Summary Pollen transport is an understudied process with consequences for plant reproductive success and floral evolution. Recently, pollinator bodies have been described as pollen competition arenas, with implications for plant community assembly. However, the identity, strength, and diversity of pollen competitive interactions and how they vary across pollinator groups is unknown. Evaluating patterns and drivers of the pollen competition landscape across different pollinator groups is central to further our understanding of plant coexistence mechanisms. Here, we integrate information on insect pollen loads with network analyses to uncover novel pollen co-transport networks and how these vary across pollinators. We evaluate differences in pollen load size, diversity and their phenological and phylogenetic attributes among insect groups and how these relate to body size and sex. Pollen co-transport networks revealed differences in the number and identity of competitors that pollen species encounter across pollinator groups. These networks were highly modular, with groups of pollen species interacting more often on pollinator bodies. Pollen load size and richness were shaped by bee size and sex. Sex also influenced the pollen phylogenetical diversity. Pollinators can impose vastly different competitive landscapes during pollen transport, with so far unknown consequences for plant reproductive success, floral evolution and community assembly.

The nectar microbiome can influence pollinator choice and plant fitness. Previous research has shown that changes in environmental conditions at large spatial scales can influence nectar microbiome composition. However, little is known about how changes in climate with increasing elevation affect nectar microbiome abundance and composition. Here, we describe the culturable nectar mycobiome (CNMB) of Rhododendron catawbiense (Ericaceae) by quantifying colony abundance, identity and richness of fungal genera. We further evaluate how the CNMB abundance, diversity and composition (i.e. the fungal species within the nectar microbiome) varies at two different elevations. Nectar samples were collected from R. catawbiense individuals at a high and low elevation and were cultured on yeast agar with 0.01% chloramphenicol media. Fungal colonies were categorized morphologically, quantified and then identified using DNA barcoding. In total, 2822 fungal colonies were recorded belonging to six genera across both elevations. Elevation did not influence CNMB diversity (Simpson's diversity index) or genera richness per flower, however only three genera were found at the high elevation while six were found at the low elevation. Elevation had a significant effect on colony abundance with a 95% increase in the number of colonies in nectar samples at low compared with the high elevation. Variation in abundance and the overall genera composition of fungal colonies across elevations may have the potential to affect nectar quantity and quality and ultimately pollination success. This study adds to our understanding of the drivers of CNMB composition across spatial scales and its potential implications for plant-pollinator interactions.

Floral visitation alone has been typically used to characterize plant-pollinator interaction networks even though it ignores differences in the quality of floral visits (e.g. transport of pollen) and thus may overestimate the number and functional importance of pollinating interactions. However, how network structural properties differ between floral visitation and pollen transport networks is not well understood. Furthermore, the strength and frequency of plant-pollinator interactions may vary across fine temporal scales (within a single season) further limiting our predictive understanding of the drivers and consequences of plant-pollinator network structure. Thus, evaluating the structure of pollen transport networks and how they change within a flowering season may help increase our predictive understanding of the ecological consequences of plant-pollinator network structure. Here we compare plant-pollinator network structure using floral visitation and pollen transport data and evaluate within-season variation in pollen transport network structure in a diverse plant-pollinator community. Our results show that pollen transport networks provide a more accurate representation of the diversity of plant-pollinator interactions in a community but that floral visitation and pollen transport networks do not differ in overall network structure. Pollen transport network structure was relatively stable throughout the flowering season despite changes in plant and pollinator species composition. Overall, our study highlights the need to improve our understanding of the drivers of plant-pollinator network structure in order to more fully understand the process that govern the assembly of these interactions in nature.

Fall applied liquid swine (Sus scrofa domesticus) manure (LSM) can lead to economic and environmental concerns due to potentially enhanced NO3 loss. Objectives of this study were to evaluate LSM application timing and use of Instinct nitrification inhibitor, and compare to anhydrous ammonia (AA). Treatments were a no-N control, AA (without Instinct), and LSM with three Instinct rates applied each of 3 yr on October 1 (early fall) and November 1 (late fall). The effect of Instinct was inconsistent. Instinct increased soil NH4-N concentrations in fall (only early fall LSM application) and spring sampled LSM injection zones. However, in the late spring Instinct had no effect to minimal positive effect on soil inorganic-N, and the high rate was not more effective than the low rate. Corn (Zea mays L.) canopy normalized difference vegetative index (NDVI) was not influenced by Instinct or different between N source, and greatest with late fall N application. Across years, Instinct increased corn grain yield only when early fall applied with the low rate. However, LSM with Instinct had lower yield compared to AA. Waiting to apply N in late fall provided increased inorganic N retention, and with LSM higher corn yield. Based on this study, AA was a better fall applied N source compared to LSM with or without Instinct. A decision to use Instinct with LSM must weigh cost of the inhibitor against other options, such as delayed fall or spring application, or use of AA.

