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Applying biochar to agricultural soils has been proposed as a means of sequestering carbon (C) while simultaneously enhancing soil health and agricultural sustainability. However, our understanding of the long‐term effects of biochar and annual versus perennial cropping systems and their interactions on soil properties under field conditions is limited. We quantified changes in soil C concentration and stocks, and other soil properties 6 years after biochar applications to corn (Zea mays L.) and dedicated bioenergy crops on a Midwestern US soil. Treatments were as follows: no‐till continuous corn, Liberty switchgrass (Panicum virgatum L.), and low‐diversity prairie grasses, 45% big bluestem (Andropogon gerardii), 45% Indiangrass (Sorghastrum nutans), and 10% sideoats grama (Bouteloua curtipendula), as main plots, and wood biochar (9.3 Mg/ha with 63% total C) and no biochar applications as subplots. Biochar‐amended plots accumulated more C (14.07 Mg soil C/ha vs. 2.25 Mg soil C/ha) than non‐biochar‐amended plots in the 0–30 cm soil depth but other soil properties were not significantly affected by the biochar amendments. The total increase in C stocks in the biochar‐amended plots was nearly twice (14.07 Mg soil C/ha) the amount of C added with biochar 6 years earlier (7.25 Mg biochar C/ha), suggesting a negative priming effect of biochar on formation and/or mineralization of native soil organic C. Dedicated bioenergy crops increased soil C concentration by 79% and improved both aggregation and plant available water in the 0–5 cm soil depth. Biochar did not interact with the cropping systems. Overall, biochar has the potential to increase soil C stocks both directly and through negative priming, but, in this study, it had limited effects on other soil properties after 6 years. The total increase in C stocks in biochar‐amended plots was nearly twice the amount of C added with biochar 6 years earlier, suggesting a negative priming effect of biochar on formation and/or mineralization of native soil organic C. Biochar has the potential to increase soil C stocks both directly and through negative priming, but, in this study, it had limited effects on other soil properties after 6 years.

The winter fallow period common in annual cropping systems leaves soils vulnerable to erosion and nutrient loss, especially to nitrogen (N) leaching. This vulnerability can be mitigated with perennial crops that have living roots in the ground year‐round. The mechanisms, magnitude, and consistency with which perennial crops retain N are not clear. We used an experiment to test whether a perennial crop, miscanthus (Miscanthus × giganteus Greef et Deu.), would leach less N than continuous maize (Zea mays L.) and how soil net N mineralization (Nmin) may explain observed leaching under varied environment and management conditions. The experiment included three crossed factors: (1) cropping system (maize, juvenile miscanthus = 1–2 years old, mature miscanthus = 3–4 years old); (2) N fertilization (0 and 224 kg N ha−1); and (3) environment (four site‐years at two locations in Iowa, USA, that differed in climate and soil fertility). We measured N cycling dynamics, including: inorganic soil N (ammonium + nitrate), in situ Nmin, N leaching, crop N uptake, and calculated system N use efficiency. There were many complex interactions among factors. On average, cumulative Nmin under juvenile miscanthus was 111% greater than maize, but as miscanthus matured, there was no difference in Nmin between the perennial crop and maize. There was no difference in N leaching between juvenile miscanthus and maize, but mature miscanthus decreased N leaching by 42% and 88% compared to maize (with and without N fertilization, respectively). Across all treatments, there was no relationship between Nmin and N leaching, suggesting other mechanisms are regulating N leaching. Overall, mature miscanthus shows promise as a tool to reduce N losses in areas dominated by annual row‐crops. We tested how soil net N mineralization (Nmin) may explain observed N leaching under Miscanthus × giganteus and Zea mays L. in a staggered‐start experiment testing different plant age, environment, and management conditions. Nmin did not explain N leaching. There was also no difference in N leaching between juvenile miscanthus and maize, but mature miscanthus decreased N leaching by 42% and 88% compared to maize (with and without N fertilization, respectively). Overall, mature miscanthus shows promise as a tool to reduce N losses in areas dominated by annual row‐crops, but the underpinning mechanisms require further investigation.

