To evaluate the radiation dose–volume effects of optic nerves and chiasm by visual psychophysical, electrophysiologic tests, and optical coherence tomography in patients with locally advanced nasopharyngeal carcinoma.55), mean dose (Dmean), highest dose to 5% of the volume (D5), and maximum dose (Dmax) for optic nerves and chiasm were evaluated for each patient. Cutoff values were identified as V55: 50%, Dmean: 50 Gy, D5: 55 Gy, and Dmax: 60 Gy. The effects of radiation dose–volume on ophthalmologic tests were evaluated.A series of visual tests including visual acuity, visual field, contrast sensitivity, visual evoked potential, and optical coherence tomography were administered to 20 patients with locally advanced (T3-T4) nasopharyngeal carcinoma who were treated with definitive chemoradiotherapy. Volume that received 55 Gy (V55 ≥ 50% and Dmean ≥ 50 Gy. Visual evoked potential latency was affected significantly with Dmean ≥ 50 Gy, D5 ≥ 55 Gy, and Dmax ≥ 60 Gy. For the chiasm, significant detrimental effect of all parameters was observed on visual acuity as well. Retinal nerve fiber layer thickness and visual evoked potential amplitude were not affected by any of the dose–volume parameters neither optic nerves nor chiasm.Ophthalmological evaluation revealed optic neuropathy with simultaneous retinopathy in 6 eyes of 4 patients and radiation retinopathy alone in both eyes of 1 patient. Regarding radiation dose–volume effects of the optic nerve, significant detrimental effect of all parameters was observed on visual acuity. Visual field and contrast sensitivity were affected significantly with VThe volume receiving the threshold dose, mean dose, and 5% of the volume receiving the maximum dose are important parameters besides maximum dose to optic nerves and chiasm. A comprehensive ophthalmological evaluation including visual field, contrast sensitivity, visual evoked potential latency, and amplitude should be performed for these patients. Visual evoked potential latency is an objective predictor of vision loss before the onset of clinical signs. Although very rare, it severely affects patients’ quality of life. Vascular occlusion, demyelination, free radical injury, DNA damage, or blood–brain barrier damage are some of the proposed factors responsible for RION.3 Similarly, radiation retinopathy is defined as an occlusive vasculopathy secondary to retinal vascular endothelial cell damage that may cause ischemia. Both are reported to occur between 3 months and 8 years, with a peak at 1 to 1.5 years.5Primary treatment of nasopharyngeal carcinoma is radiotherapy for early stage disease and chemoradiotherapy (CRT) for locally advanced stage. Due to the proximity of the nasopharynx to critical structures and the need for high radiation doses for local control, the risks of radiation-induced toxicities are substantial. Anterior visual pathways—optic nerve and chiasm—are especially under risk in locally advanced disease invading the base of the skull and extending to the cavernous or sphenoidal sinus or to the paraorbital regions.69 Nevertheless, decrease in VA may be caused by many reasons such as dry eye, refractive error, cataract, and many other media opacities. Limiting the toxicity evaluation solely on VA may be insufficient and may cause difficulty in defining the relationship between VA, dose–volume threshold levels, and the severity of the complications. The vision may be affected subclinically without overt loss at or above the tolerance doses of the anterior visual pathways. Several studies investigated the subclinical damage to the anterior visual pathways and the relationship between radiation-associated factors such as total dose and fraction size in nasopharyngeal carcinoma.1014 However, with technological advances in treatment delivery systems and treatment planning software, volumetric dose data also became very important. Studies evaluating the relationship between dose–volume levels and the subclinical damage using comprehensive visual tests are lacking.Radiation Therapy Oncology Group/European Organization for Research and Treatment of Cancer late radiation morbidity criteria which defined the injury on anterior visual pathways focused only on visual acuity (VA), and the studies were mostly concentrated on loss of vision.15 Optical coherence tomography (OCT) creates high-resolution, cross-sectional microstructure images of the retina in real time.16 Retinal nerve fiber layer (RNFL) thickness surrounding the optic disc can be measured by OCT  is the physiologic response of the occipital cortex to a sensory stimulus of vision. Visual evoked potential latency is the transmission time of the visual stimulus from the optic nerve to the occipital cortex, whereas VEP amplitude is the height of this wave which defines the magnitude of the transmission. Visual evoked potentials can provide information about optic neuritis.d by OCT .The purpose of the present study is to investigate the functions of the anterior visual pathways with psychophysical , electrophysiologic (VEP latency and VEP amplitude) tests, and OCT in 20 patients with locally advanced nasopharyngeal carcinoma and to evaluate the relation between the results of the visual tests and radiation dose–volume effects of optic nerve and chiasm for each patient.17—who were treated with definitive CRT at our institution between March 2007 and August 2012 were reviewed retrospectively after institutional review board approval. Patients with recurrent disease, history of prior radiotherapy to the same region, radiotherapy portals not including the optic nerve and chiasm, tumor invasion to the optic nerve and chiasm, vision loss, advanced refractive error, glaucoma, or keratitis history prior to radiotherapy and noncompliant patients were excluded. Median age was 49 (range: 18-65) years. Sixteen (80%) patients were males. Twelve patients (60%) had T4 tumor.The medical records of 20 patients with T3 and T4 nasopharyngeal carcinoma—according to 2010 American Joint Committee on Cancer Staging70 received ≥110% of the prescribed dose. No more than %1 of any PTV70 received ≤93% of the prescribed dose. The patients received 3 cycles of induction chemotherapy with cisplatin (75 mg/m2) and docetaxel (75 mg/m2) followed by cisplatin (75 mg/m2) concomitant with RT, at 3-week intervals.Three dimensional conformal radiotherapy was planned for 18 (90%) patients and volumetric arc therapy for 2 patients using 6 MV X-rays. Total radiotherapy dose to the gross tumor volume was 70 Gy with 2 to 2.12 Gy daily fractions, 59 to 61 Gy to high-risk area, and 50 to 54 Gy to low-risk area. The prescription dose is the isodose surface that encompasses 95% of the planning target volume (PTV). No more than 20% of any PTV55), mean dose (Dmean), highest dose to 5% of the volume (D5), and maximum dose (Dmax) for right and left optic nerves and chiasm were evaluated according to the dose–volume histograms of each patient. Cutoff values were identified as V55: 50%, Dmean: 50 Gy, D5: 55 Gy, and Dmax: 60 Gy. The correlation between the results obtained from the ophthalmologic tests and the values below and above the cutoff was evaluated.Volume that received 55 Gy  months after the completion of CRT. Visual acuity, anterior and posterior segment evaluations followed by visual field, contrast sensitivity, and VEP tests were done; and RNFL thickness measurements obtained by OCT were recorded for 40 eyes of 20 patients.Visual acuity was measured with Snellen chart on the basis of the autorefractometer results after refractive correction. Visual field was analyzed via Zeiss Humphrey Field Analyzer  using a 120 full-field protocol after correction of near-vision correlated with age if needed. Visual field defects were scored as grade 1: normal; grade 2: peripheral defects outside the central 30° field; grade 3: defects located in the central 30° field; grade 4: generalized defects.Contrast sensitivity was measured by MetroVision  under photopic conditions designating contrast sensitivity between 0.5 and 15 cycle/degree spatial frequencies. The contrast sensitivity value corresponding to each spatial frequency was plotted, and the attained line was compared to the normal line. It was scored as Grade 1: normal; Grade 2: under the normal line beginning from high-spatial frequencies (5 cycle/degree); Grade 3: under the normal line beginning from low spatial frequencies (1 cycle/degree) for each eye.Visual evoked potentials were obtained via MetroVision using pattern reversal stimulus. The latency and amplitude of the P100 potentials for each eye was recorded in milliseconds and microvolts, respectively. The reference range of VEP latency and amplitude can differ from one center to another according to the equipment used and environmental factors. In the present study, the normal range of VEP latency for the P100 potential was 106 ± 3 milliseconds, and the VEP amplitude for the P100 potential was 9 ± 4 µV, according to the normal population measurements made previously using the equipment mentioned above. Amplitudes lower than normal and/or delay in latency are considered pathological.Retinal nerve fiber layer measurements were done by spectral domain OCT  following pupillary dilatation. The values between 90 and 110 µm were considered normal.2.PASW Statistics version 18 software was used for the statistical analyses. Significance level of α, for type I error, was accepted as 0.05. Relationship between upper and lower cutoff values of radiation dose–volume parameters and ophthalmologic tests were assessed using Pearson χForty eyes of 20 patients were evaluated. Ophthalmological evaluation revealed optic neuropathy with simultaneous retinopathy in 6 eyes of 4 patients and radiation retinopathy alone in both eyes of 1 patient. Involvement was bilateral in 3 patients and unilateral in 2 patients. Retinal exudates and hemorrhage related to radiation retinopathy, optic disc edema, and splinter hemorrhages related to optic neuropathy were seen on fundus examination. Visual acuity was decreased in 6 eyes of 4 patients with optic neuropathy and radiation retinopathy. Visual acuity was not affected (VA: 10/10) in the left eye of patient 2 with isolated radiation retinopathy because of the lack of macula involvement, and also VEP latency was normal in this eye because there was no optic neuropathy accompanying retinopathy. Radiation retinopathy occurred 30 months after radiotherapy in patient 10. When his previous clinical chart which was recorded 1 year ago was reevaluated, it was seen that VA was 10/10 for both eyes despite significant delay in VEP latency at that time (right eye: 143 milliseconds and left eye: 137 milliseconds). The ophthalmological evaluation results of the whole group were indicated in 55 ≥ 50%, Dmean ≥ 50 Gy, D5 ≥ 55 Gy, and Dmax ≥ 60 Gy) was observed on VA . Visual field and contrast sensitivity were affected both with V55 ≥ 50% and Dmean ≥ 50 Gy . Visual evoked potential latency prolonged significantly with Dmean ≥ 50 Gy (P < .001), D5 ≥ 55 Gy (P = .005), and Dmax ≥ 60 Gy (P = .002). mean, Dmax, and VEP latency for the optic nerve. Retinal nerve fiber layer thickness and VEP amplitude were the only ophthalmologic parameters that were not affected by any of the dose–volume parameters  was also observed on VA . Dmean ≥ 50 Gy significantly affected visual field (P = .035), contrast sensitivity (P = .007), and VEP latency (P = .005). Dmax ≥ 60 Gy had a detrimental effect on contrast sensitivity and VEP latency as well . mean, Dmax, and VEP latency for the chiasm. Once more, RNFL thickness and VEP amplitude were the only ophthalmologic parameters that were not affected by any of the dose–volume parameters for the chiasm . Radiation-induced optic neuropathy accompanying radiation retinopathy was detected in 4 patients and radiation retinopathy without RION occurred in 1 patient who received higher doses. It was observed that a decrease in VA was accompanied by a deterioration in visual field and contrast sensitivity in our patients if V55 ≥50% , and Dmean ≥50 Gy (P = .050 and P = .003) for the optic nerves.In general, studies