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ILeVO (fluopyram) is a fungicide seed treatment for soybean sudden death syndrome (SDS) that also has nematicidal activity. ILeVO is sold with a base of insecticide Poncho (clothianidin), nematode-protectant VOTiVO (Bacillus firmus), and Acceleron fungicides (metalaxyl, fluxapyroxad, and pyraclostrobin). Yield and reproduction of the soybean cyst nematode (SCN) (Heterodera glycines) on soybean plants grown from seed treated with ILeVO plus the base were compared with those treated with only the base in 27 small-plot experiments and 12 strip-trial experiments across Iowa from 2015 to 2017. To increase the likelihood that yield results were related to effects on SCN, data were used only from 26 small-plot experiments and 12 strip trials in which symptoms of SDS were low or nonexistent. An SCN reproductive factor (RF) was calculated for each experimental unit by dividing the SCN population density at harvest by the population density at planting. ILeVO significantly reduced SCN RF by 50% in one strip-trial experiment and by 36 to 60% in four small-plot experiments but yields were not increased by ILeVO in any of those five experiments. Soybean yields were 2.8 to 3.7 bushels/acre (bu/ac) (188.3 to 248.8 kg/ha) greater with ILeVO in three small-plot experiments but SCN RF was not reduced in those experiments. Also, yield was 1.9 bu/ac (127.8 kg/ha) greater with ILeVO in one strip-trial experiment in 2016 but SCN samples were not collected at harvest from the study to assess the possible effects of ILeVO on SCN reproduction. When strip-trial data from 2015 and 2016 were combined, there was a small but significant 0.8 bu/ac (52.2 kg/ha) yield increase with ILeVO. Overall, the effects of ILeVO on SCN reproduction and soybean yield were variable in these field studies.

An on-farm research network is an organization of farmers that conducts agronomic experiments under local conditions. It is common that an elementary statistical analysis be conducted for individual studies. However, there is unexplored potential in detecting yield response variability patterns for better decision making. We developed a data-analytics framework and web-application program that allows users to analyze multiple studies that use a common protocol and can identify the conditions where an imposed treatment may or may not be effective. The development of this data-analytics framework is needed to improve predictions at the farm level that can lead to more cost-effective, sustainable and environmentally sound agricultural production. Data visualization is an important part of data-analytics. In this paper, we have developed and tested a Bayesian hierarchical model that can be used to assess the general agro-nomic performance of different management practices. Decision making related to new management practices should be based on the complete evidence, local conditions and economic considerations. The web-application includes dynamic data visualization features to enhance communication and sharing of information with the goal to reach a broader audience. core Ideas • We develop a data-analytics framework and web-application for on-farm research trials. • A Bayesian hierarchical model quantifies the uncertainty in yield response. • The model helps assess alternative practices, products, and technologies among trial locations. • The framework provides a reactive break-even economic analysis of alternative management practices.

The on-farm research network concept enables a group of farmers to test new agricultural management practices under local conditions with support from local researchers or agronomists. Different on-farm trials based on the same experimental design are conducted over several years and sites to test the effectiveness of different innovative management practices aimed at increasing crop productivity and profitability. As a larger amount of historical trial data are being accumulated, data of all the trials require analyses and summarization. Summaries of on-farm trials are usually presented to farmers as individual field reports, which are not optimal for the dissemination of results and decision making. A more practical communication method is needed to enhance result communication and decision making. R Shiny is a new rapidly developing technology for turning R data analyses into interactive web applications. For the first time for on-farm research networks, we developed and launched an interactive web tool called ISOFAST using R Shiny. ISOFAST simultaneously reports all trial results about the same management practice to simplify interpretation of multi-site and multi-year summaries. We used a random-effects model to synthetize treatment differences at both the individual trial and network levels and generate new knowledge for farmers and agronomists. The friendly interface enables users to explore trial summaries, access model outputs, and perform economic analysis at their fingertips. This paper describes a case-study to illustrate how to use the tool and make agronomic management decisions based on the on-farm trial data. We also provided technical details and guidance for developing a similar interactive visualization tool customized for on-farm research network. ISOFAST is currently available at https://analytics.iasoybeans.com/cool-apps/ISOFAST/.

