Is Global Crop Yield Enhancement and Emissions Mitigation by Biochar Application Feasible from a Biochar Resource Availability Perspective?

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Feng Zhang, wucheng Zhao, Ondřej Mašek, Zhixin Li, Yufei Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6267300/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Evaluating biochar’s potential for reducing greenhouse gas (GHG) emissions and increasing crop yields globally is essential to addressing climate change challenges. Analysis of 2140 data pairs from controlled field trials and global livestock manure and crop straw raster layers across diverse soils, climates, and management practices revealed an average global biochar production potential of 1.89 t/ha. Biochar application (BA) increased crop yields by 38% and reduced GHG emissions by an average of 23%. Specifically, biochar management with rotary tilling and deep application (20–50 cm) shows promise, reducing GHG emissions by 27% and boosting yields by 33%. In terms of biochar production technologies, the application of woody biochar and manure biochar to the deeper soil layer (20–50 cm) with large particle size (> 3 mm) and low pyrolysis temperature (200–400°C) can significantly improve crop yield by 42.6% ( p < 0.05). High pyrolysis temperature (800–1000°C) and small particle size (< 3 mm) of surface (0–20 cm) applied by straw biochar significantly reduces GHG emissions by 20.5% ( p < 0.05). However, in 32.52% of global areas there are limitations of biochar feedstock resources for practical applications, which could make its widespread adoption challenging. Sustainable biochar use can support agricultural carbon neutrality, but realizing its full benefit will require region-specific policies and management based on local biomass availability. Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation Earth and environmental sciences/Environmental sciences/Environmental impact Biochar availability evaluation Carbon neutral Greenhouse gas emission Food security Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Climate change and food security rank among the most pressing challenges of our time, threatening global stability and ecosystem resilience (Hasegawa et al., 2018 ; Shakoor et al., 2021c ; Song et al., 2022 ). Greenhouse gas (GHG) emissions from terrestrial systems significantly intensify climate change, with future impacts predicted to exceed those of other natural disasters (Liu et al., 2019 ). In response, nations worldwide have committed to the net-zero emissions targets set by the Paris Agreement (Smith et al., 2015 ). At the same time, the global population is projected to reach 9–10 billion by 2050, intensifying demands on food systems and necessitating agricultural practices that simultaneously maximize yields and minimize environmental footprints (Dijk et al., 2021 ; Zhao et al., 2023 ). The urgency of developing sustainable agricultural strategies to support this growing population while mitigating environmental impacts cannot be overstated. Biochar application (BA) has emerged as a promising approach to address both climate and agricultural challenges, showing potential to sequester carbon, improve soil fertility, and enhance crop productivity (Woolf et al., 2010 ; Li and Sun, 2019 ; Zhao et al., 2022 ; Blanco-Canqui et al., 2024 ; Zhang et al., 2024b ). By sequestrating organic carbon and enhancing nutrient availability in soils, BA is a viable tool for reducing GHG emissions and improving soil health, yet realizing its full potential depends on understanding the specific factors that drive agronomic responses to BA. These responses vary widely depending on local climate, soil properties, biochar characteristics, and management practices (Karan et al., 2023 ), and it remains unclear which of these factors plays the dominant role in shaping outcomes for crop yield and GHG reduction under BA. Understanding these key drivers is crucial not only for maximizing biochar’s efficacy in climate mitigation and productivity but also for scaling its role in achieving future carbon neutrality. Despite promising evidence from controlled studies, uncertainties persist regarding biochar’s effects on yield and GHG emissions, with field studies and meta-analyses reporting mixed results: some studies indicate increases in CO₂ and N₂O, and others report reductions in CH₄, N₂O, and CO₂ (Smith et al., 2010 ; Bruun et al., 2011 ; Liu et al., 2011 ; He et al., 2016 ; Shakoor et al., 2021a ; Jiang et al., 2023 ). Global assessments of biochar management practices are sparse, even though these practices critically influence GHG reduction and yield enhancement outcomes (Liu et al., 2011 ; He et al., 2016 ; Shakoor et al., 2021a ; Sriphirom et al., 2022 ). Furthermore, while biochar is readily accessible in small-scale trials, large-scale applications face resource limitations. The economic and logistical challenges associated with sourcing and transporting biochar materials – often from distant areas – pose significant barriers to widespread adoption. Moreover, the current scale of promising soil carbon sequestration techniques, including applications of biochar, enhanced silicate weathering, and other CO₂ removal technologies, with better soil management, are highly unlikely to balance the carbon currently emitted from fossil fuel combustion (Poulton et al., 2018 ; Schlesinger and Amundson, 2018 ). Regarding implementation of large-scale carbon sequestration and emission reduction practices, a critical yet often overlooked question is raised: can available biomass resources (e.g. straw, wood, and manure) meet the demands of large-scale BA? While small-scale trials show promising results, resource constraints may limit biochar’s broader impact, especially in achieving agricultural carbon neutrality on a global scale. Given these considerations, evaluating the global availability of biochar resources and assessing feasibility for large-scale use are essential for effective carbon accounting and climate resilience strategies (Nematian et al., 2023 ; Hu et al., 2024 ; Xie et al., 2024 ). The present study aims to expand the scope of prior research by performing, for the first time, a comprehensive evaluation of biochar feedstock materials, resource availability, and their impact on crop yields and GHG emissions by considering the potential benefit of deploying biochar technology in every country across all global regions. Specifically, we aim to (1) identify the key drivers – environmental factors, soil properties, biochar characteristics, or management practices – that influence biochar’s effects on CO₂, CH₄, and N₂O emissions and crop yield; (2) develop global maps of GHG emissions and yield outcomes related to BA; and (3) assess the global availability of biochar resources, examining supply–demand dynamics to evaluate the feasibility of biochar for yield enhancement and emissions reduction. The findings should provide essential insights for farmers and policymakers, advancing strategies for carbon mitigation and sustainable food production on a global scale. 2. Materials and methods 2.1 Data sources and literature collection We used a highly robust and rational systematic review methodology (PRISMA) to synthesize peer-reviewed literature from Web of Science ( https://www.webofscience.com/ ), China National Knowledge Infrastructure (CNKI) ( https://www.cnki.net/ ), and Google Scholar ( https://scholar.google.com/ ) up to August 2023 (Fig. S1 ). The keywords “charcoal” OR “biochar” AND “yield” AND “greenhouse gas” OR “GHG” OR “carbon dioxide” OR “nitrous oxide” OR “methane” OR “CH 4 ” OR “CO 2 ” OR “N 2 O” were used to search published articles. The three search platforms provided a total of 19,278 articles and these were further screened according to the following criteria: (a) the study must be field experiments with at least three separate replicates of the experimental treatment, and the number of replicates should be indicated in the paper. Laboratory, pot experimental, soil column, and modelling simulated results were excluded; (b) the experimental treatment is BA and control is without BA; (c) yield data must be the current season, and GHG data must be cumulative emissions, or provide average emissions and the days of growing season; and (d) for multi-year observations, each year is treated as a separate variable. The articles were selected according to Fig. S1 . Finally, a total of 286 eligible articles were screened to extract the cumulative CH 4 , CO 2 , N 2 O, and yield of each year for BA and CK. The corresponding standard deviation (SD) and replicates were also collected. We estimated SDs as 10% of the mean for data that were missing SD values (Zhang et al., 2022 ). If only the standard error (SE) was given in the paper, SD was calculated as \(\:\:\text{S}\text{E}\times\:\sqrt{n}\) , where n is the number of replicates. The values of figures were extracted using GetData Graph Digitizer 2.25 software. We also collected ancillary information like geographic (longitude and latitude), edaphic [texture, bulk density (BD), soil pH, and soil organic carbon (SOC)], climatic [MAP (mean annual precipitation), MAT (mean annual temperature], and other experimental traits if they were available in these studies. A total of 267 individual field studies and 2140 pairs of observations (CO 2 , 396; CH 4 , 497; N 2 O, 700; and yield, 547) were collected comparing response ratio of BA and CK. We tested publication bias with Egger regression before doing meta-analysis. Some single data points were eliminated using funnel plot asymmetry (Fig. S2) to achieve the purpose of no publication bias ( p > 0.05). The dataset in our study did not show publication bias and so had no significant impacts on the results. Our results were relatively robust and reliable, which could reflect the real impact of BA on crop yield and GHG emissions. Additionally, all meta-analysis data conformed to a normal distribution (Fig. S3). In general, the set of paired comparisons (2140) satisfactorily covered each frequency rectangle in a histogram (Fig. S3). There were 2140 pairs of observation data collected in our meta-analysis. The continents Asia, Europe, North America, South America, Africa, and Oceania accounted for 44.6%, 19.1%, 10.3%, 9.4%, 8.8%, and 7.8%, respectively (Fig. 1 ). These sites are distributed in all climate zones except polar climate zones. We use GHG emissions and crop yield to obtain the optimal BA rate to balance the current potential global biochar production using a quadratic curve. The balance between emission reduction and yield increase was considered comprehensively, and the experiments were collected from global in-site field studies, which could represent the current global actual BA. 2.2 Uncertainty analysis Uncertainty analysis concerns the effect of