evaluating the radiation damage by VA tried to determine a maximum threshold dose for optic nerves and chiasm. Parsons 25 Esassolak et al investigated the functions of the anterior visual pathways in locally advanced nasopharyngeal carcinoma patients both by electrophysiologic and psychophysical (VA and contrast sensitivity) tests and found that the radiation damage to the optic nerves and chiasm did not show statistically significant differences for doses over 50 Gy, but VEP latency, VEP amplitude, contrast sensitivity, and visual field were affected negatively in the radiotherapy group when compared with the control group.10 Pan et al investigated optic neuropathy in 28 patients with nasopharyngeal carcinoma by visual field and VEP tests before irradiation, at the end, and 5 years after irradiation.11 They found significant delay in VEP latencies and nonsignificant decrease in VEP amplitudes within 1 year after irradiation than preirradiation in 78.6% of the investigated eyes. After 1 year, decrease in VEP amplitudes also became significant.It is obvious that subjective tests evaluating the clinical evidence of damage on anterior visual pathways can be highly affected by the intellectual capacity and the compliance of the patient, so objective tests are crucial. Electrophysiologic tests (VEP latency and VEP amplitude) have been found to be more objective and sensitive in detecting occult disorders. These tests may be abnormal months before the loss of vision in patients with anterior visual pathway radionecrosis.26 They performed VA tests, fundoscopy, visual field tests, and VEP tests, and found a correlation between the occurrence of visual loss and a delivery of >60 GyE to 20% of the volume of the optic nerve. In the present study, Dmean ≥50 Gy, D5 ≥55 Gy, and Dmax ≥60 Gy caused delay in VEP latency for the optic nerves . The prolongation of VEP latency even in case of a maximum point dose above the threshold value (Dmax ≥60 Gy) gave an impression that VEP latency may be affected without any clinical sign. In their study which assessed the dose–response tolerance of the visual pathways and cranial nerves of the cavernous sinus to stereotactic radiosurgery, Leber et al stated that VEP may be abnormal months before the loss of vision in patients with anterior visual pathway radionecrosis.25 In our study, one of the patients who developed RION 30 months after radiotherapy had previous records of ophthalmologic tests which were done 1 year ago, and in his previous clinical chart VA was 10/10 for both eyes despite significant delay in VEP latency (right eye: 143 milliseconds and left eye: 137 milliseconds). Visual evoked potential latency might provide early evidence of optic neuropathy before the onset of visual symptoms. It is obvious that VA alone is insufficient when evaluating the effects of radiotherapy on anterior visual pathways.Hasegawa and associates analyzed the tolerance dose for retention of VA in 30 patients with head-and-neck tumors treated with carbon ion radiotherapy.5 Microangiopathic changes similar to those in diabetes, venous occlusive diseases, and telangiectatic disorders may occur in retina after radiotherapy.27 Even though radiotherapy does not affect retina directly, it may cause retrograde degeneration in retinal ganglion cells via the optic nerve damage or microvascular damage which predisposes to retinopathy.29Ophthalmological evaluation revealed radiation retinopathy either alone or accompanying RION in 5 patients although retina was not included in the radiotherapy field. As mentioned before, radiation retinopathy is defined as an occlusive vasculopathy secondary to retinal vascular endothelial cell damage after radiotherapy that may cause ischemia.21Previous studies mostly used VA, visual field, contrast sensitivity, and VEP tests. Use of OCT is rare. In the present study, RNFL thickness which was determined by OCT is the sole parameter which was not affected by radiation. It may be based on the fact that the impact of radiation occurs on the optic nerve pathway in the radiotherapy field, not primarily on retinal ganglion cell axons and neurons as it is in inflammatory diseases such as multiple sclerosis.The limitations of the present study are the relatively small number of patients evaluated, lack of preradiotherapy, and regular ophthalmological evaluations after radiotherapy as well as its retrospective nature. However, to the best of our knowledge, this is the first study evaluating the relationship between dose–volume parameters of the anterior visual pathway and visual function by more comprehensive ophthalmologic tests. A larger prospective study is required to achieve more reliable results.Despite their rarity, RION and radiation retinopathy are the important complications that may occur in patients treated with radiotherapy for nasopharyngeal carcinoma. The disabling damage to vision severely affects the patient’s quality of life, so a careful follow-up is crucial besides a careful radiotherapy planning. Previous studies have evaluated the maximum dose received by the optic nerve and chiasm. Volume dependence is not well understood. Nevertheless, in the modern radiotherapy era, the volume of the critical organ receiving a particular dose is very important. Besides, during daily treatment process, it is quite difficult to obtain the maximum point dose at the same point each and every day. For this reason, the volume receiving the threshold dose, mean dose received by the critical structure, and 5% of the volume receiving the maximum dose are important parameters to be evaluated.Regarding the visual assessment in these patients, VA test alone is inadequate due to its subjective nature and lack of reflecting the severity of subclinical damage. A more comprehensive ophthalmological evaluation including visual field, contrast sensitivity, VEP latency, and amplitude should be performed. Especially electrophysiological tests help in the early detection of radiation damage to the visual pathway. Visual evoked potential latency is quite important as an objective predictor of vision loss before the onset of clinical signs. Early recognition of RION and treatment in incipient phases is very important. No treatment has been proven effective, visual prognosis is poor, so one must be aware of this side effect. The damage caused by the dose–volume effects of irradiation on anterior visual pathways at clinical and subclinical levels will gain importance with the radiobiological results of new radiotherapy techniques, different fractionation schemes, and fraction doses. Studies regarding the potential medical or interventional treatment of radiation-induced damage to the visual pathway are needed.


Beyond the main bulk components of cereals such as the polysaccharides and proteins, lower concentration secondary metabolites largely contribute to the nutritional value. This paper outlines a comprehensive protocol for GC-MS metabolomic profiling of phenolics and organic acids in grains, the performance of which is demonstrated through a comparison of the metabolite profiles of the main northern European cereal crops: wheat, barley, oat and rye. Phenolics and organic acids were extracted using acidic hydrolysis, trimethylsilylated using a new method based on trimethylsilyl cyanide and analyzed by GC-MS. In order to extract pure metabolite peaks, the raw chromatographic data were processed by a multi-way decomposition method, Parallel Factor Analysis 2. This approach lead to the semi-quantitative detection of a total of 247 analytes, out of which 89 were identified based on RI and EI-MS library match. The cereal metabolome included 32 phenolics, 30 organic acids, 10 fatty acids, 11 carbohydrates and 6 sterols. The metabolome of the four cereals were compared in detail, including low concentration phenolics and organic acids. Rye and oat displayed higher total concentration of phenolic acids, but ferulic, caffeic and sinapinic acids and their esters were found to be the main phenolics in all four cereals. Compared to the previously reported methods, the outlined protocol provided an efficient and high throughput analysis of the cereal metabolome and the acidic hydrolysis improved the detection of conjugated phenolics. Cereals such as wheat, barley, rye and oat are amongst the mostly grown agricultural food products worldwide and the most important cereal crops for human consumption in northern Europe. The detailed chemical and functional composition of these crops is defining their use for food and feed as well as their prices. Cereals are the most important study objects in foodomics studies seeking to optimize their health beneficial factors and/or reducing deleterious metabolites. While the gross chemical composition, such as carbohydrates, proteins, dietary fibers and micronutrient contents, are important characteristics of cereal products, recent studies showed that relatively low concentration secondary metabolites such as antioxidant phenolics, organic acids and phytosterols have a significant influence on the health and nutritional values of cereals ,2. The bQuantitative and qualitative analysis of both, secondary and primary metabolites (with molecular weight of up to 1500 Da) of grains are studied within cereal metabolomics. Cereal metabolomics offers an insight into the metabolic fluctuations of cereal cultivars that may reveal effects of genetic modifications as well as of biotic and abiotic stresses . Recent Comprehensive metabolomic profiling of cereals requires a reliable protocol that enables extraction of maximum metabolic information in a high-throughput and reproducible manner. Metabolomics studies performed for uncovering single and/or multiple internal and/or external effects on cereals aim to cover as broad range of metabolites as possible. However, due to the great physico-chemical diversity of cereal metabolites, it is in practice impossible to cover the whole cereal metabolome using a single protocol. The phytochemical composition, including phenolics of wheat ,19,20, bThis study demonstrates the development of comprehensive GC-MS metabolomics protocol for profiling a broad range of phenolics and organic acids from whole grain flour samples, and applied on wheat, barley, rye and oat. Phenolics of cereals are primarily present in conjugated and bonded forms with carbohydrates, lipids and other cell membrane components that alter their solubility and thus bioavailability . AnalysiIn this study, a standardized, high-throughput and unbiased protocol was developed for GC-MS metabolomic profiling of free and conjugated phenolics and organic acids of whole-grain cereals using hydrochloric acid based hydrolysis followed by trimethylsilyl derivatization. The study demonstrates the first application of a novel trimethylsilylation method based on trimethylsilyl cyanide (TMSCN) for derivatization of cereal metabolites. When compared to other frequently used derivatization methods, the new protocol provides a more unbiased and broad-spectrum derivatization of metabolites and is able to provide reproducible metabolomics profiles of complex biological samples . The obtTr. aestivum, variety Bussard), barley , rye  and oat  were purchased in Sepetember 2012 from the Danish bread cereal producing company Aurion . All four cereals were grown under biodynamical conditions in Jutland during the season 2011/12.Whole grain samples of wheat  and the remaining flour sample was extracted a second time using the same extraction procedure. Then, the combined extracts were completely dried under nitrogen gas flow at 40 °C and hydrolyzed by using 240 µL of 6 M hydrochloric acid at 96 °C for 1 h by stirring at 1400 rpm. The hydrolyzed extracts were transferred into a fresh 2 mL glass vials and phenolics and organic acids were extracted into diethyl ether. Ether-based extraction of phenolics and organic acids was performed twice, by addition of 800 μL diethyl ether and vortexing for 25 s. The obtained ether fractions were completely dried using nitrogen gas flow and re-solubilized in 200 μL 100% methanol. Aliquots, 90 