ILeVO (fluopyram) is a fungicide seed treatment for soybean sudden death syndrome (SDS) that also has nematicidal activity. ILeVO is sold with a base of insecticide Poncho (clothianidin), nematode-protectant VOTiVO (

Narrowing row width in soybean fields leads to earlier canopy closure, which may increase capture of incoming solar radiation during critical crop stages for yield determination. Theoretically, this should enhance seed yield. However, in prior studies, the impact of narrowing row spacing on soybean yield has been inconsistent. To explore on a broader scale the potential factors underlying this inconsistency, we evaluated the yield difference between narrow (NR; ≈38 cm) and wide (WR; ≈76 cm) row spacing using two sources of yield and management information: (i) data collected from 4879 soybean production fields via a multi-year, multi-state survey of soybean producers in the North Central US region; and (ii) data extracted from 129 site-year experiments that quantified NR-WR yield difference. The producer fields were allocated to their respective climate-soil domains to enable analysis of the NR-WR yield difference within each domain. The experimental trial data originated from three US geographic regions: south, central, and north. Key crop developmental stages in each trial were estimated using a soybean crop simulation model to discern if changes in crop phenology or any weather variable occurring before versus after a specific crop stage modulated the magnitude of the NR-WR yield difference. Analysis of experimental trial data indicated that, while NR yields were overall higher than WR yields, the NR-WR yield difference varied by region: 540 (south), 104 (central), and 240 kg ha−1 (north); the respective NR yields were greater than WR yields in 92%, 68%, and 84% of the cases. In the north and south regions, the NR-WR yield difference increased when the crop cycle length decreased as a consequence of later sowing date, earlier cultivar maturity group, and/or higher temperature. The relatively smaller (and occasionally negative) NR-WR yield difference detected in the central region was likely the result of environmental conditions that favored canopy closure irrespective of row spacing. In contrast to the analysis of the experimental database, no consistent NR-WR yield differences were detected in the producer field database. We hypothesize that the apparent absence of a significant NR-WR effect in the producer dataset is likely associated with the background management used with narrow spacing, together with yield losses due to wheel damage and greater disease pressure. This complementary approach using both producer and experimental data can help evaluate if practices documented in experimental trials to enhance yield realize equivalent yield increases in producer fields and, if not, explore underlying causes for the discrepancy.

Field trials are commonly used to estimate the effects of different factors on crop yields. In the present study, we followed an alternative approach to identify factors that explain field-to-field yield variation, which consisted of farmer survey data, a spatial framework, and multiple statistical procedures. This approach was used to identify management factors with strongest association with on-farm soybean yield variation in the US North Central (NC) region. Field survey data, including yield and management information, were collected over two crop growing seasons (2014 and 2015) from rainfed and irrigated soybean fields (total of 3568 field-year observations). Fields were grouped into technology extrapolation domains (TEDs) that accounted for soil and climate variation and 9 TEDs were selected based on the number of fields needed to detect yield differences due to management as determined using power analysis. Average yield ranged from 2.5 to 5 Mg ha−1 across TEDs, with field yield distributions in half of the domains having a distributional peak that was close to maximum yields. Conditional inference trees analysis was chosen among 26 statistical procedures as the approach that best combines ability to detect and rank factors (and their interactions) with greatest influence on on-farm yield and relatively easy interpretation of results. Survey data from ca. 150 fields in each of the nine TEDs allowed us to identify key management factors influencing yields for an agricultural area that includes ca. 7 million ha sown with soybean. In five of the nine TEDs, highest yields were observed in early-sown fields. Other factors explaining on-farm yield variation were maturity group, and in-season foliar fungicide and/or insecticide application, but, in some cases, their influence on yield depended upon sowing date and water regime. While the approach proposed here cannot establish cause-effect relationships conclusively, it can certainly provide a focus to replicated field experiments in relation to which management factors to investigate. We believe that future agronomic studies based on farmer survey data can greatly benefit from ex-ante identification of most important TEDs (relative to crop area and production) as well as determination of minimum number of farmer survey data that needs to be collected from each of them based on expected yield differences and variability. The approach is generic enough to be applied in other crop producing regions as long as farmer data and associated climate and soil databases are available.

In recent years, nematode-protectant seed treatments have become available to supplement resistant soybean cultivars to manage soybean cyst nematode (Heterodera glycines; SCN). Twenty-seven small-plot and 18 strip-trial experiments were conducted comparing the effects of Clariva Complete Beans (CCB) and CruiserMaxx Advanced plus Vibrance (CMV) on SCN reproduction and soybean yield on a moderately resistant (2014) and resistant (2015 to 2016) soybean cultivar. Yield data were collected, and an SCN reproductive factor was calculated by dividing final (at harvest) SCN egg population densities by initial (at planting) population densities from soil samples collected in each small plot or sampled area in the strip trials. Relative to the CMV treatment, CCB significantly decreased SCN reproductive factor in two small-plot experiments (one each in 2014 and 2015) but not in any of the strip trials in any year. Soybean yields were significantly greater with CCB versus CMV in 5 of the 18 strip trials but not in any of the small-plot experiments, even when there were significant decreases in SCN reproduction. For unknown reasons, CCB significantly decreased yields in two small-plot experiments and at one strip-trial location. In summary, the effects of CCB seed treatment on SCN reproduction and soybean yields were variable in the years that these experiments were conducted in Iowa.

