Biochar is a carbon-rich substance produced by the pyrolysis of organic matter, such as agricultural and forestry residues.
It has garnered significant interest as a sustainable soil amendment and carbon sequestration tool.
Biochar can improve soil fertility, enhance water-holding capacity, and reduce greenhouse gas emissions.
Researchers utilize various production techniques and feedstocks to optimize biochar properties for specific applications.
This MeSH term provides a comprehensive overview of biochar's characteristics, production methods, and potential environmental and agronomic benefits.
Streamlline your biochar resesarch with the cutting-edge PubCompare.ai tool, which helps identify the most reliable protocols and enhance reproducibility.
Carbon content in feedstock (and char) was measured in triplicates on 100-mg samples that were combusted at 1030°C and analyzed in an element analyzer (Perkin-Elmer Optima 5300 DV Inductively Coupled Plasma Optical Emission Spectrometer (ICP-OES)). Wood feedstock was analyzed to contain 50.1% C, Eupatorium shrub 40.3% C, and in our parallel project in Tanzania rice husk was analyzed to contain 41.1% C, in accordance with literature values [25 ]. All biochars were characterized for cation exchange capacity by extraction with ammonium acetate at pH 7, both before and after washing with water for those samples where quenching was done with soil, and only after washing for the water-quenched samples [26 ]. Three biochars representing two different kiln types (soil pit kiln and metal cone kiln each 70°—1m50 diameter) and two feedstock (100% Eupatorium and 50:50 Eupatorium: hard wood) were analyzed by a EBC accredited laboratory following the EBC certification program and methods [4 , 27 ]. Five example biochars were further analyzed for 15 individual PAHs by 36-h exhaustive toluene Soxhlet extraction according to published procedures [28 (link), 29 (link)] and surface area by N2 adsorption at 77 K.
Cornelissen G., Pandit N.R., Taylor P., Pandit B.H., Sparrevik M, & Schmidt H.P. (2016). Emissions and Char Quality of Flame-Curtain "Kon Tiki" Kilns for Farmer-Scale Charcoal/Biochar Production. PLoS ONE, 11(5), e0154617.
One-way analysis of variance (ANOVA) was performed to assess the effects of fertilization treatments on soil properties, the diversity and abundances of soil bacterial and fungal communities, and microbial carbon metabolism and qCO2 using Bonferroni’s post hoc test in SPSS 20.0 software (SPSS, Chicago, IL, USA). The significance testing in one-way ANOVA was based on the data from which the samples originated both followed a normal distribution and had the same variances [78 ]. Principal coordinate analysis (PCoA) was used to evaluate the biochar impacts on the Bray-Curtis distances of the bacterial and fungal community compositions [79 (link)]. We conducted the “capscale” function of the R package vegan (version 3.1.2) to calculate the Bray-Curtis dissimilarities for principal coordinate analysis and “permutest” permutation-based testing for the calculation of the significance values [80 ]. The compositional similarity was calculated as 1 minus the Bray-Curtis dissimilarity. The non-amended (CK and CF) and biochar-amended (LB, MB, and HB) samples were separately examined for biochar effects on soil bacterial and fungal networks. The OTUs presented either in all non-amended or in all biochar-amended samples were kept for the subsequent network constructions, respectively. The co-occurrence patterns of the bacterial and fungal communities were constructed by calculating multiple correlations and similarities with co-occurrence network (CoNet) inference [81 (link)]. We used an ensemble approach based on the four measurements, including Pearson and Spearman correlations and Bray-Curtis and Kullback-Leibler dissimilarities between pairwise OTUs. A valid co-occurrence was considered a statistically robust correlation between taxa when the correlation threshold was above 0.7 and the P value was below 0.01. The P values were merged using Brown’s method for the four measurements [82 (link)] and then adjusted using the Benjamini-Hochberg procedure to reduce the chances of obtaining false-positive results [83 ]. Network visualization was conducted using Gephi software [84 ]. Nodes indicated individual microbial taxa (OTUs) in the microbiome network [26 (link)]. Network edges represented the pairwise correlations between nodes, suggesting a biologically or biochemically meaningful interactions [12 (link)]. The modules were the clusters of closely interconnected nodes (i.e., groups of co-existing or co-evolving microbes) [26 (link)]. The microbial networks were searched to identify highly associated nodes (clique-like structures) using Molecular Complex Detection (MCODE) introduced for the Cytoscape platform [85 (link)]. The algorithm identifies seeded nodes for expansion by computing a score of local density for each node in the graph. Over 90% accuracy of MCODE predictions yielded, when an overlap score was above 0.2 threshold. The calculated topological characteristics of the bacterial and fungal networks included the following: the numbers of positive and negative correlations, average path length, graph density, network diameter, average clustering coefficient, average connectivity, and modularity. The roles of individual nodes were estimated by two topological parameters: the within-module connectivity Z, which quantified to what extent a node connected to other nodes in its own module, and the among-module connectivity P, which quantified how well the node connected to different modules [86 (link)]. The nodes with either a high value of Z or P were defined as keystone taxa, including module hubs (Z > 0.25, P ≤ 0.62; critical to its own module coherence), connectors (Z ≤ 0.25, P > 0.62; connect modules together and important to network coherence), and network hubs (Z > 0.25, P > 0.62; vital to both the network and its own module coherence) [87 (link)]. For network modules, the module eigengene could summarize the closely connected members within a module [88 (link)]. The singular value decomposition of the module expression matrix was used to represent the module eigengene networks [89 (link)]. The module eigengene of a module was defined as the first principal component of the standardized module expression data [90 (link)]. Then, the relationships between soil properties, microbial diversity, network module eigengenes, and SOC mineralization (microbial carbon metabolism and qCO2) were