NDLI: A probabilistic Bayesian framework for progressively updating site-specific recommendations

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A probabilistic Bayesian framework for progressively updating site-specific recommendations

Content Provider	Springer Nature Link
Author	Lawrence, Patrick G. Rew, Lisa J. Maxwell, Bruce D.
Copyright Year	2014
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.
Starting Page	275
Ending Page	296
Page Count	22
File Format	PDF
ISSN	13852256
Journal	Precision Agriculture
Volume Number	16
Issue Number	3
e-ISSN	15731618
Language	English
Publisher	Springer US
Publisher Date	2014-09-09
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Site-specific experimentation Bayesian statistics Input optimization Simulation experiment Dryland agriculture Spatial variation Agriculture Soil Science & Conservation Remote Sensing/Photogrammetry Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Atmospheric Sciences
Content Type	Text
Resource Type	Article
Subject	Agricultural and Biological Sciences

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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