winter rye (Secale cereale l.) cover crop (rcc) has potential to reduce no 3 -n loss from corn (Zea mays l.) and soybean [Glycine max (l.) Merr.] fields. However, rcc effects on annual crop productivity and corn optimal n fertilization requirement are unclear. the objectives were to evaluate corn and soybean yield response to rcc and corn optimal n rate. treatments were no-rcc and rcc with six fertilizer n rates (0-225 kg n ha -1 ) applied to corn in a no-till corn-soybean (cs) rotation at four iowa sites in 2009 through 2011. the rcc biomass and n uptake was low, with a maximum of 1280 kg dry matter (dM) ha -1 and 26 kg n ha -1 , respectively. in the no-n control, the rcc reduced soil profile no 3 -n by 15 kg n ha -1 only at time of rcc control before corn planting. corn canopy sensing, plant height, and plant population indicated more n stress, reduced plant stand, and slower growth with rcc. the rcc reduced corn grain yield by 6% at the economic optimum n rate (eonr). the eonr was the same with no-rcc and rcc, but plant n uptake efficiency (Pue) was reduced at low n rates with rcc, but not above the eonr. soybean yield was not affected by rcc. results indicate n fertilization rate should be the same with or without rcc. improvement in rcc systems and management could make rcc a more viable practice within notill corn and soybean production.Abbreviations: CS, corn-soybean; DM, dry matter; EONR, economic optimum nitrogen rate; NDVI, normalized difference vegetative index; NUE, nitrogen use efficiency; PAN, plant available nitrogen; PUE, plant nitrogen uptake efficiency; RCC, rye cover crop; SOM, soil organic matter; YEONR, yield at economic optimum nitrogen rate. E nvironmental concerns related to crop N fertilization is an ongoing issue (USEPA, 2007), including reducing N in surface waters related to hypoxia in coastal surface waters Kladivko et al., 2014). Nitrogen application rate to corn is an important factor in regard to cropping system profitability and NO 3 loss. Applying only the optimal N rate will not stop NO 3 loss, nor necessarily achieve the drinking water standard (Lawlor et al., 2007). Successful development of agricultural systems that benefit water quality have to be more inclusive of several agricultural practices, rather than only N rate or timing (Hatfield et al., 2009). Therefore, additional in-field practices are needed to reduce NO 3 losses (Sainju and Singh, 2008).Nitrate losses in tile drainage water from corn production systems can range from 7 to 68 kg N ha -1 yr -1 (Lawlor et al., 2007), and with most values ranging from 29 to 56 kg N ha -1 yr -1 (Sawyer and Randall, 2008). Cover crops have shown potential for uptake of residual N from fertilizers or inorganic N released from degrading soil organic matter (SOM) in the period between annual crops (Strock et al., 2004;Tonitto et al.,

Determining specific N fertilization rates to achieve optimal return is difficult. Crop N stress sensing uses the plant as an indicator of N fertilization need and has potential to improve N management. However, for making N rate decisions, a calibrated relationship between measured N stress and optimum N rate is required. Corn (Zea mays L.) plant N stress was determined with a chlorophyll meter (CM) at 102 site-years of N rate trials conducted from 1999-2005 with corn following soybean [Glycine max (L.) Merr.] (SC) and continuous corn (CC). Normalizing CM readings to relative chlorophyll meter (RCM) values reduced variation and improved the calibration of N stress with the nitrogen rate difference (ND) from the economic optimum nitrogen rate (EONR). With SC the adjusted R 2 (adjR 2 ) was 0.53 for CM readings and 0.73 for RCM values, and with CC the adjR 2 was 0.57 for CM readings and 0.76 for RCM values. The same statistically significant (P , 0.001) relationship between RCM values and ND was found for both SC and CC, indicating RCM critical values of 0.97 and 0.98, respectively. This indicates the same calibration for N rate determination based on RCM values can be used for both rotations. Evaluation of RCM values at multiple corn growth stages indicated the same relationship to ND at the fifteenth leaf and silking growth stages, suggesting a period of time during mid-to-late vegetative growth to collect CM readings, and make in-season N rate decisions and applications. The calibration of RCM values to the rate differential from optimum N can be used by producers to determine inseason N applications for corn across varying production conditions.