evaluated using Spearman’s rank correlation test. Random forest modeling was used to quantitatively assess the important predictors of carbohydrate catabolism and qCO2 involving soil properties and the microbial community. Soil properties included soil pH, SMC, SOC, total nitrogen, total phosphorus, total potassium, available nitrogen, available phosphorus, available potassium, and cation exchange capacity, while the microbial community included the biomass, diversity, composition, and network of soil bacterial and fungal communities. The bacterial and fungal biomass were characterized by bacterial and fungal PLFAs. The bacterial and fungal diversities were represented by the Shannon index based on the rarified same sequencing depth. The compositions of soil bacterial and fungal communities were indicated by the first principal coordinates (PCoA1). The bacterial and fungal networks were represented by the module eigengenes that were significantly related to diversity and carbohydrate metabolism. The importance of each factor was evaluated by the increase in the mean square error between the observed and predicted values when the predictor was randomly permuted [91 (link)]. This accuracy of importance was measured for each tree and was averaged across the forest. Accuracy of importance was estimated for each observation using the left-out individual predictions (called “out-of-bag” estimation) and then averaged over all observations [92 (link)]. These analyses were conducted using the randomForest package [93 ], and the significance of the model and predictor importance was determined using the A3 and rfPermute packages, respectively [94 , 95 ]. Structural equation modeling (SEM) was applied to determine the direct and indirect contributions of soil properties and microbial community to carbohydrate catabolism and qCO2 [96 (link)]. SEM analysis was conducted via the robust maximum likelihood evaluation method using AMOS 20.0 (AMOS IBM, USA). The SEM fitness was examined on the basis of a non-significant chi-square test (P > 0.05), the goodness-of-fit index (GFI), and the root mean square error of approximation (RMSEA) [97 ].
Chen L., Jiang Y., Liang C., Luo Y., Xu Q., Han C., Zhao Q, & Sun B. (2019). Competitive interaction with keystone taxa induced negative priming under biochar amendments. Microbiome, 7, 77.
Below, we provide a brief overview of methods for each of the 21 NCS that we quantified. Full methodological details are provided in the Supplementary Materials. Reforestation: Additional carbon sequestration in above- and belowground biomass and soils gained by converting nonforest (<25% tree cover) to forest [>25% tree cover (45 (link))] in areas of the conterminous United States where forests are the native cover type. We excluded areas with intensive human development, including all major roads (46 ), impervious surfaces (47 ), and urban areas (48 ). To eliminate double counting with the peatland restoration pathway, we removed Histosol soils (49 ). To safeguard food production, we removed most cropland and pasture. We discounted the carbon sequestration mitigation benefit in conifer-dominated forests to account for albedo effects. Natural forest management: Additional carbon sequestration in above- and belowground biomass gained through improved management in forests on private lands under nonintensive timber management. The maximum mitigation potential was quantified on the basis of a “harvest hiatus” scenario starting in 2025, in which natural forests are shifted to longer harvest rotations. This could be accomplished with less than 10% reduction in timber supply with new timber supply from thinning treatments for fuel risk reduction until new timber from reforestation is available in 2030. Fire management: Use of prescribed fire to reduce the risk of high-intensity wildfire. We considered fire-prone forests in the western United States. We assume that treatment eliminates the risk of subsequent wildfire for 20 years, but only on the land that was directly treated. We assume that 5% of lands are treated each year, and we calculated the benefits that accrue over 20 years, finding that the initial increase in emissions associated with prescribed fire treatment is more than offset over time by the avoided impacts of wildfires. We report the average annual benefit across these 20 years. The impact of wildfires includes both direct emissions from combustion and suppression of net ecosystem productivity following wildfires. Avoided forest conversion: Emissions of CO2 avoided by avoiding anthropogenic forest conversion. Most forest clearing is followed by forest regeneration rather than conversion to another land use. To estimate the rate of persistent conversion (i.e., to another land use), we first calculated forest clearing in the conterminous United States from 2000 to 2010 and then used the proportion of forest clearing that historically was converted to another land use to estimate conversion rates in 2000 to 2010. We used estimates of avoided carbon emissions from above- and belowground biomass that are specific to each region and forest type. We did not count forest loss due to fire to avoid double counting with the improved fire management opportunity. We did not count forest loss due to pests because it is unclear whether this loss can be avoided. We reduced the benefit of avoided conversion in conifer-dominated forests to account for their albedo effects. Urban reforestation: Additional carbon sequestration in above- and belowground biomass gained by increasing urban tree cover. We considered the potential to increase urban tree cover in 3535 cities in the conterminous United States. We considered the potential for additional street trees, and for those cities not in deserts, we also considered the potential for park and yard tree plantings. The potential percent increase in tree cover was estimated on the basis of high-resolution analysis of 27 cities, which excluded sports fields, golf courses, and lawns (50 ). Improved plantations: Additional carbon sequestration gained in above- and belowground tree biomass by extending rotation lengths for a limited time in even-aged, intensively managed wood production forests. Rotation lengths were extended from current economic optimal rotation length to a biological optimal rotation length in which harvest occurs when stands reach their maximum annual growth. Cover crops: Additional soil carbon sequestration gained by growing a cover crop in the fallow season between main crops. We