (hg38) or phiX genomes was excluded by mapping the reads to the genomes using Bowtie2.To exclude bacterial DNA contamination in the VLP dataset, we performed further filtering. First, the VLP reads were assembled into contigs using MEGAHIT and the contigs were checked for virus or not. Contigs were defined as viral contigs if they were predicted as viruses by DeepVirFinder (P-value More

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Effects of Se and K-humate on nitrogen concentrationsThe N concentration in the soil varied in availability and total content in oat straw and seeds after the foliar application of Se and K-humate. Se alone increased the availability of N in the soil in the following order: Se3  > Se2  > Se1  > control. Thus, Se was found to increase the available N-soil in an application-rate-dependent manner (Table 2). The availability of N-soil after Se application was improved via the simultaneous application of K-humate with the same rate-dependence as observed with Se alone. Comparable results were found using the sum of means for analysis. The insignificant difference found between the sum of means for control and treatment at an Se concentration of 12 × 10−3 mM Se may reflect the relatively low concentration of Se used.Table 2 Effect of selenium and K-humate on nitrogen content.Full size tableThe total N-straw content increased as a result of an increased content of N-plant (Table 2). Differences were found to be insignificant between Se concentrations of 12 × 10−3 mM, 63 × 10−3 mM, and controls. Likewise, the simultaneous application of K-humate showed insignificant differences between Se concentrations of 63 × 10−3 mM and 88 × 10−3 mM. Insignificant differences were noted between the control and Se concentration of 12 × 10−3 mM and the Se concentration of 63 × 10−3 and 88 × 10−3 mM using the sum of means. The total N-seeds content increased for application rates of 12 × 10−3–88 × 10−3 mM, and the simultaneous application of K-humate augmented this increase. The application rate dependency of the effects of Se and K-humate application was identical to that observed in N-soil and N-straw. No significant differences among Se and K-humate applications were observed. An insignificant difference was observed among the sum of means for Se and K-humate applications at concentrations of 63 × 10−3 and 88 × 10−3 mM.The application of Se caused proportional increases in N-soil, N-straw, and N-seeds, and the simultaneous application of K-humate improved this effect. Previously, the application of Se resulted in an increase in the accumulation of NPK which altered N and K distribution. However, the distribution of P was not affected19. Furthermore, the application of Se ultimately resulted in an increase in the accumulation of N, calcium (Ca), K, and Mn20. A significant increase in concentrations of N and S in the rice grain plants grown under N-limiting conditions was also observed while the Ca that have been treated with Se regardless of N supply21. Thus, a synergistic interaction between Se and N in total grain proteins was reported21.Effects of Se and K-humate on PThe effect of applications of different Se concentrations without K-humate on the available P-soil showed a reduction in the following order: Se3  > Se2  > Se1  > control (Table 3). Thus, the foliar application rate of Se caused a rate-dependent increase in the available P-soil. Simultaneous application of K-humate further increased P-soil availability. A rate dependency similar to Se alone was also observed with simultaneous Se and K-humate application. A similar result was observed using the sum of means for data analysis. Significant differences were observed among all treatments.Table 3 Effect of selenium and K-humate on phosphorous content.Full size tableFoliar application of Se increased total P-straw. An insignificant difference was found between the control and Se concentrations of 12 × 10−3 and 63 × 10−3 mM, which was similar to findings observed after the application of K-humate. Moreover, insignificant differences were observed between the applications of Se and Se + K-humate. An insignificant effect was found between control and Se concentrations of (12 × 10−3 and 63 × 10−3 mM), and K-humate application using the sum of means.The application of Se having concentrations ranging from 12 × 10−3 to 88 × 10−3 mM resulted in increased P-seeds and the addition of K-humate augmented this effect (Table 3). The effect of Se and K-humate applications showed a decrease in the following order: Se3  > Se2  > Se1  > control. Insignificant differences between values were observed when Se was applied without K-humate at concentrations of 12 × 10−3 and 63 × 10−3 mM, and for the sum of means for Se and K-humate applications at concentrations of 12 × 10−3 and 63 × 10−3 mM. Thus, the application rate of Se caused a proportional increase in P-soil, P-straw, and P-seeds. Furthermore, the simultaneous application of K-humate augmented this effect.Consistently, concentrations of P and Ca increased in response to the application of selenite-Se (Na2SeO3⋅5H2O) to maize seedlings22, and the application of Se led to an increase in the accumulation of NPK, with alteration of N and K distribution. However, the distribution of P was not influenced19.Effects of the foliar application of Se and K-humate on KDifferent application rates of Se without humate increased K-soil and this effect showed a decrease in the following order: Se3  > Se2  > Se1 = control (Table 4). Again, the