There is a growing consensus that biochar‐elicited augmentation of rhizosphere microbial diversity and activity makes a major contribution to the positive impacts of biochar on both plant productivity and health (De Tender et al ., 2016 ; Jaiswal et al ., 2017 ; Kolton et al ., 2017 ). Seeing that soil moisture has well‐known and fundamental impacts on soil microbial activity and community structure (Brockett et al ., 2012 ; Manzoni et al ., 2012 ; Barnard et al ., 2013 ; Cavagnaro, 2016 ), we hypothesized that rhizosphere microbial enhancement could be promoted by biochar that is ‘activated’ by pre‐wetting in the soil before planting, much in the same way that manure or compost is often used. This simple approach may result in synergies between biochar and soil moisture that lead to increased microbial abundance, diversity and activity via pre‐planting stimulation of microbial growth and activation of dormant populations of soil microorganisms. These changes may collectively enhance the efficacy of biochar for plant growth promotion and disease suppression, as well as reduce phytotoxicity. The present study is a proof‐of‐concept using a model cucumber ( Cucumis sativus )– Pythium aphanidermatum pathosystem. P. aphanidermatum is an important soilborne pathogen that causes damping‐off and root rot diseases in young seedlings (Favrin et al ., 1988 ; Lamichhane et al ., 2017 ). Our specific research objectives were to study the influence of pre‐conditioned biochar growth medium on (1) soil bacterial and fungal composition, diversity, and activity, (2) cucumber plant performance, and (3) damping‐off caused by P. aphanidermatum .

In the majority of tested pathosystems, disease progression is retarded at relatively lower biochar doses and accelerated at relatively higher biochar doses (Jaiswal et al ., 2014 ; Copley et al ., 2015 ; Huang et al ., 2015 ). A similar dose effect also has been reported for plant growth (Rajkovich et al ., 2012 ; Spokas et al ., 2012 ; Jaiswal et al ., 2015 ), but usually, the biochar dose–response curves for plant growth and plant disease are shifted relative to each other along the biochar dose axis, a phenomenon described as the ‘shifted R max effect’ (Jaiswal et al ., 2015 ). Maximum plant growth response usually occurs at higher biochar doses than those required for maximum disease suppression. Unfortunately, diseases are sometimes stimulated at biochar doses that are optimal for plant growth (Frenkel et al ., 2017 ). As a result, a new approach is needed that will help to extend the range of biochar doses that result in a concurrence of maximum benefits for both plant productivity and disease suppression, as well as for carbon sequestration.

To date, studies testing the actual efficacy of such ‘conditioned’ biochar‐charged composts report variable positive, negative, or neutral results on plant growth (Schulz et al ., 2013 , 2014 ; Kammann et al ., 2015 ; Bass et al ., 2016 ), such that it is clear that composting is not an universal panacea for issues of biochar‐plant safety. Furthermore, composting is a costly process in terms of time (several months), space requirements, environmental nuisance (odors), and well as having its own carbon footprint as a result of energy consumption for transport of materials to and from the composting plant and the process itself (e.g. turning piles). As far as we know, co‐composted biochar has not been shown to have any impact on plant disease. By contrast, direct application of biochar in soil and potting mixtures has been shown in numerous pathosystems to impact the progress of diseases caused by both foliar and soilborne plant pathogens (Elad et al ., 2011 ; Graber et al ., 2014 ; Frenkel et al ., 2017 ).

Widescale addition of biochar (the solid co‐product of biomass pyrolysis) to soil has been suggested as a means of achieving long‐term sequestration of carbon (Lehmann, 2007 ; Laird, 2008 ), decreased greeenhouse gas emissions (Jeffery et al ., 2016 ), reduced pressure on peatlands (Vaughn et al ., 2013 ; Steiner & Harttung, 2014 ), and enhanced soil properties and plant growth (Biederman & Harpole, 2013 ; Jeffery et al ., 2015 ). This technique has yet to be widely adopted, however, for a number of reasons, including unresolved concerns about its effects on plant growth and health. For example, a recent meta‐analysis showed that biochar addition to temperate zone soils can have deleterious effects or no effect on plant growth and soil fertility, whereas its use in tropical soils often has positive impacts (Jeffery et al ., 2017 ). Moreover, there is a demonstrated effect of biochar dose on plant responses (Rajkovich et al ., 2012 ; Spokas et al ., 2012 ; Jaiswal et al ., 2015 ), and adding to the complexity, plant physical traits and plant health may demonstrate optimal responses at dissimilar biochar doses (Viger et al ., 2014 ; Jaiswal et al ., 2015 ; Rogovska et al ., 2017 ). A further concern is that some biochars are initially phytotoxic (Rajkovich et al ., 2012 ; Jaiswal et al ., 2015 ; Kammann et al ., 2015 ). As a result, commercial producers frequently promote ‘co‐composting’, whereby biochar is added at the beginning of the composting process to the organic feedstocks. This process is suggested to ‘charge’, ‘condition’, or ‘activate’ biochar, reducing its phytotoxicity and improving its soil benefits (Kammann et al ., 2015 ).