Plant–pollinator network studies have uncovered important generalities in the structure of these communities, rapidly advancing our understanding of the underlying drivers of such a structure. In spite of this, however, it is still unclear how changes in structural network properties influence overall plant pollination success. One key limitation is the lack of information on the relationship between network structural properties and aspects of pollination and plant reproductive success. Here, we estimate four plant species network structural metrics (interaction strength, weighted degree, closeness centrality, and specialization level), commonly used to describe their importance within plant–pollinator networks, at two different sites, and evaluate their effects on pollen deposition and pollen tube success. We found a positive effect of plant–pollinator specialization and a negative effect of closeness centrality on heterospecific pollen load size. We also found a marginal negative effect of closeness centrality on pollen tube success. Our results suggest that increasing plant–pollinator specialization within nested communities (pollinated by one or very few generalist insect species) may result in high levels of heterospecific pollen transfer. Furthermore, the differential effects of plant–pollinator network metrics on pollination success (pollen receipt and pollen tube success), highlight the need to integrate quantity (e.g. visitation rate) and quality (e.g. pollen delivery) aspects of pollination to achieve a more mechanistic understanding of the relationship between plant–pollinator network structure and function. Such knowledge is key to evaluate the resilience and stability of plant–pollinator communities and the services they provide in the face of increasing human disturbances.

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 (R2 = 0.77) using 35-years of historical weather was close to the observed and predicted yield at maturity (R2 = 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 (n = 31) with an average error range of ±38 kg N ha−1 (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.

Core Ideas Instinct nitrification inhibitor either did not affect or reduced grain yield.Instinct did not positively influence early growth plant height or mid‐vegetative canopy normalized difference vegetative index (NDVI).Broadcasting or injecting urea–ammonium nitrate solution (UAN) did not change corn response to Instinct. The use of nitrification inhibitors with fertilizer N application is an attempt to improve corn (Zea mays L.) N use efficiency while reducing environmental and economic concerns associated with N losses. The objective of this study was to evaluate if the encapsulated formulation of nitrapyrin [2‐chloro‐6‐(trichloromethyl) pyridine], Instinct nitrification inhibitor, would influence corn growth and production when applied with spring preplant urea–ammonium nitrate solution (UAN). A 3‐yr field study was conducted in a randomized complete block design with four replications of a factorial combination consisting of UAN at six incremental N rates (0–225 kg N ha−1), broadcast‐incorporated and injected, and with and without 2.56 L ha−1 (0.56 kg a.i. ha−1) Instinct. In 1 of 3 yr, as well as for the means across years, Instinct applied with UAN had a negative effect of reduced early growth plant height and lower mid‐vegetative canopy normalized difference vegetative index (NDVI) compared with UAN without Instinct. Corn grain yield also had a lower across N rate mean yield with Instinct application in 2 of 3 yr and across all 3 yr. The economic optimum N rate (EONR) with Instinct was 32 kg N ha−1 higher than without Instinct, applied either broadcast or injected. Because Instinct did not provide positive effects on corn growth and yield, the study results indicate that Instinct use with spring preplant applied UAN solution would not be an effective nitrification inhibitor to enhance fertilizer N supply or corn production.

Nitrogen fertilizer management can impact soil organic C (SOC) stocks in cereal-based cropping systems by regulating crop residue inputs and decomposition rates. However, the impact of long-term N fertilizer management, and associated changes in SOC quantity and quality, on the fate of N fertilizer inputs is uncertain. Using two 15-year N fertilizer rate experiments on continuous maize (Zea mays L.) in Iowa, which have generated gradients of SOC, we evaluated the legacy effects of N fertilizer inputs on the fate of added N. Across the historical N fertilizer rates, which ranged from 0 to 269 kg N ha(-1) yr(-1), we applied isotopically-labeled N fertilizer at the empirically-determined site-specific agronomic optimum rate (202 kg N ha(-1) at the central location and 269 kg N ha at the southern location) and measured fertilizer recovery in crop and soil pools, and, by difference, environmental losses. Crop fertilizer N recovery efficiency (NREcrop) at physiological maturity averaged 44% and 14% of applied N in central Iowa and southern Iowa, respectively (88 kg N ha(-1) and 37 kg N ha(-1), respectively). Despite these large differences in NREcrop, the response to historical N rate was remarkably similar across both locations: NREcrop was greatest at low and high historical N rates, and least at the intermediate rates. Decreasing NREcrop from low to intermediate historical N rates corresponded to a decline in early season fertilizer N recovery in the relatively slow turnover topsoil mineral-associated organic matter pool (0-15 cm), while increasing NREcrop from intermediate to high historical N rates corresponded to an increase in early-season fertilizer N recovery in the relatively fast turnover topsoil particulate organic matter pool and an increase in crop yield potential. Despite the variation in NREcrop along the historical N rate gradient, we did not detect an effect of historical N rate on environmental losses during the growing season, which averaged 34% and 69% of fertilizer N inputs at the central and southern locations, respectively (69 kg N ha(-1) and 185 kg N ha(-1), respectively). Our results suggest that, while beneficial for SOC storage over the long term, fertilizing at the agronomic optimum N rate can lead to significant environmental N losses.

