Hamze Dokoohaki
David Schmudde
Micah P. Dombrowski
NextjournalWhere should we apply Biochar ?
Subtitle
1. News & Media
- Biochar could boost US crops (physicsworld.com, 02 Jul 2019)
- Biochar: miracle or madness? (Geographical Magazine , 06 Jun 2019)
2. Abstract
The heating of biomass under low-oxygen conditions generates three co-products, bio-oil, biogas, and biochar. Bio-oil can be stabilized and used as fuel oil or be further refined for various applications and biogas can be used as an energy source during the low-oxygen heating process. Biochar can be used to sequester carbon in soil and has the potential to increase crop yields when it is used to improve yield-limiting soil properties. Complex bio-physical interactions have made it challenging to answer the question of where biochar should be applied for the maximum agronomic and economic benefits. We address this challenge by developing an extensive informatics workflow for processing and analyzing crop yield response data as well as a large spatial-scale modeling platform. We use a probabilistic graphical model to study the relationships between soil and biochar variables and predict the probability and magnitude of crop yield response to biochar application. Our results show an average increase in crop yields ranging from 4.7% to 6.4% depending on the biochar feedstock and application rate. Expected yield increases of at least 6.1% and 8.8% are necessary to cover 25% and 10% of US cropland with biochar. We find that biochar application to crop area with an expected yield increase of at least 5.3%–5.9% would result in carbon sequestration offsetting 0.57%–0.67% of US greenhouse gas emissions. Applying biochar to corn area is the most profitable from a revenue perspective when compared to soybeans and wheat because additional revenues accrued by farmers are not enough to cover the costs of biochar applications in many regions of the United States.
3. Introduction
The heating of plant biomass under low-oxygen conditions generates three co-products, bio-oil, biogas, and biochar. Bio-oil can be stabilized and used as fuel oil or be further refined for various applications and biogas can be used as an energy source during the low-oxygen heating process. On the other hand, biochar application to soil has been practiced to sequester carbon and it has shown the potential to increase crop yields when it is used to improve yield-limiting soil properties.
The complex nature of biochar interactions with soils and crops as well as lack of clear understanding of the mechanics of these interactions has led to reports with conflicting interpretations, even under similar conditions (Bach et al., 2016). In addition, the large amount of missing data in the literature including inconsistent reporting of soil and biochar properties has made the prediction of crop yield response to biochar a very challenging modeling exercise.
We addressed this challenge by developing an extensive informatics workflow for processing and analyzing crop yield response data as well as a large spatial-scale modeling. We used probabilistic graphical models to study the relationships between soil and biochar variables and predict the probability and magnitude of crop yield response to biochar application. These models are inherently capable of handling missing data and accounting for uncertainty associated with observations. In this platform, we combined expert knowledge(for better defining the relationship among the variables) and data (for estimating the parameters of the model) to deal with the complexity of our problem. Probabilistic graphical models are commonly used in cases with incomplete datasets and high uncertainty which makes them a suitable candidate for the current status of biochar science.
4. Material and Methods
4.1. Data
4.2. Model Development
We developed a hybrid Bayesian network model (a model that includes both discrete and continuous variables) within the R environment and by using the 'Bayes Server' software. The final design of the model’s structure resulted in 84 parameters. Given that absolute yield is not readily comparable among studies, the response ratio (RR) was used as the target variable . The RR is defined as:
A positive RR indicates a positive yield response to biochar application, whereas a RR of 0 shows no change from control treatment.
We compared our BN model and the only available statistical model for explaining the heterogeneity in yield response to soil and biochar properties (Crane et al., 2013). The authors of that study used a generalized additive model (GAM) with 162 parameters to develop smooth functions mapping of independent variables to RR. In more than 250 iterations, the BN model consistently outperformed the GAM; as the BN model average Model Efficiency (EF) and Mean Absolute Difference (MAD) values were 0.23 and 0.10, respectively, compared to -1.96 and 0.18 for the GAM.
5. Results
5.1. Descriptive analysis
The estimated average RR to biochar showed a 12% increase for all studies in our database. A large variability in RR was also observed, ranging from −24.4% to 98%, with the interquartile range ranging from 0 to 21% (figure 1). Among all soil properties, clay content, SOC, pH and CEC showed a significant negative correlation with RR, while sand and silt content were positively correlated with RR. Yield response was invariant with nitrogen application rate, biochar ash content, and biochar pH. The HPT, biochar N, and C:N ratio showed a significant negative correlation with RR. Higher biochar C content was significantly correlated with a higher RR (figure 1). A linear model analysis revealed a minor (not significant) association between feedstock and crop type with RR while no direct association was found between thermochemical technology and RR. Note that we do not make any assumptions about the functional form of yield response to biochar application. The model predicts that the biochar application rate has a diminishing marginal effect on the yield response, i.e. the yield effect is higher with low biochar application rates and flattens out at higher application rates.