All of the plant experiments were conducted twice and the two experimental repeats were pooled and analyzed jointly with ‘experiment’ as an additional factor. Data were analyzed using Jmp 13 software as a two‐way ANOVA with biochar application (0% and 3% for microbiome and microbial activity analyses or 0%, 0.5%, 1% and 3% for all the other analyses) and pre‐conditioning status (PCS and NCS) as main factors and the interaction between them. To enable the ANOVA, percentages values were normalized by the arcsine square‐root transformation (Ahrens et al ., 1990 ). Multiple comparisons of the means were conducted using the Tukey–Kramer HSD test (α = 0.05).

Subsamples of potting mixture collected at Day 45 (after pre‐conditioning but before Pythium inoculation) were used to assess the effect of pre‐conditioning on soil microbial activity. Soil respiration rates were measured using an acid‐titration technique (Ohlinger et al ., 1996 ). Soil oxidative activity was estimated by measuring dehydrogenase (DHA) activity (Casida et al ., 1964 ). Soil hydrolytic activity was estimated with the fluorescein di‐acetate (FDA) method (Schnürer & Rosswall, 1982 ). The results of these measurements were expressed as production of CO 2 , TPF and fluorescein in mg per kg dry soil for the respiration, DHA and FDA assays, respectively.

A Bray–Curtis distance matrix, using the most abundant OTUs (at least five sequences in total), was applied to estimate the β diversity, which was then visualized using nonmetric multidimensional scaling (NMDS) using Past software ( http://folk.uio.no/ohammer/past/ ). A heatmap was created using most abundant OTUs (at least 50 sequences in total) in Qiime. Likewise, the core microbiome was calculated by using the most abundant OTUs (at least five sequences in total) in Venny (Oliveros, 2007 ) to illustrate shared and unique OTUs. Statistically significant differences in bacterial and fungal taxonomic abundances and alpha diversity parameters as a result of biochar amendments were determined by Tukey–Kramer honest significant difference (HSD) test (α = 0.05) using Jmp 13 software (SAS Institute, Cary, NC, USA). Statistical differences between groups of samples were tested by permutational multivariate analysis of variance (PERMANOVA) available through Past.

Pair‐end Fastq files were merged using Pear. The Clc software (CLC Bio, Aarhus, Denmark) was used for quality and length‐trimming (retaining sequences longer than 225 bp with minimum quality score of Q30). Subsequent analysis was performed using the Quantitative Insights into Microbial Ecology (Qiime; v.1.9.1) pipeline (Caporaso et al ., 2010 ), unless stated otherwise. The ITS1 sequence was extracted from the reads using ITSx extractor v.1.0.11 (Bengtsson‐Palme et al ., 2013 ) to remove flanking SSU and 5.8S regions. Sequences were grouped into operational taxonomic units (OTUs; 97% similarity cut‐off) using Uclust. A representative sequence from each OTU was picked and aligned using PyNast to the Silva bacterial database v.119 ( https://www.arb-silva.de/ ). Fungal sequences were aligned using Mafft (Yamada et al ., 2016 ). Each representative sequence was assigned a taxonomy using the Uclust algorithm and the Silva database for bacterial sequences and using the Uclust algorithm and the Unite database v.7.1 ( https://unite.ut.ee/ ) for fungal sequences. Utilizing the taxonomic assignments and the alignment of the representative sequences, an OTU table was created. Finally, sequences identified as chimeras by Uchime, singletons, chloroplasts and mitochondria were removed from the analysis. Operational taxonomic units generated from the data processing were used to determine β (between‐sample) and α (within‐sample) diversity. Taxonomic‐based alpha diversity was calculated based on the total number of phylotypes (richness) and on Shannon's diversity index ( H ′). For α diversity analysis, sequences from each sample were evenly subsampled to 18 000 and 30 000 sequences for bacterial and fungal analyses, respectively.

A subsample of the potting mixture sampled at Day 45 from each treatment before Pythium inoculation was stored at −80°C until DNA extraction. DNA was extracted from 0.3 g of potting mixture with two technical repeats using a commercial soil DNA extraction kit (Exgene Soil SV, Geneall, Korea) according to the manufacturer's protocol. The DNA samples were PCR‐amplified from the variable V4 region of the 16S rRNA gene using the CS1_515F and CS2_806R primer sets for bacteria (Moonsamy et al ., 2013 ) and from the ITS1 region using the CS1_ITS1F and CS2_ITS2 primer sets for fungi (Walters et al ., 2015 ), as described previously (Jaiswal et al ., 2017 ). Sequencing of PCR‐amplicons was performed using Illumina MiSeq technology at the Research Resources Center of the University of Illinois at Chicago, according to the amplicon sequencing protocol of the DNA service facility ( http://www.rrc.uic.edu/dnas ). The sequence data generated in this study were submitted to the NCBI under bioproject number PRJNA415285 for the bacterial analyses and PRJNA415280 for the fungal analyses.

Plant growth medium was sampled from each treatment at three different dates: (1) before pre‐conditioning (0 d); (2) after 45 d of pre‐conditioning (or nonconditioning) but before Pythium inoculation; and (3) at 65 d, 20 d after cucumber transplanting (20 DAT). For each treatment × date combination, three of the four biological replicates were selected randomly for sampling. Plant growth medium from the five pots making up a single biological replicate was pooled. These potting media samples were divided into subsamples for analyses of (1) soil physical and chemical properties, (2) microbiome analyses and (3) enzyme activities. The soil physical and chemical properties that were determined were soil moisture content, pH, redox potential (Eh), electrical conductivity (EC), dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC), as described in Methods S1 .