Identification of causes of gaps between yield potential and producer yields has been restricted to small geographic areas. In the present study, we developed a novel approach for identifying causes of yield gaps over large agricultural areas with diversity in climate and soils. This approach was applied to quantify and explain yield gaps in rainfed and irrigated soybean in the North-Central USA (NC USA) region, which accounts for about one third of soybean global production. Survey data on yield and management were collected from 3568 producer fields over two crop seasons and grouped into 10 technology extrapolation domains (TEDs) according to their soil, climate, and water regime. Yield potential was estimated using a combination of crop modeling and boundary functions for water productivity and compared against highest producer yields derived from the yield distribution in each TED-year. Yield gaps were calculated as the difference between yield potential and average producer yield. Explanatory factors for yield gaps were investigated by identifying management practices that were concordantly associated with high- and low-yield fields. Management × TED interactions were then evaluated to elucidate the underlying causes of yield gaps. The chosen spatial TED framework accounted for about half of the regional variation in producer yield within the NC USA region. Across the 10 TEDs, soybean average yield potential ranged from 3.3 to 5.3 Mg ha −1 for rainfed fields and from 5.3 to 5.6 Mg ha −1 for irrigated fields. Highest producer yields in each TED were similar (±12%) to the estimated yield potential. Yield gap, calculated as percentage of yield potential, was larger in rainfed (range: 15–28%) than in irrigated (range: 11–16%) soybean. Upscaled to the NC USA region, yield potential was 4.8 Mg ha −1 (rainfed) and 5.7 Mg ha −1 (irrigated), with a respective yield gap of 22 and 13% of yield potential. Sowing date, tillage, and in-season foliar fungicide and/or insecticide were identified as explanatory causes for yield variation in half or more of the 10 TEDs. However, the degree to which these three factors influenced producer yield varied across TEDs. Analysis of in-season weather helped interpret management × TED interactions. For example, yield increase due to advances in sowing date was greater in TEDs with less water limitation during the pod-setting phase. The present study highlights the strength of combining producer survey data with a spatial framework to measure yield gaps, identify management factors explaining these gaps, and understand the biophysical drivers influencing yield responses to crop management.

Moderate resolution imaging spectroradiometer (MODIS) satellite imagery from 2004 to 2013 were used to assess the field-specific risks of soybean sudden death syndrome (SDS) caused by Fusarium virguliforme in Iowa. Fields with a high frequency of significant decrease (>10%) of the normalized difference vegetation index (NDVI) observed in late July to middle August on historical imagery were hypothetically considered as high SDS risk. These high-risk fields had higher slopes and shorter distances to flowlines, e.g., creeks and drainages, particularly in the Des Moines lobe. Field data in 2014 showed a significantly higher SDS level in the high-risk fields than fields selected without considering NDVI information. On average, low-risk fields had 10 times lower F. virguliforme soil density, determined by quantitative polymerase chain reaction, compared with other surveyed fields. Ordinal logistic regression identified positive correlations between SDS and slope, June NDVI, and May maximum temperature, but high June maximum temperature hindered SDS. A modeled SDS risk map showed a clear trend of potential disease occurrences across Iowa. Landsat imagery was analyzed similarly, to discuss the ability to utilize higher spatial resolution data. The results demonstrated the great potential of both MODIS and Landsat imagery for SDS field-specific risk assessment.

Previous research predicted that the biofuel-driven expansion of corn (Zea mays L.) area would increase riverine export of nitrate-nitrogen (NO3-N) in the Mississippi River Basin. Accurate information about water quality trends in agricultural watersheds is needed to better inform agricultural policy and to help quantify the effectiveness of field and landscape management practices. This study was designed to (1) characterize possible NO3-N trends within the Raccoon River watershed (RRWS) of central Iowa, and (2) explore links between the relative areas planted to corn and soybean (Glycine max) and water quality. We examined NO3-N concentration and loading data from more than 60 main stem river and tributary locations in the RRWS for the period 1999 to 2014. In addition, we assessed the role of climate, crop rotations, and simplified annual N budgets on NO3-N concentrations and loads to show that expansion of corn area has not increased Raccoon River NO3-N levels. Nitrate-N concentrations have not increased as corn area increased 19% and fertilizer N inputs increased 24% since 1999. We conclude that expansion of corn area at the expense of soybean may be affecting water quality. Better management of soybean in a corn–soybean rotation should reduce NO3-N export from the watershed, and reducing throughput of water in this artificially drained system will improve water quality.