quantified the benefit of using cover crops on all of the five major crops in the United States (corn, soy, wheat, rice, and cotton) that are not already growing cover crops (27 ), using the mean sequestration rate quantified in a recent meta-analysis (51 ). Avoided conversion of grassland: Emissions of CO2 avoided by avoiding conversion of grassland and shrubland to cropland. We quantified avoided emissions from soil and roots (for shrubs, we also considered aboveground biomass) based on the spatial pattern of conversion from 2008 to 2012. We used spatial information on location of recent conversion and variation in soil carbon and root biomass to estimate mean annual emission rate from historic conversion. We estimated a 28% loss of soil carbon down to 1 m (26 (link)). We modeled spatial variation in root biomass based on mean annual temperature and mean annual precipitation using data from (52 ). Biochar: Increased soil carbon sequestration by amending agricultural soils with biochar, which converts nonrecalcitrant carbon (crop residue biomass) to recalcitrant carbon (charcoal) through pyrolysis. We limited the source of biochar production to crop residue that can be sustainably harvested. We assumed that 79.6% of biochar carbon persists on a time scale of >100 years (53 , 54 ) and that there are no effects of biochar on emissions of N2O or CH4 (55 , 56 ). Alley cropping: Additional carbon sequestration gained by planting wide rows of trees with a companion crop grown in the alleyways between the rows. We estimated a maximum potential of alley cropping on 10% of U.S. cropland (15.4 Mha) (57 ). Cropland nutrient management: Avoided N2O emissions due to more efficient use of nitrogen fertilizers and avoided upstream emissions from fertilizer manufacture. We considered four improved management practices: (i) reduced whole-field application rate, (ii) switching from anhydrous ammonia to urea, (iii) improved timing of fertilizer application, and (iv) variable application rate within field. We projected a 4.6% BAU growth in fertilizer use in the United States by 2025. On the basis of these four practices, we found a maximum potential of 22% reduction in nitrogen use, which leads to a 33% reduction in field emissions and a 29% reduction including upstream emissions. Improved manure management: Avoided CH4 emissions from dairy and hog manure. We estimated the potential for emission reductions from improved manure management on dairy farms with over 300 cows and hog farms with over 825 hogs. Our calculations are based on improved management practices described by Pape et al. (8 ). Windbreaks: Additional sequestration in above- and belowground biomass and soils from planting windbreaks adjacent to croplands that would benefit from reduced wind erosion. We estimated that windbreaks could be planted on 0.88 Mha, based on an estimated 17.6 Mha that would benefit from windbreaks, and that windbreaks would be planted on ~5% of that cropland (8 ). Grazing optimization: Additional soil carbon sequestration due to grazing optimization on rangeland and planted pastures, derived directly from a recent study by Henderson et al. (58 ). Grazing optimization prescribes a decrease in stocking rates in areas that are overgrazed and an increase in stocking rates in areas that are undergrazed, but with the net result of increased forage offtake and livestock production. Grassland restoration: Additional carbon sequestration in soils and root biomass gained by restoring 2.1 Mha of cropland to grassland, equivalent to returning to the 2007 peak in CRP enrollment. Grassland restoration does not include restoration of shrubland. Legumes in pastures: Additional soil carbon sequestration due to sowing legumes in planted pastures, derived directly from a recent global study by Henderson et al. (58 ). Restricted to planted pastures and to where sowing legumes would result in net sequestration after taking into account potential increases in N2O emissions from the planted legumes. Improved rice management: Avoided emissions of CH4 and N2O through improved practices in flooded rice cultivation. Practices including mid-season drainage, alternate wetting and drying, and residue removal can reduce these emissions. We used a U.S. Environmental Protection Agency (EPA) analysis that projects the potential for improvement across U.S. rice fields, in comparison with current agricultural practices (59 ). Tidal wetland restoration: In the United States, 27% of tidal wetlands (salt marshes and mangroves) have limited tidal connection with the sea, causing their salinity to decline to the point where CH4 emissions increase (30 ). We estimated the potential for reconnecting these tidal wetlands to the ocean to increase salinity and reduce CH4 emissions. Peatland restoration: Avoided carbon emissions from rewetting and restoring drained peatlands. To estimate the extent of restorable peatlands, we quantified the difference between historic peatland extent [based on the extent of Histosols in soil maps (60 )] and current peatland extent. Our estimate of mitigation potential accounted for changes in soil carbon, biomass, and CH4 emissions, considering regional differences, the type of land use of the converted peatland, and whether the peatland was originally forested. Avoided seagrass loss: Avoided CO2 emissions from avoiding seagrass loss. An estimated 1.5% of seagrass extent is lost every year (61 (link)). We assumed that half of the carbon contained in biomass and sediment from disappearing seagrass beds is lost to the atmosphere (62 (link)). Seagrass restoration: Increased sequestration from restoring the estimated 29 to 52% of historic seagrass extent that has been lost and could be restored (61 (link)). We estimated the average carbon sequestration rate in the sediment of seagrass restorations based on data from six seagrass restoration sites in the United States (63 ).
Fargione J.E., Bassett S., Boucher T., Bridgham S.D., Conant R.T., Cook-Patton S.C., Ellis P.W., Falcucci A., Fourqurean J.W., Gopalakrishna T., Gu H., Henderson B., Hurteau M.D., Kroeger K.D., Kroeger T., Lark T.J., Leavitt S.M., Lomax G., McDonald R.I., Megonigal J.P., Miteva D.A., Richardson C.J., Sanderman J., Shoch D., Spawn S.A., Veldman J.W., Williams C.A., Woodbury P.B., Zganjar C., Baranski M., Elias P., Houghton R.A., Landis E., McGlynn E., Schlesinger W.H., Siikamaki J.V., Sutton-Grier A.E, & Griscom B.W. (2018). Natural climate solutions for the United States. Science Advances, 4(11), eaat1869.