Plant shoots were sampled at the end of the plant experiments. Shoots were washed with distilled water and dried for 1 wk in a ventilated oven at 60°C. The dry tissue was ground and sieved through a 20‐mesh sieve. One hundred‐mg samples were wet ashed with H 2 SO 4 ‐H 2 O 2 and analyzed for N, P and K. Ashing in HClO 4 ‐HNO 3 was used to analyze Ca and Mg. Element concentrations were determined as follows: total N and P by autoanalyzer (Zellweger Analytics, Milwaukee, WI, USA); K by flame photometer (M410, Sherwood Sci. Ltd, Cambridge, UK), and Ca and Mg by atomic absorption spectrophotometer (AAnalyst 400; Perkin Elmer, Waltham, MA, USA).

The five plants that made up each biological replicate were used to calculate the percentage of damping‐off for each of the four biological replicates (i.e. damping off incidence). Damping‐off incidence was determined daily until the disease stopped progressing (7 d after transplanting, DAT). Daily damping‐off records were used to draw disease progress curves and calculate the Area Under the Mortality Progress Curve (AUMPC in % × days), which represents the intensity of the entire epidemic. AUMPC was determined by the trapezoid method (Jaiswal et al ., 2014 ).

There were eight main treatments: (1) nonconditioned biochar‐free substrate (NCS‐CON); (2–5) nonconditioned biochar‐amended substrate at 0.5%, 1% and 3% w/w (NCS‐GHW), respectively; v) pre‐conditioned biochar‐free substrate (PCS‐CON); and (6–8) pre‐conditioned biochar‐amended substrate at 0.5%, 1% and 3% w/w (PCS‐GHW), respectively. Each treatment included eight replicates arranged in four blocks, with five plants per biological replicate (total 40 plants per treatment). In each treatment, plants were split into two groups: 20 plants were kept pathogen‐free and 20 were inoculated with Pythium as described below. Transplanted seedlings were maintained in the glasshouse for 20 d at 26 ± 1°C under optimal fertigation and irrigation regimes as in previous studies (Jaiswal et al ., 2014 ). In total, 320 plants were used. A schematic diagram of the experimental procedure is shown in Fig. S1 . The experiment as described was repeated in its entirety twice.

Cucumber ( Cucumis sativus , cv. Muhasan, Efaal, Israel) was grown from seeds in a tray containing potting mixture that was previously homogenized with or without biochar in a pest‐ and disease‐free glasshouse at 26 ± 1°C under sprinkler irrigation. After germination, a single cucumber (6‐d‐old) seedling was transplanted to each pot (0.5 l, diameter = 10 cm) containing pre‐conditioned (PCS) or nonconditioned (NCS) growth media with or without biochar (0–3% w/w). Before cucumber seedling transplanting, the moisture content of nonconditioned treatments was adjusted to that of pre‐conditioned treatments by adding water, to neutralize the effect of moisture.

Pre‐conditioning was conducted by fertilizing and irrigating pots (3 l, diameter = 16 cm) containing potting mixture previously homogenized without biochar or with different concentrations of biochar (0.5%, 1% and 3% w/w) with drippers delivering 5 : 3 : 8 NPK fertilizer once per day for 3 min (total of 40–50 ml water per pot per day). Pots were maintained in a pest‐ and disease‐free glasshouse at 26 ± 1°C for 6 wk (45 d). In parallel, nonconditioned treatments were maintained in the same glasshouse contemporaneously over the same time period without irrigation or fertilization.

Soil microbial activities estimated by FDA activity (hydrolytic activity), dehydrogenase activity (oxidative activity) and CO 2 emission (respiration rates) were significantly increased in the PCS biochar treatments ( P < 0.0001), showing an increase of 107%, 60% and 199%, respectively, as compared to the NCS nonamended treatment (Fig. 8 a–c). In the NCS treatments, there was no effect of biochar on microbial activities. In nonamended treatments, soil hydrolytic activity and respiration rates were significantly increased by pre‐conditioning (by 31% and 68%, respectively), as compared to no conditioning (Fig. 8 a,c). A two‐way ANOVA analysis revealed that pre‐conditioning, biochar amendment and the interaction between biochar amendment and pre‐conditioning had a significant effect on soil microbial activity (Table S11 ). The synergism factor of pre‐conditioning and biochar treatment for respiration rates, FDA and DHA activities were 3, 5 and 3, respectively.