microliter, of the final extracts were transferred into 200 µL glass inserts and completely dried under nitrogen gas flow, sealed and stored at −20 °C until GC-MS analysis. Each sample was spiked with an internal standard (IS) (5 μL of 0.2 mg mL−1 solution of ribitol). In order to avoid any moisture, the samples stored in the freezer were dried under reduced pressure before derivatization. Sample derivatization and injection were fully automated by using a Multi-Purpose Sampler  with DualRait WorkStation integrated to a GC-MS system from Agilent . Each sample was individually derivatized by addition of 40 µL trimethylsilyl cyanide (TMSCN) and incubated for 40 min at 40 °C. Two replicate samples per cereal were analyzed in randomized order and the MPS autosampler allowed a sequential derivatization of all samples in the same manner by keeping the derivatization time constant, throughout the analysis.Cereal metabolites were extracted from 50 mg of milled grains that were soaked into 600 μL 85% methanol and vortexed for 20 s at 3000 rpm followed by 20 min incubation at 30 °C using a Thermomixer  at 1400 rpm. After 3 min of centrifugation at 16,000× −1. Detailed information on CIS and MPS parameters are described in Khakimov et al. 2013 [−1, and the initial temperature of CIS was set to 120 °C for 0.3 min followed by heating at 5 °C s−1 until reaching 320 °C and then held for 10 min. The GC oven program was as follows: initial temperature 40 °C, equilibration time 3.0 min, heating rate 12.0 °C min−1, end temperature 300 °C, hold time 8.0 min and post run time 5 min at 40 °C. Mass spectra were recorded in the range of 50–500 m/z with a scanning frequency of 3.2 scans s−1, and the MS detector was switched off during the 8.5 min of solvent delay time and after 25.5 min of the run time. The transfer line, ion source and quadrupole temperatures were set to 290, 230 and 150 °C, respectively. The mass spectrometer was tuned according to manufacturer’s recommendation by using perfluorotributylamine (PFTBA).The GC-MS consisted of an Agilent 7890A GC and an Agilent 5975C series MSD. GC separation was performed on a Phenomenex ZB 5MSi column (30 m × 250 μm × 0.25 μm). A derivatized sample volume of 1 µL was injected into a cooled injection system (CIS port) using Solvent Vent mode at the vent pressure of 7 kPa until 0.3 min after injection at the vent flow of 100 mL minal. 2013 . HydrogeInitial analysis and visualization of the GC-MS data was performed using ChemStation software . Retention indices of detected metabolites were calculated using the Van den Dool and Kratz equation and retention times of C10-C40 alkanes that were analyzed using the same GC-MS protocol . The rawThe total ion current (TIC) chromatograms of the GC-MS data obtained from hydrolyzed extracts of the four cereals are illustrated in p-salicylic, gallic, gentisic, homovanillic and α-resorcylic acids and methyl esters of ferulic, caffeic, protocatechuic and sinapinic acids were identified. Small molecular organic acids, alcohols and their esters constituted 30 out of 89 identified metabolites. These included succinic, glyceric, maleic, fumaric, malic, pyroglutamic, azelaic acids and methyl esters of aconitic and citric acids that are part of the same or different metabolic pathways, and in addition, TMS-derivatives of 10 fatty acids and their esters, 6 sterols and a flavonoid, catechin-nTMS.Comparison of RIs and PARAFAC2 resolved mass spectra of 247 resolved peaks against the NIST05 and Golm Metabolite Database resulted in the identification of 89 metabolites  at levelversus PC2 scores plot of the PCA model  [Nine major phenolics of the cereals investigated in this study were compared with winter wheat (estivum) , Dicktooestivum) , Grandriestivum)  and Bajkestivum)  varietieThis paper outlines and demonstrates an optimized, relatively unbiased, comprehensive and high-throughput metabolomic profiling of whole-grain cereals based on new technologies developed within GC-MS metabolomics and chemometrics. A metabolite extraction protocol optimized towards phenolics and organic acids of whole-grains, and an unbiased and high-throughput protocol, was developed that allow processing of up to 60 samples per day. The hydrochloric acid based hydrolysis allowed extraction of all major cereal phenolics, free and conjugated, and enabled the detection of 32 phenolic and 30 organic acids from 50 mg of flour. A novel trimethylsilylation method based on TMSCN allowed the detection of up to 300 metabolites from the GC-MS profiles. The multi-way decomposition method PARAFAC2 facilitated deconvolution of overlapping, retention time shifted and low s/n ratio peaks with high precision and in a semi-automated manner. The resolved mass spectra of deconvoluted peaks allowed the identification of 89 metabolites using NIST and Golm metabolite databases. Multivariate and univariate analysis of phenolic profiles of cereals revealed that ferulic, caffeic and sinapinic acids and their esters were the main phenolics of whole-grain samples across the four cereals studied. Rye and oat showed higher concentrations of the most abundant phenolics acids compared to wheat and barley. Comparison of the relative concentrations of the nine most abundant phenolics of cereals with previously reported data showed that the acidic hydrolysis significantly improved detection of caffeic acid. However, metabolite profiles of cereals highly depend on several factors such as genotype, growth conditions, harvest time and storage. Thus, essential secondary metabolite profile comparisons of different cereals as well as different varieties require a strictly controlled experimental design. This paper has demonstrated a new methodology that is ready to be applied in a larger metabolomic profiling studies that may reveal biological information related to phenolic and organic acids of whole-grain cereals. Moreover, the protocol developed can easily be modified for polar metabolite fractions, including mono- and di-saccharides and amino acids, of cereals by altering metabolite extraction method and the additional of a methoximation step in GC-MS derivatization.


The last time the phylogenetic relationships among members of the family Hemerobiidae were studied quantitatively was over 12 years ago and based exclusively on morphology. Our study builds upon this morphological evidence by adding sequence data from three gene loci to provide a total evidence phylogeny of brown lacewings (Neuroptera: Hemerobiidae). Thirty-seven species representing nineteen Hemerobiidae genera were compared with outgroups from the families Ithonidae, Psychopsidae and Chrysopidae in Bayesian and parsimony analyses using a single nuclear gene (CAD) and two mitochondrial (16S rDNA and Cytochrome Oxidase I) genes. We compare divergence time estimates of Hemerobiidae cladogenesis under the two most commonly used relaxed clock models and discuss the evolution of wing venation in the family.Notiobiella Banks, and erect a new subfamily Zachobiellinae including the remaining genera previously placed in Notiobiellinae. Psychobiellinae is synonymized with Drepanacrinae. Unlike the previous hypothesis that proposed a remarkably laddered topology, our tree suggests that hemerobiids diverged as three main clades. Moreover, in contrast to the vein proliferation hypothesis, we found that hemerobiids have instead undergone multiple reductions in the number of radial veins, this scenario questions the relevance of this character as diagnostic of various subfamiliesWe recovered a phylogeny largely incongruent with previously published morphological studies, although all but two subfamilies  were recovered as monophyletic. We found the subfamily Drepanacrinae paraphyletic with respect to Psychobiellinae, and Notiobiellinae to be polyphyletic. We thus offer a revised concept of Notiobiellinae, comprising only Carobius Banks (Carobiinae) is sister to the previously unplaced genus Notherobius New in a clade more closely related to Sympherobiinae, Megalominae and Zachobiellinae subfam. nov. The addition of taxa which are not available for DNA sequencing should be the focus of future studies, especially Adelphohemerobius Oswald, which is particularly important to test our inferences regarding the evolution of wing venation in Hemerobiidae.Our phylogenetic hypothesis and divergence times analysis suggest that extant hemerobiids originated around the end of the Triassic and evolved as three distinct clades that diverged from one another during the Late Jurassic to Early Cretaceous. Contrary to earlier phylogenetic hypotheses, The online version of this article (doi:10.1186/s12862-016-0746-5) contains supplementary material, which is available to authorized users. Notiobiella Banks are green , as their common name suggests, are relatively small lacewings with brown wings and body Fig. . Most speen Fig. . Represer et al.  offer a Recent quantitative phylogenetic analyses of family level relationships among lacewings have confirmed that Hemerobiidae and Chrysopidae (green lacewings) are sister groups . O). O6]). Oswald  presenteCarobius Banks) to 13 veins in the genus Drepanepteryx Leach [Currently, Hemerobiidae includes approximately 560 species [12, 13) divided among 10 subfamilies. The subfamilies are diagnosed mainly by features of the wing venation and genitalic morphology . The mulThe importance of Oswald’s , 10, 11 Polystoechotes Burmeister), Psychopsidae (Psychopsis Newman) and Chrysopidae  and Pimachrysa Adams), the latter being the sister family of Hemerobiidae [Berothimerobius Monserrat & Deretsky was not included in the analysis as this genus, although originally described in Hemerobiidae, was subsequently synonymized with Ormiscocerus Blanchard in Gay and placed in Berothidae [Carobius, Conchopterella, Drepanacra Tillyard, Drepanepteryx, Gayomyia Banks, Hemerobius, Megalomus Rambur, Micromus, Megalomina Banks, Neuronema McLachlan, Nomerobius, Notherobius, Notiobiella, Nusalala Navás, Psectra Hagen, Psychobiella, Sympherobius Banks, Wesmaelius Krüger and Zachobiella Banks. Some genera were not available for DNA sequencing; genera absent from this analysis are Adelphohemerobius Oswald, Anapsectra Tjeder, Austromegalomus Esben-Petersen, Biramus Oswald, Hemerobiella Kimmins, Nesobiella Kimmins, Neosympherobius Kimmins, and Noius Navás. In many cases, a closely related sister genus was available as a surrogate for phylogenetic placement, for example, Hemerobius for Hemerobiella, Sympherobius for Neosympherobius, and Micromus for Noius. Unfortunately, important putative adelphotaxa either for the family  sampled here included more than one species. We used as out-groups species in the closely related families Ithonidae  or partu Oswald ) were miNotherobius was not originally included in the analysis by Oswald [The morphological matrix corresponds (with modifications) to that used by Oswald . We reviy Oswald  as mater16S rDNA and cytochrome oxidase I (COI)) along with a single nuclear gene, CPSase region of carbamoyl-phosphate synthetase-aspartate transcarbamoylase-dihydroorotase (CAD)). Primer sequences used to amplify and sequence the three gene regions are presented in Table ca. 550 bp fragment of 16S rDNA (3′-end) was generated using a single primer pair originally from Simon et al. [Genbank accession numbers, specimen voucher numbers and collection data are presented in Table n et al.  with then et al. : initialSequences were obtained using Applied Biosystems Big Dye Terminator V3.0 . Sequences were gel fractionated and bases called on an ABI 3730TM DNA sequencer (PE Applied Biosystems). Sequencing electropherograms were edited and contigs assembled and proofed using SequencherTM 5.3  and Geneious 7.1.7 .st, 2nd positions, CAD 1st, 2nd positions; 2: CAD 3rd positions, 3: COI 3rd positions). In MrBayes the nst command was set to mixed and rate to gamma which specify model averaging over the family of GTR models. For the morphological partition a gamma distribution was used with coding and ratepr commands set to variable. In all cases all the parameters in the model were unlinked. Each