We estimate the maximum additional annual mitigation potential above a business-as-usual baseline at a 2030 reference year, with constraints for food, fiber, and biodiversity safeguards (SI Appendix, Tables S1 and S2). For food, we allow no reduction in existing cropland area, but do allow the potential to reforest all grazing lands in forested ecoregions, consistent with agricultural intensification scenarios (9 ) and potential for dietary changes in meat consumption (10 ). For fiber, we assume that any reduced timber production associated with implementing our Natural Forest Management pathway is made up by additional wood production associated with Improved Plantations and/or Reforestation pathways. We also avoid activities within pathways that would negatively impact biodiversity, such as establishing forests where they are not the native cover type (11 ). For most pathways, we generated estimates of the maximum mitigation potential (Mx) informed by a review of publications on the potential extent (Ax) and intensity of flux (Fx), where Mx = Ax × Fx. Our estimates for the reforestation pathway involved geospatial analyses. For most pathways the applicable extent was measured in terms of area (hectares); however, for five of the pathways (Biochar, Cropland Nutrient Management, Grazing—Improved Feed, Grazing—Animal Management, and Avoided Woodfuel Harvest) other units of extent were used (SI Appendix, Table S1). For five pathways (Avoided Woodfuel Harvest; Grazing—Optimal Intensity, Legumes, and Feed; and Conservation Agriculture) estimates were derived directly from an existing published estimate. An overview of pathway definitions, pathway-specific methods, and adjustments made to avoid double counting are provided in SI Appendix, Table S2. See SI Appendix, pp 36–79 for methods details.
Griscom B.W., Adams J., Ellis P.W., Houghton R.A., Lomax G., Miteva D.A., Schlesinger W.H., Shoch D., Siikamäki J.V., Smith P., Woodbury P., Zganjar C., Blackman A., Campari J., Conant R.T., Delgado C., Elias P., Gopalakrishna T., Hamsik M.R., Herrero M., Kiesecker J., Landis E., Laestadius L., Leavitt S.M., Minnemeyer S., Polasky S., Potapov P., Putz F.E., Sanderman J., Silvius M., Wollenberg E, & Fargione J. (2017). Natural climate solutions. Proceedings of the National Academy of Sciences of the United States of America, 114(44), 11645-11650.
Out of 23 initially screened rhizobacteria (Table 7) isolated from wheat rhizospheric soil, collected from Old Shujabad Road (30.11°N and 71.43°E) and Akramabad (30.16°N and 71.29°E), four most efficient drought-tolerant ACC-deaminase producing PGPR were screened out after a laboratory hydroponic trial in the Department of Soil Science, Bahauddin Zakariya University Multan, Pakistan. For screening of most effective drought tolerant PGPR strains, polyethylene glycol 6000 (PEG-6000) was used (0, 10 and 20%) to maintain osmotic potential (0.05, −0.23 and −0.78 MPa) to introduce drought stress63 (link),64 . Molecular identification of the most efficient drought tolerant ACC deaminase producing PGPR was done by 16S rRNA gene sequencing using PCR primers 1492R 5′ (TAC GGY TAC CTT GTT ACG ACT T) 3′ and 27F 5′ (AGA GTT TGA TCM TGG CTC AG) 3′. The gene sequencing primers were 907R 5′ (CCG TCA ATT CMT TTR AGT TT) 3′ and 785F 5′ (GGA TTA GAT ACC CTG GTA) 3′. Finally, 16S rRNA gene sequences were aligned and relationships were deduced using BLAST analysis65 (link). Most efficient drought-tolerant ACC-deaminase producing PGPR were identified as AbW1 = Leclercia adecarboxylata (NR_104933.1), CbW2 = Agrobacterium fabrum (NR_074266.1), CbW3 = Bacillus amyloliquefaciens (FN_597644.1) and AbW5 = Pseudomonas aeruginosa (CP012001.1). These PGPR strains were able to grow at the osmotic potential −0.78 MPa generated through 20% polyethylene glycol 6000 (PEG). The DF minimal salt medium (4.0 g KH2PO4, 6.0 g Na2HPO4, 0.2 g, MgSO4.7H2O, 2.0 g glucose, 2.0 g gluconic acid and 2.0 g citric acid with trace elements: 1 mg FeSO4.7H2O, 10 mg H3BO3, 11.19 mg MnSO4.H2O, 124.6 mg ZnSO4.7H2O, 78.22 mg CuSO4.5H2O, 10 mg MoO3, pH = 7.2 and 0.5 M ACC as a sole nitrogen source) was used to grow the strains66 (link). For determination of indole acetic acid (IAA) with and without L-tryptophan, Glickmann and Dessaux67 (link) method was adopted. To confirm the presence of AcdS gene that plays key role in synthesis of ACC deaminase NCBI gene bank was consulted. From NCBI gene bank it was confirmed that B. amyloliquefaciens (https://www.ncbi.nlm.nih.gov/nuccore/KX709841.1/), A. fabrum (https://www.ncbi.nlm.nih.gov/protein/PZP48640.1/) and P. aeruginosa (https://www.ncbi.nlm.nih.gov/nuccore/CP014948.1/) have AcdS gene while work is yet continued on L. adecarboxylata. For assessing the ACC deaminase produced by PGPR methodology of El-Tarabily68 (link) and Honma and Shimomura20 was used. Pikovskaya’s medium was used to examine the phosphorus solubilizing activity of PGPR as described by Vazquez et al.69 (link). Potassium solubilizing activity of PGPR was assessed according to the