In order to illustrate the shared and unique OTUs between treatments, we used the core microbiome approach. The shared bacterial microbiome of the PCS and NCS growth media amended with or without biochar contained 352 OTUs and constituted 33.7% of the total bacterial abundance. Only a minor portion of the bacterial microbiome was specific for the NCS nonamended (three OTUs; 0.3%), NCS biochar treatment (two OTUs; 0.2%) and PCS nonamended (18 OTUs; 1.7%). However, 221 OTUs were specific for the PCS biochar treatment and constituted 17.2% of the total bacterial abundance (Fig. 7 a). Further examination of the unique 221 OTUs of the PCS biochar treatment revealed that 13% were identified as the bacterial order Sphingobacteriales , 10% as Rhizobiales , and 5–7% as Cytophagales, Flavobacteriales, Myxococcales, Opitutales and Planctomycetales . Likewise, the shared fungal microbiome contained 417 OTUs and constituted 34.5% of the total fungal abundance. The fungal specific microbiome for the NCS nonamended, NCS biochar‐amended, PCS nonamended and PCS biochar‐amended media contained 2.7% (32 OTUs), 2.8% (34 OTUs), 3.5% (42 OTUs) and 10% (121 OTUs) of the total fungal abundance, respectively (Fig. 7 b). Identification of the unique 121 fungal OTUs in the PCS biochar treatment showed that 10% was Ascomycota and 5% Basidiomycota , with the major portion of the OTUs being unassigned.

Bacterial and fungal diversity based on the overall phylotype richness (number of observed species) and Shannon's diversity clearly showed that both diversity indexes were significantly increased by the PCS biochar treatment but not by the NCS biochar as compared to the nonamended control ( P < 0.01; Fig. 6 ); by contrast, fungal Shannon's diversity was lowered in the NCS biochar treatment. Furthermore, two‐way ANOVA analysis revealed that pre‐conditioning and the interaction between biochar amendment and pre‐conditioning had a significant effect on both bacterial and fungal diversity (Table S11 ).

A Bray–Curtis distance matrix‐based NMDS ordination showed substantial shifts in the bacterial (Fig. 5 a) and fungal (Fig. 5 b) community composition as a function of biochar concentration (PERMANOVA, P = 0.0001), pre‐conditioning (PERMANOVA, P = 0.0001) and interaction of these two factors (PERMANOVA, P = 0.0001 and P = 0.0002 for bacterial and fungal communities, respectively). Furthermore, a heatmap of the most abundant OTUs revealed distinct bacterial microbiome patterns for the PCS biochar treatment as compared to the PCS nonamended and NCS (for both biochar and nonamended) treatments (Fig. S4 a). The fungal microbiome also clustered distinctly for the PCS biochar treatment in the heatmap, but the effect was weaker as compared to the bacterial community (Fig. S4 b).

The fungal genera that are possibly affiliated with plant disease, plant growth promotion, disease suppression and other possible ecological roles as previously described in the literature are listed in Table S10 with statistical analysis (only abundance > 0.1% are displayed). Genera that were stimulated by the PCS biochar treatment were: Penicillium, Umbelopsis, Solicoccozyma, Trichoderma, Cryptococcus, Mortierella, Clonostachys, Thermomyces, Zopfiella, Aspergillus and Chloridium (by two‐ to 50‐fold; P < 0.001); Blastobotrys and Scedosporium (by 40‐ to 50‐fold; P < 0.0001). By contrast, the PCS biochar treatment significantly reduced other fungal genera such as Clitopilus, Rhodotorula, Arthrinium and Gymnopilus ( P < 0.001). The genera Leucocoprinus, Oidiodendron, Devriesia, Scytalidium, Naganishia, Pseudogymnoascus, Exophiala, Archaeorhizomyces, Fusarium, Cladophialophora, Myxocephala and Cladosporium were not significantly influenced by any of the treatments ( P > 0.05).

Bacterial communities at the lower taxonomic levels included a number of OTUs that belong to genera with documented activities for plant growth promotion, disease suppression and other possible ecological roles, as described previously in the literature (Table S9 ). The genera that were stimulated by the PCS biochar treatment are listed in Table S9 , with statistical analysis (only defined genera with abundance > 0.1% are displayed). The most notable enriched genera were Pseudolabrys, Phenylobacterium, Chitinophaga, Streptomyces, Afipia, Bacillus, Arthrobacter, Bdellovibrio , Flavobacterium, Opitutus, Devosia, Pseudomonas and Mesorhizobium (by two‐ to 50‐fold; P < 0.001); Aquincola, Luteimonas , Flavisolibacter (by 60‐ to 100‐fold; P < 0.0001); Hyphomicrobium , Novosphingobium and Cellvibrio (by 100‐ to 140‐fold; P < 0.001). By contrast, the PCS biochar treatment significantly reduced other genera of bacteria such as Rhodanobacter, Rhizomicrobium, Mucilaginibacter, Burkholderia, Bradyrhizobium, Acidobacteria group ( Granulicella, Telmatobacter and Acidobacterium ) , Actinobacteria group ( Acidothermus and Conexibacter ) ( P < 0.001).

Relative abundances of major bacterial orders were significantly altered by biochar amendments and pre‐conditioning, as illustrated in Fig. 4 (a) and Table S8 . The most dominant bacterial orders that showed significantly higher abundances in the PCS biochar treatments were Rhizobiales, Myxococcales, Opitutales, Caulobacterales, Sphingomonadales, Planctomycetales, Flavobacteriales, Pseudomonadales, Gemmatimonadales, Cytophagales, Nitrosomonadales, Bacillales, Streptomycetales and Bdellovibrionales ( P < 0.01). By contrast, the relative abundances of Acidobacteriales, Xanthomonadales, Rhodospirillales, Solirubrobacterales, Acidimicrobiales, Gaiellales and Frankiales were substantially lower in the PCS biochar treatments ( P < 0.0001). The relative abundances of the fungal orders: Eurotiales , Mucorales , Filobasidiales , Sordariales and Microascales were significantly higher in the PCS biochar treatments ( P < 0.001; Fig. 4 b). Two‐way ANOVA analysis revealed that the interaction of biochar amendment and pre‐conditioning had a significant effect on the microbial (both bacterial and fungal) composition at all taxonomic levels (Tables S7–S10 ).

