Background:Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive tumor with a poor prognosis, despite the emergence of chemotherapies such as gemcitabine plus albumin-bound paclitaxel (nab-paclitaxel, AG), unmet medical needs still exist for patients with metastatic PDAC (mPDAC). Surufatinib is a small-molecule tyrosine kinase inhibitor targets vascular endothelial growth factor (VEGFR) 1, 2, 3, fibroblast growth factor receptor 1 (FGFR1), and colony stimulating factor 1 receptor (CSF-1R). This single-center, retrospective study evaluates the potential efficacy of combination therapy containing Surufatinib in advanced or metastatic pancreatic cancer. Method:We conducted a real world retrospective study of mPDAC patients who received the Surufatinib between July 2022 and July 2023 at Zhejiang Cancer Hospital. In addition, patients who received first line chemotherapy at the same period were analyzed as comparison. Result:As of November 20, 2024, 20 eligible patients were identified in this retrospective study. The median progression-free survival (mPFS) of patients who received Surufatinib treatment was 5.27 months (95% CI, 2.55-7.98), and the median overall survival(mOS) was 9.93 months (95% CI,6.55-13.32). For fist line treatment, 9 patients received Surufatinib combined with immune checkpoint inhibitors (ICIs) and chemo and the mPFS was 7.5 months (95% CI, 3.14-11.85), compared with an mPFS of 5.43 months (95% CI, 3.89-6.96) for 52 mPDAC patients received chemotherapy at the same period. Grade 3 or above Treatment Related Adverse Event (TRAE) were neutrophil count decreased (10%), and white blood cell count decreased (5%). Conclusion:Preliminary data suggest that surufatinib shows potential therapeutic benefit in mPDAC, but its efficacy needs to be further validated. This combination strategy may provide a new treatment option for patients, especially in the first-line setting. Future studies will expand the sample size and include additional evaluation parameters to fully assess its efficacy and safety. Clinical trial registration:ClinicalTrials, identifier NCT06378580.

Studies have generally shown that grain mineral density is lower in modern wheat (Triticum aestivum L.) compared to historic germplasm. The conclusion of a limited study from the US Pacific Northwest (PNW) was that grain mineral density of soft white wheat (SWW) had declined over time to meet already-low mineral density of hard red wheat (HRW), though little else is known about this. Therefore, the primary objective of this study was to better understand grain mineral density (P, K, Mg, Ca, Mn, Fe, Zn, and Cu) of modern PNW wheats, with subobjectives to compare SWW and HRW wheat classes to each other and worldwide benchmarks, quantify effects of agronomic factors on grain minerals, and evaluate minerals in refined flour. Results indicated whole-grain mineral density of PNW wheat was comparable to worldwide benchmark concentrations, with P and K most likely low, with no evidence of SWW and HRW class differences. Agronomically, there was significant variation in grain minerals among production sites and wheat varieties that could be utilized to generate mineral-enriched grain to feed malnourished populations. In the process of refining flour from whole grain, the minerals most reduced were P, Mg, Mn, Fe, Zn, and Cu (60%-90%). Refined flour mineral concentrations were largely unassociated with flour yield or quality parameters, suggesting that efforts to enhance mineral density will not affect other flour traits. Overall, these results illustrate that the mineral density of modern SWW and HRW produced in the PNW are comparable to each other and to wheat globally. Considering reported historical changes, these results suggest a modern, worldwide convergence in mineral density across wheat classes.