methodology of Candra Setiawati and Mutmainnah70 . Characterization of the PGPR isolates is provided in Table 7. For the production of biochar, timber waste was collected from local timber market. The timber waste was initially sun-dried and then pyrolyzed at 389 °C for 80 min in an especially designed pyrolyzer as described by Qayyum et al.27 . All the pyrolyzed material (biochar) was then crushed in a grinder and passed through 2 mm sieve. Finally, the fine powder of timber waste biochar (BC) was stored in air tight plastic jars27 . The pH and ECe of BC were determined by mixing the BC and water with the ration, 1:20 (w/v) as described by Qayyum et al.27 . Di-acid (HNO3: HClO4) digestion71 of biochar was done for the analysis of total phosphorus following yellow color method on spectrophotometer72 , and those of potassium and sodium on flame photometer73 . For the determination of nitrogen, H2SO4 digestion72 was done followed by distillation on Kjeldahl’s distillation apparatus74 . The volatile matter and ash content of biochar were analyzed according to Qayyum et al.75 (link) by heating the biochar in muffle furnace at 450 °C and 550 °C respectively. The fixed carbon in biochar was assessed (Table 6) using the equation as follows76 ; The plastic bag (30 cm deep × 20 cm in diameter) was used as a pot, having capacity to carry 8 kg soil. The soil was collected from the plough layer of bank of the Chenab River, Multan, Punjab, Pakistan. The soil of selected area was previously characterized as dark yellowish brown, moderately calcareous, weakly structured and well drained with Cambic subsurface horizon and an Ochric epipedon77 (link). The soil texture was determined by hydrometer method78 which was sandy loam (USDA triangle) with mixed hyperthermic Haplocambids. The organic matter in soil was determined by Walkley79 (link). The organic nitrogen in soil was determined using the equation: For extractable soil P determination, Olsen and Sommers80 method was used. Similarly, the extractable K in soil was determined according to the method described by Nadeem et al.73 (Table 6).
Characteristics of soil, timber waste biochar (BC).
ExperimentSoil
Biochar
Unit
Value
Attriburs
Unit
Value
Sand
%
55
pH
—
7.03
Silt
%
30
ECe
dS m−1
0.89
Clay
%
15
Volatile Matter
%
30.26
Texture
Sandy Loam
Ash Content
%
10.19
pHs
—
8.43
Fixed Carbon
%
59.55
ECe
dS m−1
1.95
Total N
%
0.29
Organic Matter
%
0.45
Total P
%
0.53
Organic N
%
0.023
Total K
%
1.36
Extractable P
mg kg−1
8.16
Total Na
%
0.28
Extractable K
mg kg−1
204
Characteristics of PGPR.
Isolates
PGPR traits
IAAwithout L-Tryptophan(µg/mL)
IAAwith L-Tryptophan(µg/mL)
ACC deaminase(µmol α-ketobutyrate nmol mg−1 protein h−1)
Phosphorus solubilization(µg/mL)
Potassium solubilization(µg/mL)
BbW6
—
—
104.2 ± 10.9
—
12.4 ± 1.22
BbW12
—
—
94.9 ± 6.91
—
—
AbW4
0.86 ± 0.07
8.11 ± 1.26
131.3 ± 10.1
—
10.6 ± 1.91
CbW4
0.66 ± 0.12
9.52 ± 0.60
84.2 ± 7.19
6.22 ± 0.34
13.7 ± 1.63
AbW1
3.42 ± 0.27
67.8 ± 2.20
304.9 ± 24.1
26.6 ± 1.04
20.1 ± 1.02
BbW9
0.62 ± 0.06
7.33 ± 0.40
134.6 ± 20.6
10.2 ± 0.22
14.5 ± 1.58
AbW9
—
—
94.7 ± 15.3
—
9.84 ± 1.33
AbW8
0.16 ± 0.04
2.14 ± 0.17
181.2 ± 259
9.41 ± 0.29
11.7 ± 1.26
CbW3
1.12 ± 0.60
17.3 ± 2.34
313.2 ± 34.3
20.9 ± 2.48
23.4 ± 1.92
AbW16
—
—
144.3 ± 23.2
11.2 ± 0.12
15.6 ± 1.20
CbW5
—
—
153.5 ± 21.7
10.6 ± 0.27
13.3 ± 1.18
CbW2
2.43 ± 0.34
58.8 ± 3.27
349.6 ± 21.4
16.2 ± 1.48
26.7 ± 1.49
BbW14
—
—
149.6 ± 11.1
9.84 ± 0.10
14.7 ± 1.38
AbW3
—
—
209.2 ± 29.4
—
11.9 ± 1.61
BbW8
0.12 ± 0.04
3.44 ± 0.37
179.3 ± 26.8
8.21 ± 0.38
—
AbW20
—
—
172.0 ± 20.1
11.4 ± 0.22
13.6 ± 1.73
CbW6
0.36 ± 0.02
1.52 ± 0.35
159.6 ± 31.3
7.43 ± 0.19
15.2 ± 1.56
AbW5
3.16 ± 0.21
24.8 ± 1.49
245.4 ± 19.5
22.8 ± 1.36
17.9 ± 1.02
BbW4
0.56 ± 0.11
6.14 ± 1.06
349.6 ± 21.4
—
11.6 ± 1.44
BbW10
—
—
119.7 ± 24.9
13.4 ± 0.24
—
AbW11
—
—
194.7 ± 10.6
12.8 ± 0.29
—
AbW2
0.76 ± 0.05
14.7 ± 1.09
129.6 ± 7.46
13.0 ± 0.35
10.9 ± 1.41
CbW7
0.46 ± 0.09
10.4 ± 1.16
89.4 ± 10.1
11.9 ± 0.12
10.3 ± 1.28
In each plastic pot, 8 kg soil was filled. To fulfil macro nutrients requirement nitrogen (N), phosphorus (P) and potassium (K) fertilizers were added at the rate of 120: 90 and 60 kg ha−1 respectively, as recommended dose keeping in mind the nutrients concentration of biochar where it was applied81 (link). The urea was added in three split doses. As far as diammonium phosphate (DAP) and muriate of potash (MOP) fertilizers are concerned, the recommended rates of fertilizers were applied in a single dose at the time of sowing. Timber waster biochar was added at three different rates including: control i.e., no biochar (0BC), 0.75% of soil (60 g biochar per 8 Kg soil) biochar (1BC) and 1.50% of soil (120 g biochar per 8 Kg soil) biochar (2BC). The seeds of wheat (Galaxy-2013) were obtained from the certified seed dealer of the Government of Punjab, Pakistan. Healthy seeds were separated from broken and weak seeds. The seeds were surface-sterilized with sodium hypochlorite (5%) followed by 3 washes with ethanol (95%). Finally, all the seeds were washed