The vast majority of bacteria (86–93%) in the present study were associated with five primary phyla: Proteobacteria , Bacteroidetes , Acidobacteria , Actinobacteria and Verrucomicrobia . The relative abundance of Proteobacteria was significantly higher in the biochar‐amended media as compared to the nonamended treatment but the effect was much higher for the PCS biochar treatment than the NCS treatment ( P < 0.0001). The relative abundances of Verrucomicrobia , Planctomycetes, Gemmatimonadetes and Firmicutes were significantly higher only in the PCS biochar treatment as compared to the other treatments ( P < 0.001; Fig. 3 a; Table S7 ). By contrast, the relative abundances of Acidobacteria and Actinobacteria were significantly lower in the biochar treatments as compared to the nonamended treatments, with the decline in the PCS biochar treatment being significantly greater than in the NCS treatment ( P < 0.0001; Fig. 3 a; Table S7 ).

Altogether, 12 samples were analyzed for bacterial communities, generating a total of 581 665 reads with an average of 48 472 reads per sample from which 341 540 high‐quality sequences were selected for downstream analysis. Likewise, for the fungal analyses, a total of 658 837 reads with an average of 54 903 reads per sample were generated from 12 samples from which 508 848 high‐quality sequences were selected for downstream analysis. Quality control screening and binning resulted in a total of 8994 and 2721 unique OTUs for bacterial and fungal sequences, respectively.

Several fluorescent Pseudomonas, Flavobacterium and Bacillus strains that were selected randomly from amongst those that were cultured and isolated from the biochar treatments showed antagonistic activity against P . aphanidermatum and inhibition of mycelial growth by 35–61%, depending on the strain. Potent antagonistic strains identified using 16S rRNA gene sequencing showed highest similarity (i.e. 98–99%) to Pseudomonas fluorescens , Ps. putida , Ps. koreensis , Ps. moraviensis, Ps. monteilii , Bacillus subtilis and Flavobacterium johnsoniae , all of which have been described previously as plant growth promoting or biocontrol agents (Bais et al ., 2004 ; Kloepper et al ., 2004 ; Haas & Defago, 2005 ; Bakker et al ., 2007 ; Kolton et al ., 2014 ).

Culture counts of general bacteria, fluorescent Pseudomonas spp. and Bacillus spp. were not affected by the NCS treatment at any biochar concentration, whereas Actinomycetes spp. and filamentous fungi abundance slightly increased at higher biochar concentrations (Table S6 ). However, the PCS treatment significantly increased the abundance of general bacteria, fluorescent Pseudomonas spp., Bacillus spp., Actinomycetes spp. and filamentous fungi by six‐, eight‐, two‐, 14‐ and two‐fold, respectively, as compared to the NCS nonamended control ( P < 0.001). A linear ANOVA model was applied to investigate the effect of biochar concentration and its interaction with pre‐conditioning of biochar growth medium on abundance of culturable microorganisms (Table S6 ). For most of the tested microorganisms, the factors ‘biochar concentration’ and ‘pre‐conditioning’ had a significant effect, along with the interaction between those two main factors (‘biochar concentration × pre‐conditioning’, P < 0.05). The synergism factors of pre‐conditioning and biochar treatment for abundance of general bacterial, fluorescent Pseudomonas spp., Bacillus spp., Actinomycetes spp. and filamentous fungi reduction were 12, 33, 2, 4 and 3, respectively.

The Pythium population in the soil at the end of the plant experiment was significantly reduced by biochar in both the NCS and PCS treatments as compared to the nonamended control ( P < 0.01; Fig. S3 ). The Pythium population in the PCS treatment was significantly lower than in the NCS treatment, with no significant differences between nonamended controls of the NCS and PCS treatments (Fig. S3 ). The synergistic factor of pre‐conditioning and biochar treatment for Pythium population reduction was 2.

The water‐holding capacity of the growth media was not influenced by biochar amendments (Table S5 ). The addition of 3% biochar significantly increased the pH in both the NCS and PCS treatments by 0.4 and 1.1 pH units, respectively, over the control; at the same time, the pre‐conditioning process reduced pH in all treatments at the beginning of the plant experiments (Table S5 ). The addition of biochar resulted in significant increases in EC values and in DOC and DIC contents in the NCS treatment but not in the PCS treatment (Table S5 ; Fig. S2 ). The rinsing of water‐soluble alkaline salts during pre‐conditioning is partly or wholly responsible for lower pH and EC values in the PCS biochar treatments. Some of these salts are water soluble bicarbonates or carbonates, as denoted by the reduction in water soluble inorganic carbon (DIC) in the PCS biochar treatments. The redox potential value was reduced by biochar addition in the NCS treatment but there was no effect of biochar addition on redox potential in the PCS treatment at the start of the experiment (Table S5 ).

