Rising production costs and declining commodity prices have compelled farmers to reassess their input and management strategies. Soybean [Glycine max (L.) Merr] seed costs, in particular, have continued to increase due to advances in biotechnology, higher-yielding germplasm, and available seed treatments, accounting for 32-35% of the total operational expenses in current soybean production systems. Optimizing seeding rate is therefore one area of consideration for improving profitability and maximizing return on investment. However, soybean yield response to plant population density is influenced by the combination of several factors, including water availability, temperature, radiation interception, planting practices, and crop growth and development. Unlike many other crops, soybean exhibits high phenotypic plasticity, allowing plants to adjust their growth and yield components (e.g., number of pods and seeds per plant) in response to varying plant population densities. As a result, the literature has shown that soybean can produce comparable yields across a wide range of plant population densities. The objectives of this management guide were to (i) provide an overview of factors influencing soybean yield response to seeding rate and plant population density and (ii) summarize recommendations by land grant university Extension agronomists across soybean-producing states in the United States.

Recent studies highlight conservation management practices as an effective strategy to enhance soil health. However, results vary, particularly regarding which soil health parameters respond most sensitively to these practices. More studies covering a wide range of soil types and climatic conditions are needed to assist farmers in making management decisions on production practices related to soil health. In this study, we collected soil samples (0-15 cm) from 21 (4-50 years) soybean [Glycine max (L.) Merr.]-based cropping systems trials across the United States (US) to assess the impact of management practices on soil health indicators. Soil indicators included wet aggregate stability (WAS), permanganate oxidizable carbon (POXC), organic matter loss-on-ignition (OM-LOI), mineralizable carbon (Min-C), water extractable organic carbon (WEOC), total organic carbon (TOC), soil extractable protein (ACE-N), total nitrogen (TN), pH, soil test phosphorus (STP), and soil test potassium (STK). Our objectives were: (i) to assess the effects of crop rotation, tillage, cover cropping, and artificial drainage on soil health; (ii) to inform soybean farmers about the management practices that are associated with improvements on soil health; , (iii) to develop and share a unique and open soil health dataset with the research community for future global meta-studies. To assess the effects of management practices on soil health indicators, both meta-analysis approach and linear mixed-effect models were used. Two-crop rotations were associated with greater STP values compared to a single-crop. The inclusion of cover crops was associated with greater Min-C and WEOC compared to no cover crops. No-tillage showed more acidic pH than conventional tillage. The remaining soil health indicators tested did not change in response to the management practices assessed. There were no statistically significant differences in observed soil tests between tile-drained and un- drained treatments. Overall results suggest that cover crops can play an important role in building soil health in soybean-based cropping systems. Our open-access dataset provides a valuable resource for future research and meta-studies, ultimately contributing to the development of more effective management strategies for promoting more sustainable soybean cropping systems.

Context: Winter cover crops, like cereal rye (Secale cereale), are one of the most promising solutions to environmental sustainability in the maize (Zea mays L.)-soybean (Glycine max L. merr.) cropping systems of the Midwestern US. However, one of the largest barriers to adoption is the "yield drag," or decrease in cash crop yield that occurs when following cereal rye (especially with maize). Reports of maize yield drag are inconsistent, and the underlying mechanisms are unclear. Objective: Our primary research questions were: i. Does fertilizer N alleviate maize yield drag? ii. does hillslope position change soil N dynamics and maize N needs after cereal rye cover crop? iii. what factors across site-years and hillslopes best predict maize yields? Methods: We conducted a split-plot experiment [cover/no-cover x six nitrogen (N) rates] across three hillslope positions (summit, backslope, and toeslope) over three site-years. Fertilizer N rates ranged from 0 to 336 kg N ha(-1). In response, we measured soil N dynamics (net N mineralization, inorganic and organic N pools), crop grain yield, and response to N fertilizer. We also ran a random forest model to identify the most important factors affecting yield drag. Results: There were few, weak interactive effects of hillslope position and cover crops on soil N dynamics. Toeslope positions tended to increase soil N pools. Cover crops had weaker effects on soil N dynamics but did decrease soil nitrate by 26%, on average, across the field. There were more complex hillslope x cover crop interactions on maize yield response to N fertilizer. While fertilizer N partially alleviated maize yield drag, yields remained consistently lower under cover crops compared to winter fallow, particularly at summit and backslope positions characterized by lower soil organic matter (SOM). Random forest modeling identified N application rate, cereal rye biomass, soil test phosphorus, and SOM as key predictors of maize yield, and revealed complex interactions between management and hillslope factors (i.e., soil properties). Conclusions: Our findings emphasize the importance of considering landscape variability when optimizing N management in cereal rye-maize systems to mitigate yield penalties and enhance nutrient use efficiency. Implications: The soil variation that comes from hillslope positions has complex interactions with cover crops and fertilizer N. These interactions need to be considered, and managed appropriately, to ensure farmers have reliable maize crop yields when following a cereal rye winter cover crop.