Process-based agroecosystem models are powerful tools to assess performance of managed landscapes, but their ability to accurately represent reality is limited by the types of input data they can use. Ensuring these models can represent cropping field heterogeneity and environmental impact is important, especially given the growing interest in using agroecosystem models to quantify ecosystem services from best management practices and land use change. We posited that augmenting process-based agroecosystem models with additional field-specific information such as topography, hydrologic processes, or independent indicators of yield could help limit simulation artifacts that obscure mechanisms driving observed variations. To test this, we augmented the agroecosystem model Agricultural Production Systems Simulator (APSIM) with field-specific topography and satellite imagery in a simulation framework we call Foresite. We used Foresite to optimize APSIM yield predictions to match those created from a machine learning model built on remotely sensed indicators of hydrology and plant productivity. Using these improved subfield yield predictions to guide APSIM optimization, total N O 3 − N loss estimates increased by 39% in maize and 20% in soybeans when summed across all years. In addition, we found a disproportionate total amount of leaching in the lowest yielding field areas vs the highest yielding areas in maize (42% vs 15%) and a similar effect in soybeans (31% vs 20%). Overall, we found that augmenting process-based models with now-common subfield remotely sensed data significantly increased values of predicted nutrient loss from fields, indicating opportunities to improve field-scale agroecosystem simulations, particularly if used to calculate nutrient credits in ecosystem service markets.

Quantifying the carbon (C) uptake of Miscanthus × giganteus (M × g) in both aboveground and belowground structures (e.g., net primary productivity (NPP)) and differences among methodological approaches is crucial. Our objectives were to directly measure Mxg NPP and evaluate the effects of nitrogen application, location, and belowground biomass sampling methods. We hypothesize that increased nitrogen application increases the overall NPP of M × g and that quantifying rhizome biomass using excavations will produce the lowest variability between replicates. We collected biomass from mature M × g stands from three locations in Iowa with three nitrogen application rates and one site in Illinois. We destructively sampled at two time points, when rhizome mass is anticipated to be at a minimum (initial) and anticipated to be at its maximum (peak). Biomass was collected from 1 × 1 m quadrats in which one in‐clump and one beside‐clump cores were collected and then excavated to 30 cm depth to extract all rhizomes. We found that aboveground M × g NPP ranged from 15.4 Mg Da ha–1 year–1 to 36.4 Mg Da ha–1 year–1 and belowground M × g NPP ranged from 4.4 Mg Da ha–1 year–1 to 19.6 Mg Da ha–1 year–1. M × g NPP varied across sites, fertilization, and calculation assumptions. Aboveground NPP (yield) was on average 68.7% of the total NPP. Root‐to‐shoot ratios at peak biomass decreased with nitrogen application rate, from an average of 1.9 for 0 N plots to 0.89 for 224 N fertilized plots. There was more variation in core data than from excavations; however, when in‐clump and beside‐clump cores were averaged together, core and excavation averages were not different. Overall, these results show that the range of mature M × g NPP is driven by aboveground productivity, influenced by nitrogen application and site. Our results provide useful data to constrain agro‐ecosystem models and provide crucial insights for future perennial belowground sampling. Many estimates of Miscanthus × giganteus (M × g) productivity focus on aboveground harvestable yields and do not directly address belowground biomass in this perennial crop. To more accurately constrain the amount of carbon taken up by M × g in both aboveground and belowground plant parts, we calculated the net primary productivity (NPP) of mature M × g at three sites with three nitrogen application rates using collections of aboveground and belowground biomass at two time points during the growing season. These estimates help improve our understanding of M × g carbon sequestration potential and will improve the representation of M × g in agro‐ecosystem models.