ProblemMillions of people worldwide lack diversified diets and rely on staple foods, like wheat, that are inherently poor in minerals as primary sources of nutrition. Efforts have been made to enhance grain mineral density in wheat through plant breeding and crop management, though there is concern that mineral “dilution” will occur if higher yield is also sought. Much of that concern comes from studies comparing historic and modern wheat genotypes with impactful genetic differences (e.g., tall vs. semi-dwarf), but there is much less clarity in studies focused on modern genotypes.ObjectiveThe objective of this research was to better understand factors affecting concentrations of eight minerals in whole-grain soft white wheat, especially yield.MethodsThis was done by collecting a genetically and environmentally diverse grain mineral dataset and analyzing it in multiple ways.ResultsThere was no significant effect of yield on density of any tested mineral in grain when factors such as production environment and test weight were controlled, though regression coefficients for yield were generally negative. Thus, the negative effect of yield was present, but it was insignificant and small relative to other sources of variation. Production environment had the greatest impact on grain minerals, contributing between 50% and 90% of variation in the data, depending on the mineral. Wheat genotype had an impact on density of most minerals and genotype by environment interactions were common. Achieving genetic gain in elevating grain minerals through plant breeding may be most successful for Zn, P, and Mg, because genotypic variation and heritability were relatively high for these minerals. Further analysis of Zn showed that breeding selection for simultaneously high-Zn, high-yield wheat varieties is possible, because individual wheat varieties tended to have consistent grain Zn relative to the mean, regardless of yield level or production environment. Grain test weight also had a significant impact on concentrations of two minerals (K and Cu). Factors that impact plant health and metabolism, and ultimately grain test weight, are likely to also affect plant mineral uptake and translocation, possibly causing this association.ImplicationsIn summary, this analysis provides evidence that the impact of yield on grain mineral density among modern wheat germplasm is small and insignificant. In the quest to improve the mineral nutrient value of wheat through plant breeding, crop management, and other approaches, the minor negative impact of yield should be considered secondarily to much more significant factors like production environment, wheat genotype, and even test weight.

Brassicaceae oilseed crops have proven potential as vegetable oil feedstock for biofuels and food uses. However, meeting a growing demand for vegetable oils for food and industrial uses will require identifying oilseed species that are best suited for various growing environments within a particular region. The objective of this study was to compare growth dynamics, seasonal water use (WU), seed yields, and water use efficiency (WUE) of canola ( Brassica napus L.), camelina ( Camelina sativa L.), and white mustard ( Sinapis alba L.) to determine their suitability under three different environments within the northern Great Plains. Comparisons were made among these species over three growing seasons between 2013 and 2016 at Morris, MN; Mandan, ND; and Sidney, MT, situated along a strong precipitation gradient from east to west. Generally, growing season precipitation was similar at Morris and Mandan, but both were considerably greater than at Sidney. Seasonal WU was similar among these oilseed species at Morris and Mandan but was greatest for camelina at the drier Sidney environment. Canola seed yield was the greatest at Morris and had higher WUE than camelina and white mustard. At Mandan and Sidney, canola and camelina had similar seed yields and WUE, which were generally greater than white mustard. Under abundant moisture and low stress (e.g., Morris), seed yield per millimeter of water used could be maximized by growing canola, while in a drier more stressful environment like Sidney, seed yield per millimeter of water used could be maximized by growing camelina.

Precision nitrogen (N) application methods have been developed for dryland wheat that utilize site-specific measurements of grain protein concentration (GPC) to determine N fertilizer recommendations for the next season. The objectives of this study were to determine the critical protein level and N equivalent to a unit change in GPC from relationships between GPC, and grain yield or plant-available N in soft white winter wheat (SWW, Triticum aestivum L.), and assess the consistency of these relationships across SWW cultivars grown under a wide range of precipitation. A 3-year study was undertaken near two sites: Lexington (225 mm of mean annual precipitation) and Adams (450 mm) in Oregon. Differences in precipitation and N fertilization rates between sites were used to induce variability in grain yield and GPC of four cultivars. A critical protein concentration of 117.5 g kg-1 was defined by Cate-Nelson analysis of scatter plots of relative yield versus GPC. Critical protein among cultivars ranged between 105 and 118 g kg-1 suggesting that 117.5 g kg-1 might be used as a general indicator of N sufficiency. Slopes of the regression of available N on GPC were consistent across cultivars and revealed that 4.2-8.4 kg N ha-1 is equivalent to a unit change protein (1 g kg-1) in lower precipitation areas of the region where SWW is under water stress during grain filling. This information is useful in calculating the N to apply from the GPC in the previous season to meet crop requirements in the next season. A critical grain protein level can indicate N sufficiency for yield of winter wheat under water stress.The N equivalent to a unit change in protein concentration is generalizable across winter wheat cultivars.Critical protein level and N equivalent to unit change in protein are useful for precision N management.

