Biochar may improve the health of environmentally sensitive soils (i.e., low C, sandy, sloping) especially if combined with cover crops (CCs), but research is scant. We assessed how wood biochar (836 g C kg−1) applied at 0, 6.25, 12.5, 25, and 50 Mg ha−1 to sandy, sloping, and semi‐arid soils with and without CCs affects soils and crop yields in the central US Great Plains for 3 years. We measured crop yields and CC biomass each year, and most soil properties in Years 1 and 3. Biochar did not interact with CCs, suggesting the combination was no better than biochar or CC alone. In the semi‐arid soil, crop and CC did not establish due to persistent droughts. Biochar benefits were highly site‐specific. Biochar improved some soil properties but only in the sandy and sloping soils and at the biochar application depth (0‐ to 15‐cm soil depth). The 50 Mg biochar ha−1 improved the soil's ability to sorb water (0.08 cm s−1/2). Also, in the sandy soil, it increased soil organic matter concentration (2.5 g kg−1), soil pH (0.65 units), and available water (0.07 m3 m−3) only in Year 1, suggesting a biochar benefit in sandy soils is short‐lived. In the sloping soil, >25 Mg biochar ha−1 reduced bulk density (0.16 Mg m−3) and increased soil mean weight diameter of water‐stable aggregates (0.58 mm), organic matter concentration (11.42 g kg−1), infiltration (9.35 cm), CC biomass production (0.27 Mg ha−1), and some microbial biomass groups. Biochar did not affect crop yields. Overall, >25 Mg biochar ha−1 improved properties in some soils without interacting with CCs.

Does biochar combined with cover crops improve health and productivity of sandy, sloping, and semi‐arid soils?

Introduction Cover crop (CC) is an essential tool to improve or maintain soil health, potentially improving cash crop productivity. Several recent reports of cash crop yield reduction following cover cropping necessitated this research to guide efficient CC decisions in the season before corn (Zea mays) or soybean (Glycine max) is to be grown. Methods Therefore, we designed this multi-year, multi-location study to include the farmers who plant CC following the harvest of a small grain crop, majorly wheat (Triticum aestivum) or oats (Avena sativa), and then grow corn or soybean cash crop in the subsequent season. We also selected the farmers who used a fall CC mix that was winter-terminated, to avoid further complexities. The major objective of this study was to document soil health changes and cash crop yields following CC in eight selected locations around SD for three consecutive CC seasons between 2017-2020. Experimental plots were laid out at the farmer-cooperators’ CC fields, where no cover (NC) ‘control’ was tested against CC in a randomized complete block design (RCBD). Soil samples were analyzed for selected soil health indicators (SHIs): potentially mineralizable nitrogen (PMN), permanganate oxidizable carbon (POXC), soil respiration (SR), soil microbial biomass (SMB), soil nitrate-nitrogen, soil organic matter (SOM), and other basic soil properties (pH, electrical conductivity, etc.); crop and residue biomass were calculated, and cash crop economic yields were measured. Results and discussion No statistically significant (p<0.05) advantage was found for SHIs or cash crop yields under CC plots compared to NC plots as these fields had healthy soils (long-term no-till was practiced, high SOM levels >30 g kg-1). These findings directed us to investigate hydroclimatological parameters and climatological indices such as accumulated precipitation, standardized precipitation index (SPI), and standardized precipitation-evapotranspiration index (SPEI) for their impact on CC’s influence on cash crop yields. Conclusion Our analyses indicated that hydroclimatology, especially SPEI for the month before CC planting can be used as a tool to guide successful CC decisions, reducing the risk of cash crop yield loss. Further investigations with SPI and SPEI, along with other climatological parameters are needed to explore and design better CC management tools.

Cover crops did not improve soil health but hydroclimatology may guide decisions preventing cash crop yield loss

Winter rye (Secale cereale L.) biomass and chemical composition influences decomposition rates, nutrient cycling and effects on cash crop growth. Management that maintains rye biomass while lowering carbon (C) to nitrogen (N) ratios and concentrations of structural carbohydrates including hemicellulose, cellulose, and lignin could maintain some ecosystem services while accelerating nutrient cycling. Two independent studies examined (1) five rye seeding rates ranging from 0 to 90 kg ha−1 and (2) five termination dates ranging from April 19 to May 31 (585 to 1084 growing degree days) to understand management induced relationships between rye biomass, nutrient concentrations, and composition of hemicellulose, cellulose, and lignin. Rye biomass increased at seeding rates of 90 kg ha−1 as compared to the 22–67 kg ha−1 seeding rates while trends toward higher plant structural components were observed as seeding rate increased. Rye biomass accumulated rapidly beyond 819 (May 13) growing degree days while increasing rye C:N ratio and concentrations of hemicellulose and cellulose. Rye C:N ratio was strongly correlated to rye biomass (R2 = 0.80, p < .001) with a C:N ratio of 25 associated with 2000 kg ha−1 of rye biomass. Changes in rye residue quality were strongly influenced by biomass accumulation with delaying termination most significantly driving biomass changes. Trends toward lower C:N ratios and concentrations of structural carbohydrates were seen at the 22–67 kg ha−1 seeding rates even without differences in biomass suggesting lower seeding rates could cycle nutrients quicker without limiting biomass production potential. 