International audience; The aim of on-farm research is to identify and test a new technology, product or management practice (e.g. more efficient seeding rate, enhanced row spacing, better disease management treatment, etc.) suited to local conditions by comparing it to a standard farmer practice across several farmers' fields. Typically, each trial includes two treatments (new practice vs. standard control practice) replicated at least three times in each field. The statistical analysis of yield data collected in such trials provides growers with useful information about the effectiveness of the tested farming practice on crop productivity and its uncertainty. We used a random-effects model to i) estimate the performance of a treatment compared to a control in individual trials, ii) estimate the overall mean yield response across all trials, iii) compute prediction intervals describing a range of plausible yield response for a new (out-of-sample) field at the trial level, and iv) compute the probability that the tested management practice will be ineffective in a new field. We used frequentist (classical) and Bayesian approaches for data collected in 26 on-farm trial categories managed by the Iowa Soybean Association. Depending on the level of between-trial variability, we found that prediction intervals were 2.2-12.1 times larger than confidence intervals for the estimated mean yield responses for all tested management practices. We conclude that prediction intervals should be systematically reported to provide additional information about future trials or experiments with associated uncertainties. Nevertheless, prediction intervals should be interpreted with caution when the between-trial variance is small. Using prediction intervals and, when appropriate, the probability of ineffective treatment will prevent farmers from overoptimistic expectations that a significant effect at the overall population level will lead with high certainty to a yield gain on their own farms.

Large databases containing producer field-level yield and management records can be used to identify causes of yield gaps. A relevant question is how to account for the diverse biophysical background (i.e., climate and soil) across fields and years, which can confound the effect of a given management practice on yield. Here we evaluated two approaches to group producer fields based on biophysical attributes: (i) a technology extrapolation domain spatial framework (‘TEDs’) that delineates regions with similar (long-term average) annual weather and soil water storage capacity and (ii) clusters based on field-specific soil properties and weather during each crop phase in each year. As a case study, we used yield and management data collected from 3462 rainfed fields sown with soybean across the North Central US (NC-US) during four growing seasons (2014–2017). Following the TED approach, fields were grouped into 18 TEDs based on the TED that corresponded to the geographic location of each field. In the cluster approach, fields were grouped into clusters based on similarity of in-season weather and soil. To evaluate how the number of clusters would affect the results, fields were grouped separately into 5, 10, 18, and 30 clusters. The two stratification approaches (TEDs and clusters) were compared on their ability to explain the observed yield variation and yield response to key management factors (sowing date and foliar fungicide and/or insecticide). Lack of stratification of producer fields based on their biophysical background ignored management by environment (M × E) interactions, leading to spurious relationships and results that are not relevant at local level. In the case of the cluster approach, a fine stratification (18 and 30 clusters) explained a larger portion of the yield variance compared with a coarse stratification (5 and 10 clusters). However, for our case study in the NC-US region, we did not find strong evidence that the data-rich clustering approach outperformed the TEDs on the ability to explain yield variation and identify M × E interactions. Only the stratification into 30 clusters exhibited a small improved ability at explaining yield variation compared with the TEDs. However, the use of the clustering approach had important trade-offs, including large amount of data requirements and difficulties to scale results to different regions and over time. The choice of the stratification method should be based on objectives, data availability, and expected variation in yield due to erratic weather across regions and years.

Soybean yield response variability to foliar fungicide applications was evaluated in on-farm replicated strip trials (OFTs) and small-plot trials (SPTs) from 2008 through 2015 in Iowa. A total of 230 OFTs and 49 SPTs were compared for yield response to pyraclostrobin, pyraclostrobin + fluxapyroxad, or trifloxystrobin + prothioconazole fungicides. OFTs (18 to 55 m wide and 200 to 800 m long strips) were harvested with farmers’ combines equipped with yield monitors and GPS, while SPTs (3.0 to 4.6 m wide and 10.7 to 15.3 m long plots) were harvested by small research plot combines. Variance component and power analyses were conducted with a subset of data consisting of 12 OFTs and SPTs, each with pyraclostrobin and evaluated in 2008 and 2009. While average yield responses were similar, the residual random yield variation was smaller in OFTs than SPTs. Power analysis showed that SPTs need more replications than OFTs to detect the same overall treatment differences. To detect a yield response of 134 kg/ha, it would require at least three treatment replications with 12 locations in OFTs and seven replications with 12 locations in SPTs. Researchers need to acknowledge the differences in statistical power of detecting yield responses to foliar fungicide on soybean in different types of field experiments, especially with smaller plot sizes in situations with less foliar disease.

x CHAPTER I. GENERAL INTRODUCTION 1 Dissertation Organization 2 CHAPTER II. SOIL pH EFFECTS ON NITRIFICATION OF FALLAPPLIED ANHYDROUS AMMONIA 3 Abstract 3 Introduction 4 Materials and Methods 8 Results and Discussions 13 Conclusions 20 Acknowledgments 21 References 21 CHAPTER III. ESTIMATING EX POST ECONOMIC OPTIMUM RATES OF NITROGEN FERTILIZATION BY DISCRETE MARGINAL ANALYSIS 33 Abstract 33 Introduction 34 Materials and Methods 38 Results and Discussion 40 Conclusions 53 References 55