three times with sterilized deionized water82 (link). For PGPR inoculation, 10 ml of inoculum (0.5 optical density at 535 nm wavelength)83 (link) was added along 10% sugar (glucose) in 100 g sterilized seeds. After proper mixing of seeds, inoculum and sugar solution, top dressing of seeds was done with a mixture of peat and clay (3:1 ratio) as described by Ahmad et al.84 (link). Before inoculation of seeds, the peat and clay mixture was sterilized at 121 °C for 20 min in an autoclave83 (link). All the control treatment seeds were also top dressed with peat and clay mixture along with 10% sugar solution without inoculum85 (link). In each pot, 10 seeds of wheat were initially sown. In control, the soil normal moisture (NM) was maintained at the level of 70% of field capacity (FC70) throughout the experiment on weight basis. However, to introduce mild drought (MD) and severe drought (SD) stress as per treatment plan, the soil moisture was maintained at the level of 50% and 30% of field capacity (FC50 and FC30), respectively, throughout the trial as suggested by Boutraa et al.86 (link). After germination of seeds, five healthy seedlings were kept in each pot by thinning. The pot experiment was conducted in the research area of the Department of Soil Science, Bahauddin Zakariya University Multan, Pakistan under drought stress on wheat. There were 15 treatments with 3 replications, following factorial completely randomized design (CRD). The treatments included: Control (No PGPR + No BC), L. adecarboxylata, A. fabrum, P. aeruginosa, B. amyloliquefaciens, 1BC, L. adecarboxylata + 1BC, A. fabrum + 1BC, P. aeruginosa + 1BC, B. amyloliquefaciens + 1BC, 2BC, L. adecarboxylata + 2BC, A. fabrum + 2BC, P. aeruginosa + 2BC and B. amyloliquefaciens + 2BC. Leaf gas exchange parameters (net photosynthetic rate, net transpiration rate and stomatal conductance) were determined with the help of Infra-Red Gas Analyzer (CI-340 Photosynthesis system, CID, Inc. USA) by joining 4 leaves of wheat. On a sunny day, the readings were taken between 10:30 and 11:30 AM at saturating intensity of light87 (link). After 50 days of sowing, the seedlings were harvested from each pot for the measurement of shoot length and determination of electrolyte leakage, proline contents, photosynthetic pigments level and nutrients concentration in the shoot. The electrolyte leakage (EL) was determined following the procedure given by Lutts et al.88 (link). The leaves were washed with deionized water and then cut using a steel cylinder having diameter 1 cm. One gram of uniform sized leaf pieces were immersed in a test tube containing deionized water (20 ml) and incubated at 25 °C for 24 h. The electrical conductivity (EC1) was determined using pre-calibrated EC meter. The second EC (EC2) was noted heating the test tubes in a water bath at 120 °C for 20 min. The final value of EL was calculated using the equation as follows; For proline assessment in wheat leaves, methodology stated by Bates et al.89 (link) was followed. The proline was extracted from fresh (0.1 g) leaves in 2 ml of 40% methanol. After extraction, the 1 ml mixture of glacial acetic acid and 6 M orthophosphoric acid (3:2 v/v) was mixed in 1 ml extract along with 25 mg ninhydrin. Then the solution was incubated at 100 °C for 60 min. After cooling down, 5 ml Toluene was added. For the estimation of proline contents, absorbance was noted on spectrophotometer at 520 nm wavelength. The chlorophyll a, chlorophyll b and total chlorophyll contents were determined in the fresh leaves of wheat according to the protocol given by Arnon90 (link). The extract was taken from the leaves using acetone (80%) solution. For the estimation of chlorophyll a and chlorophyll b, the absorbance was taken at 663 and 645 nm wavelength, respectively on spectrophotometer. Final calculations were made using the following relations; where, OD = Optical density (wavelength). V = Final volume made. W = Fresh leaf made (g). The samples were digested with sulfuric acid72 followed by distillation on Kjeldahl’s distillation apparatus74 . The yellow colour method was used for the determination of phosphorus concentration noting absorbance at 420 nm on spectrophotometer72 . As far as the K concentration in wheat shoot and grain is concerned, the samples were digested and then run on flame photometer as described by Nadeem et al.73 . The wheat plants were harvested after 125 days of sowing for the determination of grains yield pot−1, straw yield pot−1 and 100-grain weight. Weight of 100 grains, straw and grains yield pot−1 were assessed on top weight balance. For straw yield, plants were harvested at 4 inches above the ground surface. Sun dried 100 grains of wheat were counted randomly and manually and then weighed on top weight balance. Total wheat grains collected from a single pot were weighed and considered as grain yield pot−1. Statistical analyses of the data were carried out using standard statistical procedures91 . All the treatments were compared using Tukey’s test at p ≤ 0.05.