Abstract Fully realizing the benefits of cover crops and minimizing the yield penalty requires knowing when to terminate them. Cereal rye ( Secale cereale L.) is the dominant cover crop used in the Midwest US, and previous work has shown that corn ( Zea mays L.) yield penalties began to occur more frequently when cereal rye is at ∼1000 lb dry biomass ac −1 . However, farmers do not have an easy, reliable way of estimating when this biomass occurs without time‐consuming or destructive sampling. This study evaluates the effectiveness of the Robel pole method as a rapid, practical tool for estimating aboveground cereal rye biomass accumulation in Iowa, aiming to help farmers optimize cover crop termination timing. Over two growing seasons (2022–2023) and multiple field sites, we established strong correlations (< 0.0001) between biomass accumulation, crop obstruction height, and accumulated growing degree days (GDD). Cereal rye biomass accumulation was proportional to crop obstruction height, validating the Robel pole's utility as a non‐destructive, instantaneous assessment tool. Cereal rye biomass increased linearly with GDD after a threshold of 3222 GDD, reaching up to 2617 lb ac − 1 near termination. The Robel pole method provided an easy‐to‐use proxy for biomass estimation, requiring neither temperature data nor planting dates, enabling quick decision‐making to balance environmental benefits while minimizing potential negative impacts on subsequent crops.

Mung bean [Vigna radiata (L.) Wilczek] is a legume with high nutritional value and adaptability to diverse environmental conditions. However, optimizing its agronomic practices in the Midwestern United States, particularly regarding planting dates, remains underexplored. This study evaluated the effect of planting date on mung bean growth and yield in Iowa. Field experiments were conducted in 2023 and 2024, each year at a different location, using a randomized complete block design with two genotypes (ISU-B and OK2000) and four planting dates. Early planting (mid-May to early June) significantly improved pod length and grain yield, with maximum grain yield reaching 2527 lb ac-1, whereas late planting (late June to early July) reduced yield by up to 46.7% and increased the risk of frost damage before maturity. Genotype & times; planting date interactions revealed that ISU-B was more responsive to early planting, achieving a higher grain yield than OK2000 under early planting conditions, while OK2000 exhibited greater stability under delayed planting. These findings suggest that optimal planting dates are critical for maximizing mung bean productivity in temperate regions, with early planting offering the best grain yield potential.

Abstract Soybean ( Glycine max [L.] Merr.) seed prices have increased by more than 190% from 1998 to 2018 in the US, making seeding rate one of the most cost‐sensitive management decisions. While soybean can maintain maximum seed yield across a wide range of plant populations due to their morphological and reproductive plasticity, the decreasing commodity prices and increased seed cost necessities re‐evaluation of economic optimum seeding rates. This study aimed to evaluate soybean yield responses and economic return across a wide range of seeding rates and environments in Iowa, as well as seed quality. Across site‐years, the agronomic optimum seeding rate (AOSR) was 234,300 seeds ha −1, where seed yield plateaued at 4.7 Mg ha −1 . Results showed a higher number of pods and seeds at seeding rates lower than 234,300 seeds ha −1 ; however, at higher seeding rates, the number of pods and seeds were consistently lower. There was no relationship between individual seed weight, oil and protein content, and seeding rates. Seeding rates above the AOSR resulted in no benefit under favorable conditions, since higher plant populations mostly benefit environments exposed to weather events that reduce population (e.g., hail). The economic optimum seeding rate (EOSR) averaged approximately 90.8% of the AOSR. Seed yields are maximized by targeting the AOSR; however, the additional yield from the difference between AOSR and EOSR did not bring financial gains.