Published in Agron. J. 105:85–94 (2013) doi:10.2134/agronj2012.0218 Available freely online through the author-supported open access option. Copyright © 2013 by the American Society of Agronomy, 5585 Guilford Road, Madison, WI 53711. 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. N management of corn under rainfed conditions in the U.S. Midwest has been studied extensively during the last 50 yr but has become a subject of intensive scientifi c and public debate only during the last two decades. Th e recent focus on N management is attributed to environmental concerns about pollution of water bodies by NO3 escaping from corn fi elds, emission of greenhouse gases (i.e., N2O) to the atmosphere, and the large amount of petroleum-based energy required to produce, transport, and apply N. Historically, N fertilizer recommendations for corn have been based on a simplifi ed empirical formula called the yield goal (Hoeft et al., 2000; Stanford, 1973). Th e major premise of yield goal recommendations is that corn N requirements or optimal N rates are proportional to corn yields, with a constant multiplier of 21.4 kg N Mg–1 corn grain. Th ese calculations were based on the assumption of a constant supply of N from the soil under a wide range of soil and weather conditions. While the yield goal recommendations were based on a mass balance approach (N rates should approximate N removed by grain plus adjustments for N losses and N supplied by the soil), several studies have shown a low correlation of corn yields and optimal N rates (Scharf et al., 2006; Vanotti and Bundy, 1994). Th e low correlation is oft en attributed to large variability in the N supply from the soil and variable N losses by diff erent mechanisms such as leaching, volatilization, or denitrifi cation. Another method for estimating N fertilizer needs for corn was developed in the late 1950s based on conducting the so-called yield response trials. Th is method considered applying a wide range of N rates in small-size plots, measuring the yield at each applied N rate, and fi tting a model (i.e., fi tting regression curves) to the yield values to calculate the rates at which the marginal increase in grain value would equal the marginal N fertilizer cost (Heady et al., 1955, p. 292–332; Voss, 1975). Th is calculation produced economically optimum N rates (EONR) that would maximize, aft er the fact, the return to N per unit of area. Unlike the yield goal approach, the economic optimization method indirectly considered the variability in N supplied from the soil and fl uctuations in the prices of N fertilizer and corn with time. Except when calibrating soil and plant tissue tests in soil fertility studies, the EONR method has seldom been used as the basis for N fertilizer recommendations in production agriculture. Recently, combined eff orts involving several land grant universities led to the creation of a multistate database of yield response trials to estimate the EONR for corn (Sawyer et al., 2006). Th ese data enable researchers to partially address large spatial and temporal variability in the EONR as well as fl uctuations in the prices of N fertilizer and corn grain. At least two issues remain to be resolved, however. Nitrogen yield ABSTRACT Current systems for developing N recommendations for corn (Zea mays L.) lack methods to quantify the eff ects of factors infl uencing yield responses to N and quantify the uncertainty in N recommendations. We utilized hierarchical modeling and Bayesian analysis to quantify the risk from reducing N to corn using on-farm observations. Across Iowa, farmers conducted 34 trials in 2006 and 22 trials in 2007. Each trial had a farmer’s normal N rate alternating with a reduced rate (by about 30% less) in three or more replications. Yield losses (YLs) from reduced N were calculated at 35-m intervals. Posterior distributions were used to identify across-fi eld and within-fi eld factors aff ecting YL and to quantify the risk of economic YL (>0.31 Mg ha–1) from reducing N in unobserved fi elds. In 2006 (dry May and June), the economic YL for corn aft er soybean (C-S) was predicted to be 20% larger than that for corn aft er corn. Also in 2006, C-S fi elds with above-normal June rainfall had economic YLs 35% larger than those with below-normal June rainfall, and sidedress applications were about 20% riskier than spring applications. In 2007 for C-S, N reductions with above-normal spring rainfall were riskier than with below-normal spring rainfall. Areas with higher soil organic matter (SOM) had economic YLs about 20% smaller than those with lower SOM. Many on-farm trials can be conducted across the state and the use of the proposed statistical methodology can improve decisions on where to reduce N applications across and within fi elds.