analysis consisted of four MCMC chains run simultaneously for 55 million generations. Trees were sampled every 500th generation and the burn-in fraction was set to 0.25 (25 %). Convergence was assessed using the standard deviation of split frequencies diagnostic given by MrBayes, set to stop the chain once a value of 0.01 was reached. A majority rule consensus tree was calculated with posterior probabilities (PP) for each node. Finally, unambiguous morphological changes were plotted over this topology using MacClade 4.06 [Alignment of all sequences was done manually, although CAD and COI were aligned with reference to translated amino acid sequences using Mesquite 3.02 . All aliade 4.06 .Megalomus caucasicus Makarkin [Notiobiella thaumasta Oswald [Sympherobius siriae Jepson et al. [Wesmaelius mathewesi Makarkin et al. [Psychopsis from the remaining taxa) was constrained with a uniform prior of 200–230 Myr following Winterton et al. [We conducted a divergence time analysis in PhyloBayes 3.3  using thMakarkin , and Nota Oswald  were setn et al. , and Wesn et al.  were setn et al. . A birthThe total sequence length after alignment was 2760 base pairs (bp), comprising 542 bp of 16S (229 variable sites), 876 bp of CAD (503 variable) and 1478 bp of COI (568 variable). A 49 bp long A-T rich fragment of 16S and an indel rich 88 bp length of CAD fragment were both unalignable with any confidence in homology and were excluded from the phylogenetic analysis. Exploratory phylogenetic analyses were carried out excluding third codon positions, but were discarded since in each instance it reduced tree resolution noticeably and violated monophyly of well-supported clades based on extraneous evidence  .Notiobiella as sister to Drepanacrinae, while the remaining Notiobiellinae genera (Zachobiella and Psectra) were recovered sister to Megalominae and Carobiinae. Drepanacrinae was rendered paraphyletic by the inclusion of Psychobiellinae  with a high level of statistical support in all analyses. Figure Separate analyses of either the morphological partition or each molecular marker (with parsimony or Bayesian inference) produced topologies with little resolution, suggesting that neither character system is capable of producing a robust hypothesis in isolation .The combined tree Fig.  supportsFigure During the last 100 years Hemerobiidae has undergone a variety of taxonomic arrangements that divided its current members at first in two separate families , subsequMicromus), Hemerobiinae  and Drepanacrinae  to more than four radial veins in some members of Microminae , Hemerobiinae , Depranacrinae  and in all members of Drepanepteryginae.Hemerobiidae are a well-supported monophyletic family of lacewings based on a series of morphological and molecular characters Fig. . ApomorpPromegalomus anomalus Panvolov in Dolin (Late Jurassic) and Cretohemerobius disjunctus Ponomarenko (Early Cretaceous), but both are stem fossils and not assignable to any crown lineage. Consequently, while they do provide a minimum age for the family Hemerobiidae, they do not provide enough information to place a minimum age on any particular subfamilial lineage. Regardless of the clock model used, both analyses resulted in wide confidence intervals around the mean. Estimates of divergence times are improved by having as many calibrations as possible around nodes of interest; when trying to elucidate the evolutionary time table for a family such Hemerobiidae, this means having  calibrations spread across the tree. Therefore the wide confidence interval are most likely a reflection of our shallow calibrations being unsuitable to accommodate the variation found at deeper parts of the tree.Divergence time estimates for Hemerobiidae based on the total evidence tree obtained here suggest that the family diverged from Chrysopidae during the Late Triassic or Early Jurassic, depending on the clock model used  and Conchopterella (Drepanacrinae), were estimated to have diverged largely contemporaneously under the UGAM model . This suggests that the initial separation between southern South America + Antarctica and Australia during 90 myr [Based on the age of the oldest hemerobiid fossil and the presence of genera with species in both Old World and New World, Oswald  suggesteg 90 myr  might acDrepanepteryx , Neuronema  and Gayomyia . Relationships among these genera are equivocal in this analysis, but Oswald [Drepanepteryx as sister to Gayomyia based on three homoplasious transformations. All members in this subfamily are large, distinctive hemerobiids characterized by their broad, often falcate wings usually with numerous radial veins and broad humeral costal area. Microminae is represented by Micromus (cosmopolitan), Megalomina , Nusalala  and Nois , with all but Nois sampled here. The subfamily is well supported here as defined by Oswald [Nusalala as sister to Micromus rather than Megalomina, all with high levels of support.Drepanepteryginae and Microminae (Clade A) are strongly supported here as sister groups (PP: 0.99). This was also found by Oswald  althought Oswald  recoverey Oswald  and is cy Oswald , we recoNotherobius New to be sister to Carobius Banks and thus part of Carobiinae. This relationship was obtained in the DNA-based tree and in the tree with combined evidence in both cases with high PP values. Oswald [Notherobius to Sympherobiinae  although he rejected this hypothesis arguing the lack of synapomorphic features and consequently did not provide a definitive position for the genus. The presence of a mediocubital crossvein 3 m-cu also supports (char. 38:1) this relationship . This represents a novel finding for a genus whose phylogenetic origins had remained obscure for almost 30 years [Notherobius and Carobius and the sister group relationship between the two genera is now unambiguous.Clade B diverged from Clade C during the Late Jurassic to Early Cretaceous and comprises four subfamilies that are united by a single homoplasious character transformation, the reduction of forewing radial veins to two (28:1). This is a not a homogenous feature in the clade with multiplication of FW radial veins occurring once again in Megalominae 28:3). We found the previously enigmatic genus . We foun30 years . Indeed,30 years  remarkedMegalomus) and Carobinae is relatively weakly supported, with no synapomorphies for the clade identified. Oswald [The sister-group relationship between Megalominae . The sister relationship between the genera included here (Nomerobius and Sympherobius) is supported by three unique morphological transformations: a well developed and rounded distal convexity of the orad margin of the right mandible (char. 12:1), the posteroventral angle of the 9th tergite as a narrow membrane-margined lobe (char. 47:0)  Fig. , and the:0) Fig. ., Notiobiellinae (sensu stricto) and Drepanacrinae (inclusive of Psychobiellinae)) are well supported based on molecular data, but lack any morphological synapomorphies to support the clade. Oswald [Notiobiella (Notiobiellinae s.s.). Drepanacrinae (represented here by Drepanacra and Conchopterella) was rendered paraphyletic by the inclusion of the Australian genus Psychobiella (Psychobiellinae). This result was obtained with the molecular and combined evidence with 100 % posterior probability values in both cases. Consequently, we consider Psychobiellinae syn. nov. (containing only Psychobiella) as a synonym of Drepanacrinae. Drepanacrinae can be defined by the presence of a scabriculous region in the gonosaccal membrane (below mediuncus) (char. 76:1) . The sister relationship between Psychobiella and Drepanacra was found by the DNA evidence alone and by the combined evidence, in both cases with high statistical support values. Figure Conchopterella is supported by multiple character transformations  Fig. , which ions Fig. ; one of ons Fig.   as defined by Oswald [Notiobiella with the clade formed by Psectra, Anapsectra and Zachobiella. Likewise, Nakahara [Notiobiella from all others hemerobiids into its own subfamily, mainly based on the presence of the phallolingua, which remains an autopomorphy of Notiobiella (char. 77:1).y Oswald , was recNakahara  suggesteNesobiella, Biramus and Hemerobiella, our study corroborates the monophyly of Hemerobiinae based on multiple representatives of Hemerobius and Wesmaelius. This clade was supported with 100 % posterior probability values in both the molecular and the combined evidence trees. Figure th gonocoxite  and proximal convexity of orad margin of right mandible, prominently convex and strongly angulate (char.11:1). The monophyly of Wesmaelius and Hemerobius is supported by multiple apomorphies  divided, composed of a pair of adjacent, narrow sclerotized straps . The posterior probability value for this node varied from 0.5 to 0.80 under different analytical settings.The sister relationship between clade B and C was only recovered with the combined evidence analysis and was not recovered in trees sourced solely from either molecular data or the morphology data , and that alternative topologies could produce a different character optimization regarding the transformation in the number of radial veins. We conducted a character optimization under the two alternative scenarios: in the first one Clade C is sister to the rest of Hemerobiids (thus clades A and B are sister clades), in the second one, Clade B is sister to the rest of the ingroup (thus clades A and C as sister clades). Our hypothesis of multiple reductions of radial veins is supported also under the first scenario, and under the second scenario the ancestral character reconstruction would be equivocal. Thus, although the sister relationship of clades B and C was not decisively supported, a scenario where multiple reductions in the number of radial veins has occurred is not contingent upon this relationship, since having 4 or more radial veins is the ancestral character reconstruction at the root of Hemerobiidae in 2 of the 3 possible alternative topologies. In other words, under any reconstruction of relationship of clades A, B and C, no scenario supports the hypothesis of progressive increase in radial veins throughout the family.Adelphohemerobius enigmaramus Oswald [A. enigmaramus is a female, this was done in the absence of male genitalic features that could have potentially confirmed or contradicted his hypothesis; the female genitalia, while unusual, are not necessarily considered to exhibit typical plesiomorphic characteristics. Oswald [A. enigmaramus in support of this ‘adelphotaxon’ hypothesis, but most represent plesiomorphies , autapomorphies , or their homology is subject to interpretation . Moreover, as Oswald [A. enigmaramus is a very typical hemerobiid. In contrast to Oswald, we interpret the crossvein 2ir1 of Oswald [Neosympherobius. Further evidence is required of this enigmatic taxon to place it with more certainty within Hemerobiidae, including addition of DNA sequence data and discovery of the male.A tenth subfamily, Adelphohemerobiinae, was not included in this analysis as it is known only from a single specimen of s Oswald . Accordis Oswald  this taxs Oswald  preferen. Oswald  lists vas Oswald  states, f Oswald  as a secZachobiellinae subfam. nov.Zachobiella Banks, 1920: 335.Type genus. th tergite from a broad membrane-marginated lobe to an elongated subectoproctal lobe  with a free distal process , Psectra Hagen  instead as sister to Drepanacrinae (inclusive of Psychobiellinae).Comments. The absence of the intraradial crossvein 4ir1 is an apomorphy for this clade but, according to Oswald  this cro. Oswald  noted whCarobius. Rather than a laddered set of relationships, we found the family is composed of three main lineages. Certain taxa not available for DNA sequencing should be the focus of future studies, especially Adelphohemerobius, which is particularly important to test our inferences regarding the evolution of wing venation in Hemerobiidae.The phylogenetic systematics of Neuroptera is steadily catching up to other holometabolous orders to produce family level phylogenetic hypotheses based on combined evidence , 47–49.