Decision-making criteria to accurately predict nitrogen (N) rates in corn (Zea mays L.) would greatly benefit canopy sensor-guided variable rate N (VRN) management with positive implications for water quality. The objectives of this study were to measure corn yield response to VRN applied at the midvegetative corn growth stage and compare yield and agronomic efficiency (AE) between a one-time spring-N application (preplant or early sidedress) and two VRN management strategies, split-N (VRNS) and rescue-N (VRNR). Field sites located across Iowa received spring-N fertilizer at six application rates, with additional N potentially applied with each VRN spring-N rate and for the VRNS and VRNR strategies at the V10 growth stage based on canopy sensing. Drought conditions were evident during 2012 and 2013 in Iowa, which resulted in reduced corn yield and requirement for N fertilizer at many site-years. The VRN with 0 and 56 kg N ha−1 (50 lb N ac−1) applied in the spring was most closely aligned with calculated economic optimum N rate and AE. The VRNS and VRNR strategies applied more N than was needed, compared to spring-N only. Canopy sensors did not detect adequate to excess N, and therefore N applied prior to VRN application should be part of sensing-based algorithm criteria. Mean corn yield with the soybean (Glycine max [L.] Merr.)–corn rotation was not maintained with VRN when no spring-N was applied. Yield comparison showed no differences between spring-N, VRNS, and VRNR for both rotations. However, the greatest AE was achieved with spring-N and the VRNS strategy in the soybean–corn rotation. Weather events that occur during and after canopy sensing and VRN application remain an important factor for successful corn yield response to N rate and timing, and should be considered with canopy sensor VRN. Results from this study provide feedback on how to translate remote sensing data into VRN management.

Introduction This project was designed to study the N fertilization needs in continuous corn (CC) and corn rotated with soybean (CS) as influenced by location and climate. Multiple rates of fertilizer N were spring applied, with the intent to measure yield response to N within each rotation on a yearly basis for multiple years at multiple sites across Iowa. This will allow determination of N requirements for each rotation, differences that exist between the two rotations, responses to applied N across different soils and climatic conditions, and evaluation of tools used to adjust N application.

Winter cereal rye continues to be promoted as a viable cover crop within Iowa for corn and soybean production systems. Several environmental benefits accrue with use of cover crops, including reduction of nitrate in drainage water, soil erosion control, and reduced phosphorus (P) runoff loss. In the Iowa Nutrient Reduction Strategy, a rye cover crop is estimated to reduce nitrate-N concentration by 31%, a sizeable impact. However, questions remain about rye cover crop effects on corn nitrogen (N) fertilization requirement, N supply from the cover crop, and crop yield. To help answer these questions, a series of studies were conducted to evaluate corn economic optimum N rate (EONR), rye cover crop degradation and N recycling following termination, rye shoot and root biomass and nutrient composition, corn and soybean yield, and agronomic practices to improve corn yield in a rye cover crop system.

Nitrogen fertilization is critical to optimize short-term crop yield, but its long-term effect on soil organic C (SOC) is uncertain. Here, we clarify the impact of N fertilization on SOC in typical maize-based (Zea mays L.) Midwest U.S. cropping systems by accounting for site-to-site variability in maize yield response to N fertilization. Within continuous maize and maize-soybean [Glycine max (L.) Merr.] systems at four Iowa locations, we evaluated changes in surface SOC over 14 to 16 years across a range of N fertilizer rates empirically determined to be insufficient, optimum, or excessive for maximum maize yield. Soil organic C balances were negative where no N was applied but neutral (maize-soybean) or positive (continuous maize) at the agronomic optimum N rate (AONR). For continuous maize, the rate of SOC storage increased with increasing N rate, reaching a maximum at the AONR and decreasing above the AONR. Greater SOC storage in the optimally fertilized continuous maize system than in the optimally fertilized maize-soybean system was attributed to greater crop residue production and greater SOC storage efficiency in the continuous maize system. Mean annual crop residue production at the AONR was 22% greater in the continuous maize system than in the maize-soybean system and the rate of SOC storage per unit residue C input was 58% greater in the monocrop system. Our results demonstrate that agronomic optimum N fertilization is critical to maintain or increase SOC of Midwest U.S. cropland.