Purpose The prognosis of early-stage hepatocellular carcinoma (HCC) patients after radical resection has received widespread attention, but reliable prediction methods are lacking. Radiomics derived from enhanced computed tomography (CT) imaging offers a potential avenue for practical prognostication in HCC patients. Methods We recruited early-stage HCC patients undergoing radical resection. Statistical analyses were performed to identify clinicopathological and radiomic features linked to recurrence. Clinical, radiomic, and combined models (incorporating clinicopathological and radiomic features) were built using four algorithms. The performance of these models was scrutinized via fivefold cross-validation, with evaluation metrics including the area under the curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) being calculated and compared. Ultimately, an integrated nomogram was devised by combining independent clinicopathological predictors with the Radscore. Results From January 2016 through December 2020, HCC recurrence was observed in 167 cases (64.5%), with a median time to recurrence of 26.7 months following initial resection. Combined models outperformed those solely relying on clinicopathological or radiomic features. Notably, among the combined models, those employing support vector machine (SVM) algorithms exhibited the most promising predictive outcomes (AUC: 0.840 (95% Confidence interval (CI): [0.696, 0.984]), ACC: 0.805, SEN: 0.849, SPE: 0.733). Hepatitis B infection, tumour size > 5 cm, and alpha-fetoprotein (AFP) > 400 ng/mL were identified as independent recurrence predictors and were subsequently amalgamated with the Radscore to create a visually intuitive nomogram, delivering robust and reliable predictive performance. Conclusion Machine learning models amalgamating clinicopathological and radiomic features provide a valuable tool for clinicians to predict postoperative HCC recurrence, thereby informing early preventative strategies.

Abstract Some producers in the inland Pacific Northwest (PNW) are interested in diversifying the traditional 2‐yr sequence of winter wheat (Triticum aestivum L.) (WW)–summer fallow (SF) with oilseed crops to capture break crop effects. The objective of this study was to compare production costs and economic returns of 2‐yr sequences with those of intensified 3‐yr sequences at a low‐precipitation (<300 mm) site where the long‐term rotation has been WW‐SF. A 5‐year (2014–2018) cropping sequence study was conducted that included summer fallow with intensive tillage (SF) and reduced tillage (RTF) in 2‐yr rotations with WW; and RTF in 3‐yr rotations with WW, winter canola (WN; Brassica napus L.), or spring carinata (SC; Brassica carinata A. Braun) as a primary crop and spring wheat (SW), spring barley (SB; Hordeum vulgare L.), or SC as a secondary crop. Reduced tillage fallow increased WW yields by 14% compared with SF. Production of WN and SC in WN‐SW‐RTF and SC‐SW‐RTF, expressed by equivalent WW yield, was 42%, and 35% of WW in WW‐RTF vs. 67% needed to compensate for 1/3 less cropping with WW. Production of SC in WW‐SC‐RTF was 21% of WW in WW‐RTF vs. 33% needed to compensate. Mean net returns over total costs were negative with WW‐RTF least unprofitable at −US$162 ha−1 followed by WW‐SB‐RTF at −$167 ha−1, WW‐SF at −$180 ha−1, WW‐SC‐RTF at −$191 ha−1, SC‐SW‐RTF at −$205 ha−1, and WN‐SW‐RTF at −$229 ha−1. Including oilseeds in 3‐yr rotations with WW and fallow apparently may be less profitable than WW in 2‐yr rotations with fallow.