Winter Rye Cover Crop Seeding Rate and Termination Timing Effects on Cover Crop Biomass and Quality

Ranking the contribution of genotype, environment, and management (G × E × M) on maize's economic optimum nitrogen fertilizer rate (EONR) variability could improve understanding and predictability of EONR. We performed a simulation experiment using the Agricultural Production Systems sIMulator model with the objectives to (1) rank the effects of 24 individual G × E × M factors on the magnitude and interannual variability of the EONR across the US Midwest and (2) investigate the impact of G × M factors on the EONR variability under present and future climate scenarios. Results indicate that genetics (27%), management (31%), and environmental conditions (41%) each influence the EONR variability. Within these broad categories, the top three individual factors impacting the EONR were interannual weather variability, crop radiation use efficiency, and the soil inorganic N carryover from the previous year. The G × E × M factors influenced the yield response to N fertilizer in different ways. Soil‐related factors (e.g., organic matter and residual nitrate) influenced grain yields at the low N rates, while management factors (e.g., planting date and density) influenced yield at all N rates. Combining increases in plant density and changes in genetics synergistically increased the EONR by 15% from baseline. Future climate scenarios without adaptation decreased the EONR and yield loss, but crop adaptation was buffered against the negative climate change impacts. We concluded that 59% of the annual EONR variability is manageable (due to genetics and management) and that G × M factors could buffer climate change's negative effects on crop production. Present results can inform experimental research on N fertilizer and N rate decisions.

Ranking genotype, environment, management effects on the optimum nitrogen rate for maize: A cropping system modeling analysis

Globally, agricultural systems face the complex challenges of producing enough food, fiber, and fuel to sustain a growing population while mitigating the negative environmental consequences of intensive agricultural management. To that end, optimizing nitrogen (N) fertilizer management has become a major focus, as it is commonly the most limiting nutrient to maximizing cereal crop growth, while over-application increases the potential for reactive N to leach into groundwater and be emitted as a potent greenhouse gas. The goals of this dissertation were to 1) quantify the interannual changes of the economic optimum N rate while enhancing our understanding of the tradeoff between crop production and environmental degradation; 2) improve the capacity of a process-based crop model to simulate yield response to N and the economic optimum N rate; and 3) rank and quantify the sensitivity of the yield response to N and the optimum N rate to individual environmental, crop management, and genetic factors. To meet our goals, we used process-based modeling and measured grain yields from 14 long-term maize yield response to N experiments across Iowa and Illinois, USA, thereby allowing us to quantify temporal trends of measured optimum N rates and provide a systems-level analysis of the yield response to N using process-based modeling. Analysis of these data revealed the economic optimum N rates have been increasing over time by 2.06 and 2.80 kg N ha-1 yr-1 for continuous maize and maize following soybean crop rotation and that reducing N rates below the optimum to mitigate negative environmental effects of N application will have more severe consequences on yield production than reductions in N losses. Moreover, we provide a solution for process-based models to reproduce the continuous maize yield penalty and identify bias associated with fitting simulated yield response curves leading to the lower capacity of process-based models to predict the economic optimum N rate relative to grain yield (RRMSE = 33 and 19%). Further complicating the prediction of the economic optimum N rate, we determined that crop genetics, management, and growing environment have similar contributions to the annual sensitivity of the economic optimum N rate. This dissertation serves as a benchmark to guide future research priorities toward increasing economic optimum N rate prediction and inform future N fertilizer rate recommendations, research, and policy decisions. 

Enhancing our understanding of optimal nitrogen fertilizer rates for maize: A synthesis of long-term experimentation and crop modeling

Precipitation and not Cover crop Composition influenced corn economic optimal n rate and yield

Evaluating the influence of grass or broadleaf cover crops on soil health measurements is common in the U.S Midwest. However, the comparison among different cover crop mixtures, including blends of both grass and broadleaf species is limited. Eleven cover crop experiments were conducted in South Dakota from 2018-2020. Cover crops were planted in the fall after small grains harvest as mixtures of dominantly grasses or broadleaves, a 50/50 grass/broadleaf mixture, and a no cover crop control. Soil and plant surface residue samples were collected in the fall before winter kill and in the spring before cover crop termination and corn planting. Soil samples were analyzed for permanganate oxidizable carbon (POXC), potentially mineralizable nitrogen (PMN), and soil respiration. Cover crops regardless of composition compared to the no cover crop control did not affect fall or spring cover crop/previous crop residue biomass in 7 of the 11 site-years, suggesting growing cover crops may accelerate decomposition of previous crop residue. Cover crops did not improve soil health measurements compared to the no cover crop control or were there differences among cover crop mixtures. Weather and soil properties (precipitation, soil organic matter, and pH) were related to differences in soil heath measurements among site-years. In the first year of planting a multi-species mixture of grasses and/or broadleaves after small grain harvest, growers should not expect to find differences in soil health measurements. Long-term trials are needed to determine whether these different cover crop mixtures over time result in changes in soil health.

Cover crop composition in long‐term no‐till soils in Semi‐Arid environments do not influence soil health measurements after one year

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Precipitation and not Cover crop Composition influenced corn economic optimal n rate and yield

Cover crop composition in long‐term no‐till soils in Semi‐Arid environments do not influence soil health measurements after one year