Algorithms for In‐Season Nutrient Management in Cereals

Franzen, David W.; Kitchen, Newell R.; Holland, Kyle; Schepers, James S.; Raun, W. R.

doi:10.2134/agronj2016.01.0041

Cited by 71 publications

(57 citation statements)

References 49 publications

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“…Franzen et al (2016) described the current status of interpretations for the central United States. More study comparing different interpretations is needed to determine which interpretations work best in which environments.…”

Section: Limitations Of Sensing Spectral Propertiesmentioning

confidence: 99%

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: Limitations Of Sensing Spectral Propertiesmentioning

confidence: 99%

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

et al. 2018

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“…Early work from Van Es et al (2006) noted that accurate estimation of optimum N rates year-to-year and field-to-field remains elusive. Nonetheless, in more recent work, Franzen et al (2016) report that viable midseason sensor-based options are available for maize and wheat producers in many regions of the world.…”

Section: Review and Interpretationmentioning

confidence: 99%

“…Sensor-based N rate recommendations can vary spatially and temporally, have been further refined by location and crop (Oklahoma State University, 2016), and are currently available to producers (Franzen et al, 2016). Researchers have studied and validated in-season yield potential prediction using NDVI sensors (Crain et al, 2012;Teal et al, 2006).…”

Section: Review and Interpretationmentioning

confidence: 99%

Variability in Optimum Nitrogen Rates for Maize

Dhital

Raun

2016

Agronomy Journal

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Maize (Zea mays L.) grain yield levels and the response to fertilizer nitrogen (N) are expected to change from year to year and from location to location. Because yield level and N response have been documented to be independent and are known to infl uence N demand, optimum N rates at the same location vary each year due to unpredictable changes in the environment. Th e objective of this study was to further analyze maize grain yield levels and optimum fertilizer N rates from published data in maize growing regions of the United States. Optimum N rates were determined by calculating the diff erence in N uptake between the highest-yielding plot and the check plot (no N applied [0-N]). Th e diff erence in grain N uptake between the fertilized plot and the 0-N check plot was then divided by 0.33 (the assumed average N use effi ciency) to estimate optimum N rate by site and year. For the 213 site-years of data included in this study, grain yields in both the high N rate and check (0-N) plots were highly variable. Optimum N rates fl uctuated from year to year at all locations. Optimum N rates were not highly correlated with the high-N rate yield (R 2 = 0.20) or 0-N check yield (R 2 = 0.16). Th e wide range in optimum N rates observed in all maize experiments suggests the need to adjust N rates by year and location. A potential solution is to use midseason sensor-based technologies that can accurately predict yield potential and simultaneously encumber N responsiveness known to be independent of yield.Plant and Soil Sciences Dep., 044 N Ag Hall,

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“…The challenge for adaptation planning is to select, implement, and effectively manage appropriate adaptation at the appropriate time. Additionally, a better understanding of climate patterns and signals that impact agricultural resource management (Baumhardt et al 2016;Mauget et al 2014a, b;Stout and Lee 2003) improved modeling frameworks for evaluation of alternative tactics and strategies (Ahuja et al 2007), and information-based management tools (Franzen et al 2016) are needed to provide managers with increased knowledge to inform management decisions.…”

Section: Adaptation and Adaptive Capacity Of Southern Plains Agriculturementioning

confidence: 99%

Vulnerability of Southern Plains agriculture to climate change

et al. 2017

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Projections of greater interannual and intrannual climate variability, including increasing temperatures, longer and more intense drought periods, and more extreme precipitation events, present growing challenges for agricultural production in the Southern Plains of the USA. We assess agricultural vulnerabilities within this region to support identification and development of adaptation strategies at regional to local scales, where many management decisions are made. Exposure to the synergistic effects of warming, such as fewer and more intense precipitation events and greater overall weather variability, will uniquely affect rain-fed and irrigated cropping, high-value specialty crops, extensive and intensive livestock production, and forestry. Although the sensitivities of various agricultural sectors to climatic stressors can be difficult to identify at regional scales, we summarize that crops irrigated from the Ogallala aquifer possess a high sensitivity; rangeland beef cattle production a low sensitivity; and rain-fed crops, forestry, and specialty crops intermediate sensitivities. Numerous adaptation strategies have been identified, including drought contingency planning, increased soil health, improved forecasts and associated decision support tools, and implementation of policies and financial instruments for risk management. However, the extent to which these strategies are adopted is variable and influenced by both biophysical and socioeconomic considerations. Inadequate local-and regional-scale climate risk and resilience information suggests that climate vulnerability research and climate adaptation approaches need to include bottom-up approaches such as learning networks and peer-to-peer communication.

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Algorithms for In‐Season Nutrient Management in Cereals

Cited by 71 publications

References 49 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

Variability in Optimum Nitrogen Rates for Maize

Vulnerability of Southern Plains agriculture to climate change

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