Fertilizing maize at an optimum nitrogen rate is imperative to maximize productivity and sustainability. Using a combination of long-term (n = 379) and short-term (n = 176) experiments, we show that the economic optimum nitrogen rate for US maize production has increased by 2.7 kg N ha−1 yr−1 from 1991 to 2021 (1.2% per year) simultaneously with grain yields and nitrogen losses. By accounting for societal cost estimates for nitrogen losses, we estimate an environmental optimum rate, which has also increased over time but at a lower rate than the economic optimum nitrogen rate. Furthermore, we provide evidence that reducing rates from the economic to environmental optimum nitrogen rate could reduce US maize productivity by 6% while slightly reducing nitrogen losses. We call for enhanced assessments and predictability of the economic and environmental optimum nitrogen rate to meet rising maize production while avoiding unnecessary nitrogen losses. Maize production is dependent on Nitrogen fertilizer input. Here, the authors use long-term and short-term experiments to demonstrate that economic and environmental optimum nitrogen fertilization rates have increased between 1991 and 2021.

In 2022, over 198.8 million ha was insured across the United States, with indemnity payments exceeding $18.2 billion. In the US Midwest, strong wind gusts associated with thunderstorms often cause significant stalk breakage in maize (Zea mays L.) fields. This study investigated whether crop insurance adjustment tables continue to be accurate when adjusting for maize yield after stalk breakage has occurred. Two experiments were conducted to assess the effect of below-ear and above-ear stalk breakage at four levels of severity (0%, 25%, 50%, and 75%) and at three timings (V13, V17, and tassel stage [VT]). Trials spanned nine site-years across Iowa, Minnesota, and Nebraska from 2019 to 2022. Below-ear breakage results in a loss of the primary ear, while above-ear breakage reduces the size and weight of the harvestable ear. Plant density, primary and secondary ear counts, grain yield, and kernel mass were measured. The number of secondary ears was highly influenced by the damage type. When breakage occurred above the ear node, 25% damage severity resulted in a 35% increase in the number of secondary ears compared to the untreated control. When breakage occurred below the ear node, the impact was much more evident, with an 85% increase of secondary ears. On average, breakage above the ear resulted in a 9.5%, 18.6%, and 25.2% yield penalty for 25%, 50%, and 75% severity, respectively. As expected, for the same damage severity, below-ear breakage resulted in much higher yield penalties (13.3%, 32.6%, and 55.0%). There was no clear relationship between damage severity and kernel weight.

Context: The use of biostimulant seed treatments (BST) in soybean (Glycine max (L.) Merr.) is a growing market, with many different products and active ingredients (microorganisms) available. Companies promote the use of biostimulant seed treatment products by claiming several potential benefits to crop production, including seed yield increase. There are limited field evaluations on the efficacy of biostimulant seed treatments to increase soybean seed yield in the USA, and many of the microorganisms included in biostimulant products that are marketed to soybean farmers have never been documented in the peer-reviewed literature. Objective: Here, we evaluated the effect of several commercially available biostimulant seed treatment products on soybean seed yield. Methods: Field trials were established using a standard protocol during the 2022 and 2023 growing seasons across 22 states (103 site-years) in the USA. The 103 site-years were separated into four environmental clusters based on weather and soil properties. Results: No significant yield differences were observed due to biostimulant seed treatment across and within clusters in both years. We believe that the lack of yield response may have been due to factors such as competition of active ingredients with native soil microorganisms or lack of favorable conditions for plant-microbe interactions. Overall results suggest that management practices, such as row spacing, seeding rate, foliar insecticide, and tillage affect yield more than any of the examined biostimulant seed treatments. Conclusion: We argue that there is a need for further evaluations and standard requirements on the registration, production, commercialization, handling, and viability testing of biostimulants in the USA.

Context or problem: The associations among soil health, management practices, and environmental conditions are complex, and research often focuses on specific practices or regional contexts. This have led to varying results regarding which soil health parameters are most influential for soybean yield. Objective: In this study, we investigated the effects of soil health measurements, agricultural management practices (4-40 years), inherent soil properties, location-specific factors, and soil fertility analytical results on soybean (Glycine max L. Merr.) seed yield. Methods: Soil samples (0-15 cm) were collected in 2023 from 17 agricultural research trials across the US. Soil health measurements, inherent soil properties, and soil fertility analytical results were assessed. Field management history and yield data were reported by the collaborators, and publicly available weather data (precipitation and temperature) were retrieved. Conditional inference trees were used to identify soybean yield influential factors. Results: Soybean seed yield was mainly driven by planting date. Trials planted before 26 May averaged 4809 kg ha-1 , 55 % greater yields than planting after 26 May (2649 kg ha-1). Longitude, along with soil organic carbon (SOC), autoclaved citrate extractable N (ACE-N), and soil test potassium (STK) were also important factors explaining yield variability. Conclusions: Our results demonstrated that planting date was the most critical factor driving soybean seed yield, yet yield responses are modulated to a lesser extent by longitude, SOC, ACE-N, and STK. Implications: To optimize soybean yield, conservation practices should prioritize early planting and soil health improvement. These findings can help identify soil health parameters associated with soybean seed yield for future long-term research.