Automatic classification of major depression disorders (MDD) is an arduous task. When constructing the brain network automatic classification model based on functional magnetic resonance imaging (fMRI), the selection of global signal regression (GSR) and brain atlas are two key factors. However, their impact on the classification has not reached a consensus so far. The main reasons include the following two points: first, the sample size of previous studies is small, and different studies lead to inconsistent results; second, there are too many parameters in their models, which could not clearly reveal the effects of the above two factors. Therefore, we believe that only by using the data of multi-center and large samples, it is possible to find out the influence of these two factors on the classification results. To test our hypothesis, data sets (The REST-meta-MDD project) from 17 centers were used in this study. The set was divided into two parts, training set and independent validation set. The training set used 10-fold cross-validation to evaluate the classification performance, and the independent validation set used the features of the first part to classify directly. Feature selection adopted two sample t-test plus least absolute shrinkage and selection operator (LASSO), and the classifier was linear support vector machine (SVM). Finally, the classification effect of factors was confirmed by statistical analysis. The results showed that the impact of GSR on the classification results was related to the selection of brain atlas. In anatomical automatic labeling (AAL)-based networks, GSR would reduce the classification accuracy. But for Dosenbach networks, GSR would improve the classification performance. The classification ability of networks constructed by different brain templates was different, and the AAL was the best. In conclusion, the choice of brain atlas was a key factor affecting classification performance in MDD classification. (C) 2022 Society for Imaging Science and Technology.

乳腺癌患者化疗后容易出现认知功能受损(俗称"化学脑").最近十几年来人们开始将影像和神经心理学评估结合起来,希望了解其背后的生理学机制.虽然越来越多的影像学证据被找到,但对"化学脑"背后的病理机制知之甚少.笔者对乳腺癌患者"化学脑"的发病机制以及其结构的神经影像学研究进展作一综述,旨在总结化疗后认知功能损伤乳腺癌患者大脑的结构变化,希望找到一些潜在的生理学意义.

Abstract A time‐series of yield monitor data may be used to identify field areas of consistently low or high yield to serve as productivity zones for site‐specific crop management. However, transient factors that affect yield in 1 yr, but not every year, detract from this approach. The objective of this study was to illustrate Moran eigenvector spatial filtering (MESF) with results from analysis of multi‐year crop yield data from two farm fields in the United States. The MESF method accounts for temporal autocorrelation within a common factor map representing the correlation across years and partitions stochastic geographic variation into spatially structured and unstructured components. Crop rotation data were utilized from a dryland field in east‐central South Dakota and an irrigated field in southwestern Georgia. A random effects (RE) model was estimated that utilized eigenfunctions of a geographic connectivity matrix to account for spatially structured random effects (SSRE) and unstructured random effects (SURE) in standardized z scores of multi‐year crop yield. The MESF method was evaluated with conventional averaging of unfiltered yield data as a reference for comparison. In South Dakota, the SSRE accounted for 26% of the yield variance shared across years. Distinct patterns appeared to be related to changes in soil type and landscape position. The Georgia field yielded similar results. The MESF is effective for revealing structured variation in a time series of yield monitor data and may be useful for defining productivity zones within fields.

Crop residue management strategies have exhibited significant effects on crop growth and soil properties, which in turn may influence soil phosphorus (P) transformation and availability. In this study, the effect of long-term (83-year) crop residue management treatments (straw plus 45 or 90 kg N ha−1; straw burning in fall or spring; straw plus manure) on soil P availability and storage capacity in the surface (0–0.3 m) and subsurface (0.3–0.6 m) were investigated relative to straw incorporated into soil (control) in a wheat-fallow rotation in the Pacific Northwest. Compared to the control, N application significantly decreased soil available P by 37–49%, measured as Olsen-P, due to the higher P removal by the wheat crop. The significant decrease in NaOH-extractable inorganic P (Pi) by 31–42% and Oxalate-extractable Fe by 20–27% suggests N application induced Fe associated-Pi release to supply crop growth. Straw burning had no significant effect on soil P balance but decreased available P by 20–36%, which can be attributed to the transformation of labile Pi and/or moderately labile Pi to stable Pi and P downward transport due to the increased pH of 0.4–0.9 and the loss of organic carbon. Fall burning appeared to have a greater effect on soil properties and P chemistry than spring burning. Manure application significantly increased soil available P by 245% in surface soil in 2014 while resulted in obvious negative soil P storage capacity (− 103 mg P kg−1) and high potential of P downward transport due to long-term positive P surplus together with the increase in soil pH of 1.2.