Core Ideas Corn stover has many uses, including recent interest for cellulosic bioenergy production. For corn stover use in ethanol production, an increased understanding is needed of plant component biomass and N content within a stover harvest system, and the impact on N cycling and corn N use. Improving N use efficiency in corn is important for optimizing yield and reducing environmental impacts. Corn (Zea mays L.) stover has become an important commodity for many uses, including cellulosic ethanol production. However, there are concerns about the impacts of aggressive stover removal at the industrial scale. The objective of this study was to evaluate the effect of continuous stover removal (SR) on plant component productivity, N uptake, and nitrogen use efficiency (NUE). Treatments were none, partial, and complete SR, no‐till (NT) and chisel plow (CP), and 0, 168, and 280 kg N ha−1 rates. Total plant, vegetative, grain, and cob dry matter (DM) increased with SR (5.2–7.5%), but no difference was detected between partial and complete removal and there were no interactions with tillage system or N rate. Chisel plow and N application increased total and plant component DM, with the same overall effect on plant components from tillage as occurred with SR. Grain harvest index (GHI) was not influenced by SR (mean of 51% with N application). Stover removal had little effect on NUE measures, with only partial factor productivity (PFP), total production efficiency (TPE), and system efficiency (SYE) increasing with SR. Increasing N rate decreased NUE, with no differential effect from SR or tillage system. Stover removal in this continuous corn system provided a soil environment conducive to increased overall productivity, plant N uptake, and NUE; with a similar effect with CP compared to NT. Nitrogen management will need to account for specific biomass removal because corn production level and N removal can be differentially affected by plant component harvest.

Recent years of high rainfall and prolonged wet soil conditions in Iowa have renewed interest to protect losses of fertilizer nitrogen (N) in corn. This study evaluated effect of N additives and a slow‐release urea product on the soil NO 3 –N fraction of total inorganic N, mid–vegetative growth N stress, grain yield, and corn nitrogen use efficiency. Earn 1 CEU in Nutrient Management by reading this article and taking the quiz at www.certifiedcropadviser.org/education/classroom/classes/516 .

INTRODUCTION Water quality issues have renewed interest in timing of nitrogen (N) application as a means to improve use efficiency in corn and reduce losses. Improved economic return is also desired as N fertilization is one of the most costly inputs to corn production. Time of fertilizer application is a component of the site-specific 4R nutrient management stewardship programs. In Iowa, the Nutrient Reduction Strategy has a 7% (37% std. dev.) nitrate-N reduction with a 0% (3% std. dev.) corn yield change for sidedress compared to pre-plant N application (SP 0435A). A main area of emphasis for sidedressing is the potential to apply N during the time of rapid plant N uptake, reducing the time applied N is subject to potential losses with wet conditions. Sidedress and in-season N application may also allow for adjustment of application rate. However, producers can be reluctant to apply N in-season as they are busy with other operations, concerned about yield loss due to early N stress, or concerned that wet weather will prevent application. Delay in sidedress applications can reduce yield, but the potential can be mitigated with use of split application where part of the N is applied at or before planting. However, as corn accumulates approximately 70% of total N uptake by R1 (Woli et al., 2016), N limitation during vegetative growth can affect yield potential. Sidedress (split) application has historically shown consistent benefit on coarse textured soils. On mediumto fine-textured soils, the yield, water quality, and economic return have not been consistent. This report will summarize studies conducted in the past twelve years on the effect of N application timing on corn production.

Winter rye (Secale cereale L.) cover crop (RCC) use in corn (Zea mays L.) and soybean [Glycine max. (L.) Merr.] production can alter N dynamics compared to no RCC. The objectives of this study were to evaluate RCC biomass production (BP) and subsequent RCC degradation (BD) and N recycling in a no‐till corn–soybean (CS) rotation. Aboveground RCC was sampled at spring termination for biomass dry matter (DM), C, and N. To evaluate BD and remaining C and N, RCC biomass was put into nylon mesh bags, placed on the soil surface, and collected multiple times over 105 d. Treatments included rye cover crop following soybean (RCC‐FS) and corn (RCC‐FC), and prior‐year N applied to corn. Overall, the RCC BP and N was low due to low soil profile NO3–N. Across sites and years, the greatest BP was with RCC‐FC that received 225 kg N ha−1 (1280 kg DM ha−1), with similar N uptake as with RCC‐FS (27 kg N ha−1). The RCC biomass and N remaining decreased over time following an exponential decay. An average 62% biomass with RCC‐FS and RCC‐FC degraded after 105 d; however, N recycled was greater with RCC‐FS than RCC‐FC [22 (80%) vs. 14 (64%) kg N ha−1, respectively], and was influenced by the RCC C/N ratio. The RCC did not recycle an agronomically meaningful amount of N, which limited N that could potentially be supplied to corn. Rye cover crops can conserve soil N, and with improved management and growth, recycling of crop‐available N should increase.

