A probabilistic Bayesian framework for progressively updating site-specific recommendations

A probabilistic Bayesian framework for progressively updating site-specific recommendations The goal of this research was to create an agricultural adaptive management framework that enables the probabilistic optimization of N fertilizer to achieve maximized net returns under multiple uncertainties. These uncertainties come in the form of bioclimatic variables that drive crop yield, and economic variables that determine profitability. Taking advantage of variable rate application (VRA), spatial monitoring technologies, and historical datasets, we demonstrate a comprehensive spatiotemporal modeling approach that can achieve optimal efficiency for the producer under such uncertainties. The utility of VRA fertilizer research for producers is dependent upon a localized accurate understanding of crop responses under a range of possible climatic regimes. We propose an optimization framework that continuously updates by integrating annual on-site experiments, VRA prescriptions, crop prices received, input prices, and climatic conditions observed each year under a dryland spring wheat (Triticum aestivum) cropping system. The spatio-temporal Bayesian framework used to assimilate these data sources also enables calculation of the probabilities of economic returns and the risks associated with different VRA strategies. The results from our simulation experiments indicated that our framework can successfully arrive at optimum N management within 6–8 years using sequential Bayesian analysis, given complete uncertainty in water as a driver of crop yield. Once optimized, the spatial N management approach increased net returns by $23–25 ha−1 over that of uniform N management. By identifying small-scale targeted treatments that can be merged with VRA prescriptions, our framework ensures continuous reductions in parameter uncertainty. Thus we have demonstrated a useful decision aid framework that can empower agricultural producers with site-specific management that fully accounts for the range of possible conditions farmers must face. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

A probabilistic Bayesian framework for progressively updating site-specific recommendations

Loading next page...
 
/lp/springer_journal/a-probabilistic-bayesian-framework-for-progressively-updating-site-zdS7WaJ9yl
Publisher
Springer US
Copyright
Copyright © 2014 by Springer Science+Business Media New York
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-014-9375-4
Publisher site
See Article on Publisher Site

Abstract

The goal of this research was to create an agricultural adaptive management framework that enables the probabilistic optimization of N fertilizer to achieve maximized net returns under multiple uncertainties. These uncertainties come in the form of bioclimatic variables that drive crop yield, and economic variables that determine profitability. Taking advantage of variable rate application (VRA), spatial monitoring technologies, and historical datasets, we demonstrate a comprehensive spatiotemporal modeling approach that can achieve optimal efficiency for the producer under such uncertainties. The utility of VRA fertilizer research for producers is dependent upon a localized accurate understanding of crop responses under a range of possible climatic regimes. We propose an optimization framework that continuously updates by integrating annual on-site experiments, VRA prescriptions, crop prices received, input prices, and climatic conditions observed each year under a dryland spring wheat (Triticum aestivum) cropping system. The spatio-temporal Bayesian framework used to assimilate these data sources also enables calculation of the probabilities of economic returns and the risks associated with different VRA strategies. The results from our simulation experiments indicated that our framework can successfully arrive at optimum N management within 6–8 years using sequential Bayesian analysis, given complete uncertainty in water as a driver of crop yield. Once optimized, the spatial N management approach increased net returns by $23–25 ha−1 over that of uniform N management. By identifying small-scale targeted treatments that can be merged with VRA prescriptions, our framework ensures continuous reductions in parameter uncertainty. Thus we have demonstrated a useful decision aid framework that can empower agricultural producers with site-specific management that fully accounts for the range of possible conditions farmers must face.

Journal

Precision AgricultureSpringer Journals

Published: Sep 9, 2014

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve Freelancer

DeepDyve Pro

Price
FREE
$49/month

$360/year
Save searches from Google Scholar, PubMed
Create lists to organize your research
Export lists, citations
Access to DeepDyve database
Abstract access only
Unlimited access to over
18 million full-text articles
Print
20 pages/month
PDF Discount
20% off