various inputs and outputs on the whole model or system, in other words it measures how the uncertainty of input parameters translates to output parameters. The model simulation and availability evaluation considered the uncertainties of all processes (Figs. S4–S7). A bootstrapping strategy was employed to address the uncertainty inherent in machine learning-based predictions. Using the optimal set of hyper parameters, we trained 100 random forest (RF) models with bootstrapped samples. This approach allowed the calculation of the coefficient of variation for yield and GHG emission predictions across each grid cell, based on the outputs from the 100 RF models (Xu et al., 2024a ). This measure of variability offers an evaluation of the model prediction uncertainty, providing valuable insights into the reliability of our yield and GHG emission estimates. In addition, 10,000 Monte Carlo simulations were performed to characterize the overall uncertainty of yield and GHG emission when the simulated yield and GHG emission were normally distributed. Specifically, the yield and GHG emission were calculated several times according to the bootstrapping strategy, and the 30% and 70% quartiles were used as the characteristic uncertainty of the estimation (Xu et al., 2024). We assessed the environmental datasets used for the model by determining the degree of extrapolation for the environmental factor layer. This was according to the method of van den Hoogen et al. (2020). This involves counting the number of rasters in the environment layer that are beyond the scope of the observed data. Furthermore, the extrapolation ratio of the environment layer (the proportion of each environment layer is the same) in each grid is adjusted. 2.3 Data analysis The “metafor” and “forestplot” packages in R (version 4.2.2) ( https://www.r-project.org/ ) were used in all data analysis processes. Meta-regression was also used to evaluate the effect of continuous variables on CH 4 , CO 2 , N 2 O, and yield. Aside from the global collection of livestock feces (pigs, cattle, sheep, goats, chickens, and ducks) and crop straw (wheat, maize, rice, bean, potato, cotton, sugarcane, and rape straws) and forest above-ground biomass, this study does not take into account any economic, social, or cultural barriers that might further limit the adoption of biochar technology in calculating potential biochar production. These global livestock density spatial layers were downloaded from https://www.visualcapitalist.com/ . The crop straw maps were downloaded from MapSPAM (Yu et al., 2020 ). Biochar conversion rates and other data are referenced from the studies of Lefebvre et al. ( 2023 ), Schmidt et al. (2012), and Yang et al. ( 2021a ). The process of biochar production potential is presented in Supplementary Material. Biochar production potential availability was calculated using ArcGIS Pro 2023. Data processing, graphing, and tabulation were performed with R version 4.2.2 and Origin 2021 Pro. 3. Results 3.1 Effect of biochar on crop yield and GHG emission The BA boosted crop yield by 16%, varying with environment and management (Figs. S8 and S9). In dry areas ( 15°C) saw a 9% increase. Irrigation further boosted yields by 18%. Wheat yield with BA had the biggest increase (38%), followed by maize and vegetables yield. The yield of upland soil benefited most under BA. The BA generally reduced N 2 O emissions by 21% (Fig. S10 and S11(A)), with the most significant inhibition (24%) in areas with 500–1000 mm precipitation. In cooler regions (MAT < 15°C), BA decreased N 2 O emissions by 27%. Maize fields with BA had the largest reduction (32%), followed by grassland and soybean fields. However, in vegetable fields, BA increased N 2 O emissions by 52%. BA in upland ecosystems had the greatest inhibitory impact on N 2 O (17%) (Fig. S10). BA had no effect on CO 2 emissions ( p > 0.05) overall (Fig. S10 and S11(B)). Specifically, BA reduced CO 2 in drier and wetter extremes while promoting it in moderate-precipitation zones and warmer regions. Vegetable fields and plantations with BA showed increased CO 2 emissions, while maize and upland fields showed decreases. The BA reduced CH 4 emissions by 15% (Fig. S10 and S11(C)), with significant inhibition (19%) in areas with MAP of 1000–1500 mm and cooler regions. The BA increased CH 4 emissions in irrigated fields but reduced them under non-irrigated conditions ( p 0.05). The BA inhibited CH 4 emissions in upland, paddy fields, and grassland ( p 3mm) and low pyrolysis temperature (200–400℃) significantly ( p < 0.05) improved crop yield by 11% (Fig. S9). High pyrolysis temperature (800–1000℃) and small particle size ( 20 cm) applied straw biochar significantly ( p < 0.05) reduced GHG emissions by 11% (Fig. S11). 3.2 Driving factors for yield and GHG emissions with BA The structural equation modeling (SEM) analysis identified environmental factors as the primary driver of relative changes in yield (path coefficient: 0.25), N₂O emissions (0.31), CO₂ emissions (0.25), and CH₄ emissions (0.38) (Fig. 2 A). Secondary influences on yield included biochar management (0.21), biochar properties (0.17), and soil properties (-0.11). For N₂O emissions, biochar management (-0.31), soil properties (0.25), and biochar properties (-0.16) were key drivers (Fig. 2 B). The CO₂ emissions were similarly influenced by soil properties (0.23), biochar properties (0.23), and management practices (-0.22) (Fig. 2 C). The CH₄ emissions were most impacted by soil properties (-0.32), followed by biochar management (-0.22) and soil (-0.17) (Fig. 2 D). Biochar properties had the largest indirect effects on yield and GHG emissions, with path coefficients of 0.42, 0.63, 0.48, and 0.52, respectively. The SEM models explained 73%, 66%, 58%, and 65% of the variance in yield, N₂O, CO₂, and CH₄ changes, respectively, under BA (Fig. 2 ). The model-averaged analysis of predictor variables revealed that the nitrogen fertilizer (1), BA rate (0.92), MAP (0.9), and SOC (0.86) were most important for the effect of BA on crop yield considering whole factors (Fig. 3 A). Nitrogen fertilizer (1) and MAP (0.84) were most important for the effect of BA on N 2 O emissions considering whole factors (Fig. 3 B). Concerning the effect of BA on CO 2 emissions, MAT (1), BD (1), BA depth (0.96), pH (0.85), and SOC (0.8) were the most important predictors in the whole factor model (Fig. 3 C). For the effect of BA on CH 4 emissions, SOC (1), irrigation (0.98), BD (0.97), pH (0.83), and BA depth (0.8) were the most important predictors in the whole factor model (Fig. 3 D). 3.3 Global biochar supply potential 3.3.1 Deficit ratio from optimal BA Our analysis estimates the average global biochar supply potential as approximately 1.89 t/ha (Fig. S12). When BA rates are considered alongside supply potential, a distinct geographical pattern emerges in biochar availability. Biochar supply decreases with increasing latitude, showing significant deficits in high-latitude regions of both hemispheres, which indicates that these biochar-deficit areas could achieve the target of increasing production and reducing emissions if there were sufficient biochar production source materials. Specifically, the Northern Hemisphere had a maximum deficit of -14.5%, while the Southern Hemisphere reached − 8.26%. Conversely, tropical and subtropical regions near the equator demonstrated biochar surpluses, with a surplus ratio of 25.3% (Fig. 4 ). 3.3.2 Inability regions of biochar application The potential for biochar supply is constrained by the availability of biomass resources. Globally, 32.52% of global areas face a deficit in biochar resources, indicating that the maximum biochar supply potential in these regions is insufficient to meet local demand, and so are insufficient to meet basic BA demands without reliance on imported raw materials. Deficit areas include northern Asia, northern North America, central South America, the Sahara Desert in Africa, the Qinghai-Tibet Plateau in China, and southern Australia. In these biochar-deficit areas, strictly speaking, when BA strategies are implemented on a large scale, there are insufficient biochar sources as a sustainable measure to increase production and reduce emissions. Even field studies of biochar in these areas show that it has benefits of increasing production and reducing emissions. The data show that the countries with a major surplus for BA are India, New Zealand, Brazil, Argentina, Mexico, Iran, Bangladesh, Netherlands, Spain, and Ethiopia, with surplus ratios of 95%, 79%, 75%, 71%, 69%, 64%, 62%, 61%, 21%, and 18%, respectively (Fig. S13). The major deficit countries are Russia, Canada, Papua New Guinea, Australia, Finland, Sweden, Indonesia, Congo, Saudi Arabia, and Norway with deficit ratios of 98%, 94%, 92%, 91%, 72%, 64%, 61%, 52%, 41%, and 12%, respectively (Fig. S14). 3.4 Global biochar yield increase and GHG reduction Globally, BA shows the greatest emission reduction effect on N₂O (93.8%), followed by CH₄ (78.4%) and CO₂ (56.7%) (Fig. 5 B–D). The BA also increases crop yields, with a global yield improvement of 90.6% (Fig. 5 A). Spatial simulations indicate that BA generally enhances crop yields worldwide, with decreases observed in limited areas (9.8% of the evaluated areas), including parts of western North America, northern South America, northern Africa, and southern Australia. The BA significantly reduces N₂O and CH₄ emissions across most regions, with only 5.3% of areas showing increases: N₂O emissions rose in southern Australia and central South America; CH₄ emissions increased in South Asia, central Africa, and southern South America; and CO₂ emissions rose in West, South, and East Asia. Overall, under the optimal BA ratio on each continent, considering the limitation of biochar resource sources, BA reduced GHG emissions by 23.4% and increased crop yield by 32.4% compared with no BA (Fig. 5 A–D). 