Triploid × (Greef et Deu. ex Hodkinson et Renvoize) is a sterile, perennial grass used for biomass production in temperate environments. While . × has been intensively researched, a scale standardizing description of . × morphological stages has not been developed. Here we provide such a scale by adapting the widely-used Biologische Bundesanstalt, Bundessortenamt, CHemische Industrie (BBCH) scale and its corresponding numerical code to describe stages of morphological development in . × using observations of the \"Freedom\" and \"Illinois\" clone in Iowa, USA. Descriptive keys with images are also presented. Because . × plants overlap in the field, the scale was first applied to individual stems and then scaled up to assess plants or communities. Of the 10 principal growth stages in the BBCH system, eight were observed in . × . Each principal stage was subdivided into secondary stages to enable a detailed description of developmental progression. While . × does not have seed development stages, descriptions of those stages are provided to extend the scale to other genotypes. We present methods to use morphological development data to assess phenology by calculating the onset, duration, and abundance of each developmental stage. This scale has potential to harmonize previously described study-specific scales and standardize results across studies. Use of the precise staging presented here should more tightly constrain estimates of developmental parameters in crop models and increase the efficacy of timing-sensitive crop management practices like pest control and harvest.

The cultivation of sterile giant miscanthus (Miscanthus × giganteus, M × g) for bioenergy and bioproducts has expanded into grain‐cropped land in the United States (US) as local markets developed for this high‐yielding perennial grass (10–30 Mg DM ha−1). However, the magnitude of spatial and temporal variability in yield within US Corn Belt fields, along with impacts on economic return and sustainable land management, is poorly understood. This study established a diagnostic model relating remote sensing‐derived vegetation indices to ground truth data from 105 hand‐harvested stem biomass samples, which were strategically selected to represent the full range of vegetation index observations. The high‐resolution satellite‐sensed vegetation indices captured > 90% of the yield variation measured within fields. This model was then used to predict yield variability and assess economic performance across four of the first commercial M × g fields in the Corn Belt state of Iowa, US. Significant spatial variability in biomass dry matter (DM) yields (9.3–18.1 Mg DM ha−1) and net profits ( $83 to $ 1211.5 ha−1) was observed. All fields were profitable in all site‐years. When low profit occurred, it was explained by limited management experience of the crop in Iowa. The breakeven yield at a selling price of$130 Mg−1 varied from 9.0–12.1 Mg ha−1 at 15% moisture content (7.6–10.3 Mg DM ha−1). Breakeven prices ranged from $ 73 to $122.4 Mg−1, matching ranges used in the Department of Energy Billion Ton Report (US Department of Energy, 2023). Notably, M × g yield and profits were commensurate with grain crops particularly with favorable precipitation. This study provides insight on the M × g management “learning curve”, performance on marginal land and in drought conditions, and demonstrates that addressing yield gaps, reducing costs, and implementing precision agriculture strategies can enhance profitability. These findings emphasize the value of remote sensing technologies in guiding sustainable and competitive commercial‐scale M × g production. We developed, tested, and used a satellite remote sensing method to predict the productivity and profitability of sterile Miscanthus × giganteus. Using high‐resolution imagery and ground measurements from Iowa's first commercial miscanthus fields, we made a robust model that accurately estimated yield over 8 site‐years. We found strong profitability and identified where better management could further boost returns. This is the first study with actual yield, costs, and returns of commercial miscanthus in the US. It demonstrates that satellite‐based tools can guide efficient and sustainable biomass production, supporting both farmers and industry.