Danish S, & Zafar-ul-Hye M. (2019). Co-application of ACC-deaminase producing PGPR and timber-waste biochar improves pigments formation, growth and yield of wheat under drought stress. Scientific Reports, 9, 5999.
The biochar used in our experiment (BioDea ® ) was produced (by BioEsperia srl, Arezzo, Italy) through pyrolysis at temperatures ranging from 600 to 650 °C, using a mixture of agricultural woody residues, (i.e., Castanea sativa Mill., Robinia pseudoacacia L., Fraxinus ornus L., Alnus glutinosa (L.) Gaertn., and Quercus robur L.). Subsequently, the biochar was mechanically collected, resulting in a product with minimal ash content and a high concentration of organic carbon. Finally, the biochar was finely ground and put into an aqueous solution to allow for its application by fertigation in crop fields. The chemical characteristics of the used biochar are reported in Table 1.
Fedeli R., Fiaschi T., Simone L.d., Angiolini C., Maccherini S., Loppi S., & Fanfarillo E. (2024). Low Concentrations of Biochar Improve Germination and Seedling Development in the Threatened Arable Weed Centaurea cyanus. Environments, 11(9), 189-189.
To produce sugar syrup waste biochar, the initial step involves collecting and thoroughly drying the sugar syrup waste to eliminate all moisture. Next, the dried waste is combined with sulfuric acid [30 ]. To create the biochar, the mixture is subsequently heated to approximately 400 ± 15 °C in an oxygen-free environment, like a pyrolysis reactor. The characteristics of biochar include: pH = 8.15; ECe (dS/m) = 5.05; Ash Content (%) = 30; Volatile Matter (%) = 20; Fixed carbon (%) = 50; Total Nitrogen (%) = 0.11; Total Phosphorus (%) = 0.49; Total Potassium (%) = 0.41; Surface area (m²/g) = 300 and CEC (meq./100 g) = 425.
Huang P., Huang S., Ma Y., Danish S., Hareem M., Syed A., Elgorban A.M., Eswaramoorthy R, & Wong L.S. (2024). Alleviation of salinity stress by EDTA chelated-biochar and arbuscular mycorrhizal fungi on maize via modulation of antioxidants activity and biochemical attributes. BMC Plant Biology, 24, 63.
Biochar used in this study was commercially purchased from Sonnenerde company, Riedlingsdorf, Austria. It was produced by the pyrolysis of mixed paper fibre sludge and grain husks (in a ratio of 1:1 by mass) at 550 °C for 30 min in a Pyreg reactor (Pyreg GmbH, Dörth, Germany) . The seller also provided information on the biochar properties such as specific surface area that was measured according to DIN 66132/ISO 9277 and the contents of cations (Ca, Mg, K, and Na) which were determined according to DIN EN ISO 11885. The biochar contained 53.1% C, 1.4% N (DIN 51732), 38.3% ash (DIN 51719). The C: N ratio of biochar was 37.9 and biochar pH was 8.8. Biochar contained particles with size ranging between 1-5 mm. The specific surface area of biochar particles was 21.7 m 2 g -1 . Further, biochar contained 57 g Ca kg -1 , 3.9 g Mg kg -1 , 15 g K kg -1 and 0.77 g Na kg -1 (Horák et al. 2020; (link)Aydin et al. 2020b ).
Matin N.H., Igaz D., Aydın E., & Hořák J. (2024). Biochar application regulating soil inorganic nitrogen and organic carbon content in cropland in the Central Europe: a seven-year field study. Biochar, 6(1).
The invasive, ubiquitous, and nonpalatable forest weed "Eupatorium adenophorum" (locally named "Banmara"), also known as Ageratina adenophora (Spreng), was used as a feedstock for biochar production. Biochar (BC) was produced using a novel flame curtain soil pit Kon-Tiki kiln with a pyrolysis temperature ranging from 450-700 • C [22] (link). Sundried feedstock weighing 250 kg was used for biochar production. After a pyrolysis time of about 2 h, the biochar was snuffed with soil and kept overnight (12-18 h) before collection. Biochar yield was 20% of the dry feedstock (50 kg). Biochar produced from Eupatorium using Kon-Tiki was characterized by a high pH (10.4), organic carbon (70%), CEC (72 cmolc kg -1 ), and surface area (74.6 m 2 g -1 ) [23] (link). For the preparation of urine-enriched biochar (BU), 1 kg biochar was added in a bucket containing 5 L cattle urine (approximately 1:1 volume ratio of biochar and urine) and stirred thoroughly to prepare the urine-biochar slurry. The next day, the urine-enriched biochar slurry was collected and applied to the respective experimental plots.
Pandit N.R., Sipkhan P., Sharma S.S., Dawadi D., Vista S.P., & Raut P.B. (2024). Cattle-Urine-Enriched Biochar Enhances Soil Fertility, Nutrient Uptake, and Yield of Maize in a Low-Productive Soil. Nitrogen, 5(1), 16-27.
To create the biochar, it was initially washed using tap water to eliminate any impurities. After removing the ash content, the biochar was subjected to meticulous rinsing with deionized water to completely remove any residual ash residues. The biochar was allowed to air-dry in a well-ventilated environment until it was dry. Subsequently, the deashed biochar was appropriately stored for future utilization [12 (link), 29 ].