The content of nutritional elements (N, P, K, Ca and Mg) in the cucumber shoots of the noninoculated treatments was not significantly affected by either the NCS or PCS biochar treatments, with the exception of Ca (Table S4 ). Shoot Ca content was increased in a similar way in the NCS and PCS biochar treatments as compared to the respective nonamended controls ( P < 0.001; Table S4 ). Shoot Ca was increased by 20%, 32% and 77% in the NCS 0.5%, 1% and 3% biochar concentrations, respectively, as compared with the PCS nonamended control. Compared with the nonamended PCS control, increases in Ca content for the 0.5%, 1% and 3% biochar amended PCS treatments were 18%, 41% and 70%, respectively.

In the noninoculated plants, net photosynthesis rate was not significantly affected by the NCS biochar treatment ( P = 0.4519; Fig. 2 c), whereas the 3% biochar amended PCS treatments significantly improved the photosynthesis rate by 23% as compared to the nonamended control ( P < 0.01; Fig. 1 c). In the presence of the pathogen, the NCS biochar treatment enhanced the photosynthesis rate by 20% and 15% at 0.5% and 1% biochar doses, respectively, as compared to the nonamended control treatment ( P < 0.001; Fig. 1 d). The PCS biochar treatments enhanced the photosynthesis rate at all biochar concentrations (by 30%, 35% and 37% for 0.5%, 1% and 3%, respectively) as compared to the nonamended control ( P < 0.0001; Fig. 1 d). Similar to the disease suppression results, pre‐conditioning of the biochar medium also increased the efficacy of biochar for plant performance in the 3% biochar treatments (Fig. 1 c,d). The synergistic factor of pre‐conditioning and biochar treatment for the canopy DW and photosynthesis rate in the noninoculated treatment was 7 and 5, respectively, and 2 and 11, respectively, in the inoculated treatment.

Effect of nonconditioned (NCS) and pre‐conditioned (PCS) biochar treatments at concentrations of 0%, 0.5%, 1% and 3% biochar on (a, b) canopy DW; (c, d) photosynthesis rate, (b, d) with Pythium inoculation (I) and (a, c) without inoculation (NI). Columns labeled by a different uppercase letter and lowercase letter are significantly different at P ≤ 0.05 according to Tukey–Kramer honest significant difference test within the NCS and PCS biochar treatments, respectively. Significant difference at *, P ≤ 0.05 according to Student's t ‐test between the NCS and PCS biochar treatments at the same concentration. Bars represent ± SE.

The effect of biochar on plant growth and physiological parameters in the presence and absence of the pathogen is presented in Fig. 2 (a–d). In the noninoculated plants, the NCS biochar treatments increased canopy DW at 0.5% and 1% concentration by 29% and 35% compared with the control, respectively ( P < 0.001; Fig. 2 a), with no effect at 3% biochar. Thus, a nonmonotonic, U‐shaped biochar dose–response curve for plant growth in the NCS treatment was exhibited. However, pre‐conditioned biochar at the 3% concentration significantly enhanced plant DW by 52% as compared with the PCS nonamended treatment ( P < 0.001; Fig. 2 a). In the presence of the pathogen, canopy DW was increased by 57% and 62% in the NCS 0.5% and 1% biochar concentrations, respectively, as compared with the NCS nonamended control. Compared with the nonamended PCS control, increases in DW for the 0.5%, 1% and 3% biochar amended PCS treatments were 45%, 59% and 70%, respectively ( P < 0.001; Fig. 2 b).

The linear ANOVA model was applied to investigate the effect of biochar concentration and its interaction with pre‐conditioning on Pythium diseases (Table S3 ). All ANOVA models were highly significant. The factors ‘biochar concentration’ and ‘pre‐conditioning’ had significant effects on both disease parameters, as did the interaction between those two main factors (‘biochar concentration × pre‐conditioning’, P < 0.001). The synergism factors calculated from the Abbott's equation for the pre‐conditioning plus biochar treatment for final damping‐off and AUMPC were 8 and 4, respectively.

Effect of (a) nonconditioned (NCS) and (b) pre‐conditioned (PCS) growth medium with or without GHW‐350 biochar on the progress of cucumber damping‐off, (c) final damping‐off and (d) area under the mortality progress curve (AUMPC). Columns labeled with a different uppercase and lowercase letters are significantly different at P ≤ 0.05 according to the Tukey–Kramer honest significant difference test within the NCS and PCS biochar treatments, respectively. Significant difference at *, P ≤ 0.05 according to Student's t ‐test between the NCS and PCS biochar treatments at the same concentration. Bars represent ± SE.

