Search

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.

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.

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.

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.

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.

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.