Increased transpiration efficiency  could lead to increased drought tolerance under some water deficit scenarios. Intrinsic  TE is usually considered as the primary source of variation in whole-plant TE, but empirical data usually contradict this assumption. Sunflower has a significant variability in TE, but a better knowledge of the effect of leaf and plant-level traits could be helpful to obtain more efficient genotypes for water use. The objective of this study was, therefore, to assess if genotypic variation in whole-plant TE is better related to leaf- or plant-level traits. Three experiments were conducted, aimed at verifying the existence of variability in whole-plant TE and whole-plant and leaf-level traits, and to assess their correlation. Sunflower public inbred lines and a segregating population of recombinant inbred lines were grown under controlled conditions and subjected to well-watered and water-deficit treatments. Significant genotypic variation was found for TE and related traits. These differences in whole-plant transpiration efficiency, both between genotypes and between plants within each genotype, showed no association to leaf-level traits, but were significantly and negatively correlated to biomass allocation to leaves and to the ratio of leaf area to total biomass. These associations are likely of a physiological origin, and not only a consequence of genetic linkage in the studied population. These results suggest that genotypic variation for biomass allocation could be potentially exploited as a source for increased transpiration efficiency in sunflower breeding programmes. It is also suggested that phenotyping for TE in this species should not be restricted to leaf-level measurements, but also include measurements of plant-level traits, especially those related to biomass allocation between photosynthetic and non-photosynthetic organs. Drought is one of the major limitations to crop yields worldwide, and global climate change scenarios predict a future increase in the risk of drought Dai, . MoreoveIncreased transpiration efficiency (the ratio of biomass to water transpired) could then represent an interesting trait to achieve a more conservative use of water  is used to define different expressions such as the ratio of photosynthesis to stomatal conductance (intrinsic TE) and the ratio of biomass to transpiration in a plant (whole-plant TE). Intrinsic TE is usually considered as the primary source of variation in whole-plant TE. However, intrinsic TE is not always a reliable estimator of whole-plant TE , and physiological traits such as leaf photosynthetic capacity, biochemical composition of the plant biomass, and hydraulic limitations to water flow in the plant. The physiological traits considered by Sinclair (Transpiration efficiency (either intrinsic or whole-plant) depends on physiological traits of the plant and on the environment. Sinclair  mentionsSinclair  are consSinclair , has freSinclair . If the Sinclair . MoreoveSinclair .The integration of organ-level traits into whole-plant phenotypes is key to plant adaptation  or plant-level traits .8 seeds resulting from six cycles of selfing by single-seed descent from a segregating population of F2 plants, derived from the cross of HAR2 and HA64.Three experiments were conducted using sunflower public inbred lines and a population of recombinant inbred lines (RIL). The inbred lines used included HAR2 and HA64, which had shown a contrasting response of leaf growth to water deficit  and water deficit (WD) conditions. Experiment 2 included 71 RILs from the HAR2 × HA64 population, and was aimed mainly at assessing the genetic variability in TE under WW and WD conditions. In experiment 3, 23 randomly selected RILs from the same population were grown under WD conditions, and traits underlying whole-plant TE were analyzed.In all experiments, the intensity of the soil water deficit reached was mild (−0.65 MPa), and daily adjustment of soil water content ensured that all genotypes were subjected to a similar water deficit treatment irrespective of their leaf area or water use rate, which has been shown to increase the repeatability of rankings of genotype performance under drought . On day 10 after sowing, plants were transferred to an automatic phenotyping platform . Pots were weighed and watered automatically to a soil water content of 0.28 g water·g soil−1 (−0.05 MPa) daily until plants had 8–10 fully expanded leaves in all genotypes. At this moment (day 25 after sowing), two treatments were imposed: soil water content of half of the plants of each genotype was gradually decreased from 0.29 to 0.19 g water·g soil−1 (−0.65 MPa) during a period of 12 days , while the rest of the plants were watered to a constant soil water content of 0.29 g water·g soil−1 .In experiment 1, 16–20 plants of each of the inbred lines HA64, HAR2, HA89, and RHA801 were sown in 2.75 L pots filled with 2,400 g of soil , and arranged following a completely randomized design. Soil water content was measured initially by oven-drying soil samples at 105°C for 48 h, and then monitored daily by weighing and watering of the pots. Plants were grown without water limitations (−0.05 MPa) for 9 days in a growth chamber , arranged following a complete block design. Pots were weighed and watered manually to a soil water content of 0.25 g water·g soil−1 (−0.05 MPa) daily until plants had 10 fully expanded leaves on average. At this moment (day 25 after sowing) two treatments were imposed: the soil water content of half of the plants of each genotype was decreased to 0.17 g water·g soil−1 (−0.65 MPa), maintained at that value during a period of 8 days, and finally increased to 0.25 g water·g soil−1  during 3 days; the rest of the plants were watered to a constant soil water content of 0.25 g water·g soil−1 .In experiment 2, 71 RILs and the parental lines HA64 and HAR2 were sown as in Experiment 1. Four plants of each RIL and 8 plants of the parental genotypes were grown in a greenhouse , arranged following a complete block design. Pots were weighed and watered automatically to a soil water content of 0.29 g water·g soil−1 (−0.05 MPa) daily until plants had 10 fully expanded leaves on average. At this moment (day 19 after emergence), the soil water content of all the plants was gradually decreased from 0.29 to 0.19 g water·g soil−1 (−0.65 MPa) during a period of 14 days .In experiment 3, 23 randomly-selected RILs from the same population were sown as in Experiment 1. As in Experiment 1, four plants of each RIL were initially grown in a growth chamber (same conditions) and then transferred to the automatic phenotyping platform GlyPh in greenhouse .Total water use was measured daily for each pot, and plant water use was calculated by subtracting direct soil evaporation . At the end of each experiment, shoots  and roots (experiments 1 and 3) were harvested. Shoot were further separated into stems (plus petioles) and leaf blades. Dry mass of the samples was determined after oven-drying the samples at 60°C for 48 h. In experiments 1 and 3, final leaf area was measured by image analysis of photographs of sampled leaves . In experiment 2, leaf area was estimated through non-destructive leaf length and width measurements prior to harvest, as in Pereyra-Irujo et al. .s) was calculated for all three experiments using shoot dry mass; whole-plant transpiration efficiency (TEwp) was calculated using total plant dry mass (shoot + root) for experiments 1 and 3.Transpiration efficiency was calculated for each plant as the ratio between final dry mass and plant water use . Shoot transpiration efficiency  or using whole-plant dry mass  in experiments 1 and 3.Carbon allocation between different organs was analyzed according to the framework used by , by dividing the daily plant water used by the plant leaf area. At the end of the experiment, imprints from the middle portion of the lamina midway between the midrib and leaflet edge on the adaxial and abaxial side of the fourth leaf were taken by the acrilic varnish method ; leaf temperature was measured with an infrared thermometer ; photochemical efficiency of Photosystem II was measured using a fluorometer .13C/12C ratio (δ13C), determined by EA-IRMS  according to Coplen et al. . In the same experiment, leaf samples of three plants of six selected genotypes which had shown differences in TE were used to determine carbon isotope discrimination (CID) as the n et al.  at the IFor experiment 1 data, analyses of variance (ANOVA) were carried out with the R statistical package software . Correlations were analyzed either using data from each experiment, or with the joint dataset. Phenotypic, genotypic, and environmental correlations (and their standard error) were calculated using multivariate restricted maximum likelihood estimation using the Proc MIXED of the SAS statistical software. The method implemented by Holland  for calc−1  and 1.8 to 3.5 g L−1 (water deficit), but with no significant genotype × treatment interaction. This population showed transgressive segregation, as shown in Figures wp, and TE under drought calculated using shoot biomass, TEs) under drought among the 23 evaluated lines, with mean values ranging from 3 to 4.2 g L−1 for TEs , and also in Experiment 1 .In experiment 2, a significant variability in TE was found both under well-watered and water deficit conditions among the 72 RILs, ranging from 1.9 to 3.3 g Ls Figure  and 4.2 p Figure . A highlExperiments 2 and 3 were analyzed jointly, considering the set of traits measured in both experiments and the 23 shared RILs. The analyses showed significant genotypic variance for all the evaluated traits, and a significant effect of the environment Table . DespiteThe correlation between TE and other possibly related traits was analyzed in each of the experiments involving the RIL population, and also jointly using the set of shared traits and genotypes. First, the correlation between TE and its components (plant biomass and water use) was explored. Phenotypic correlation between TE and total plant water use was found to be not significant in all cases Table . NeverthSignificant phenotypic correlations with TE were found for most biomass allocation traits Table . The pheSignificant genotypic variation was found for TE in sunflower genotypes, both in a group of four inbred lines, and in a population of RILs derived from the cross between two of them, as found previously for this species . Lambrides et al.  and AdirA detailed analysis of whole-plant traits and TE showed variation in the magnitude of genotypic and environmental correlations. Genotypic correlations can arise from a linkage between genes underlying the studied traits or pleiotropic effects of the same gene or genes. These correlations cannot be generalized, since alleles and linkage desequilibrium between them can differ between populations of a species. On the other hand, environmental correlations can indicate the presence of physiological relationship between traits  and to LAR ; correlation with LMF was also negative but not significant . Despite some differences between the results obtained in both studies, which could be a consequence of different methods and environmental conditions  genotypes were analyzed. This fact could represent a limitation, since most cultivated varieties of sunflower are hybrids and, therefore, studies using inbreds might not be able to capture the whole range of potential variability. Studies using inbred lines can, however, help shed light on the physiology of complex traits, and there are many examples in the literature in which this kind of studies have been carried out (e.g., Chimenti et al., Surrogate traits most frequently used as proxies for TE are carbon isotope discrimination and canopy temperature, and also chlorophyll content and SLA as indirect estimates of photoynthetic capacity (Condon et al., Differences in whole-plant transpiration efficiency between sunflower genotypes, and between plants within each genotype, were closely associated to biomass allocation patterns, and less closely associated to leaf-level traits. This association is probably of a physiological origin, and not only a consequence of genetic linkage in the studied population. Genotypic variation for biomass allocation could be potentially exploited as a source for increased transpiration efficiency in breeding programmes, independently of the frequently used intrinsic TE-related traits.GP and LA conceived and designed the experiments. LV and IA performed the experiments and measurements. CP performed leaf-level measurements. LV, IA, and GP analyzed the data. LV, IA, CP, LA, and GP discussed the results and wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