4. Discussion 4.1 Driving factors of crop yield and GHG emissions with BA 4.1.1 BA impact on crop yield It should be noted the responses of crop yield to BA are functions of various factors like climate (e.g. precipitation, temperature, and aridity) (Zhang et al., 2023 ), soil properties, soil management practices (e.g. tillage, mulching, and irrigation) (Faloye et al., 2017 ), and biochar management practices (e.g. modified biochar, application method, application depth, and particle size) (He et al., 2016 ). In our results, in dry areas (< 1000 mm precipitation), BA raised yields by 17% ( p < 0.05). Combining BA with irrigation further boosted yields by 18% ( p < 0.05). Upland soil benefited most under BA (Figs. S8 and S9). Many studies indicate that the reason for raised crop yields may be that the developed specific surface area and porous structure provide enough space and aeration for hydraulic and nutrient retention (Zhao et al., 2022 ), thus improving water and nutrient utilization efficiency (Faloye et al., 2017 ; Razzaghi et al., 2020 ), and creating favorable conditions for propagation of bacteria and fungi (Palansooriya et al., 2019 ; Xu et al., 2023 ). This eventually changes the soil structure of physical, chemical and biological, which can be attributed to BA accelerating plant growth through regulating soil water, nutrients, aeration, temperature respiration, and photosynthesis (Smith et al., 2010 ; Palansooriya et al., 2019 ; Han et al., 2023 ; Zhang et al., 2024a ). At the same time, SEM demonstrated that environment and biochar management were dominant direct factors for crop yield, which emphasizes the role of biochar management in regulation of biochar yield, which was neglected in most previous studies (Dai et al., 2020 ; Xu et al., 2021 ; Su et al., 2024 ). These factors are precisely the driving factors affecting the formation of yield discussed above. The BA method is a critical aspect of biochar management. Traditional broadcast and plowing application of biochar may not completely mix biochar and soil particles, while the large plow arm and mechanical tumbling intensity of a rotary tiller not only easily mix biochar and soil particles, but also increase the depth of BA (Schneider et al., 2017 ; Li et al., 2018 ). This effectively increases the probability of biochar recombination of subsoil nutrients and microorganisms, so that crop root nutrient utilization is expanded in the soil profile, thereby increasing crop productivity (Joseph et al., 2021 ). Therefore, we should not only strengthen the transformation properties of biochar itself, but the difference due to the application method should be one future main direction for biochar management. 4.1.2 BA impact on GHG emissions The three GHG emissions were generally inhibited by biochar. Separately, BA reduced N 2 O and CH 4 emissions by 21% ( p < 0.05) and 15% ( p < 0.05) compared with no BA in general, respectively, and there was no significant response of CO 2 emissions to BA (Figs. S10 and S11). The BA may affect conditions that drive nitrification and denitrification through regulating organic matter amendments and soil physico-chemical properties for N 2 O mitigation (Bruun et al., 2011 ; Saarnio et al., 2013 ; Sánchez-García et al., 2014 ). Biochar’s climate-mitigation potential stems mostly from its highly recalcitrant nature, which retards the rate of photosynthetically fixed carbon returned to the atmosphere (Kuzyakov et al., 2009 ; Woolf et al., 2010 ; Lehmann et al., 2021 ). Reduction in CH 4 emissions may be attributed to the stimulated methanotrophic and inhibited methanogenic activity caused by BA that led to lower CH 4 emissions in an ambient system (Han et al., 2016). Our meta-analysis results and the previous studies on the effect of biochar on GHG emissions were based on site-experiments, without considering the available biochar sources (Hyun and Yoo, 2023 ). In addition, both incubation and field plot experiments assumed that biochar was abundant. In previous studies, some experimental biochar was purchased at other allopatric locations and transported to the experimental sites for implementation, which unwittingly increases the economic costs and resources involved in implementing biochar tests, indirectly increasing global carbon emissions (Woolf et al., 2010 ). Few studies have linked biochar management to soil, environmental, and other factors when analyzing its impact on GHG emissions. The path coefficients of environmental N 2 O, CO 2 , and CH 4 emissions are 0.31, 0.25, and 0.38, and for biochar management are − 0.33, -0.21, and − 0.22, respectively (Fig. S15). The dominant factors affecting soil GHG emissions under biochar are management and environmental factors, whose coefficients are − 0.25 and 0.33, respectively, much higher than the − 0.03 and 0.04 of biochar peculiarity and soil factors, respectively. However, general conclusions have been drawn about the biochar pyrolysis temperature, applied biochar amount, soil properties, annual rainfall, and average annual temperature (Cayuela et al., 2014 ; Wu et al., 2019 ; Zhang et al., 2023 ; Meng et al., 2024 ). Our study explained the possibility of emission reduction in the biochar model from four aspects and then made some emission-reduction exploration models in terms of controllable factors such as biochar management and application environment, and the application mode of plant and animal source biochar. 4.2 Global spatial distribution of crop yield and GHG emission Global BA showed notable spatial variability in its effects on crop yield and GHG emissions. High-latitude regions, such as northwest Asia, northern Europe, central North America, and Central Asia, exhibited significant yield increases. This effect is likely due to the physical properties of biochar – its high porosity, large surface area, and honeycombed structure allowing for improved water storage and solar radiation absorption, which enhance water use efficiency and crop growth (Karer et al., 2013 ; Tan et al., 2019 ; Edeh et al., 2020 ; Liao et al., 2020 ; Zhao et al., 2022 ). In contrast, certain tropical regions, including the southern Sahara Desert, central North America, southern Australia, and northern India, experienced yield reductions under BA, with LNR values averaging 2.6. These regions may face nitrogen limitations due to increased microbial immobilization or the release of toxic compounds from biochar, which could suppress crop productivity (Liao et al., 2020 ). The BA has minimal negative effects on crop yield, confined to small regions such as central Africa, central North America, northern India, and isolated areas in southern and western Australia (Fig. 4 ). This pattern is potentially due to regional suitability or possibly because of soil functional variation caused by BA toxicity, which can reduce crop yields (Xu et al., 2024b ). Regarding N₂O emissions, biochar exhibited a similar latitudinal trend of above yield spatial distribution. High-latitude regions, except for some areas in western and southern Australia, as well as parts of central Africa and central South America, showed a reduction in N₂O emissions (Fig. 5 B). This is consistent with the enhanced nitrogen utilization efficiency in these cooler, low-precipitation regions, where biochar inhibits the activity of nitrite and nitrate reductases, thus reducing nitrification and denitrification processes (Cayuela et al., 2014 ; Shakoor et al., 2021b ; Huang et al., 2023 ). Biochar accelerated CO 2 emissions in the Eurasian region, the Indian, and some hot spots in East Asia; however, there were hot spots of inhibition in the North America, South America and central Europe (Fig. 5 C). These spatial distribution results reinforce the results of our meta-analysis above. For CH₄, biochar primarily suppressed emissions in high-latitude regions such as northern North America, central Europe, and northern Asia, where lower temperatures and reduced soil flooding contributed to the CH₄ suppression effect (Fig. 5 D). In contrast, in tropical regions near the equator characterized by high temperatures, moisture, and a large proportion of flooded rice fields, biochar increased CH₄ emissions, likely due to enhanced methanogen activity (He et al., 2016 ; Somboon et al., 2024 ). These findings support the idea that the effectiveness of biochar in mitigating GHG emissions is context-dependent, influenced by both regional climate and soil management practices. Overall, the spatial variability observed in both crop yield and GHG emissions under BA emphasizes the need for region-specific management strategies and careful consideration of local environmental conditions for BA at a global scale. 4.3 Biochar production potential availability evaluation The biochar availability is evaluated by the biochar production potential and the BA rate, and the potential production is determined by considering the global available biomass resources and the yield of different biochar types. This measure can objectively reflect the maximum yield of potential sources of biochar globally and is an indispensable step in evaluating biochar availability. It is concluded that the global biochar-deficit area accounted for 32.52% by calculating the balance of the global biochar production potential and average BA rates layer, which means the global straw and manure biological resources basically meet local consumption considering the maximum biochar potential yield. The average global biochar potential production is about 1.89 t/ha. Deng et al. ( 2024 ) estimated the CO 2 reduction of biochar by using agricultural, forest, grass resource, and energy crop residues in China, and this satisfied the negative emission demands in most mitigation scenarios. Using a simulation, Yang et al. ( 2021b ) found that China could use 73% of national crop biomass residues as biochar to reduce GHG emissions by 8620 Mt CO 2 -eq by 2050, contributing 13–31% of the global GHG emission reduction goal in the moderate and maximum bio-negative emission technologies scenarios. However, with the penetration of biochar research and carbon policy in recent years, this prediction requires further verification. Global production of livestock manure residue is increasing year by year. When considering plant- and animal-derived biochar, our study shows whether global biochar can meet the needs of production increase and emission reduction, and whether the required amount can be adequately supplied. In other words, when investigating the benefits of biochar in increasing production and reducing emissions, it is ridiculous not to consider the supply of biochar feedstock sources. The amount of straw and manure available locally is insufficient to support the amount of biochar applied in actual agriculture to increase production and reduce emissions. It is unwise to use biochar achieving carbon neutrality in these areas when considering the biochar feedstock resources limitation. If biochar is transported from off-site places, this will exacerbate the increase in energy costs, resulting in greater carbon emissions. The mismatch between localized BA and global biomass resources is 32.52%, suggesting that conclusions derived from single-point BA experiments may not reflect real-world conditions. These findings underscore the importance of considering the spatial availability of biochar resources when assessing its potential for emission reductions and yield increases on a global scale. The biochar potential production in northern North America, northern South America, northern and central Africa, northern Asia, central Australia, parts of the Arabian Peninsula, and parts of the Tibetan Plateau of China cannot meet the BA rate. Globally, the regions with deficits of biochar resources are mostly deserts and polar wasteland, and the resources available for biochar production in these regions are low due to scarcity of straw, wood, or livestock resources. Considering the real supply of biochar feedback resources, it is unlikely that biochar has the capacity to increase yield and reduce emissions on a global scale, because some regions have insufficient resources to support their production of biochar. This is a challenge not considered in most previous biochar studies, which indicated that biochar was a suitable material to reduce carbon emissions and increase crop yields (Woolf et al., 2010 ; Smith, 2016 ). 