Soybean [ Glycine max (L.) Merr.] yield loss from hailstorms depends on the growth stage when hail occurs and the magnitude of plant damage. We evaluated how soybean canopy recovery, yield, and seed quality were affected by simulated hail damage in Iowa and Indiana from 2016 to 2018. Five levels of hail damage were simulated by defoliating 0%, 25%, 50%, 75%, and 100% leaves at the full‐pod (R4) and beginning of seed‐fill (R5) stages. Canopy closure was similar for plants with 0%–50% defoliation but significantly reduced for plants with 75% and 100% defoliation. The normalized difference vegetation index (NDVI) and normalized difference red edge index (NDRE) predicted defoliation levels better than canopy closure, with NDRE being more sensitive for detecting canopy variation among defoliation rates. Soybean yield and yield components decreased quadratically with increasing defoliation severity. Yield loss was minimal with 25% defoliation, regardless of growth stage or location. Soybean yield declined more with 100% defoliation at the R5 stage (80%–83%) compared to the R4 stage (67%–79%). The yield loss when plants were defoliated greater than 25% was due to a reduction in seed numbers (up to 54–88 seeds plant −1 ) and seed weight (up to 0.022–0.052 g seed −1 ). Defoliation at both stages minimally affected seed protein but decreased oil concentrations when defoliation reached 75%–100%. Soybean yield and seed quality loss should not be an issue of concern for fields with up to 25% hail defoliation damage at the R4–R5 stages. Results will help refine crop insurance guidelines, improving damage assessment for farmers.

Optimal soybean [Glycine max (L.) Merr.] production requires accurate, stage-specific management practices to mitigate abiotic and biotic stressors. From emergence to full maturity, a soybean plant's physiological needs and vulnerabilities change as it transitions through its vegetative and reproductive cycles. This management guide details each growth stage, provides clear descriptions, and identifies the common risks encountered. For different growth stages, strategic management recommendations are presented, emphasizing proactive approaches to mitigate potential yield limitations. The objectives of this management guide are (a) to clearly define the distinct growth stages of the soybean plant and (b) to discuss common risks and provide research-based management recommendations applicable at each stage.

Harvest aids, such as foliar-applied crop defoliants or desiccants, are tools available to soybean [Glycine max (L.) Merr.] farmers that can homogenize maturity and help facilitate earlier soybean harvest, particularly when extreme weather events are forecasted. Prior research has shown soybean harvest aids increase harvest efficiency but has been limited primarily to states in the southern United States. Further investigation on the utility of harvest aids across the majority of US soybean production is warranted as incorrect desiccation timing can significantly reduce seed, protein, and oil yield. In 2024, a study was conducted at 19 sites across 13 US states to test the effect of planting date, maturity group, and desiccation timing on soybean seed yield, protein and oil yield, green stem incidence, and harvest timing. Seed yield reductions were rare when desiccation occurred at R7 but were common at R6.5 applications. Seed protein and oil yield was not affected by desiccation at R7 but was reduced with some R6.5 applications. Desiccation at R6.5 and R7 reduced green stem incidence 50% of the time. Harvest could occur 15 days earlier in the southern United States when desiccation occurred at R6.5 or R7, while harvest could occur 4 and 3 days earlier in the northern United States, respectively. Overall, the data show a harvest aid applied at soybean R7 can allow farmers to harvest earlier with low risk of seed, protein, or oil yield reductions.