A combine harvester provides unique capabilities as a mobile sensing platform. This chapter aims to contribute to the advancement of on-combine sensor use for obtaining site-specific crop data by trying to convince potential users in the agricultural community of its value and accessibility. Today, mass/volume flow and electrical capacitance sensors are widely used for measuring grain yield and moisture. A variety of other sensors have been used in crop analysis and process control that include photoelectronic spectrometers for analysis of crop quality attributes as well as ultrasonic and laser sensors for quantifying aboveground biomass. Applications of this information include precision N management, post-harvest assessment of crop stress, grain segregation by protein concentration and mapping of late-season weed infestations. Barriers challenging wider adoption of on-combine sensing techniques include the need for (i) software for exploring multi-year yield data and constructing profit zones, (ii) inexpensive spectrometers for grain quality measurement and mapping, (iii) commercial firms offering services in spectroscopy, custom mapping and data fusion, (iv) stand-alone units with user interface and firmware for multi-sensor data collection, and (v) field studies demonstrating economic benefits of various applications of information from on-combine sensing.

Weed maps created late in the growing season are potentially useful in regions where late maturing weeds are problematic and need to be controlled before they produce seed. The objectives of this study were to (1) spatially characterize the population dynamics of predominant weed species and apply this information into quantifying the effect of treated and untreated weed infestations on wheat (Triticum aestivum L.) yields and (2) evaluate potential herbicide savings with post-harvest site-specific treatments. Multi-year grain yield and weed data were acquired at harvest in each of four years (2015–18) within a dryland production field (9.2 ha) in eastern Oregon. Abundance of weed species (2015) and percent cover of weed species (2016, 2017 and 2018) were visually estimated on a square grid based on dividing the field into 7-m2 cells. Spatial patterns in the weed community were subject to rapid change and depended on year, crop and weed control strategy. While tumble mustard (Sisymbrium altissimum) was the most predominant and competitive species, the spatial distribution of this weed and that of other species varied each year. Tumble mustard and prickly lettuce (Lactuca serriola) were equally problematic in spring wheat and winter wheat whereas Russian thistle (Salsola tragus) was problematic in spring wheat and downy brome (Bromus tectorum) in winter wheat. Potential savings from site-specific herbicide application varied from 10 to 95% based on percentage of field infested. Weed maps at harvest are useful for studying weed dynamics, identifying potentially herbicide-resistant weeds and planning site-specific weed management. Combined with yield maps, weed maps at harvest are also useful for explaining crop yield variability that is associated with weed competition and weed control in furtherance of integrated weed management strategies.

Purpose Early detection of pulmonary nodules is an effective way to improve patients' chances of survival. In this work, we propose a novel and efficient way to build a computer-aided detection (CAD) system for pulmonary nodules based on computed tomography (CT) scans. Methods The system can be roughly divided into two steps: nodule candidate detection and false positive reduction. Considering the three-dimensional (3D) nature of nodules, the CAD system adopts 3D convolutional neural networks (CNNs) in both stages. Specifically, in the first stage, a segmentation-based 3D CNN with a hybrid loss is designed to segment nodules. According to the probability maps produced by the segmentation network, a threshold method and connected component analysis are applied to generate nodule candidates. In the second stage, we employ three classification-based 3D CNNs with different types of inputs to reduce false positives. In addition to simple raw data input, we also introduce hybrid inputs to make better use of the output of the previous segmentation network. In experiments, we use data augmentation and batch normalization to avoid overfitting. Results We evaluate the system on 888 CT scans from the publicly available LIDC-IDRI dataset, and our method achieves the best performance by comparing with the state-of-the-art methods, which has a high detection sensitivity of 97.5% with an average of only one false positive per scan. An additional evaluation on 115 CT scans from local hospitals is also performed. Conclusions Experimental results demonstrate that our method is highly suited for the detection of pulmonary nodules.