4.4 Limitations of the current study and future outlook Our research strives to provide detail (Fig. 6 ), but uncertainties and limitations remain. First, our analysis focuses on common biochar feedstocks (plant biomass and livestock dung), leaving out others like sludge biochar feedstock, which makes the value of 1.89 t/ha of average potential biochar production less reliable. Second, our research does not fully consider dynamic influences such as off-site transportation and carbon prices, which could all affect biochar’s economic viability. Accounting for these uncertainties, although the positive effects of biochar are well established in some areas, the limited availability of raw materials for biochar does not translate into actual agricultural activities, such as the Tibetan Plateau in China (ln R -3.8), central Africa (ln R -10.1), northern Asia (ln R -6.4), central Australia (ln R -4.7), and South America (ln R -7.2), which also hinder the progress of carbon neutrality regionally. Future research should investigate biochar co-benefits in crop production and GHG emissions considering its feedstock biomass resources. On a global scale, there are regional inconsistencies in the development of agriculture and animal husbandry. Therefore, the follow-up study aims to analyze regional differences of biochar produced from plant biomass and animal dung biomass in a more detailed way and identify the advantageous biomass sources of biochar produced in regions, which offers a more holistic understanding of biochar’s role in climate change mitigation. This is of great significance for promoting global carbon neutrality. 5. Conclusions The potential benefits of biochar in increasing yield and reducing emissions are feasible for 67.48% of global area and not feasible for 32.52%. Under the optimal BA ratio on each continent, considering the limitation of biochar resources, BA reduced GHG emissions by 23.4% and increased crop yield by 32.4% compared with no BA. The global data synthesis found that biochar management practices and environmental factors dominate GHG emission and yield growth globally, and BA increased crop yields by 38% and reduced GHG emissions by an average of 23%. Especially, rotary tiller and deep application, which can decrease GHG emissions by 27% and increase crop yield by 33%, have great potential in biochar management. The average global biochar production potential is about 1.89 t/ha. Future research should approach claims of biochar’s yield-enhancing and emission-reducing effects with caution and consider the actual availability of biochar feedstock resources. Regional variability in biochar’s effectiveness underscores the uncertainty of its global impacts. Locally specific decision support must recognize these relationships and trade-offs to establish carbon reduction and crop production considering judicious BA commensurate with climate change mitigation needs. Declarations CRediT authorship contribution statement Wucheng Zhao: Conceptualization, Methodology, Validation, Formal analysis, Visualization, Writing original draft. Ondřej Mašekb: Methodology, Writing-review and editing. Feng Zhang: Conceptualization, Methodology, Writing-review and editing, Supervision, Funding acquisition. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This study was supported by the National Natural Science Foundation of China (Grant Nos. 32071550), Gansu Science and Technology Major Project (22ZD6NA007). This work was also supported by the Supercomputing Center of Lanzhou University. References Blanco-Canqui, H., Creech, C.F., Easterly, A.C., 2024. How does biochar impact soils and crops in a semi-arid environment? A 5-yr assessment. Field Crops Research, 310. Bruun, E.W., Müller-Stöver, D., Ambus, P., Hauggaard-Nielsen, H., 2011. Application of biochar to soil and N 2 O emissions: potential effects of blending fast-pyrolysis biochar with anaerobically digested slurry. 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Purple, pink, orange, and avocado green represent variables belonging to biochar peculiarity, biochar management, and soil and environmental factors, respectively. Red dashed lines indicate negative effects, and solid lines indicate positive effects; the numbers marked on lines are standardized path coefficients, and the width of lines indicates size of the path coefficient. All relationships are significant (\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001 or \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.0001). R\u003csup\u003e2\u003c/sup\u003e represents the percentage of the dependent variable that the arrow points to can be explained by the model.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6267300/v1/1ae23a14296327eaa823b20f.png"},{"id":79440201,"identity":"6342b62b-50a0-4d0f-b0ff-3a311c1da61b","added_by":"auto","created_at":"2025-03-28 12:50:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":25460,"visible":true,"origin":"","legend":"\u003cp\u003eModel-averaged importance of the predictors for the effects of biochar on GHG emissions and yield.\u003c/p\u003e\n\u003cp\u003eAnnotation: The importance of value is based on the sum of Akaike weights. The cut-off is set at 0.8 to differentiate between essential and nonessential predictors. NF, nitrogen fertilizer; BAR, biochar application rate; pH, potential of hydrogen; MAP, mean annual precipitation; MAT, mean annual temperature; SOC, soil organic carbon; BT, biochar type; BD, bulk density; BPT, biochar pyrolysis temperature; BAM, biochar application method; NMC, N-fertilizer management cycle; BPS, biochar particle size; BAD, biochar application depth; IRR, irrigation.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6267300/v1/7e87bda3ad72efb42217eb87.png"},{"id":79441297,"identity":"275201ee-3ed7-48d7-b194-508c912f9cfb","added_by":"auto","created_at":"2025-03-28 12:58:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":464371,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal spatial evaluation of biochar availability trade-off imposed by global biochar potential yield and biochar application.\u003c/p\u003e\n\u003cp\u003eBPY: Biochar Potential Yield, data were calculated from global livestock (distribution layers for pigs, cattle, sheep, goats, chickens, and ducks) manure production sources and biomass (wheat, maize, rice, bean, potato, cotton, sugarcane, and rape straws, and forest aboveground biomass distribution layers) sources. BA, biochar application rate. In order to visually compare the size of the biochar deficiency regionally.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6267300/v1/5607f178b47e4200ad6f0afa.png"},{"id":79440207,"identity":"054be0e4-eedd-41bd-a9b9-b335cedb5ab8","added_by":"auto","created_at":"2025-03-28 12:50:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":745211,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal spatial simulation and performance of models for crop yield and GHG emissions under BA.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6267300/v1/fff3d6f336ab55862bd1b71f.png"},{"id":79440210,"identity":"cc81ae2f-60e8-4381-9ca3-7b16b9574b01","added_by":"auto","created_at":"2025-03-28 12:50:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":809075,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of biochar potential availability evaluation.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6267300/v1/32af424b34fe396766f3e4d5.png"},{"id":81723427,"identity":"470b5e39-8208-4969-b830-4e7deccaf2ca","added_by":"auto","created_at":"2025-04-30 16:44:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3352046,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6267300/v1/61217ff7-db12-4623-b5f6-724c169d9951.pdf"},{"id":79440216,"identity":"c8adb108-25be-4ae5-ad9f-fe444663123b","added_by":"auto","created_at":"2025-03-28 12:50:13","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":12711460,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6267300/v1/5789e870ddb7e2eaa0974e1a.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Is Global Crop Yield Enhancement and Emissions Mitigation by Biochar Application Feasible from a Biochar Resource Availability Perspective?","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eClimate change and food security rank among the most pressing challenges of our time, threatening global stability and ecosystem resilience (Hasegawa et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Shakoor et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021c\u003c/span\u003e; Song et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Greenhouse gas (GHG) emissions from terrestrial systems significantly intensify climate change, with future impacts predicted to exceed those of other natural disasters (Liu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In response, nations worldwide have committed to the net-zero emissions targets set by the Paris Agreement (Smith et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). At the same time, the global population is projected to reach 9\u0026ndash;10\u0026nbsp;billion by 2050, intensifying demands on food systems and necessitating agricultural practices that simultaneously maximize yields and minimize environmental footprints (Dijk et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The urgency of developing sustainable agricultural strategies to support this growing population while mitigating environmental impacts cannot be overstated.\u003c/p\u003e \u003cp\u003eBiochar application (BA) has emerged as a promising approach to address both climate and agricultural challenges, showing potential to sequester carbon, improve soil fertility, and enhance crop productivity (Woolf et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Li and Sun, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Blanco-Canqui et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). By sequestrating organic carbon and enhancing nutrient availability in soils, BA is a viable tool for reducing GHG emissions and improving soil health, yet realizing its full potential depends on understanding the specific factors that drive agronomic responses to BA. These responses vary widely depending on local climate, soil properties, biochar characteristics, and management practices (Karan et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and it remains unclear which of these factors plays the dominant role in shaping outcomes for crop yield and GHG reduction under BA. Understanding these key drivers is crucial not only for maximizing biochar\u0026rsquo;s efficacy in climate mitigation and productivity but also for scaling its role in achieving future carbon neutrality.