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 , 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.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 The United States is the world’s largest consumer of crude oil, resulting in two major problems: 1) low energy security and (2) high greenhouse gas (GHG) emissions. To increase national energy independence and decrease GHG emissions, the U.S. Congress enacted the Energy Independence and Security Act (EISA) in 2007 (110. P.L. 140). The EISA aims to increase the production of clean renewable fuels within the USA. Biofuels, such as biodiesel and renewable jet fuel from oilseed crops, are an alternative to petroleum-based fuels [ low enerThe Department of Defense is the leading consumer of fuel within the USA. The U.S. military consumes 23 billion liters of aviation fuel a year. The United States military desires a secure fuel source that is not threatened or controlled by world events. Biofuels developed from crops grown within the borders of the USA are a secure source of fuel uninfluenced by the same world events that affect petroleum-based fuels. To diversify their fuel sources the U.S. military set a goal of 5% of their yearly aviation fuel needs (1.15 billion liters per year) from biofuel.Even though alternative fuels may not be price competitive, there is a long-term commitment by the military to use diversification in fuel sources as a means of reducing risk . The milTechnology is available to produce biofuels, such as biodiesel and renewable jet fuel, from oilseed crops. The common oilseed crops for temperate regions include canola (rapeseed), sunflower, soybean, flax, safflower, and mustard. Some oilseed crops, such as mustard, show considerable salt tolerance.−1).Because of its climate, which enables year-round crop growth, and its reasonably secure source of irrigation water from surface, ground, and/or degraded water sources, California’s San Joaquin Valley (SJV) is an ideal agricultural area for a secure source of biofuel. In the SJV, biofuel feedstock that can grow on marginally productive soil of poor quality, particularly saline soils, is advantageous for cost reduction since marginal soils are usually fallow or produce yields too low to be profitable. The U.S. Department of Agriculture and the U.S. Navy’s Office of Naval Research identified the SJV as a potentially strategic location for biofuel production to meet 10% of the yearly biofuel production of aviation fuel . Current estimates of salt-affected soil for the WSJV using National Resource Conservation Service’s (NRCS) Soil Survey Geographic Database  are 3.6 × 105 ha. Recent estimates using satellite imagery place this even higher at 5.5 × 105 ha [Marginally productive lands in California are an ample potential resource that can be used to great advantage to reduce cost. In the early 1980s, Backlund and Hoppes  estimateSinapis alba L.), are salt tolerant and produce reasonably high oil yields. Moreover, after the oil is pressed out from its seeds, the residual Ida Gold mustard seed meal can act as an effective biodegradable bioherbicide and can provide Se as a nutritional supplement in livestock feed. Se-enriched seed meal is a unique and extra cash-value product that can only be produced in the Se-laden soils of the WSJV.Regardless of the estimate that is accepted, there is extensive salt-affected soil within the SJV to grow salt-tolerant oilseed crops for conversion to biofuel that would not compete with food crops for land use. Salt and drought tolerant biofuel feedstock, such as mustard oilseed, has tremendous potential when grown on marginally productive salt-affected soils. Specially bred mustard varieties, such as Ida Gold mustard  for evaluating the feasibility of oilseed production on marginal soils to support a 115 ML yr−1 biofuel conversion facility in the SJV.It is the objective of this study (1) to formulate a crop yield model relating Ida Gold mustard oilseed yield to edaphic properties for the WSJV and (2) to use the crop yield model to predict the yield of Ida Gold mustard oilseed on salt-affected soils  directed soil sampling; (2) identification of marginally productive salt-affected soils for oilseed production in the SJV; (3) development of a spatial database of edaphic factors influencing mustard yield for the SJV derived from satellite imagery and the SSURGO database and (4) applying the Ida Gold mustard oilseed yield model on marginally productive soil for the SJV and performing Monte Carlo simulations to show the range and probability of potential biofuel production in the region. The feasibility evaluation involves four steps: (1) development of an Ida Gold mustard oilseed yield model for marginal SJV soils using apparent soil electrical conductivity  identification of sites within the field where crop yield and soil core samples are taken that reflect the range and variability of edaphic influences on oilseed yield; (4) analysis of chemical and physical properties of the soil cores thought to influence yield and (5) statistical analysis and crop yield model formulation.The development of a field-based Ida Gold mustard oilseed yield model involves the following steps: (1) selection of an appropriate field that has a full range of edaphic properties that are suspected of influencing Ida Gold mustard oilseed yield; (2) conducting an intensive EC–1). The texture in the top 55 cm is a clay loam and 0.55–1.55 m is a sandy clay loam. The parent material is alluvium derived from sedimentary rock. The mean annual precipitation is 25 cm.The study site was a 16.2-ha field  located west of Los Banos in Merced County, California on the WSJV . The sita surveys were conducted on 14 January and 28 January 2014, following a pre-plant irrigation to bring the water content in the root zone to field capacity. The methods and materials used in the ECa surveys followed the protocols and guidelines outlined in Corwin and Lesch [a surveys. Geospatial ECa measurements in the vertical (EMv) and horizontal (EMh) coil configurations were taken simultaneously every 3–5 m. Each ECa measurement was geo-referenced using GPS. The GPS receiver accuracy had sub-meter accuracy. The preliminary ECa survey determined whether the study site provided a sufficiently wide range in soil properties influencing Ida Gold mustard oilseed yield to meet the objective of formulating a crop yield model. The preliminary survey consisted of making six east-west traverses. From the geospatial ECa data set, six locations were selected to take core samples (0–1.5 m depth increment), which were immediately analyzed for pHe, saturation percentage (SP), B, and ECe.Preliminary and intensive ECnd Lesch ,10. An Ea survey, two separate intensive ECa surveys were conducted. One ECa survey for the entire 16.2 ha and another survey confined to the southeastern corner of the field. a survey data for EMv and EMh. The reason for taking the two intensive ECa surveys was because the preliminary cursory ECa survey indicated the greatest variability in salinity over the range that would affect Ida Gold mustard oilseed yield was in the southeast corner of the field; consequently, a separate survey and crop yield/soil sampling were performed for the southeast corner. The intention was to provide a range of soil properties, particularly with regard to salinity and boron, which would influence Ida Gold mustard oilseed yield to varying degrees thereby providing the data to formulate a more robust statistical model of crop yield.Following the preliminary ECa surveys were completed, ESAP software version 2.10R [a survey data. The ESAP software package uses a model-based sampling strategy  to identify sample site locations. The ESAP software identifies sites that characterize the range and variation in the geospatial ECa measurements, reflecting the observed spatial variability in ECa, while minimizing any clustering of the sample sites by maximizing the spatial uniformity of the sampling design across the study area. A detailed discussion of the application of the response surface sampling design using ECa survey data is in Lesch et al. [Once the two intensive ECon 2.10R ,12,13 waThe sample design for the 16.2-ha field consisted of 20 sample site locations for the entire field and 20 sample site locations for the southeast corner as selected by ESAP . Soil co2 area where each soil sample location was the centroid of the 1 m2 plant sample area. The biomass and oilseed yield were collected on 28–29 May 2014. Six of the 40 sample site locations had no Ida Gold mustard oilseed yield. All subsequent referral to yield is with respect to oilseed yield.At each of the 40 sample-site locations biomass and Ida Gold mustard oilseed yield were determined by hand within a 1 mg. Saturation pastes were prepared for all 288 soil samples and saturation paste extracts were obtained following the procedure of Rhoades [e, SP, pHe, 5 major anions , 4 major cations , B, and sodium adsorption ratio (SAR). The chemical analysis procedures followed were those found in Sparks [h ECa divided by EMv ECa and (2) the ratio of Cl concentration in the irrigation water and Cl concentration at 1.2–1.5 m. The LF reflects the excess water applied to translocate salts from the root zone. Each property selected for analysis had the potential to influence Ida Gold mustard oilseed yield.In the field, a subsample (100–300 g) of each soil core sample was taken for soil moisture determination. The subsamples were weighed in the field to minimize error due to moisture evaporation. The subsamples were