All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. P recision agriculture technologies are becoming an integral part of farming operations for crop production, including fertilizer management in the U.S. Corn Belt. Active canopy sensors are continually being developed and tested as an input variable used to implement variable rate N fertilizer management strategies in corn. Canopy sensors can diff erentiate site-specifi c N need, thus potentially reducing N fertilizer application across fi elds while maintaining crop yields. Improving N use effi ciency by addressing spatial variability of corn fertilizer N requirements can increase profi tability and promotes equitable land stewardship.Since the early 1990s, ongoing research has been conducted using the SPAD chlorophyll meter (SPAD-502) as a plant based testing method to determine N fertilization need. Schepers et al. (1992) found signifi cant diff erences in SPAD readings between sites, corn hybrids, and growth stages suggesting that normalization procedures are needed to standardize readings in order for the SPAD-502 to be a practical N management tool. Normalization procedures require a nonlimiting N fertilized fi eld area as a comparison to areas where N may be defi cient. Th is normalization produces a relative SPAD value (rSPAD), sometimes called a suffi ciency index (Schepers et al., 1992). Th is normalization has been shown to be important by others with the SPAD-502 (Varvel et al., 1997;Scharf et al., 2006;Hawkins et al., 2007;Varvel et al., 2007;Ziadi et al., 2008).Researchers from Missouri summarized a regional experiment in the north central United States evaluating the SPAD-502 to predict corn N fertilization (Scharf et al., 2006). Th ey found rSPAD values correlated well to the economic optimum N rate (EONR) and yield response to N in corn, and were more accurate in predicting N need when N fertilizer was not previously applied, when sensing was performed later in the growing season, and when readings were normalized. Findings from Hawkins et al. (2007) in Iowa and Varvel et al. (2007) in Nebraska indicated that in-season N stress in corn can be detected with the SPAD-502 and N rate algorithms could be developed to make in-season N rate applications between the V8 to V12 growth stages. Although, the SPAD-502 can detect N defi ciencies, a study by Zhang et al. (2008) indicated limited potential for use in production corn fi elds under moderate N stress and near optimum N.Canopy refl ectance measurement at optical wavelengths with active canopy sensors is a relatively new method of remote sensing. ABSTRACTIn-season corn (Zea mays L.) N sensing with active canopy sensors can provide input variables that direct variable rate N fertilizer applications. Th e objectives of this study were to assess corn N stress at the V10 to V12 growth sta...

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.

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.

a b s t r a c tThe 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, N 2 O, and soil NO 3 À compared to the default simulation. Inverse modeling substantially reduced predictive model error relative to the default model for all model predictions, except for soil NO 3 À and NH 4 þ . Post-processing analyses provided insights into parametereobservation 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.

tion and nodulation, thus reducing N 2 fixation capacity (Shibles, 1998). Application of N before planting or dur-Nitrogen application during soybean [Glycine max (L.) Merrill] ing the early growth stages can suppress the N 2 fixation reproductive stages has the potential to increase soybean productivity. The objective of this study was to determine the impact of N fertilizer process. Fertilizer N applied during soybean reproducapplied to the soil at the beginning pod growth stage on soybean yield tive stages (R1 to R5) might increase the capacity and and grain quality. Additional objectives were to study alternative N duration of the inorganic N utilization period while mainfertilizer and application practices that might enhance soybean use taining N 2 fixation. of applied N. A field study was conducted at five locations in Iowa A study conducted in Kansas with irrigated soybean during 1999 and 2000. Nitrogen treatments were urea and polymerfound significant soybean yield response when N was coated urea broadcast and subsurface band placed between the rows applied between the R3 and full pod stage (R4) (Wesley at 45 and 90 kg N ha Ϫ1 and a no-N control. The study showed few, et al., 1998). Their work showed an average yield insmall, and inconsistent effects of N material, placement, and rate on crease of 464 kg ha Ϫ1 at six of eight sites. The two grain yield and quality components at individual sites or when comnonresponsive sites had low yield, below 3360 kg ha Ϫ1 . bined across individual sites. There were no significant effects on grain yield, with only a 39 kg ha Ϫ1 increase from applied N. Grain protein, oil, Nitrogen application did not enhance grain protein and and fiber concentrations were the same with or without N application.oil. This study suggests that in soybean with high yield Aboveground plant dry matter (DM) at the R6 growth stage was potential (3700 kg ha Ϫ1 or greater), 22 kg N ha Ϫ1 would greater with the higher N rate, but plant DM with N application was produced a positive yield response. Gascho (1993) in lower than the no-N control. Nitrogen concentration in plant DM Georgia studied a variety of N application treatments was significantly increased with applied N. In conclusion, N application Published in Agron. J. 97: 615-619 (2005).

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.