\u003c/p\u003e \u003cp\u003eDespite promising evidence from controlled studies, uncertainties persist regarding biochar\u0026rsquo;s effects on yield and GHG emissions, with field studies and meta-analyses reporting mixed results: some studies indicate increases in CO₂ and N₂O, and others report reductions in CH₄, N₂O, and CO₂ (Smith et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Bruun et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; He et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Shakoor et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Global assessments of biochar management practices are sparse, even though these practices critically influence GHG reduction and yield enhancement outcomes (Liu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; He et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Shakoor et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e; Sriphirom et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, while biochar is readily accessible in small-scale trials, large-scale applications face resource limitations. The economic and logistical challenges associated with sourcing and transporting biochar materials \u0026ndash; often from distant areas \u0026ndash; pose significant barriers to widespread adoption. Moreover, the current scale of promising soil carbon sequestration techniques, including applications of biochar, enhanced silicate weathering, and other CO₂ removal technologies, with better soil management, are highly unlikely to balance the carbon currently emitted from fossil fuel combustion (Poulton et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Schlesinger and Amundson, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Regarding implementation of large-scale carbon sequestration and emission reduction practices, a critical yet often overlooked question is raised: can available biomass resources (e.g. straw, wood, and manure) meet the demands of large-scale BA? While small-scale trials show promising results, resource constraints may limit biochar\u0026rsquo;s broader impact, especially in achieving agricultural carbon neutrality on a global scale. Given these considerations, evaluating the global availability of biochar resources and assessing feasibility for large-scale use are essential for effective carbon accounting and climate resilience strategies (Nematian et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hu et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Xie et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe present study aims to expand the scope of prior research by performing, for the first time, a comprehensive evaluation of biochar feedstock materials, resource availability, and their impact on crop yields and GHG emissions by considering the potential benefit of deploying biochar technology in every country across all global regions. Specifically, we aim to (1) identify the key drivers \u0026ndash; environmental factors, soil properties, biochar characteristics, or management practices \u0026ndash; that influence biochar\u0026rsquo;s effects on CO₂, CH₄, and N₂O emissions and crop yield; (2) develop global maps of GHG emissions and yield outcomes related to BA; and (3) assess the global availability of biochar resources, examining supply\u0026ndash;demand dynamics to evaluate the feasibility of biochar for yield enhancement and emissions reduction. The findings should provide essential insights for farmers and policymakers, advancing strategies for carbon mitigation and sustainable food production on a global scale.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Data sources and literature collection\u003c/h2\u003e\n \u003cp\u003eWe used a highly robust and rational systematic review methodology (PRISMA) to synthesize peer-reviewed literature from Web of Science (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.webofscience.com/\u003c/span\u003e\u003c/span\u003e), China National Knowledge Infrastructure (CNKI) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cnki.net/\u003c/span\u003e\u003c/span\u003e), and Google Scholar (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://scholar.google.com/\u003c/span\u003e\u003c/span\u003e) up to August 2023 (Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). The keywords \u0026ldquo;charcoal\u0026rdquo; OR \u0026ldquo;biochar\u0026rdquo; AND \u0026ldquo;yield\u0026rdquo; AND \u0026ldquo;greenhouse gas\u0026rdquo; OR \u0026ldquo;GHG\u0026rdquo; OR \u0026ldquo;carbon dioxide\u0026rdquo; OR \u0026ldquo;nitrous oxide\u0026rdquo; OR \u0026ldquo;methane\u0026rdquo; OR \u0026ldquo;CH\u003csub\u003e4\u003c/sub\u003e\u0026rdquo; OR \u0026ldquo;CO\u003csub\u003e2\u003c/sub\u003e\u0026rdquo; OR \u0026ldquo;N\u003csub\u003e2\u003c/sub\u003eO\u0026rdquo; were used to search published articles. The three search platforms provided a total of 19,278 articles and these were further screened according to the following criteria: (a) the study must be field experiments with at least three separate replicates of the experimental treatment, and the number of replicates should be indicated in the paper. Laboratory, pot experimental, soil column, and modelling simulated results were excluded; (b) the experimental treatment is BA and control is without BA; (c) yield data must be the current season, and GHG data must be cumulative emissions, or provide average emissions and the days of growing season; and (d) for multi-year observations, each year is treated as a separate variable.\u003c/p\u003e\n \u003cp\u003eThe articles were selected according to Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e. Finally, a total of 286 eligible articles were screened to extract the cumulative CH\u003csub\u003e4\u003c/sub\u003e, CO\u003csub\u003e2\u003c/sub\u003e, N\u003csub\u003e2\u003c/sub\u003eO, and yield of each year for BA and CK. The corresponding standard deviation (SD) and replicates were also collected. We estimated SDs as 10% of the mean for data that were missing SD values (Zhang et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). If only the standard error (SE) was given in the paper, SD was calculated as\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\text{S}\\text{E}\\times\\:\\sqrt{n}\\)\u003c/span\u003e\u003c/span\u003e, where n is the number of replicates. The values of figures were extracted using GetData Graph Digitizer 2.25 software. We also collected ancillary information like geographic (longitude and latitude), edaphic [texture, bulk density (BD), soil pH, and soil organic carbon (SOC)], climatic [MAP (mean annual precipitation), MAT (mean annual temperature], and other experimental traits if they were available in these studies. A total of 267 individual field studies and 2140 pairs of observations (CO\u003csub\u003e2\u003c/sub\u003e, 396; CH\u003csub\u003e4\u003c/sub\u003e, 497; N\u003csub\u003e2\u003c/sub\u003eO, 700; and yield, 547) were collected comparing response ratio of BA and CK.\u003c/p\u003e\n \u003cp\u003eWe tested publication bias with Egger regression before doing meta-analysis. Some single data points were eliminated using funnel plot asymmetry (Fig. S2) to achieve the purpose of no publication bias (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The dataset in our study did not show publication bias and so had no significant impacts on the results. Our results were relatively robust and reliable, which could reflect the real impact of BA on crop yield and GHG emissions. Additionally, all meta-analysis data conformed to a normal distribution (Fig. S3). In general, the set of paired comparisons (2140) satisfactorily covered each frequency rectangle in a histogram (Fig. S3). There were 2140 pairs of observation data collected in our meta-analysis. The continents Asia, Europe, North America, South America, Africa, and Oceania accounted for 44.6%, 19.1%, 10.3%, 9.4%, 8.8%, and 7.8%, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). These sites are distributed in all climate zones except polar climate zones. We use GHG emissions and crop yield to obtain the optimal BA rate to balance the current potential global biochar production using a quadratic curve. The balance between emission reduction and yield increase was considered comprehensively, and the experiments were collected from global in-site field studies, which could represent the current global actual BA.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Uncertainty analysis\u003c/h2\u003e\n \u003cp\u003eUncertainty analysis concerns the effect of various inputs and outputs on the whole model or system, in other words it measures how the uncertainty of input parameters translates to output parameters. The model simulation and availability evaluation considered the uncertainties of all processes (Figs. S4\u0026ndash;S7). A bootstrapping strategy was employed to address the uncertainty inherent in machine learning-based predictions. Using the optimal set of hyper parameters, we trained 100 random forest (RF) models with bootstrapped samples. This approach allowed the calculation of the coefficient of variation for yield and GHG emission predictions across each grid cell, based on the outputs from the 100 RF models (Xu et al., \u003cspan class=\"CitationRef\"\u003e2024a\u003c/span\u003e). This measure of variability offers an evaluation of the model prediction uncertainty, providing valuable insights into the reliability of our yield and GHG emission estimates. In addition, 10,000 Monte Carlo simulations were performed to characterize the overall uncertainty of yield and GHG emission when the simulated yield and GHG emission were normally distributed. Specifically, the yield and GHG emission were calculated several times according to the bootstrapping strategy, and the 30% and 70% quartiles were used as the characteristic uncertainty of the estimation (Xu et al., 2024). We assessed the environmental datasets used for the model by determining the degree of extrapolation for the environmental factor layer. This was according to the method of van den Hoogen et al. (2020). This involves counting the number of rasters in the environment layer that are beyond the scope of the observed data. Furthermore, the extrapolation ratio of the environment layer (the proportion of each environment layer is the same) in each grid is adjusted.