The production of advanced perennial bioenergy crops within marginal areas of the agricultural landscape is gaining interest due to its potential to sustainably produce feedstocks for biofuels and bioproducts while also improving the sustainability and resilience of commodity crop production. However, predicting the biomass yields of this production system is challenging because marginal areas are often relatively small and spread around agricultural fields and are typically associated with various abiotic conditions that limit crop production. Machine learning (ML) offers a viable solution as a biomass yield prediction tool because it is suited to predicting relationships with complex functional associations. The objectives of this study were to (1) evaluate the accuracy of commonly applied ML algorithms in agricultural applications for predicting the biomass yields of advanced switchgrass cultivars for bioenergy and ecosystem services and (2) determine the most important biomass yield predictors. Datasets on biomass yield, weather, land marginality, soil properties, and agronomic management were generated from three field study sites in two U.S. Midwest states (Illinois and Iowa) over three growing seasons. The ML algorithms evaluated in the study included random forests (RFs), gradient boosting machines (GBMs), artificial neural networks (ANNs), K-neighbors regressor (KNR), AdaBoost regressor (ABR), and partial least squares regression (PLSR). Coefficient of determination (R2) and mean absolute error (MAE) were used to evaluate the predictive accuracy of the tested algorithms. Results showed that the ensemble methods, RF (R2 = 0.86, MAE = 0.62 Mg/ha), GBM (R2 = 0.88, MAE = 0.57 Mg/ha), and GBM (R2 = 0.78, MAE = 0.66 Mg/ha), were the most accurate in predicting biomass yields of the Independence, Liberty, and Shawnee switchgrass cultivars, respectively. This is in agreement with similar studies that apply ML to multi-feature problems where traditional statistical methods are less applicable and datasets used were considered to be relatively small for ANNs. Consistent with previous studies on switchgrass, the most important predictors of biomass yield included average annual temperature, average growing season temperature, sum of the growing season precipitation, field slope, and elevation. This study helps pave the way for applying ML as a management tool for alternative bioenergy landscapes where understanding agronomic and environmental performance of a multifunctional cropping system seasonally and interannually at the sub-field scale is critical.

Historically crop models have been used to evaluate crop yield responses to nitrogen (N) rates after harvest when it is too late for the farmers to make in-season adjustments. We hypothesize that the use of a crop model as an in-season forecast tool will improve current N decision-making. To explore this, we used the Agricultural Production Systems sIMulator (APSIM) calibrated with long-term experimental data for central Iowa, USA (16-years in continuous corn and 15-years in soybean-corn rotation) combined with actual weather data up to a specific crop stage and historical weather data thereafter. The objectives were to: (1) evaluate the accuracy and uncertainty of corn yield and economic optimum N rate (EONR) predictions at four forecast times (planting time, 6th and 12th leaf, and silking phenological stages); (2) determine whether the use of analogous historical weather years based on precipitation and temperature patterns as opposed to using a 35-year dataset could improve the accuracy of the forecast; and (3) quantify the value added by the crop model in predicting annual EONR and yields using the site-mean EONR and the yield at the EONR to benchmark predicted values. Results indicated that the mean corn yield predictions at planting time ( = 0.77) using 35-years of historical weather was close to the observed and predicted yield at maturity ( = 0.81). Across all forecasting times, the EONR predictions were more accurate in corn-corn than soybean-corn rotation (relative root mean square error, RRMSE, of 25 vs. 45%, respectively). At planting time, the APSIM model predicted the direction of optimum N rates (above, below or at average site-mean EONR) in 62% of the cases examined ( = 31) with an average error range of ±38 kg N ha (22% of the average N rate). Across all forecast times, prediction error of EONR was about three times higher than yield predictions. The use of the 35-year weather record was better than using selected historical weather years to forecast (RRMSE was on average 3% lower). Overall, the proposed approach of using the crop model as a forecasting tool could improve year-to-year predictability of corn yields and optimum N rates. Further improvements in modeling and set-up protocols are needed toward more accurate forecast, especially for extreme weather years with the most significant economic and environmental cost.