Using uncalibrated digital aerial imagery (DAI) for diagnosing in-season nitrogen (N) status of corn (Zea mays L.) is challenging because of the dynamic nature of corn growth and the difficulty of obtaining timely imagery. Late-season DAI is more accurate for identifying areas deficient in N than early-season imagery. Even so, the quantitative use of the imagery across many fields is still limited because DAI is often not radiometrically calibrated. This study tested whether spectral characteristics of corn canopy derived from normalized uncalibrated late-season DAI could predict final corn N status. Color and near-infrared (NIR) imagery was collected in late August or early September across Iowa from 683 corn fields in 2006, 824 in 2007, and 828 fields in 2007. Four sampling areas (one within a target-deficient area) were selected within each field for conducting the end-of-season corn stalk nitrate test (CSNT). Each image was enhanced to increase the dynamic range within each field and to normalize reflectance values across all fields within a year. The reflectance values of individual bands and three vegetation indices were used to predict corn N status expressed as Deficient and Sufficient (a combination of marginal, optimal, and excessive CSNT categories) using a binary logistic regression (BLR). The green reflectance had the highest prediction rate, which was 70, 64, and 60% in 2006, 2007, and 2008, respectively. The results suggest that the normalized (enhanced) late-season uncalibrated DAI can be used to predict final corn N status in large-scale on-farm evaluation studies.

Despite growing interests in variable-rate nitrogen (VRN) fertilizer applications, we still lack basic knowledge and practical methodology for identifying major factors that can be used to guide VRN application to corn (Zea mays L.). Th e objective was to develop a methodology for identifying a predictable relationship between economic yield response (YR) to N and commonly measured soil and terrain attributes in the presence of spatial dependence. Six 30-ha no-till fi elds in central Iowa were studied during 6 yr. Urea-ammonium nitrate solution was sidedressed at 112 and 140 kg N ha −1 in alternating strips replicated from 10 to 22 times. Yield responses to the high rate were calculated in a 20 by 25 m grid pattern and classifi ed into profi table and nonprofi table categories within each fi eld. Autologistic models were used to identify which (if any) of the following factors economically aff ected YR: elevation, apparent soil electrical conductivity (ECa), slope, topographic wetness index (TWI), or digital soil map units. Signifi cant eff ects of some of the factors were found within 8 of 15 site-years. Within fi ve of these site-years, well-drained areas with lower ECa and TWI, and higher elevation and slope had the higher probability of profi table YR, but these eff ects were not stable over time. Within the proposed methodology, a high spatial resolution of YR is used that increases the ability to identify areas profi table to N, and farmers can explore VRN possibilities by applying a small fertilizer increment below or above a uniform optimal rate in many alternating strips across fi elds.

Nitrogen (N) applied in bands across cornfields often induces differences in plant height, leaf color, and growth stage of corn (Zea mays L.). Especially during wet springs, plants growing immediately over the bands are often noticeably taller and greener for a short period. Plants growing between the bands experience N deficiency until their roots reach the bands. The impacts of such short periods of N deficiency on plant early growth have received little attention. We studied the effects of tracks left by fertilizer applicator on corn growth stage, plant height, and leaf chlorophyll meter readings (CMRs) in a field where conditions seemed favorable for a fertilizer-induced advancement in growth stage. Measurements showed that the reduced plant height or leaf color attributable to a temporary N deficiency was mainly associated with the delay of growth stages and might have little influence on final grain yield.

To evaluate the temporal patterns of N deficiencies in corn and assess the ability of remote sensing to diagnose N deficiencies during the vegetative growth of corn, three field-scale experiments were conducted with various rates (56, 112, and 168 kg N ha −1 ), timing (early and late applications) and placement (injected into soil and dribbled on soil surface) of N fertilization in a split-plot design. Relationships between canopy reflectance during the growing season and yield data at the end of growing season were studied for different treatments. Results showed significant variation in both grain yields and canopy reflectance among the three cornfields. The N fertilization made in early June resulted in low canopy reflectance in early July, but the differences disappeared as the season progressed. The effect of N rates on canopy reflectance was not significant in early July but it gradually became detectable in mid-July and thereafter. The fertilizer placement had a significant effect on grain yields only in one field but not on canopy reflectance in all three fields. These observations suggest that the deficiency of N developed under field conditions is a dynamic phenomenon, which adds complexity for accurately defining “N deficiency” and effectively developing management strategies for in-season correction. Remote sensing throughout the season helps collect information about important interactions that have not been given enough attention in the past.