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Data analysis\u003c/h2\u003e\n \u003cp\u003eThe \u0026ldquo;metafor\u0026rdquo; and \u0026ldquo;forestplot\u0026rdquo; packages in R (version 4.2.2) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003c/span\u003e) were used in all data analysis processes. Meta-regression was also used to evaluate the effect of continuous variables on CH\u003csub\u003e4\u003c/sub\u003e, CO\u003csub\u003e2\u003c/sub\u003e, N\u003csub\u003e2\u003c/sub\u003eO, and yield.\u003c/p\u003e\n \u003cp\u003eAside from the global collection of livestock feces (pigs, cattle, sheep, goats, chickens, and ducks) and crop straw (wheat, maize, rice, bean, potato, cotton, sugarcane, and rape straws) and forest above-ground biomass, this study does not take into account any economic, social, or cultural barriers that might further limit the adoption of biochar technology in calculating potential biochar production. These global livestock density spatial layers were downloaded from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.visualcapitalist.com/\u003c/span\u003e\u003c/span\u003e. The crop straw maps were downloaded from MapSPAM (Yu et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Biochar conversion rates and other data are referenced from the studies of Lefebvre et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Schmidt et al. (2012), and Yang et al. (\u003cspan class=\"CitationRef\"\u003e2021a\u003c/span\u003e). The process of biochar production potential is presented in Supplementary Material. Biochar production potential availability was calculated using ArcGIS Pro 2023. Data processing, graphing, and tabulation were performed with R version 4.2.2 and Origin 2021 Pro.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Effect of biochar on crop yield and GHG emission\u003c/h2\u003e\n \u003cp\u003eThe BA boosted crop yield by 16%, varying with environment and management (Figs. S8 and S9). In dry areas (\u0026lt;\u0026thinsp;1000 mm precipitation), BA raised yields by 17%. Warm regions (\u0026gt;\u0026thinsp;15\u0026deg;C) saw a 9% increase. Irrigation further boosted yields by 18%. Wheat yield with BA had the biggest increase (38%), followed by maize and vegetables yield. The yield of upland soil benefited most under BA. The BA generally reduced N\u003csub\u003e2\u003c/sub\u003eO emissions by 21% (Fig. S10 and S11(A)), with the most significant inhibition (24%) in areas with 500\u0026ndash;1000 mm precipitation. In cooler regions (MAT\u0026thinsp;\u0026lt;\u0026thinsp;15\u0026deg;C), BA decreased N\u003csub\u003e2\u003c/sub\u003eO emissions by 27%. Maize fields with BA had the largest reduction (32%), followed by grassland and soybean fields. However, in vegetable fields, BA increased N\u003csub\u003e2\u003c/sub\u003eO emissions by 52%. BA in upland ecosystems had the greatest inhibitory impact on N\u003csub\u003e2\u003c/sub\u003eO (17%) (Fig. S10). BA had no effect on CO\u003csub\u003e2\u003c/sub\u003e emissions (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) overall (Fig. S10 and S11(B)). Specifically, BA reduced CO\u003csub\u003e2\u003c/sub\u003e in drier and wetter extremes while promoting it in moderate-precipitation zones and warmer regions. Vegetable fields and plantations with BA showed increased CO\u003csub\u003e2\u003c/sub\u003e emissions, while maize and upland fields showed decreases. The BA reduced CH\u003csub\u003e4\u003c/sub\u003e emissions by 15% (Fig. S10 and S11(C)), with significant inhibition (19%) in areas with MAP of 1000\u0026ndash;1500 mm and cooler regions. The BA increased CH\u003csub\u003e4\u003c/sub\u003e emissions in irrigated fields but reduced them under non-irrigated conditions (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). All crop types except for forests showed inhibited CH\u003csub\u003e4\u003c/sub\u003e emissions with BA, while plantations showed a slight increase (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The BA inhibited CH\u003csub\u003e4\u003c/sub\u003e emissions in upland, paddy fields, and grassland (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In terms of biochar production technologies, application of woody biochar and manure biochar to the bottom layer (20\u0026ndash;50 cm) with large particle size (\u0026gt;\u0026thinsp;3mm) and low pyrolysis temperature (200\u0026ndash;400℃) significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) improved crop yield by 11% (Fig. S9). High pyrolysis temperature (800\u0026ndash;1000℃) and small particle size (\u0026lt;\u0026thinsp;3 mm) of the surface (\u0026gt;\u0026thinsp;20 cm) applied straw biochar significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) reduced GHG emissions by 11% (Fig. S11).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Driving factors for yield and GHG emissions with BA\u003c/h2\u003e\n \u003cp\u003eThe structural equation modeling (SEM) analysis identified environmental factors as the primary driver of relative changes in yield (path coefficient: 0.25), N₂O emissions (0.31), CO₂ emissions (0.25), and CH₄ emissions (0.38) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). Secondary influences on yield included biochar management (0.21), biochar properties (0.17), and soil properties (-0.11). For N₂O emissions, biochar management (-0.31), soil properties (0.25), and biochar properties (-0.16) were key drivers (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). The CO₂ emissions were similarly influenced by soil properties (0.23), biochar properties (0.23), and management practices (-0.22) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). The CH₄ emissions were most impacted by soil properties (-0.32), followed by biochar management (-0.22) and soil (-0.17) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD). Biochar properties had the largest indirect effects on yield and GHG emissions, with path coefficients of 0.42, 0.63, 0.48, and 0.52, respectively.\u003c/p\u003e\n \u003cp\u003eThe SEM models explained 73%, 66%, 58%, and 65% of the variance in yield, N₂O, CO₂, and CH₄ changes, respectively, under BA (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The model-averaged analysis of predictor variables revealed that the nitrogen fertilizer (1), BA rate (0.92), MAP (0.9), and SOC (0.86) were most important for the effect of BA on crop yield considering whole factors (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). Nitrogen fertilizer (1) and MAP (0.84) were most important for the effect of BA on N\u003csub\u003e2\u003c/sub\u003eO emissions considering whole factors (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). Concerning the effect of BA on CO\u003csub\u003e2\u003c/sub\u003e emissions, MAT (1), BD (1), BA depth (0.96), pH (0.85), and SOC (0.8) were the most important predictors in the whole factor model (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). For the effect of BA on CH\u003csub\u003e4\u003c/sub\u003e emissions, SOC (1), irrigation (0.98), BD (0.97), pH (0.83), and BA depth (0.8) were the most important predictors in the whole factor model (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Global biochar supply potential\u003c/h2\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.1 Deficit ratio from optimal BA\u003c/h2\u003e\n \u003cp\u003eOur analysis estimates the average global biochar supply potential as approximately 1.89 t/ha (Fig. S12). When BA rates are considered alongside supply potential, a distinct geographical pattern emerges in biochar availability. Biochar supply decreases with increasing latitude, showing significant deficits in high-latitude regions of both hemispheres, which indicates that these biochar-deficit areas could achieve the target of increasing production and reducing emissions if there were sufficient biochar production source materials. Specifically, the Northern Hemisphere had a maximum deficit of -14.5%, while the Southern Hemisphere reached \u0026minus;\u0026thinsp;8.26%. Conversely, tropical and subtropical regions near the equator demonstrated biochar surpluses, with a surplus ratio of 25.3% (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.2 Inability regions of biochar application\u003c/h2\u003e\n \u003cp\u003eThe potential for biochar supply is constrained by the availability of biomass resources. Globally, 32.52% of global areas face a deficit in biochar resources, indicating that the maximum biochar supply potential in these regions is insufficient to meet local demand, and so are insufficient to meet basic BA demands without reliance on imported raw materials. Deficit areas include northern Asia, northern North America, central South America, the Sahara Desert in Africa, the Qinghai-Tibet Plateau in China, and southern Australia. In these biochar-deficit areas, strictly speaking, when BA strategies are implemented on a large scale, there are insufficient biochar sources as a sustainable measure to increase production and reduce emissions. Even field studies of biochar in these areas show that it has benefits of increasing production and reducing emissions. The data show that the countries with a major surplus for BA are India, New Zealand, Brazil, Argentina, Mexico, Iran, Bangladesh, Netherlands, Spain, and Ethiopia, with surplus ratios of 95%, 79%, 75%, 71%, 69%, 64%, 62%, 61%, 21%, and 18%, respectively (Fig. S13). The major deficit countries are Russia, Canada, Papua New Guinea, Australia, Finland, Sweden, Indonesia, Congo, Saudi Arabia, and Norway with deficit ratios of 98%, 94%, 92%, 91%, 72%, 64%, 61%, 52%, 41%, and 12%, respectively (Fig. S14).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Global biochar yield increase and GHG reduction\u003c/h2\u003e\n \u003cp\u003eGlobally, BA shows the greatest emission reduction effect on N₂O (93.8%), followed by CH₄ (78.4%) and CO₂ (56.7%) (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB\u0026ndash;D). The BA also increases crop yields, with a global yield improvement of 90.6% (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). Spatial simulations indicate that BA generally enhances crop yields worldwide, with decreases observed in limited areas (9.8% of the evaluated areas), including parts of western North America, northern South America, northern Africa, and southern Australia. The BA significantly reduces N₂O and CH₄ emissions across most regions, with only 5.3% of areas showing increases: N₂O emissions rose in southern Australia and central South America; CH₄ emissions increased in South Asia, central Africa, and southern South America; and CO₂ emissions rose in West, South, and East Asia. Overall, under the optimal BA ratio on each continent, considering the limitation of biochar resource sources, BA reduced GHG emissions by 23.4% and increased crop yield by 32.4% compared with no BA (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA\u0026ndash;D).