Climate change projections indicate that precipitation events in the central United States are expected to become more intense, more frequent in the spring, and less frequent in the summer. Such a precipitation shift could adversely impact crop yields, especially in subfield areas known as farmed potholes, which are highly susceptible to flooding and ponding, and crop death is more likely to occur, particularly early in the growing season. This suggests that planting alternative crops, such as more flood tolerant perennials, in these areas may be a more profitable option. Using observations of crop growth and yield along with ponding depth of a specific field and farmed pothole in the central United States, we developed a spatially explicit version of the agroecosystem model Agro‐IBIS to estimate water depth and crop yield. After evaluating the model, we conducted a case study for a specific farmed pothole with a range of future precipitation scenarios with Agro‐IBIS to simulate the effects of contemporary (2002–2016) and future precipitation on a conventional corn/soybean (Zea mays L. and Glycine max Merr.) rotation and an alternative perennial miscanthus (Miscanthus × giganteus Greef et Deu.) cropping system. The depth and frequency of ponding increased under most future precipitation scenarios. The corn/soybean rotation had greater total loss (i.e., no yield) on average (>30%) for all scenarios in comparison to miscanthus (<10%). Under one future precipitation scenario with increased spring precipitation, both the corn/soybean rotation and miscanthus simulations showed an increase in yield. A simple budget analysis indicated that it is more profitable to plant miscanthus instead of corn or soybeans where yields in farmed potholes are consistently poor. Our findings show that potholes can be individually modeled, and their influence on yield can be quantified for use in future management decisions dictated by change in climate. Farmed potholes are low‐lying, frequently flooded portions of agricultural fields that consistently see yield reductions due to excess water. These areas are particularly prone to predicted changes in climate and are hotspots for negative environmental impacts. This research investigates the viability of planting miscanthus under contemporary and future precipitation scenarios using the crop model AgroIBIS‐VSF to simulate both miscanthus and a conventional corn/soybean rotation in order to model crop productivity. We found that overall, miscanthus was more productive than the corn/soybean rotation, and that it was more profitable to plant than taking yearly yield losses on corn and soybean.

Switchgrass (Panicum virgatum L.) is a promising feedstock for bioenergy and bioproducts; however, its inherent variability in chemical attributes creates challenges for uniform conversion efficiencies and product quality. It is necessary to understand the range of variation and factors (i.e., field management, environmental) influencing chemical attributes for process improvement and risk assessment. The objectives of this study were to (1) examine the impact of nitrogen fertilizer application rate, year, and location on switchgrass chemical attributes, (2) examine the relationships among chemical attributes, weather and soil data, and (3) develop models to predict chemical attributes using environmental factors. Switchgrass samples from a field study spanning four locations including upland cultivars, one location including a lowland cultivar, and between three and six harvest years were assessed for glucan, xylan, lignin, volatiles, carbon, nitrogen, and ash concentrations. Using variance estimation, location/cultivar, nitrogen application rate, and year explained 65%–96% of the variation for switchgrass chemical attributes. Location/cultivar × year interaction was a significant factor for all chemical attributes indicating environmental‐based influences. Nitrogen rate was less influential. Production variables and environmental conditions occurring during the switchgrass field trials were used to successfully predict chemical attributes using linear regression models. Upland switchgrass results highlight the complexity in plant responses to growing conditions because all production and environmental variables had strong relationships with one or more chemical attributes. Lowland switchgrass was limited to observations of year‐to‐year environmental variability and nitrogen application rate. All explanatory variable categories were important for lowland switchgrass models but stand age and precipitation relationships were particularly strong. The relationships found in this study can be used to understand spatial and temporal variation in switchgrass chemical attributes. The ability to predict chemical attributes critical for conversion processes in a geospatial/temporal manner would provide state‐of‐the‐art knowledge for risk assessment in the bioenergy and bioproducts industry. Switchgrass is a promising feedstock for bioenergy and bioproducts. Chemical attributes were assessed for switchgrass from a field study spanning five locations and up to six harvest years. Production variables and environmental conditions occurring during the switchgrass field trials were used to successfully predict chemical attributes using linear regression models. The relationships found in this study can be used to understand spatial and temporal variation in switchgrass chemical attributes. The ability to predict chemical attributes critical for conversion processes in a geospatial/temporal manner would provide state‐of‐the‐art knowledge for risk assessment in the bioenergy and bioproducts industry.