Context Soybean [Glycine max (L.) Merr.] is one of the major crops worldwide. Identification of environmental factors that improve both yield and N-2-fixation remain of high importance. Objective The study aimed to i) assess the effect (estimate and uncertainty) of sulfur (S) fertilization on seed yield and N-2-fixation (as N derived from the atmosphere, Ndfa), and (ii) evaluate the influence of soil and weather variables on these estimates and uncertainties. Methods Thirty-five studies from nine US states were analyzed, comparing no fertilization (Check) with S fertilization at planting (S), using a regression tree approach to assess environmental effects on yield and Ndfa. Results For both treatments, precipitation from full-pod to full-seed explained 40 % of the yield variation. For the Check, [soil organic matter, SOM/(clay+silt)] was a secondary factor. For the S, seasonal precipitation above 73 mm resulted in the highest yield (4.9 Mg ha(-1)), with 51 % Ndfa and 135 kg ha(-1) of fixed-N. Yield uncertainty, averaging 1.2 Mg ha(-1), was associated with soil clay content below 11 %. Vapor-pressure-deficit from full-bloom to full-pod influenced Ndfa, accounting for 40 % of its variation between treatments. For both treatments, the highest Ndfa (similar to 65 %) required vapor-pressure-deficit below 0.92 kPa. Soil clay was pivotal to the uncertainty in Ndfa, explaining 34 % and 40 % of the variation for Check and S, but with a reduction in uncertainty when soil clay was above 26 %. Conclusion The main regulators of yield and Ndfa were precipitation, temperature, SOM, and soil texture. Sulfur fertilization moderately increased yield and Ndfa, especially in environments with high plant N-demand. Ndfa uncertainty was more related to crop growth factors, with high seed yield correlating with high Ndfa. Implications Future research should focus on controlled studies to improve the knowledge of the identified soil and weather factors and their interplay with seed yield and Ndfa.

Soybean [ Glycine max (L.) Merr.] is a crucial crop for global food, feed, and biofuel industries, with its yield influenced by agronomic practices such as row spacing and seeding rate. This study aimed to evaluate the effects of these practices on soybean yield across 7 years (2016–2023) in Iowa. Using a split‐split‐plot design, we examined three row spacings (15, 20, and 30 inches) and varying seeding rates at two experimental sites. The research was conducted under typical Iowa conditions with different soybean cultivars and soil types. Grain yield data were standardized to 13% moisture and analyzed using ANOVA to assess the interactions between row spacing, seeding rate, and cultivar. Results indicated the effects of row spacing and seeding rate on yield were inconsistent across years and locations. Narrower row spacings (15 and 20 inches) tended to improve yield in high‐productivity environments, while wider spacing (30 inches) performed better in some low‐yielding environments. The seeding rate response varied, with no clear pattern across site‐years, suggesting that soybean plants can compensate for lower planting densities by adjusting branching and pod set. These findings highlight the adaptability of soybean to different planting practices, offering farmers flexibility in optimizing seeding rates and row spacings without significant yield loss. This research provides valuable insights into potentially reducing input costs while maintaining productivity in soybean production.

Iowa Learning Farms, based at Iowa State University since 2004, is a nationally recognized conservation outreach program that has successfully engaged with farmers and landowners to deliver information and provide tools and guidance encouraging the implementation of agricultural practices that enhance water quality, improve soil health and productivity, and contribute to building a Culture of Conservation in Iowa and beyond. Iowa Learning Farms programs all focus directly on reaching and engaging with farmers. The program's success comes from the processes employed to create, test, and deliver programs that serve demographic groups including traditional row crop, new or next-generation, female, nontraditional crop, and livestock farmers, as well as tenants, landowners, and conservation professionals. Our programs include field days, webinars, Conservation Station trailers, rapid needs assessment and response workshops, Leadership Circle listening sessions, a youth education program, infographic-style factsheets, and a Whole Farm Conservation Best Practices Manual, as well as a newsletter, blog, and social media presence. Our programs foster a collaborative relationship with farmer partners, agencies, the university, researchers, and the public. The development, feedback, and iterative process of creating and refining programmatic elements will be highlighted.

Achieving high yields in the large-scale agricultural systems that dominate the US landscape requires critical machine-enabled field operations to be executed in narrow windows of time. Novel cropping systems hold great promise to increase ecosystem services from these large-scale systems, but researchers and end-users need effective methods of representing the timing requirements of such systems. This gap prompted our exploration of approaches to visualize the timing of critical machine-enabled field operations. We refer to the resulting graphic as a field operations visualizer (FOV). We iterated multiple versions of the FOV through a user-centered process involving extensive stakeholder feedback. The resulting FOV version offers a straightforward method of visualizing operation sequences and identifying potential conflicts. Survey results suggest that the FOV provides significant operational insights to users about the timing challenges (or benefits) of novel cropping systems. The FOV may therefore be useful in guiding efforts to improve novel cropping systems and to thereby ultimately increase their deployment to deliver ecosystem services.