The effects of PCS and NCS growth media with or without biochar (0%, 0.5%, 1% and 3%) on cucumber damping‐off progress curves are presented in Fig. 1 (a,b). In general, damping off was observed 24 h after inoculation, with no significant difference between the treatments during the first 2 d. Damping‐off incidence progressed rapidly and became stable 7 DAT (Fig. 1 a,b). The effect of biochar on final damping‐off of cucumber is presented in Fig. 1 c. In the NCS treatments, the addition of biochar did not significantly suppress the damping‐off as compared with the nonamended control ( P = 0.4; Fig. 1 c). However, the PCS biochar treatment exhibited significantly improved disease suppression ( P < 0.0001; Fig. 1 c). Damping‐off incidence decreased by 28%, 42% and 58% in the 0.5%, 1% and 3% biochar concentrations, respectively, as compared with the PCS nonamended control. Compared with the nonamended NCS control, reductions for the 0.5%, 1% and 3% biochar amended PCS treatments were 33%, 46% and 62%, respectively. Daily damping‐off records during the 7 d of the infection period were used to calculate AUMPC disease incidence values. The AUMPC values show a similar pattern to the final damping‐off ( P < 0.0001; Fig. 1 d), decreasing by 24%, 40% and 59% in the 0.5%, 1% and 3% biochar concentrations, respectively, as compared with the PCS nonamended control. Reductions for the 0.5%, 1% and 3% biochar amended PCS treatments were 33%, 46% and 63%, respectively, as compared with the nonamended NCS control. Comparisons between the NCS and PCS treatments within each biochar concentration show that pre‐conditioning increased the efficacy of biochar for disease suppression by 20–62% (Fig. 1 c,d).

Discussion

One of the difficulties in knowing how to use biochar in agriculture is that nonmonotonic dose–response curves are quite prevalent. Often, a maximum in plant responses (growth and/or disease resistance) is observed at some intermediate biochar dose, and frequently, there is an offset between the best dose for growth and the best dose for plant protection (Jaiswal et al., 2015). This effect, possibly due to low concentrations of phytotoxins that are brought in with the biochar, has spawned an industry effort to pre‐condition biochar, reducing toxins and ‘charging’ it with beneficial microorganisms and nutrients, usually by co‐composting biochar and other organic feedstocks (Kammann et al., 2015). However, co‐composting is a costly, time‐consuming and so‐far unproven practice in terms of added value for plant health. Co‐composts also have variable effects (positive, negative or neutral) on plant growth (Schulz et al., 2013, 2014; Kammann et al., 2015; Bass et al., 2016). The issue is further complicated by the fact that compost is a very heterogeneous material whose qualities depend strongly on the composting feedstock, process and maturity (Noble, 2011). In the current study, we found that a simple biochar conditioning procedure, fertigation of the growth medium for 6 wk before planting, led to significantly enhanced cucumber growth as well as to improved resistance against damping‐off caused by Pythium. Moreover, conditioning the biochar brought the dose–response curves for both growth and disease resistance into sync. From the results it is clear that the cause must be related to mechanisms that require synergy between wetting with a nutrient solution and addition of biochar. This requirement reduces the main candidate mechanisms to two: (1) biochar‐enhanced nutrient availability as a result of reactions between the fertilizer solution and the biochar over time (as in Joseph et al., 2013), or (2) alterations in the root‐zone microbiome that resulted from the conditioning of the biochar‐amended growth medium with fertilizer solution. Regarding the first possibility, although biochar treatments caused an increase in cucumber shoot Ca, which can suppress diseases caused by soilborne plant pathogens by increasing plant membrane structural integrity (Bateman & Basham, 1976; Kelman et al., 1989), pre‐conditioning had no significant effect compared with nonconditioning. Likewise, the lack of effect on plant N demonstrates there was no problem of N immobilization (for example, as suggested by Bonanomi et al., 2017). Thus, enhanced nutrient availability to plants apparently is not a factor. The key synergetic difference that remains is differences in the bacterial and fungal microbiomes that developed in the biochar pre‐conditioned treatment, relative to the other three treatments.

In general, the microbiome of the plant rhizosphere can be affected by a host of individual and inter‐related physical and chemical attributes. These may include moisture content, pH, redox potential (Eh), electrical conductivity (EC), root exudates, nutrients and toxins. Many of these are irrelevant for the studied system. For instance, the moisture content in the nonconditioned (NCS) treatments was adjusted to that of the pre‐conditioned (PCS) treatments before transplanting the seedlings in order to eliminate a moisture effect. Likewise, fertilizer solution during the seedling growth was supplied equally in all the treatments. Many soilborne pathogens thrive under narrow ranges of pH, Eh and EC (Bonanomi et al., 2010; Husson, 2013) that can be altered by adding biochar and by watering with a fertilizer solution. Yet, although the highest concentration of biochar slightly increased the pH value, there was no difference in pH between PCS and NCS biochar treatments. Moreover, there was no difference in Eh between any of the biochar proportions (0–3%) in the PCS treatment.

It is noteworthy that EC was greater in the NCS biochar treatment than in the PCS biochar treatment, which is evidence for leaching of water soluble salts from the biochar during the pre‐conditioning stage. Water soluble organic compounds (DOC) were also eliminated from the biochar‐treated soil during the pre‐conditioning stage, either by leaching or by microbial consumption. Among the eliminated organic compounds may have been those that contributed to phytotoxicity in the NCS biochar treatment. Labile toxic compounds can damage plant root tissues (Li et al., 2015) and predispose them to pathogen attack (Jaiswal et al., 2014, 2015).