foliar application rate of Se causes a proportional increase, in this case, in K-soil. The application of K-humate with Se augmented this effect. A similar rate dependency was also observed with simultaneous application and when the sum of means was used. An insignificant difference was observed between the sum of means for controls and Se concentrations of 12 × 10−3 mM.Table 4 Effect of selenium and K-humate on potassium content.Full size tableThe foliar application of Se led to a slight increase in the total K-straw content (Table 4). An insignificant change was observed for Se concentrations from 12 × 10−3 to 88 × 10−3 mM, and similar results were found with the additional application of K-humate.The application of Se at concentrations from 12 × 10−3 to 88 × 10−3 mM resulted in a slight increase in K-seeds, and the additional application of K-humate only slightly increased the accumulation of K (Table 4). An insignificant difference was observed between Se alone and with K-humate. Similar findings were noted when the sum of means was used for analysis. Se application rates thus produce a proportional increase in K-soil but not in K-straw or K-seeds. Comparable data were noted after K-humate addition. Concentrations of K previously decreased in response to selenite-Se (Na2SeO3⋅5H2O) application to maize seedlings; however, magnesium (Mg) concentrations did not change22. Moreover, the application of Se led to the accumulation of NPK and altered N and K distribution without affecting the P distribution19. Consistently, the application of Se ultimately resulted in increasing K accumulation20.Effects of Se and K-humate application on oat growthApplication of Se improved the yield, which was assessed as kg × 10−3/feddan (Table 5). Higher concentrations of Se produced a higher yield of oat. The effect of Se showed a reduction in the following order: Se3  > Se2  > Se1  > control. The simultaneous application of K-humate increased the yield only slightly, resulting in insignificant differences. Similar findings were also observed when the sum of means was used. In contrast, seed production was not significantly affected, and plant length (m × 10–2) did not show a significant response. In contrast, Se application to potato plants enhanced tuber yield, plant growth, and quality compared with controls. Moreover, Se application along with different N additions ultimately increased potato productivity compared with Se or N alone23. Similarly, the grain yield increased when Se was applied; this application was significant at low levels24.Table 5 Effect of Se and K-humate application on oat growth.Full size tableEffects of Se and K-humate applications on OMS (%) and non-enzymatic antioxidants and total phenols in oat plantsThe total OMS content increased with increasing Se concentrations, perhaps due to stimulation of root growth or microbial biomass. This effect showed a decrease in the following order: Se3  > Se2  > Se1  > control. The addition of K-humate by foliar application significantly augmented the OMS content (%) (Table 6). Application of Se also increased the non-enzymatic antioxidant content; however, the increases were insignificant at Se concentrations of 12 × 10−3 and 63 × 10−3 mM. The highest values for non-enzymatic antioxidants were observed at Se concentrations of 88 × 10−3 mM. The application of K-humate along with Se did not significantly augment the effects observed after the application of Se alone. Analyses using the sum of means were completely consistent with these findings.Table 6 Effect of selenium and K-humate application on organic matter in soil (OMS), non-enzymatic antioxidant, and total phenols in oats.Full size tableSe positively enhanced the total phenol content with effects decreasing in the following order: Se3  > Se2  > Se1  > control. Furthermore, this effect was significantly amplified with the simultaneous application of K-humate. Analysis using the sum of means gave comparable results. Se enhances the ability of plants to cope with stress by stimulating plant cell antioxidant capacity though the upregulating of antioxidant enzymes, such as CAT, SOD, and GSH-Px. Se also increases the synthesis of PCs, GSH, proline, ascorbate, alkaloids, flavonoids, and carotenoids. Se may also induce the spontaneous dismutation of the superoxide radical into H2O2. Elevated antioxidant capacity can reduce lipid peroxidation by lowering ROS accumulation under metal-induced oxidative stress conditions25. Application of Se using foliar spray also induced an increase in the concentration of rosmarinic acid20.Effects of Se and K-humate applications on Se contentAfter the application of Se, Se-soil concentrations increased. The effects of Se concentrations decreased in the following order: Se3  > Se2  > Se1  > control. The additional application of K-humate significantly amplified these effects (Table 7). The treatment of K-humate that increased Se content in the soil may be owing to experimental errors, however, increasing Se content in either straw or seeds may be owing to the increased stimulating movement from soil to different parts of the plant. Se-straw content increased with increasing the Se foliar application; this effect decreased in the following order: Se3  > Se2  > Se1  > control. The simultaneous application of K-humate augmented the effects observed after the application of Se alone. Total Se concentration also increased Se-seeds like Se-straw for Se alone, Se with K-humate, and using the sum of means for