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Driving factors of crop yield and GHG emissions with BA\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e4.1.1 BA impact on crop yield\u003c/h2\u003e \u003cp\u003eIt should be noted the responses of crop yield to BA are functions of various factors like climate (e.g. precipitation, temperature, and aridity) (Zhang et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), soil properties, soil management practices (e.g. tillage, mulching, and irrigation) (Faloye et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and biochar management practices (e.g. modified biochar, application method, application depth, and particle size) (He et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In our results, in dry areas (\u0026lt;\u0026thinsp;1000 mm precipitation), BA raised yields by 17% (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Combining BA with irrigation further boosted yields by 18% (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Upland soil benefited most under BA (Figs. S8 and S9). Many studies indicate that the reason for raised crop yields may be that the developed specific surface area and porous structure provide enough space and aeration for hydraulic and nutrient retention (Zhao et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), thus improving water and nutrient utilization efficiency (Faloye et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Razzaghi et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and creating favorable conditions for propagation of bacteria and fungi (Palansooriya et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This eventually changes the soil structure of physical, chemical and biological, which can be attributed to BA accelerating plant growth through regulating soil water, nutrients, aeration, temperature respiration, and photosynthesis (Smith et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Palansooriya et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Han et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). At the same time, SEM demonstrated that environment and biochar management were dominant direct factors for crop yield, which emphasizes the role of biochar management in regulation of biochar yield, which was neglected in most previous studies (Dai et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Su et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These factors are precisely the driving factors affecting the formation of yield discussed above. The BA method is a critical aspect of biochar management. Traditional broadcast and plowing application of biochar may not completely mix biochar and soil particles, while the large plow arm and mechanical tumbling intensity of a rotary tiller not only easily mix biochar and soil particles, but also increase the depth of BA (Schneider et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This effectively increases the probability of biochar recombination of subsoil nutrients and microorganisms, so that crop root nutrient utilization is expanded in the soil profile, thereby increasing crop productivity (Joseph et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, we should not only strengthen the transformation properties of biochar itself, but the difference due to the application method should be one future main direction for biochar management.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e4.1.2 BA impact on GHG emissions\u003c/h2\u003e \u003cp\u003eThe three GHG emissions were generally inhibited by biochar. Separately, BA reduced N\u003csub\u003e2\u003c/sub\u003eO and CH\u003csub\u003e4\u003c/sub\u003e emissions by 21% (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and 15% (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared with no BA in general, respectively, and there was no significant response of CO\u003csub\u003e2\u003c/sub\u003e emissions to BA (Figs. S10 and S11). The BA may affect conditions that drive nitrification and denitrification through regulating organic matter amendments and soil physico-chemical properties for N\u003csub\u003e2\u003c/sub\u003eO mitigation (Bruun et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Saarnio et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; S\u0026aacute;nchez-Garc\u0026iacute;a et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Biochar\u0026rsquo;s climate-mitigation potential stems mostly from its highly recalcitrant nature, which retards the rate of photosynthetically fixed carbon returned to the atmosphere (Kuzyakov et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Woolf et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Lehmann et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Reduction in CH\u003csub\u003e4\u003c/sub\u003e emissions may be attributed to the stimulated methanotrophic and inhibited methanogenic activity caused by BA that led to lower CH\u003csub\u003e4\u003c/sub\u003e emissions in an ambient system (Han et al., 2016). Our meta-analysis results and the previous studies on the effect of biochar on GHG emissions were based on site-experiments, without considering the available biochar sources (Hyun and Yoo, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition, both incubation and field plot experiments assumed that biochar was abundant. In previous studies, some experimental biochar was purchased at other allopatric locations and transported to the experimental sites for implementation, which unwittingly increases the economic costs and resources involved in implementing biochar tests, indirectly increasing global carbon emissions (Woolf et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFew studies have linked biochar management to soil, environmental, and other factors when analyzing its impact on GHG emissions. The path coefficients of environmental N\u003csub\u003e2\u003c/sub\u003eO, CO\u003csub\u003e2\u003c/sub\u003e, and CH\u003csub\u003e4\u003c/sub\u003e emissions are 0.31, 0.25, and 0.38, and for biochar management are \u0026minus;\u0026thinsp;0.33, -0.21, and \u0026minus;\u0026thinsp;0.22, respectively (Fig. S15). The dominant factors affecting soil GHG emissions under biochar are management and environmental factors, whose coefficients are \u0026minus;\u0026thinsp;0.25 and 0.33, respectively, much higher than the \u0026minus;\u0026thinsp;0.03 and 0.04 of biochar peculiarity and soil factors, respectively. However, general conclusions have been drawn about the biochar pyrolysis temperature, applied biochar amount, soil properties, annual rainfall, and average annual temperature (Cayuela et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Meng et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Our study explained the possibility of emission reduction in the biochar model from four aspects and then made some emission-reduction exploration models in terms of controllable factors such as biochar management and application environment, and the application mode of plant and animal source biochar.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Global spatial distribution of crop yield and GHG emission\u003c/h2\u003e \u003cp\u003eGlobal BA showed notable spatial variability in its effects on crop yield and GHG emissions. High-latitude regions, such as northwest Asia, northern Europe, central North America, and Central Asia, exhibited significant yield increases. This effect is likely due to the physical properties of biochar \u0026ndash; its high porosity, large surface area, and honeycombed structure allowing for improved water storage and solar radiation absorption, which enhance water use efficiency and crop growth (Karer et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Tan et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Edeh et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liao et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In contrast, certain tropical regions, including the southern Sahara Desert, central North America, southern Australia, and northern India, experienced yield reductions under BA, with LNR values averaging 2.6. These regions may face nitrogen limitations due to increased microbial immobilization or the release of toxic compounds from biochar, which could suppress crop productivity (Liao et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The BA has minimal negative effects on crop yield, confined to small regions such as central Africa, central North America, northern India, and isolated areas in southern and western Australia (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This pattern is potentially due to regional suitability or possibly because of soil functional variation caused by BA toxicity, which can reduce crop yields (Xu et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding N₂O emissions, biochar exhibited a similar latitudinal trend of above yield spatial distribution. High-latitude regions, except for some areas in western and southern Australia, as well as parts of central Africa and central South America, showed a reduction in N₂O emissions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). This is consistent with the enhanced nitrogen utilization efficiency in these cooler, low-precipitation regions, where biochar inhibits the activity of nitrite and nitrate reductases, thus reducing nitrification and denitrification processes (Cayuela et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Shakoor et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Biochar accelerated CO\u003csub\u003e2\u003c/sub\u003e emissions in the Eurasian region, the Indian, and some hot spots in East Asia; however, there were hot spots of inhibition in the North America, South America and central Europe (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). These spatial distribution results reinforce the results of our meta-analysis above. For CH₄, biochar primarily suppressed emissions in high-latitude regions such as northern North America, central Europe, and northern Asia, where lower temperatures and reduced soil flooding contributed to the CH₄ suppression effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). In contrast, in tropical regions near the equator characterized by high temperatures, moisture, and a large proportion of flooded rice fields, biochar increased CH₄ emissions, likely due to enhanced methanogen activity (He et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Somboon et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These findings support the idea that the effectiveness of biochar in mitigating GHG emissions is context-dependent, influenced by both regional climate and soil management practices.\u003c/p\u003e \u003cp\u003eOverall, the spatial variability observed in both crop yield and GHG emissions under BA emphasizes the need for region-specific management strategies and careful consideration of local environmental conditions for BA at a global scale.