Plain Language SummaryAs profit margins become increasingly smaller for Midwest farmers, it is crucial to understand how a crop reacts to weather‐related stress. Cornfield edge effect is a new phenomenon Iowa farmers are experiencing where grain yield is lower at the field edge and progressively improves towards the field interior. Our objective was to verify if cornfield edge effect is occurring in Iowa along southern or western field edges when soybean is the adjacent field. And, if an edge effect is observed, determining the timing of the effect through the analysis of grain yield components. Field data collection was conducted in two regions of Iowa in 2019 and 2020 with observations at four distances from the field edge to interior. A field edge effect was detected in three of seven locations. We believe cornfield edge effect is occurring and that often it is a result of lower kernel numbers per ear and/or kernel weight.

Adverse weather conditions from acute events (e.g., storms causing lodging, flooding, or hail) or short-duration weather patterns (i.e., periods of cold events; extended waterlogged field conditions) can result in yield losses, though management practices may play key roles in aiding with crop recovery or avoidance of these stress events. This review summarizes current knowledge (with emphasis placed on the US Midwest) related to corn response to short-term weather stresses of (i) cold temperature, (ii) excess water, (iii) hail/defoliation damage, and (iv) wind damage. Each section presents summaries of how corn growth and yield are affected, provides context into past events experienced, identifies agronomic or production recommendations to correct or alleviate the stress condition, and proposes areas where future research is needed. This review also highlights challenges associated with controlled simulation work on these stressors, and also identifies key areas to expand future research efforts. In general, yield losses associated with strong storms and short-term weather events often ranged from 5% to 35%, but extreme cases could result in up to 80%-100% yield loss. Much of the literature on these topics was published prior to 1995, though it still forms the basis for modern agronomic guidance, which is problematic given the changes in agriculture in the last 20 years in management practices, available genetics and technologies, and changing environmental conditions. Revisiting these foundational studies and expanding them to examine current and future weather conditions are critical for better informing agronomic recommendations, for devising mitigation strategies, and for determining accurate yield loss expectations following these stresses. Climatic shifts are resulting in more frequent strong storms and adverse weather for corn. Storms and weather stress reduce corn yield 5%-35%, though greater losses are possible. Foundational knowledge produced prior to 1995 should be re-assessed to ensure accuracy in modern systems. Updating agronomic recommendations with modern technology is critical to ensure crop security.

Knowledge about how on -farm management decisions influence maize residue quantity and quality is limited but necessary to better manage the effects of residue on cropping systems carbon, nutrient turnover, and water dynamics. Our objectives were to i) Quantify the interactive effects of previous crop, nitrogen (N) fertilizer rate, plant density, and hybrid on maize residue quantity and quality characteristics, and ii) Explore relationships and potential trade-offs between residue attributes and grain yield to aid decision making. Experiments were carried out in central Iowa, USA in 2021 and 2022. We evaluated three commercial hybrids across three N fertilizer rates (0, 146, 336 kg N ha(-1) ), and three plant densities (3.6, 7.5, 11.3 plants m(-2) ) in fields with maize or soybean as the previous crop, totaling 54 replicated treatments. Biomass samples were collected at physiological maturity, and 12 residue -related attributes were assessed together with the final grain yield. The experimental factors generated a large variability in maize grain yield (1.5 -15.4 Mgha(-1) ), residue amount (2.6 -14.1 Mg ha(-1) ), residue CN ratio (33 -130), residue N (11 -167 kg N ha(-1) ), and residue lignin concentration (1.0 -3.6%). Nitrogen fertilizer explained most of the variability in residue quantity and CN ratio while plant density explained most of the variability in residue biochemical composition. Significant interactions among studied factors on residue attributes were few and mostly related to hybrids by plant density and previous crop by N rate interactions. Our results indicated that the higher the grain yield, the higher the residue quantity (r = 0.79), the residue lignin concentration (r = 0.46), and the lower the residue CN ratio (r = -0.51). Grain yields lower than 8 Mg ha(-1) generated comparatively higher residue quantities (grain harvest index < 0.50), while the opposite occurred at grain yields above 8 Mg ha(-1) . Management practices that resulted in high grain yields generated residues with low CN ratio but high lignin concentration, indicating a tradeoff in residue quality. The observed large effects of management on residue quality and quantity imply that there are opportunities to manipulate residue attributes via management for maximizing profitability and soil health.