analysis.Table 7 Effects of Se and K-humate applications on Se content.Full size tableEffects of Se and K-humate application on Cr contentThe highest concentrations of Cr were observed in control plants followed by Se2  > Se3  > Se1. In response to Se application, the Cr-straw content decreased (Table 8). The difference between Se2 and Se3 was insignificant. K-humate addition induced a notable increase in Cr-straw in the following order: control  > Se3  > Se2  > Se1. This may be owing to the increased stimulating movement of Cr from soil to different parts of the plant. Results obtained from Se treatments varied depending on the presence of K-humate. Cr-seeds decreased in the following order: Se2  > Se3  > Se2  > control. The addition of K-humate increased the Cr-seed content compared with Se alone; however, the difference between Se2 and Se3 was insignificant. Analysis using the sum of means did not produce significant differences.Table 8 Effects of Se and K-humate application on Cr content.Full size tableEffects of Se and K-humate applications on Fe contentVariable effects were produced using different application rates of Se on Fe-straw, and this effect was observed in the following order: Se3  > Se1  > control  > Se2 (Table 9). Differences were insignificant among control, Se1, and Se2. K-humate caused concentrations of Fe-straw to significantly increase in the following order: control  > Se3  > Se2  > Se1. Differences between control and Se3 as well as Se1 and Se2 were insignificant. Analysis using the sum of means was similar. Neither Se nor Se with K-humate applications produced significant changes in Fe-seeds. Analysis using the sum of means was similar. Low concentration of Se application may enhance plant productivity and encourage phytoremediation by improving plant tolerance to stress and enhancing photosynthesis25. Further, a significant increase was observed in concentrations of Fe and S in rice grain grown in N-limiting conditions while Ca that have been treated with Se regardless of N supply21.Table 9 Effects of Se and K-humate applications on Fe content.Full size tableEffects of Se and K-humate application on Mn contentApplication of Se reduced the Mn-straw content, and this effect was observed in the following order: control  > Se2  > Se1  > Se3. No significant difference was found between control and Se1 (Table 10). In contrast, K-humate addition further reduced Mn-straw concentrations in the following order: control  > Se1  > Se3  > Se2. The control and Se1 were not significantly different when using the sum of means for analysis. Likewise, no significant difference was seen between Se1 and Se3. Accumulation of Mn in seeds varied among treatments in the following order: control  > Se2  > Se3  > Se1. K-humate addition altered this order to be in the following order: control  > Se2  > Se1  > Se3. No significant differences were observed between Se2 and Se3 when the sum of means for analysis was used. Previously, the application of Se increased the concentrations of Mg and molybdenum in grains grown in 16 and 24 mM N compared with N-limited plants21.Table 10 Effects of Se and K-humate application on Mn content.Full size tableEffect of Se and K-humate applications on Zn content in oat plantsApplication of Se2—the middle concentration of Se—resulted in highest accumulation in Zn-straw, and this effect was observed in the following order: Se2  > Se1  > control  > Se3 (Table 11). The application of K-humate with Se resulted in some insignificant variations compared with the application of Se alone. Control, Se1, and Se3 were insignificantly different when the sum of means was used for the analysis. Concentrations of Zn in seeds were reduced after Se application. K-humate with Se foliar application altered the concentration of Zn in seeds with impacts in the following order: control  > Se3  > Se1  > Se2. The difference between Se1 and Se3 was insignificant. Additionally, insignificant differences in Zn concentrations after application of Se1, Se2, and Se3 were found when the sum of means was used for analysis. Low concentrations of Se possibly enhance plant productivity and phytoremediation capacity by improving the ability of plants to tolerate stress and enhancing photosynthesis25.Table 11 Effect of Se and K-humate applications on Zn containing oat plant.Full size tableEffects of Se and K-humate application on Cu contentIncreasing concentrations of Se from 12 × 10−3 to 88 × 10−3 mM increased the concentration of Cu-seed, and this effect was observed in the following order: Se1  > control  > Se2  > Se3 as it shown in Table 12. Application of Se with K-humate showed significant changes in the Cu-straw content in the following order: Se1  > Se2  > control  > Se3. No significant differences were observed using the sum of means for analyses. In contrast, the foliar application of Se resulted in increases in Cu-seed at concentrations of Se1 and Se3; however, at 63 × 10−3 mM (Se2), a reduction in Cu-seed was observed. K-humate with Se simultaneously resulted in increased Cu-seed content with impacts decreasing in the following order: Se3  > Se1  > control  > Se2. The sum of means analysis showed no significant variation between control and Se2. Previously, the application of Se led to a decrease in the concentrations of Cu in grains grown in 16 and 24 mm N compared with N-limited plants21.
Table 12 Effects of Se and K-humate application on Cu content.Full size table More

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