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Sensors 2017, 17(10), 2343; doi:10.3390/s17102343

Evaluating Oilseed Biofuel Production Feasibility in California’s San Joaquin Valley Using Geophysical and Remote Sensing Techniques

1
USDA-ARS, U.S. Salinity Laboratory, 450 West Big Springs Road, Riverside, CA 92507, USA
2
USDA-ARS, Water Management Systems Research, 2150 Center Ave., NRRC Building D, Fort Collins, CO 80526, USA
3
Department of Earth and Planetary Sciences, Northrop Hall, 221 Yale Blvd. NE, University of New Mexico, Albuquerque, NM 87131, USA
4
USDA-ARS, Water Management Research Unit, San Joaquin Valley Agricultural Sciences Center, 9611 South Riverbend Ave., Parlier, CA 93648, USA
5
Riverside Public Utilities—Resources Division, 3435 14th St., Riverside, CA 92501, USA
*
Author to whom correspondence should be addressed.
Received: 2 September 2017 / Revised: 3 October 2017 / Accepted: 5 October 2017 / Published: 14 October 2017
(This article belongs to the Special Issue Sensors in Agriculture)
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Abstract

Though more costly than petroleum-based fuels and a minor component of overall military fuel sources, biofuels are nonetheless strategically valuable to the military because of intentional reliance on multiple, reliable, secure fuel sources. Significant reduction in oilseed biofuel cost occurs when grown on marginally productive saline-sodic soils plentiful in California’s San Joaquin Valley (SJV). The objective is to evaluate the feasibility of oilseed production on marginal soils in the SJV to support a 115 ML yr−1 biofuel conversion facility. The feasibility evaluation involves: (1) development of an Ida Gold mustard oilseed yield model for marginal soils; (2) identification of marginally productive soils; (3) development of a spatial database of edaphic factors influencing oilseed yield and (4) performance of Monte Carlo simulations showing potential biofuel production on marginally productive SJV soils. The model indicates oilseed yield is related to boron, salinity, leaching fraction, and water content at field capacity. Monte Carlo simulations for the entire SJV fit a shifted gamma probability density function: Q = 68.986 + gamma (6.134,5.285), where Q is biofuel production in ML yr−1. The shifted gamma cumulative density function indicates a 0.15–0.17 probability of meeting the target biofuel-production level of 115 ML yr−1, making adequate biofuel production unlikely. View Full-Text
Keywords: apparent soil electrical conductivity; ECa-directed soil sampling; electromagnetic induction; proximal sensor; response surface sampling; salt tolerance; boron tolerance; soil mapping; soil salinity; spatial variability apparent soil electrical conductivity; ECa-directed soil sampling; electromagnetic induction; proximal sensor; response surface sampling; salt tolerance; boron tolerance; soil mapping; soil salinity; spatial variability
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Corwin, D.L.; Yemoto, K.; Clary, W.; Banuelos, G.; Skaggs, T.H.; Lesch, S.M.; Scudiero, E. Evaluating Oilseed Biofuel Production Feasibility in California’s San Joaquin Valley Using Geophysical and Remote Sensing Techniques. Sensors 2017, 17, 2343.

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