Marked spatial and temporal variability in yield response to N fertilizer observed in individual yield response trials creates a high degree of uncertainty when estimating economic optimum rates (EORs) of N for a group of trials and when extrapolating these rates from one location to another. A survey was conducted to characterize and classify variability in yield response to N on subfield and field scales. Fertilizer N was applied at five rates (56, 84, 112, 140, and 168 kg N ha -1 ) in many (6-12) replicated strips within three 18- to 24-ha no-till fields during two corn (Zea mays L.) growing seasons. Yield responses or yield differences between two adjacent strips were measured in 22 to 25 grid cells ha -1 within each field. Cumulative probability distributions (CPDs) were used to estimate the probability that a given N rate produces a yield response less or equal to a specified quantity. The yield responses were classified into potential categories with different N fertilizer requirements using apparent soil electrical conductivity (EC a ), digital soil map units, and relative elevation. Analysis indicated that the classifications explained

Deficiencies of N during the growth of corn (Zea mays L.) are often diagnosed by using chlorophyll meters that measure chlorophyll content in leaves. The diagnoses are based on the assumption that above-optimal supplies ofN do not significantly influence chlorophyll meter readings (CMRs). The objective of this research was to assess the possibility that above-optimal supplies of N impacted chlorophyll concentration and the effects imposed a limitation on the minimum N deficiencies that can be detected by chlorophyll meters. Our approach was to monitor temporal patterns in CMRs of nonirrigated corn that received various rates of N at various times. The results showed that the time at which N deficiency symptoms first become detectable was closely related to the amounts by which N rates fell short for maximizing grain yield. The measured symptoms of N deficiency changed with time. Temporal patterns in CMRs were affected by N treatments while yields were not greatly affected. In-season N applications made to plants that started to show N deficiencies caused CMRs to converge with those taken on plants that always had adequate N. These observations suggest that above-optimal supplies of N may induce a luxury production of chlorophyll that is analogous to luxury uptake of nutrients. These problems severely limit the value of using chlorophyll meters to guide in-season fertilization in fields having near-optimal supplies of N. The underlying problem is the uncertainty caused by difficulties associated with distinguishing luxury production of chlorophyll from symptoms of N deficiencies.

Evidence that nitrogen (N) fertilization tends to accelerate maturation as well as increase rates of growth has received little attention when diagnosing N deficiencies in corn (Zea mays L.). Such a tendency could be a potential source of errors when the diagnosis is solely based on comparing plants with different rates of growth. Whether N fertilization could accelerate rates of growth and maturation was tested in a field study with 12 paired plots representing relatively large variability in soil properties and landscape positions. The plots were located under conditions where preplant N fertilization reduced or avoided temporary N shortages for some plants but did not reduce for other plants early in the season. We measured corn heights to the youngest leaf collar, stages of growth and chlorophyll meter readings (CMRs). The added N advanced growth stages as well as increased corn heights and CMRs at any given time. Fertilization effects on corn heights, growth stages and ear weights were statistically significant (P < 0.05) despite substantial variability associated with landscape. Reductions in growth due to a temporary shortage of N within a growth stage might be partially offset by longer periods of growth within that stage to physiological maturity. Temporary shortages of N, therefore, may produce symptoms of N deficiency in situations where subsequent additions of N should not be expected to increase yields. Recognition of these two somewhat different effects (i.e., increase growth rates and advance growth stages) on corn growth could help to define N deficiency more precisely and to improve the accuracy of diagnosing N status in production agriculture.

Assessing economic and environmental impacts of nitrogen (N) fertilization in production agriculture is important for preventing unnecessary application of N fertilizer and avoiding losses of this N into water body. Chlorophyll meters are often used to evaluate N management practices and diagnose deficiencies of N in corn (Zea mays L.). Chlorophyll meter readings (CMRs) are usually interpreted relative to reference readings taken from plants having ample N to maximize rates of growth. Although measurements taken with chlorophyll meters provide a scale for estimating the sufficiency of N for corn growth, effects of above-optimal supplies of N on the measurements have not been studied when diagnosing N deficiencies during vegetative growth stages. Here, temporal trends in chlorophyll measurements were monitored in trials where various rates of N were applied soon after planting and (or) after symptoms of N deficiency had developed due to the changes of N status in soil and demand for N during corn growth. Divergence of chlorophyll measurements from the reference readings occurred in situations where plants having too little N were compared with plants having adequate N. In contrast, convergence of chlorophyll measurements with the reference readings after application of fertilizer N during the growing season indicated partial or complete recovery of the plants from the deficiency of N. The recovery can be explained by considering that luxury production of chlorophyll occurred at higher rates of fertilization or by interactions of N with soil water and other nutrients supplied during corn growth. Observations that plants can partially or completely recover from periods with inadequate N for chlorophyll production suggest that the leaf chlorophyll measurements taken early in the season should not be always expected to highly correlate with final yields of grain. Therefore, it is important to recognize the possible recovery of chlorophyll production and to avoid N losses to the environment.