Younis U., Danish S., Datta R., Alahmadi T.A, & Ansari M.J. (2024). Sustainable remediation of chromium-contaminated soils: boosting radish growth with deashed biochar and strigolactone. BMC Plant Biology, 24, 115.
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The D8 Advance is a versatile X-ray diffractometer (XRD) designed for phase identification, quantitative analysis, and structural characterization of a wide range of materials. It features advanced optics and a high-performance detector to provide accurate and reliable results.
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The ESCALAB 250Xi is a high-performance X-ray photoelectron spectroscopy (XPS) system designed for surface analysis. It provides precise and reliable data for the characterization of materials at the nanoscale level.
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The S-4800 is a high-resolution scanning electron microscope (SEM) manufactured by Hitachi. It provides a range of imaging and analytical capabilities for various applications. The S-4800 utilizes a field emission electron gun to generate high-quality, high-resolution images of samples.
The ASAP 2460 is a surface area and porosity analyzer that uses gas adsorption techniques to measure the surface area, pore size, and pore volume of solid materials. It is capable of determining these properties across a wide range of materials, including powders, granules, and porous solids.
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The ASAP 2020 is a surface area and porosity analyzer from Micromeritics. It is designed to measure the specific surface area and pore size distribution of solid materials using the principles of gas adsorption.
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Sodium hydroxide is a chemical compound with the formula NaOH. It is a white, odorless, crystalline solid that is highly soluble in water and is a strong base. It is commonly used in various laboratory applications as a reagent.
Sodium hydroxide is a chemical compound with the formula NaOH. It is a white, crystalline solid that is highly soluble in water. Sodium hydroxide has a wide range of applications in various industries, including as a pH regulator, cleaning agent, and chemical intermediate.
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Hydrochloric acid is a commonly used laboratory reagent. It is a clear, colorless, and highly corrosive liquid with a pungent odor. Hydrochloric acid is an aqueous solution of hydrogen chloride gas.
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The UV-1800 is a UV-Visible spectrophotometer manufactured by Shimadzu. It is designed to measure the absorbance or transmittance of light in the ultraviolet and visible wavelength regions. The UV-1800 can be used to analyze the concentration and purity of various samples, such as organic compounds, proteins, and DNA.
Biochar offers several noteworthy benefits as a soil amendment and carbon sequestration tool. It can improve soil fertility by enhancing nutrient retention and water-holding capacity. Biochar also has the potential to reduce greenhouse gas emissions from agricultural practices. Additionally, it can serve as a long-term carbon sink, helping to mitigate climate change.
Biochar properties can be tailored by adjusting the production techniques and feedstocks used. Researchers utilize various pyrolysis methods and source materials to create biochars with desired characteristics, such as high surface area, porosity, or nutrient content. Experimenting with different production parameters can help you engineer biochar that is best suited for your intended application, whether it's improving soil health or enhancing carbon sequestration.
Biochar can be produced from a wide range of organic feedstocks, including agricultural residues, forestry waste, and even municipal solid waste. The type of feedstock, as well as the pyrolysis conditions, can result in biochars with distinct physical and chemical properties. For example, biochar made from woody biomass may have different characteristics than that derived from crop residues. Understanding these variations is key to selecting the right biochar for your specific needs.
PubCompare.ai allows you to streamline your biochar research in several ways. First, it helps you screen the protocol literature more efficiently by leveraging AI to pinpoint critical insights. The platform's comparative analysis can highlight key differences in the effectiveness of various biochar production protocols, enabling you to identify the most reliable and reporducible methods for your specific research goals. This can save you time and improve the accuracy of your biochar experiments and product development.
One common challenge in using biochar is ensuring consistent quality and performance across different batches or sources. PubCompare.ai can help address this by allowing you to systematically compare the characteristics and efficacy of multiple biochar products or production processes. This can help you identify the most reliable and high-performing biochar options to use in your applications. The platfrom's AI-driven analysis can also reveal subtle differences that may impact biochar's effectiveness, enabling you to make more informed decisions about the best biochar to use for your needs.
More about "Biochar"
Biochar is a carbon-rich material produced through the pyrolysis, or thermal decomposition, of organic matter such as agricultural and forestry residues.
This versatile substance has gained significant attention for its potential as a sustainable soil amendment and carbon sequestration tool.
Biochar can enhance soil fertility, improve water-holding capacity, and even reduce greenhouse gas emissions.
Researchers employ a variety of production techniques and feedstocks to optimize biochar properties for specific applications.
Some key methods include the use of D8 Advance, ESCALAB 250Xi, S-4800, ASAP 2460, and ASAP 2020 instruments for characterization and analysis.
Chemical treatments with sodium hydroxide (NaOH) and hydrochloric acid (HCl) can also be used to modify biochar properties.
The UV-1800 spectrophotometer is another valuable tool for analyzing the composition and quality of biochar samples.
By leveraging these advanced analytical techniques, scientists can refine biochar production processes and develop tailored products to meet the specific needs of various applications, such as soil amelioration, carbon sequestration, and environmental remediation.
To streamline biochar research and enhance reproducibility, the cutting-edge PubCompare.ai tool can be utilized.
This AI-driven platform helps researchers identify the most reliable protocols from literature, preprints, and patents, ensuring they can confidently select the best biochar products and processes for their work.
Biochar's versatility and potential benefits make it a rapidly evolving field of study, with PubCompare.ai playing a key role in advancing the science and applications of this remarkable material.