A lack of plant available water limits the ability to intensify the summer fallow-winter wheat (SF-WW) rotation in low precipitation (<350 mm) areas of the inland Pacific Northwest (PNW). The objective of this study was to compare crop yield, water use efficiency, precipitation capture, and soil water storage between conventional-tillage SF and reduced tillage fallow (RTF) and among different 2-yr and 3-yr cropping sequences. After full initiation of the experiment, eight sequences were evaluated over a 3-yr period (2016-18) including SF-WW, RTF-WW, RTF-WW-spring barley (SB), RTF-winter napus canola (WN)-spring wheat (SW), RTF-spring carinata (SC)-SW, RTF-WN-spring forage triticale (ST), and RTF-winter forage triticale (WT)-SC. Growing season precipitation was near average (269 mm) each year. Ponded infiltration rates were significantly higher (P <= 0.05) in 2-yr rotations managed with RTF (77.68 +/- 24.56 mm h(-1)) than SF (37.08 +/- 13.03 mm h(-1)). Water use efficiency and yields of WW were generally greater following RTF than for WW after SF. Water use and yield of winter cereals WW and WT after fallow were greater than for oilseeds WN and SC that also followed fallow. Pre-plant soil water contents were significantly lower following a primary crop than after fallow. Consequently, water use and yield of secondary crops were <50 % of primary crops. Of 3-yr cropping sequences, RTF-WW-SB and RTF-WW-SC had the highest water use efficiencies with annualized yields generally approaching that of RTF-WW and SF-WW. These results support integration of spring barley and spring carinata under low precipitation dryland conditions in the PNW to increase diversification and improve conservation of water.

The analysis of variance (ANOVA) is used by agronomists to compare the means of treatment groups in field trials. To perform an ANOVA, the treatment groups are assumed to be randomly sampled, normally distributed, and have equal variances. This chapter proposes such a procedure involving a spatial statistical technique known as autoregressive response (AR) modeling. It advocates the method of analysis of field experiments for ANOVA of agronomic field trials because, through its specification of the geographic configuration of areal units, the AR model accommodates plots that are located on the nodes of a regular lattice. The chapter provides guidance for the use of AR-based ANOVA and accompanying SAS computer code for the analysis of agronomic field data. It shows clearly the basic difference between AR-based ANOVA and conventional ANOVA with regard to the results from the 102-plot variety trial.

AbstractMonitoring crop phenology is crucial for making site‐specific management decisions for crop protection and nutrition. The prominent yellow bloom associated with canola (Brassica napus L.) and similar yellow‐flowering plants can provide cues about spatial differences as well as timing of crop input requirements. The objective of this study was to remotely characterize the phenological development of Brassicaceae oilseeds such as canola and carinata (B. carinata A. Braun) in terms of spectral‐temporal dynamics between vegetation density and yellow flower density. Temporal variation of spectral indices (normalized difference vegetation index [NDVI], normalized difference yellowness index [NDYI], and visible atmospherically resistant index [VARI]) were measured in small plots over the growing season in relation to changes in vegetation density and flower density in winter canola and spring carinata. Phenological change between vegetative and reproductive development could be automatically detected using the difference in the change of the sign of ΔIndex values between VARI and NDYI. An overall accuracy of 85% was obtained when testing the algorithm with Landsat 8 data of canola fields near Olds, AB, Canada. The contrasting behavior between reproductive and vegetation indices across flowering transitions was confirmed for three independent datasets across a range of genetic variation in Brassica oilseeds as well as geographic variation in soil types and management practices. A bivariate time series analysis procedure was developed for automatically estimating flowering transitions based on predictable, relative differences between vegetative and reproductive indices. Researchers and land managers can exploit optimal phenology windows to improve site‐specific models and disease risk assessments.