Discussions of N fertilizer needs for corn (Zea mays L.) usually are based on the assumption that economic optimum rates (EORs) of N provide the best benchmark to indicate the rates most likely to maximize profits for producers. We describe methods for calculating some additional benchmarks and illustrate how efforts to improve N management could be advanced by recognizing several alternative benchmarks simultaneously. We analyzed data from 54 small-plot trials with a new procedure that disaggregates problems associated with systematic model bias and variability in crop yield responses to fertilizer N. We compared the commonly used method for calculating EORs to a method that is described as discrete marginal analysis at near-optimal rates of N fertilization. We described methods for calculating the incremental break-even rate, the incremental rate that gives no crop response, and the incremental rate of N that gives the desired level of profit. Discussions of the relative merits of each benchmark suggest that problems associated with calculating and using the concept of EORs can be avoided by presenting the alternative benchmarks as proposed in this paper.

Efforts to calculate economic optimum rates (EORs) of N fertilization for corn (Zea mays L.) have been hampered by a lack of methods for disaggregating problems caused by model bias and variability in crop responses to N. We illustrate how the concepts of ex post and ex ante analyses can be used in a multistep procedure to disaggregate these problems when calculating ex post EORs when large amounts of data are available. Five models were used to describe yield responses from a collection of 54 small-plot trials that included seven rates of N. The multistep procedure included steps to reduce model bias and steps to reduce unexplained variability by forming categories based on information available at the time of fertilization. The concepts of ex post and ex ante analyses were used to clarify what information is important at each stage of the procedure and to ensure that information generated in early steps is used effectively in later steps. Analyses illustrate that calculated values for EORs should be expected to vary with the amounts of information available and that the new procedure can be described as a systematic search for the best EOR that can be calculated with existing information. Although this procedure may have little practical use when data are collected in traditional small-plot trials, illustration of this method by using data collected in such trials revealed the problems of model bias and variability in yield response as well as the potential for solving these problems in production systems where advances in technology make it practical to collect unprecedented amounts of data.

From 2005 through 2009, 282 on-farm evaluation trials were conducted in soybean fields across Iowa to identify when a foliar application of pyraclostrobin produced profitable yield responses. Because of a delay in plant maturity, 218 trials exhibited a fungicide-induced “greening effect” documented using the late-season color infrared (CIR) digital aerial imagery of the soybean canopy. These 218 trials were approximately 35% more likely to produce profitable yield responses (65% vs 30%) than those without “the greening effect.” In addition, greater yield responses were observed in trials that received more than 12 inches of cumulative March through May rainfall. Potentially, site-specific observations of spring rainfall could be used to identify fields that are or are not likely to produce above break-even yield responses, and therefore, help farmers avoid unnecessary foliar fungicide applications on soybean. Accepted for publication 30 July 2013. Published 28 September 2013.

Soil temperature at the time of application has been the primary factor used to predict rates of nitrification and assess the risks associated with losses of N applied in the fall as anhydrous ammonia in the Corn Belt. We report studies assessing the importance of soil pH as a factor affecting nitrification rates and losses of this N before corn (Zea Mays L.) begins rapid growth in June. Data were collected in a series of field studies conducted during 4 yr. Anhydrous ammonia was applied in the fall after soils had cooled to 7.5. Significant relationships between soil pH and percentage nitrification were observed each year. Means of measurements made in mid-April (when planting begins) indicated 89% nitrification of fertilizer N in soils having pH > 7.5 and 39% nitrification of this N in soils having pH < 6.0. The finding that soil pH influenced when nitrification occurred helps to explain why the effects of nitrification inhibitors have been variable in this region. Significant relationships between soil pH and recovery of fertilizer N as exchangeable NH + 4 and NO - 3 were observed in years with above-average rainfall before samples were collected in April. The effects of soil pH on nitrification, therefore, influenced the amounts of NO - 3 lost by denitrification or leaching during spring rainfall. The observed effects of pH on nitrification rates suggest that economic and environmental benefits of delaying application of fertilizer N may be greater in higher-pH soils than in lower-pH soils.

Nitrification inhibitors (NI) can be used with liquid swine manure (LSM) to decrease potential NO3 losses, but knowledge specifying when and where NI can increase corn (Zea mays L.) yields is limited. Eleven on-farm evaluation trials (OET) were conducted in 2009 and 15 in 2010 to identify site-specific factors for using Instinct (an encapsulated form of nitrapyrin) with LSM in Iowa. Farmers injected LSM in the fall in at least three field-long strips with and without NI. Yield responses (YR) to NI were calculated by dividing yield monitor data into 50-m cells within each field. Hierarchical models were used to estimate predictive probabilities of profitable YR for two categories of monthly average rainfall and soil drainage. On average, NI produced no YR in relatively normal 2009 and a 0.15 Mg ha−1 YR in extremely wet 2010. The NI did not change late-season corn N status but half of corn stalk nitrate test (CSNT) samples were N deficient in 2009 and about 65 % in 2010. Fields receiving >90 cm March through August rainfall in 2010 were predicted 65 % more likely to have economic YR (>0.13 Mg ha−1) than fields receiving