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Biochar production potential availability evaluation\u003c/h2\u003e \u003cp\u003eThe biochar availability is evaluated by the biochar production potential and the BA rate, and the potential production is determined by considering the global available biomass resources and the yield of different biochar types. This measure can objectively reflect the maximum yield of potential sources of biochar globally and is an indispensable step in evaluating biochar availability. It is concluded that the global biochar-deficit area accounted for 32.52% by calculating the balance of the global biochar production potential and average BA rates layer, which means the global straw and manure biological resources basically meet local consumption considering the maximum biochar potential yield. The average global biochar potential production is about 1.89 t/ha. Deng et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) estimated the CO\u003csub\u003e2\u003c/sub\u003e reduction of biochar by using agricultural, forest, grass resource, and energy crop residues in China, and this satisfied the negative emission demands in most mitigation scenarios. Using a simulation, Yang et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e) found that China could use 73% of national crop biomass residues as biochar to reduce GHG emissions by 8620 Mt CO\u003csub\u003e2\u003c/sub\u003e-eq by 2050, contributing 13\u0026ndash;31% of the global GHG emission reduction goal in the moderate and maximum bio-negative emission technologies scenarios. However, with the penetration of biochar research and carbon policy in recent years, this prediction requires further verification. Global production of livestock manure residue is increasing year by year. When considering plant- and animal-derived biochar, our study shows whether global biochar can meet the needs of production increase and emission reduction, and whether the required amount can be adequately supplied. In other words, when investigating the benefits of biochar in increasing production and reducing emissions, it is ridiculous not to consider the supply of biochar feedstock sources. The amount of straw and manure available locally is insufficient to support the amount of biochar applied in actual agriculture to increase production and reduce emissions. It is unwise to use biochar achieving carbon neutrality in these areas when considering the biochar feedstock resources limitation. If biochar is transported from off-site places, this will exacerbate the increase in energy costs, resulting in greater carbon emissions. The mismatch between localized BA and global biomass resources is 32.52%, suggesting that conclusions derived from single-point BA experiments may not reflect real-world conditions. These findings underscore the importance of considering the spatial availability of biochar resources when assessing its potential for emission reductions and yield increases on a global scale.\u003c/p\u003e \u003cp\u003eThe biochar potential production in northern North America, northern South America, northern and central Africa, northern Asia, central Australia, parts of the Arabian Peninsula, and parts of the Tibetan Plateau of China cannot meet the BA rate. Globally, the regions with deficits of biochar resources are mostly deserts and polar wasteland, and the resources available for biochar production in these regions are low due to scarcity of straw, wood, or livestock resources. Considering the real supply of biochar feedback resources, it is unlikely that biochar has the capacity to increase yield and reduce emissions on a global scale, because some regions have insufficient resources to support their production of biochar. This is a challenge not considered in most previous biochar studies, which indicated that biochar was a suitable material to reduce carbon emissions and increase crop yields (Woolf et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Smith, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Limitations of the current study and future outlook\u003c/h2\u003e \u003cp\u003eOur research strives to provide detail (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), but uncertainties and limitations remain. First, our analysis focuses on common biochar feedstocks (plant biomass and livestock dung), leaving out others like sludge biochar feedstock, which makes the value of 1.89 t/ha of average potential biochar production less reliable. Second, our research does not fully consider dynamic influences such as off-site transportation and carbon prices, which could all affect biochar\u0026rsquo;s economic viability. Accounting for these uncertainties, although the positive effects of biochar are well established in some areas, the limited availability of raw materials for biochar does not translate into actual agricultural activities, such as the Tibetan Plateau in China (ln R -3.8), central Africa (ln R -10.1), northern Asia (ln R -6.4), central Australia (ln R -4.7), and South America (ln R -7.2), which also hinder the progress of carbon neutrality regionally. Future research should investigate biochar co-benefits in crop production and GHG emissions considering its feedstock biomass resources. On a global scale, there are regional inconsistencies in the development of agriculture and animal husbandry. Therefore, the follow-up study aims to analyze regional differences of biochar produced from plant biomass and animal dung biomass in a more detailed way and identify the advantageous biomass sources of biochar produced in regions, which offers a more holistic understanding of biochar\u0026rsquo;s role in climate change mitigation. This is of great significance for promoting global carbon neutrality.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe potential benefits of biochar in increasing yield and reducing emissions are feasible for 67.48% of global area and not feasible for 32.52%. Under the optimal BA ratio on each continent, considering the limitation of biochar resources, BA reduced GHG emissions by 23.4% and increased crop yield by 32.4% compared with no BA. The global data synthesis found that biochar management practices and environmental factors dominate GHG emission and yield growth globally, and BA increased crop yields by 38% and reduced GHG emissions by an average of 23%. Especially, rotary tiller and deep application, which can decrease GHG emissions by 27% and increase crop yield by 33%, have great potential in biochar management. The average global biochar production potential is about 1.89 t/ha. Future research should approach claims of biochar\u0026rsquo;s yield-enhancing and emission-reducing effects with caution and consider the actual availability of biochar feedstock resources. Regional variability in biochar\u0026rsquo;s effectiveness underscores the uncertainty of its global impacts. Locally specific decision support must recognize these relationships and trade-offs to establish carbon reduction and crop production considering judicious BA commensurate with climate change mitigation needs.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWucheng Zhao: Conceptualization, Methodology, Validation, Formal analysis, Visualization, Writing original draft. Ondřej Ma\u0026scaron;ekb: Methodology, Writing-review and editing. Feng Zhang: Conceptualization, Methodology, Writing-review and editing, Supervision, Funding acquisition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (Grant Nos. 32071550), Gansu Science and Technology Major Project (22ZD6NA007). This work was also supported by the Supercomputing Center of Lanzhou University.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eBlanco-Canqui, H., Creech, C.F., Easterly, A.C., 2024. How does biochar impact soils and crops in a semi-arid environment? 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Computers and Electronics in Agriculture, 214.\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":false,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Biochar availability evaluation, Carbon neutral, Greenhouse gas emission, Food security","lastPublishedDoi":"10.21203/rs.3.rs-6267300/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6267300/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEvaluating biochar\u0026rsquo;s potential for reducing greenhouse gas (GHG) emissions and increasing crop yields globally is essential to addressing climate change challenges. Analysis of 2140 data pairs from controlled field trials and global livestock manure and crop straw raster layers across diverse soils, climates, and management practices revealed an average global biochar production potential of 1.89 t/ha. Biochar application (BA) increased crop yields by 38% and reduced GHG emissions by an average of 23%. Specifically, biochar management with rotary tilling and deep application (20\u0026ndash;50 cm) shows promise, reducing GHG emissions by 27% and boosting yields by 33%. In terms of biochar production technologies, the application of woody biochar and manure biochar to the deeper soil layer (20\u0026ndash;50 cm) with large particle size (\u0026gt;\u0026thinsp;3 mm) and low pyrolysis temperature (200\u0026ndash;400\u0026deg;C) can significantly improve crop yield by 42.6% (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). High pyrolysis temperature (800\u0026ndash;1000\u0026deg;C) and small particle size (\u0026lt;\u0026thinsp;3 mm) of surface (0\u0026ndash;20 cm) applied by straw biochar significantly reduces GHG emissions by 20.5% (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, in 32.52% of global areas there are limitations of biochar feedstock resources for practical applications, which could make its widespread adoption challenging. Sustainable biochar use can support agricultural carbon neutrality, but realizing its full benefit will require region-specific policies and management based on local biomass availability.\u003c/p\u003e","manuscriptTitle":"Is Global Crop Yield Enhancement and Emissions Mitigation by Biochar Application Feasible from a Biochar Resource Availability Perspective?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-28 12:50:08","doi":"10.21203/rs.3.rs-6267300/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"67cd5f1d-2175-4922-b728-5d4ad21a1017","owner":[],"postedDate":"March 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46316375,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation"},{"id":46316376,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"}],"tags":[],"updatedAt":"2025-10-23T11:40:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-28 12:50:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6267300","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6267300","identity":"rs-6267300","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}