subsequently oven-dried at 110ºC for 24 h and weighed again to determine θ Rhoades . The satn Sparks . Leaching, ECe, SP, pHe, Cl−, HCO3−, PO43−, NO3−, SO42−, Na+, K+, Ca2+, Mg2+, B, SAR, and LF. Correlations between the edaphic properties and ECa and between oilseed yield and ECa were determined. Scatter plots of yield vs. individual soil-related properties were also obtained.Simple correlations were determined between yield and the edaphic properties of θThe correlation analyses and scatter plots served as a basis for the development of the yield model for Ida Gold mustard oilseed. The correlations were useful to determine what properties were likely significant in influencing oilseed yield, while the scatter plots helped to determine the general form of the oilseed yield model. The oilseed yield model was developed using  multiple regression techniques . The edarY is the relative crop yield, a is the salinity threshold (dS m−1), b is the slope expressed in yield decrement percentage per dS m−1, and −1). Maas and Hoffman [a and b were determined from a compilation of salt tolerance data available for Ida Gold mustard oilseed for the SJV, including the data collected within this study and the work of Maas [a-directed soil sampling and from 10 supplemental sites. The locations of the supplemental sites were from a transect covering a range of yields. Salt tolerance data were from those sites varying in oilseed yield where all soil properties were optimal except salinity, which varied over a wide range, while B tolerance data were from those sites where all soil properties were optimal except B, which varied over a wide range. If no salinity and B data were available, then all properties were considered optimal for yield.In some instances, sufficient input data for the Ida Gold mustard oilseed yield model did not exist or fell outside the range of data that were used to develop the oilseed yield model. In those instances, the two-piece linear salt tolerance model of Maas and Hoffman  was used Hoffman  proposed of Maas  and Grie of Maas . If sali of Maas  and firs of Maas  was usede) above 4 dS m−1. Above 4 dS m−1 very sensitive, sensitive, and moderately salt-sensitive crops will show yield decrements. From 4–8 dS m−1 the yields of many crops are restricted, from 8–16 dS m−1 only salt tolerant crops yield satisfactorily, and above 16 dS m−1 only a few very tolerant crops produce satisfactory yields [To assess the potential production of Ida Gold oilseed in the SJV, the yield model was applied over all salt-affected soils in the valley. Bohn et al.  defines y yields .a surveys conducted at the 22 fields, collected 41,779 ECa readings at an average density of 175 readings ha−1. Simultaneous EMh and EMv measurements of ECa were taken. Across the 22 fields, 267 soil-sampling locations were identified using ESAP. Soil cores were taken to a depth of 1.2 m, representing the root-zone depth. Details of the ECa survey and soil sampling are in Scudiero et al. [One means of identifying salt-affected soils in the SJV is the use of SSURGO. However, the accuracy and reliability of soil salinity in SSURGO is dubious because salinity is a spatially and temporally variable soil property influenced by crop and irrigation management strategies. Recent NRCS reports , provide; dS m−1), gravimetric water content , pHe, and saturation percentage (SP) using procedures presented in Methods of Soil Analysis Part 3 [Part 4 [e hard and ECa soft data. The hard data  were averaged for each field. The soft data  were calibrated to the ECe data using spatial linear regression models [2 = 0.93 by Scudiero et al. [Soil samples were analyzed for salinity  for the SJV exists. The supplemental data set, collected over a period of 25 years by Corwin and colleagues, is a compilation of data that appeared in publications by Bourgault et al. [g, and B for the composite depth increment of 0–1.2 m, which was found to be the root zone depth for Ida Gold mustard oilseed. In general, SSURGO provides water content and B ranges associated with soil type. The PDFs, defined within the ranges of water content and B provided by SSURGO, were used as input for Monte Carlo simulations with the oilseed yield model. In instances where ranges of water content or B were not given in SSURGO, then B was assumed optimal and water content was estimated from a pedotransfer function using SSURGO texture data.To supplement the SSURGO data an extensive spatial database of quantitative soils information . To incorporate this uncertainty into the Monte Carlo simulations, PDFs were established for each salinity category. The PDFs were defined by the average residuals and standard deviation of the residuals between observed salinities from the ground-truth salinity measurements of Scudiero et al. [The use of degraded soil is crucial to driving down the cost of biofuel production in the SJV. Subsequently, the reliability of spatial salinity data for the SJV was of paramount importance for identifying salt-affected soils and for model input data. There were concerns regarding the reliability of the salinity ranges provided in SSURGO for the root zone due to anthropogenic influences ; consequently, as discussed in detail in o et al.  and predo et al. .h ECa to EMv ECa and from a ratio of Cl in irrigation water to Cl concentration below the root zone). Only those LFs where EM and Cl ratios agreed to within 5% were used.Leaching fraction is a difficult edaphic property to obtain and is not found in SSURGO. The supplemental data set of Corwin and colleagues was used to determine the frequency distribution, average, range, and standard deviation of LF for the SJV. Leaching fraction was determined from a ratio of EM−1) and probability of those yields, which are easily converted to L ha−1 of biofuel. This was done first for the WSJV and then for the entire SJV. The mean, median, standard deviation, skewness, and kurtosis of the Monte Carlo simulation distribution were calculated to characterize quantitatively the PDF and subsequently derive the cumulative density function (CDF) of biofuel production. Once the CDF is known, the probability (and thereby feasibility) of oilseed production in the SJV to support sufficiently a conversion facility becomes evident.Monte Carlo simulations with the Ida Gold mustard oilseed yield model were performed with 10,000 iterations to provide a range of potential yields ) or B tolerance model was used, whichever was more limiting at the site. For instances where insufficient input data existed and only ECe and B input data were available, then either the ECe ) or B tolerance model was used, whichever was more limiting at the site. If insufficient input data existed and only ECe input data was available, then the ECe tolerance model (Equation (1)) was used.Sufficient input data was not always available for each field in the WSJV or SJV or sometimes the input data was outside the range of data used to develop the crop yield model for Ida Gold mustard oilseed. For these instances, an alternative model was needed see . When a a survey revealed that the greatest variation in salinity over the range that would result in oilseed yield decrements was in the southeast corner of the study site; consequently, two intensive ECa surveys were conducted with separate soil sampling designs for each survey. One ECa survey and associated soil sampling covered the full field and the other focused on the SE corner. h) and vertical coil configuration (EMv) ECa surveys. The combined soil sampling designs  provided a full range of soil properties and oilseed yields to build a robust Ida Gold mustard oilseed yield model. The only property potentially influencing Ida Gold mustard oilseed yield that did not vary significantly at the study site was texture. In general, the fine-textured soils (mainly loams and clay loams), which predominate the WSJV, do not vary to a significant extent because the soil is a consequence of lacustrine deposits and of fine-grained alluvium material originating from the Coastal Ranges [The preliminary ECl Ranges ,45.a measurements, both EMh and EMv, were higher on the west side of the 16.2-ha field than on the east side , major cations , major anions , SAR, micro-elevation, depth to groundwater, and groundwater EC. The measured edaphic properties that were felt to potentially influence Ida Gold mustard oilseed yield included: ECe) increases with depth up to the 0.3–0.6, levels off at the 0.3–0.6 and 0.6–0.9 m depth increments, and then decreases with depth. Saturation percentage (SP) is reasonably constant over depth ranging from means of 48.13% at 1.2–1.5 m to 54.85% at 0.3–0.6 m, indicating uniform texture through the soil profile. Gravimetric water content (θg) at field capacity increases with depth from 0.10 kg kg−1 at 0–0.15 m to 0.24 kg kg−1 at 1.2–1.5 m. Boron levels tend to be lower below 0.3–0.6 m. pH increases with depth from 7.29 at 0–0.15 m to 7.98 at 1.2–1.5 m. SAR increases with depth from 6.53 at 0–0.15 m to 19.04 at 0.3–0.6 m, then decreases to 10.72 at 1.2–1.5 m.e, various cations and anions , and SAR, while the lowest CVs are for pHe, SP, and θg. All edaphic properties in g, SP, HCO3−, and B. The range, minimum, and maximum of the edaphic properties in e, pHe, B, and SAR cover broad ranges from low to very high. For the composite depth of 0–1.5 m, ECe ranged from 2.05 to 36.22 dS m−1, pHe ranged from 7.18 to 8.53, B ranged from 2.65 to 35.06 mg L−1, and SAR ranged from 3.48 to 112.13. The SP is the only soil property that is narrow in range, nonetheless it reflects a texture that is typical of the WSJV.a and oilseed yield for the 0–1.2 m depth increment. The edaphic properties most significantly correlated to ECa include θg, ECe, B, SAR, LF (determined by the ratio EMh ECa /EMv ECa), and SP. The edaphic properties most significantly correlated to oilseed yield include θg, ECe, B, LF, and SP.Exploratory statistical analyses revealed that Ida Gold mustard oilseed yield was most significantly correlated to individual edaphic properties for the top 1.2 m. 2) reflects the root zone of the crop. The top 1.2 m resulted in the most statistically significant and best-fit Ida Gold mustard oilseed yield model (see Equation (3)). Consequently, the 0–1.2 m soil interval was taken to represent the root zone of Ida Gold mustard at the study site. Subsequently, all data presented and discussed are with respect to the 0–1.2 m composite depth or to individual depth increments that lie within the composite depth of 0–1.2 m.Corwin and Lesch  indicateg, ECe, B, and LF were the most influential edaphic properties on oilseed yield. Scatter plots of these properties vs oilseed yield indicated quadratic relationships of ECe and B to oilseed yield and linear relationships of LF and θg to yield. Based on the initial exploratory correlation and multiple linear regression analysis, the following regression model structure was proposed to describe edaphic property effects on oilseed yield:Y is the Ida Gold mustard oilseed yield (kg ha−1); B is boron concentration (mg L−1); eEC is electrical conductivity of the saturation extract (dS m−1); LF is the leaching