Effect of biochar application on corn and soybean yield in Michigan and Ohio

Abstract

Biochar soil amendment, a product of anoxic thermochemical conversion of biomass through a pyrolysis process, may help mitigate greenhouse gas (GHG) emissions from agricultural soils (Huang et al., 2023; Verheijen et al., 2010). Biochar application to field soils include improvements in pH of acidic soils, cation exchange capacity, and water holding capacity (Agegnehu et al., 2017; Alkharabsheh et al., 2021; Tokas et al., 2021; Ye et al., 2020).

Crop yield response to biochar application is variable but has been generally found as neutral or positive, with largest yield responses in acidic soils likely due to a liming effect of the biochar (Huang et al., 2023). Although crop yield response to biochar application has been globally studied, there have been limited field studies conducted

under field conditions in the Midwestern United States. In growing environments similar to Ohio and Michigan, a meta-analysis and modeling approaches found crop yield response to biochar to be small (< 10% across crops and −2.6 to 0.6% for corn (Zea mays L.) (Aller et al., 2018; Huang et al., 2023).

Although there have been previous studies on biochar's effect on corn and soybean [Glycine max (L.) Merr.] yield, farmers in Ohio and Michigan need to understand the potential yield outcomes of biochar application using production practices and crop rotations representative of the region. The objective of this research was to evaluate biochar application on corn and soybean yield.

A field experiment was established at three locations in Fall 2020 with biochar application and continued in 2021 with corn planting and 2022 with soybean planting. Locations included The Ohio State University (OSU) Western Agricultural Research Station (WARS) near South Charleston, OH (39°51′41.40″ N, 83°40′30.36″ W), OSU Northwest Agricultural Research Station (NWARS) near Custar, OH (41°13′6.6″ N, 83°45′48.24″ W), and Michigan State University (MSU) Agronomy Farm in East Lansing, MI (42°42′52.41″ N, 84°27′42.40″ W). The soil series are Kokomo (fine, mixed, superactive, mesic Typic Argiaquolls), Hoytville (fine, illitic, mesic Mollic Epiaqualfs), and Riddles (fine-loamy, mixed, active, mesic Typic Hapludalfs)–Hillsdale (coarse-loamy, mixed, active, mesic Typic Hapludalfs) complex at WARS, NWARS, and MSU, respectively. Prior to experiment initiation, 20 soil samples were collected from the entire field area, homogenized, and tested for soil properties (Table 1). Based on state guidelines, P, K, Ca, and Mg levels were sufficient (Culman et al., 2020).

The experiment was a randomized complete block design with two treatments (biochar and non-treated control) and four replications. At MSU, plots were 40 ft long by 20 ft wide, and the field was fallow in 2020 due to COVID-19 restrictions. At NWARS and WARS, each plot was 30 ft long by 10 ft wide and the previous crop was soybean. A softwood pine-derived biochar (Wakefield Biochar™; Table 2) was applied by hand in Fall 2020 at each location and the entire field area tilled to a depth of four inches using a field cultivator. Biochar was applied at a rate of 4.5 tons acre⁻¹. Crop yield response to biochar application occurs at rates ≥4.5 tons acre⁻¹ (Huang et al., 2023). Production practices are shown in Table 3.

In Ohio, monthly average temperature and cumulative precipitation were obtained from weather stations located at NWARS and WARS (College of Food, Agricultural, and Environmental Sciences, 2022) (Figure 1). For MSU, weather information was obtained for the East Lansing weather station (WeatherWX, 2022). Soil moisture at a 6-inch depth was collected approximately every two weeks during the growing season, three times per plot (HydroSense II, Campbell Scientific).

Corn and soybean yield were adjusted to 15.5% and 13.0% moisture content, respectively. Yield was analyzed by location due to a treatment by location interaction. The effect of biochar on yield was evaluated using the Proc Mixed procedure (SAS 9.4). Fixed effect was biochar treatment and random effect was replication. Normality and homogeneity of variance assumptions were not violated.

Corn grain yield was not influenced by the application of biochar (Table 4). Similarly, at MSU and NWARS, soybean yield was not influenced by biochar application. However, at WARS, soybean yield was 7.4 bu acre⁻¹ greater when biochar was applied compared to the control. For soybean, adequate water supply during the R3 to R5 growth stages is critical to maximize yield (Rattalino Edreira et al., 2017), which generally occurs late July through August. In August 2022, precipitation at WARS was below average (Figure 1), but the average soil moisture on July 28, August 18, and September 1 was greater in the biochar treatment (23.4%) compared to the control (21.0%) (α = .09). Thus, the yield response at WARS in 2022 may have been due to greater plant available water in the biochar treatment compared to the control.

In a meta-analysis examining the use of biochar across cropping systems, researchers found an average increase of 15.7% in crop yield in fields treated with biochar compared to a control (Huang et al., 2023). However, researchers noted yield response to biochar was variable depending on the environment and soil properties with crop yield responses either neutral or positive.

For corn, there was no yield response with biochar application. In soybean, a yield response occurred in one out of three locations. While there may be environmental and soil quality benefits associated with the use of biochar, it is important to understand the effect on corn and soybean yield as the cost of biochar amendment and application is high (Sorensen & Lamb, 2018). Due to lack of yield response in corn and soybean from field trials, biochar may not be economically viable to farmers in the short-term. Additional studies on the long-term effect of biochar on GHG emissions, soil properties, and crop yield are needed.

Gabriela Silva-Pumarada: Formal analysis; investigation; writing—original draft. Raj K. Shrestha: Conceptualization; funding acquisition; methodology; writing—review and editing. Marília Chiavegato: Methodology; supervision; writing—review and editing. Kristin Mercer: Supervision; writing—review and editing. Benjamin K. Agyei: Investigation; Writing—original draft. Maninder P. Singh: Conceptualization; funding acquisition; investigation; supervision; writing—review and editing. Laura E. Lindsey: Conceptualization; funding acquisition; supervision; writing—review and editing.

The authors declare no conflict of interest.