Rapid water infiltration is important for improving water storage and reducing erosion potential in arid and semiarid areas. Taprooted crops are sometimes credited with improving water infiltration. In a study repeated over three years, we tested whether adding a single brassica crop year to the traditional wheat (Triticum aestivum)–summer fallow rotation would improve water infiltration. We found significantly faster rates of ponded water infiltration in subsequent wheat crops following canola (Brassica napus; 29.68 ± 19.83 mm h–1 [1.17 ± 0.78 in hr–1], n = 24) and mustard (Brassica carinata; 25.27 ± 17.32 mm h–1 [0.99 ± 0.68 in hr–1], n = 19) as compared to wheat following winter wheat (21.14 ± 15.78 mm h–1 [0.83 ± 0.62 in hr–1], n = 24). The greater infiltration rates measured following brassicas compared to wheat were not attributed to root channels due to the lack of visible channels. Crop diversification with oilseeds can increase water infiltration after only a single crop year.

An on-combine, near infrared (NIR) spectrometer is now commercially available for mapping grain protein concentration (GPC) within farm fields. Costing > US$20,000, this specialized instrument may not be affordable for all producers. The objective of this study was to adapt and evaluate a moderately priced reflectance spectrometer ( < US$5500, Avantes AvaSpec ULS2048 x 16-USB2) for on-combine sensing of GPC. A "leave-one-out" cross-validation partial least square (PLS) regression model for GPC was developed for flowing grain with the spectrometer's probe-head attached to the lower end of an inclined trough. Good correlation (r(2) = 0.92) was observed between estimated and reference GPC in grain samples when NIR spectra (850-1040 nm) and GPC reference values were used for developing the PLS model. The NIR estimation achieved a standard error of < 5 g of protein kg(-1) of grain. An inexpensive external temperature control system was built that stabilized the temperature of the instrument's input/output converter board within 60 m of activation. Placed on a test stand machine with standard 12.5 cm diameter auger, the instrument predicted GPC to < 5 and < 10 g kg(-1) in two and three of four wheat cultivars thus indicating that the model produced on the trough may be applied into predicting GPC on a combine. When placed on a combine, the calibrated instrument produced a protein map that correlated well (r = 0.88) with a map derived from an expensive reflectance spectrometer (US$32,000, Polytec 1721). Thus, a relatively inexpensive NIR reflectance spectrometer may be adapted for mapping GPC across fields.

Brassicaceae oilseed crops can provide rotation benefits to dryland wheat (Triticum aestivum L.) and supply feedstock for biofuel production. However, growers face decisions about what oilseed crop is best suited for an environment. The objective of this study was to determine how varying production environments affect the agronomic performance of modern cultivars of six Brassicaceae crop species and identify ideal genotypes for seven growing environments spanning four ecoregions. A field experiment was replicated in Colorado, Idaho, Iowa, Minnesota, Montana, North Dakota, and Oregon, USA, between 2013 and 2016 to measure seed and oil yields of seed for four cultivars of Brassica napus, two of B. carinata, two of B. juncea, two of Sinapis alba, one of B. rapa, and one of Camelina sativa. Also, δ13C signature of seed was used as an indicator of water limitation. Generally, across all genotypes, seed and oil yields increased with increased growing season precipitation. Modern commercial cultivars of B. napus and B. juncea had the highest seed oil contents and generally produced the greatest oil yields across most environments, although they were not always the highest seed yielders. For instance, B. carinata over six site years in North Dakota and Minnesota yielded greater than B. napus producing as much as 2471 kg ha−1 in Minnesota. Camelina produced competitive seed yields in some of the drier environments and its δ13C signature indicated that it had the greatest drought resistance. However, seed oil content of some of these high yielding genotypes may need improvement before they are viable as biofuel feedstock.