fraction; gθ is the gravimetric water content (kg kg−1); 0β, 1β, 2β, . . . , 6β are the regression model parameters, and ε is the random error component, initially assumed to be normally distributed and spatially independent. Ordinary least squares (OLS) regression techniques resulted in a fitted regression equation with a R2 = 0.89 and adjusted R2 = 0.78. Adjusting for spatial autocorrelation using the maximum-likelihood approach resulted in the following Ida Gold mustard oilseed yield model (Equation (3)):Exploratory statistical analyses revealed that θLF and gθ parameters are highly significant at or near the 0.01 level, and the eEC (linear and quadratic) and B (linear and quadratic) parameter estimates are significant at or near the 0.05 level. The LF and gθ parameters are both positive, implying that the yield increased as either LF or gθ increased, which is physically sound since increased leaching reduces osmotic stress and increased water content increases the plant-available water reducing matric stress. The positive linear and negative quadratic eEC terms imply that the yield increased at low eEC up to a point of maximum yield with respect to eEC and decreased beyond the maximum. The point of maximum yield with respect to eEC was calculated by setting the first partial derivative of the fitted regression to zero with respect to eEC, which resulted in a value of 6.8 dS m−1. Similarly, the positive linear and negative quadratic B terms imply that the yield increased under low B up to a point of maximum yield with respect to B and decreased beyond the maximum. The point of maximum yield with respect to B was 4 mg L−1.Equation (3) represents the most parsimonious and robust model for marginally productive salt-affected soils of the WSJV. Any locations where no oilseed yield was obtained were not used in the model development. The −1, which is the term a in Equation (1), and a yield decrement slope of 17%, which is the term b in Equation (1) . Equation (1) established the upper limit of the salinity range of salt-affected soils that would grow Ida Gold mustard oilseed. A 17% yield decrement for each 1 dS m−1 increase in root-zone soil salinity beyond 8.3 dS m−1 resulted in no oilseed yield above 14.3 dS m−1. It is important to note that the salinity range of 4–14.3 dS m−1 was not necessarily economically viable. Once the feasibility of reaching the 115 ML yr−1 goal is established, then the maximum yield decrement that is economically viable could be determined. Viability would take into account other economically relevant factors such as (1) selling the seed meal remaining, after the oilseed has been pressed to extract the oil, as Se-enriched meal for livestock or as a herbicide used in organic agriculture and (2) incorporation of gasification to generate power to run the oil press.In those instances where sufficient input data for Equation (3) did not exist or fell outside the range of data that was used to develop the model, the two-piece linear salt tolerance model ) of Maas and Hoffman  was usedtion (1) a. The sae and setting it equal to 0, ECe corresponds to the peak mustard oilseed yield, which is 6.8 dS m−1. This is equal to the ECe producing the maximum yield in Equation (3). Similarly, a quadratic relationship between yield and ECe was also found by Corwin et al. [e and yield of cotton seed and mustard oilseed is that when the plant is osmotically stressed it puts energy into the development of the reproductive part of the plant rather than vegetative tissue; consequently, as ECe increases up to the salinity threshold biomass yield of both crops remains constant or may decrease, while mustard oilseed and cotton seed yield steadily increase. After the salinity threshold is reached then oilseed and cotton seed yield and the biomass of both crops decrease as salinity increases. This quadratic relationship indicates that more breeding research is needed to obtain new cultivars of mustard that have their peaks of potential yield at different ranges of salinity to maximize production in the SJV.The two-piece linear salt tolerance model ) of Maas and Hoffman  was not −1 ) produced a comparable fit to the B tolerance data  for the SJV. −1 for SSURGO and the Scudiero et al. [e was estimated to be <4 dS m−1 was disregarded and not included in the Monte Carlo simulations.Because of the inaccuracy of salinity in SSURGO the Scudiero et al.  regionalo et al.  regionalo et al.  salinity−1 was used for salinity, rather than the mean ECe level of 10.0 dS m−1 from −1 represents the point of maximum yield with respect to the quadratic salinity response pattern. For the same reason a B baseline of 4.0 mg L−1 was used in the sensitivity analysis. The calculated percentage yield change shown in e, then LF, and finally θg.The reliability and accuracy of the spatial data that serves as input into any model are just as critical as the model itself, exemplified by the old adage, garbage in garbage out. Sensitivity analysis of the crop yield model provides an indication of the input variables that need the greatest level of accuracy and therefore need particular scrutiny when building a spatial database of inputs for the crop yield model. Sensitivity analysis  establise, oilseed yield model input values were obtained from the 30 x 30 m predictions from Scudiero et al. [e for each field for the root zone (0–1.2 m) within the SJV. For the Monte Carlo simulations, PDFs were defined by the average residual and standard deviation of the residuals for each category of salinity  using the predicted and ground-truth ECe data from Scudiero et al. [e of each pixel in the Monte Carlo simulations was determined from the predicted ECe and residual PDF. −1 were not used since only those fields with an average root zone ECe of greater than 4 dS m were used in the Monte Carlo simulations. There was not a substantial difference in the Monte Carlo simulation findings using the PDF for the entire data set in e residual data set.In the case of ECo et al.  and usedo et al. . The inpg for the SJV, which were subsequently used as input data in the Monte Carlo simulations. The PDFs for B, LF, and θg were log normally distributed.The edaphic property data collected and presented by Corwin and colleagues  and sold as 20% biofuel in California.Oilseed yield must be converted to biofuel yield. The oil content of Ida Gold mustard oilseed was 26% and the extraction efficiency was 64%. Subsequently, for every 1000 kg ha−1 of biofuel produced, which is well below the goal of 115 ML yr−1. Subsequently, Monte Carlo simulations were performed for the entire SJV. To do this, the regional-scale salinity model of Scudiero et al. [5 ha. Monte Carlo simulations resulted in the PDF and associated CDF shown in −1. A comparison of the means, medians, standard deviations, skewness, and kurtosis for the measured Monte Carlo simulation data and for the estimates from Equation (6) shows excellent agreement, with means of 101.4 and 101.4 ML yr−1, medians of 97.8 and 99.4 ML yr−1, standard deviations of 14.1 and 14.1 ML yr−1, skewness of 0.87 and 0.87, and kurtosis of 4.17 and 4.14, respectively  was not applied. Rather, the two-piece linear salt tolerance model of Maas and Hoffman  presenteEven though the feasibility of meeting desired oilseed production levels is dubious, there are circumstances that could improve the likelihood of meeting the 115 ML lower limit of production. Water in terms of water content and water available for leaching are two impactful properties pertaining to oilseed yield in the model. Maintaining the root zone water content at high levels with adequate leaching could drive up yields sufficiently high to make oilseed a viable biofuel in the SJV. In particular, high frequency irrigation to maintain root zone water contents near field capacity would have significant impact. However, this is only feasible when the SJV has sufficient irrigation water supplies, which may be the exception rather than the rule as shown by the recent 6-year California drought (2011–2016).e > 4 dS m−1) or non-salt-affected (ECe ≤ 4 dS m−1) soils, could play a major role in enhancing the feasibility of biofuel production in the SJV. Planting mustard oilseed between rows in orchards, sometimes referred to as intercropping or alley cropping, provides tremendous acreage for crop growth on lands that are otherwise left fallow in many instances. Intercropping has several additional advantages, including weed control, wind and water erosion control, dust management, improved percolation, more effective use of land and resources, and additional crop revenue. There are roughly 130,000 ha of orchards in the SJV. By planting an oilseed crop between rows as a secondary crop, biofuel production could increase roughly 15–30%, which would increase the probability of meeting the 115 ML yr−1 target from 15–17% to 60–80% for the entire SJV. Even though intercropping with oilseed may make biofuel production feasible, it certainly does not necessarily make it economically viable. Furthermore, secondary crops from intercropping are a management challenge for producers filled with additional management problems.Orchards, whether in salt-affected  tolerance studies provided in the past. Furthermore, past plant salt tolerance studies did not evaluate the interaction of edaphic properties on crop yield.Even though biofuel feasibility evaluation is a valuable application of sensor technology, the unique aspect of this study is that it presents an innovative regional-scale approach for modeling the interaction of edaphic properties on crop yield by characterizing the spatial variability of soil properties influencing yield using proximal and satellite sensors. In essence, each sampling location identified from an ECThe same approach can be used to predict drought impacts on crop productivity within an agricultural region, to identify reclamation needs to optimize crop productivity, and to manage resources  and edaphic properties  to optimize crop yield. Because of its regional-scale application and quantitative probability assessment, the approach provides land and water resource specialists with a tool to make regional- and landscape-scale resource decisions with a clear understanding of the likelihood of impact on agricultural productivity.−1 of biofuel from oilseed indicates that there is a 15–17% probability of meeting the minimum production level when all salt-affected soils in the SJV are utilized for oilseed production (−1), moderate B levels , high LF, and high available water content, which are soils that can either be easily reclaimed to produce higher cash crops or used to grow salt and B tolerant crops of higher cash return than oilseed, such as pistachio.This study exemplifies the combined use of proximal and satellite sensors to answer a practical agricultural question of national significance with strategic military implications concerning the feasibility of biofuel production in California’s San Joaquin Valley to help meet the aviation fuel needs of the U.S. military. A quantitative assessment of the probability of attaining a minimum of 115 ML yroduction . The low