Research has demonstrated the importance of the root microbiome for plant health (Berendsen et al., 2012; Berg et al., 2014; Vandenkoornhuyse et al., 2015). Insomuch as members of the root microbiome are mainly recruited from the surrounding soil during seed/seedling–soil contact (Garbeva et al., 2004; Bulgarelli et al., 2013), any manipulation of indigenous soil microorganisms may strongly influence the microbiome along with plant health. Early colonization of beneficial microbial communities on plant roots can restrict soilborne pathogens from establishing large populations on their host and thereby reduce their ability to infect plant tissue (Raaijmakers et al., 2009; Berendsen et al., 2012; Mendes et al., 2013). There are numerous approaches to managing and manipulating the root microbiome involving soil amendments, (bio)solarization and management practices such as no‐till or cover crops, for increasing plant productivity and disease suppression (Katan, 2017; Wallenstein, 2017). Manipulating the indigenous microorganisms could have several advantages over introduction of biocontrol agents (due to difficulties in survival, reproduction and colonization in the infection sites) as: (1) they are already present, (2) they are adapted to the environment, and (3) they can form stable complex communities. Pre‐conditioning of biochar‐amended growth media in the present study was demonstrated to significantly manipulate the bacterial and fungal microbiomes, as well as to increase their diversity and activity, with good results for plant health and productivity. A number of previous studies have established a strong link between diversity and activity of the soil microbiome and enhanced plant productivity (Van der Heijden et al., 2008; Wagg et al., 2011), suppression of soilborne pathogens (Mendes et al., 2011, 2013; Raaijmakers & Mazzola, 2016) and provision of other ecological services (Bell et al., 2005; Wagg et al., 2014; Delgado‐Baquerizo et al., 2016). Changes in microbial community structure, diversity and activity induced by pre‐conditioning may be associated with a synergy between moisture and biochar that increases liquid diffusion rates (Banerjee et al., 2016), providing microorganisms with biochar‐borne substrates such as soluble organic compounds (Table S2).

It is still a great challenge to determine bacterial and fungal function based on taxonomic groups, let alone attempt to determine correlations between the biochar‐stimulated microbiome and agro‐ecological services. Genera may contain species having harmful, beneficial or no effect on plant performance and disease suppression. Nevertheless, there was an increase in relative abundances of microbes from genera such as Pseudomonas, Bacillus and Trichoderma whose members often display the potential to produce compounds that inhibit pathogens and elicit systemic plant resistance, parasitize pathogens, compete with pathogens for resources, produce or modulate phytohormones such as indol‐3‐acetic acid (IAA, auxin), cytokinins, gibberellins, jasmonic acid (JA), salicylic acid (SA) and ethylene (ET) (Lugtenberg & Kamilova, 2009; Pieterse et al., 2009, 2014; Shigenaga & Argueso, 2016). Indeed, several fluorescent Pseudomonas, Flavobacterium and Bacillus strains that were isolated from the PCS biochar treatment showed antagonistic activity in vitro against Pythium. They also shared high sequence identity with species that have been described previously as plant growth‐promoting or biocontrol agents (Bais et al., 2004; Kloepper et al., 2004; Haas & Defago, 2005; Bakker et al., 2007; Kolton et al., 2014). In addition, the Pythium population was significantly lower in the PCS biochar treatments. Pythium spp. are relatively aggressive plant pathogens but poor competitors in soil and their saprophytic activities are greatly restricted in the presence of other root‐colonizing organisms (Hendrix & Campbell, 1973; Rankin & Paulitz, 1994). Thus, manipulation of indigenous microorganisms by pre‐conditioning could have played an important role in reducing Pythium and damping‐off disease in the present study. Additionally, the higher abundance of general and specific bacteria may have influenced the Pythium population by competing for space and resources as previously suggested (Lugtenberg & Kamilova, 2009; Berendsen et al., 2012).

Intensive agricultural practices and monoculture have led to a reduction of microbial and fungal diversity and activity which has contributed to an increase in soilborne plant diseases and decrease in plant performance (Campbell, 2006; Katan, 2017). Agricultural practices that support recruitment and maintenance of beneficial microbial communities in the soil and rhizosphere can help to enhance the efficiency and sustainability of intensive crop production and disease suppression. This study demonstrates that pre‐conditioning of a biochar‐amended growth medium enhanced the efficacy of biochar for plant performance and suppression of soilborne disease through enriching the medium in beneficial soil microorganisms, and increasing microbial and fungal diversity and activity. Moreover, the pre‐conditioning step helped to bring growth promotion and disease suppression dose–response curves into sync, facilitating maximum benefits for both plant productivity and disease suppression. Review of the available literature suggests that direct application of biochar in pathosystems involving late‐stage diseases such as Fusarium in tomatoes and asparagus (Matsubara et al., 2002; Elmer & Pignatello, 2011; Akhter et al., 2016; Jaiswal et al., 2017), or foliar diseases including Botrytis, Phytophthora and Powdery mildew in tomatoes and strawberries (Elad et al., 2010; Meller Harel et al., 2012; Zwart & Kim, 2012; Mehari et al., 2015; De Tender et al., 2016), resulted in good disease suppression and plant performance without a pre‐conditioning stage. However, adding unconditioned biochar followed by infections with pathogens such as Rhizoctonia and Pythium that cause early stage diseases occasionally results in neutral or negative effects (Copley et al., 2015). Altogether, these findings suggest that a pre‐conditioning stage should be incorporated as an important stage during biochar application in nurseries and soilless media, and possibly into soil.