Improved prediction of optimal N fertilizer rates for corn (Zea mays L.) can reduce N losses and increase profits. We tested the ability of the Agricultural Production Systems sIMulator (APSIM) to simulate corn and soybean (Glycine max L.) yields, the economic optimum N rate (EONR) using a 16-year field-experiment dataset from central Iowa, USA that included two crop sequences (continuous corn and soybean-corn) and five N fertilizer rates (0, 67, 134, 201, and 268 kg N ha 1) applied to corn. Our objectives were to: (a) quantify model prediction accuracy before and after calibration, and report calibration steps; (b) compare crop model-based techniques in estimating optimal N rate for corn; and (c) utilize the calibrated model to explain factors causing year to year variability in yield and optimal N. Results indicated that the model simulated well long-term crop yields response to N (relative root mean square error, RRMSE of 19.6% before and 12.3% after calibration), which provided strong evidence that important soil and crop processes were accounted for in the model. The prediction of EONR was more complex and had greater uncertainty than the prediction of crop yield (RRMSE of 44.5% before and 36.6% after calibration). For long-term site mean EONR predictions, both calibrated and uncalibrated versions can be used as the 16-year mean differences in EONR's were within the historical N rate error range (40-50 kg N ha(-1)). However, for accurate year-by-year simulation of EONR the calibrated version should be used. Model analysis revealed that higher EONR values in years with above normal spring precipitation were caused by an exponential increase in N loss (denitrification and leaching) with precipitation. We concluded that long-term experimental data were valuable in testing and refining APSIM predictions. The model can be used as a tool to assist N management guidelines in the US Midwest and we identified five avenues on how the model can add value toward agronomic, economic, and environmental sustainability.

Corn ( Zea mays L.) N use is of continued interest due to agronomic performance and environmental issues. This 2‐yr study evaluated era hybrid response to fertilizer nitrogen (FN) rate in a factorial arrangement of one popular hybrid per five decades (1960–2000 eras) and five N rates (0–224 kg N ha −1 ). An additional hybrid per era was grown at 168 kg N ha −1 . Hybrid productivity and nitrogen use efficiency (NUE) increased across the eras, but not between the 1980 and 1990 eras. Grain yield (GY) increased 65% and total plant biomass 43%, however, total plant nitrogen uptake (PNU) increased only 19% and across N rates was only higher for the 2000 era. At the agronomic optimum nitrogen rate (AONR), there was a linear GY increase of 0.13 Mg ha −1 yr −1 and GY N response of 0.091 Mg ha −1 yr −1 , indicating considerable genetic gain. There was no trend in AONR across eras. For plant N status measures, SPAD readings decreased and canopy index values increased across eras. All NUE measures indicated significant improvement in NUE. The apparent nitrogen recovery efficiency (NRE) at N rates near the AONR of each era, however, was not highest for the most recent eras. Harvest index (HI), grain nitrogen harvest index (GNHI), and fraction of total PNU accumulated by R1 were the same among eras. The grain nitrogen concentration (GNC), however, was 24% lower for the 2000 compared to the 1960 era. Corn hybrid development across the 50‐yr period improved productivity and NUE, but not the AONR.

The ability of biogeochemical ecosystem models to represent agro-ecosystems depends on their correct integration with field observations. We report simultaneous calibration of 67 DayCent model parameters using multiple observation types through inverse modeling using the PEST parameter estimation software. Parameter estimation reduced the total sum of weighted squared residuals by 56% and improved model fit to crop productivity, soil carbon, volumetric soil water content, soil temperature, N2O, and soil NO3− compared to the default simulation. Inverse modeling substantially reduced predictive model error relative to the default model for all model predictions, except for soil NO3− and NH4+. Post-processing analyses provided insights into parameter–observation relationships based on parameter correlations, sensitivity and identifiability. Inverse modeling tools are shown to be a powerful way to systematize and accelerate the process of biogeochemical model interrogation, improving our understanding of model function and the underlying ecosystem biogeochemical processes that they represent.