Calibrating Reflectance Measurements to Predict Optimal Sidedress Nitrogen Rate for Corn

Scharf, Peter C.; Lory, John A.

doi:10.2134/agronj2008.0111

Cited by 58 publications

(37 citation statements)

References 56 publications

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“…Early studies on corn focused on the ability of reflectance measurements to distinguish N fertilizer rate treatments (Walburg et al, 1982;McMurtrey et al, 1994;Blackmer et al, 1994;Ma et al, 1996), with subsequent studies focused on the development of N rate recommendations (Bausch and Duke, 1996;Kitchen et al, 2010;Dellinger et al, 2008;Schmidt et al, 2009;Scharf and Lory, 2009;Tubaña et al, 2008;Bausch and Delgado, 2003;Solari et al, 2010). Most of the earlier studies were based on measurements of reflected sunlight, while most of the later studies are based on reflectance of pulsed light originating from the sensor.…”

Section: Sensing Spectral Properties For Nitrogen Recommendations Formentioning

confidence: 99%

“…The most widely published approach for developing N rate recommendations for corn has been to measure the EONR and reflectance across a range of environments, then regress EONR vs. reflectance (Kitchen et al, 2010;Dellinger et al, 2008;Schmidt et al, 2009;Scharf and Lory, 2009;Barker and Sawyer, 2010). This regression relationship can then be used to predict EONR in the future.…”

Section: Sensing Spectral Properties For Nitrogen Recommendations Formentioning

confidence: 99%

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Strengths and Limitations of Nitrogen Rate Recommendations for Corn and Opportunities for Improvement

et al. 2018

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A gronomy J our n al • Volume 110 , I ssue 1 • 2 018 1 T he goal of an N recommendation system is to accurately estimate the gap between the N provided by the soil and the N required by the plant. Accurately estimating this gap depends on the ability of the recommendation system to accurately estimate fi eld or subfi eld specifi c economically optimal nitrogen rates (EONR). Current recommendation systems are not as accurate as needed to provide consistently reliable estimates of N needs across years at the fi eld or subfi eld scale. Uncontrollable factors like temperature, rainfall timing, intensity and amount, and interactions of temperature and rainfall with factors such as N source, timing and placement, plant genetics, and soil characteristics combine to make N rate recommendations for an individual fi eld or rates for subfi elds a process guided as much by science as by the best professional judgement of farmers and farm advisors.Substantial evidence has accumulated that EONRs can vary widely across fi elds, within fi elds and over years in the same fi eld for a wide range of crops and geographies. Examples ABSTRACTNitrogen fi xation by the Haber-Bosch process has more than doubled the amount of fi xed N on Earth, signifi cantly infl uencing the global N cycle. Much of this fi xed N is made into N fertilizer that is used to produce nearly half of the world's food. Too much of the N fertilizer pollutes air and water when it is lost from agroecosystems through volatilization, denitrifi cation, leaching, and runoff . Most of the N fertilizer used in the United States is applied to corn (Zea mays L.), and the profi tability and environmental footprint of corn production is directly tied to N fertilizer applications. Accurately predicting the amount of N needed by corn, however, has proven to be challenging because of the eff ects of rainfall, temperature, and interactions with soil properties on the N cycle. For this reason, improving N recommendations is critical for profi table corn production and for reducing N losses to the environment. Th e objectives of this paper were to review current methods for estimating N needs of corn by: (i) reviewing fundamental background information about how N recommendations are created; (ii) evaluating the performance, strengths, and limitations of systems and tools used for making N fertilizer recommendations; (iii) discussing how adaptive management principles and methods can improve recommendations; and (iv) providing a framework for improving N fertilizer rate recommendations.