5.2. Spatial modeling
We compared our BN model and the only available statistical model for explaining the heterogeneity in yield response to soil and biochar properties [2]. The authors of that study used a generalized additive model (GAM) with 162 parameters to develop smooth functions mapping of independent variables to RR. In more than 250 iterations, the BN model consistently outperformed the GAM; as the BN model average EF and MAD values were 0.23 and 0.10, respectively, compared to −1.96 and 0.18 for the GAM. Negative EF implies that the observed mean is a better predictor than the model; and the positive average EF proves the merit of the BN and indicates that it can be used to explore different scenarios of soil and biochar properties within the scope of the training dataset. Given the proficiency of the BN model, spatially explicit analysis of response to biochar was explored for cropland in the US under different biochar scenarios. Figure 2 shows the estimated probability, magnitude, and expected yield increase for hardwood biochar and 15 Mg ha−1 application rate scenarios. We focus on locations that have a positive expected yield increase because farmer's will not apply biochar if the expected yield change is negative. This assumes that the only incentive for farmers to apply biochar is the yield increase.
The following interactive map shows all the suitable places found by our model with more than 75% chance of yield increase following a switchgrass biochar application.
Regions known to have high soil quality (e.g. Des Moines lobe in north central Iowa) showed a low probability of having a yield increase under all biochar scenarios. Our model indicates a high probability of a positive RR in areas with highly weathered soils (e.g. Eastern half of San Joaquin valley in California and Mississippi Valley). Yield response was predicted to be the lowest in areas with very high SOC, CEC, or soil pH such as those found in north Texas and Minnesota (figure 2).Our results show the highest increase in expected yields of 6.43% for biochar derived from hardwood with an application rate of 15 Mg ha−1. The lowest increase increase of 4.69% was observed for biochar derived from soybeans at an application rate of 5 Mg ha−1. Assuming farmers with the highest expected yield increase are the first to apply biochar to the field, then minimum yield increases ranging from 8.8% (soybeans biochar at 5 Mg ha−1) to 11.4% (hardwood biochar 15 Mg ha−1) are necessary to cover 10% of crop area. A minimum yield increases ranging from 6.1% (switchgrass biochar at 5 Mg ha−1) to 7.9% (hardwood biochar 15 Mg ha−1) are necessary to cover 25% of crop area.
The above map shows the probability of yield increase following a soybean biochar application in Mississippi Valley.
6. Discussion
The BN developed in this study is computationally fast and accurate enough to be used for large scale modeling, nonetheless, our results have several caveats. For example, our understanding suggests that various management practices (such as residual removal, increases in N fertilization rates, etc), soil properties (such as inorganic N available in the soil), and biochar properties (such the size of labile C and N pools) are potential drivers of crop yield responses that are not explicitly defined in the model. Adequate data is currently unavailable in the literature to cover all possible soil-biochar interactions, and therefore the model focuses on available information. The inference domain of the models output is limited to the extent of the training dataset. However our model paves the way for the use of more computationally intensive process-based crop models by identifying regions, soil types, and biochar types with high and low probability of crop yield response.
A one-time application of 5 or 15 Mg ha−1 was the only option considered here; in the future other management options, such as annual co-applications of biochar with fertilizer, may need to be considered. Although the results of this study are not directly pertinent, the pattern of responsive soils identified are likely to be similar under different application scenarios. Future research may open up different pathways of biochar use that do not have to apply 5 or 15 Mg ha−1 of biochar but a lower amount (e.g. when used as part of a composite fertilizer) which would change the economics and very likely also the range of soils where it can be applied. If smaller amounts of biochar as part of a composite fertilizer can increase yields, it may even prompt farmers or farming regions to get their own pyrolysis unit, use the heat for greenhouses and the biochar for producing carbon fertilizers. Farmers may start to plant say hedgerows to get their own biochar at lower costs and create carbon credits while doing so, depending on the political decisions for future land use changes and CO2 prices.
7. Apps
The following apps allow users to directly interact with our model.
In order to predict the probability of yield increase following biochar application, you need to enter your basic soil (The units are in percentage like 30% sand) and biochar properties and then click on the predict button. Then this interface will use those properties to estimate and plot the probability of the yield increase, the mean response ratio as well as the variance around our estimate. You're allowed to compare as many scenarios of biochar x soil as you wish.
The suggested probability of yield increase by our model is not a professional advice for biochar application by any means. It's also necessary to mention that the raw outputs or any derivative of our model's results is not free for commercial and Governmental use.
7.1. Spatial modeling
The following app shows the probability of yield increase following biochar application. This app retrieves your soil properties from gSSURGO database (exclusively for USA) and then uses a hypothetical biochar type to estimate the probability of yield increase at different soil types within the selected area.
The suggested probability of yield increase by our model is not a professional advice for biochar application by any means. It's also necessary to mention that the raw outputs or any derivative of our model's results is not free for commercial and Governmental use.
suppressPackageStartupMessages({shiny::shinyAppDir("/BNShinyAppSpatial")})