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Section: Sensing Spectral Properties For Nitrogen Recommendations Formentioning

confidence: 99%

Section: Sensing Spectral Properties For Nitrogen Recommendations Formentioning

confidence: 99%

Strengths and Limitations of Nitrogen Rate Recommendations for Corn and Opportunities for Improvement

et al. 2018

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“…The use of the CC sensors has been directed toward development of algorithms for in-season N application. Relationships of sensor readings to yield and in-season N status, as well as algorithms for use in determining in-season N rates have been developed by several researchers Solari et al, 2008;Sripada et al, 2008;Scharf and Lory, 2009;Barker and Sawyer, 2010;Solari et al, 2010;Oliviera et al, 2013;Franzen et al, 2014).…”

Section: Crop Circlementioning

confidence: 99%

Comparison of Satellite Imagery and Ground‐Based Active Optical Sensors as Yield Predictors in Sugar Beet, Spring Wheat, Corn, and Sunflower

Sharma

Denton

et al. 2017

Agronomy Journal

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Algorithms using active-optical (AO) sensors have been developed to direct in-season N application to crops. Many farmers in the United States have a large number of farm fi elds to manage. Farmers using AO technology must visit each fi eld and operate the sensor across the entire fi eld in order to conduct in-season N application. A fi eld might be driven over with an on-the-go N fertilizer applicator, but the application might not be required. Th e objective of this study was to determine whether satellite imagery might be used to predict yield in sugar beet, spring wheat, corn and sunfl ower similar to the yield prediction possible using AO sensors. If so, the algorithms produced could be used to select fi elds that would benefi t from in-season N application. Two N-rate studies in sugar beet, spring wheat, corn and sunfl ower, were conducted with experimental unit size of 9 by 9 m large enough to fi t a satellite pixel of 5 by 5 m size within each unit. Th e AO sensor and satellite imagery data were related to yield of sugar beet, spring wheat, corn and sunfl ower in some site-years. Th e problem is the ability to acquire the satellite imagery early enough in the season to be useful as a screening tool. Th ese results indicate that even though satellite imagery could be used as a fi eld screening tool, a better option may be to mount an AO sensor on a farm implement for an early season activity, or to explore the use of unmanned aerial vehicles (UAVs).

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“…For example, timing fertilizer addition to coincide with early summer crop growth has been shown in many cases to increase the efficiency of nutrient use and crop productivity (Tran et al 1997, Ma et al 2003, Scharf and Lory 2009 Fig. 3A).…”

Section: Thresholds and Nonlinear System Dynamicsmentioning

confidence: 99%

A sustainability framework for assessing trade-offs in ecosystem services

Cavender‐Bares¹,

Polasky²,

King³

et al. 2015

E&S

150

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ABSTRACT. Achieving sustainability, i.e., meeting the needs of current populations without compromising the needs of future generations, is the major challenge facing global society in the 21st century. Navigating the inherent trade-offs between provisioning, regulating, cultural, and supporting ecosystem services, and doing so in a way that does not compromise natural capital needed to provide services in the future, is critical for sustainable resource management. Here we build upon existing literature, primarily from economics and ecology, to present an analytical framework that integrates (1) the ecological mechanisms that underpin ecosystem services, (2) biophysical trade-offs and inherent limits that constrain management options, (3) preferences and values of stakeholders, and (4) explicit analysis of how systems evolve through time to ensure the goal of meeting the needs of future generations. Well-known ecological models define the relationships and trade-offs among services that represents an "efficiency frontier." Well-known methods in economics that combine preferences that define the willingness of stakeholders to trade off ecosystem services on the efficiency frontiers illuminate desirable outcomes that meet human needs. System dynamics show how the system will evolve with consequent impacts on ecosystem services and human well-being and the effects this has on achieving sustainability. Heterogeneity in biophysical constraints, uncertainty, technological advances, and obstacles imposed by societal factors and governance regimes influence potential and realized ecosystem services. Using a set of contrasting scenarios, we illustrate how progress can be made toward sustainability and the important obstacles that must be addressed in doing so. Our framework for analyzing sustainability drawn from economics and ecology is intended to make an integration of concepts from both disciplines accessible to a wider audience.

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Calibrating Reflectance Measurements to Predict Optimal Sidedress Nitrogen Rate for Corn

Cited by 58 publications

References 56 publications

Strengths and Limitations of Nitrogen Rate Recommendations for Corn and Opportunities for Improvement

Strengths and Limitations of Nitrogen Rate Recommendations for Corn and Opportunities for Improvement

Comparison of Satellite Imagery and Ground‐Based Active Optical Sensors as Yield Predictors in Sugar Beet, Spring Wheat, Corn, and Sunflower

A sustainability framework for assessing trade-offs in ecosystem services

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